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CNeuroMod-THINGS, a densely-sampled fMRI dataset for visual neuroscience
arXiv:2507.09024v1 Announce Type: new Abstract: Data-hungry neuro-AI modelling requires ever larger neuroimaging datasets. CNeuroMod-THINGS meets this need by capturing neural representations for a wide set of semantic concepts using well-characterized stimuli in a new densely-sampled, large-scale fMRI dataset. Importantly, CNeuroMod-THINGS exploits synergies between two existing projects: the THINGS initiative (THINGS) and the Courtois Project on Neural Modelling (CNeuroMod). THINGS has developed a common set of thoroughly annotated images broadly sampling natural and man-made objects which is used to acquire a growing collection of large-scale multimodal neural responses. Meanwhile, CNeuroMod is acquiring hundreds of hours of fMRI data from a core set of participants during controlled and naturalistic tasks, including visual tasks like movie watching and videogame playing. For CNeuroMod-THINGS, four CNeuroMod participants each completed 33-36 sessions of a continuous recognition paradigm using approximately 4000 images from the THINGS stimulus set spanning 720 categories. We report behavioural and neuroimaging metrics that showcase the quality of the data. By bridging together large existing resources, CNeuroMod-THINGS expands our capacity to model broad slices of the human visual experience.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-07-15 04:00:00 UTC.
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Co-evolutionary Balance State of the Autism inter-Brain Network: A Neurofunctional Framework for Biomarker Discovery
arXiv:2507.09045v1 Announce Type: new Abstract: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social communication and repetitive behaviors; however, objective neurophysiological biomarkers remain lacking. We propose a coevolutionary balance paradigm that quantifies network level energy via a Hamiltonian integrating regional activity measured by fractional amplitude of low frequency fluctuations (fALFF) and resting state functional connectivity (FC). Analysis of resting state fMRI data from 93 adult males with ASD and 93 matched controls revealed that empirical networks showed lower energy than 1000 topology preserving null models (paired t = -4.12, p less than or equal to 1e-4). Participants with ASD exhibited more negative whole brain energy (t = -3.239, p = 0.0015), driven by increased agreement links and reduced imbalanced same motifs. Subnetwork analysis indicated greater energy in the Default Mode Network after false discovery rate correction (p less than 0.016) and enhanced energy between the Default Mode, Salience and Dorsal Attention networks (p less than 0.032). Energy metrics and inter network connectivity correlated with Autism Diagnostic Interview Revised and Autism Diagnostic Observation Schedule severity scores (absolute correlation greater than or equal to 0.29, p less than 0.02). A k nearest neighbors classifier using nine principal features including motif proportions, global node link alignment, inter network fALFF weighted and FC strengths, subnetwork magnetization and pairwise energy achieved an accuracy of 79 percent with balanced sensitivity and specificity. These results demonstrate that coevolutionary energy detects interpretable network disruptions and establishes a robust framework for ASD classification.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-07-15 04:00:00 UTC.
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Cellular Mechanisms of Phase Maintenance in a Pyloric Motif of a Central Pattern Generator
arXiv:2507.09360v1 Announce Type: new Abstract: In many neural networks, patterns controlling rhythmic behaviors are maintained across a wide range of periods. In the crustacean pyloric central pattern generator (CPG), a constant bursting pattern is preserved over a three-to-fivefold range of periods. We idescribe how neuromodulation could adjust neuronal properties to preserve phase relations as the period changes. We developed a biophysical model implementing a reduced pyloric network motif, which has a bursting neuron and two follower neurons interconnected through inhibitory synaptic coupling. We described cellular mechanisms supporting phase maintenance and investigated possible coordination between these mechanisms in four dynamically distinct ensembles of a pyloric CPG producing a triphasic pattern. The coordinated variation of the voltages of half-activation for potassium (VK2) and hyperpolarization-activated (Vh) currents provides a family of three mechanisms for control of burst duration, interburst interval, and latency to spiking. The mechanisms are determined by the Cornerstone bifurcation, one of the Shilnikov blue sky catastrophe scenarios. In Mechanism 1, in a bursting neuron, the burst duration increases as VK2 nears a blue-sky catastrophe bifurcation, while the interburst interval grows as Vh approaches a saddle-node on an invariant circle bifurcation. In Mechanism 2, a silent neuron responds with a single burst to short input; the burst duration grows as VK2 approaches a saddle-node bifurcation for periodic orbits. In Mechanism 3, a spiking neuron responds with a pause to short input; the pause duration grows as Vh nears a saddle-node bifurcation for stationary states. In all three mechanisms, the measured quantities grow without bound as the bifurcation parameter nears its critical value, consistent with an inverse-square-root law.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-07-15 04:00:00 UTC.
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Self-supervised pretraining of vision transformers for animal behavioral analysis and neural encoding
arXiv:2507.09513v1 Announce Type: new Abstract: The brain can only be fully understood through the lens of the behavior it generates -- a guiding principle in modern neuroscience research that nevertheless presents significant technical challenges. Many studies capture behavior with cameras, but video analysis approaches typically rely on specialized models requiring extensive labeled data. We address this limitation with BEAST (BEhavioral Analysis via Self-supervised pretraining of Transformers), a novel and scalable framework that pretrains experiment-specific vision transformers for diverse neuro-behavior analyses. BEAST combines masked autoencoding with temporal contrastive learning to effectively leverage unlabeled video data. Through comprehensive evaluation across multiple species, we demonstrate improved performance in three critical neuro-behavioral tasks: extracting behavioral features that correlate with neural activity, and pose estimation and action segmentation in both the single- and multi-animal settings. Our method establishes a powerful and versatile backbone model that accelerates behavioral analysis in scenarios where labeled data remains scarce.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-07-15 04:00:00 UTC.
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Dissociating Cognitive Load and Stress Responses Using Single-Channel EEG: Behavioral and Neural Correlates of Anxiety Across Cognitive States
arXiv:2507.10093v1 Announce Type: new Abstract: Identifying neural markers of stress and cognitive load is key to developing scalable tools for mental state assessment. This study evaluated whether a single-channel high-density EEG (hdrEEG) system could dissociate cognitive and stress-related activity during a brief auditory task-based protocol. Sixty-eight healthy adults completed resting state recordings, cognitively demanding auditory tasks, and exposure to unpredictable literalized startle stimuli. Participants also rated their stress and anxiety using a modified State-Trait Anxiety Inventory (STAI). EEG analysis focused on frequency bands (Theta, Gamma, Delta) and machine-learning-derived features (A0, ST4, VC9, T2). A double dissociation emerged: Theta and VC9 increased under cognitive load but not startle, supporting their sensitivity to executive function. In contrast, Gamma and A0 were elevated by the startle stimulus, consistent with stress reactivity. ST4 tracked cognitive effort and worry, while T2 negatively correlated with self-reported calmness, indicating relevance to emotional regulation. These results demonstrate that a short, uniform assessment using portable EEG can yield multiple reliable biomarkers of cognitive and affective states. The findings have implications for clinical, occupational, and educational settings, and may inform future neurofeedback protocols targeting simultaneous regulation of attention and stress.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-07-15 04:00:00 UTC.
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The Evaluation of Breathing 5:5 effect on resilience, stress and balance center measured by Single-Channel EEG
arXiv:2507.10175v1 Announce Type: new Abstract: Slow-paced breathing is a promising intervention for reducing anxiety and enhancing emotional regulation through its effects on autonomic and central nervous system function. This study examined the neurophysiological and subjective effects of a 5:5 breathing protocol on stress-related EEG biomarkers using a mobile single-channel EEG system. Thirty-eight healthy adults were randomly assigned to either an intervention group (n = 20), which completed two sessions spaced two weeks apart with daily breathing practice, or a control group (n = 18), which completed one session. In each session, participants underwent an auditory EEG assessment with resting, mental load, and startle conditions. The intervention group also completed a guided breathing session during the first visit and practiced the technique between sessions. EEG biomarkers (ST4, Alpha, Delta, Gamma, VC0) and subjective anxiety levels (STAI) were assessed before and after the intervention. A significant reduction in Gamma power was observed in the intervention group immediately following the first breathing session during mental load (p = .002), indicating acute stress reduction. Across sessions, long-term breathing practice led to increased Alpha and Delta power and reduced ST4 activity, suggesting cumulative improvements in emotional regulation and cognitive efficiency. Correlational analyses revealed that changes in VC0 and Alpha were significantly associated with subjective reports of tension, focus difficulty, and calmness. Guided slow-paced breathing at a 5:5 rhythm produces both immediate and sustained effects on neural markers of stress and cognition, with corresponding improvements in subjective anxiety. These findings support EEG-based monitoring as a scalable method for evaluating breath-based interventions and promoting real-time emotional self-regulation.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-07-15 04:00:00 UTC.
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Beyond vividness: Content analysis of induced hallucinations reveals the hidden structure of individual differences in visual imagery
arXiv:2507.09011v1 Announce Type: cross Abstract: A rapidly alternating red and black display known as Ganzflicker induces visual hallucinations that reflect the generative capacity of the visual system. Recent proposals regarding the imagery spectrum, that is, differences in the visual system of individuals with absent imagery, typical imagery, and vivid imagery, suggest these differences should impact the complexity of other internally generated visual experiences. Here, we used tools from natural language processing to analyze free-text descriptions of hallucinations from over 4,000 participants, asking whether people with different imagery phenotypes see different things in their mind's eye during Ganzflicker-induced hallucinations. Strong imagers described complex, naturalistic content, while weak imagers reported simple geometric patterns. Embeddings from vision language models better captured these differences than text-only language models, and participants with stronger imagery used language with richer sensorimotor associations. These findings may reflect individual variation in coordination between early visual areas and higher-order regions relevant for the imagery spectrum.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-07-15 04:00:00 UTC.
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Algorithm Development in Neural Networks: Insights from the Streaming Parity Task
arXiv:2507.09897v1 Announce Type: cross Abstract: Even when massively overparameterized, deep neural networks show a remarkable ability to generalize. Research on this phenomenon has focused on generalization within distribution, via smooth interpolation. Yet in some settings neural networks also learn to extrapolate to data far beyond the bounds of the original training set, sometimes even allowing for infinite generalization, implying that an algorithm capable of solving the task has been learned. Here we undertake a case study of the learning dynamics of recurrent neural networks (RNNs) trained on the streaming parity task in order to develop an effective theory of algorithm development. The streaming parity task is a simple but nonlinear task defined on sequences up to arbitrary length. We show that, with sufficient finite training experience, RNNs exhibit a phase transition to perfect infinite generalization. Using an effective theory for the representational dynamics, we find an implicit representational merger effect which can be interpreted as the construction of a finite automaton that reproduces the task. Overall, our results disclose one mechanism by which neural networks can generalize infinitely from finite training experience.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-07-15 04:00:00 UTC.
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Leveraging Swin Transformer for enhanced diagnosis of Alzheimer's disease using multi-shell diffusion MRI
arXiv:2507.09996v1 Announce Type: cross Abstract: Objective: This study aims to support early diagnosis of Alzheimer's disease and detection of amyloid accumulation by leveraging the microstructural information available in multi-shell diffusion MRI (dMRI) data, using a vision transformer-based deep learning framework. Methods: We present a classification pipeline that employs the Swin Transformer, a hierarchical vision transformer model, on multi-shell dMRI data for the classification of Alzheimer's disease and amyloid presence. Key metrics from DTI and NODDI were extracted and projected onto 2D planes to enable transfer learning with ImageNet-pretrained models. To efficiently adapt the transformer to limited labeled neuroimaging data, we integrated Low-Rank Adaptation. We assessed the framework on diagnostic group prediction (cognitively normal, mild cognitive impairment, Alzheimer's disease dementia) and amyloid status classification. Results: The framework achieved competitive classification results within the scope of multi-shell dMRI-based features, with the best balanced accuracy of 95.2% for distinguishing cognitively normal individuals from those with Alzheimer's disease dementia using NODDI metrics. For amyloid detection, it reached 77.2% balanced accuracy in distinguishing amyloid-positive mild cognitive impairment/Alzheimer's disease dementia subjects from amyloid-negative cognitively normal subjects, and 67.9% for identifying amyloid-positive individuals among cognitively normal subjects. Grad-CAM-based explainability analysis identified clinically relevant brain regions, including the parahippocampal gyrus and hippocampus, as key contributors to model predictions. Conclusion: This study demonstrates the promise of diffusion MRI and transformer-based architectures for early detection of Alzheimer's disease and amyloid pathology, supporting biomarker-driven diagnostics in data-limited biomedical settings.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-07-15 04:00:00 UTC.
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Intrinsic frequency distribution characterises neural dynamics
arXiv:2507.10145v1 Announce Type: cross Abstract: Decomposing multivariate time series with certain basic dynamics is crucial for understanding, predicting and controlling nonlinear spatiotemporally dynamic systems such as the brain. Dynamic mode decomposition (DMD) is a method for decomposing nonlinear spatiotemporal dynamics into several basic dynamics (dynamic modes; DMs) with intrinsic frequencies and decay rates. In particular, unlike Fourier transform-based methods, which are used to decompose a single-channel signal into the amplitudes of sinusoidal waves with discrete frequencies at a regular interval, DMD can derive the intrinsic frequencies of a multichannel signal on the basis of the available data; furthermore, it can capture nonstationary components such as alternations between states with different intrinsic frequencies. Here, we propose the use of the distribution of intrinsic frequencies derived from DMDs (DM frequencies) to characterise neural activities. The distributions of DM frequencies in the electroencephalograms of healthy subjects and patients with dementia or Parkinson's disease in a resting state were evaluated. By using the distributions, these patients were distinguished from healthy subjects with significantly greater accuracy than when using amplitude spectra derived by discrete Fourier transform. This finding suggests that the distribution of DM frequencies exhibits distinct behaviour from amplitude spectra, and therefore, the distribution may serve as a new biomarker by characterising the nonlinear spatiotemporal dynamics of electrophysiological signals.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-07-15 04:00:00 UTC.
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A new mathematical model for brain memory working. Optimal control behavior for Hopfield networks
arXiv:2305.14360v5 Announce Type: replace Abstract: Recent works have highlighted the need for a new dynamical paradigm in the modeling of brain function and evolution. Specifically, these models should incorporate non-constant and asymmetric synaptic weights $T_{ij}$ in the neuron-neuron interaction matrix, moving beyond the classical Hopfield framework. Krotov and Hopfield proposed a non-constant yet symmetric model, resulting in a vector field that describes gradient-type dynamics, which includes a Lyapunov-like energy function. Firstly, we will outline the general conditions for generating a Hopfield-like vector field of gradient type, recovering the Krotov-Hopfield condition as a particular case. Secondly, we address the issue of symmetry, which we abandon for two key physiological reasons: (1) actual neural connections have a distinctly directional character (axons and dendrites), and (2) the gradient structure derived from symmetry forces the dynamics towards stationary points, leading for every pattern to a recognition or to a free association, if the equilibrium is rather far from the input. We propose a novel model that incorporates a set of limited but variable controls $|\xi_{ij}|\leq K$, which are used to adjust an initially constant interaction matrix, $T_{ij}=A_{ij}+\xi_{ij}$ according to a controlled variational functional. We simulate three potential outcomes when a pattern is submitted: (1) if the dynamics converges to an existing stationary point without activating controls, the system has \emph{recognized} an incoming pattern; (2) if a new stationary point is reached through control activation, the system has \emph{learned} a new pattern; and (3) if the dynamics \emph{wanders}, the system is unable to recognize or learn the submitted pattern. An additional feature (4) models the processes of \emph{forgetting and restoring} memory. Numerical simulations on a basic neural network model support the theoretical results.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-07-15 04:00:00 UTC.
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Fairness, not Emotion, Drives Socioeconomic Decision Making
arXiv:2409.10322v2 Announce Type: replace Abstract: Emotion and fairness play a key role in mediating socioeconomic decisions in humans; however, the underlying neurocognitive mechanism remains largely unknown. This exploratory study unraveled the interplay between agents' emotions and the fairness of their monetary proposal in rational decision-making, backed by ERP analyses at a group as well as a strategic level. In a time-bound ultimatum-game paradigm, 40 participants were exposed to three distinct proposers' emotions (Happy, Neutral, Disgusted) followed by one of the three offer ranges (Low, Intermediate, High). Our findings show a robust influence of economic fairness on acceptance rates. A multilevel generalized linear model showed offer as the dominant predictor of trial-specific responses. Subsequent clustering grouped participants into five clusters, which the Drift Diffusion Model corroborates. Pertinent neural markers demonstrated the recognition of facial expressions; however, they had minimal effect during socioeconomic decision-making. Our study explores individualistic decision-making processes revealing different cognitive strategies.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-07-15 04:00:00 UTC.
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Emergent functions of noise-driven spontaneous activity: Homeostatic maintenance of criticality and memory consolidation
arXiv:2502.10946v2 Announce Type: replace Abstract: Unlike digital computers, the brain exhibits spontaneous activity even during complete rest, despite the evolutionary pressure for energy efficiency. Inspired by the critical brain hypothesis, which proposes that the brain operates optimally near a critical point of phase transition in the dynamics of neural networks to improve computational efficiency, we postulate that spontaneous activity plays a homeostatic role in the development and maintenance of criticality. Criticality in the brain is associated with the balance between excitatory and inhibitory synaptic inputs (EI balance), which is essential for maintaining neural computation performance. Here, we hypothesize that both criticality and EI balance are stabilized by appropriate noise levels and spike-timing-dependent plasticity (STDP) windows. Using spiking neural network (SNN) simulations and in vitro experiments with dissociated neuronal cultures, we demonstrated that while repetitive stimuli transiently disrupt both criticality and EI balance, spontaneous activity can develop and maintain these properties and prolong the fading memory of past stimuli. Our findings suggest that the brain may achieve self-optimization and memory consolidation as emergent functions of noise-driven spontaneous activity. This noise-harnessing mechanism provides insights for designing energy-efficient neural networks, and may explain the critical function of sleep in maintaining homeostasis and consolidating memory.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-07-15 04:00:00 UTC.
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Distinct Modes of Functional Neural Organization in Autism: Insights from Dynamical Systems Analysis of Resting-State EEG
arXiv:2506.23013v2 Announce Type: replace Abstract: While differences in patterns of functional connectivity and neural synchronization have been reported between individuals on the autism spectrum and neurotypical peers at various age stages, these differences appear to be subtle and may not be captured by typical quantitative measures of EEG. We used the dynamical systems approach to analyze resting-state EEG to investigate fine-grained spatiotemporal organization of brain networks in autistic and neurotypical young adults. While power spectra showed minimal group differences, autistic participants exhibited higher Lyapunov exponents (indicating less stable neural dynamics), weaker phase synchronization, and lower clustering/efficiency of functional networks during eyes-open resting state, suggesting more random and less stably connected neural dynamics in comparison to those of neurotypical peers. Closing the eyes regularized neural dynamics in autistic but not neurotypical participants, with increases in synchrony strength, transient desynchronization patterning, and functional connectivity observed in the autistic group. The results point to the distinct modes of neural dynamics organization likely reflecting cumulative developmental adaptations to sensory inputs that shape both resting-state neural activity and cognitive processing strategies.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-07-15 04:00:00 UTC.
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Dynamical stability for dense patterns in discrete attractor neural networks
arXiv:2507.10383v1 Announce Type: cross Abstract: Neural networks storing multiple discrete attractors are canonical models of biological memory. Previously, the dynamical stability of such networks could only be guaranteed under highly restrictive conditions. Here, we derive a theory of the local stability of discrete fixed points in a broad class of networks with graded neural activities and in the presence of noise. By directly analyzing the bulk and outliers of the Jacobian spectrum, we show that all fixed points are stable below a critical load that is distinct from the classical \textit{critical capacity} and depends on the statistics of neural activities in the fixed points as well as the single-neuron activation function. Our analysis highlights the computational benefits of threshold-linear activation and sparse-like patterns.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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Bayesian Theory of Consciousness as Exchangeable Emotion-Cognition Inference
arXiv:2407.09488v3 Announce Type: replace-cross Abstract: This paper proposes a unified framework in which consciousness emerges as a cycle-consistent, affectively anchored inference process, recursively structured by the interaction of emotion and cognition. Drawing from information theory, optimal transport, and the Bayesian brain hypothesis, we formalize emotion as a low-dimensional structural prior and cognition as a specificity-instantiating update. This emotion-cognition cycle minimizes joint uncertainty by aligning emotionally weighted priors with context-sensitive cognitive appraisals. Subjective experience thus arises as the informational footprint of temporally extended, affect-modulated simulation. We introduce the Exchangeable Integration Theory of Consciousness (EITC), modeling conscious episodes as conditionally exchangeable samples drawn from a latent affective self-model. This latent variable supports integration, via a unified cause-effect structure with nonzero irreducibility, and differentiation, by preserving contextual specificity across episodes. We connect this architecture to the Bayesian theory of consciousness through Rao-Blackwellized inference, which stabilizes inference by marginalizing latent self-structure while enabling adaptive updates. This mechanism ensures coherence, prevents inference collapse, and supports goal-directed simulation. The formal framework builds on De Finetti's exchangeability theorem, integrated information theory, and KL-regularized optimal transport. Overall, consciousness is reframed as a recursive inference process, shaped by emotion, refined by cognition, stabilized through exchangeability, and unified through a latent self-model that integrates experience across time.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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Credit Card Fraud Detection Using RoFormer Model With Relative Distance Rotating Encoding
arXiv:2507.09385v1 Announce Type: new Abstract: Fraud detection is one of the most important challenges that financial systems must address. Detecting fraudulent transactions is critical for payment gateway companies like Flow Payment, which process millions of transactions monthly and require robust security measures to mitigate financial risks. Increasing transaction authorization rates while reducing fraud is essential for providing a good user experience and building a sustainable business. For this reason, discovering novel and improved methods to detect fraud requires continuous research and investment for any company that wants to succeed in this industry. In this work, we introduced a novel method for detecting transactional fraud by incorporating the Relative Distance Rotating Encoding (ReDRE) in the RoFormer model. The incorporation of angle rotation using ReDRE enhances the characterization of time series data within a Transformer, leading to improved fraud detection by better capturing temporal dependencies and event relationships.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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BrainFLORA: Uncovering Brain Concept Representation via Multimodal Neural Embeddings
arXiv:2507.09747v1 Announce Type: new Abstract: Understanding how the brain represents visual information is a fundamental challenge in neuroscience and artificial intelligence. While AI-driven decoding of neural data has provided insights into the human visual system, integrating multimodal neuroimaging signals, such as EEG, MEG, and fMRI, remains a critical hurdle due to their inherent spatiotemporal misalignment. Current approaches often analyze these modalities in isolation, limiting a holistic view of neural representation. In this study, we introduce BrainFLORA, a unified framework for integrating cross-modal neuroimaging data to construct a shared neural representation. Our approach leverages multimodal large language models (MLLMs) augmented with modality-specific adapters and task decoders, achieving state-of-the-art performance in joint-subject visual retrieval task and has the potential to extend multitasking. Combining neuroimaging analysis methods, we further reveal how visual concept representations align across neural modalities and with real world object perception. We demonstrate that the brain's structured visual concept representations exhibit an implicit mapping to physical-world stimuli, bridging neuroscience and machine learning from different modalities of neural imaging. Beyond methodological advancements, BrainFLORA offers novel implications for cognitive neuroscience and brain-computer interfaces (BCIs). Our code is available at https://github.com/ncclab-sustech/BrainFLORA.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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Effective Self-Attention-Based Deep Learning Model with Evolutionary Grid Search for Robust Wave Farm Energy Forecasting
arXiv:2507.09847v1 Announce Type: new Abstract: Achieving carbon neutrality, a key focus of UN SDG #13, drives the exploration of wave energy, a renewable resource with the potential to generate 30,000 TWh of clean electricity annually, surpassing global demand. However, wave energy remains underdeveloped due to technical and economic challenges, particularly in forecasting wave farm power output, which is vital for grid stability and commercial viability. This study proposes a novel predictive framework to enhance wave energy integration into power grids. It introduces a hybrid sequential learning model combining Self-Attention-enhanced Convolutional Bi-LSTM with hyperparameter optimization. The model leverages spatial data from Wave Energy Converters (WECs) and is validated using datasets from wave farms in Adelaide, Sydney, Perth, and Tasmania, Australia. Benchmarked against ten machine learning algorithms, the model achieves superior accuracy, with R2 scores of 91.7% (Adelaide), 88.0% (Perth), 82.8% (Tasmania), and 91.0% (Sydney). It outperforms conventional ML and deep learning methods, offering robust and scalable predictions for wave energy output across diverse marine environments, supporting reliable integration into energy systems.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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Physics-informed neural networks for high-dimensional solutions and snaking bifurcations in nonlinear lattices
arXiv:2507.09782v1 Announce Type: cross Abstract: This paper introduces a framework based on physics-informed neural networks (PINNs) for addressing key challenges in nonlinear lattices, including solution approximation, bifurcation diagram construction, and linear stability analysis. We first employ PINNs to approximate solutions of nonlinear systems arising from lattice models, using the Levenberg-Marquardt algorithm to optimize network weights for greater accuracy. To enhance computational efficiency in high-dimensional settings, we integrate a stochastic sampling strategy. We then extend the method by coupling PINNs with a continuation approach to compute snaking bifurcation diagrams, incorporating an auxiliary equation to effectively track successive solution branches. For linear stability analysis, we adapt PINNs to compute eigenvectors, introducing output constraints to enforce positivity, in line with Sturm-Liouville theory. Numerical experiments are conducted on the discrete Allen-Cahn equation with cubic and quintic nonlinearities in one to five spatial dimensions. The results demonstrate that the proposed approach achieves accuracy comparable to, or better than, traditional numerical methods, especially in high-dimensional regimes where computational resources are a limiting factor. These findings highlight the potential of neural networks as scalable and efficient tools for the study of complex nonlinear lattice systems.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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Evolution of Fear and Social Rewards in Prey-Predator Relationship
arXiv:2507.09992v1 Announce Type: cross Abstract: Fear is a critical brain function for detecting danger and learning to avoid specific stimuli that can lead to danger. While fear is believed to have evolved under pressure from predators, experimentally reproducing the evolution is challenging. To investigate the relationship between environmental conditions, the evolution of fear, and the evolution of other rewards, such as food reward and social reward, we developed a distributed evolutionary simulation. In our simulation, prey and predator agents co-evolve their innate reward functions, including a possibly fear-like term for observing predators, and learn behaviors via reinforcement learning. Surprisingly, our simulation revealed that social reward for observing the same species is more important for prey to survive, and fear-like negative reward for observing predators evolves only after acquiring social reward. We also found that the predator with increased hunting ability (larger mouth) amplified fear emergence, but also that fear evolution is more stable with non-evolving predators that are bad at chasing prey. Additionally, unlike for predators, we found that positive rewards evolve in opposition to fear for stationary threats, as areas with abundant leftover food develop around them. These findings suggest that fear and social reward have had a complex interplay with each other through evolution, along with the nature of predators and threats.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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Effects of structural properties of neural networks on machine learning performance
arXiv:2507.10005v1 Announce Type: cross Abstract: In recent years, graph-based machine learning techniques, such as reinforcement learning and graph neural networks, have garnered significant attention. While some recent studies have started to explore the relationship between the graph structure of neural networks and their predictive performance, they often limit themselves to a narrow range of model networks, particularly lacking mesoscale structures such as communities. Our work advances this area by conducting a more comprehensive investigation, incorporating realistic network structures characterized by heterogeneous degree distributions and community structures, which are typical characteristics of many real networks. These community structures offer a nuanced perspective on network architecture. Our analysis employs model networks such as random and scale-free networks, alongside a comparison with a biological neural network and its subsets for more detailed analysis. We examine the impact of these structural attributes on the performance of image classification tasks. Our findings reveal that structural properties do affect performance to some extent. Specifically, networks featuring coherent, densely interconnected communities demonstrate enhanced learning capabilities. The comparison with the biological neural network emphasizes the relevance of our findings to real-world structures, suggesting an intriguing connection worth further exploration. This study contributes meaningfully to network science and machine learning, providing insights that could inspire the design of more biologically informed neural networks.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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Fast Estimations of Hitting Time of Elitist Evolutionary Algorithms from Fitness Levels
arXiv:2311.10502v3 Announce Type: replace Abstract: The fitness level method is a widely used technique for estimating the mean hitting time of elitist evolutionary algorithms on level-based fitness functions. However, this paper identifies its main limitation: the linear lower bound derived from traditional fitness level partitioning is not tight when applied to many non-level-based fitness functions. A new subset level method is introduced to address this limitation. It selects a subset of non-optimal solutions, partitions them into levels, and then estimates linear bound coefficients based on drift analysis. Explicit expressions are proposed to compute the lower bound on the mean hitting time of elitist evolutionary algorithms. The proposed method is validated using six instances of the knapsack problem. Results show that the new method can be used to quickly estimate the lower bound on the mean hitting time of elitist evolutionary algorithms. This expands the application scope of the fitness level method to non-level-based functions.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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Dynamic Spiking Framework for Graph Neural Networks
arXiv:2401.05373v4 Announce Type: replace Abstract: The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks (GNNs) is gradually attracting attention due to the low power consumption and high efficiency in processing the non-Euclidean data represented by graphs. However, as a common problem, dynamic graph representation learning faces challenges such as high complexity and large memory overheads. Current work often uses SNNs instead of Recurrent Neural Networks (RNNs) by using binary features instead of continuous ones for efficient training, which would overlooks graph structure information and leads to the loss of details during propagation. Additionally, optimizing dynamic spiking models typically requires propagation of information across time steps, which increases memory requirements. To address these challenges, we present a framework named \underline{Dy}namic \underline{S}p\underline{i}king \underline{G}raph \underline{N}eural Networks (\method{}). To mitigate the information loss problem, \method{} propagates early-layer information directly to the last layer for information compensation. To accommodate the memory requirements, we apply the implicit differentiation on the equilibrium state, which does not rely on the exact reverse of the forward computation. While traditional implicit differentiation methods are usually used for static situations, \method{} extends it to the dynamic graph setting. Extensive experiments on three large-scale real-world dynamic graph datasets validate the effectiveness of \method{} on dynamic node classification tasks with lower computational costs.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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Continuous Spiking Graph Neural Networks
arXiv:2404.01897v2 Announce Type: replace Abstract: Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) by introducing continuous dynamics. They typically draw inspiration from diffusion-based methods to introduce a novel propagation scheme, which is analyzed using ordinary differential equations (ODE). However, the implementation of CGNNs requires significant computational power, making them challenging to deploy on battery-powered devices. Inspired by recent spiking neural networks (SNNs), which emulate a biological inference process and provide an energy-efficient neural architecture, we incorporate the SNNs with CGNNs in a unified framework, named Continuous Spiking Graph Neural Networks (COS-GNN). We employ SNNs for graph node representation at each time step, which are further integrated into the ODE process along with time. To enhance information preservation and mitigate information loss in SNNs, we introduce the high-order structure of COS-GNN, which utilizes the second-order ODE for spiking representation and continuous propagation. Moreover, we provide the theoretical proof that COS-GNN effectively mitigates the issues of exploding and vanishing gradients, enabling us to capture long-range dependencies between nodes. Experimental results on graph-based learning tasks demonstrate the effectiveness of the proposed COS-GNN over competitive baselines.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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A Pairwise Comparison Relation-assisted Multi-objective Evolutionary Neural Architecture Search Method with Multi-population Mechanism
arXiv:2407.15600v2 Announce Type: replace Abstract: Neural architecture search (NAS) enables researchers to automatically explore vast search spaces and find efficient neural networks. But NAS suffers from a key bottleneck, i.e., numerous architectures need to be evaluated during the search process, which requires a lot of computing resources and time. In order to improve the efficiency of NAS, a series of methods have been proposed to reduce the evaluation time of neural architectures. However, they are not efficient enough and still only focus on the accuracy of architectures. In addition to the classification accuracy, more efficient and smaller network architectures are required in real-world applications. To address the above problems, we propose the SMEM-NAS, a pairwise comparison relation-assisted multi-objective evolutionary algorithm based on a multi-population mechanism. In the SMEM-NAS, a surrogate model is constructed based on pairwise comparison relations to predict the accuracy ranking of architectures, rather than the absolute accuracy. Moreover, two populations cooperate with each other in the search process, i.e., a main population guides the evolution, while a vice population expands the diversity. Our method aims to provide high-performance models that take into account multiple optimization objectives. We conduct a series of experiments on the CIFAR-10, CIFAR-100 and ImageNet datasets to verify its effectiveness. With only a single GPU searching for 0.17 days, competitive architectures can be found by SMEM-NAS which achieves 78.91% accuracy with the MAdds of 570M on the ImageNet. This work makes a significant advance in the important field of NAS. Our code is publicly available at https://github.com/ccz-enas/SMEM-NAS.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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Transformers Can Solve Non-Linear and Non-Markovian Filtering Problems in Continuous Time For Conditionally Gaussian Signals
arXiv:2310.19603v4 Announce Type: replace-cross Abstract: The use of attention-based deep learning models in stochastic filtering, e.g. transformers and deep Kalman filters, has recently come into focus; however, the potential for these models to solve stochastic filtering problems remains largely unknown. The paper provides an affirmative answer to this open problem in the theoretical foundations of machine learning by showing that a class of continuous-time transformer models, called \textit{filterformers}, can approximately implement the conditional law of a broad class of non-Markovian and conditionally Gaussian signal processes given noisy continuous-time (possibly non-Gaussian) measurements. Our approximation guarantees hold uniformly over sufficiently regular compact subsets of continuous-time paths, where the worst-case 2-Wasserstein distance between the true optimal filter and our deep learning model quantifies the approximation error. Our construction relies on two new customizations of the standard attention mechanism: The first can losslessly adapt to the characteristics of a broad range of paths since we show that the attention mechanism implements bi-Lipschitz embeddings of sufficiently regular sets of paths into low-dimensional Euclidean spaces; thus, it incurs no ``dimension reduction error''. The latter attention mechanism is tailored to the geometry of Gaussian measures in the $2$-Wasserstein space. Our analysis relies on new stability estimates of robust optimal filters in the conditionally Gaussian setting.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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BEExformer: A Fast Inferencing Binarized Transformer with Early Exits
arXiv:2412.05225v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) based on transformers achieve cutting-edge results on a variety of applications. However, their enormous size and processing requirements hinder deployment on constrained resources. To enhance efficiency, binarization and Early Exit (EE) have proved to be effective solutions. However, binarization may lead to performance loss as reduced precision affects gradient estimation and parameter updates. Besides, research on EE mechanisms is still in its early stages. To address these challenges, we introduce Binarized Early Exit Transformer (BEExformer), the first-ever selective learning-based transformer integrating Binarization-Aware Training (BAT) with EE for efficient and fast textual inference. Each transformer block has an integrated Selective-Learn Forget Network (SLFN) to enhance contextual retention while eliminating irrelevant information. The BAT employs a differentiable second-order approximation to the sign function, enabling gradient computation that captures both the sign and magnitude of the weights. This aids in 21.30 times reduction in model size. The EE mechanism hinges on fractional reduction in entropy among intermediate transformer blocks with soft-routing loss estimation. This accelerates inference by reducing FLOPs by 52.08% and even improves accuracy by 2.89% by resolving the "overthinking" problem inherent in deep networks. Extensive evaluation through comparison with the SOTA methods and various ablations across six datasets covering multiple NLP tasks demonstrates its Pareto-optimal performance-efficiency trade-off.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing
arXiv:2504.13355v2 Announce Type: replace-cross Abstract: Measurements acquired from distributed physical systems are often sparse and noisy. Therefore, signal processing and system identification tools are required to mitigate noise effects and reconstruct unobserved dynamics from limited sensor data. However, this process is particularly challenging because the fundamental equations governing the dynamics are largely unavailable in practice. Reservoir Computing (RC) techniques have shown promise in efficiently simulating dynamical systems through an unstructured and efficient computation graph comprising a set of neurons with random connectivity. However, the potential of RC to operate in noisy regimes and distinguish noise from the primary smooth or non-smooth deterministic dynamics of the system has not been fully explored. This paper presents a novel RC method for noise filtering and reconstructing unobserved nonlinear dynamics, offering a novel learning protocol associated with hyperparameter optimization. The performance of the RC in terms of noise intensity, noise frequency content, and drastic shifts in dynamical parameters is studied in two illustrative examples involving the nonlinear dynamics of the Lorenz attractor and the adaptive exponential integrate-and-fire system. It is demonstrated that denoising performance improves by truncating redundant nodes and edges of the reservoir, as well as by properly optimizing hyperparameters, such as the leakage rate, spectral radius, input connectivity, and ridge regression parameter. Furthermore, the presented framework shows good generalization behavior when tested for reconstructing unseen and qualitatively different attractors. Compared to the extended Kalman filter, the presented RC framework yields competitive accuracy at low signal-to-noise ratios and high-frequency ranges.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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RsGCN: Rescaling Enhances Generalization of GCNs for Solving Scalable Traveling Salesman Problems
arXiv:2506.00533v3 Announce Type: replace-cross Abstract: Neural traveling salesman problem (TSP) solvers face two critical challenges: poor generalization for scalable TSPs and high training costs. To address these challenges, we propose a new Rescaling Graph Convolutional Network (RsGCN). Focusing on the scale-dependent features (i.e., features varied with problem scales) related to nodes and edges that influence the sensitivity of GCNs to the problem scales, a Rescaling Mechanism in RsGCN enhances the generalization capability by (1) rescaling adjacent nodes to construct a subgraph with a uniform number of adjacent nodes for each node across various scales of TSPs, which stabilizes the graph message aggregation; (2) rescaling subgraph edges to adjust the lengths of subgraph edges to the same magnitude, which maintains numerical consistency. In addition, an efficient training strategy with a mixed-scale dataset and bidirectional loss is used in RsGCN. To fully exploit the heatmaps generated by RsGCN, we design an efficient post-search algorithm termed Re2Opt, in which a reconstruction process based on adaptive weight is incorporated to help avoid local optima. Based on a combined architecture of RsGCN and Re2Opt, our solver achieves remarkable generalization and low training cost: with only 3 epochs of training on the mixed-scale dataset containing instances with up to 100 nodes, it can be generalized successfully to 10K-node instances without any fine-tuning. Extensive experiments demonstrate our state-of-the-art performance across uniform distribution instances of 9 different scales from 20 to 10K nodes and 78 real-world instances from TSPLIB, while requiring the fewest learnable parameters and training epochs among neural competitors.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-07-15 04:00:00 UTC.
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Higher resolution pooled genome-wide CRISPR knockout screening in Drosophila cells using integration and anti-CRISPR (IntAC)
Nature Communications, Published online: 15 July 2025; doi:10.1038/s41467-025-61692-3
Genome-wide CRISPR screens map how genes support survival and contribute to diverse biological functions. Here, the authors use antiCRISPR to enhance genome-wide CRISPR screening in Drosophila and generate higher-resolution maps of cell fitness, toxin, and drug-resistance.in Nature Communications on 2025-07-15 00:00:00 UTC.
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Colchicine in patients with aortic stenosis undergoing transcatheter aortic valve replacement: a double-blind randomized trial
Nature Communications, Published online: 15 July 2025; doi:10.1038/s41467-025-61916-6
An inflammatory process may increase the risk of arrhythmias after transcatheter aortic valve replacement. Here, the authors show that periprocedural treatment with colchicine may reduce the incidence of new-onset arrhythmias and subclinical leaflet thrombosis after transcatheter aortic valve replacement.in Nature Communications on 2025-07-15 00:00:00 UTC.
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Designing multi-metal-site nanosheet catalysts for CO2 photoreduction to ethylene
Nature Communications, Published online: 15 July 2025; doi:10.1038/s41467-025-61850-7
Based on Bader charge descriptors, a Cu2ZnSnS4 nanosheet with S defects is developed for photocatalytic CO2 reduction. Under visible light irradiation, C2H4 is produced from CO2 with a yield of 25.16 µmol g-1 h-1 with ~50% selectivity.in Nature Communications on 2025-07-15 00:00:00 UTC.
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Eradicating fungal biofilm-based infections by ultrasound-assisted semiconductor sensitized upconversion photodynamic therapy
Nature Communications, Published online: 15 July 2025; doi:10.1038/s41467-025-61519-1
A nonantibiotic-dependent therapy capable of destroying the biofilm is desired for the treatment of drug-resistant fungal infections but underdeveloped. Here, the authors engineer semiconductor coated upconversion nanohybrids for the synergistic actions of ultrasound and near-infrared light that promote drug penetration and reactive oxygen species-mediated disruption of Candida albicans biofilms.in Nature Communications on 2025-07-15 00:00:00 UTC.
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High-performance ZrNiSn-based half-Heusler thermoelectrics with hierarchical architectures enabled by reactive sintering
Nature Communications, Published online: 15 July 2025; doi:10.1038/s41467-025-61868-x
The authors develop a scalable reactive sintering method to produce ZrNiSn-based thermoelectrics without melting, achieving a high zT of 1.33 at 873 K through hierarchical microstructure engineering and detailed property analysis.in Nature Communications on 2025-07-15 00:00:00 UTC.
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Deciphering the role of CAPZA2 in neurodevelopmental disorders: insights from mouse models
Communications Biology, Published online: 15 July 2025; doi:10.1038/s42003-025-08385-1
CAPZA2 deficiency impairs synaptic structure in the hippocampus and prefrontal cortex and disrupts synaptic protein expression, leading to cognitive and other behavioral abnormalities, revealing its critical role in brain development and function.in Nature communications biology on 2025-07-15 00:00:00 UTC.
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Deciphering cancer therapy resistance via patient-level single-cell transcriptomics with CellResDB
Communications Biology, Published online: 15 July 2025; doi:10.1038/s42003-025-08457-2
CellResDB is a patient-derived single-cell database of therapy resistance, featuring 4.7 million cells across 24 cancers. It includes clinical annotations and an AI-powered robot for interactive analysis.in Nature communications biology on 2025-07-15 00:00:00 UTC.
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Old age variably impacts chimpanzee engagement and efficiency in stone tool use
We know vanishingly little about how long-lived apes experience senescence in the wild, particularly with respect to their foraging behaviors. Chimpanzees use tools during foraging, and given the cognitive and physical challenges presented by tool use, tool-use behaviors are potentially at a heightened risk of senescence, though this has never been investigated in wild individuals. Accordingly, we sampled data from a longitudinal video archive that contained footage of wild chimpanzees using stone hammers and anvils to crack hard-shelled nuts (nut cracking) at an ‘outdoor laboratory’ over a 17-year period (with focal chimpanzees aging from approximately 39–44 to 56–61 years across this period). Over time, elderly chimpanzees began attending experimental nut-cracking sites less frequently than younger individuals. Several elderly chimpanzees exhibited reductions in efficiency across multiple stages of nut cracking, including taking longer to both select stone tools prior to use and use tools to crack open nuts and consume the associated pieces of kernel. Two chimpanzees began using less streamlined behavioral sequences to crack nuts, including a greater number of actions (such as more numerous hammer strikes). Notably, we report interindividual variability in the extent to which elderly chimpanzees’ tool-use behaviors changed during our sample period – ranging from small to profound reductions in engagement and efficiency – as well as differences in the specific aspects of nut cracking that changed for each individual. We discuss the possible causes of these changes – and recommendations for future research – with reference to literature surrounding the senescence of captive and wild primates.in eLife on 2025-07-15 00:00:00 UTC.
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Embracing the complexity of cooperation
A theoretical framework for analyzing the evolution of nonlinear cooperative interactions is taking shape.in eLife on 2025-07-15 00:00:00 UTC.
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Higher-order crossmodal representations in the auditory cortex of deaf and hearing individuals
The study of early deafnes provides unique insights into how sensory experience shapes brain function and organisation. Here we investigated whether the auditory cortex of deaf individuals can implement higher-order cognitive functions when its sensory input is absent or significantly reduced. Crossmodal plasticity research has shown that auditory areas of the human brain are recruited for executive processing tasks in deaf individuals (Cardin et al., 2018; Ding et al., 2015; Manini et al., 2022; Zimmermann et al., 2021). What is the role of the auditory cortex during such executive processes in deaf individuals? One possibility is that it has a role in sensory processing, extracting information about relevant features. Alternatively, they it may represent higher-order information, such as coding task rules or modulating attentional states. To distinguish between these hypotheses, we conducted an fMRI delay-to-match experiment in either the visual or somatosensory modality in deaf (N=13) and hearing (N=18) individuals. Representational Similarity Analysis (RSA) showed that the auditory cortex of deaf individuals contains information about higher-level processes such as task and sensory modality. We also found significant representations of somatosensory frequency. Critically, task and modality representations were also found in the auditory cortex of hearing individuals. These findings suggest that crossmodal plasticity relies on the enhancement of representations that are present in hearing individuals, rather than through the implementation of novel ones. In conclusion, we show that sensory experience shapes cognitive processing and the function of sensory regions in the brain, and that the functional destiny of cortical regions can be shaped by early sensory experience.in bioRxiv: Neuroscience on 2025-07-15 00:00:00 UTC.
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Proximity to an SGC-DLPFC Individualized Functional Target and outcomes in large rTMS clinical trials for Treatment-Resistant Depression
Background Targeting methods for repetitive transcranial magnetic stimulation (rTMS) in patients with depression now include the use of individual functional scans to target specific functional connectivity (FC) patterns obtained from functional magnetic resonance imaging (fMRI). Potential biomarkers of rTMS response include target FC with the subgenual anterior cingulate cortex (SGC) or the causal depression circuit (CDC), each of which may be candidates for individualized functional targets (iFTs). We assessed the relationship of these two approaches to clinical outcomes in two large rTMS clinical trials. Methods 501 subjects with moderate to severe depression underwent 4-6 weeks of daily rTMS to the left dorsolateral prefrontal cortex (DLPFC), targeted using neuronavigation to a common group-based functional target. Resting-state scans acquired at baseline were used to retrospectively compute iFTs using either SGC-DLPFC or CDC-DLPFC FC. The Euclidean distance from the group-based target used in the trial to the centre of gravity of each iFT was computed and correlated with outcomes. Results Most subjects' iFTs were within 2cm of their group-based target. Proximity to either the SGC- or CDC-iFT was not associated with better outcomes. Sensitivity analyses accounting for treatment target FC, methodology, data quality, or treatment parameters did not change the results. Conclusions Proximity to SGC- or CDC-derived iFTs was not associated with better outcomes in patients who received neuronavigated rTMS to a group-based target. Prospective randomized clinical trials comparing neuronavigated group-based target to neuronavigated iFTs are needed.in bioRxiv: Neuroscience on 2025-07-15 00:00:00 UTC.
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White matter reorganization of motor and affective-motivational networks in pain-indifferent carriers of the R221W mutation
Congenital insensitivity to pain (CIP) due to the R221W mutation on the nerve growth factor gene results in reduced peripheral C-nociceptor density and behavioural indifference to painful stimuli. While functional neuroimaging has revealed altered cortical and sub-cortical pain processing in R221W carriers, structural white matter changes remain unexplored and may suggest an anatomical basis of symptoms. Heterozygous R221W carriers (n = 11) and age-, sex-, education-matched controls (n = 11) diffusion tensor imaging data were compared using fixel-based analysis, and complimentary edge and node analyses using graph theory, and network-based statistics. Whole-brain and region of interest (ROI) fixel-based analyses revealed significantly reduced fibre density and fibre-bundle cross-section in brainstem motor tracts of R221W carriers, encompassing the corticospinal pathways, corona radiata, external capsule, cerebellar peduncles, and pontine crossing (p < 0.05). Graph theory analysis of pain-processing ROIs demonstrated reduced local efficiency in right anterior cingulate cortex (ACC) and altered betweenness centrality in bilateral insula and left ACC of R221W carriers. Despite R221W carriers showing higher node degrees in the somatosensory cortex and ACC, these connections had reduced efficiency and integration with cortical network regions. Network-based statistics identified a possible compensatory subnetwork with stronger connectivity from right thalamus to left ACC and left insula in R221W carriers (p < 0.019). These findings suggest that congenitally reduced peripheral nociception could lead to abnormalities in the thalamocortical and motor efferent pathway, but not sensory afferent pathways. The combination of reduced brainstem motor tract integrity and altered cortical network efficiency, alongside potentially compensatory thalamo-cortical connectivity, could support a model of R221W CIP as motor under-reactivity rather than sensory insensitivity.in bioRxiv: Neuroscience on 2025-07-15 00:00:00 UTC.
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Multiscale Metabolic Covariance Networks Uncover Stage-Specific Biomarker Signatures Across the Alzheimer's Disease Continuum.
Background Connectomics studies analyze neural connections and their roles in cognition and disease. Beyond regional comparisons, recent research has revealed inter-regional brain relationships via graph theory of brain network connectivity. Within these networks, path length measures a network's efficiency in communication. These connections can be quantified as inter-subject covariance networks related to functional connectivity, with alterations reported in neurodegenerative diseases. Methods Retrospective analysis of ADNI 18F-FDG PET images using metabolic covariance analysis and hierarchical clustering was used to assess regional brain networks in subjects from cognitively normal (CN) to AD. We evaluated AD stage changes by calculating whole brain entropy, connection strength, and clustering coefficients. Additionally, estimates of shortest path for positive and negative correlations as a measure of network efficiency. We also developed a novel region set enrichment analysis (RSEA) to detect brain functional changes based on metabolic variations. Results were aligned with transcriptomic signatures and clinical cognitive assessments. Findings In AD subjects, whole brain metabolic connectivity revealed an increase in entropy, connection strength, and clustering coefficients, which indicates brain network reorganization as compensatory mechanisms of pathological disruption. As AD advances, path lengths between brain regions decrease from CN to MCI; however, path lengths significantly increased in AD. RSEA indicated functional changes in motor, memory, language, and cognition functions related to disease progression. Interpretation Metabolic covariance analysis of whole brain, and regional connectomics, track with AD progression. Moreover, path lengths permitted AD stages determination via alterations in brain connectivity. Furthermore, RSEA facilitated the identification of functional changes based on metabolic readouts.in bioRxiv: Neuroscience on 2025-07-15 00:00:00 UTC.
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Modulation of prefrontal functional connectivity by anodal tDCS over left DLPFC predicts performance enhancement in competitive swimmers: A simultaneous tDCS-fNIRS, double-blind, sham-controlled crossover study
While transcranial direct current stimulation (tDCS) has been proposed as a method to enhance physical performance in athletes, the underlying neural mechanisms and the reasons for the widely reported individual variability in its effects remain unclear. This study investigated whether prefrontal hemodynamic responses, measured by functional near-infrared spectroscopy (fNIRS), are associated with the effects of anodal tDCS over the left dorsolateral prefrontal cortex (DLPFC) on swimming performance. In a double-blind, sham-controlled, crossover design, eight trained male swimmers performed 100 m freestyle trials under both anodal tDCS and sham conditions. We recorded prefrontal cortical activation and functional connectivity using fNIRS during a resting-state period and a subsequent stimulation period. While tDCS led to a numerical improvement in 100 m freestyle time, the overall effect was not statistically significant. The fNIRS analyses revealed that tDCS significantly reduced intra-hemispheric functional connectivity, especially in the stimulated left prefrontal cortex. Crucially, the magnitude of this connectivity reduction correlated with the degree of performance improvement, suggesting a direct brain-behavior link. Exploratory analyses further suggested that baseline functional connectivity could predict an individual's neural response to tDCS, with those having higher baseline connectivity showing a greater reduction. These findings suggest that tDCS over the left DLPFC may enhance physical performance by increasing the neural efficiency of prefrontal networks. Therefore, baseline functional connectivity is a promising physiological biomarker that could be used to personalize neuromodulation protocols in athletes.in bioRxiv: Neuroscience on 2025-07-15 00:00:00 UTC.
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Prevalence, satisfaction and preference of tooth shades and their correlation with age, gender and skin color : A cross sectional study [version 3; peer review: 1 approved, 2 approved with reservations]
Shade selection is a crucial factor influencing patient satisfaction with dental prostheses and the overall success of treatment. Conventional visual methods of shade selection are inherently subjective, regardless of the availability of dental records or photographs. This study aims to assess the prevalence, satisfaction, and preference of tooth shades in a cross-section of the local Indian population and to evaluate their correlation with age, gender, and skin color in a diverse sample of 120 participants visiting the outpatient department of this Institution. Methods A total of 120 participants, comprising 60 males and 60 females across four age groups (20-30, 30-40, 40-50, and 50-60 years), were visually evaluated using the VITA 3D Master shade guide. Participants also indicated their satisfaction with their current tooth shade and their preferred shade. Results Value 2 was the most prevalent existing shade (52.5%) and the most preferred across all groups, especially among individuals aged 41-50 years (p < 0.001). Satisfaction was highest among males aged 41–50 years. No significant association was found between skin color and tooth shade preference or satisfaction. Conclusions The results suggest that shades in the Value 2 category are universally preferred, offering a useful reference for shade selection in clinical practice.in F1000Research on 2025-07-14 16:26:20 UTC.
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Examining types of Organizational Structure in Private Chartered Universities in Western Uganda and their Impact on Academic Staff Performance [version 1; peer review: awaiting peer review]
Background This study examines the types of organizational structures in privately chartered universities in Western Uganda and how these structures impact academic staff performance. Grounded in Henri Fayol’s Administrative Management Theory and Vroom’s Expectancy Theory, this study integrates structural and motivational perspectives to explore the impact of institutional design on academic operations. Method A concurrent triangulation research design was employed to combine quantitative and qualitative approaches. Data were collected from 186 academic staff members using structured questionnaires and 10 academic deans through in-depth interviews. Results Quantitative findings revealed that functional and hierarchical structures were the most common, with 55.4% of respondents reporting highly centralized decision-making and 42.5% reporting poor communication flow. A significant positive correlation was observed between organizational structure and academic staff performance (r = 0.512, p < 0.01). Regression analysis showed that organizational structure explained 26.2% of the variance in academic staff performance (R2 = 0.262, F (1, 184) = 65.46, p < 0.001). Qualitative data supported these results, with participants highlighting that rigid and bureaucratic structures limit flexibility, innovation, and collaboration, whereas excessive centralization undermines academic autonomy (Yusoff & Isa, 2021). Conclusion The study concludes that, while traditional structures dominate private chartered universities, they often hinder academic performance. To enhance staff effectiveness, universities should adopt adaptive and participatory structures (Berkowitz, 2023). Aligning Fayol’s principles of work specialization, centralization, and communication flow with Vroom’s motivational framework offers a strategic path for organizational improvement.in F1000Research on 2025-07-14 14:38:37 UTC.
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Should We Adopt Continuous Infusions of β-lactam Antibiotics in Clinical Practice at This Time? [version 1; peer review: awaiting peer review]
This correspondence discusses the BLING III study by Dulhunty and colleagues, which reported no difference in 90-day mortality between continuous and intermittent infusions of β-lactam antibiotics in critically ill patients with sepsis. Both this study and subsequent meta-analysis are in favor of continuous infusions for the potential benefits of secondary endpoints though the primary endpoints is not significant. We elaborate that continuous infusion of time-dependent antimicrobials might result in treatment failure as well as the emergence of antimicrobial resistance if the steady state concentration is below the desired target. That might be a potential explanation of the negative results observed in BLING III and other studies. In our opinion, extended or prolonged infusion of β-lactams (over 3 hours) might provide a better target attainment, even without therapeutic drug monitoring.in F1000Research on 2025-07-14 14:34:32 UTC.
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Absent in Melanoma 2 Gene Associated Periodontitis and Coronary Heart Disease [version 2; peer review: 1 approved, 1 approved with reservations]
Aims To study the association between AIM2 gene polymorphisms and the tendency for periodontal infection and coronary heart disease, and to determine whether males or females are more susceptible to these diseases. Additionally, we examined its association with the features of periodontal disease. Methods 140 patients were enrolled in this study, and those who took part were divided into four groups as follows: healthy (c), periodontal disease (P), coronary heart disease with intact periodontium (AS-C), and coronary heart disease with periodontal disease (AS-P). Information on entrants, including age ranging from 40 to 70 years old, sex of both males and females who fulfilled the study’s criteria, body mass index with ordinary rang less than 30 kg/m2, socioeconomic status, tooth brushing frequency at least two times per- day, and indicators of periodontal disease severity, was documented. Blood samples were collected, and AIM2 gene polymorphisms were evaluated by polymerase chain reaction test, gel phase, and sequences. Results Genetic analysis of AIM2 G/T (rs2793845) revealed a high frequency of the (T) allele and (GT and TT) genotypes that were detected in the periodontal disease and coronary heart disease groups in males. The Hardy-Weinberg equilibrium of alleles and genotypes did not differ significantly (p > 0.05) between the study groups. Gene polymorphisms were also significantly (p ≤ 0.05) correlated with indicators of periodontal disease severity. Conclusion High frequenting of (T) alleles and (GT, TT) genotypes in AIM2 single nucleotide polymorphisms (SNP) were associated with an increased tendency to develop periodontal disease and coronary heart disease. It can be supposed that it has a causative function in the pathophysiology of both disorders, and the validity of SNP as a potential genomic factor for the risk of both disorders in Iraqi males.in F1000Research on 2025-07-14 14:31:47 UTC.
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Multi-scale phylodynamic modelling of rapid punctuated pathogen evolution
by Quang Dang Nguyen, Sheryl L. Chang, Carl J. E. Suster, Rebecca J. Rockett, Vitali Sintchenko, Tania C. Sorrell, Mikhail Prokopenko
Computational multi-scale pandemic modelling remains a major and timely challenge. Here we identify specific requirements for a new class of models simulating pandemics across three scales: (1) pathogen evolution, often punctuated by the rapid emergence of new variants, (2) human interactions within a heterogeneous population, and (3) public health responses which constrain individual actions to control the disease transmission. We then present a pandemic modelling framework satisfying these requirements and capable of simulating feedback loops between dynamics unfolding at these different scales. The developed framework comprises a stochastic agent-based model of pandemic spread, coupled with a phylodynamic model that incorporates within-host pathogen evolution. It is validated with a case study, modelling the punctuated evolution of SARS-CoV-2, based on global and contemporary genomic surveillance data, which captures a large heterogeneous population. We demonstrate that the model replicates the essential features of the COVID-19 pandemic and virus evolution, while retaining computational tractability and scalability.in PLoS Computational Biology on 2025-07-14 14:00:00 UTC.
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Statistical analysis supports the size control mechanism of Chlamydia development
by Jinsu Kim, Christine Sütterlin, Ming Tan, German Enciso
Chlamydia is an intracellular bacterium that reproduces via an unusual developmental cycle that only occurs within a eukaryotic host cell. A replicating form of the bacterium (RB) repeatedly divides to produce about a thousand progeny, which convert in a delayed and asynchronous manner into the infectious form (EB). The regulatory mechanisms that control this developmental switch are unknown, but they could potentially include extrinsic signals from the host cell or other chlamydiae, or an intrinsic signal such as chlamydial cell size. In this paper, we investigated the regulation of RB-to-EB conversion by developing and analyzing three mathematical models, each based on a different regulatory mechanism. To test these models, we derived statistical evidence from parameters, including number, size and location of RBs and EBs, obtained from experimental measurements and model fitting. All three models successfully reproduced the experimentally measured timing of RB-to-EB conversion and growth curves of the developmental forms in an infected cell. However, only the size control model, which postulates that RB size is an intrinsic signal that regulates the timing of RB-to-EB conversion, reproduced two additional statistical properties of the intracellular infection. These properties are a positive correlation between the number of RBs and EBs throughout the developmental cycle and the monotonic evolution of the coefficient of variation of EB number. This analysis thus provides support for the size control model.in PLoS Computational Biology on 2025-07-14 14:00:00 UTC.
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Coarse-grained model of serial dilution dynamics in synthetic human gut microbiome
by Tarun Mahajan, Sergei Maslov
Many microbial communities in nature are complex, with hundreds of coexisting strains and the resources they consume. We currently lack the ability to assemble and manipulate such communities in a predictable manner in the lab. Here, we take a first step in this direction by introducing and studying a simplified consumer resource model of such complex communities in serial dilution experiments. The main assumption of our model is that during the growth phase of the cycle, strains share resources and produce metabolic byproducts in proportion to their average abundances and strain-specific consumption/production fluxes. We fit the model to describe serial dilution experiments in hCom2, a defined synthetic human gut microbiome with a steady-state diversity of 63 species growing on a rich media, using consumption and production fluxes inferred from metabolomics experiments. The model predicts serial dilution dynamics reasonably well, with a correlation coefficient between predicted and observed strain abundances as high as 0.8. We applied our model to: (i) calculate steady-state abundances of leave-one-out communities and use these results to infer the interaction network between strains; (ii) explore direct and indirect interactions between strains and resources by increasing concentrations of individual resources and monitoring changes in strain abundances; (iii) construct a resource supplementation protocol to maximally equalize steady-state strain abundances.in PLoS Computational Biology on 2025-07-14 14:00:00 UTC.
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Decoding metastatic microenvironments through single-cell omics reveals new insights into niche dynamics and tumor evolution
by Fengshuo Liu, Xiang H.-F. Zhang
Metastasis is the predominant cause of cancer mortality, primarily driven by complex tumor–host interactions within specialized metastatic niches. Recent advances in single-cell technologies have provided unprecedented insights into metastatic niche formation, evolution and function, including how primary tumors precondition distant organs for metastases and how disseminated tumor cells dynamically interact with host cells to modulate their environments. Integrated single-cell studies across multiple cancer types have also revealed divergent and convergent metastatic adaptation strategies. These findings collectively highlight metastasis as a dynamic, cooperative process shaped by intricate tumor–host interactions, and provide a foundation for novel therapeutic strategies targeting components of the metastatic microenvironment.in PLoS Biology on 2025-07-14 14:00:00 UTC.
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Bi-directional metabolic reprogramming between cancer cells and T cells reshapes the anti-tumor immune response
by Yajing Qiu, Yihan Xu, Xinyuan Ding, Congcong Zhao, Hongcheng Cheng, Guideng Li
Cancer cells and T cells engage in dynamic crosstalk within the tumor microenvironment (TME), shaping tumor progression and anti-tumor immunity. While cancer cells reprogram metabolism to support growth and immune evasion, T cells must adapt their metabolic states to maintain effector functions. Tumor-driven metabolic perturbations, such as nutrient depletion and accumulation of immunosuppressive metabolites, profoundly impair T cell function and fate. Conversely, metabolically reprogrammed T cells can modulate the TME and influence tumor growth. This reciprocal metabolic crosstalk represents both metabolic competition and intercellular communication, offering promising therapeutic targets.in PLoS Biology on 2025-07-14 14:00:00 UTC.
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Mutation bias alters the distribution of fitness effects of mutations
by Mrudula Sane, Shazia Parveen, Deepa Agashe
Mutation bias is an important factor determining the diversity of genetic variants available for selection. As adaptation proceeds and some beneficial mutations are fixed, new beneficial mutations become rare, limiting further adaptation. The depletion of beneficial mutations is especially stark within the mutational class favored by the existing mutation bias. Recent theoretical work predicts that this problem may be alleviated by a change in the direction of mutation bias (i.e., a bias reversal). If populations sample previously underexplored types of mutations, the distribution of fitness effects (DFE) of mutations should shift towards more beneficial mutations. Here, we test this prediction using Escherichia coli, which has a transition mutation bias, with ~54% single-nucleotide mutations being transitions compared to the unbiased expectation of ~33% transitions. We generated mutant strains with a wide range of mutation biases, from 97% transitions to 98% transversions, either reinforcing or reversing the wild-type transition bias. Quantifying DFEs of ~100 single mutations obtained from mutation accumulation experiments for each strain, we find strong support for the theoretical prediction. Strains that oppose the ancestral bias (i.e., with a strong transversion bias) have DFEs with the highest proportion of beneficial mutations, whereas strains that exacerbate the ancestral transition bias have up to 10-fold fewer beneficial mutations. Such dramatic differences in the DFE should drive large variation in the rate and outcome of adaptation, suggesting an important and generalized evolutionary role for mutation bias shifts.in PLoS Biology on 2025-07-14 14:00:00 UTC.
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Sensory and motor contents are prioritized dynamically in working memory
by Irene Echeverria-Altuna, Sage E. P. Boettcher, Freek van Ede, Anna C. Nobre
Internal selective attention prioritizes both sensory and motor contents in working memory to guide prospective behavior. Prior research has shown how attention modulation of sensory contents is flexible and temporally tuned depending on access requirements, but whether the prioritization of motor contents follows similar flexible dynamics remains elusive. Also uncharted is the degree of co-dependence of sensory and motor modulation, which gets at the nature of both working-memory representations and internal attention functions. To address these questions, we independently tracked the prioritization of sensory and motor working-memory contents as a function of dynamically evolving temporal expectations in human participants. The design orthogonally manipulated when an item location (left versus right side) and associated prospective action (left versus right hand) would be relevant. Contralateral-vs-ipsilateral modulation of posterior alpha (8–12 Hz) activity in electroencephalography (EEG) tracked prioritization of the item location, while contralateral-vs-ipsilateral modulation of central mu/beta (8–30 Hz) activity tracked response prioritization. Proactive and dynamic alpha and mu/beta modulation confirmed the flexible and temporally structured prioritization of sensory and motor contents alike. Intriguingly, the prioritization of sensory and motor contents was temporally uncoupled, showing dissociable patterns of modulation. The findings reveal multiple modulatory functions of internal attention operating in tandem to prepare relevant aspects of internal representations for adaptive behavior.in PLoS Biology on 2025-07-14 14:00:00 UTC.
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The locus coeruleus influences behavior by coordinating effective integration of fear memories and sensory input
by Haoyu Duan, Tianyu Wang, Xinyang Zhang, Dan Xia, Zeyi Wang, Tsz Hei Fong, Tianxiang Li, Rongzhen Yan, Yang Zhan, Yulong Li, Wen-Jun Gao, Qiang Zhou
An essential function of memory is to guide behavior for better survival and adaptation. While memory formation has been extensively studied, far less is understood about how memory retrieval influences behaviors. In the auditory Pavlovian threat conditioning paradigm using C57BL/6J mice, retrieving a conditioned threat memory is associated with spiking in two dorsomedial prefrontal cortex (dmPFC) neurons with transient (T-neurons) and sustained (S-neurons) patterns. We show here that T-neurons and S-neurons are two distinct neuronal populations with different neuronal and synaptic properties and mRNA profiles. S-neuron spiking matches freezing behavior and is required for freezing. This sustained activity in S-neurons requires auditory inputs and the release of norepinephrine (NE) in the dmPFC. The activation of the locus coeruleus (LC) is initiated by dmPFC T-neuron inputs, sustained by auditory inputs, and is required for the transition to freezing by enhancing S-neuron activity. Interestingly, LC activation precipitates a brief period during which nonconditioned cues also induce freezing. Our findings highlight the critical contribution of the LC/NE system in the transition from memory to behavior, which coordinates the effective integration of memory, sensory inputs and emotional state for optimal adaptation.in PLoS Biology on 2025-07-14 14:00:00 UTC.
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A major trade-off between growth and defense in Arabidopsis thaliana can vanish in field conditions
by Derek S. Lundberg, Sonja Kersten, Ezgi Mehmetoğlu Boz, Pratchaya Pramoj Na Ayutthaya, Wangsheng Zhu, Karin Poersch, Wei Yuan, Sophia Swartz, David Müller, Ilja Bezrukov, HARVEST TEAM , Detlef Weigel
When wild plants defend themselves from pathogens, this often comes with a trade-off: the same genes that protect a plant from disease can also reduce its growth and fecundity in the absence of pathogens. One protein implicated in a major growth-defense trade-off is ACCELERATED CELL DEATH 6 (ACD6), an ion channel that modulates salicylic acid (SA) synthesis to potentiate a wide range of defenses. Wild Arabidopsis thaliana populations maintain significant functional variation at the ACD6 locus, with some alleles making the protein hyperactive. In the greenhouse, plants with hyperactive ACD6 alleles are resistant to diverse pathogens, yet they are of smaller stature, their leaves senesce earlier, and they set fewer seeds compared to plants with the standard allele. We hypothesized that ACD6 hyperactivity would not only affect the growth of microbial pathogens but also more generally change leaf microbiome assembly. To test this in an ecologically meaningful context, we compared plants with hyperactive, standard, and defective ACD6 alleles in the same field-collected soil, both outdoors and in naturally lit and climate-controlled indoor conditions, taking advantage of near-isogenic lines as well as a natural accession and a CRISPR-edited derivative. We surveyed visual phenotypes, gene expression, hormone levels, seed production, and the microbiome in each environment. The genetic precision of CRISPR-edited plants allowed us to conclude that ACD6 genotype had no effect on mature field plants in our setting, despite reproducibly dramatic effects on greenhouse plants. We conclude that additional abiotic and/or microbial signals present outdoors—but not in the greenhouse—greatly modulate ACD6 activity. This raises the possibility that the fitness costs of other commonly studied immune system genes may be grossly misjudged without field studies.in PLoS Biology on 2025-07-14 14:00:00 UTC.
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A paradigm to study the learning of muscle activity patterns outside of the natural repertoire
Journal of Neurophysiology, Volume 134, Issue 1, Page 347-360, July 2025.
in Journal of Neurophysiology on 2025-07-14 12:38:22 UTC.
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Experience-induced NPAS4 reduces dendritic inhibition from CCK+ inhibitory neurons and enhances plasticity
Journal of Neurophysiology, Volume 134, Issue 1, Page 361-371, July 2025.
in Journal of Neurophysiology on 2025-07-14 12:38:21 UTC.
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Anisomycin selectively inhibits orientation tuning shifts in mouse visual cortex
Journal of Neurophysiology, Volume 134, Issue 1, Page 372-381, July 2025.
in Journal of Neurophysiology on 2025-07-14 12:38:20 UTC.
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Is the degree of post-activation depression similar between soleus responses evoked by transcutaneous spinal cord and peripheral nerve stimulation?
Journal of Neurophysiology, Ahead of Print.
in Journal of Neurophysiology on 2025-07-14 12:11:16 UTC.
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N-methyl d-aspartate receptor hypofunction reduces steady state visual evoked potentials
Journal of Neurophysiology, Ahead of Print.
in Journal of Neurophysiology on 2025-07-14 12:11:14 UTC.
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Quantifying evolutionary rescue probabilities upon standing genetic variation and de novo mutations
Author(s): Paulo R. A. Campos
This study quantifies the probabilities of evolutionary rescue (ER) in populations facing abrupt environmental changes. We consider a population of asexual haploid individuals that evolves without recombination. The research integrates standing genetic variation and de novo mutations within a framew…
[Phys. Rev. E 112, 014404] Published Mon Jul 14, 2025
in Physical Review E: Biological physics on 2025-07-14 10:00:00 UTC.
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Role of gap junctions and clustered connectivity in emergent synchronization patterns of inhibitory neuronal networks
Author(s): Hélène Todd, Mathieu Desroches, Alex Cayco-Gajic, and Boris Gutkin
Inhibitory interneurons, ubiquitous in the central nervous system, form networks connected through both chemical synapses and gap junctions. These networks are essential for regulating the activity of principal neurons, especially by inducing temporally patterned dynamic states. Here, we aim to unde…
[Phys. Rev. E 112, 014405] Published Mon Jul 14, 2025
in Physical Review E: Biological physics on 2025-07-14 10:00:00 UTC.
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Intracellular cargo transport along multiple filaments with lane switching
Author(s): Naruemon Rueangkham and Rhoda J. Hawkins
Cargo transport mostly involves multiple molecular motors that move along cytoskeletal filaments. Such motors have the ability to detach and reattach on a filament and possibly switch onto another filament. Cargo transport by multiple motors on a single filament has been widely studied; however, car…
[Phys. Rev. E 112, 014406] Published Mon Jul 14, 2025
in Physical Review E: Biological physics on 2025-07-14 10:00:00 UTC.
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Popularity and diversity: The negative relationship in baby names in the United Kingdom [version 2; peer review: 2 approved with reservations]
Background Previous research has shown that popular names have become less popular over time. Simultaneously, accumulated evidence has indicated that names have become more diverse. However, the association between these two phenomena was unclear. This association should be revealed for a better understanding of names and naming practices. Therefore, this study investigated the relationship between the popularity and diversity of names. Methods I analyzed the data provided in a previous study in the U.K., which included complete records of all live births between 1996 and 2016 (N = 12,985,140). Results I found that the correlations between diversity and popularity indicators were highly negative, showing that they are conceptually strongly related. This means that when diversity is high, popularity is low. Conclusions Based on this study, we can predict one indicator from the other indicator. Because raw data on names are generally difficult to collect, this prediction is useful for understanding names and naming practices.in F1000Research on 2025-07-14 09:44:12 UTC.
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Connectivity of a Conspicuous Layered Structure in the Dorsal Telencephalon of the Peacock Gudgeon, Tateurndina ocellicauda
Tract tracing experiments of a layered structure in the dorsal telencephalon (Dx) in a gobiiform fish revealed intratelencephalic projections and projections from nuclei of the preglomerular complex. Injections into the dorsomedial (Dm) and dorsolateral (dDl) telencephalon showed that Dx and Dm are two distinct zones with their own connectivity profiles. Dx and dDl projections show some overlap and different areas of the preglomerular complex project to distinct dorsal telencephalic target zones.
ABSTRACT
Within the mammalian pallium, layered structures, such as the six-layered isocortex and the three-layered hippocampal formation, are crucial for integrating sensory cues from the environment and for forming and recalling memories. Similar layered pallial systems have also been shown in avian and non-avian reptiles. Despite sharing similar needs for processing external information and remembering important sites, teleosts have generally not evolved such defined layered organizations in their dorsal telencephalon. One exception is gobiiform fishes in which a subregion of the dorsal telencephalon is organized into several fiber-rich and soma-dense layered subregions. We investigated the connectivity of these layered subregions (referred to as the dorsal telencephalic area X, Dx), as well as the connectivity of the medial (Dm) and dorsolateral (dDl) parts of the dorsal telencephalon through tracer injections. We found that extratelencephalic projections reach Dm, Dx, and dDl from different regions within the preglomerular complex (PG): Dm receives input from different PG regions: PG region 1 (PG1), PG region 2 (PG2), and the commissural PG (PGc), but not from the nucleus prethalamicus (PTh). In contrast, both Dx and dDl receive projections from the lateral PTh (PTh-l) and the medial PTh (PTh-m). We find that projections to dDl come from more ventral regions of PTh-m than those that project to Dx. The majority of ascending connections could be found within the telencephalon itself, with each of the telencephalic zones receiving its own distinct pattern of intratelencephalic afferent connectivity. From our results, we conclude that Dm and Dx constitute two distinct zones of the dorsal telencephalon.
in Journal of Comparative Neurology on 2025-07-14 07:00:00 UTC.
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Feeding State-Specific Hormonal Tuning of Neural Circuit Modulation
Journal of Neurophysiology, Ahead of Print.
in Journal of Neurophysiology on 2025-07-14 04:41:27 UTC.
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Corticospinal excitability during timed interception depends on the speed of the moving target
Journal of Neurophysiology, Ahead of Print.
in Journal of Neurophysiology on 2025-07-14 04:41:13 UTC.
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Physiological and injury-induced microglial dynamics across the lifespan
Tieu, T. et al. leveraged in vivo two-photon live imaging of microglia, the brain’s resident immune cells, to reveal striking differences in their morphology, basal dynamics, and injury responses across life stages.in Cell Reports: Current Issue on 2025-07-14 00:00:00 UTC.
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A common mechanism of singular gene choice is revealed by broadly expressed odorant receptor transgenes within the olfactory epithelium
Makhlouf et al. utilize two OR minigenes carrying strong gene choice enhancers and characterize both their glomerular coalescence and impact on endogenous expression of class I, class II, and TAAR genes using bulk RNA-seq. They reveal a universal mechanism of gene choice and an underappreciated dorsal to ventral pattern of glomerular formation.in Cell Reports: Current Issue on 2025-07-14 00:00:00 UTC.
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Discovery and structural basis of endogenous and exogenous inhibitors of the proton-activated-chloride channel
Zhang et al. report a series of cholesterol analogs and structurally related dyes as inhibitors of the proton-activated chloride (PAC) channel, with deoxycholic acid (DCA) and Evans blue (EB) as their representatives. Cryo-EM structures and patch-clamp recordings reveal their differential inhibitory mechanisms, forming a solid basis for drug discovery targeting this channel.in Cell Reports: In press on 2025-07-14 00:00:00 UTC.
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Bispecific antibody-antigen complex structures reveal activity enhancement by domain rearrangement
Structural insight into the bispecific antibody (BsAb) that bridges two different antigens is critical for cancer drug development. Sato et al. determine cryo-EM structures of diabody-type BsAbs in ternary complex with their antigens, EGFR and CD3. These structures reveal how domain rearrangements enhance their anti-cancer activity.in Cell Reports: In press on 2025-07-14 00:00:00 UTC.
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Time is encoded by methylation changes at clustered CpG sites
Ochana et al. profiled DNA methylation across clustered CpG sites in blood from over 300 healthy individuals. They identified two distinct modes of age-related change—stochastic and block-like—and developed a deep learning methylation clock that predicts chronological age from just two genomic loci with a median accuracy of 1.36 years.in Cell Reports: In press on 2025-07-14 00:00:00 UTC.
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Non-canonical activating roles of RCoR2 sustain transcription in adrenergic neuroblastoma
Aloisi et al. identified RCoR2 as an exclusively expressed marker of adrenergic neuroblastomas. While CoREST family members are traditionally repressors, RCoR2 uniquely facilitates enhancer-promoter interactions of genes controlled by the core regulatory transcription factors, sustaining oncogenic programs and tumor cell survival, establishing it as a pivotal neuroblastoma vulnerability.in Cell Reports: In press on 2025-07-14 00:00:00 UTC.
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Non-canonical role of RCoR2: The repressor turns activator
Transcriptional programs are strictly regulated during normal development, in a balance between activators and repressors. In this issue, Aloisi et al. demonstrate a non-canonical role of the corepressor RCoR2 that turned out to play an activating role in adrenergic neuroblastoma.in Cell Reports: In press on 2025-07-14 00:00:00 UTC.
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Publisher Correction: Targeting GRPR for sex hormone-dependent cancer after loss of E-cadherin
Nature, Published online: 14 July 2025; doi:10.1038/s41586-025-09353-9
Publisher Correction: Targeting GRPR for sex hormone-dependent cancer after loss of E-cadherinin Nature on 2025-07-14 00:00:00 UTC.
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Experimental demonstration of logical magic state distillation
Nature, Published online: 14 July 2025; doi:10.1038/s41586-025-09367-3
Experimental demonstration of logical magic state distillationin Nature on 2025-07-14 00:00:00 UTC.
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Scientists everywhere must be protected
Nature, Published online: 14 July 2025; doi:10.1038/d41586-025-02213-6
Scientists everywhere must be protectedin Nature on 2025-07-14 00:00:00 UTC.
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A motorcycle ride through the forest: how I protect Nigeria’s wildlife
Nature, Published online: 14 July 2025; doi:10.1038/d41586-025-02211-8
Emmanuel Barde Elisha is a research coordinator for a non-profit organization dedicated to managing protected areas for conservation and local development.in Nature on 2025-07-14 00:00:00 UTC.
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Embattled US scientific society names palaeontologist to top job
Nature, Published online: 14 July 2025; doi:10.1038/d41586-025-02236-z
Neil Shubin, a University of Chicago researcher known for co-discovering a ′missing link’ fossil fish, will lead the National Academy of Sciences if ratified by members.in Nature on 2025-07-14 00:00:00 UTC.
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Microbiota-driven antitumour immunity mediated by dendritic cell migration
Nature, Published online: 14 July 2025; doi:10.1038/s41586-025-09249-8
A newly identified bacterial strain (YB328) isolated from the faeces of patients who responded to immune checkpoint blockade therapies can promote antitumour immunity through the activation of tumour-specific CD8+ T cells.in Nature on 2025-07-14 00:00:00 UTC.
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Need to update your data? Follow these five tips
Nature, Published online: 14 July 2025; doi:10.1038/d41586-025-02210-9
Researchers engaged in long-term projects often need to update their data sets over time. Here’s how to do it while maintaining reproducibility.in Nature on 2025-07-14 00:00:00 UTC.
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Giant radio telescope was ‘a natural magnet’ for African talent
Nature, Published online: 14 July 2025; doi:10.1038/d41586-025-01984-2
Roger Deane has seen the investment in astronomy on the continent pay off both in his own career and with more young scientists joining the field.in Nature on 2025-07-14 00:00:00 UTC.
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AI hype, crackling northern lights and more: take it all in with these holiday reads
Nature, Published online: 14 July 2025; doi:10.1038/d41586-025-02198-2
Ten of Nature’s recent contributors share their current book obsessions.in Nature on 2025-07-14 00:00:00 UTC.
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Why I study trauma's genetic legacy
Nature, Published online: 14 July 2025; doi:10.1038/d41586-025-02233-2
Rana Dajani's research includes identifying risk factors for type 2 diabetes, and genetic links to trauma.in Nature on 2025-07-14 00:00:00 UTC.
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Why do ageing rates vary by country? Massive study says politics play a part
Nature, Published online: 14 July 2025; doi:10.1038/d41586-025-02181-x
Social inequality and the decay of democratic institutions are linked to accelerated ageing — but education seems to slow the process.in Nature on 2025-07-14 00:00:00 UTC.
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Exclusive: NIH to dismiss dozens of grant reviewers to align with Trump priorities
Nature, Published online: 14 July 2025; doi:10.1038/d41586-025-02221-6
The move would undo years of work, leaving advisory councils understaffed, and without the full expertise needed for reviews.in Nature on 2025-07-14 00:00:00 UTC.
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Found: a human gut microbe that makes cancer therapy more effective in mice
Nature, Published online: 14 July 2025; doi:10.1038/d41586-025-02224-3
Cancer therapies called checkpoint inhibitor drugs, which ratchet up the immune system, work better when this bacterium is around.in Nature on 2025-07-14 00:00:00 UTC.
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China’s shift towards ‘organized research’: how can coordination and innovation co-exist?
Nature, Published online: 14 July 2025; doi:10.1038/d41586-025-02199-1
Beijing’s reorganization of science is a broad, systemic effort to reshape how research is conducted, applied and evaluated nationwide.in Nature on 2025-07-14 00:00:00 UTC.
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Direct interactions between the human insula and hippocampus during memory encoding
Nature Neuroscience, Published online: 14 July 2025; doi:10.1038/s41593-025-02005-1
The hippocampus and insula communicate when processing emotional memories. Discrete sites in the human insular cortex showed changes that predicted later memory recall, while others responded to emotional content.in Nature Neuroscience on 2025-07-14 00:00:00 UTC.
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Response of spatially defined microglia states with distinct chromatin accessibility in a mouse model of Alzheimer’s disease
Nature Neuroscience, Published online: 14 July 2025; doi:10.1038/s41593-025-02006-0
The dynamics of microglia states adjacent to or far from amyloid-beta plaques are unclear. Here the authors show that non-plaque-associated microglia modulate the cell population expansion in response to amyloid deposition, and Csf1 signaling regulates their transition to the amyloid-associated state.in Nature Neuroscience on 2025-07-14 00:00:00 UTC.
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An expanded subventricular zone supports postnatal cortical interneuron migration in gyrencephalic brains
Nature Neuroscience, Published online: 14 July 2025; doi:10.1038/s41593-025-01987-2
Neonatal gyrated brains harbor an elaborate subventricular zone, termed the Arc, supporting cortical migratory streams of diverse interneurons.in Nature Neuroscience on 2025-07-14 00:00:00 UTC.
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Fast, cost-effective and flexible DNA sequencing by roll-to-roll fluidics
Nature Methods, Published online: 14 July 2025; doi:10.1038/s41592-025-02730-2
This work introduces roll-to-roll fluidics, a method that enhances reagent efficiency and reduces turnaround time, enabling rapid and economical next-generation sequencing.in Nature Methods on 2025-07-14 00:00:00 UTC.
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Author Correction: Elevated heterotrophic activity in Guaymas Basin hydrothermal plumes influences deep-sea carbon cycling
Nature Communications, Published online: 14 July 2025; doi:10.1038/s41467-025-61906-8
Author Correction: Elevated heterotrophic activity in Guaymas Basin hydrothermal plumes influences deep-sea carbon cyclingin Nature Communications on 2025-07-14 00:00:00 UTC.
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Genome-wide antibiotic-CRISPRi profiling identifies LiaR activation as a strategy to resensitize fluoroquinolone-resistant Streptococcus pneumoniae
Nature Communications, Published online: 14 July 2025; doi:10.1038/s41467-025-61814-x
Streptococcus pneumoniae, the causative agent of pneumococcal disease, has become increasingly resistant to fluoroquinolones. Through CRISPRi-seq, the authors identify the role of the LiaFSR operon in resensitizing S. pneumoniae to fluoroquinolones.in Nature Communications on 2025-07-14 00:00:00 UTC.
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A neurofunctional signature of affective arousal generalizes across valence domains and distinguishes subjective experience from autonomic reactivity
Nature Communications, Published online: 14 July 2025; doi:10.1038/s41467-025-61706-0
Using fMRI, this study developed a brain signature for affective arousal that demonstrates high sensitivity across valence while remaining distinct from autonomic arousal and wakefulness, offering broad applications.in Nature Communications on 2025-07-14 00:00:00 UTC.
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Ultrafast Pockels laser
Nature Physics, Published online: 14 July 2025; doi:10.1038/s41567-025-02978-7
Ultrafast Pockels laserin Nature Physics on 2025-07-14 00:00:00 UTC.
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Spatial sound decomposer
Nature Physics, Published online: 14 July 2025; doi:10.1038/s41567-025-02979-6
Spatial sound decomposerin Nature Physics on 2025-07-14 00:00:00 UTC.
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Elusive isotope ratios
Nature Physics, Published online: 14 July 2025; doi:10.1038/s41567-025-02955-0
Unresolved discrepancies between isotope ratio measurements are not uncommon and have even slowed down the revision of the International System of Units, explains Juris Meija.in Nature Physics on 2025-07-14 00:00:00 UTC.
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Too late to be careless
Nature Physics, Published online: 14 July 2025; doi:10.1038/s41567-025-02967-w
Too late to be carelessin Nature Physics on 2025-07-14 00:00:00 UTC.
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Take a tour of telescopes or tokamaks
Nature Physics, Published online: 14 July 2025; doi:10.1038/s41567-025-02981-y
It’s never too early to plan for a regular dose of physics during your vacation. If you are looking for inspiration, we have some hidden physics gems for you.in Nature Physics on 2025-07-14 00:00:00 UTC.
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Entanglement accelerates quantum simulation
Nature Physics, Published online: 14 July 2025; doi:10.1038/s41567-025-02945-2
Large quantum systems with high entanglement are difficult to simulate with classical methods, but now it is shown that entanglement may be beneficial for quantum simulations.in Nature Physics on 2025-07-14 00:00:00 UTC.
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The Chongqing Adolescent Twin Study: An Integrative Multimodal Brain Imaging and Non-imaging Dataset
Scientific Data, Published online: 14 July 2025; doi:10.1038/s41597-025-05449-z
The Chongqing Adolescent Twin Study: An Integrative Multimodal Brain Imaging and Non-imaging Datasetin Nature scientific data on 2025-07-14 00:00:00 UTC.
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Rapid detection and capture of clinical Escherichia coli strains mediated by OmpA-targeting nanobodies
Communications Biology, Published online: 14 July 2025; doi:10.1038/s42003-025-08345-9
Two thoroughly characterized nanobodies are sufficient to detect over 90% of clinical Escherichia coli isolates by targeting the conserved and abundant outer membrane protein OmpA.in Nature communications biology on 2025-07-14 00:00:00 UTC.
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Normative evidence weighing and accumulation in correlated environments
The brain forms certain deliberative decisions following normative principles related to how sensory observations are weighed and accumulated over time. Previously we showed that these principles can account for how people adapt their decisions to the temporal dynamics of the observations (Glaze et al., 2015). Here, we show that this adaptability extends to accounting for correlations in the observations, which can have a dramatic impact on the weight of evidence provided by those observations. We tested online human participants on a novel visual-discrimination task with pairwise-correlated observations. With minimal training, the participants adapted to uncued, trial-by-trial changes in the correlations and produced decisions based on an approximately normative weighing and accumulation of evidence. The results highlight the robustness of our brain’s ability to process sensory observations with respect to not just their physical features but also the weight of evidence they provide for a given decision.in eLife on 2025-07-14 00:00:00 UTC.
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Polyphosphate discriminates protein conformational ensembles more efficiently than DNA promoting diverse assembly and maturation behaviors
Disordered proteins and domains often assemble into condensates with polyanionic nucleic acids, primarily via charge complementarity, regulating numerous cellular functions. However, the assembly mechanisms associated with the other abundant and ubiquitous, anionic, stress–response regulating polymer, polyphosphate (polyP), are less understood. Here, we employ the intrinsically disordered DNA-binding domain (DBD) of cytidine repressor (CytR) from E. coli to study the nature of assembly processes with polyP and DNA. CytR forms metastable liquid-like condensates with polyP and DNA, while undergoing liquid-to-solid transition in the former and dissolving in the latter. On mutationally engineering the ensemble to exhibit more or less structure and dimensions than the WT, the assembly process with polyP is directed to either condensates with partial time-dependent dissolution or spontaneous aggregation, respectively. On the other hand, the CytR variants form only liquid-like but metastable droplets with DNA which dissolve within a few hours. Polyphosphate induces large secondary-structure changes, with two of the mutants adopting polyproline II-like structures within droplets, while DNA has only minimal structural effects. Our findings reveal how polyphosphate can more efficiently discern conformational heterogeneity in the starting protein ensemble, its structure, and compactness, with broad implications in assembly mechanisms involving polyP and stress response in bacterial systems.in eLife on 2025-07-14 00:00:00 UTC.
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Harnessing AlphaFold to reveal hERG channel conformational state secrets
To design safe, selective, and effective new therapies, there must be a deep understanding of the structure and function of the drug target. One of the most difficult problems to solve has been the resolution of discrete conformational states of transmembrane ion channel proteins. An example is KV11.1 (hERG), comprising the primary cardiac repolarizing current, Ikr. hERG is a notorious drug anti-target against which all promising drugs are screened to determine potential for arrhythmia. Drug interactions with the hERG inactivated state are linked to elevated arrhythmia risk, and drugs may become trapped during channel closure. While prior studies have applied AlphaFold to predict alternative protein conformations, we show that the inclusion of carefully chosen structural templates can guide these predictions toward distinct functional states. This targeted modeling approach is validated through comparisons with experimental data, including proposed state-dependent structural features, drug interactions from molecular docking, and ion conduction properties from molecular dynamics simulations. Remarkably, AlphaFold not only predicts inactivation mechanisms of the hERG channel that prevent ion conduction but also uncovers novel molecular features explaining enhanced drug binding observed during inactivation, offering a deeper understanding of hERG channel function and pharmacology. Furthermore, leveraging AlphaFold-derived states enhances computational screening by significantly improving agreement with experimental drug affinities, an important advance for hERG as a key drug safety target where traditional single-state models miss critical state-dependent effects. By mapping protein residue interaction networks across closed, open, and inactivated states, we identified critical residues driving state transitions validated by prior mutagenesis studies. This innovative methodology sets a new benchmark for integrating deep learning-based protein structure prediction with experimental validation. It also offers a broadly applicable approach using AlphaFold to predict discrete protein conformations, reconcile disparate data, and uncover novel structure–function relationships, ultimately advancing drug safety screening and enabling the design of safer therapeutics.in eLife on 2025-07-14 00:00:00 UTC.
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S100a4+ alveolar macrophages accelerate the progression of precancerous atypical adenomatous hyperplasia by promoting the angiogenic function regulated by fatty acid metabolism
Lung cancer is preceded by premalignant lesions, and what factors drive this transformation and the potential regulatory mode in the context of tumor initiation remain to be elucidated. In the course of precancerous lesions in mice, we found a phasic shift in metabolic patterns. Macrophages are a heterogeneous cell population with high plasticity in the tumor microenvironment. Single-cell interaction and metabolic analyses highlighted a cellular state, S100a4+ alveolar macrophages, which exhibited distinct fatty acid metabolic activity, such as palmitic acid metabolism, at the atypical adenomatous hyperplasia stage, accompanied by an angiogenic-promoting function in a pre-neoplastic setting of mice. These findings were reproducible in human single-cell transcriptomes and had been confirmed by histopathological staining and in vitro cell coculture assays. Taken together, the results from this study demonstrated that the S100a4+ alveolar macrophage subset contributes to tumorigenesis by altering its metabolic state, suggesting that metabolic interventions targeting this cell state in the early stage of disease may delay neoplastic transformation of the lung epithelium.in eLife on 2025-07-14 00:00:00 UTC.
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PRDM16 functions as a co-repressor in the BMP pathway to suppress neural stem cell proliferation
BMP signaling acts as an instructive cue in various developmental processes such as tissue patterning, stem cell proliferation, and differentiation. However, it is not fully understood how this signaling pathway generates different cell-specific outputs. Here, we have identified PRDM16 as a key co-factor for BMP signaling in the mouse brain. PRDM16 contributes to a repressive role of BMP signaling on neural stem cell (NSC) proliferation. We demonstrate that PRDM16 regulates the genomic distribution of BMP pathway transcription factors, the SMAD4/pSMAD complex, preventing the activation of cell proliferation genes. When Prdm16 is lost, the SMAD complex relocates to nearby genomic regions, leading to abnormal upregulation of BMP target genes. This function of PRDM16 is also required for the specification of choroid plexus (ChP) epithelial cells. Through a single-cell resolution fluorescent in situ approach, we have observed that genes co-repressed by SMAD and PRDM16, such as Wnt7b and several cell cycle regulators, become overexpressed in Prdm16 mutant ChP. Our findings elucidate a mechanism through which SMAD4 and pSMAD1/5/8 repress gene expression. Moreover, our study suggests a regulatory circuit composed of BMP and Wnt signaling, along with PRDM16, in controlling stem cell behaviors.in eLife on 2025-07-14 00:00:00 UTC.
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Self-other generalisation shapes social interaction and is disrupted in borderline personality disorder
Generalising information from ourselves to others, and others to ourselves allows for both a dependable source of navigation and adaptability in interpersonal exchange. Disturbances to social development in sensitive periods can cause enduring and distressing damage to lasting healthy relationships. However, identifying the mechanisms of healthy exchange has been difficult. We introduce a theory of self-other generalisation tested with data from a three-phase social value orientation task – the Intentions Game. We involved humans with (n=50) and without (n=53) a diagnosis of borderline personality disorder and assessed whether infractions to self-other generalisation may explain prior findings of disrupted social learning and instability. Healthy controls initially used their preferences to predict others and were influenced by their partners, leading to self-other convergence. In contrast, individuals with borderline personality disorder maintained distinct self-other representations when learning about others. This allowed for equal predictive performance compared to controls despite reduced updating sensitivity. Furthermore, we explored theory-driven individual differences underpinning contagion. Overall, the findings provide a clear explanation of how self-other generalisation constrains and assists learning, and how childhood adversity is associated with separation of internalised beliefs. Our model makes clear predictions about the mechanisms of social information generalisation concerning both joint and individual reward.in eLife on 2025-07-14 00:00:00 UTC.
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Cell-type-specific plasticity in synaptic, intrinsic, and sound response properties of deep-layer auditory cortical neurons after noise trauma
Peripheral damage drives auditory cortex (ACtx) plasticity, but the underlying synaptic and cellular mechanisms remain poorly understood. We used a combination of in vitro slice electrophysiology, optogenetics, and in vivo two-photon imaging to investigate layer 5 extratelencephalic (ET) and layer 6 corticothalamic (CT) neuronal plasticity in mice, following noise-induced hearing loss (NIHL). Thalamocortical (TC) input was initially balanced between CTs and ETs but shifted to CT-dominant one day post-NIHL and then normalized by day seven. This transient shift was accompanied by increased quantal size and suprathreshold excitability in CTs, with minimal changes in ETs. In vivo, CTs exhibited persistent elevation in sound intensity thresholds, while ETs showed a transient shift in frequency tuning and reduced high-frequency responsiveness that recovered within a week. These findings reveal distinct, cell-type-specific plasticity mechanisms in deep-layer ACtx neurons following peripheral damage and highlight potential targets for treating hearing loss-related disorders such as tinnitus and hyperacusis.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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Functional connectivity differences in adult's ADHD - a MEG study
The neurobiology of adult Attention-Deficit/Hyperactivity Disorder (ADHD), particularly functional brain network connectivity, remain poorly understood. Magnetoencephalography (MEG) can reveal frequency-specific network dynamics given its high temporal resolution. Here, we investigated intrinsic functional connectivity differences between adults with ADHD (n = 24) and healthy controls (n = 44) using MEG data from the Open MEG Archive (OMEGA) dataset. We employed source reconstruction (Destrieux atlas), weighted phase-lag index (wPLI) connectivity across six frequency bands, graph theory metrics (Characteristic Path Length (CPL), node strength, clustering coefficient), and network-based statistics (NBS). We observed widespread hypo-connectivity in the high-gamma band (50-150 Hz) in adults with ADHD compared to controls. NBS identified a significant high-gamma sub-network, predominantly involving dorsal and ventral attention networks (DAN/VAN) and default mode network (DMN) nodes centered around a left fusiform gyrus hub, where all constituent connections exhibited consistently lower connectivity in the ADHD group. Globally, this was reflected in reduced gamma network integration (longer CPL) within the DAN and VAN. Locally, reduced high-gamma clustering was observed in VAN nodes (e.g., insula) and reduced node strength in a DAN region (postcentral sulcus). Predictive modeling using ElasticNet regression confirmed the importance of high-gamma metrics, with CPL measures yielding moderate classification accuracy (AUC = 0.70-0.73). In contrast to high-gamma findings, the alpha band showed increased integration (shorter CPL) within the DMN and between the VAN and DAN in the ADHD group, alongside differences in alpha and beta band node properties in cingulate and somatomotor regions. Our findings reveal robust, frequency-dependent functional network alterations in adult ADHD, particularly highlighting disrupted high-frequency communication within and between key cognitive networks.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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The impact of a psychedelic drug on olfactory search behavior by mice
Animals use their sense of smell for survival and well-being. Any disruption to olfactory perception, such as in the case of olfactory hallucinations, can lead to devastating consequences and decreased quality of life. Psychedelics interrupt and distort accurate perception, yet little is known about the impact of psychedelics on olfactory behaviors. Using an olfactory search task, we investigated the impact of the psychedelic 2,5-dimethoxy-4-iodoamphetamine (DOI) on olfactory search behavior of mice. We found that DOI decreases search accuracy, alters movement, and increases sniff rate. These findings suggest that the olfactory behaviors are altered by DOI, elucidating psychedelic-induced changes to olfactory processes.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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Change-resistance distinguishes the representational geometries of human spatial memory and mouse CA1 in deformed environments
Prior work has highlighted qualitative similarities between the neural instantiations of cognitive maps in rodents and memory-guided navigation in humans, suggesting a conservation of representational structure across species. Yet evidence of cross-species differences in neural coding continues to mount. Our ability to reconcile these similarities and differences has been inherently limited by the qualitative nature of our cross-species comparisons. To overcome this limitation, here we combine recent technical and theoretical advances to characterize the representational geometry of human spatial memory during a diverse set of environmental deformations and compare this geometry to that of mouse CA1. Across three untethered immersive virtual reality experiments (n > 100 participants per experiment), we find that deformations induce compounding local distortions in human spatial memory. These distortions yield a representational geometry which closely resembles a change-resistant version of that of mouse hippocampal CA1 during analogous deformations. The geometries of mouse CA1 subpopulations with higher firing rates, spatial tuning stability, and spatial tuning specificity all better resembled that of human spatial memory. The precision, but not accuracy, of human spatial memory also modulated cross-species resemblance. The local impact of deformations scaled up when humans navigated a larger environment, preserving representational geometry and cross-species resemblance. Neither geometry nor cross-species resemblance depended on the human visual advantage during retrieval. Together, these results establish a common cross-species resemblance in the representational geometry of mouse CA1 and human spatial memory during environmental deformations, with a notable difference in the resistance to change between these assays.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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Recursive Entropic Time: A Neural Framework forthe Informational Construction of SubjectiveDuration
The conception of time as a universal and independent parameter is a foundational assumption in physical models. However, it does not address the subjective nature of temporal perception and leads to inconsistencies in complex systems. This paper introduces the Recursive Entropic Time framework, a theory proposing that subjective time is not fixed but instead emerges from neural systems involved in interpretation and association. We hypothesize that the brain uses a divided system for processing time. Primary sensory cortices handle objective clock-based time, while higher-order associative cortices construct subjective time through a mechanism in which the rate of temporal flow is inversely influenced by the amount of information being processed. To test this theory, we conducted a two-part investigation. In the first part, we used a public dataset involving brain scans of subjects under the influence of a hallucinogenic substance. This revealed that the Recursive Entropic Time model had greater effectiveness in associative regions of the brain compared to primary sensory areas. This finding suggested a region-specific effect rather than a global one. In the second part, we examined brain activity during a temporal reproduction task and analyzed two trials where participants produced nearly identical time durations. Despite the behavioral similarity, the information processing differed between the trials. The Recursive Entropic Time model accurately predicted these outcomes by reflecting internal durations derived from the information load. These findings support Recursive Entropic Time as a falsifiable and mechanistic explanation of how the brain constructs subjective time. We argue that time, as it is experienced, is not a simple reflection of external reality but a mental construction shaped by higher cognition. This framework provides a measurable and testable method for understanding subjective time and may lead to applications such as brain-based time atlases and insights into cognitive disorders.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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Spatiotemporal Abstraction Theory: Re-Interpretation of Localized Cortical Networks
The brain excels at extracting meaning from noisy and degraded input, yet the computational principles that underlie this robustness remain unclear. We propose a theory of spatiotemporal abstraction (STA), in which localized cortical networks integrate inputs across space and time to produce multi-scale, concept-level representations that remain stable despite loss of detail. We demonstrate how this principle explains a long-standing paradox of how cochlear implant patients can understand speech despite severely scrambled neural patterns. STA provides a unified framework that explains fundamental questions: Why do we have so many neurons that respond very similarly in one cortical location? Why do we have different inhibitory neurons? It also forces us to re-examine long-standing explanations of memory, creativity, illusions, attractor dynamics, excitatory-to-inhibitory balance, and the structure and purpose of the ubiquitous canonical circuits seen throughout the brain. We conclude with STA implications for improving neural implants and artificial neural networks.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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Dynamic extracellular interactions with AMPA receptors
Synaptic plasticity in the central nervous system enables the encoding, storing, and integrating new information. AMPA-type glutamate receptors (AMPARs) are ligand-gated ion channels that mediate most fast excitatory synaptic transmission in the brain, and plasticity of AMPARs signaling underlies the long-lasting changes in synaptic efficacy and strength important for learning and memory.1,2 Recent work has indicated that the enigmatic N-terminal domain (NTD) of AMPARs may be a critical regulator of synaptic targeting and plasticity of AMPARs. However, few synaptic proteins have been identified that regulate AMPAR plasticity through interactions with AMPAR NTDs. Moreover, the scope of AMPAR NTD interactors that are important for synaptic plasticity remains unknown. Here, we present the dynamic, extracellular interactome for AMPARs during synaptic plasticity. Using surface-restricted proximity labeling and BioSITe-based proteomics, we identified 70 proteins that were differentially labeled by APEX2-tagged AMPARs after induction of chemical Long-term potentiation of synapses (cLTP) in cultured neurons. Included in this list, were four members of the IgLON family of GPI-anchored proteins (Ntm, OBCAM/Opcml, Negr1, Lsamp). We show OBCAM and NTM directly interact with the extracellular domains of AMPARs. Moreover, overexpression of NTM significantly attenuates the mobility of surface AMPARs in dendritic spines. These data represent a significant first step at uncovering the unexplored extracellular regulation of AMPARs, with broad implications for synapse function and synaptic plasticity.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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A Sexually Dimorphic Neuronal Cluster in the Mouse Medial Amygdala Exhibits Binary Activation Mode Based on Male Sexual Status
Increasing scientific interest has been directed toward understanding sexual dimorphism in the brain. Although various brain structures exhibit masculine or feminine characteristics, no strictly binary anatomical feature, such as those seen in genitalia, has been identified. In this study, we identified a dense, sexually dimorphic cluster of neurons in the posterodorsal medial amygdala (MeApd), which we named DIMPLE, that exhibited a remarkable binary pattern of cFos activation. Using the TRAP2 (Targeted Recombination in Active Populations) transgenic mouse model, we found that it was consistently labeled in all females, regardless of age or sexual experience. In males, however, DIMPLE was not labeled in any of the adult virgins but was evident pre-weaning and following mating. Surgical removal of gonads (ovariectomy or orchiectomy) did not alter the labeling pattern of DIMPLE in either sex. Interestingly, a single intraperitoneal injection of prolactin, a hormone known to increase in males after mating, induced DIMPLE labeling in virgin males. However, treatment with cabergoline, a potent inhibitor of prolactin secretion, did not prevent DIMPLE labeling in females or in post-mating males. Given the established role of the MeApd in social and reproductive behaviors, we propose that DIMPLE may support neural mechanisms underlying female-typical behavior and potentially contribute to post-mating behavioral shifts in males.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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Responses of inferior colliculus neurons to notched noises in awake mice: putative neural correlates of auditory enhancement and Zwicker tone
Sensory systems are well adapted to constantly changing statistics of the environment and to process specific spectral features of sounds, such as spectral notch (i.e. low energy frequency band) embedded in broadband stimuli. Spectral notches can be added to the stimulus spectrum due to filtering by the outer ear, and can be used as monaural cues related to head or pinna position for localizing sound sources. In addition, broadband sounds with spectral notch are known to produce auditory enhancement, a perceptual phenomenon in which a target within a spectrally notched masker can become salient if preceded by a copy of the masker. Notched noise can also produce an auditory illusion, called Zwicker Tone (ZT), which is perceived immediately after stimulation and whose pitch corresponds to the spectral notch. The present study aimed to further investigate the mechanisms of auditory enhancement, including those of ZT, in the inferior colliculus of awake mice. We show that neural activity can be strongly suppressed during NN stimulation and enhanced immediately after NN stimulation. These effects depend on notch center frequency relative to the best frequency of neurons, stimulus level and notch width. Our results are consistent with the mechanisms described for post-inhibitory rebound in the central auditory system: NN could hyperpolarize the membrane potential, which can then activate several cationic conductances, leading to a rebound of neural activity. We discuss auditory enhancement and ZT as collateral effects of an essential neural mechanism aimed at enhancing the central representation of acoustic spectral contrasts.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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Neurophysiological correlates of passive movements are speed- and type-dependent
Introduction: The supraspinal involvement in the control of passive movements remains elusive. Mechanoreceptor properties, their change in the context of ageing and the somatotopically organized supraspinal connections between sensory and motor systems provide a neuroanatomical basis for the prediction that cortical structures are involved in the control of passive movements. Previous electromyographic evidence indeed show movement speed and -type-dependent changes in muscle activity. This study aimed to provide electrophysiological evidence for the involvement of frontal cortex inhibition and corticomotor interactions in the control of passive movements. Methods: Continuous and discontinuous passive elbow movements were performed in healthy younger (n = 20, 22.5 +/- 2.31 y) and older (n = 20, 72.7 +/- 5.73 y) adults at three movement speeds (20, 60, and 100 bpm) while electro-encephalographic (EEG) and electromyographic (EMG) data were acquired. Alpha power and beta corticomuscular connectivity were used as measures of frontal cortex inhibition and brain-muscle connectivity, respectively. Results: Frontal cortex inhibition decreased (p = 0.036) and brain-muscle connectivity increased (p < 0.001) with increasing movement speeds. In addition, frontal cortex inhibition was 17% higher in the discontinuous condition as compared to the continuous condition (p = 0.005) while corticomuscular coherence was 25.9% higher in the continuous vs. the discontinuous condition (p < 0.001). These effects were independent of age. Conclusion: The present results provide insights into the control of passive movements and show that frontal cortex inhibition and brain-muscle interactions depend on movement speed and movement type.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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Neural Representation of Associative Threat Learning in Pulvinar Divisions, Lateral Geniculate Nucleus, and Mediodorsal Thalamus in Humans
Understanding the neural mechanisms underlying associative threat learning is essential for advancing behavioral models of threat and adaptation. We investigated distinct activation patterns across thalamic pulvinar divisions, lateral geniculate nucleus (LGN), and mediodorsal thalamus (MD) during the acquisition of associative threat learning in the MRI. We revealed parallel thalamic learning systems within the anterior pulvinar and MD, supporting distinct mechanisms of automatic survival vs. more deliberate learning. Additionally, our findings support a novel hierarchical pulvinar model during fear conditioning: the medial pulvinar mediates basic threat information from the inferior and lateral divisions to the anterior pulvinar for integrative learning. Pulvinar divisions and MD support extinction learning. These regions also process salience and modulate safe/threat memory expression during extinction recall and threat renewal. The LGN sustains feedforward processing of anticipated visual input throughout all threat phases. This study extends dominant brain models of threat learning and memory, reframing our understanding of distinct thalamic roles in these psychological processes.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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Temporal Dynamics of Sensorimotor Integration for Non-Visual Target Information During Movement Preparation and Online Control
Online movement variability in non-visual (auditory and proprioceptive) target reaching is modality-dependent. This study investigated whether such modality-specific effects emerge during movement preparation and whether this phase influences subsequent online control. Participants performed reaching movements toward auditory, proprioceptive, or audio-proprioceptive targets. Electromyography and movement kinematics were recorded to examine the effects of sensory modality on sensory encoding and motor coordination during movement preparation, as well as online control at 5%, 50%, and 100% of movement time. Results revealed a modality-dependent sensory encoding phase and a modality-independent motor coordination. Movement variability was greater for auditory targets than for proprioceptive and audio-proprioceptive targets at 50% and 100% of movement. Only motor coordination influenced early online control (5%), but this effect was modality-independent. These findings demonstrate that the influence of sensory modality extends beyond execution to the preparatory phase of movement. The results support a four-stage model of action control: a modality-dependent sensory encoding phase of preparation and late online control, alongside a modality-independent motor coordination phase and early online control. These findings offer new insight into the temporal dynamics of sensorimotor control without vision, indicating that non-visual sensory information is differentially used during distinct phases of movement preparation and execution.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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Connectivity and function are coupled across cognitive domains throughout the brain
Decades of neuroimaging have revealed that the functional organization of the brain is roughly consistent across individuals and at rest it is resembles group-level task-evoked networks. A fundamental assumption in the field is that the functional specialization of a brain region arises from its connections to the rest of the brain, but limitations in the amount of data that can be feasibly collected in a single individual, leaves open the question: Is the association between task activation and connectivity consistent across the brain and many cognitive tasks? To answer this question, we fit ridge regressions models to activation maps from 33 cognitive domains (generated with NeuroQuery) using resting-state functional connectivity data from the Human Connectome Project as the predictor. We examine how well functional connectivity fits activation and find that all regions and all cognitive domains have a very robust relationship between brain activity and connectivity. The tightest relationship exists for higher-order, domain-general cognitive functions. These results support the claim that connectivity is a general organizational principle of brain function by comprehensively testing this relationship in a large sample of individuals for a broad range of cognitive domains and provide a reference for future studies engaging in individualized predictive models.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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Bridging local and global dynamics: a biologically grounded model for cooperative and competitive interactions in the brain
Functional brain networks exhibit both cooperative and competitive interactions, yet existing models--assuming purely excitatory long-range coupling--fail to account for the widespread anti-correlations observed in fMRI. Starting from a laminar neural mass framework, where each mass comprises distinct slow (alpha-band) and fast (gamma-band) oscillatory pyramidal subpopulations (P1 and P2), we show how laminar-specific long-range excitatory projections across neural mass parcels can give rise to both cooperation and competition via cross-frequency envelope coupling. We demonstrate that homologous connections across parcels (e.g., P1[->]P1 or P2[->]P2) induce positive correlations between the infra-slow amplitude fluctuations of alpha band envelopes in each parcel, as well as in the simulated fMRI BOLD signals. Conversely, heterologous connections (P1[->]P2) induce negative correlations. We tested this mechanism by building personalized whole-brain models for a cohort of 60 subjects in two steps. First, we inferred signed inter-parcel generative effective connectivity directly from resting-state fMRI using regularized maximum-entropy (Ising) models. Then we connected laminar neural masses to simulate BOLD dynamics by implementing positive and negative Ising connections via homologous and heterologous projections, respectively. Ising-derived cooperative/competitive connectivity modeling faithfully reproduced both static and dynamic functional connectivity patterns, as well as gamma power-BOLD correlation and partial alpha power-BOLD anticorrelation-outperforming structurally constrained and cooperative-only variants. This further demonstrates that functional data alone suffices to infer individualized connectivity. Together, these results provide a biologically grounded mechanistic model on how long-range excitatory circuits and local cross-frequency interactions shape the balance of cooperation and competition in large-scale brain dynamics.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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The selective amyloid-driven failure of cholinergic medial septal neurons in aging mice perturbs REM sleep, cognition and emotion, and broadcasts amyloid to other brain regions
Early degeneration of basal forebrain cholinergic neurons is a key feature of Alzheimer's Disease (AD). We expressed in mice of both sexes an AppNL-G-F allele, harboring familial AD mutations, specifically in cholinergic medial septum (MS) neurons, and compared the phenotype to mice with global AppNL-G-F expression. Over the course of 14 months, as mice reached late middle age, targeted expression led to the loss of about one-third of MS cholinergic neurons and widespread amyloid deposition in their terminal fields, especially in the hippocampus and, to a lesser extent, on blood vessels. This selective vulnerability of ageing cholinergic cells to amyloid, markedly reduced REM sleep and caused cognitive and emotional alterations resembling those in mice with the mutation expressed throughout the brain. Mice with global AppNL-G-F expression also had a previously unreported selective death of about 20% of their medial septal cholinergic cells. Although the broadcasting of amyloid by medial septal cholinergic cells is a notable feature, and potentially important in human pathology, selective genetic lesioning of about one third of the medial septal cholinergic cells, independently of amyloid, gave the same REM sleep, cognitive and emotional phenotypes. Thus, it is the killing of the cholinergic cells by amyloid, and therefore the missing acetylcholine, and not the secreted/deposited amyloid in the hippocampus and other areas that is the critical feature. These findings underscore the interest in revitalizing the classic cholinergic hypothesis of AD. Restricting pathological amyloid expression to MS cholinergic neurons, so that their health is compromised by amyloid, is sufficient to reproduce many AD-like symptoms, highlighting the critical role of these cells in early AD pathogenesis, REM sleep regulation, emotion and cognition.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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IsoDGR-Induced Endothelial Cytoskeletal Disruption Drives Age-Related Blood-Brain Barrier Breakdown
Brain aging is characterized by progressive breakdown of the blood-brain barrier (BBB), which correlates with neuroinflammation and cognitive decline. Emerging evidence implicates degenerative modifications of the vascular proteins as a key driver of BBB dysfunction. In particular, spontaneous deamidation of Asp-Gly-Arg (NGR) motifs generates isoAsp-Gly-Arg (isoDGR) sequences that structurally mimic canonical Arg-Gly-Asp (RGD) integrin-binding ligands. Here, we show that age-associated accumulation of isoDGR in the brain cortex induces endothelial cytoskeletal collapse and tight junction disorganization, leading to BBB breakdown. Using mice lacking the L-isoaspartyl repair enzyme PCMT1 (which accelerates isoDGR accumulation) and wild type aged mice, we found markedly elevated isoDGR in brain tissues accompanied by focal microhemorrhages and increased BBB permeability. Recent whole-genome sequencing suggests that a common PCMT1 variant is linked to neurodegenerative disease risk, indicating potential clinical relevance in vascular aging. Remarkably, systemic treatment with an isoDGR-neutralizing antibody largely prevented capillary breaches and leakage, and even restored barrier integrity in aged wild-type mice. To uncover the molecular mechanism, we exposed brain endothelial cells to synthetic isoDGR-peptides, which recapitulated these effects. Unbiased RNA-sequencing reinforced these findings, revealing broad transcriptomic reprogramming of cytoskeletal, cell-cell junction, inflammatory, and stress-response pathways. Functional studies demonstrated that isoDGR triggered collapse of F-actin stress fibers, disrupted junctional ZO-1 and VE-cadherin, increased monolayer permeability to macromolecules, and impaired endothelial cell migration and proliferation. IsoDGR-treated endothelial cells exhibited increased oxidative stress, upregulation of ICAM-1/VCAM-1/CCL-2, and adopted a senescent phenotype. Our results suggest that isoDGR hijacks endothelial integrin signaling to destabilize the actin cytoskeleton and tight junctions, a process that breaches the BBB and subsequently activates inflammatory and senescence programs. In summary, we identify BBB disruption via isoDGR-induced cytoskeletal dysfunction as a central pathology of vascular aging, and demonstrate that targeting isoDGR damage preserves BBB integrity and attenuates neuroinflammation.in bioRxiv: Neuroscience on 2025-07-14 00:00:00 UTC.
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KIF5A downregulation in spinal muscular atrophy links axonal regeneration defects with ALS
Spinal muscular atrophy (SMA) is a devastating neuromuscular disorder caused by mutations in the Survival Motor Neuron 1 (SMN1) gene, leading to decreased SMN levels and motor neuron dysfunction. SMN-restoring therapies offer clinical benefit, but the downstream molecular consequences of SMN reduction remain incompletely understood. Here, we demonstrate that SMN deficiency results in downregulation of KIF5A in human neurons and in a mouse model of SMA. We provide evidence that reduced SMN levels impair axon regeneration, which is rescued by KIF5A overexpression and that the RNA-binding protein SMN functions to stabilize KIF5A mRNA. These findings provide evidence of a molecular link between SMA and ALS pathophysiology, highlighting KIF5A as a new SMN target. Our findings suggest SMN-independent interventions targeting KIF5A could represent a complementary therapeutic approach for SMA and other motor neuron diseases.in bioRxiv: Neuroscience on 2025-07-13 00:00:00 UTC.
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Impact of sex and glial tau expression on heat shock protein induction in a Drosophila model of tauopathy
Heat shock proteins (Hsps) are central components of the cellular stress response and serve as the first line of defense against protein misfolding and aggregation. Disruption of this proteostasis network is a hallmark of neurodegenerative diseases, including tauopathies -- a class of neurodegenerative diseases characterized by intracellular tau accumulation in neuronal and glial cells. Although specific Hsps are enriched in glial cells, and some have been shown to directly bind tau and influence its aggregation, the broader interplay between Hsps and tau remains poorly understood. In particular, it is unclear whether tau expression affects the heat shock response, and whether this interaction is modulated in a sex-specific fashion. Here, we used a Drosophila model of tauopathy to examine both inducible and constitutive Hsp expression in response to heat stress in the context of glial tau expression. We found that Hsp expression displays sexually dimorphic expression patterns at basal levels and in response to heat stress. Moreover, tau expression in glia disrupts the normal induction of specific heat shock proteins following heat stress. This work provides new insight into how tau interacts with the cellular stress response, and highlights sex-specific differences in Hsp regulation. Understanding these molecular connections is crucial to understanding how the presence of tau in glial cells influences the stress response, and potentially contributes to tauopathy pathogenesis.in bioRxiv: Neuroscience on 2025-07-13 00:00:00 UTC.
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High-Throughput Tracking of Freely Moving Drosophila Reveals Variations in Aggression and Courtship Behaviors
Aggression is a nearly universal behavior used to secure food, territory, and mates across species, including the fruit fly Drosophila melanogaster. In fruit flies, both sexes display aggression through stereotypical motor patterns. This, along with their sophisticated genetic and molecular toolkit, makes Drosophila melanogaster an excellent model for studying aggression. While male- and female-specific aggressive motor programs have been qualitatively described, automated systems for quantifying these behaviors in freely moving flies remain limited in their ability to combine high-resolution analysis with high throughput. Here, we pair a high-resolution, high-throughput imaging system (the Kestrel) with DeepLabCut pose estimation to create a pipeline that tracks multiple freely moving fly pairs and quantifies social dynamics with high fidelity. We validated body-part tracking using published benchmarks. The platform reliably reproduced a known phenotype: heightened female aggression following thermogenetic activation of cholinergic pC1 neurons in female brain. It also detected increased unilateral wing extension, a courtship display inversely related to aggression, between two males upon activating a previously uncharacterized ~40-neuron group in the male brain. Pose-based analysis revealed locomotive differences between experimental and control groups, and subtle, genotype-specific variations in head butts and UWEs. This workflow enables high-throughput screening and mechanistic dissection of social behaviors.in bioRxiv: Neuroscience on 2025-07-13 00:00:00 UTC.
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Autophagy-driven Presynaptic Reorganization as a Molecular Signature of Brain Resilience.
Neural circuits must remain functionally stable while responding flexibly to changing demands, stressors, and aging-related decline. While this balance is thought to be maintained through plasticity programs that integrate molecular, metabolic, and activity-dependent signals to reconfigure synapses structurally and functionally, direct mechanistic models of how such adaptations are orchestrated remain scarce. Here, we show that targeted impairment of autophagy in the Drosophila mushroom body (MB), a key sleep-regulatory and integrative center in the fly brain, triggers a brain-wide remodeling at presynaptic active zones (AZ). Quantitative proteomics revealed a specific upregulation of AZ scaffold proteins (including BRP, RIM, and Unc13A), accompanied by reduced levels of calcium channel subunits and increased Shaker-type potassium channels. These changes occurred largely independent of transcription and highlight a coordinated, excitability-tuning response centered on the AZ. Behaviorally, MB-specific autophagy impairment increased sleep and modestly extended lifespan. These adaptations resembled a previously described resilience program termed PreScale, which promotes restorative sleep homeostasis in response to sleep deprivation and early, still reversible brain aging. Conversely, overexpression of Atg5 in the MB delayed the onset of PreScale. Notably, autophagic disruption confined to MB neurons also caused widespread, non-cell autonomous accumulation of Ref(2)P and ATG8a-positive aggregates across the brain, revealing systemic propagation of proteostatic stress. Together, our findings identify MB autophagy as a key regulator of synaptic architecture and sleep-associated resilience. Such early acting programs may actively preserve circuit function and behavioral output by regulating synaptic plasticity, and define a genetically tractable model for how local stress signals can orchestrate brain-wide adaptation via post-transcriptional synaptic reprogramming.in bioRxiv: Neuroscience on 2025-07-13 00:00:00 UTC.
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Correction to: Large-scale parameters framework with large convolutional kernel for encoding visual fMRI activity information
This is a correction to: Shuxiao Ma, Linyuan Wang, Senbao Hou, Chi Zhang, Bin Yan, Large-scale parameters framework with large convolutional kernel for encoding visual fMRI activity information, Cerebral Cortex, Volume 34, Issue 7, July 2024, bhae257, https://doi.org/10.1093/cercor/bhae257.in Cerebral Cortex on 2025-07-13 00:00:00 UTC.
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Piano Training Induces Dynamic Neuroplasticity of Bimanual Coordination but Not Auditory Processing in Young Adults
Twenty-six weeks of piano training result in adaptations in the activation of the motor but not the auditory networks. The changes in brain activation in the motor system are task-demand dependent, and cannot be fully explained by a single model of brain plasticity.
ABSTRACT
Piano training enables the study of the interplay between the auditory and motor domains in the acquisition of complex skill. Here, we uniquely combine longitudinal and cross-sectional designs to show how the motor and auditory brain systems respond in novice pianists over a 6-month training period. In the auditory domain, we found no differences in brain activation between novice pianists and a passive control group. In a specially designed piano task on an MRI-compatible keyboard, we demonstrate that the time course of neuroplastic reorganization in the cortical and subcortical regions reflects the shift from spatial attention to automated movements, but depends on task demands related to bimanual coordination. Importantly, no single model of brain plasticity can fully explain the observed dynamic time courses of functional changes. Finally, we demonstrate that the increased activation in the dorsal premotor and parietal cortices in novice pianists compared to skilled musicians while performing the motor task vanishes within the first 6 months of training. These results present converging evidence that the dynamic musical-training-related plasticity is highly contextual, and underscore the importance of ecological designs in research on skill acquisition.
in Journal of Neuroscience Research on 2025-07-12 08:04:25 UTC.
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Mapping Postictal Aphasia through Signal Complexity: A Stereo‐Electroencephalography Study
Objective
The postictal period provides an opportunity to investigate the pathophysiology underlying aphasia and recovery following epileptic seizures. This study examines postictal aphasia in stereo-electroencephalography (SEEG)-explored patients to identify brain regions associated with task-specific language deficits using signal complexity analysis.
Methods
We evaluated video-SEEG-recorded focal seizures with and without postictal aphasia in patients with SEEG-confirmed hemispheric language dominance. SEEG traces were analyzed using permutation entropy (PE), with the postictal period quantified by a PE-based metric, the Postictal Alteration Time (PAT). Brain region PAT was correlated with language function recovery (eg, repetition). Electro-clinical recuperation was also assessed within the dorsal-ventral language stream framework. Additionally, a bedside testing battery was developed to evaluate postictal aphasia severity and task-specific deficits.
Results
A total of 322 seizures from 98 patients were analyzed. Seizures with postictal aphasia had longer PAT than those without. Task-specific language recovery correlated with regional PAT (eg, naming – middle temporal gyrus). Moreover, the dorsal stream recovered faster than the ventral stream. Additionally, the Postictal Aphasia Scale (PAS) was developed, evaluating naming, reading, repetition, and comprehension (verbal and written) and automatic speech. Higher PAS scores (indicating milder deficits) correlated with faster regional complexity recovery. At 5 and 10 minutes postictally, PAS revealed a global aphasia pattern, with comprehension deficits gradually resolving. By 15 minutes, aphasia was primarily production-related, particularly affecting naming.
Interpretation
This study provides new insights into the pathophysiology of postictal aphasia and introduces PAS as a tool for assessing postictal aphasia severity and domain-specific deficits, aiding surgical planning and rehabilitation. ANN NEUROL 2025
in Annals of Neurology on 2025-07-12 04:39:44 UTC.
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Sex Differences in Associations of Lewy Body Disease with Alzheimer's Disease and Cognitive Decline
Objective
To investigate how sex and age at menopause influence the interplay between Alzheimer's disease (AD) and Lewy body disease (LBD) neuropathologies, and their associations with cognitive decline.
Methods
We analyzed data from: (1) three Rush Alzheimer's Disease Center cohorts (i.e., the Religious Orders Study, Rush Memory and Aging Project, and Minority Aging Research Study), and (2) the National Alzheimer's Coordinating Center Neuropathology Data Set. Neuropathological evaluation assessed LBD (neocortical/limbic-type vs none) and AD, including neuritic plaques (β-amyloid plaques surrounded by dystrophic neurites) and neurofibrillary tangles. In each dataset, we tested interactive associations between LBD and sex on neuritic plaques, neurofibrillary tangles, and cognitive decline. Additionally, in the Rush dataset, we tested whether age at spontaneous menopause modified the associations of LBD with neuritic plaques, neurofibrillary tangles, and cognitive decline in women.
Results
In the Rush dataset, we included 1,277 women and 579 men. In the National Alzheimer's Coordinating Center dataset, we included 3,283 women and 3,563 men. Across both datasets, men were more likely to have LBD, whereas women showed greater neuritic plaque and neurofibrillary tangle burdens. Sex modified the associations of LBD with neurofibrillary tangles (but not neuritic plaques), whereby LBD was more strongly associated with greater neurofibrillary tangle burden in women than men. Men showed faster LBD-related cognitive decline, whereas women showed faster neurofibrillary tangle-related decline, after adjusting for copathologies (neuritic plaques, neurofibrillary tangles, and LBD, as appropriate). In women, earlier age at menopause exacerbated the associations of LBD with neurofibrillary tangle burden and episodic memory decline.
Interpretation
Sex may influence AD and LBD neuropathologies, highlighting the need for precision approaches to dementia prevention and intervention. ANN NEUROL 2025
in Annals of Neurology on 2025-07-12 04:29:46 UTC.
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Propagating Motor Cortical Dynamics Facilitate Movement Initiation
(Neuron 106, 526–536.e1–e4; May 6, 2020)in Neuron: In press on 2025-07-12 00:00:00 UTC.
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LRRTM2 Functions as a Neurexin Ligand in Promoting Excitatory Synapse Formation
(Neuron 64, 791–798; December 24, 2009)in Neuron: In press on 2025-07-12 00:00:00 UTC.
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Oxidative stress and Kras mutation in Mist1+ cells act in a double-hit manner to drive gastric tumorigenesis
Chen et al. show that Mist1+ cells resist oxidative stress by upregulating Bnip3/Tmed6, with sustained ROS activating YAP to drive cell proliferationwhile synergizing with Kras mutation in a “double-hit” mechanism to promote tumorigenesis.in Cell Reports: Current Issue on 2025-07-12 00:00:00 UTC.
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Multimodal CRISPR screens uncover DDX39B as a global repressor of A-to-I RNA editing
Wei, Li, Lei et al. develop CREDITS and scCREDIT-seq, two complementary CRISPR-based platforms for systematic discovery of A-to-I RNA editing regulators. They identify DDX39B as a global repressor via dsRNA regulation and demonstrate its utility in enhancing RNA editing technologies and anti-HDV therapeutic strategies.in Cell Reports: Current Issue on 2025-07-12 00:00:00 UTC.
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TNF-α-NF-κB activation through pathological α-Synuclein disrupts the BBB and exacerbates axonopathy
Pathological α-Synuclein reveals vascular damage through the endothelial cells’ response. Jeon et al. show that the endothelial TNF-NF-κB pathway is a mechanistic target to mediate synucleinopathy-induced BBB degeneration via GAN deep learning techniques and biological experiments. They demonstrate that regulation of endothelial TNF pathways can modulate synucleinopathy-induced axonal damages.in Cell Reports: Current Issue on 2025-07-12 00:00:00 UTC.
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Single-nucleus profiling of the left ventricle of the mouse heart after chronic stress
Scientific Data, Published online: 12 July 2025; doi:10.1038/s41597-025-05556-x
Single-nucleus profiling of the left ventricle of the mouse heart after chronic stressin Nature scientific data on 2025-07-12 00:00:00 UTC.
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Near-complete reference genome assembly of Hoya carnosa
Scientific Data, Published online: 12 July 2025; doi:10.1038/s41597-025-05587-4
Near-complete reference genome assembly of Hoya carnosain Nature scientific data on 2025-07-12 00:00:00 UTC.
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Variation of winter wheat phenology dataset in Huang Huai Hai Plain of China from 1981 to 2021
Scientific Data, Published online: 12 July 2025; doi:10.1038/s41597-025-05368-z
Variation of winter wheat phenology dataset in Huang Huai Hai Plain of China from 1981 to 2021in Nature scientific data on 2025-07-12 00:00:00 UTC.
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A Cross-Species Brain Magnetic Resonance Imaging and Histology Database of Vertebrates
Scientific Data, Published online: 12 July 2025; doi:10.1038/s41597-025-05540-5
A Cross-Species Brain Magnetic Resonance Imaging and Histology Database of Vertebratesin Nature scientific data on 2025-07-12 00:00:00 UTC.
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A nationwide dataset of stable isotopes in meteoric and terrestrial water across Peru
Scientific Data, Published online: 12 July 2025; doi:10.1038/s41597-025-05413-x
A nationwide dataset of stable isotopes in meteoric and terrestrial water across Peruin Nature scientific data on 2025-07-12 00:00:00 UTC.
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A Topsoil Salinity Observatory for Arable Lands in Coastal Southwest Bangladesh
Scientific Data, Published online: 12 July 2025; doi:10.1038/s41597-025-05447-1
A Topsoil Salinity Observatory for Arable Lands in Coastal Southwest Bangladeshin Nature scientific data on 2025-07-12 00:00:00 UTC.
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A combined naturalistic driving, clinical, and neurobehavioral data set for investigating aging and dementia
Scientific Data, Published online: 12 July 2025; doi:10.1038/s41597-025-05554-z
A combined naturalistic driving, clinical, and neurobehavioral data set for investigating aging and dementiain Nature scientific data on 2025-07-12 00:00:00 UTC.
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Intricate interactions between fine-scale genetic structure, lifestyle, and dietary habits in the Japanese population
Communications Biology, Published online: 12 July 2025; doi:10.1038/s42003-025-08479-w
Advanced genetic clustering analysis reveals the intricate interplay between Japan’s genetic structure and lifestyle and dietary habits, introducing a new framework in genetic epidemiology that integrates both genetic and environmental factors.in Nature communications biology on 2025-07-12 00:00:00 UTC.
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Differences in brain activity during sentence repetition in people who stutter: a combined analysis of four fMRI studies
Our understanding of the neural correlates of developmental stuttering benefits from the use of functional MRI (fMRI) during speech production. Despite two decades of research, however, we have reached little consensus. In the current study, we analysed pooled fMRI data from four different studies that used the same sentence reading task and methodological approach. The combined sample included 56 adolescents and adults who stutter and 53 demographically matched typically fluent controls. A sparse-sampling design was used in each study, in which participants spoke during the silent period between measurements of brain activity. Sentence reading evoked activity in both groups across frontal and temporal regions bilaterally. At statistical thresholds corrected for family-wise error, there were no significant group differences. An uncorrected threshold was applied to explore group differences in areas previously identified in earlier fMRI studies on stuttering. People who stutter (PWS) showed greater activity compared with controls in right frontal pole, right anterior insula extending to frontal operculum, left planum temporale, and midbrain, at the level of red nucleus. In contrast, PWS showed lower activity in left superior frontal sulcus, subgenual medial prefrontal cortex, right anterior temporal lobe, and portions of inferior parietal lobe bilaterally including the angular gyrus on the left. Despite pooling data across multiple studies to achieve a relatively large sample, group differences in regions involved in speech-motor control only emerged at an uncorrected voxel-wise threshold. Some of these findings align with previous fMRI studies, such as increased activity in the right anterior insular cortex.in bioRxiv: Neuroscience on 2025-07-12 00:00:00 UTC.
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ALS-Linked FUS and SOD1 Mutations Elevate MCM2 in Human Motor Neurons
Background: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterised by progressive motor neuron loss. In typically post-mitotic neurons, abnormal reactivation of cell cycle regulators and DNA replication licensing factors is observed in ALS pathogenesis. Emerging evidence links components of the minichromosome maintenance (MCM) complex, notably MCM2, to replication stress and genomic instability, implying a mechanistic role in ALS pathogenesis. Objective: To determine whether ALS-associated mutations in FUS and SOD1 influence MCM2 expression and localisation in human induced pluripotent stem cell (hiPSC)-derived spinal motor neurons (MNs). Methods: We differentiated isogenic hiPSC lines carrying FUS P525L-GFP and R495QfsX527, a SOD1 mutant line, and matched wild-type controls into spinal MNs ([≥]28 days in vitro). QRT-PCR quantified MCM2 mRNA levels ({Delta}{Delta}Ct method; n=3 biological replicates, technical triplicates). Protein expression was assessed by Western blot densitometry (n=3). Subcellular distribution of MCM2 in FUS mutants was evaluated by immunofluorescence (pilot, N=1; [≥]50 cells quantified). Results: FUS P525L MNs exhibited a modest, non-significant increase in MCM2 mRNA (1.3-fold vs. WT; p=0.1319) but a significant 1.8-fold elevation in MCM2 protein levels (p=0.034). The R495QfsX527 line showed a comparable trend at the transcript level (1.2-fold; p > 0.05) and a 1.6-fold increase in protein (p = 0.041). SOD1 mutant MNs demonstrated a pronounced 2.3-fold MCM2 protein upregulation (p=0.008). Immunofluorescence in FUS mutant MNs revealed no significant nuclear-to-cytoplasmic shift in MCM2 localisation, indicating that elevated MCM2 levels are not driven by subcellular mislocalization. Conclusion: ALS-linked FUS and SOD1 mutations upregulate MCM2 protein in human spinal MNs, suggesting post-transcriptional or stability-driven regulation. The absence of relocalisation in FUS mutants shows that this impact is caused by overexpression rather than mislocalisation. MCM2 may be a biomarker of disease-associated replication stress. Future studies will explore whether MCM2 overexpression exacerbates DNA damage or serves as a compensatory response, clarifying its role in ALS pathogenesis.in bioRxiv: Neuroscience on 2025-07-12 00:00:00 UTC.
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Bioenergetic and Protein Processing Imbalances Synergize in iPSC-Dopamine neurons from Individuals with Idiopathic Parkinsons Disease
Patient induced pluripotent stem cell (iPSC)-based models represent a powerful human system to gain insights into the etiopathology of Parkinsons disease (PD). Here, we study several iPSC-derived dopamine neuron (iPSC-DAN) lines, from individuals with idiopathic PD, which is the most common form of PD. Specifically, using iPSC-DAN differentiated for 50-55 days, we performed an in-depth analysis of different bioenergetic pathways and cellular quality control mechanisms in the cells. Our results showed wide ranging impairments in oxidative phosphorylation (OXPHOS), glycolysis and creatine kinase pathways in the PD dopamine (DA) neurons. Specifically, the PD neurons exhibited reduced oxygen consumption rates (OCR) at baseline and after challenges with mitochondrial inhibitors, as well as decreased glycolytic reserves measured via ECAR. This translated to lower OCR:ECAR ratios signifying more reliance on glycolysis vs OXPHOS in the PD cells. Moreover, a mislocalization of creatine kinase B to mitochondria was seen in the PD cells. These energetic changes synergized with the enhanced expression of mitochondrial fission proteins, disrupted mitophagy and oxidative stress. Additionally, the PD neurons contained more monomeric, phosphorylated and aggregated forms of alpha synuclein and displayed reduced viability. Ultrastructural examination through immuno-electron microscopy showed more alpha synuclein gold particles directly associated with mitochondria and packing autophagic vesicles. In essence, these data capture a web of key changes in human iPSC-DAN from idiopathic PD subjects associated with neuronal degeneration.in bioRxiv: Neuroscience on 2025-07-12 00:00:00 UTC.
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Individual differences in learning and decision-making: the role of COMT Val158Met polymorphism in transitive inference
Understanding the ordinal relationships between items requires constructing a rank order supporting decision-making between options. This process depends on the ability to learn reciprocal relationships and to select the best option available when making a choice. In such forms of decision-making, the prefrontal cortex (PFC) plays a crucial role in encoding the relative value of alternatives as a decision is formed. Higher-order cognitive abilities are influenced by genetic factors that affect dopamine availability in the PFC, potentially contributing to individual differences. Here, we examined the performance of 83 participants in a transitive inference task (TI), grouped by genotype based on the Val158Met single-nucleotide polymorphism in the Catechol-O-Methyltransferase (COMT) gene. The task included a learning phase in which participants acquired the reciprocal relationships among a set of hierarchically ranked items (A>B>C>D>E>F), followed by a test phase in which they were required to compare all possible item pairs and select the higher-ranked one. While genotype did not significantly influence test-phase performance, it did affect learning efficiency. Specifically, Val homozygotes took a longer learning procedure than both heterozygotes and Met homozygotes during the learning phase. Drift diffusion modelling (DDM) revealed that task performance was explained by the efficiency of evidence accumulation, which was lower in Val homozygotes, accounting for their poorer performance not only during initial learning but also when required to switch to a reversed hierarchical structure (A -
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Cellular basis for cortical network aging in primates
Large-scale brain networks are vulnerable to change with aging and become dysregulated. How these networks are altered at the cellular level remains unclear owing to challenges of bridging data across scales. Here, we integrate in vivo cortical similarity networks with whole brain spatial transcriptomics to characterize the aging brain in a lifespan cohort of macaques (N=64, ages 1-26 years). Deep-layer excitatory neurons and oligodendrocytes emerged as dominant correlates of cortical similarity, linking infragranular cell type composition to macroscopic network structure. Age-related declines in network strength were most pronounced in transmodal networks, including default mode and limbic, and aligned with regions enriched in inhibitory and glial cell types. Parvalbumin-enriched chandelier cells showed the strongest association with regional vulnerability, suggesting a role in network disconnection. Cell-type enrichment was conserved across species, with both human and macaque transcriptomic data aligning with the cortical functional hierarchy. These findings uncover a cellular basis for cortical network aging and highlight the value of imaging-transcriptomic integration across scales.in bioRxiv: Neuroscience on 2025-07-12 00:00:00 UTC.
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Electroencephalography-based diagnosis of schizophrenia using machine learning
AbstractSchizophrenia is a mental disorder with a high social burden. Identification of quantitative biomarkers has the potential to facilitate the diagnosis process. This study aims to explore a routine to gain such biomarkers using quantitative analysis of electroencephalography (EEG) data. Previous studies suggest that EEG data can be used to differentiate schizophrenia patients from healthy subjects. Various EEG features were used for such diagnostics using machine learning (ML) algorithms, but selecting the optimal EEG features and the classifiers is still insufficient. We propose an automatic selection of ML parameters using the Waikato Environment for Knowledge Analysis software. Using Waikato Environment for Knowledge Analysis’s “Supervised Attribute Selection” tool, we identified attributes that allow the identification of schizophrenia patients with a high accuracy of 93%. The attributes identified were EEG signals enriched for alpha and gamma frequencies from specific brain areas (frontal right, central, parietal, and occipital). This proposed strategy can effectively identify schizophrenia patients with high accuracy. It could be used as an ML tool to support diagnosis and potentially provide insights into the underlying disease mechanism of schizophrenia.in Cerebral Cortex on 2025-07-12 00:00:00 UTC.
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Altered resting-state brain entropy by repetitive transcranial magnetic stimulation across the human cortex
AbstractRepetitive transcranial magnetic stimulation (rTMS) is a non-invasive neuromodulation technique used to treat neuropsychiatric disorders. Despite its efficacy, its neuro-mechanisms remain unclear. Brain entropy (BEN), a measure of the irregularity and complexity of brain activity, has been shown to reflect the effects of high-frequency rTMS (HF-rTMS). However, it remains unknown whether BEN is sensitive to low-frequency rTMS (LF-rTMS), as well as to target-specific effects.Eighteen healthy adult participants underwent continuous theta burst stimulation (cTBS) over the left dorsolateral prefrontal cortex (L-DLPFC), and 23 healthy adult participants underwent LF-rTMS targeting the L-DLPFC, left temporoparietal junction (L-TPJ), and left occipital cortex (L-OCC). Magnetic resonance imaging scans were performed pre- and post-stimulation, and BEN maps were calculated from the preprocessed functional images.Results showed that cTBS over L-DLPFC increased BEN in the medial orbitofrontal cortex (MOFC), while L-DLPFC LF-rTMS increased BEN in the MOFC, subgenual anterior cingulate cortex, and putamen. LF-rTMS at the L-TPJ increased BEN in the right TPJ, while LF-rTMS at the L-OCC decreased BEN in the posterior cingulate cortex. These findings demonstrate BEN remains sensitive to LF-rTMS and exhibits target-specific effects. Furthermore, this work advances BEN as a promising biomarker for rTMS effects beyond motor cortex paradigms.in Cerebral Cortex on 2025-07-12 00:00:00 UTC.
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Ten simple rules for success as a trainee-led outreach organization in computational biology education
by Jennifer Blanc, Margaret C. Steiner, Lauren E. Blake, Elizabeth Gibbons, Mariadaria K. Ianni-Ravn, Roxroy C. Morgan, Suzanna Parkinson, Christian Porras, Ethan Zhong
in PLoS Computational Biology on 2025-07-11 14:00:00 UTC.
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Ten simple rules for developing a training program
by Leanna B. Blevins, Amy M. Harrigan, Kevin A. Janes, Jason A. Papin
in PLoS Computational Biology on 2025-07-11 14:00:00 UTC.
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Perceptual clustering in auditory streaming
by Nathanael Larigaldie, Tim Yates, Ulrik R. Beierholm
Perception is dependent on the ability to separate stimuli from different objects and causes in order to perform inference and further processing. We have models of how the human brain can perform such causal inference for simple binary stimuli (e.g., auditory and visual), but the complexity of the models increases dramatically with more than two stimuli. To characterize human perception with more complex stimuli, we developed a Bayesian inference model that takes into account a potentially unlimited number of stimulus sources: it is general enough to factor in any discrete sequential cues from any modality. Because the model employs a non-parametric prior, increased signal complexity does not necessitate the addition of more parameters. The model not only predicts the number of possible sources, but also specifies the source with which each signal is associated. As a test case, we demonstrate that such a model can explain several phenomena in the auditory stream perception literature, that it provides an excellent fit to experimental data, and that it makes novel predictions that we experimentally confirm. These findings have implications not just for human auditory temporal perception, but for a wide range of perceptual phenomena across unisensory and multisensory stimuli.in PLoS Computational Biology on 2025-07-11 14:00:00 UTC.
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Time-series modeling of epidemics in complex populations: Detecting changes in incidence volatility over time
by Rachael Aber, Yanming Di, Benjamin D. Dalziel
Trends in infectious disease incidence provide important information about epidemic dynamics and prospects for control. Higher-frequency variation around incidence trends can shed light on the processes driving epidemics in complex populations, as transmission heterogeneity, shifting landscapes of susceptibility, and fluctuations in reporting can impact the volatility of observed case counts. However, measures of temporal volatility in incidence, and how volatility changes over time, are often overlooked in population-level analyses of incidence data, which typically focus on moving averages. Here we present a statistical framework to quantify temporal changes in incidence dispersion and to detect rapid shifts in the dispersion parameter, which may signal new epidemic phases. We apply the method to COVID-19 incidence data in 144 United States (US) counties from January 1st, 2020 to March 23rd, 2023. Theory predicts that dispersion should be inversely proportional to incidence, however our method reveals pronounced temporal trends in dispersion that are not explained by incidence alone, but which are replicated across counties. In particular, dispersion increased around the major surge in cases in 2022, and highly overdispersed patterns became more frequent later in the time series. These increases potentially indicate transmission heterogeneity, changes in the susceptibility landscape, or that there were changes in reporting. Shifts in dispersion can also indicate shifts in epidemic phase, so our method provides a way for public health officials to anticipate and manage changes in epidemic regime and the drivers of transmission.in PLoS Computational Biology on 2025-07-11 14:00:00 UTC.
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Neural network approaches, including use of topological data analysis, enhance classification of human induced pluripotent stem cell colonies by treatment condition
by Alexander Ruys de Perez, Paul E. Anderson, Elena S. Dimitrova, Melissa L. Kemp
Understanding how stem cells organize to form early tissue layers remains an important open question in developmental biology. Helpful in understanding this process are biomarkers or features that signal when a significant transition or decision occurs. We show such features from the spatial layout of the cells in a colony are sufficient to train neural networks to classify stem cell colonies according to differentiation protocol treatments each colony has received. We use topological data analysis to derive input information about the cells’ positions to a four-layer feedforward neural network. We find that despite the simplicity of this approach, such a network has performance similar to the traditional image classifier ResNet. We also find that network performance may reveal the time window during which differentiation occurs across multiple conditions.in PLoS Computational Biology on 2025-07-11 14:00:00 UTC.
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Allocating limited surveillance effort for outbreak detection of endemic foot and mouth disease
by Ariel Greiner, José L. Herrera-Diestra, Michael Tildesley, Katriona Shea, Matthew Ferrari
Foot and Mouth Disease (FMD) affects cloven-hoofed animals globally and has become a major economic burden for many countries around the world. Countries that have had recent FMD outbreaks are prohibited from exporting most meat products; this has major economic consequences for farmers in those countries, particularly farmers that experience outbreaks or are near outbreaks. Reducing the number of FMD outbreaks in countries where the disease is endemic is an important challenge that could drastically improve the livelihoods of millions of people. As a result, significant effort is expended on surveillance; but there is a concern that uninformative surveillance strategies may waste resources that could be better used on control management. Rapid detection through sentinel surveillance may be a useful tool to reduce the scale and burden of outbreaks. In this study, we use an extensive outbreak and cattle shipment network dataset from the Republic of Türkiye to retrospectively test three possible strategies for sentinel surveillance allocation in countries with endemic FMD and minimal existing FMD surveillance infrastructure that differ in their data requirements: ranging from low to high data needs, we allocate limited surveillance to [1] farms that frequently send and receive shipments of animals (Network Connectivity), [2] farms near other farms with past outbreaks (Spatial Proximity) and [3] farms that receive many shipments from other farms with past outbreaks (Network Proximity). We determine that all of these surveillance methods find a similar number of outbreaks – 2-4.5 times more outbreaks than were detected by surveying farms at random. On average across surveillance efforts, the Network Proximity and Network Connectivity methods each find a similar number of outbreaks and the Spatial Proximity method always finds the fewest outbreaks. Since the Network Proximity method does not outperform the other methods, these results indicate that incorporating both cattle shipment data and outbreak data provides only marginal benefit over the less data-intensive surveillance allocation methods for this objective. We also find that these methods all find more outbreaks when outbreaks are rare. This is encouraging, as early detection is critical for outbreak management. Overall, since the Spatial Proximity and Network Connectivity methods find a similar proportion of outbreaks, and are less data-intensive than the Network Proximity method, countries with endemic FMD whose resources are constrained could prioritize allocating sentinels based on whichever of those two methods requires less additional data collection.in PLoS Computational Biology on 2025-07-11 14:00:00 UTC.
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Structural elucidation of the haptoglobin–hemoglobin clearance mechanism by macrophage scavenger receptor CD163
by Ching-Shin Huang, Hui Wang, Joshua B. R. White, Oksana Degtjarik, Cindy Huynh, Kristoffer Brannstrom, Mark T. Horn, Stephen P. Muench, William S. Somers, Javier Chaparro-Riggers, Laura Lin, Lidia Mosyak
Intravascular hemolysis releases hemoglobin into the bloodstream, which can damage vascular and renal tissues due to its oxidative nature. Circulating haptoglobin acts as a primary defense by binding to free hemoglobin, forming a haptoglobin–hemoglobin (HpHb) complex that is then recognized and cleared by the CD163 scavenger receptor on macrophages. While the function and structure of HpHb complex are mostly well-defined, the molecular mechanism underlying its interaction with CD163 remains unclear. Here we report the cryo-electron microscopy structures of human CD163 in its unliganded state and in its complex with HpHb. These structures reveal that CD163 functions as a trimer, forming a composite binding site at its center for one protomer of the dimeric HpHb, resulting in a 3:1 binding stoichiometry. In the unliganded state, CD163 can also form a trimer, but in an autoinhibitory configuration that occludes the ligand binding site. Widespread electrostatic interactions mediated by calcium ions are pivotal in both pre-ligand and ligand-bound receptor assemblies. This calcium-dependent mechanism enables CD163/HpHb complexes to assemble and, once internalized, disassemble into individual components upon reaching the endosome, where low calcium and lower pH conditions prevail. Collectively, this study elucidates the molecular mechanism by which CD163-mediated endocytosis efficiently clears different isoforms of HpHb.in PLoS Biology on 2025-07-11 14:00:00 UTC.
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H3K4me2 marks the enhancer: Enhancer logic in the zebrafish embryo
by Noura Maziak, Juan M. Vaquerizas
At a key point in development, the embryo activates its genome: a shift that is largely coordinated by maternally derived factors. A new study in PLOS Biology identifies H3K4me2-marked enhancers in zebrafish that function independently and mirror the gamete state. At a key point in development, the embryo activates its genome: a shift that is largely coordinated by maternally derived factors. A new study in PLOS Biology identifies H3K4me2-marked enhancers in zebrafish that function independently and mirror the gamete state.in PLoS Biology on 2025-07-11 14:00:00 UTC.
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Neural activity patterns stabilize during wakefulness and conscious experience
by Simon van Gaal
Several neuronal markers have been proposed to differentiate the global brain states that underly states of consciousness. A new pre-registered study in PLOS Biology compares neural markers of loss of consciousness in flies when awake, asleep, and anesthetized. Which neuronal markers can differentiate global brain states that underly states of consciousness? This Primer explores the findings of a new study published in PLOS Biology, which compares neural markers of loss of consciousness in flies when awake, asleep and anesthetized.in PLoS Biology on 2025-07-11 14:00:00 UTC.
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Cilia in the brain display region-dependent oscillations of length and orientation
by Roudabeh Vakil Monfared, Sherif Abdelkarim, Pieter Derdeyn, Kiki Chen, Hanting Wu, Kenneth Leong, Tiffany Chang, Justine Lee, Sara Versales, Surya M. Nauli, Kevin Beier, Pierre Baldi, Amal Alachkar
In this study, we conducted high-throughput spatiotemporal analysis of primary cilia length and orientation across 22 mouse brain regions. We developed automated image analysis algorithms, which enabled us to examine over 10 million individual cilia, generating the largest spatiotemporal atlas of cilia. We found that cilia length and orientation display substantial variations across different brain regions and exhibit fluctuations over a 24-h period, with region-specific peaks during light-dark phases. Our analysis revealed unique orientation patterns of cilia, suggesting that cilia orientation within the brain is not random but follows specific patterns. Using BioCycle, we identified rhythmic fluctuations in cilia length across five brain regions: the nucleus accumbens core, somatosensory cortex, and the dorsomedial, ventromedial, and arcuate hypothalamic nuclei. Our findings present novel insights into the brain cilia dynamics, and highlight the need for further investigation into cilia’s role in the brain’s response to environmental changes and regulation of oscillatory physiological processes.in PLoS Biology on 2025-07-11 14:00:00 UTC.
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Heavy Metal Contamination: Sources, Health Impacts, and Sustainable Mitigation Strategies with Insights from Nigerian Case Studies [version 4; peer review: 3 approved with reservations]
Heavy metal contamination has gradually become a highly significant global issue due to its continual existence in the environment and bioaccumulation in the ecosystems, posing deleterious risks to human health. The major objectives of the review is to investigate the sources, pathways, and toxicological impacts of heavy metals such as cadmium, lead, mercury, and arsenic, elucidating their health consequences and plausible mitigation strategies. Furthermore, the review explores the dual origins of heavy metal contamination; natural geological processes and anthropogenic activities such as industrial emissions, mining, and agricultural practices. These heavy metals seep into soil, water, and food chains, leading to bioaccumulation, bio-magnification and causing significant health risks, including cardiovascular diseases, neurological disorders, and reproductive toxicity. Additionally, the addition of indigenous case studies from Nigeria, such as lead poisoning in Zamfara State and contamination in the Great Kwa River of Cross Rivers State underscores the disproportionate impact of heavy metal pollution in developing nations. The key findings from this review via the selected case studies revealed the socio-economic and environmental dimensions of the issue, providing a contextual understanding of region-specific vulnerabilities and health outcomes. To address these problems, the review evaluates already existing mitigation strategies, including chelation therapy and phytoremediation, while proposing sustainable, cost-effective solutions for reducing exposure and mitigating impacts. It emphasizes the importance of integrative approaches involving policy, community engagement, and technological innovations to fight heavy metal contamination effectively. In conclusion, this review contributes to the understanding of heavy metal toxicity, giving and showcasing very much important insights into the sources and health implications of contamination. By integrating theoretical perspectives with practical solutions, this review provides a robust framework for informing policy makers and advancing sustainable environmental management practices.in F1000Research on 2025-07-11 13:02:09 UTC.
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Digital literacy in the use of digital platforms in public administration in the Lambayeque Region [version 1; peer review: awaiting peer review]
Background Nowadays, digital literacy plays a key role in improving public management, especially in the most vulnerable communities. In this regard, we aim to understand how the knowledge and use of digital tools impact the work life of the vulnerable population in Lambayeque, facilitating their access to digital platforms and, in turn, improving their interaction with public services. The research aimed to analyze how the implementation of digital literacy improved the use of digital platforms in public administration among the vulnerable working population of the Lambayeque Region during the year 2023. Methods To achieve this, an applied approach with a descriptive-propositional scope was adopted. Additionally, a non-experimental and cross-sectional design was employed, considering a population of 1,356 inhabitants of Lambayeque, from which 673 were selected using a statistical formula. Results The results evidenced a positive relationship between digital literacy and the use of digital platforms. It was observed that the majority of respondents had a high level of digital literacy, which favored efficiency in the use of these tools within public administration. However, it was also identified that a sector of the population still presented a medium level, highlighting the need to strengthen the development of digital skills. Furthermore, the findings coincided with previous research that emphasized the importance of digital literacy in various sectors, such as commerce, small and medium enterprises, and financial inclusion. Conclusions Consequently, it was concluded that training in digital literacy was key to the adoption of new technologies and the utilization of digital services, thereby promoting a more active and empowered citizenship in the digital environment.in F1000Research on 2025-07-11 13:00:34 UTC.
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Digital Leadership in Education: A Bibliometric Analysis of Research Trends from 2010 to 2024 [version 1; peer review: awaiting peer review]
The Fourth Industrial Revolution (4IR), the COVID-19 pandemic, and the rapidly digitising educational system due to advances in artificial intelligence (AI) have made a change in leadership imperative. A key framework for improving organisational effectiveness in handling these changes is digital leadership. It combines technological competencies with traditional leadership. Even with increased scholarly interest, there remains a gap in the thorough analysis of the field’s intellectual framework, thematic evolution, and collaborative dynamics. This study addresses this gap by conducting a bibliometric analysis of digital leadership research in education from 2010 to 2024, employing RStudio to map publication trends, influential sources, author productivity, conceptual themes, and social structures. Data from 338 Scopus-indexed documents reveal a significant rise in publications post-2010, peaking in 2023, with core journals such as Education and Information Technologies and Cogent Education dominating the field. Prolific authors like Karakose T., Altinay F., and Z. underscore the centrality of collaborative research, while thematic mapping identifies key clusters: digital competence, virtual leadership, and institutional innovation. Thematic evolution highlights a post-pandemic pivot toward digital transformation and AI integration, though niche areas like K-12 digital leadership remain underdeveloped. Social network analysis reveals fragmented yet growing global collaborations, with the United States, Turkey, and the United Kingdom as dominant hubs, while disparities persist in Global South participation. The study’s implications emphasize the need for interdisciplinary research, equitable global partnerships, and policy frameworks that prioritize digital leadership training for educators. Practitioners are encouraged to implement adaptive strategies to leverage emerging technologies, ensuring sustainable learning outcomes. This analysis provides a foundation for future research and practice in digital leadership by delineating the field’s conceptual and social networks, thereby bridging the divides between theory, policy, and institutional practice.in F1000Research on 2025-07-11 12:59:15 UTC.
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Confirmatory Factor Analysis and Propensity to Cheat Scale Validation in the Ethiopian Public Higher Education Institutions [version 3; peer review: 2 approved with reservations, 2 not approved]
Background This study aimed to validate a comprehensive and psychometrically sound instrument—the Propensity to Cheat Scale (PCS)—designed to measure undergraduate students’ propensity toward academic dishonesty in Ethiopian universities. Based on Ajzen’s Theory of Planned Behavior, the PCS was validated to assess students’ attitudes, subjective norms, and perceived behavioral control related to various forms of cheating, including cheating on tests and examinations, cheating on assignments, cheating on research work (plagiarism), and theft and mutilation of library materials. Methods The present study employed an explanatory research design using a questionnaire based on the Propensity to Cheat Scale (PCS). The questionnaire was administered to 500 university students (male = 367 [73.4%]; female = 133 [26.6%]) selected from three Ethiopian public universities between November and January 2022. In order to measure the underlying variables of propensity towards cheating, a factor model is developed using exploratory factor analysis (EFA), and confirmatory factor analysis was employed to validate the students’ perceived PTC. The internal consistency of the PTC scale was assessed using reliability analysis, and validity evaluations were conducted to confirm the scale’s discriminant and convergent validity. Results Confirmatory factor analysis (CFA) results revealed a good fit to the data, and the internal consistency of the PCS was found to be strong, providing a reliable measure of students’ propensity for cheating. Validity evaluations, including discriminant validity and convergent validity, confirmed the validity of the scale. The average variance extracted (AVE) and composite reliability values also supported the scale’s convergent validity. The multidimensional concept of the PTC was supported by a four-factor solution consisting of 26 reliable and valid items. Conclusion The findings of the study demonstrate that the scale has also provided sufficient evidence of convergent and discriminant validity. By establishing discriminant and convergent validity, as well as reliability, through different validation procedures, the study has provided strong evidence for the effectiveness of the PCS as an instrument for determining whether university students are likely to engage in cheating behavior.in F1000Research on 2025-07-11 12:49:49 UTC.
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From simple lab tasks to the virtual court: Bayesian integration in tennis
Journal of Neurophysiology, Volume 134, Issue 1, Page 303-313, July 2025.
in Journal of Neurophysiology on 2025-07-11 12:22:20 UTC.
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Muscle function and electromyography: (almost) 70 years since Doty and Bosma (1956)
Journal of Neurophysiology, Volume 134, Issue 1, Page 337-346, July 2025.
in Journal of Neurophysiology on 2025-07-11 12:22:18 UTC.
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Unified measures quantifying intensity and similarity of pain and somatosensory percepts
Journal of Neurophysiology, Volume 134, Issue 1, Page 292-302, July 2025.
in Journal of Neurophysiology on 2025-07-11 12:22:17 UTC.
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Complementary functional profiles of mismatch responses mediated by adaptation and deviance detection point to two distinct auditory short-term memory systems
Journal of Neurophysiology, Volume 134, Issue 1, Page 314-336, July 2025.
in Journal of Neurophysiology on 2025-07-11 12:22:15 UTC.
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Patients perspectives on an interprofessional care model in healthcare for patients with immune-mediated inflammatory diseases: A qualitative study [version 1; peer review: awaiting peer review]
Background Managing immune-mediated inflammatory diseases(IMIDs) is complex for patients and healthcare professionals. Research indicates healthcare fragmentation due to mono-disciplinary approaches that address numerous comorbid conditions. While acknowledged as critical to IMIDs care, the joint coordination and management of somatic disorders, psychological disorders, and socioeconomic factors receive limited attention in current clinical practices. Research calls for more interprofessional approaches addressing IMIDs patients’ needs. Therefore, this study explored patients’ experiences participating in an interprofessional patient-centred complex IMIDs intervention to inform future development. Materials & Methods The study was based on semi-structured interviews with 11 participants. We used Thematic Analysis to analyse data. Results We identified three overarching themes: Bringing well-known actors into a new concept, Expanding interprofessional care, and Bridging interprofessional care. From the patients’ perspective, integrating expertise from well-known professionals and a broader spectrum of professionals was critical to addressing their complex IMIDs needs and avoiding fragmentation. Moreover, coordination administered by care coordinators was vital to their experiences of success. Conclusions This study presents a two-fold conceptualisation of interprofessional care to inform future IMIDs intervention development. Our results underscore the importance of individualising and tailoring treatment through patient-centred care to help patients improve their IMIDs and self-management skills.in F1000Research on 2025-07-11 11:23:57 UTC.
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LitSieve: An integrated literature search and triage tool for biocuration [version 1; peer review: awaiting peer review]
Biomedical databases are an important part of the scientific infrastructure for organising and synergising research outputs. Many of these databases abstract content from the rapidly expanding scientific literature. Therefore, database curators require effective literature search methods in order to capture research relevant to their domain. This article describes LitSieve, a literature search tool with filtering based on text mined annotations, and flexible article organisation features. It allows users to define filters based on biomedical entities like genes, diseases and species to include or exclude particular articles within their results. By combining a search query with a filter, curators are able to identify articles relevant to the database which they are curating. LitSieve uses APIs provided by Europe PMC, from which abstracts, article full text and text mined annotations are drawn. LitSieve is available at https://www.ebi.ac.uk/europepmc/litsieve/in F1000Research on 2025-07-11 11:03:14 UTC.
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Predicting interface residues for protein polymers based on module division
Author(s): Shuhong Yu, Zicheng Xie, Jiudong Wang, and Xinqi Gong
In the field of computational biology, AlphaFold has facilitated remarkable progress in predicting protein structures. However, for some multimeric complexes, the accuracy of its predictions still needs improvement. Enhancing the prediction of binding sites in protein oligomers contributes to the pr…
[Phys. Rev. E 112, 014403] Published Fri Jul 11, 2025
in Physical Review E: Biological physics on 2025-07-11 10:00:00 UTC.
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The Brain of the African Wild Dog. V. The Somatosensory System and Vestibular Nuclear Complex
Lateral view of the African wild dog brain showing the location of the somatosensory cortical areas in relation to the parietal multisensory region (PP), occipital visual cortical areas (17, 18, 19, 21, SS, T), and temporal auditory cortical areas (AAF, AI AII). These broad relations of the sensory cortex reflect that observed in many other mammals.
ABSTRACT
Social behaviors in the African wild dog (Lycaon pictus) commonly involve a range of tactile aspects, including biting, pushing, embracing, mounting, face and muzzle licking, nose–chin and muzzle contact, paw placement, play fighting, and wrestling, supported by the vestibular system. We employed an array of architectural and immunohistochemical stains to provide a qualitative description of the somatosensory and vestibular systems in the brain of one representative African wild dog individual. The appearance of both systems does not appear to differ from that reported in other Carnivora. The six nuclei forming the vestibular system, and their relationship to each other and the incoming vestibular branch of the eighth cranial nerve, appear like those observed in many mammalian species. The location and appearance of the dorsal column nuclei, the trigeminal sensory column, the colliculi, somatosensory nuclei of the dorsal thalamus, and the five somatosensory cortical areas observed in the African wild dog are like those observed in the domestic dog and other Carnivora. This study of the somatosensory and vestibular systems of the African wild dog completes our series of studies describing the major sensory systems in the African wild dog brain. It appears reasonable to conclude that, at the systems level of analysis, no overt specializations of any of the sensory systems are present. Thus, the neural underpinnings of the complex sociality of the African wild dog may be supported by nonsensory neural systems, such as motor, neuromodulatory, limbic, or cognitive systems, or levels of organization like receptor expression patterns or connectivity.
in Journal of Comparative Neurology on 2025-07-11 08:05:03 UTC.
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Cognitive Performance in Early Neuronal Synuclein Disease with Hyposmia but without Motor Disability: Association with Dopamine Deficiency and Isolated Rapid Eye Movement Sleep Behavior Disorder
Objective
To determine the impact of dopamine deficiency and isolated rapid eye movement (REM) sleep behavior disorder (iRBD) on cognitive performance in early neuronal α-synuclein disease (NSD) with hyposmia but without motor disability.
Methods
Using Parkinson's Progression Markers Initiative baseline data, cognitive performance was assessed with a cognitive summary score (CSS) derived from robust healthy control (HC) norms. Performance was examined for participants with hyposmia in early NSD-Integrated Staging System (NSD-ISS), either stage 2A (cerebrospinal fluid α-synuclein seed amplification assay [SAA]+, dopamine transporter scan [DaTscan]−) or 2B (SAA+, DaTscan+).
Results
Participants were stage 2A (n = 101), stage 2B (N = 227), and HCs (n = 158). Although stage 2 had intact Montreal Cognitive Assessment scores (mean [SD] = 27.0 [2.3]), stage 2A had a numerically worse CSS (z-score mean difference = 0.05, p = NS; effect size = 0.09) and stage 2B a statistically worse CSS (z-score mean difference = 0.23, p < 0.05; effect size = 0.40) compared with HCs. In stage 2A, hyposmia alone was associated with normal cognition, but those with comorbid iRBD had significantly worse cognition (z-score mean difference = 0.33, p < 0.05, effect size =0.50). In stage 2B, hyposmia alone had abnormal cognition (z-score mean difference = 0.18, p = 0.0078, effect size = 0.29), and superimposed iRBD had a statistically significant additive effect.
Interpretation
Using a novel CSS, we demonstrated that hyposmia is associated with cognitive deficits in prodromal NSD without motor disability, particularly when comorbid dopamine system impairment or comorbid iRBD is present. Therefore, it is critical to include and assess cognition at all stages when studying synuclein disease, even in the absence of motor disability. ANN NEUROL 2025
in Annals of Neurology on 2025-07-11 07:24:20 UTC.
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Domain engineering in ferroelectric nematics for nonlinear optical modulation
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Structural and genetic determinants of zebrafish functional brain networks
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Proteins with cognition-associated structural changes in a rat model of aging exhibit reduced refolding capacity
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Discovering high-entropy electrocatalysts through a batch-alloy targeting approach
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Dynamically covalent lipid nanoparticles mediate CRISPR-Cas9 genome editing against choroidal neovascularization in mice
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Cold-air outbreaks in the continental US: Connections with stratospheric variations
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Kilometric sea level changes during the Messinian salinity crisis caused by river erosion and climate
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Stripe rust fungi hijack a host transcriptional repressor to induce TaSWEET14d for enhanced sugar uptake in wheat
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Decoding sexual dimorphism of the sex-shared nervous system at single-neuron resolution
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Constraining biorecalcitrance of carboxyl-rich alicyclic molecules in the ocean
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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BRD4770 protects against DOX-induced cardiotoxicity by inhibiting apoptosis and ferroptosis
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Cypin regulates K63-linked polyubiquitination to shape synaptic content
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Exogenous activation of the adhesion GPCR ADGRD1/GPR133 protects against bone loss by negatively regulating osteoclastogenesis
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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G proteins of the G12 family expressed by POMC neurons regulate key metabolic functions
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Transition of cellular senescence to pyroptosis mediates recurrence of small cell lung cancer after chemotherapy
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Picoeukaryote-based biohybrid microrobots for active delivery in the kidney
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Wearing bacteriophages individually with an adhesive drug-loadable nanohelmet for treating ocular infections
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Parallel evolution of salinity tolerance in Arabidopsis thaliana accessions from Cape Verde Islands
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Change-of-mind neuroeconomic decision-making is modulated by LINC00473 in medial prefrontal cortex in a sex-dependent manner
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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UFMylation maintains tumor suppressor pVHL stability by activating the deubiquitinase BAP1
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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PD-1 and ICOS are coexpressed in T follicular helper cells but define three stages of maturation of T follicular regulatory cells
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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Complex I inhibition combined with TLR activation in the breast tumor microenvironment educates cytotoxic neutrophils
Science Advances, Volume 11, Issue 28, July 2025.
in Science Advances on 2025-07-11 07:00:00 UTC.
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