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Neural correlates of perceptual consciousness from within: a narrative review of human intracranial research
arXiv:2510.08736v1 Announce Type: new Abstract: Despite many years of research, the quest to identify neural correlates of perceptual consciousness (NCC) remains unresolved. One major obstacle lies in methodological limitations: most studies rely on non-invasive neural measures with limited spatial or temporal resolution making it difficult to disentangle proper NCCs from concurrent cognitive processes. Additionally, the relatively low sensitivity of non-invasive neural measures limits the interpretation of null findings in studies targeting proper NCCs. In this review, we discuss how human intracranial recordings can advance the search for NCCs, by offering high spatiotemporal resolution, improved signal sensitivity, and broad cortical and subcortical coverage. We review studies that have examined NCCs at the level of single neurons and populations of neurons, and evaluate their implications on the debates between cognitive and sensory theories of consciousness. Finally, we highlight the limits of current intracranial human recordings and propose future directions based on emerging technologies and novel experimental paradigms.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-10-13 04:00:00 UTC.
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Brain2Text Decoding Model Reveals the Neural Mechanisms of Visual Semantic Processing
arXiv:2503.22697v2 Announce Type: replace Abstract: Decoding sensory experiences from neural activity to reconstruct human-perceived visual stimuli and semantic content remains a challenge in neuroscience and artificial intelligence. Despite notable progress in current brain decoding models, a critical gap still persists in their systematic integration with established neuroscientific theories and the exploration of underlying neural mechanisms. Here, we present a novel framework that directly decodes fMRI signals into textual descriptions of viewed natural images. Our novel deep learning model, trained without visual information, achieves state-of-the-art semantic decoding performance, generating meaningful captions that capture the core semantic content of complex scenes. Neuroanatomical analysis reveals the critical role of higher-level visual cortices, including MT+ complex, ventral stream visual cortex, and inferior parietal cortex, in visual semantic processing. Furthermore, category-specific analysis demonstrates nuanced neural representations for semantic dimensions like animacy and motion. This work provides a more direct and interpretable framework to the brain's semantic decoding, offering a powerful new methodology for probing the neural basis of complex semantic processing, refining the understanding of the distributed semantic network, and potentially developing brain-sinpired language models.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-10-13 04:00:00 UTC.
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Confidence-weighted integration of human and machine judgments for superior decision-making
arXiv:2408.08083v3 Announce Type: replace-cross Abstract: Large language models (LLMs) can surpass humans in certain forecasting tasks. What role does this leave for humans in the overall decision process? One possibility is that humans, despite performing worse than LLMs, can still add value when teamed with them. A human and machine team can surpass each individual teammate when team members' confidence is well-calibrated and team members diverge in which tasks they find difficult (i.e., calibration and diversity are needed). We simplified and extended a Bayesian approach to combining judgments using a logistic regression framework that integrates confidence-weighted judgments for any number of team members. Using this straightforward method, we demonstrated its effectiveness in both image classification and neuroscience forecasting tasks. Combining human judgments with one or more machines consistently improved overall team performance. Our hope is that this simple and effective strategy for integrating the judgments of humans and machines will lead to productive collaborations.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-10-13 04:00:00 UTC.
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Spike-frequency and h-current based adaptation are dynamically equivalent in a Wilson-Cowan field model
arXiv:2510.08436v2 Announce Type: replace-cross Abstract: During slow-wave sleep, the brain produces traveling waves of slow oscillations (SOs; $\leq 2$ Hz), characterized by the propagation of alternating high- and low-activity states. The question of internal mechanisms that modulate traveling waves of SOs is still unanswered although it is established that it is an adaptation mechanism that mediates them. One mechanism investigated is spike-frequency adaptation, a hyperpolarizing feedback current that is activated during periods of high-activity. An alternative mechanism is based on hyperpolarization-activated currents, which are positive feedback currents that are activated in low-activity states. Both adaptation mechanisms were shown to feature SO-like dynamics in neuronal populations, and the inclusion of a spatial domain seems to enhance observable differences in their effects. To investigate this in detail, we examine a spatially extended two-population Wilson-Cowan model with local spatial coupling and the excitatory populations equipped with either one of the two adaptation mechanisms. We describe them with the same dynamical equation and include the inverse mode of action by changing the signs of adaptation strength and gain. We show that the dynamical systems are mathematically equivalent under a compensatory external input, which depends on the adaptation strength, leading to a shift in state space of the otherwise equivalent bifurcation structure. Strong enough adaptation is required to induce traveling waves. Additionally, adaptation modulates the properties of the spatio-temporal activity patterns, such as temporal and spatial frequencies, and the speed of the traveling waves, all of which increase with increasing strength. Though being dynamically equivalent, our results also explain why location-dependent variations in feedback strength cause differences in the propagation of traveling waves between both adaptation mechanisms.in arXiv: Quantitative Biology: Neurons and Cognition on 2025-10-13 04:00:00 UTC.
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Estimating Brain Activity with High Spatial and Temporal Resolution using a Naturalistic MEG-fMRI Encoding Model
arXiv:2510.09415v1 Announce Type: cross Abstract: Current non-invasive neuroimaging techniques trade off between spatial resolution and temporal resolution. While magnetoencephalography (MEG) can capture rapid neural dynamics and functional magnetic resonance imaging (fMRI) can spatially localize brain activity, a unified picture that preserves both high resolutions remains an unsolved challenge with existing source localization or MEG-fMRI fusion methods, especially for single-trial naturalistic data. We collected whole-head MEG when subjects listened passively to more than seven hours of narrative stories, using the same stimuli in an open fMRI dataset (LeBel et al., 2023). We developed a transformer-based encoding model that combines the MEG and fMRI from these two naturalistic speech comprehension experiments to estimate latent cortical source responses with high spatiotemporal resolution. Our model is trained to predict MEG and fMRI from multiple subjects simultaneously, with a latent layer that represents our estimates of reconstructed cortical sources. Our model predicts MEG better than the common standard of single-modality encoding models, and it also yields source estimates with higher spatial and temporal fidelity than classic minimum-norm solutions in simulation experiments. We validated the estimated latent sources by showing its strong generalizability across unseen subjects and modalities. Estimated activity in our source space predict electrocorticography (ECoG) better than an ECoG-trained encoding model in an entirely new dataset. By integrating the power of large naturalistic experiments, MEG, fMRI, and encoding models, we propose a practical route towards millisecond-and-millimeter brain mapping.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-13 04:00:00 UTC.
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Adaptive Decoding via Hierarchical Neural Information Gradients in Mouse Visual Tasks
arXiv:2510.09451v1 Announce Type: cross Abstract: Understanding the encoding and decoding mechanisms of dynamic neural responses to different visual stimuli is an important topic in exploring how the brain represents visual information. Currently, hierarchically deep neural networks (DNNs) have played a significant role as tools for mining the core features of complex data. However, most methods often overlook the dynamic generation process of neural data, such as hierarchical brain's visual data, within the brain's structure. In the decoding of brain's visual data, two main paradigms are 'fine-grained decoding tests' and 'rough-grained decoding tests', which we define as focusing on a single brain region and studying the overall structure across multiple brain regions, respectively. In this paper, we mainly use the Visual Coding Neuropixel dataset from the Allen Brain Institute, and the hierarchical information extracted from some single brain regions (i.e., fine-grained decoding tests) is provided to the proposed method for studying the adaptive topological decoding between brain regions, called the Adaptive Topological Vision Transformer, or AT-ViT. In numerous experiments, the results reveal the importance of the proposed method in hierarchical networks in the visual tasks, and also validate the hypothesis that "the hierarchical information content in brain regions of the visual system can be quantified by decoding outcomes to reflect an information hierarchy." Among them, we found that neural data collected in the hippocampus can have a random decoding performance, and this negative impact on performance still holds significant scientific value.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-13 04:00:00 UTC.
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A Neural Surrogate-Enhanced Multi-Method Framework for Robust Wing Design Optimization
arXiv:2510.08582v1 Announce Type: new Abstract: This paper introduces a modular and scalable design optimization framework for the wing design process that enables faster early-phase design while ensuring aerodynamic stability. The pipeline starts with the generation of initial wing geometries and then proceeds to optimize the wing using several algorithms. Aerodynamic performance is assessed using a Vortex Lattice Method (VLM) applied to a carefully selected dataset of wing configurations. These results are employed to develop surrogate neural network models, which can predict lift and drag rapidly and accurately. The stability evaluation is implemented by setting the control surfaces and components to fixed positions in order to have realistic flight dynamics. The approach unifies and compares several optimization techniques, including Particle Swarm Optimization (PSO), Genetic Algorithms (GA), gradient-based MultiStart methods, Bayesian optimization, and Lipschitz optimization. Each method ensures constraint management via adaptive strategies and penalty functions, where the targets for lift and design feasibility are enforced. The progression of aerodynamic characteristics and geometries over the optimization iterations will be investigated in order to clarify each algorithm's convergence characteristics and performance efficiency. Our results show improvement in aerodynamic qualities and robust stability properties, offering a mechanism for wing design at speed and precision. In the interest of reproducibility and community development, the complete implementation is publicly available at https://github.com/AmirHosseinGhaemi2000/CHIMERA.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-13 04:00:00 UTC.
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Evolutionary Computation as Natural Generative AI
arXiv:2510.08590v1 Announce Type: new Abstract: Generative AI (GenAI) has achieved remarkable success across a range of domains, but its capabilities remain constrained to statistical models of finite training sets and learning based on local gradient signals. This often results in artifacts that are more derivative than genuinely generative. In contrast, Evolutionary Computation (EC) offers a search-driven pathway to greater diversity and creativity, expanding generative capabilities by exploring uncharted solution spaces beyond the limits of available data. This work establishes a fundamental connection between EC and GenAI, redefining EC as Natural Generative AI (NatGenAI) -- a generative paradigm governed by exploratory search under natural selection. We demonstrate that classical EC with parent-centric operators mirrors conventional GenAI, while disruptive operators enable structured evolutionary leaps, often within just a few generations, to generate out-of-distribution artifacts. Moreover, the methods of evolutionary multitasking provide an unparalleled means of integrating disruptive EC (with cross-domain recombination of evolved features) and moderated selection mechanisms (allowing novel solutions to survive), thereby fostering sustained innovation. By reframing EC as NatGenAI, we emphasize structured disruption and selection pressure moderation as essential drivers of creativity. This perspective extends the generative paradigm beyond conventional boundaries and positions EC as crucial to advancing exploratory design, innovation, scientific discovery, and open-ended generation in the GenAI era.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-13 04:00:00 UTC.
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The Enduring Dominance of Deep Neural Networks: A Critical Analysis of the Fundamental Limitations of Quantum Machine Learning and Spiking Neural Networks
arXiv:2510.08591v1 Announce Type: new Abstract: Recent advancements in QML and SNNs have generated considerable excitement, promising exponential speedups and brain-like energy efficiency to revolutionize AI. However, this paper argues that they are unlikely to displace DNNs in the near term. QML struggles with adapting backpropagation due to unitary constraints, measurement-induced state collapse, barren plateaus, and high measurement overheads, exacerbated by the limitations of current noisy intermediate-scale quantum hardware, overfitting risks due to underdeveloped regularization techniques, and a fundamental misalignment with machine learning's generalization. SNNs face restricted representational bandwidth, struggling with long-range dependencies and semantic encoding in language tasks due to their discrete, spike-based processing. Furthermore, the goal of faithfully emulating the brain might impose inherent inefficiencies like cognitive biases, limited working memory, and slow learning speeds. Even their touted energy-efficient advantages are overstated; optimized DNNs with quantization can outperform SNNs in energy costs under realistic conditions. Finally, SNN training incurs high computational overhead from temporal unfolding. In contrast, DNNs leverage efficient backpropagation, robust regularization, and innovations in LRMs that shift scaling to inference-time compute, enabling self-improvement via RL and search algorithms like MCTS while mitigating data scarcity. This superiority is evidenced by recent models such as xAI's Grok-4 Heavy, which advances SOTA performance, and gpt-oss-120b, which surpasses or approaches the performance of leading industry models despite its modest 120-billion-parameter size deployable on a single 80GB GPU. Furthermore, specialized ASICs amplify these efficiency gains. Ultimately, QML and SNNs may serve niche hybrid roles, but DNNs remain the dominant, practical paradigm for AI advancement.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-13 04:00:00 UTC.
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A Denoising Diffusion-Based Evolutionary Algorithm Framework: Application to the Maximum Independent Set Problem
arXiv:2510.08627v1 Announce Type: new Abstract: Denoising diffusion models (DDMs) offer a promising generative approach for combinatorial optimization, yet they often lack the robust exploration capabilities of traditional metaheuristics like evolutionary algorithms (EAs). We propose a Denoising Diffusion-based Evolutionary Algorithm (DDEA) framework that synergistically integrates these paradigms. It utilizes pre-trained DDMs for both high-quality and diverse population initialization and a novel diffusion-based recombination operator, trained via imitation learning against an optimal demonstrator. Evaluating DDEA on the Maximum Independent Set problem on Erd\H{o}s-R\'enyi graphs, we demonstrate notable improvements over DIFUSCO, a leading DDM solver. DDEA consistently outperforms it given the same time budget, and surpasses Gurobi on larger graphs under the same time limit, with DDEA's solution sizes being 3.9% and 7.5% larger on the ER-300-400 and ER-700-800 datasets, respectively. In out-of-distribution experiments, DDEA provides solutions of 11.6% higher quality than DIFUSCO under the same time limit. Ablation studies confirm that both diffusion initialization and recombination are crucial. Our work highlights the potential of hybridizing DDMs and EAs, offering a promising direction for the development of powerful machine learning solvers for complex combinatorial optimization problems.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-13 04:00:00 UTC.
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Fast and robust parametric and functional learning with Hybrid Genetic Optimisation (HyGO)
arXiv:2510.09391v1 Announce Type: new Abstract: The Hybrid Genetic Optimisation framework (HyGO) is introduced to meet the pressing need for efficient and unified optimisation frameworks that support both parametric and functional learning in complex engineering problems. Evolutionary algorithms are widely employed as derivative-free global optimisation methods but often suffer from slow convergence rates, especially during late-stage learning. HyGO integrates the global exploration capabilities of evolutionary algorithms with accelerated local search for robust solution refinement. The key enabler is a two-stage strategy that balances exploration and exploitation. For parametric problems, HyGO alternates between a genetic algorithm and targeted improvement through a degradation-proof Dowhill Simplex Method (DSM). For function optimisation tasks, HyGO rotates between genetic programming and DSM. Validation is performed on (a) parametric optimisation benchmarks, where HyGO demonstrates faster and more robust convergence than standard genetic algorithms, and (b) function optimisation tasks, including control of a damped Landau oscillator. Practical relevance is showcased through aerodynamic drag reduction of an Ahmed body via Reynolds-Averaged Navier-Stokes simulations, achieving consistently interpretable results and reductions exceeding 20% by controlled jet injection in the back of the body for flow reattachment and separation bubble reduction. Overall, HyGO emerges as a versatile hybrid optimisation framework suitable for a broad spectrum of engineering and scientific problems involving parametric and functional learning.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-13 04:00:00 UTC.
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Man-Made Heuristics Are Dead. Long Live Code Generators!
arXiv:2510.08803v1 Announce Type: cross Abstract: Policy design for various systems controllers has conventionally been a manual process, with domain experts carefully tailoring heuristics for the specific instance in which the policy will be deployed. In this paper, we re-imagine policy design via a novel automated search technique fueled by recent advances in generative models, specifically Large Language Model (LLM)-driven code generation. We outline the design and implementation of PolicySmith, a framework that applies LLMs to synthesize instance-optimal heuristics. We apply PolicySmith to two long-standing systems policies - web caching and congestion control, highlighting the opportunities unraveled by this LLM-driven heuristic search. For caching, PolicySmith discovers heuristics that outperform established baselines on standard open-source traces. For congestion control, we show that PolicySmith can generate safe policies that integrate directly into the Linux kernel.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-13 04:00:00 UTC.
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FedL2T: Personalized Federated Learning with Two-Teacher Distillation for Seizure Prediction
arXiv:2510.08984v1 Announce Type: cross Abstract: The training of deep learning models in seizure prediction requires large amounts of Electroencephalogram (EEG) data. However, acquiring sufficient labeled EEG data is difficult due to annotation costs and privacy constraints. Federated Learning (FL) enables privacy-preserving collaborative training by sharing model updates instead of raw data. However, due to the inherent inter-patient variability in real-world scenarios, existing FL-based seizure prediction methods struggle to achieve robust performance under heterogeneous client settings. To address this challenge, we propose FedL2T, a personalized federated learning framework that leverages a novel two-teacher knowledge distillation strategy to generate superior personalized models for each client. Specifically, each client simultaneously learns from a globally aggregated model and a dynamically assigned peer model, promoting more direct and enriched knowledge exchange. To ensure reliable knowledge transfer, FedL2T employs an adaptive multi-level distillation strategy that aligns both prediction outputs and intermediate feature representations based on task confidence. In addition, a proximal regularization term is introduced to constrain personalized model updates, thereby enhancing training stability. Extensive experiments on two EEG datasets demonstrate that FedL2T consistently outperforms state-of-the-art FL methods, particularly under low-label conditions. Moreover, FedL2T exhibits rapid and stable convergence toward optimal performance, thereby reducing the number of communication rounds and associated overhead. These results underscore the potential of FedL2T as a reliable and personalized solution for seizure prediction in privacy-sensitive healthcare scenarios.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-13 04:00:00 UTC.
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Neural Codecs as Biosignal Tokenizers
arXiv:2510.09095v1 Announce Type: cross Abstract: Neurophysiological recordings such as electroencephalography (EEG) offer accessible and minimally invasive means of estimating physiological activity for applications in healthcare, diagnostic screening, and even immersive entertainment. However, these recordings yield high-dimensional, noisy time-series data that typically require extensive pre-processing and handcrafted feature extraction to reveal meaningful information. Recently, there has been a surge of interest in applying representation learning techniques from large pre-trained (foundation) models to effectively decode and interpret biosignals. We discuss the challenges posed for incorporating such methods and introduce BioCodec, an alternative representation learning framework inspired by neural codecs to capture low-level signal characteristics in the form of discrete tokens. Pre-trained on thousands of EEG hours, BioCodec shows efficacy across multiple downstream tasks, ranging from clinical diagnostic tasks and sleep physiology to decoding speech and motor imagery, particularly in low-resource settings. Additionally, we provide a qualitative analysis of codebook usage and estimate the spatial coherence of codebook embeddings from EEG connectivity. Notably, we also document the suitability of our method to other biosignal data, i.e., electromyographic (EMG) signals. Overall, the proposed approach provides a versatile solution for biosignal tokenization that performs competitively with state-of-the-art models. The source code and model checkpoints are shared.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-13 04:00:00 UTC.
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Differential Analysis of Pseudo Haptic Feedback: Novel Comparative Study of Visual and Auditory Cue Integration for Psychophysical Evaluation
arXiv:2510.09570v1 Announce Type: cross Abstract: Pseudo-haptics exploit carefully crafted visual or auditory cues to trick the brain into "feeling" forces that are never physically applied, offering a low-cost alternative to traditional haptic hardware. Here, we present a comparative psychophysical study that quantifies how visual and auditory stimuli combine to evoke pseudo-haptic pressure sensations on a commodity tablet. Using a Unity-based Rollball game, participants (n = 4) guided a virtual ball across three textured terrains while their finger forces were captured in real time with a Robotous RFT40 force-torque sensor. Each terrain was paired with a distinct rolling-sound profile spanning 440 Hz - 4.7 kHz, 440 Hz - 13.1 kHz, or 440 Hz - 8.9 kHz; crevice collisions triggered additional "knocking" bursts to heighten realism. Average tactile forces increased systematically with cue intensity: 0.40 N, 0.79 N and 0.88 N for visual-only trials and 0.41 N, 0.81 N and 0.90 N for audio-only trials on Terrains 1-3, respectively. Higher audio frequencies and denser visual textures both elicited stronger muscle activation, and their combination further reduced the force needed to perceive surface changes, confirming multisensory integration. These results demonstrate that consumer-grade isometric devices can reliably induce and measure graded pseudo-haptic feedback without specialized actuators, opening a path toward affordable rehabilitation tools, training simulators and assistive interfaces.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-13 04:00:00 UTC.
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Enabling Population-Level Parallelism in Tree-Based Genetic Programming for GPU Acceleration
arXiv:2501.17168v5 Announce Type: replace Abstract: Tree-based Genetic Programming (TGP) is a widely used evolutionary algorithm for tasks such as symbolic regression, classification, and robotic control. Due to the intensive computational demands of running TGP, GPU acceleration is crucial for achieving scalable performance. However, efficient GPU-based execution of TGP remains challenging, primarily due to three core issues: (1) the structural heterogeneity of program individuals, (2) the complexity of integrating multiple levels of parallelism, and (3) the incompatibility between high-performance CUDA execution and flexible Python-based environments. To address these issues, we propose EvoGP, a high-performance framework tailored for GPU acceleration of TGP via population-level parallel execution. First, EvoGP introduces a tensorized representation that encodes variable-sized trees into fixed-shape, memory-aligned arrays, enabling uniform memory access and parallel computation across diverse individuals. Second, EvoGP adopts an adaptive parallelism strategy that dynamically combines intra- and inter-individual parallelism based on dataset size, ensuring high GPU utilization across a broad spectrum of tasks. Third, EvoGP embeds custom CUDA kernels into the PyTorch runtime, achieving seamless integration with Python-based environments such as Gym, MuJoCo, Brax, and Genesis. Experiments show that EvoGP attains a peak throughput exceeding $10^{11}$ GPops/s, with speedups of up to $528\times$ over GPU-based TGP implementations and $18\times$ over the fastest CPU-based libraries, while maintaining comparable accuracy and improved scalability across large population sizes. EvoGP is open source and accessible at: https://github.com/EMI-Group/evogp.in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-13 04:00:00 UTC.
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Author Correction: Valvulogenesis of a living, innervated pulmonary root induced by an acellular scaffold
Communications Biology, Published online: 13 October 2025; doi:10.1038/s42003-025-08927-7
Author Correction: Valvulogenesis of a living, innervated pulmonary root induced by an acellular scaffoldin Nature communications biology on 2025-10-13 00:00:00 UTC.
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Ant phylogeny is not resolved by the application of site heterogeneous models
Communications Biology, Published online: 13 October 2025; doi:10.1038/s42003-025-07849-8
Ant phylogeny is not resolved by the application of site heterogeneous modelsin Nature communications biology on 2025-10-13 00:00:00 UTC.
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Tachykinin 1-expressing neurons in the lateral habenula signal negative reward prediction error
Evaluating outcomes to accurately predict which actions lead to reward is crucial for survival. Discrepancies between expected and realized outcomes, termed reward prediction errors, serve as a teaching signal to update subsequent predictions and promote adaptive behavior. Neural correlates of prediction errors have been identified in several brain regions, including the lateral habenula (LHb), which contains a subpopulation of neurons encoding negative RPEs (nRPEs) that are excited by worse-than-expected outcomes and inhibited by better-than-expected outcomes. LHb projections to the midbrain shape firing in dopaminergic neurons and play a well-established role in reward learning and decision-making. However, the LHb engages in a wide variety of behaviors beyond reward processing, and it remains unclear whether these distinct functions are mediated by specific transcriptionally defined cell types. Little is known about the transcriptomic identity of nRPE-encoding neurons, limiting our understanding of the specific role of these signals in outcome valuation. Using cell type-specific recording in mice performing reward-guided tasks, we demonstrate neurons expressing the neuropeptide gene Tachykinin-1 (Tac1) represent a subpopulation of LHb neurons encoding reward prediction errors. We found LHbTac1 reward outcome responses are sensitive to changes in both the expected value and realized value of rewards, and their activity scales with the magnitude of the difference. Further, LHbTac1 neurons show little modulation to other task-related events, or to innately aversive stimuli that engage a broader population of LHb cell types. Together, these data demonstrate that Tac1 marks a subpopulation of LHb neurons that preferentially encodes nRPE. Our results provide insight into cell type-specific contributions of habenular neurons in RPE signaling and open avenues for more targeted manipulations of nRPE-encoding neurons to understand their role in reward-guided behavior.in bioRxiv: Neuroscience on 2025-10-13 00:00:00 UTC.
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Transcriptional regulation of disease-relevant microglial activation programs
Microglia, the brain's innate immune cells, can adopt a wide variety of activation states relevant to health and disease. Dysregulation of microglial activation occurs in numerous brain disorders, and driving or inhibiting specific states could be therapeutic. To discover regulators of microglial activation states, we conducted CRISPR interference screens in iPSC-derived microglia for inhibitors and activators of six microglial states. We identified transcriptional regulators for each of these states and characterized 31 regulators at the single-cell transcriptomic and cell-surface proteome level in two distinct iPSC-derived microglia models. Finally, we functionally characterized several regulators. STAT2 knockdown inhibits interferon response and lysosomal function. PRDM1 knockdown drives disease-associated and lipid-rich signatures and enhanced phagocytosis. DNMT1 knockdown results in widespread loss of methylation, activating negative regulators of interferon signaling. These findings provide a framework to direct microglial activation to selectively enrich microglial activation states, define their functional outputs, and inform future therapies.in bioRxiv: Neuroscience on 2025-10-13 00:00:00 UTC.
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Coral die-off marks Earth’s first climate ‘tipping point’, scientists say
Nature, Published online: 12 October 2025; doi:10.1038/d41586-025-03316-w
A surge in global temperatures has caused widespread bleaching and death of warm-water corals around the world.in Nature on 2025-10-12 00:00:00 UTC.
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A 'brain-first' mouse model of progressive alpha-synuclein pathology via intranasal rotenone administration
The precise aetiology of Parkinson's disease (PD) is still poorly understood, but it is thought to arise due to an intricate relationship between genes and the environment. Our study takes a unique approach to understanding the effect of environmental factors on the onset and progression of -synuclein (aSyn) pathology, a key feature of PD, from the olfactory bulb (OB) to other brain regions. In the present study, we evaluated the time-dependent progression of PD-like pathology by administering rotenone intranasally for 5.5 months in C57BL/6 male mice. We performed olfactory and motor tests and examined the aSyn accumulation, glial cell activation and dopaminergic neurodegeneration after 3, 4 and 5.5 months of rotenone exposure by immunoblotting and immunofluorescence techniques. We observed a time-dependent progression of aSyn accumulation from the OB to other brain regions, including the mid-brain and cortex. Consistently, we observed a time-dependent behavioural impairment, OB atrophy, progression of aSyn pathology, neuroinflammation and neurodegeneration. Our findings also established a link between distinct astrocyte activation and dopaminergic (DAergic) activity. In conclusion, this chronic and progressive mouse model mimics the brain-first type of progression of PD-like pathology in some PD patients, opening the possibility for testing potential disease-modifying interventions.in bioRxiv: Neuroscience on 2025-10-12 00:00:00 UTC.
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LRRK2 Kinase Mediates Increased GCase Activity in Microglia in Response to Proinflammatory Stimuli
Variants in the LRRK2 and GBA1 genes are among the most common risk factors associated with Parkinson's disease (PD). Both patients carrying PD-associated variants in GBA1, encoding lysosomal enzyme glucocerebrosidase (GCase), and a subset of non-carrier patients have been shown to have reduced GCase enzymatic activity, suggesting that reduced GCase activity may be a feature of both genetic and a subset of sporadic PD. However, the effect of PD-associated variants in LRRK2, encoding a serine/threonine kinase, on GCase activity remains controversial, with conflicting results in various tissues and cell types. Moreover, rare patients carrying both GBA1 and LRRK2 risk alleles seem to have a more benign disease course than carriers of GBA1 variants alone, suggesting a complex interplay between these two genes in PD. Here we evaluate the effect of LRRK2 kinase activity on GCase activity in human induced pluripotent stem cell (iPSC)-derived microglia (iMGs), a PD-relevant brain cell type expressing high levels of LRRK2. Using CRISPR editing, isogenic control iPSC lines were generated to match PD patient-derived iPSC lines harbouring the LRRK2 p.G2019S, p.M1646T, or p.N551K-p.R1398H protective haplotype variants. Whereas iMGs harbouring the p.M1646T variant, and the protective haplotype, respectively increased and decreased phosphorylation of canonical LRRK2 substrate, Rab10, GCase protein levels and activity were not altered in any of the LRRK2 variant lines. Additionally, whereas pharmacological inhibition of LRRK2 kinase activity had no impact on GCase activity in iMGs under basal conditions, it attenuated the increase in GCase activity elicited in response to interferon {gamma} (IFN{gamma}) treatment. Moreover, GCase activity induced by IFN{gamma} was reduced in PD risk LRRK2 p.M1646T iMGs and increased in p.N551K-p.R1398H protective haplotype iMGs compared to their isogenic corrected controls, congruent with their respective effects on LRRK2 kinase activity and PD risk. Thus, our data suggests a role for LRRK2 kinase activity in regulation of GCase activity in response to neuroinflammation.in bioRxiv: Neuroscience on 2025-10-12 00:00:00 UTC.
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Cytokines of the Interleukin-1 superfamily and Interleukin-6 are associated with neuroinflammation and brain injury in Herpes Simplex Virus Encephalitis
Early adjunctive anti-inflammatory therapy could modulate the immune response and improve clinical outcomes in Herpes Simplex Virus encephalitis (HSE). However, data on the array of inflammatory mediators to target and the immunomodulatory drugs to use are limited. This study aimed to determine key cytokines associated with HSV-induced neuroinflammation, brain injury, and clinical outcomes, and the effect of dexamethasone on their blood levels. We profiled the levels of forty-eight cytokines and four brain injury biomarkers (GFAP, NFL, Tau and UCHL-1) in CSF and serum samples collected from HSE adult patients; recruited in a randomised clinical trial to receive either adjunctive dexamethasone plus intravenous aciclovir or aciclovir alone. We found that cytokines of the IL-1 superfamily (IL-1, IL-18, IL-1RA) and IL-6 were most consistently associated with neuroinflammation, brain injury and poor clinical outcomes. Spearman correlation analysis revealed positive associations between CSF concentrations of IL-1RA, IL-18, and IL-10 and serum concentrations of IL-1RA and IL-6 with the astrocytic marker, Glial fibrillary acidic protein (GFAP). Levels of CSF IL-1RA and IL-18 were associated with increased volume of cerebral oedema on MRI and were significantly raised (p < 0.05) in both patients with abnormal GCS scores (< 15) and worse outcomes (LoS <3). Moreover, the blood levels of these key mediators were unaffected by adjunctive dexamethasone treatment. Taken together, this work identified key targets for direct, targeted adjunctive anti-inflammatory and neuroprotective therapy to be taken forward in clinical trials, aiming to improve outcomes following HSE.in bioRxiv: Neuroscience on 2025-10-12 00:00:00 UTC.
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Divergent disruption of brain networks following total and chronic sleep loss: a longitudinal fMRI study
Study objectives: Sleep loss significantly disrupts cognitive and emotional functioning, yet the neural consequences of different types of sleep deprivation remain unclear. Methods: In a within-subject resting-state fMRI study, we examined how acute total sleep deprivation (TSD) and chronic sleep restriction (CSR) alter intrinsic functional brain organization in 28 healthy adults scanned under three conditions: rested wakefulness (RW), after one night of TSD, and after five nights of CSR. To quantify network-level disruption, we applied graph-theoretical analyses, including a novel within-subject adaptation of the Hub Disruption Index and Covariate-Constrained Manifold Learning (CCML), an unsupervised embedding technique sensitive to subject-level covariates. Moreover, we assessed subjective sleep quality, sleepiness, and circadian traits. Results: Both TSD and CSR were associated with a consistent reorganization of graph topology relative to RW. Furthermore, direct comparisons revealed that TSD and CSR affect different brain hubs. Regional changes in degree, closeness, and clustering coefficients were most prominent in subsystems of the default mode network, frontoparietal network, and cerebellum. These differences were also captured in CCML embeddings, supporting the hypothesis that acute and chronic sleep deprivation exert divergent effects on brain connectivity. Findings were robust across graph thresholds, brain atlases, and nodal metrics. Moreover, these results were further supported by the subjective measures - sleepiness was associated with reduced network integration in RW, and circadian phenotype emerged as a key determinant of individual sensitivity to sleep loss. Conclusions: Our results show that TSD and CSR induce distinct alterations in brain functional organization, offering new insights into their neural impact.in bioRxiv: Neuroscience on 2025-10-12 00:00:00 UTC.
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Decoding state specific connectivity during speech production and perception
Understanding how dynamic brain networks support language perception and production is central to cognitive neuroscience. A vast network based literature has employed functional connectivity (FC), primarily using resting-state and task-based fMRI. However, methodological limitations have hindered this approach in language processing, particularly during speech production. Here, we address this gap by employing a large cohort of electrocorticographic (ECoG) patients (N=42) to investigate the networks driving speech perception and production. We acquired data while patients were engaged in a controlled battery of speech production tasks focusing on five cognitive states (auditory perception, picture perception, reading perception, speech production, and baseline). Using linear classifiers we were able to robustly decode cognitive states from single-trial FC (i.e. Pearson correlations) of the neural activity patterns, achieving a mean accuracy of 64.4%. These classifiers revealed distinct network signatures underlying auditory and visual perception as well as speech production via stable network connectivity. Importantly, the network signatures included both regions with robust local neural activity and those with minimal or no detectable activation. Such signatures indicate that even low-activity regions contribute critically to differentiating cognitive states. Our findings underscore the significance of functional connectivity analysis as a complementary dimension to investigating local neural activity, and suggest that the functional networks supporting speech extend beyond the most metabolically active regions.in bioRxiv: Neuroscience on 2025-10-12 00:00:00 UTC.
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Nonlinear hippocampal coding of the pair-bonded partner in prairie voles
Neural representation of specific opposite-sex partners remains largely unexplored. Using monogamous prairie voles, we demonstrated that partner preference required the ventral hippocampus, in which 3-4 Hz oscillations were enhanced following partner interaction. We identified ventral hippocampal neurons responding selectively to the pair-bonded partner and stranger, which encoded identity-related information in a nonlinear manner, shedding light upon how mammalian brains create distinct neural representations of romantic opposite-sex partners.in bioRxiv: Neuroscience on 2025-10-12 00:00:00 UTC.
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Astrocyte SEMA3C reduction improves Rett Syndrome phenotypes
During typical neurodevelopment, astrocytes secrete proteins that support neuronal connectivity. This process is disrupted in Rett Syndrome (RTT), a regressive neurodevelopmental disorder characterized by motor, sensory, and cognitive impairments. While astrocytes typically promote neuron outgrowth, co-culture of RTT astrocytes with wildtype neurons inhibits their outgrowth, implicating secreted astrocyte factors in RTT pathology. However, the specific factors and their contributions to RTT deficits remain poorly defined. To address this, we focused on the class 3 semaphorin SEMA3C, which shows increased astrocyte secretion in RTT and other neurodevelopmental disorders. Using astrocyte and neuron cell culture, we find that SEMA3C is inhibitory to dendrite outgrowth via PLXND1 and NRP2 receptors in cortical neurons. Genetic reduction of astrocyte SEMA3C in female RTT model mice enhances dendritic arborization and normalizes synaptic activity. Behaviorally, astrocyte SEMA3C reduction normalizes visual acuity and motor behavior, which are established clinical features in RTT. Together, these findings identify astrocyte SEMA3C as a contributor to RTT pathology and highlight the SEMA3C-NRP2-PLXND1 signaling pathway as a potential therapeutic target in disordered neurodevelopment.in bioRxiv: Neuroscience on 2025-10-12 00:00:00 UTC.
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The neural computations underlying context dependent attribute-based valuation of complex stimuli.
Adaptive decision making requires value computations to be flexible because we often need to value a stimulus differently as the context changes. A stimulus might be highly desirable in one context but completely unappealing in another. However, it is not known how the brain can support such flexible modulation of overall stimulus value. Here we test a model of flexible value construction whereby individual attributes of a stimulus are converted into contextually dependent attribute-specific value representations before being combined into an overall integrated stimulus value. To test this framework, human participants (online n=95, MRI n=35) provided ratings for 75 unique high-dimensional clothing stimuli under three instructed `goal-contexts` designed to elicit differences in overall value judgments for the items. Integrated value ratings for each stimulus were found to be goal-context dependent, while individual stimulus-attributes varied markedly in how they contributed to value ratings across goal contexts. In the fMRI data, representations of the absolute levels of particular attributes were revealed in visual areas. In contrast, encoding of individual attributes in value space, alongside integrated overall stimulus value, was present within distinct regions of prefrontal cortex. More specifically, behaviorally relevant attributes in value space and integrated stimulus value were found in vmPFC and dmPFC respectively. These findings indicate that the construction of value for high-dimensional stimuli is achieved through the computation of goal-context-dependent attributes in value space, providing mechanistic insight into how the brain can flexibly modulate stimulus-values as context changes.in bioRxiv: Neuroscience on 2025-10-12 00:00:00 UTC.
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Shared and distinct neural signatures of inhibitory control deficits in attention-deficit/hyperactivity disorder and autism
Background: Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) share deficits in inhibitory control, yet direct comparisons of their neurobiological underpinnings remain sparse and are often confounded by comorbidity. We examined shared and distinct abnormalities in brain activation and circuit connectivity associated with inhibitory control in non-comorbid ADHD and ASD. Methods: Brain activity and hyperdirect pathway connectivity during inhibitory control were examined in children with non-comorbid ADHD (age: 11.1{+/-}0.24y) and children with non-comorbid ASD (age: 11.1{+/-}0.39y), compared with typically developing (TD) children (age: 10.9{+/-}0.09y), and benchmarked against healthy adults (age: 24.1{+/-}1.26y), using fMRI stop-signal task data. Results: Both ADHD and ASD groups showed reduced activation in salience and frontoparietal network regions alongside increased activation in default mode network (DMN) regions, consistent with triple-network dysfunction. These activation deficits were more pronounced in ASD, whose activation profile also diverged most from adults. At the circuit level, only ASD showed abnormal hyperdirect connectivity, involving insula-STN and preSMA-STN pathways and further reflected in a composite insula/IFG-STN connectivity measure, indicating broader cortical-STN disruption. Importantly, across all children, reduced salience/frontoparietal relative to DMN engagement correlated with greater parent-reported inhibitory control deficits, and in ASD, individual differences in insula/IFG-STN connectivity showed the same association, highlighting the behavioral relevance of both network- and circuit-level abnormalities. Conclusions: ADHD and ASD share network-level but differ in circuit-level abnormalities underlying inhibitory control deficits. Together, these insights advance the neurobiology of inhibitory control in ADHD and ASD and provide potential targets for neuroscience-informed interventions addressing both shared and disorder-specific mechanisms.in bioRxiv: Neuroscience on 2025-10-12 00:00:00 UTC.
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A role for prefrontal dopamine signaling in control of goal-directed actions
Impairments in behavioral flexibility commonly found across psychiatric disorders have often been attributed to a dysfunction of the medial prefrontal cortex (mPFC), notably in relation with altered dopamine transmission. However, how dopamine transmission shapes neuronal activity in the mPFC to allow adapting to a changing situation remains largely elusive. Here, we show that dopamine dynamics in the mPFC encode the value of instrumental actions, in particular during reversal learning. Such signal shapes the activity of mPFC dopaminoceptive neurons during reversal learning through the recruitment of heteromers formed by dopamine D1 and D2 receptors with NMDA receptors. In accordance, blockade of either D1/NMDA or D2/NMDA heteromers in the mPFC selectively impairs reversal of outcome identities but not learning and expression of initial action-outcome associations. Our findings provide mechanistic evidence for a central role of dopamine in the mPFC to allow updating goal-directed actions.in bioRxiv: Neuroscience on 2025-10-12 00:00:00 UTC.
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Spatially patterned cytoskeletal organization shapes astrocyte branch complexity
Astrocytes, the most abundant cells in the brain, extend elaborate branches that enable diverse functions, from synapse maintenance to blood-brain-barrier integrity. The cytoskeletal basis of this architecture has remained unclear, since traditional culturing methods produce minimal branching. Using immunopanning and serum-free conditions, we generated primary rodent astrocytes with in-vivo-like morphology and surveyed their cytoskeleton using confocal microscopy and cryogenic electron tomography. We show that astrocyte microtubules are oriented primarily plus-ends-out. Proximally, microtubules appear stabilized by post-translational modifications (PTMs) and microtubule inner proteins (MIPs). Distal regions lack stabilizing microtubule PTMs, and are enriched in intermediate filament (IF) GFAP. Additionally, diverse actin microstructures, including reticular webbing, extend astrocyte boundaries beyond the microtubule--IF framework. Our results uncover fundamental principles of astrocyte cytoskeletal organization that underlie their intricate branching.in bioRxiv: Neuroscience on 2025-10-12 00:00:00 UTC.
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Thalamus-cortex interactions drive cell type-specific cortical development in human pluripotent stem cell-derived assembloids
The thalamus plays a pivotal role in the development and function of neural circuits in the cerebral cortex. However, how thalamus-cortex interactions influence human cortical development remains unknown primarily because of the inaccessibility of the human embryonic brain. Here, we demonstrate thalamus-dependent gene expression, circuit organization, and neural activity during corticogenesis using human thalamocortical assembloids (hThCAs). Human cortical and thalamic organoids (hCOs and hThOs) derived from induced pluripotent stem cells exhibited region-specific gene expression and spontaneous neuronal activity. Upon the fusion of these organoids, hThCAs reconstructed reciprocal thalamus-cortex axonal projections and synaptic connections. Transcriptomic analysis revealed thalamus-dependent acceleration of cortical maturation, with upregulation of programs linked to axon development, subplate/cortical plate identity, and activity-regulated genes. Histological analysis showed expanded progenitor pools and increased deep-layer neurons within hThCAs. Wide-field Ca2+ imaging demonstrated that wave-like activity originated in the thalamic region and propagated to the cortical region. Furthermore, two-photon Ca2+ imaging of cortical neurons revealed that synchronous activity emerged exclusively in pyramidal tract (PT) and corticothalamic (CT) neurons, whereas intratelencephalic (IT) neurons remain asynchronous, highlighting cell type-specific circuit integration within hThCAs. These synchronized events were absent in isolated hCOs or in cortico-cortical assembloids, underscoring the specificity of thalamic input. Our findings suggest that diffusible thalamic cues broadly enhance progenitor expansion, while long-range thalamic input organizes cell type-specific synchronous activity. This study demonstrates the thalamus-dependent acquisition of mature cortical phenotypes in a cell type-specific manner in hThCAs, establishing developmental mechanisms linking regional interactions and cell type-specific circuit specification. Thus, hThCAs provide a tractable human platform for dissecting human-specific developmental processes and modeling neurodevelopmental disorders with disrupted thalamocortical communication at the molecular, cellular, and circuit levels.in bioRxiv: Neuroscience on 2025-10-12 00:00:00 UTC.
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Sensorimotor circuits formed by dI3 neurons have distinct connectivity within and across the lumbar and cervical spinal cord
Journal of Neurophysiology, Ahead of Print.
in Journal of Neurophysiology on 2025-10-11 10:24:47 UTC.
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Single‐Cell RNA‐Seq and Machine Learning Reveal Key Feature Genes of Astrocyte in Perioperative Neurocognitive Disorders
Single-cell RNA-seq of the aged mouse hippocampus, integrated with CellChat, pseudotime analysis, hdWGCNA, and machine learning, reveals astrocyte-specific molecular alterations in perioperative neurocognitive disorders. Four key genes linked to neuroinflammation and cognitive decline were identified, offering promising biomarkers and potential therapeutic targets for PND disease.
ABSTRACT
Perioperative neurocognitive disorder (PND) is common in older patients after anesthesia and surgery, significantly increasing morbidity and mortality. However, the role of astrocytes in its pathogenesis remains limitedly understood. Here, we analyzed 9976 and 9484 single-cell transcriptomes from hippocampal cells of aged mice undergoing PND surgery or sham operation, focusing on astrocytes. We identified 13 distinct clusters corresponding to nine cell types, including astrocytes, microglia, neurons, oligodendrocytes, and other relevant cell populations. Specifically, five astrocyte subclusters were identified, with cluster 0 being the most abundant. Its proportion decreased by 5.51% in the PND surgery condition, showing the largest difference between the groups. Functional enrichment analysis revealed that astrocytes are involved in crucial neurodevelopmental processes and pathways associated with nervous system development and synaptic regulation. Pseudotime analysis placed cluster 0 at the early stage of differentiation, suggesting it as a key responsive population. Gene co-expression network further identified modules with peak activity in cluster 0. By intersecting cluster 0 markers with differentially expressed genes (DEGs) and applying machine learning methods, including LASSO, XGBoost, and Random Forest, we pinpointed four feature genes Dbp, PISD, Id4, and Tsc22d3 with differential expression patterns between surgery and control conditions. Transcription factor activity analysis highlighted distinct regulatory networks in astrocytes, providing insights into their functional differences in response to PND surgery. These findings offer a detailed aged hippocampal landscape of astrocyte dynamics and intercellular communication, contributing to our understanding of neuroinflammation and cognitive dysfunction following surgical stress.
in Journal of Neuroscience Research on 2025-10-11 08:56:32 UTC.
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Functional dissection of the CRP-family transcription factor DevH and its interplay with NtcA in a cyanobacterium
Xu et al. uncovered how duplicated transcription factors DevH and NtcA achieve functional specialization in Anabaena. Unlike NtcA, DevH operates at higher cellular levels with broader regulatory capacity. Their work demonstrates how gene duplication diversifies transcriptional networks to optimize cyanobacterial adaptation.in Cell Reports: Current Issue on 2025-10-11 00:00:00 UTC.
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Molecular mechanisms governing the formation of distinct Upf1-containing complexes in yeast
Iermak et al. show that the conformational state of yeast Upf1 regulates its constitutive association in an endogenous multimeric complex containing the 5′-3′ mRNA exoribonuclease Xrn1. Structural rearrangement of Upf1 facilitates transition to the Upf1-2-3 complex involved in the activation of nonsense-mediated mRNA decay.in Cell Reports: Current Issue on 2025-10-11 00:00:00 UTC.
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Paternal SARS-CoV-2 infection impacts sperm small noncoding RNAs and increases anxiety in offspring in a sex-dependent manner
Nature Communications, Published online: 11 October 2025; doi:10.1038/s41467-025-64473-0
Whether paternal pre-conceptual SARS-CoV-2 infection impacts sperm RNA content, or effects offspring phenotypes, has not been previously investigated. Here authors report changes in sperm noncoding RNAs in SARS-CoV-2 infected sires and increased anxiety-like behaviors in offspring.in Nature Communications on 2025-10-11 00:00:00 UTC.
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Early menopause is associated with reduced global brain activity
Menopause affects the aging process in women through significant ovarian hormone production decline in midlife. Women who experience early menopause face an accelerated physiological aging rate, along with impaired memory and increased risks of neurodegenerative diseases. However, it remains elusive how the timing of menopause affects brain activity, which could be crucial for understanding menopause-related acceleration of aging and increased risk of dementia. Recent studies have revealed a highly structured infra-slow (< 0.1 Hz) global brain activity across species and linked it to arousal and memory functions, as well as waste clearance in Alzheimer's diseases (AD). In this study, we examined how this global brain activity relates to age of menopause using resting-state fMRI data from the Human Connectome Project-Aging dataset. We found that women who experienced earlier menopause (mean menopausal age 45{+/-}3.5 yr) exhibited weaker global brain activity (p = 5.0x10-4) with reduced coupling to cerebrospinal fluid (CSF) flow (p = 0.017) compared to age-matched later-menopausal women (mean menopausal age 54{+/-}1.2 yr). Differences appeared mainly in higher-order brain regions, where activation levels correlated with memory performance in earlier but not in intermediate or later menopausal women. These findings highlight brain activity changes linked to early menopause, suggesting a potential mechanism underlying memory decline and the increased risk of AD and dementias in early-onset menopausal women.in bioRxiv: Neuroscience on 2025-10-11 00:00:00 UTC.
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Convergent Control of NREM Sleep and Anesthesia by Prefrontal Layer 5 Extratelencephalic Neurons
The cortical mechanisms governing the transition from consciousness to unconsciousness remain poorly understood. While most cortical neurons are suppressed during NREM sleep and anesthesia, we identify a distinct population of prefrontal cortical (PFC) neurons that are preferentially active in both states. Chemogenetic activation of these excitatory NREM- and Anesthesia-Promoting (NAP) neurons enhances anesthetic potency and deepens NREM sleep, whereas their inhibition blunts anesthetic effects. Anatomical, biophysical, and transcriptomic, biophysical, and anatomical analyses reveal that NAPs are Layer 5 extratelencephalic (L5 ET) neurons that form reciprocal connections with subcortical arousal centers including anterior and reticular thalamic nuclei, hypothalamus, and claustrum. Moreover, L5 ET specific recordings and chemogenetic manipulations reproduced NAP-like activity under anesthesia and its ability to promote NREM sleep. These findings define an excitatory, molecularly and anatomically characterized PFC circuit that contributes to loss of consciousness, consistent with a corticothalamic mechanism of arousal-state transitions in sleep and anesthesia.in bioRxiv: Neuroscience on 2025-10-11 00:00:00 UTC.
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Brain infiltrating T cells mediate microglial dysregulation and neuronal loss following SAH
The contribution of T cells to neuroinflammation after aneurysmal subarachnoid hemorrhage (SAH) remains poorly understood. Using a murine pre-chiasmatic injection model of SAH we demonstrate that T cell infiltration into the brain modulates microglial activation and promotes neuronal death. Targeted transcriptomic profiling revealed a sustained neuroimmune response at 7 days post injury (dpi) characterized by a major involvement of T cells and microglia activation. Immunohistochemistry confirmed focal CD3+ T cell infiltration, predominantly CD4+, in the brain at the site of blood injection (BI), choroid plexus and meninges in SAH mice at 3- and 7-dpi. This temporal pattern was also observed in the CSF of a human SAH cohort. T cell presence spatially correlated with regions of microglial reactivity and neuronal loss. Notably, CD3-knockout mice exhibited reduced microglial activation and preserved neuronal viability. These findings identify T cells as key amplifiers of post-SAH neuroinflammation and neuronal damage. Targeting T cell-microglia crosstalk may represent a novel therapeutic avenue for SAH.in bioRxiv: Neuroscience on 2025-10-11 00:00:00 UTC.
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Rethinking Alzheimer's: Harnessing Cannabidiol to Modulate IDO and cGAS Pathways for Neuroinflammation Control
Alzheimer's disease (AD) has traditionally been associated with amyloid-β plaques, but growing evidence underscores the role of neuroinflammation in disease progression. The autoinflammatory hypothesis of AD suggests chronic immune dysfunction contributes to neuronal damage, making immune modulation a promising therapeutic strategy. Cannabidiol (CBD), a phytocannabinoid with anti-inflammatory properties, may offer therapeutic potential. This study investigates how CBD independently influences two key neuroinflammatory regulators in AD: the indoleamine 2,3-dioxygenase (IDO) pathway and the cyclic GMP-AMP synthase (cGAS) pathway. Though mechanistically distinct, both shape CNS immune responses. Targeting these immune-metabolic axes provides a mechanistic alternative to amyloid- or tau-based approaches by addressing upstream drivers of neuroinflammation and immune dysregulation. Using the male 5XFAD transgenic AD mouse model, we administered CBD via inhalation and assessed IDO and cGAS expression using flow cytometry, immunofluorescence (IF), and gene expression analysis. We evaluated cytokine levels and used STRING-based bioinformatics to identify CBD-target interactions. CBD treatment significantly reduced IDO and cGAS expression, correlating with decreased proinflammatory cytokines, including TNF-α, IL-1β, and IFN-. Bioinformatics identified potential interactions between CBD and immune targets such as AKT1, TRPV1, and GPR55. These targets were prioritized based on their roles in neuroinflammatory signaling and high-confidence interactions with CBD. AKT1 regulates inflammatory signaling and cell survival, TRPV1 modulates nociception and neuroinflammation, and GPR55 influences immune cell activation. These findings support CBD as a potential monotherapy or adjunctive treatment for AD by targeting distinct neuroinflammatory pathways, including IDO and cGAS. Further studies are warranted to fully explore its therapeutic potential.
in eNeuro on 2025-10-10 16:30:19 UTC.
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Energy Constraints Determine the Selection of Reaching Movement Trajectories in Macaque Monkeys
Reaching movements, while seemingly simple, involve complex motor control mechanisms that select specific trajectories from infinite possibilities. Despite inherent variability in volitional movements, both humans and monkeys frequently exhibit stereotyped trajectories. The literature has offered numerous explanations for invariant trajectory shapes, including a common planning space in hand space or joint space, as well as factors like kinetic energy (KE) minimization and sensory feedback. However, since most studies have relied on single-session data, crucial insights into the motor principles guiding trajectory selection and their evolution through extended practice remain underexplored. This study fills this gap by investigating how specific trajectories are selected and evolve with practice across multiple sessions, using data from two rhesus monkeys (one male, one female) performing a reaching task in a biomechanically constrained 2D setup. Our behavioral study challenges the idea of a common planning space, revealing instead a significant influence of KE on trajectory shapes. Through a novel biomechanical modeling, we quantified KE for a wide range of trajectory shapes. We discovered that trajectory selection and evolution are not simply about minimizing KE or achieving straight paths. Instead, the monkeys’ motor systems appear to prioritize maintaining a "safe KE range," where slight changes in trajectory shapes have minimal impact on energy expenditure. These findings provide new insights into the adaptive motor control strategies, suggesting that trajectory selection involves balancing energy efficiency and flexibility. Our study enhances the understanding of trajectory selection principles, with implications for rehabilitation strategies, robotics, and broader study of motor control mechanisms.
in eNeuro on 2025-10-10 16:30:19 UTC.
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Single-cell RNA sequencing of cerebrospinal fluid reveals the expansion of innate lymphoid cells with upregulated transposable elements in multiple sclerosis [version 1; peer review: awaiting peer review]
Background Multiple sclerosis (MS) is a chronic and often immune-mediated demyelinating disease with no definitive treatment. Transposable elements (TEs) are receiving increasing attention as potential contributors to neurodegenerative diseases. However, to the best of our knowledge, no studies have examined the possible association of TE expression and its potential role in MS pathogenesis at the single-cell level. Results In this study, we reanalyzed single-cell RNA sequencing data from human cerebrospinal fluid (CSF) samples. Our results revealed that TEs are overexpressed in a cluster of cells annotated as innate lymphoid cells (ILCs). Furthermore, enrichment analysis of the associated transcription factors (TFs) with highly upregulated TEs in ILCs revealed the relevance of these TFs to immune pathways and cis-regulatory regions in DNA. Conclusions We propose that upregulated TEs in ILCs are consistent with the plasticity of these cells, as TEs can insert themselves in coding or regulatory regions of immune-related genes and represent themselves as immune-related TF binding sites. We also hypothesize that ILCs with overexpressed TEs could present TE-derived antigens, potentially reactivating T cell-mediated immunity in the central nervous system (CNS) of MS patients. Therefore, this study indicates a possible mechanism involving TEs in ILC plasticity and their potential role in MS pathogenicity. Additionally, we suggest that repurposing nucleoside reverse transcriptase inhibitors (NRTIs) or developing new high-efficacy NRTIs could be a feasible approach to treating MS.in F1000Research on 2025-10-10 16:13:14 UTC.
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Evaluating artificial intelligence for accurate detection of hand and wrist fractures: a systematic review and meta-analysis [version 1; peer review: awaiting peer review]
Background and Objectives Hand and wrist fractures are among the most frequently encountered injuries in emergency departments and are often misdiagnosed, particularly when interpreted by non-specialist clinicians. These diagnostic errors can lead to treatment delays and long-term complications. Artificial intelligence (AI), particularly deep learning algorithms, is emerging as a promising adjunct to improve diagnostic accuracy in radiographic fracture detection. This study aims to evaluate the effectiveness of Artificial Intelligence (AI) in detecting hand and wrist fractures compared to manual radiographic interpretation by clinicians. Materials and Methods A systematic review and meta-analysis were conducted to assess the diagnostic performance of AI models in detecting hand and wrist fractures compared to conventional radiographic interpretation by clinicians. A comprehensive search of PubMed, Google Scholar, Science Direct, and Wiley Online Library was performed. Eligible studies included those utilizing AI for fracture detection with sensitivity and specificity data. Pooled estimates were calculated using fixed- and random-effects models. Heterogeneity was assessed via I2 statistics, and publication bias was examined using funnel plots and Egger’s test. Results Eighteen studies met inclusion criteria. The pooled sensitivity and specificity under the random-effects model were 0.910 and 0.912, respectively, indicating high diagnostic accuracy of AI models. However, substantial heterogeneity (I2 = 99.09% for sensitivity; 96.43% for specificity) and publication bias were observed, likely due to variations in AI algorithms, sample sizes, and study designs. Conclusions Most AI models demonstrated good diagnostic accuracy, with high sensitivity and specificity scores (≥90%). However, some models fell short in sensitivity and specificity (≤90%), indicating performance variations across different AI models or algorithms. From a clinical perspective, AI models with lower sensitivity scores may fail to detect hand and wrist fractures, potentially delaying treatment, while those with lower specificity scores could lead to unnecessary interventions—treating hands and wrists that are not fractured.in F1000Research on 2025-10-10 16:11:04 UTC.
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Hypoglycemic effect of the aqueous extract from Acalypha argomuelleri Briq. (Euphorbiaceae) 'Sweet stick' leaves in Rattus rattus var. albinus [version 3; peer review: 2 approved, 1 not approved]
Introduction Diabetes mellitus (DM) is a chronic metabolic disease representing a global public health concern and is associated with severe complications such as cardiovascular and renal diseases. Although several species of the genus Acalypha have demonstrated biological activity, no prior studies have evaluated the hypoglycemic effect of Acalypha argomuelleri Briq., making this study relevant. Method The hypoglycemic effect of the aqueous leaf extract of A. argomuelleri Briq. (AAAE) was evaluated in an experimental model using Rattus rattus var. albinus (males). A randomized, prospective design was employed, consisting of a control group and three treatment groups receiving doses of 100, 150, and 300 mg/kg of the extract, respectively. Hyperglycemia was induced via oral glucose administration. Results The qualitative phytochemical analysis of AAAE revealed the presence of flavonoids, phenols, cardiotonic glycosides, and diterpenes, with no reducing sugars. The 300 mg/kg dose produced a significant and sustained reduction in blood glucose levels, reaching near-normal values at 90 minutes, demonstrating a dose- and time-dependent hypoglycemic effect. Discussion The study confirmed that AAAE has a dose-dependent hypoglycemic effect, with optimal efficacy at 300 mg/kg. This dose showed a faster and more sustained reduction in glucose levels compared to 100 and 150 mg/kg, suggesting higher efficacy at elevated concentrations. The identified flavonoids and phenols, associated with glucose metabolism modulation and pancreatic β-cell protection, likely explain the observed effect. The absence of reducing sugars indicates the hypoglycemic effect is linked to secondary metabolites. Conclusions The AAAE exhibited a significant dose- and time-dependent hypoglycemic effect, with optimal efficacy at 300 mg/kg after 90 minutes. These findings support the potential of A. argomuelleri Briq. as a natural alternative for blood glucose control, though further studies are needed to assess its safety and efficacy in clinical models.in F1000Research on 2025-10-10 16:09:26 UTC.
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Project-based learning in mathematics and science: a review of contributions, prevalence, and challenges [version 1; peer review: awaiting peer review]
The integration of project-based learning (PjBL) in Mathematics and Science has received much attention because of its potential to engage students and expose them in real-world problem-solving. However, research examining the contributions, prevalence, and challenges of Project-Based Learning in mathematics and science remains limited. This review employed a systematic review approach, with articles systematically retrieved from multiple academic databases to investigate the contributions, prevalence, and challenges of PjBL in Mathematics and science education. Thus, 202 articles downloaded from Google Scholar, Academia, Search 4 Life, Scopus, and Web of Science databases. Through the filtering process, 20 articles fell into the study’s scope and were considered and used for analysis. The results from the reviewed studies showed that project-based learning contributes to enhancing students’ engagement, creativity, communication, and conceptual understanding in Mathematics and Sciences. Also, the reviewed literature showed that PjBL dominates in Mathematics and Physics with the highest prevalence of 35% each in applying PjBL pedagogy. Integrated Science shows 10% of prevalence, while Chemistry records the least with only 5%. Nevertheless, challenges such as limited resources, rigid curriculum, and inadequate teacher training on monitoring students’ projects and providing adequate assessment were also identified. This study recommends a need for teacher training and resources mobilization in schools support educators to effectively embrace Project-based learning pedagocy in mathematics and science subjects.in F1000Research on 2025-10-10 16:08:13 UTC.
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Exploring the relationship between student-content interaction, student-student interaction, self-efficacy, and learning achievements in a student-centered classroom [version 2; peer review: 1 approved, 1 approved with reservations]
Background With the growing mismatch between traditional academic training and the demands of the modern workforce, student-centered education has emerged as a key reform in higher education. This study examines the relationships among student-content interaction, student-student interaction, self-efficacy, and learning achievement in student-centered classrooms at Chinese application-oriented universities. Methods Data were collected from 524 undergraduate students via online questionnaires and analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). Results The results indicate that both student-content and student-student interactions significantly enhance learning achievement, with self-efficacy playing a critical mediating role. Higher levels of self-efficacy were also directly associated with improved academic achievement, underscoring its central importance in student success. Conclusions These findings highlight the value of fostering interactive learning environments and promoting the development of self-efficacy to improve educational outcomes. Educators are encouraged to implement instructional strategies that facilitate meaningful engagement, thereby supporting both the cognitive and motivational dimensions of learning. This study provides empirical evidence to inform the design of effective student-centered teaching practices in higher education.in F1000Research on 2025-10-10 16:07:03 UTC.
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Exploring the Potential of Peptides Derived from the Coelomic Fluid of Echinometra lucunter Targeting Inflammatory Cytokines in Placental Syndromes: In Silico Study [version 1; peer review: awaiting peer review]
Background Placental syndromes—encompassing pregnancy loss, preterm birth, gestational diabetes mellitus, and preeclampsia—have been strongly linked to dysregulated inflammatory responses at the maternal–fetal interface. This study aims to explore the potential of peptides derived from the coelomic fluid of Echinometra lucunter targeting the inflammatory cytokines involved in placental syndromes using in silico approaches. Methods The 3D molecular structure of peptides was modeled using the UCSF Chimera application. The absorption, distribution, metabolism, and excretion (ADMET) properties were analysed using SwissADME and the ProTox web server. The PerMM web server was used to estimate membrane permeability. Crystal structures of target proteins—including c-Met, Interleukin-1β (IL-1β), Interleukin-10 (IL-10), Macrophage Migration Inhibitory Factor (MIF), Platelet-Derived Growth Factor (PDGF), and TNF-Related Apoptosis-Inducing Ligand (TRAIL)—were obtained from the Protein Data Bank Japan (PDBj). Molecular docking and structural visualization were conducted using Molecular Operating Environment (MOE) software, while molecular dynamics simulations were subsequently performed using the YASARA Dynamics software to assess the stability and conformational behavior of the ligand-receptor complexes. Results Peptide A was selected based on favorable ADMET properties. Molecular docking results revealed that Peptide A exhibits low binding affinities toward pro-inflammatory mediators, including TRAIL (–10.02 kcal/mol), MIF (–9.32 kcal/mol), IL-1β (–8.29 kcal/mol), and PDGF (–10.44 kcal/mol). Furthermore, Peptide A showed potential agonistic interactions with IL-10 (–10.26 kcal/mol) and c-Met (–9.27 kcal/mol), indicating a possible role in restoring anti-inflammatory and angiogenic signaling. Molecular dynamics simulations supported the stability of the peptide–receptor complexes. Conclusions Peptide A holds promise as a dual-function therapeutic agent in placental syndromes. However, experimental validation is necessary to confirm its biological efficacy and safety.in F1000Research on 2025-10-10 16:04:04 UTC.
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Differential equation modeling of cell population dynamics in skeletal muscle regeneration from single-cell transcriptomic data
by Renad Al-Ghazawi, Hassan Lezzeik, Xiaojian Shao, Theodore J. Perkins
Skeletal muscle regeneration is a complex process orchestrated by diverse cell populations within a dynamic niche. In response to muscle damage and intercellular signaling, these cells undergo cell fate and migration decisions including quiescence, activation, proliferation, differentiation, infiltration, apoptosis, and exfiltration. The emergence of single-cell RNA sequencing (scRNA-seq) studies of muscle regeneration offers a significant opportunity to refine models of regeneration and enhance our understanding of cellular interactions. To better understand how crosstalk between cell types governs cell fate decisions and cell population dynamics, we developed a novel non-linear ordinary differential equation model guided by scRNA-seq data. Our model consists of 9 variables and 17 parameters, capturing the dynamics of key myogenic lineage and immune cell types. We calibrated time-series scRNA-seq data to units of cells per cubic millimeter of tissue and fit our model’s parameters to capture the observed dynamics, validating on an independent time series. The model successfully captures key features of regeneration dynamics, particularly after incorporating a novel regulatory interaction between M2 macrophages and satellite cells that has been hypothesized in the literature. Our model lays a foundation for future computational explorations of muscle regeneration, modeling of disease conditions, and in silico testing of therapeutic strategies.in PLoS Computational Biology on 2025-10-10 14:00:00 UTC.
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Learning spatial hearing via innate mechanisms
by Yang Chu, Wayne Luk, Dan F. M. Goodman
The acoustic cues used by humans and other animals to localise sounds are subtle, and change throughout our lifetime. This means that we need to constantly relearn or recalibrate our sound localisation circuit. This is often thought of as a “supervised” learning process where a “teacher” (for example, a parent, or your visual system) tells you whether or not you guessed the location correctly, and you use this information to update your localiser. However, there is not always an obvious teacher (for example in babies or blind people). Using computational models, we showed that approximate feedback from a simple innate circuit, such as that can distinguish left from right (e.g. the auditory orienting response), is sufficient to learn an accurate full-range sound localiser. Moreover, using this mechanism in addition to supervised learning can more robustly maintain the adaptive neural representation. We find several possible neural mechanisms that could underlie this type of learning, and hypothesise that multiple mechanisms may be present and provide examples in which these mechanisms can interact with each other. We conclude that when studying spatial hearing, we should not assume that the only source of learning is from the visual system or other supervisory signals. Further study of the proposed mechanisms could allow us to design better rehabilitation programmes to accelerate relearning/recalibration of spatial hearing.in PLoS Computational Biology on 2025-10-10 14:00:00 UTC.
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Epstein–Barr Virus, Lower Vitamin D, Low Sun Exposure, and HLA‐DRB1*1501 Risk Variant Share Common Epigenetic Pathways Leading to Multiple Sclerosis Onset
Objectives
Multiple sclerosis (MS) onset risk factors include Epstein–Barr virus (EBV) indices (including host response), lower serum 25-vitamin D (25(OH)D) levels, low sun exposure, and HLA-DRB1*1501. The underlying molecular mechanisms are unclear. Here, we examined mediation through differential DNA methylation (DNAm) to better understand possible epigenetic programming.
Methods
Two case-control studies (Ausimmune Study, Australia = 206 cases + 348 controls; and Epidemiologic Investigations of MS [EIMS], Sweden = 140 cases + 139 controls). DNAm was measured using Illumina arrays. Dimension-reduction methods generated MS-associated DNAm modules. Pathway enrichment analyses were used to describe DNAm modules’ system-level biological characteristics. Individual and joint associations with MS risk were assessed using logistic regression. DNAm module mediation of risk factor-outcome associations were assessed using mediation analysis. A range of temporality analyses were used.
Results
EBV indices (infectious mononucleosis history and anti-EBNA IgG titer), lower 25(OH)D, low sun exposure, and HLA-DRB1*1501 risk variant were individually and jointly associated with MS risk. In each study, 2 DNAm modules were found which mediated multiple exposure-MS associations. Proportions mediated ranged from 21 to 47% in Ausimmune and 25 to 53% in EIMS. Results were robust to sensitivity analyses. Top-ranked genomewide association study (GWAS) MS risk-associated genes were over-represented in both Ausimmune DNAm modules, A1 3.5-fold (p = 0.004) and A2 3-fold (p = 0.015). Reactome pathways enriched for DNAm had cross-study overlap – 45% of pathways enriched in Ausimmune DNAm modules were also enriched in EIMS (4.82-fold, p < 0.001).
Interpretation
EBV, lower vitamin D, low sun exposure, and HLA-DRB1*1501 risk variant act in concert and through common epigenetic pathways to impact MS onset risk. ANN NEUROL 2025
in Annals of Neurology on 2025-10-10 11:21:01 UTC.
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Letter on the Role of Electroencephalography in Predicting Post‐Stroke Seizures and an Updated Prognostic Model (SeLECT‐EEG)
in Annals of Neurology on 2025-10-10 10:16:25 UTC.
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Reply to “The Role of EEG in Predicting Post‐Stroke Seizures and an Updated Prognostic Model (SeLECT‐EEG)”
in Annals of Neurology on 2025-10-10 10:15:47 UTC.
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Inter- and intrahemispheric sources of vestibular signals to V1
Proceedings of the National Academy of Sciences, Volume 122, Issue 41, October 2025.
SignificanceInformation about head motion is fundamental to the visual interpretation of our environment. Indeed, head motion signals originating from the vestibular system robustly modulate activity in the visual cortex (VC). Despite this profound ...in PNAS on 2025-10-10 07:00:00 UTC.
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A midbrain-to-ventral-striatum dopaminergic pathway orchestrates odor-guided insect predation in mice
Proceedings of the National Academy of Sciences, Volume 122, Issue 41, October 2025.
SignificanceUnderstanding how predators identify and select nutritious prey is fundamental to deciphering interspecies interactions and energy flow within ecosystems. This study reveals that both laboratory and wild rodents preferentially feed on ...in PNAS on 2025-10-10 07:00:00 UTC.
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Activity-dependent citrate dynamics in neurons
Proceedings of the National Academy of Sciences, Volume 122, Issue 41, October 2025.
SignificanceDifficulty in quantifying the small-molecule modulators of glycolysis dynamically and in specific compartments of live cells limits our understanding of metabolic regulation. We engineered a quantitative fluorescent biosensor to study the ...in PNAS on 2025-10-10 07:00:00 UTC.
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A quantitative definition of accommodation: Implications for understanding and prediction of strata
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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From gas to ice giants: A unified mechanism for equatorial jets
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Attention to quantum complexity
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Uncovering the regulatory landscape of early human B cell lymphopoiesis and its implications in the pathogenesis of B-ALL
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Concise network models of memory dynamics reveal explainable patterns in path data
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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A DNA-containing phase-separated compartment links the nuclear envelope to chromosome axes to promote homolog pairing in C. elegans
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Plasticity of the cytotoxic Nep1-like protein enables promiscuity in binding to its lipid receptor glycosylinositol phosphorylceramides
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Targeting the ATM-TRMT10A-BRCA1 axis confers synthetic lethality to PARP inhibition in metastatic castration-resistant prostate cancer
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Bacterially grown living materials with resistant and on-demand functionality
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Hyperactive 20S proteasome enhances proteostasis and ERAD in C. elegans via degradation of intrinsically disordered proteins
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Structural insights into the progressive recovery of α7 nicotinic acetylcholine receptor from nicotine-induced desensitization
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Ancient pangenomic origins of noncanonical NLR genes underlying the recent evolutionary rescue of a staple crop
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Mechanism of USP21 autoinhibition and histone H2AK119 deubiquitination
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Molecular mechanism underlying phosphate distribution by SULTR family transporter SPDT in Oryza sativa
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Focused thermal energy at atomic microwave antenna sites for ecocatalysis
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Generation of vascularized retinal organoids containing microglia based on a PDMS microwell platform
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Spatiotemporal toughness modulation in hydrogels through on-demand cross-linking
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Unlocking nucleophilic reactivity in Co-MHAT catalysis: Stereospecific backside-backside displacement by anion-activated organocobalt
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Geometry-defect-spin coupling in chiral high-entropy systems: Multiscale mechanisms of GHz electromagnetic dissipation
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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A unique mechanism involving cocatalysis of enzyme and nonenzyme to form β-carboline and spirotryprostatins in Aspergillus fumigatus
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Mediating role of food web structure in linking diversity to multidimensional stability: Evidence from global marine ecosystems
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Soma-derived 30-nt small RNAs are coupled with chromosome breakage and precisely target nontransposon DNA against elimination in Euplotes vannus
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Plasmonic pathway to hybrid nanomaterials through energy transfer
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Correlated quantum machines beyond the standard second law
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Experimental realization and synchronization of a quantum van der Pol oscillator
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Genome-wide CRISPR screen identifies splicing factor SF3B4 in driving hepatocellular carcinoma
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Functional recovery of islet β cells in human type 2 diabetes: Transcriptome signatures unveil therapeutic approaches
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Loss-of-function variants in ciliary genes confer high risk for tetralogy of Fallot
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Multiuser all-optical quantum network based on metasurfaces
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Emergent Rashba spin-orbit coupling in bulk gold with buried network of nanoscale interfaces
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Digital twin enables radiosensitive organic speciation in 3D
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Orbital angular momentum–driven multistate photomemory
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Erratum for the Research Article “Ultrafast infrared nano-imaging of local electron-hole dynamics in CVD-grown single-walled carbon nanotubes” by J. Nishida et al.
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Erratum for the Research Article “ACTR5 controls CDKN2A and tumor progression in an INO80-independent manner” by X. Xu et al.
Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-10 07:00:00 UTC.
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Transcutaneous conditioning electrical stimulation alters the cold detection threshold
Journal of Neurophysiology, Ahead of Print.
in Journal of Neurophysiology on 2025-10-10 04:50:24 UTC.
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Regionally mapped astrocytic responses to cortical and white matter stroke show differential roles in astrocyte-induced vascular remodeling
Gleichman et al. identify and profile zones of reactive astrocytes after two clinically distinct forms of ischemic stroke, revealing similarities and differences. Astrocytes in cortical, but not white matter, stroke drive vascular remodeling, partially through a Lamc1-dependent mechanism, which can be exogenously induced in white matter astrocytes to increase repair.in Neuron: In press on 2025-10-10 00:00:00 UTC.
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Integrated omics reveals disease-associated radial glia-like cells with epigenetically dysregulated interferon response in multiple sclerosis
Park, Nicaise, Tsitsipatis, and colleagues discover transcriptional and epigenetic markers in human PMS iNSCs and fibroblasts that may underlie their senescent and inflammatory state. Novel DARGs with the potential to fuel smoldering inflammation are found in chronic lesions from people with PMS.in Neuron: In press on 2025-10-10 00:00:00 UTC.
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Lcn2 from neutrophil extracellular traps induces astrogliosis and post-stroke emotional disorders
Liu et al. reveal that neutrophil extracellular traps (NETs) serve as markers of post-stroke emotional disorders. The infiltration of NETs into the brain parenchyma leads to the morphological and functional remodeling of astrocytes through the release of Lcn2. Early cathodal transcranial direct current stimulation (tDCS), which inhibits Lcn2 release, significantly alleviates secondary emotional disorders following stroke.in Neuron: In press on 2025-10-10 00:00:00 UTC.
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Role of AAA-ATPase Cdc48p in peroxisomal quality control
Yusuf et al. demonstrate that in the absence of Pex5p recycling, Pex5p is degraded via the RADAR pathway during the exponential growth phase of yeast. Using the GAL1 promoter to regulate gene expression, they establish that Cdc48p, in conjunction with its cofactors Ufd1p and Npl4p, is crucial for RADAR-mediated Pex5p degradation.in Cell Reports: Current Issue on 2025-10-10 00:00:00 UTC.
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Interplay of host and viral genetic variations in modulating antibody responses to genotype 3a hepatitis C virus: Implications for vaccine design
Wang et al. investigate why antibody responses to the hepatitis C virus vary between individuals. They find that specific genetic changes in both the virus (genotype 3a) and the infected person significantly impact antibody effectiveness. Understanding this host-virus interplay is crucial for designing vaccines against diverse HCV strains.in Cell Reports: Current Issue on 2025-10-10 00:00:00 UTC.
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HUWE1 stimulates mTORC1 activity by enhancing Rheb interaction with mTORC1 and supports de novo pyrimidine synthesis
Ikeda et al. identified that HUWE1 interacts with ubiquitinated Rheb, enhancing Rheb’s binding to mTORC1 and CAD, leading to their activation. The study proposed that HUWE1 acts as a key organizer of the ubiquitinated Rheb complex, playing a vital role in enhancing mTORC1 activity and de novo pyrimidine synthesis.in Cell Reports: Current Issue on 2025-10-10 00:00:00 UTC.
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The ribonuclease DISL-2 mediates piRNA degradation in C. elegans
Pastore et al. systematically measure piRNA half-lives in C. elegans, which reveals a broad spectrum of stability influenced by 3′ terminal nucleotides. They show that DISL-2 degrades piRNAs with both templated and PUP-1-mediated non-templated uridines. DISL-2 also eliminates misprocessed piRNAs, highlighting its role in piRNA quality control and homeostasis.in Cell Reports: Current Issue on 2025-10-10 00:00:00 UTC.
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Author Correction: Loss-of-function mutations in PLD4 lead to systemic lupus erythematosus
Nature, Published online: 10 October 2025; doi:10.1038/s41586-025-09692-7
Author Correction: Loss-of-function mutations in PLD4 lead to systemic lupus erythematosusin Nature on 2025-10-10 00:00:00 UTC.
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Current mosquitoes evolved more recently than previously thought
Nature, Published online: 10 October 2025; doi:10.1038/d41586-025-03244-9
Genetic analysis reveals that most living genera of mosquitoes emerged around 73 million years ago.in Nature on 2025-10-10 00:00:00 UTC.
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European bats capture migrating birds and eat them on the wing
Nature, Published online: 10 October 2025; doi:10.1038/d41586-025-03282-3
Recordings of chewing sounds combined with altitude data bear evidence of a gruesome night-time feast.in Nature on 2025-10-10 00:00:00 UTC.
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Portable muon beam could accelerate archaeology scans
Nature, Published online: 10 October 2025; doi:10.1038/d41586-025-03245-8
Laser plasma device could be light enough to be used on the field.in Nature on 2025-10-10 00:00:00 UTC.
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Rebuilding Gaza: don’t sideline Palestinian scientists, say experts
Nature, Published online: 10 October 2025; doi:10.1038/d41586-025-03324-w
Researchers warn against ‘top down’ solutions as ceasefire is agreed in the first phase of a peace deal.in Nature on 2025-10-10 00:00:00 UTC.
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Future solitude
Nature, Published online: 10 October 2025; doi:10.1038/d41586-025-03251-w
Alone but not lonely.in Nature on 2025-10-10 00:00:00 UTC.
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Dangerous ‘nitazene’ opioids are on the rise: researchers are worried
Nature, Published online: 10 October 2025; doi:10.1038/d41586-025-03280-5
This class of synthetic opioids is more potent than heroin and morphine.in Nature on 2025-10-10 00:00:00 UTC.
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The world’s first plastics treaty is in crisis: can it be salvaged?
Nature, Published online: 10 October 2025; doi:10.1038/d41586-025-03332-w
Hopes of securing a United Nations treaty on plastic pollution are fading after the final round of negotiations ended without an agreement.in Nature on 2025-10-10 00:00:00 UTC.
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Should genetically modified wildlife be banned? Scientists weigh the risks
Nature, Published online: 10 October 2025; doi:10.1038/d41586-025-03288-x
Conservationists debate the pros and cons of using synthetic biology techniques to alter wild species.in Nature on 2025-10-10 00:00:00 UTC.
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Deciphering single-cell epigenomic language with a foundation model
Nature Methods, Published online: 10 October 2025; doi:10.1038/s41592-025-02851-8
EpiAgent, a transformer-based foundation model pretrained on approximately 5 million cells and over 35 billion tokens, has advanced single-cell epigenomics by encoding chromatin accessibility as ‘cell sentences’. Benefiting from this framework, EpiAgent achieved state-of-the-art performance in typical downstream tasks and enabled perturbation response prediction and in silico chromatin region knockouts.in Nature Methods on 2025-10-10 00:00:00 UTC.
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Author Correction: Carbon storage through China’s planted forest expansion
Nature Communications, Published online: 10 October 2025; doi:10.1038/s41467-025-64762-8
Author Correction: Carbon storage through China’s planted forest expansionin Nature Communications on 2025-10-10 00:00:00 UTC.
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Author Correction: Hydrogel dressings with intrinsic antibiofilm and antioxidative dual functionalities accelerate infected diabetic wound healing
Nature Communications, Published online: 10 October 2025; doi:10.1038/s41467-025-64329-7
Author Correction: Hydrogel dressings with intrinsic antibiofilm and antioxidative dual functionalities accelerate infected diabetic wound healingin Nature Communications on 2025-10-10 00:00:00 UTC.
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Digitally fabricated 3D slippery architectures for multifunctional liquid manipulation
Nature Communications, Published online: 10 October 2025; doi:10.1038/s41467-025-64078-7
Control of liquid-based materials is important for developing materials based on these, but topological flexibility is limited. Here, the authors report a method for digital fabrication of slippery objects with solid-liquid composite interfaces and geometric design freedom.in Nature Communications on 2025-10-10 00:00:00 UTC.
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AI-powered high-throughput digital colony picker platform for sorting microbial strains by multi-modal phenotypes
Nature Communications, Published online: 10 October 2025; doi:10.1038/s41467-025-63929-7
Phenotype-based screening is a major bottleneck in the development of microbial cell factories. Here the authors build an AI-powered digital colony picker for single-cell-resolved, contactless screening and export of microbial strains, which identified lactate-tolerant Zymomonas mobilis mutants.in Nature Communications on 2025-10-10 00:00:00 UTC.
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Molecular bases of the interactions of ATG16L1 with FIP200 and ATG8 family proteins
Nature Communications, Published online: 10 October 2025; doi:10.1038/s41467-025-64097-4
ATG16L1 is a key autophagic protein. Here, the authors elucidate the molecular basis of ATG16L1 interactions with FIP200 and ATG8 family proteins, showing that the interaction of ATG16L1 with FIP200 is indispensable for the effective autophagic flux.in Nature Communications on 2025-10-10 00:00:00 UTC.
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Two complementary NLRs from wild emmer wheat confer powdery mildew resistance
Nature Communications, Published online: 10 October 2025; doi:10.1038/s41467-025-64052-3
Powdery mildew is a devasting disease for both common wheat and durum wheat. Here, the authors report the powdery mildew resistance locus derived from wild emmer wheat is conferred by the combined effect of two complementary nucleotide-binding and leucine-rich repeat genes.in Nature Communications on 2025-10-10 00:00:00 UTC.
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LARGE1 processively polymerizes length-controlled matriglycan on prodystroglycan
Nature Communications, Published online: 10 October 2025; doi:10.1038/s41467-025-64080-z
LARGE1 glycosyltransferase synthesizes matriglycan (xylose-glucuronate)n on dystroglycan, and short matriglycan can cause neuromuscular disorders. Authors show that LARGE1 processively polymerizes matriglycan of defined length on prodystroglycan.in Nature Communications on 2025-10-10 00:00:00 UTC.
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A high-resolution temporal transcriptomic and imaging dataset of porcine wound healing
Scientific Data, Published online: 10 October 2025; doi:10.1038/s41597-025-05921-w
A high-resolution temporal transcriptomic and imaging dataset of porcine wound healingin Nature scientific data on 2025-10-10 00:00:00 UTC.
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CanCPLD: Convective Parameters and Lightning Data to Support Future Thunderstorm Projections in North America
Scientific Data, Published online: 10 October 2025; doi:10.1038/s41597-025-05924-7
CanCPLD: Convective Parameters and Lightning Data to Support Future Thunderstorm Projections in North Americain Nature scientific data on 2025-10-10 00:00:00 UTC.
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Development of a large-scale grounded vision language dataset for chest CT analysis
Scientific Data, Published online: 10 October 2025; doi:10.1038/s41597-025-05922-9
Development of a large-scale grounded vision language dataset for chest CT analysisin Nature scientific data on 2025-10-10 00:00:00 UTC.
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Neural representation of mixed feelings during real-time processing of negative words in pun-humor
Communications Biology, Published online: 10 October 2025; doi:10.1038/s42003-025-08857-4
This study reveals that processing negative words in pun-humor sentences elicits mixed feelings, in which neural representations of negativity and amusement coexist within a shared temporal window, demonstrating the highly simultaneous pattern.in Nature communications biology on 2025-10-10 00:00:00 UTC.
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Temporal recurrence as a general mechanism to explain neural responses in the auditory system
Communications Biology, Published online: 10 October 2025; doi:10.1038/s42003-025-08858-3
This study demonstrates that deep recurrent neural networks outperform other models (e.g., CNNs, Transformers) in capturing auditory cortex activity across species. It also introduces an automated tool to extract spatio-temporal receptive fields (STRFs) for each neural unit.in Nature communications biology on 2025-10-10 00:00:00 UTC.
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Forecasting the spatial spread of an Ebola epidemic in real time: Comparing predictions of mathematical models and experts
-/-in eLife on 2025-10-10 00:00:00 UTC.
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Cancer–immune coevolution dictated by antigenic mutation accumulation
The immune system is one of the first lines of defence against cancer. When effector cells attempt to suppress tumour, cancer cells can evolve methods of escape or inhibition. Knowledge of this coevolutionary system can help to understand tumour–immune dynamics both during tumourigenesis and during immunotherapy treatments. Here, we present an individual-based model of mutation accumulation, where random mutations in cancer cells trigger specialised immune responses. Unlike previous research, we explicitly model interactions between cancer and effector cells and incorporate stochastic effects, which are important for the expansion and extinction of small populations. We find that the parameters governing interactions between the cancer and effector cells induce different outcomes of tumour progress, such as suppression and evasion. While it is hard to measure the cancer–immune dynamics directly, genetic information of the cancer may indicate the presence of such interactions. Our model demonstrates signatures of selection in sequencing-derived summary statistics, such as the single-cell mutational burden distribution. Thus, bulk and single-cell sequencing may provide information about the coevolutionary dynamics.in eLife on 2025-10-10 00:00:00 UTC.
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Metabolic regulation of immune memory and function of microglia
Innate immune cells possess memory-like properties. Exposure to infections or sterile inflammation can prime them, leading to either exacerbated inflammatory responses, a process called trained immunity, or reduced responsiveness to pro-inflammatory signals, a process termed immune tolerance. Microglia, the resident innate immune cells of the central nervous system, are central players in neurodegenerative diseases. Characterizing trained immunity and tolerance in microglia is necessary for a better understanding of neurodegenerative diseases. Cell metabolic processes orchestrate microglia inflammatory responses and promote epigenetic changes shaping immune memory in microglia. Here, we review current knowledge on the role of cell metabolic pathways in microglia innate immune memory formation, focusing on glucose, glutamine, and lipid metabolism. Moreover, we address the significance of microglial immune memory in disease pathology and discuss the potential of therapeutic targeting of cell metabolic pathways in neurodegenerative disorders.in eLife on 2025-10-10 00:00:00 UTC.
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Wnt induces FZD5/8 endocytosis and degradation and the involvement of RSPO-ZNRF3/RNF43 and DVL
Frizzled (FZD) proteins are the principal receptors of the Wnt signaling pathway. However, whether Wnt ligands induce FZD endocytosis and degradation remains elusive. The transmembrane E3 ubiquitin ligases ZNRF3 and RNF43 promote the endocytosis and degradation of FZD receptors to inhibit Wnt signaling, and their function is antagonized by R-spondin (RSPO) proteins. However, the dependency of RSPO-ZNRF3/RNF43-mediated FZD endocytosis and degradation on Wnt stimulation, as well as the specificity of this degradation for different FZD, remains unclear. Here, we demonstrated that Wnt specifically induces FZD5/8 endocytosis and degradation in a ZNRF3/RNF43-dependent manner. ZNRF3/RNF43 selectively targets FZD5/8 for degradation upon Wnt stimulation. RSPO1 enhances Wnt signaling by specifically stabilizing FZD5/8. Wnt promotes the interaction between FZD5 and RNF43. We further demonstrated that DVL proteins promote ligand-independent endocytosis of FZD but are dispensable for Wnt-induced FZD5/8 endocytosis and degradation. Our results reveal a novel negative regulatory mechanism of Wnt signaling at the receptor level and illuminate the mechanism by which RSPO-ZNRF3/RNF43 regulates Wnt signaling in human cells, which may provide new insights into regenerative medicine and cancer therapy.in eLife on 2025-10-10 00:00:00 UTC.
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Long-term neprilysin gene transfer is associated with reduced levels of intracellular Abeta and behavioral improvement in APP transgenic mice
in BMC Neuroscience on 2025-10-10 00:00:00 UTC.
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A Chimera Model for Motion Anticipation in the Retina and the Primary Visual Cortex
AbstractWe propose a mean field model of the primary visual cortex (V1), connected to a realistic retina model, to study the impact of the retina on motion anticipation. We first consider the case where the retina does not itself provide anticipation—which is then only triggered by a cortical mechanism, the “anticipation by latency”—and unravel the effects of the retinal input amplitude, of stimulus features such as speed and contrast and of the size of cortical extensions and fiber conduction speed. Then we explore the changes in the cortical wave of anticipation when V1 is triggered by retina-driven anticipatory mechanisms: gain control and lateral inhibition by amacrine cells. Here, we show how retinal and cortical anticipation combine to provide an efficient processing where the simulated cortical response is in advance over the moving object that triggers this response, compensating the delays in visual processing.in Neural Computation on 2025-10-10 00:00:00 UTC.
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Modeling Higher-Order Interactions in Sparse and Heavy-Tailed Neural Population Activity
AbstractNeurons process sensory stimuli efficiently, showing sparse yet highly variable ensemble spiking activity involving structured higher-order interactions. Notably, while neural populations are mostly silent, they occasionally exhibit highly synchronous activity, resulting in sparse and heavy-tailed spike-count distributions. However, its mechanistic origin—specifically, what types of nonlinear properties in individual neurons induce such population-level patterns—remains unclear. In this study, we derive sufficient conditions under which the joint activity of homogeneous binary neurons generates sparse and widespread population firing rate distributions in infinitely large networks. We then propose a subclass of exponential family distributions that satisfy this condition. This class incorporates structured higher-order interactions with alternating signs and shrinking magnitudes, along with a base-measure function that offsets distributional concentration, giving rise to parameter-dependent sparsity and heavy-tailed population firing rate distributions. Analysis of recurrent neural networks that recapitulate these distributions reveals that individual neurons possess threshold-like nonlinearity, followed by supralinear activation that jointly facilitates sparse and synchronous population activity. These nonlinear features resemble those in modern Hopfield networks, suggesting a connection between widespread population activity and the network’s memory capacity. The theory establishes sparse and heavy-tailed distributions for binary patterns, forming a foundation for developing energy-efficient spike-based learning machines.in Neural Computation on 2025-10-10 00:00:00 UTC.
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Encoding of Numerosity With Robustness to Object and Scene Identity in Biologically Inspired Object Recognition Networks
AbstractNumber sense, the ability to rapidly estimate object quantities in a visual scene without precise counting, is a crucial cognitive capacity found in humans and many other animals. Recent studies have identified artificial neurons tuned to numbers of items in biologically inspired vision models, even before training, and proposed these artificial neural networks as candidate models for the emergence of number sense in the brain. But real-world numerosity perception requires abstraction from the properties of individual objects and their contexts, unlike the simplified dot patterns used in previous studies. Using novel synthetically generated photorealistic stimuli, we show that deep convolutional neural networks optimized for object recognition encode information on approximate numerosity across diverse objects and scene types, which could be linearly read out from distributed activity patterns of later convolutional layers of different network architectures tested. In contrast, untrained networks with random weights failed to represent numerosity with abstractness to other visual properties and instead captured mainly low-level visual features. Our findings emphasize the importance of using complex, naturalistic stimuli to investigate mechanisms of number sense in both biological and artificial systems, and they suggest that the capacity of untrained networks to account for early-life numerical abilities should be reassessed. They further point to a possible, so far underappreciated, contribution of the brain's ventral visual pathway to representing numerosity with abstractness to other high-level visual properties.in Neural Computation on 2025-10-10 00:00:00 UTC.
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Feature Normalization Prevents Collapse of Noncontrastive Learning Dynamics
AbstractContrastive learning is a self-supervised representation learning framework where two positive views generated through data augmentation are made similar by an attraction force in a data representation space, while a repulsive force makes them far from negative examples. Noncontrastive learning, represented by BYOL and SimSiam, gets rid of negative examples and improves computational efficiency. While learned representations may collapse into a single point due to the lack of the repulsive force at first sight, Tian et al. (2021) revealed through learning dynamics analysis that the representations can avoid collapse if data augmentation is sufficiently stronger than regularization. However, their analysis does not take into account commonly used feature normalization, a normalizer before measuring the similarity of representations, and hence excessively strong regularization may still collapse the dynamics, an unnatural behavior under the presence of feature normalization. Therefore, we extend the previous theory based on the L2 loss by considering the cosine loss instead, which involves feature normalization. We show that the cosine loss induces sixth-order dynamics (while the L2 loss induces a third-order one), in which a stable equilibrium dynamically emerges even if there are only collapsed solutions with given initial parameters. Thus, we offer a new understanding that feature normalization plays an important role in robustly preventing the dynamics collapse.in Neural Computation on 2025-10-10 00:00:00 UTC.
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The AMPK-related kinase NUAK1 regulates neuronal morphogenesis through the RNA splicing co-factor SON
In recent years, alternative splicing emerged as a major mechanism controlling gene-regulatory networks during brain development, yet how alternative splicing is tuned to the dynamic alterations underlying neuronal maturation remains poorly understood. Here, we identified that NUAK1, an AMPK-related kinase linked to neurodevelopmental disorders, is a key regulator of alternative splicing in developing cortical neurons. Mechanistically, NUAK1 exerts its function through phosphorylation of the splicing co-factor SON, regulating a group of highly conserved splicing events in genes crucial for neurodevelopment. We demonstrate that SON plays an important role in cortical neuron development, which is consistent with the neurodevelopmental phenotypes observed in Zhu-Tokita-Takenouchi-Kim (ZTTK) syndrome, a genetic disorder caused by SON haploinsufficiency. Together, our findings uncover a novel pathway involving NUAK1 and SON, which orchestrate a splicing program required for proper neuronal development.in bioRxiv: Neuroscience on 2025-10-10 00:00:00 UTC.
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Vagal nerve stimulation alters task-evoked pupillary responses in older adults but not younger adults in a single-blind sham-controlled crossover trial
Introduction: The locus coeruleus (LC) undergoes age-related changes and is involved in Alzheimer's disease pathogenesis. Vagal nerve stimulation (VNS) may modulate LC activity and could be used therapeutically, but age-related differences in VNS responses remain unexplored. Methods: We used a single-blind, sham-controlled, crossover design in 41 participants (21 younger, 20 older adults). Participants completed a visual oddball task with pupillometry during transcutaneous auricular VNS (verum: cymba concha; sham: earlobe) with ~30-minute washout between conditions. Results: Older adults showed smaller baseline pupil diameter but larger normalized task-evoked responses than younger adults a priori. VNS produced age-specific effects: older adults demonstrated increased tonic pupil size throughout stimulation and reduced oddball-evoked responses, with stronger effects with more current. Younger adults showed no consistent VNS effects. Discussion: VNS affects LC-related physiological measures differently across age groups, with older adults showing more robust responses. These age-specific effects may reflect different baseline LC activity states.in bioRxiv: Neuroscience on 2025-10-10 00:00:00 UTC.
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Attention Improves Population Codes by Warping Neural Manifolds in Human Visual Cortex
Decades of research on visual attention have revealed its numerous effects on neural responses. Two competing models have been proposed for how these effects lead to improved population representations: one highlights changes in neural tuning, while the other points towards changes in trial-by-trial noise correlations. Here, we develop a neural population manifold framework that interprets changes in neural responses as geometric transformations in high-dimensional neural space, allowing us to disentangle and quantify the effects of tuning changes and correlation changes induced by attention. Applying this framework to extensive measurements of cortical responses during different attentional tasks, we find that tuning changes are the primary driver of improved population representations. In contrast, correlation changes, though present, have minimal--or even detrimental--effects to information content due to its strong interactions with other changes (e.g., tuning, variability). Our results support the "tuning change" model of visual attention and demonstrate a general framework for adjudicating how different aspects of neural coding affect information processing.in bioRxiv: Neuroscience on 2025-10-10 00:00:00 UTC.
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Altered Sense of Agency in First-Episode Schizophrenia Patients: A Comparative Study Across Two Sites
Background: Disturbances in the sense of agency are a key marker of schizophrenia and are closely linked to core symptoms such as hallucinations and delusions of influence. Neuroimaging studies have implicated cortical midline structures and the default mode network (DMN) associated with self-related processing, but replication across sites and larger samples is needed. Methods: We examined neural correlates of self- versus other-agency judgments in two independent cohorts of first-episode schizophrenia patients (n = 177) and controls (n = 123) recruited at separate MRI centers. During fMRI, participants performed an agency-related task. Data were analyzed using independent component analysis and statistical parametric mapping, focusing on networks associated with self-referential processing. Results: Across both sites, self-agency consistently engaged the anterior and posterior cingulate cortex and the precuneus, key regions of the DMN. Schizophrenia patients exhibited significantly reduced DMN activation during self-referential processing compared to controls. While site-related differences were noted in the magnitude of activation, the core pattern of DMN disruption was robust across analytic approaches and datasets. Conclusions: This study replicates and extends prior findings of altered self-agency processing in schizophrenia, demonstrating consistent DMN hypo-activation in first-episode patients across two independent cohorts. These results validate the agency paradigm as a reliable probe of self-related neural mechanisms and support models of schizophrenia as a disorder of altered self-perception.in bioRxiv: Neuroscience on 2025-10-10 00:00:00 UTC.
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Notch canonical activity in a subset of glial cells regulates short-term memory in Drosophila
Notch is a transmembrane receptor expressed at the cell surface that mediates transcriptional responses upon binding to its ligands, in a variety of contexts. Evolutionarily conserved, Notch plays a key role in numerous cell fate decisions during development and is also required in post-mitotic brain neurons for the consolidation of long-term memory and long-term habituation. Notch signaling is highly expressed in glial cells, where it plays a key role in regulating their development and proliferation. More recently, a Notch-dependent neuroglial pathway has been implicated in modulating the susceptibility of short-term memory to sleep deprivation in Drosophila. In this study, we demonstrate that canonical Notch signaling in glia -mediated by the Delta ligand and the transcription factor Suppressor of Hairless- is activated in a subset of cortex and ensheathing glial cells. This signaling is essential for the formation of short-term memory in the aversive phototaxic suppression assay, which is sensitive to sleep deprivation. Dopaminergic transmission is known to be required for this type of learning and is negatively impacted by sleep deprivation, suggesting a possible interaction between Notch and dopamine pathways. Supporting this idea, we find that modulating dopaminergic transmission downregulates canonical Notch activity in glial cells. Conversely, activating Notch signaling in glia near dopaminergic neurons prevents the learning impairments typically caused by sleep deprivation. Notably, Notch signaling itself does not appear to alter dopamine levels in the brain. Together, these findings indicate that canonical Notch signaling in a specific subset of glial cells is essential for short-term memory formation and is modulated by dopaminergic signaling. This suggests that sleep loss-induced disruption of dopaminergic transmission impairs learning by downregulating canonical Notch signaling. Since Notch homologs are highly expressed in mammalian glia, this pathway may be conserved and functionally relevant in other species, including humans.in bioRxiv: Neuroscience on 2025-10-10 00:00:00 UTC.
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NATO3 protects dopaminergic neurons in mouse in vivo and human in vitro Parkinson's disease models
Parkinson's disease (PD) is a devastating neurodegenerative disorder primarily characterized by the progressive and unstoppable loss of dopaminergic (DA) neurons in the substantia nigra. We previously identified NATO3 (FERD3L), a conserved developmental transcription factor, as essential for maintaining DA neuron function during aging. Here, we show that AAV-mediated Nato3 gene transfer into the mouse substantia nigra prevents DA neuron degeneration in both MPTP-induced and -synuclein (-Syn) overexpression PD models. This neuroprotective effect is achieved by improving autophagic flux and -Syn clearance. Furthermore, lentiviral-mediated NATO3 overexpression in human midbrain DA neurons, derived from induced pluripotent stem cells carrying the pathological -Syn A53T mutation, effectively reversed key disease hallmarks. These include -Syn accumulation, aberrant mitochondrial morphology, autophagic impairments, and compromised neurite structure. Collectively, these in vivo and in vitro findings highlight NATO3's role in safeguarding DA neurons against pathological cellular events, positioning NATO3 as a therapeutic target for PD.in bioRxiv: Neuroscience on 2025-10-10 00:00:00 UTC.
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Sensorimotor Theta Oscillations Coordinate Speech Movements
Fluent speech depends on precisely timed motor commands that coordinate rapid transitions between successive articulatory gestures. Using direct cortical recordings, we identified a prominent sensorimotor theta oscillation (6-10 Hz) that supports this coordination. During articulation, premotor speech circuits exhibited enhanced theta phase coherence, with elevated population activity near theta troughs. The oscillation's frequency remained remarkably stable across varying speech rates and cognitive states, consistent with an intrinsically generated rhythm. Vocal-tract kinematics revealed pulse-like movements at 6-10 Hz, tightly coupled to cortical theta phase. At a mesoscopic scale, theta cycles structured sequential sensorimotor activations encoding articulatory gestures, with syllable identity optimally decodable following theta troughs. These findings identify theta oscillations as an intrinsic timing mechanism that coordinates the distributed and synergistic motor control underlying fluent speech.in bioRxiv: Neuroscience on 2025-10-10 00:00:00 UTC.
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Belief updating in uncertain environments are differentially sensitive to reward and punishment learning: Evidence from ERP
Learning from rewards and punishments is crucial for adaptive decision-making, but it is still unclear how individuals integrate prediction errors to update beliefs in uncertain environments, particularly the distinctions between reward and punishment learning. We employed a probabilistic classification task and situated electroencephalography (EEG) signals within a hierarchical Bayesian framework, investigating distinctions between reward and punishment learning. Our findings indicate that participants exhibited superior learning performance in reward context compared to punishment context. Fitting the hierarchical Bayesian model revealed that punishment drives faster Bayesian belief updates, although these did not translate into improved behavioral outcomes. At the neural level, higher-level precision-weighted prediction error (pwPE2) was significantly positively correlated with FRN amplitude in the punishment context but not in the reward context, and the positive effect of pwPE2 on P300 amplitude was stronger in the punishment than the reward condition. These results provide electrophysiological signatures of punish context driving faster belief updates in uncertain environments. Our results provide novel evidence for a dual-process framework in reinforcement learning, underscoring distinct neural mechanisms underlying reward and punishment learning.in bioRxiv: Neuroscience on 2025-10-10 00:00:00 UTC.
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Spatial and Temporal Regulation of K+ Buffering in Hippocampal Astrocytes
Distinct subregions within the same structure (such as along the dorsoventral axis of the hippocampus) can exhibit unique molecular and physiological properties that relate to their functional specificity. These properties can be further modulated by circadian cycles. Understanding how extracellular potassium (K) is regulated within this complex spatial and temporal framework is essential, as K dynamics directly influence neuronal excitability in both health and disease. Here, we reveal that hippocampal K dynamics exhibit pronounced subregional modulation and circadian specificity, with the ventral hippocampus (VH) showing faster K clearance than the dorsal hippocampus (DH), despite reduced functional Kir4.1 currents in VH astrocytes. This interplay between subregional organization, circadian regulation, astroglial K signaling, and Kir4.1 function provides new insights into the homeostatic control of hippocampal K, offering a framework for understanding space- and time-dependent dysfunction in conditions such as epilepsy.in bioRxiv: Neuroscience on 2025-10-10 00:00:00 UTC.
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Proteome Complexity Scales with Architecture in Human Neural Models
Mass spectrometry-based proteomics enables high-throughput identification and quantification of proteins, providing molecular insight into neural development and cellular organization. Applying this approach to in vitro systems of increasing architectural complexity, we compared immortalized monolayer cell lines with two tissue-like neural models. Here, we present a comparative proteomic analysis of three human neural models: SH-SY5Y neuroblastoma cells (2D), neurospheres, and cerebral organoids. Protein profiling revealed a stepwise increase in molecular complexity, with enhanced detection of neural-related proteins linked to axon guidance, synapse formation, and GTPase signaling. This trend was most pronounced in tissue-like models, underscoring their suitability for studying neuronal maturation and circuit assembly. Functional enrichment analyses showed progressive acquisition of neurodevelopmental programs, including synaptic vesicle cycling, presynaptic organization, neurotransmitter regulation, and late-stage gliogenesis, accompanied by increased expression of astrocytic and oligodendrocytic markers. Kinase diversity also increased across models, reaching up to 210 regulatory kinases in organoids, many implicated in neural development and degeneration. Gene set enrichment for neurological pathways mirrored this trend, aligning proteomic complexity with disease relevance. Regional brain mapping further indicated that organoids most closely recapitulate the protein architecture of the human CNS. Together, these findings demonstrate that tissue-like neural models provide richer proteomic landscapes that approximate in vivo brain biology and support the application of integrative proteomics in neuroscience and translational research.in bioRxiv: Neuroscience on 2025-10-10 00:00:00 UTC.
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Optimal composition of multiple value functions for dopamine-mediated efficient, safe and stable learning
The seminal reward prediction error theory of dopamine function faces several key challenges. Most notable is the difficulty learning multiple rewards simultaneously, inefficient on-policy learning, and accounting for heterogeneous striatal responses in the tail of the striatum. We propose a normative framework, based on linear reinforcement learning, that redefines dopamine's computational objective. We propose that dopamine optimises not just cumulative rewards, but a reward value function augmented by a penalty for deviating from a default behavioural policy, which effectively confers value on controllability. Our simulations show that this single modification enables optimal value composition, fast and robust adaptation to changing priorities, safer exploration in the context of threats, and stable learning amid uncertainty. Critically, this unifies disparate striatal observations, parsimoniously reconciling threat and action prediction error signals within the striatal tail. Our framework refines the core principle governing striatal dopamine, bridging theory with neural data and offering testable predictions.in bioRxiv: Neuroscience on 2025-10-10 00:00:00 UTC.
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Synchronized path-integration recalibration but distinct landmark-control dynamics in head direction and CA1 place cells
Accurate spatial navigation relies on path integration, a process of tracking one's location by integrating self-motion cues. Path integration uses a gain factor relating self-motion signals to displacement on the cognitive map. This gain is plastic, recalibrating rapidly to match perceived displacements relative to external cues. To elucidate the mechanism of recalibration, we simultaneously recorded from place cells, which instantiate the cognitive map, and head direction (HD) cells, thought to orient the map. Persistent conflict between self-motion and visual feedback induced functionally identical recalibration of path-integration gain in the two neural populations during forward locomotion; however, during locomotor immobility accompanied by head-scanning, HD cells did not exhibit recalibration. Moreover, the two populations manifested differential field-shifting dynamics relative to landmarks during recalibration. These results uncover a tightly coordinated yet behavior-dependent recalibration process across the navigation circuit that achieves a robust yet flexible coupling of the internal sense of position and direction.in bioRxiv: Neuroscience on 2025-10-10 00:00:00 UTC.
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Microglia-dependent LPS preconditioning prevents neuroinflammation-induced behavioral deficits in male mice
Neuroinflammation contributes to psychiatric disorders, but preventive strategies targeting brain immune cells remain unexplored. Here we demonstrate that low-dose lipopolysaccharide (LPS) preconditioning prevents systemic inflammation-induced behavioral abnormalities through microglia-dependent mechanisms in male mice. Mice received preconditioning with 0.2 mg/kg LPS or saline for two consecutive days, followed by high-dose LPS challenge (5 mg/kg) or saline seven days later. Behavioral assessment revealed that preconditioning specifically prevented social preference deficits induced by systemic inflammation (preference score: -0.49+/-0.19 vs 0.14+/-0.10, p<0.01), while showing limited effects on locomotor activity and depression-like behaviors. Additionally, LPS preconditioning prevented anxiety-like behavior in a chronic corticosterone model and attenuated hippocampal inflammatory gene expression. Immunohistochemical analysis demonstrated that preconditioning suppressed microglial activation in hippocampal CA1 region, particularly reducing PBR/IBA1 ratio (37.5+/-2.4% vs 27.6+/-2.8%, p<0.01), with less pronounced effects in CA3. Critically, pharmacological microglial depletion using PLX3397 during the preconditioning period completely abolished these protective effects, establishing the causal role of microglia. Flow cytometric analysis revealed preconditioning-induced shifts in brain macrophage subpopulations defined by TMEM119 and CD45 expression patterns. Transcriptomic profiling identified subpopulation-specific responses, with one subset showing LPS-response pathway enrichment despite minimal gene expression changes, while another displayed extensive but functionally non-specific transcriptional alterations. These findings establish microglial preconditioning as a novel preventive strategy for neuroinflammation-induced social behavioral deficits and suggest potential therapeutic applications for psychiatric disorders involving neuroinflammatory components.in bioRxiv: Neuroscience on 2025-10-10 00:00:00 UTC.
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Application of value stream mapping to design and develop an inventory management system in a hospital [version 2; peer review: 2 approved with reservations]
Background Insufficient drug reserves in hospitals pose significant challenges in management, procurement, and distribution. Rising demand often leads to shortages, while overstocking causes waste and expiration. These issues reflect inefficiencies in medical supply warehouse management and highlight the need for improvement. Therefore, enhanced resource management processes are essential to ensure a balanced and efficient medication reserve system. Lean Management is a systematic approach to eliminate waste, reduce costs, and improve efficiency. Value Stream Mapping (VSM), a core Lean tool, supports process redesign by categorizing activities as value-added (VA), necessary but non-value-added (NNVA), or non-value-added (NVA). In this study, VSM was employed to comprehensively analyze the drug disbursement process within the medical supply warehouse management system and to design and develop an enhanced drug inventory management system to improve efficiency and effectiveness. Method This research employed action research methodology with 22 participants from the drug warehouse, outpatient, and inpatient medicine rooms. A user-centered system was planned through focus group discussions. This phase emphasized conceptual framework development and planning. Potential improvements were expected to enhance satisfaction, reduce waiting time, and increase convenience, sufficiency, and availability. Result Phukhieo Chaleomphrakeit Hospital created a future state value stream map, reducing process steps from seven to six. The current system required 1,925 minutes. The redesigned system is expected to take 435 minutes, including 395 minutes for valuable activities. The proportion of value-added time increased to 20.52%, significantly reducing waste from waiting. Conclusion The study demonstrates the effectiveness of VSM in identifying inefficiencies and redesigning processes. The collaboratively developed system eliminated unnecessary steps, reduced waiting times, and enhanced operational efficiency. This provides a practical model for improving pharmaceutical supply chain management in similar hospital contexts.in F1000Research on 2025-10-09 15:13:56 UTC.
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A Generic Requirement Engineering Framework for Social Commerce Platforms: Front-end and Back-end Features [version 1; peer review: awaiting peer review]
Background Social commerce (s-commerce) represents the convergence of social media and electronic commerce, where online shopping is integrated with social networking functionalities enabled by Web 2.0 technologies. As a driver of digital transformation, s-commerce continues to evolve rapidly in response to technological advancements and shifting consumer behaviors. Despite its growing, research addressing the systematic development of platforms that support this business model, particularly in relation to the requirements engineering (RE) process remains limited. Method This study applies the Design Science Research Methodology (DSRM) to design and evaluate a comprehensive RE framework for s-commerce platforms. The six DSRM steps were followed: problem identification, objectives of a solution, design and development, evaluation, and communication. The framework integrates variability concept through requirements reuse. A structured knowledge base of front-end requirements, formalized with Feature Description Language (FDL) and Unified Modeling Language (UML), ensures consistency, traceability, and reusability. Evaluation was conducted through prototype artifacts developed using Visual Studio Code, Docker Desktop, and TableTools, as well as through meta-criteria assessment. Moreover, the framework was evaluated through expert’s evaluation Central ofInformation System (CIS)developers from Sultan Qaboos University (SQU). The steps of the method were implemented and tested over a period from January 2023 to September 2025. Results The framework is implemented as a web-based prototype artifact that establishes a structured process for requirements elicitation, specification, negotiation, validation, and management. It consolidates a generic set of 28 front-end and 15 back-end requirements for s-commerce platforms, demonstrating their practical use in deriving platform-specific requirements. The framework’s prototype is supported by two complementary tools: generates requirements and recommends additional requirements in the knowledge base. Conclusion This study provides actionable guidance for e-commerce and s-commerce developers. By focusing on requirements that enhance economic value, strengthen customer relationships, and improve platform architecture, the framework contributes to more reliable, adaptable, and efficient s-commerce systems.in F1000Research on 2025-10-09 14:56:43 UTC.
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Association of urinary sulfated glycosaminoglycan with kidney function in type 2 diabetes mellitus patients [version 1; peer review: awaiting peer review]
Background Renal glomerular endothelial cells contain glycocalyx, a sulfated glycosaminoglycan-rich (sGAG) protective layer that helps selectively filter molecules. Glycocalyx damage caused by chronic hyperglycemia alters the permeability of endothelial, representing early diabetic kidney disease (DKD). Therefore, this study assessed the association between sGAG and chronic kidney disease (CKD) risk in patients with type 2 diabetes mellitus. Methods This cross-sectional study was conducted at the Pasar Minggu and Depok Jaya Primary Health Centers. Blood and urine samples were collected for the measurement of eGFR, HbA1c, UACR, and sGAG. Subjects were categorized into two groups according to their eGFR and UACR, forming the basis of the modified CKD risk criteria: low-risk and moderate-to-high-risk. Results Data from 207 participants were analyzed. There was no linear correlation between the sGAG and eGFR. Interestingly, urinary sGAG was significantly higher in the moderate-to-high-risk group than in the low-risk group (2.45 (0.4 – 13.5) vs 1.99 (0 – 17); p=0.013) with crude OR 1.140 (95% CI 1.016 – 1.278). However, after adjusting for confounders, sGAG were no longer significantly associated with a higher CKD risk (OR=1.109, 95% CI 0.978 – 1.258). Conclusion The significant difference observed in urinary sGAG levels between moderate-to-high-risk and low-risk subjects suggests that glycocalyx layer breakdown is one of the most important mechanisms in DKD. However, HbA1c level, duration of T2DM, obesity, and female sex interfere with the breakdown mechanism in the initial stage of DKD.in F1000Research on 2025-10-09 14:42:13 UTC.
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Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow [version 1; peer review: awaiting peer review]
Haematoxylin and eosin (H&E) remain the foundation of tissue diagnosis, yet many clinical questions, tumour–immune architecture, spatial heterogeneity, and predictors of therapy response, require molecular context that routine slides cannot provide. Spatial omics closes this gap by mapping RNA and proteins in situ while preserving morphology, and recent platforms are increasingly compatible with formalin-fixed paraffin-embedded (FFPE) tissue, enabling use in routine pathology and retrospective cohorts. This mini-review offers a pragmatic, step-by-step workflow for integrating spatial assays with H&E: define the clinical decision; select a fit-for-purpose modality (whole-transcriptome spot/grid vs targeted in situ RNA; multiplex proteomics); lock pre-analytics aligned to histology (sectioning, staining, de-crosslinking, storage); pre-specify regions of interest (ROIs), registration, and segmentation rules; analyse with quality-assurance gates (normalisation, deconvolution, batch handling, spatial statistics); and validate and report using orthogonal assays and multi-site replication. FFPE-ready platforms and typical use-cases are summarised, with emphasis on pre-analytical factors that materially affect signal and analysis “recipes” distilled from recent benchmarks. Brief clinical exemplars illustrate how H&E-anchored spatial maps change decisions by pinpointing actionable niches (e.g., immune neighbourhoods, vascular niches, layer-specific programmes). Common limitations are also outlined, including technology trade-offs, pre-analytics, sampling bias, segmentation and deconvolution error, batch effects, cost, turnaround, and regulatory considerations. Future directions include standards and metadata, cross-platform integration, prospective evidence, automation and quality assurance, and multi-omic detection. Overall, the goal is to support pathology and translational teams in adopting spatial omics in FFPE with both discipline and speed, focusing on clinically meaningful decisions while ensuring reproducibility and credibility.in F1000Research on 2025-10-09 14:40:48 UTC.
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Nonlinear Effects of VAT on Household Consumption in South Africa: A NARDL Analysis [version 1; peer review: awaiting peer review]
This study empirically investigates the nonlinear effects of value added tax (VAT), interest rate, household disposable income, and economic growth on household consumption in South Africa, using annual data from 1994 to 2023 obtained from the South African Reserve Bank (SARB) and World Bank. The analysis employs a Nonlinear Autoregressive Distributed Lag (NARDL) model to capture potential asymmetric relationships between the explanatory variables and household consumption, allowing for different effects of positive and negative changes over the short and long run. The findings reveal that in the long run, positive changes in VAT significantly reduce household consumption, while negative changes lead to an increase, indicating an asymmetric and inverse relationship. Similarly, positive changes in interest rates significantly reduce household consumption, whereas negative changes lead to an increase in consumption, indicating an asymmetric relationship. Similarly, positive and negative changes in household disposable income are associated with corresponding increases and decreases in household consumption, respectively, and these relationships are statistically significant in the long run. Economic growth, despite exhibiting an asymmetric pattern, is also found to be insignificantly related to household consumption in the long term. In the short run, both positive and negative VAT and interest rate shocks result in a decline in household consumption, with the effects of positive and negative changes being statistically significant. A positive change in household disposable income causes a rise in household consumption in the short run, which is statistically significant. Additionally, a negative change in household disposable income causes a drop in household consumption in the short run. This study suggests that the South African government should exercise caution when adjusting VAT and interest rates as increases may suppress household consumption, particularly among low-to middle-income households. Additionally, efforts to enhance disposable income through targeted fiscal measures may support consumption and promote overall economic stability.in F1000Research on 2025-10-09 14:37:51 UTC.
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Environmental motion presented ahead of self-motion modulates heading direction estimation
by Liana Nafisa Saftari, Jongmin Moon, Oh-Sang Kwon
The ability of a moving observer to accurately perceive their heading direction is essential for effective locomotion and balance control. While previous studies have shown that observers integrate visual and vestibular signals collected during movement, it remains unclear whether and how observers use visual signals collected before their movement to perceive heading direction. Here we investigate the effect of environmental motion that occurred ahead of self-motion on the perception of self-motion. Human observers sat on a motion platform, viewed visual motion stimuli, and then reported their perceived heading after the platform moved. The results reveal that environmental motion presented before the observers’ movement significantly modulates their heading perception. We account for this effect using a normative computational model that takes into account the causal relationship between visual signals generated before and during the observers’ movement. Overall, our study highlights the crucial role of environmental motion presented before self-motion in heading perception, broadening the current perspective on the computational mechanisms behind heading estimation.in PLoS Computational Biology on 2025-10-09 14:00:00 UTC.
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Mouse and human striatal projection neurons compared - somatodendritic arbor, spines and in silico analyses
by Alexander Kozlov, Lidia Blazquez-Llorca, Ruth Benavides-Piccione, Asta Kastanauskaite, Ana I. Rojo, Alberto Muñoz, Antonio Cuadrado, Javier DeFelipe, Sten Grillner
Dysfunction of the basal ganglia is implicated in a wide range of neurological and psychiatric disorders. Our understanding of the operation of the basal ganglia is largely derived on data from studies conducted on mice, which are frequently used as model organisms for various clinical conditions. The striatum, the largest compartment of the basal ganglia, consists of 90–95% striatal projection neurons (SPNs). It is therefore crucial to establish if human and mouse SPNs have distinct or similar properties, as this has implications for the relevance of mouse models for understanding the human striatum. To address this, we compared the general organization of the somato-dendritic tree of SPNs, the dimensions of the dendrites, the density and size of spines (spine surface area), and ion channel subtypes in human and mouse SPNs. Our findings reveal that human SPNs are significantly larger, but otherwise the organisation of the dendritic tree (dendrogram) with an average of approximately 5 primary dendrites, is similar in both species. Additionally in both humans and mice, over 90% of the spines are located on the terminal branches of each dendrite. Human spines are somewhat larger (4.3 versus 3.1 μm2) and the terminal dendrites have a uniform diameter in both humans and mice, although somewhat broader in the latter (1.0 versus 0.6 μm). The composition of ion channels is also largely conserved. These data have been used to simulate human SPNs building on our previous detailed simulation of mouse SPNs. We conclude that the human SPNs essentially appear as enlarged versions of the mouse SPNs. This similarity suggests that both species process information in a comparable manner, supporting the relevance of mouse models for studying the human striatum.in PLoS Computational Biology on 2025-10-09 14:00:00 UTC.
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Diverse and flexible behavioral strategies arise in recurrent neural networks trained on multisensory decision making
by Thomas S. Wierda, Shirin Dora, Cyriel M. A. Pennartz, Jorge F. Mejias
Behavioral variability across individuals leads to substantial performance differences during cognitive tasks, although its neuronal origin and mechanisms remain elusive. Here we use recurrent neural networks trained on a multisensory decision-making task to investigate inter-subject behavioral variability. By uniquely characterizing each network with a random synaptic-weights initialization, we observed a large variability in the level of accuracy, bias and decision speed across these networks, mimicking experimental observations in mice. Performance was generally improved when networks integrated multiple sensory modalities. Additionally, individual neurons developed modality-, choice- or mixed-selectivity, these preferences were different for excitatory and inhibitory neurons, and the concrete composition of each network reflected its preferred behavioral strategy: fast networks contained more choice- and mixed-selective units, while accurate networks had relatively less choice-selective units. External modulatory signals shifted the preferred behavioral strategies of networks, suggesting an explanation for the recently observed within-session strategy alternations in mice.in PLoS Computational Biology on 2025-10-09 14:00:00 UTC.
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Assessing parameter identifiability of a hemodynamics PDE model using spectral surrogates and dimension reduction
by Mitchel J. Colebank
Computational inverse problems for biomedical simulators suffer from limited data and relatively high parameter dimensionality. This often requires sensitivity analysis, where parameters of the model are ranked based on their influence on the specific quantities of interest. This is especially important for simulators used to build medical digital twins, as the amount of data is typically limited. For expensive models, such as blood flow models, emulation is employed to expedite the simulation time. Parameter ranking and fixing using sensitivity analysis are often heuristic, though, and vary with the specific application or simulator used. The present study provides an innovative solution to this problem by leveraging polynomial chaos expansions (PCEs) for both multioutput global sensitivity analysis and formal parameter identifiability. For the former, we use dimension reduction to efficiently quantify time-series sensitivity of a one-dimensional pulmonary hemodynamics model. We consider both Windkessel and Structured Tree boundary conditions. We then use PCEs to construct univariate profile-likelihood confidence intervals and show how changes in experimental design improve identifiability. Our work presents a novel approach to determining parameter identifiability and leverages a common emulation strategy for enabling profile-likelihood analysis in problems governed by partial differential equations.in PLoS Computational Biology on 2025-10-09 14:00:00 UTC.
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Importance of localized dilatation and distensibility in identifying determinants of thoracic aortic aneurysm with neural operators
by David S. Li, Somdatta Goswami, Qianying Cao, Vivek Oommen, Roland Assi, Jay D. Humphrey, George E. Karniadakis
Thoracic aortic aneurysms (TAAs) stem from diverse mechanical and mechanobiological disruptions to the aortic wall that can also increase the risk of dissection or rupture. There is increasing evidence that dysfunctions along the aortic mechanotransduction axis, including reduced integrity of elastic fibers and loss of cell-matrix connections, are particularly capable of causing thoracic aortopathy. Because different insults can produce distinct mechanical vulnerabilities, there is a pressing need to identify interacting factors that drive progression. In this work, we employ a finite element framework to generate synthetic TAAs arising from hundreds of heterogeneous insults that span a range of compromised elastic fiber integrity and cellular mechanosensing. From these simulations, we construct localized dilatation and distensibility maps throughout the aortic domain to serve as training data for neural network models to predict the initiating combined insult. Several candidate architectures (Deep Operator Networks, UNets, and Laplace Neural Operators) and input data formats are compared to establish a standard for handling future subject-specific information. We further quantify the predictive capability when networks are trained on geometric (dilatation) information alone, which mimics current clinical guidelines, versus training on both geometric and mechanical (distensibility) information. We show that prediction errors based on dilatation data are significantly higher than those based on dilatation and distensibility across all networks considered, highlighting the benefit of obtaining local distensibility measures in TAA assessment. Additionally, we identify UNet as the best-performing architecture across all training data formats. These findings demonstrate the importance of obtaining full-field measurements of both dilatation and distensibility in the aneurysmal aorta to identify the mechanobiological insults that drive disease progression, which will advance personalized treatment strategies that target the underlying pathologic mechanisms.in PLoS Computational Biology on 2025-10-09 14:00:00 UTC.
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Superspreading and the evolution of virulence
by Xander O’Neill, Andy White, Graham R. Northrup, Chadi M. Saad-Roy, P. Signe White, Mike Boots
Superspreading, where a small proportion of a population can cause a high proportion of infection transmission, is well known to be important to the epidemiology of a wide range of pathogens, including SARS-CoV-2. However, despite its ubiquity in important human and animal pathogens, the impact of superspreading on the evolution of pathogen virulence is not well understood. Using theory and both deterministic and stochastic simulations we examine the evolution of pathogen virulence under a range of different distributions of infection transmission for the host. Importantly, for many pathogens, superpreader events may be associated with increased tolerance to infection or asymptomatic infection and when we account for this superspreading selects for higher virulence. In contrast, in animal populations where highly connected individuals, that are linked to superspreader events, also have fitness benefits, superspreading may select for milder pathogens. In isolation, the transmission distribution of the host does not impact selection for pathogen virulence. However, superspreading reduces the rate of pathogen evolution and generates considerable variation in pathogen virulence. Therefore, the adaptation of an emerging infectious disease, that exhibits superspreading, is likely to be slowed and characterised by the maintenance of maladaptive variants. Taken as a whole, our results show that superspreading can have important impacts on the evolution of pathogens.in PLoS Computational Biology on 2025-10-09 14:00:00 UTC.
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Spatial distribution of cytoskeleton-mediated feedback controls cell polarization: A computational study
by Parijat Banerjee, Jonathan A. Kuhn, Dhiman Sankar Pal, Yu Deng, Tatsat Banerjee, Peter N. Devreotes, Pablo A. Iglesias
In the social amoeba Dictyostelium, cell motility is regulated through a signal transduction excitable network that interfaces with the cytoskeleton to control actin polymerization patterns. In turn, the cytoskeleton influences the signaling machinery via several feedback loops, but the nature and function of this feedback remain poorly understood. In this study, we use computational models to discern the essential role of complementary positive and negative feedback loops in polarizing cells. We contrast two potential mechanisms for the negative feedback: local inhibition and global inhibition. Our results indicate that both mechanisms can stabilize the leading edge and inhibit actin polymerization in other sites, preventing multipolarity. While some experimental perturbations align more closely with the local inhibition model, statistical analyses reveal its limited polarization potential within a wide excitability range. Conversely, global inhibition more effectively suppresses secondary and tertiary leading-edge formation, making it a more robust polarization mechanism. This raises an intriguing question: if local inhibition better replicates experimental observations but is less effective for polarization than local excitation and global inhibition, could there be a supplementary mechanism enhancing its polarization potential? To address this, we propose a novel mechanism involving the dynamic partitioning of back molecules which enhances communication between the front and back of the cell and can be leveraged by local feedback interactions between the cytoskeleton and signal transduction to improve polarization efficiency.in PLoS Computational Biology on 2025-10-09 14:00:00 UTC.
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Persistent viral infections impact key biological traits in Drosophila melanogaster
by Mauro Castelló-Sanjuán, Rubén González, Ottavia Romoli, Hervé Blanc, Jared C. Nigg, Maria-Carla Saleh
Persistent viral infections have been assumed to impose minimal fitness costs for insects. We established persistent mono-infections of Drosophila melanogaster with four different enteric RNA viruses: Drosophila A virus (DAV), Drosophila C virus (DCV), Bloomfield virus, and Nora virus. We observed that these infections significantly reduce fly survival, alter the number of viable offspring per female, modulate microbiome composition, impact locomotor abilities, and change activity patterns. These results demonstrate the significant impact of persistent viral infections on key biological traits and expand our understanding of the fitness costs of persistent viral infections for the host. In addition, the four viruses displayed different accumulation kinetics and elicited unique transcriptional profiles with no common core responses. The transcriptional changes triggered by DCV infection persisted even after viral clearance. This comprehensive comparative dataset represents a valuable resource for researchers studying host-pathogen interactions, providing detailed transcriptional profiles, and behavioral measurements across different viral infections and time points. Our findings reveal that persistent viral infections modulate critical aspects of insect biology, affecting host physiology and behavior.in PLoS Biology on 2025-10-09 14:00:00 UTC.
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DISSeCT: An unsupervised framework for high-resolution mapping of rodent behavior using inertial sensors
by Romain Fayat, Marie Sarraudy, Clément Léna, Daniela Popa, Pierre Latouche, Guillaume P. Dugué
Decomposing behavior into elementary components remains a central challenge in computational neuroethology. The current standard in laboratory animals involves multi-view video tracking, which, while providing unparalleled access to full-body kinematics, imposes environmental constraints, is data-intensive, and has limited scalability. We present an alternative approach using inertial sensors, which capture high-resolution, environment-independent, compact 3D kinematic data, and are commonly integrated into rodent neurophysiological devices. Our analysis pipeline leverages unsupervised, computationally efficient change-point detection to break down inertial time series into variable-length, statistically homogeneous segments. These segments are then grouped into candidate behavioral motifs through high-dimensional, model-based probabilistic clustering. We demonstrate that this approach achieves detailed rodent behavioral mapping using head inertial data. Identified motifs, corroborated by video recordings, include orienting movements, grooming components, locomotion, and olfactory exploration. Higher-order behavioral structures can be accessed by applying a categorical hidden Markov model to the motif sequence. Additionally, our pipeline detects both overt and subtle motor changes in a mouse model of Parkinson’s disease and levodopa-induced dyskinesia, highlighting its utility for behavioral phenotyping. This methodology offers the possibility of conducting high-resolution, observer-unbiased behavioral analysis at minimal computational cost from easily scalable and environmentally unconstrained recordings.in PLoS Biology on 2025-10-09 14:00:00 UTC.
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Visualization of Trypanosoma brucei flagellar pocket collar biogenesis identifies two new cytoskeletal structures
by Marie Zelená, Elina Casas, Chloé Lambert, Nicolas Landrein, Denis Dacheux, Eloïse Bertiaux, Kim Ivan Abesamis, Gang Dong, Vladimir Varga, Derrick Roy Robinson, Mélanie Bonhivers
Understanding how cells assemble internal structures is central to cell biology. In Trypanosoma brucei, the flagellar pocket (FP) is essential for nutrient uptake, and immune evasion, and its formation depends on a cytoskeletal structure called the flagellar pocket collar (FPC). However, the mechanisms underlying FPC assembly remain poorly understood. In this study, we used cutting-edge ultrastructure expansion microscopy (U-ExM) to investigate FPC biogenesis in T. brucei. We mapped the formation of the proximal part of the new microtubule quartet (nMtQ) alongside flagellum growth, providing new insights into its assembly. Additionally, we tracked the localization dynamics of key structural proteins—BILBO1, MORN1, and BILBO2—during the biogenesis of the FPC and the hook complex (HC). Notably, we identified two previously undetected structures: the proFPC and the transient FPC-interconnecting fiber (FPC-IF), both of which appear to play crucial roles in linking and organizing cellular components during cell division. By uncovering these novel aspects of FPC biogenesis, our study significantly advances the understanding of cytoskeletal organization in trypanosomes and opens new avenues for exploring the functional significance of these structures.in PLoS Biology on 2025-10-09 14:00:00 UTC.
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FAIR-SMART expands access to supplementary materials for research transparency
by Chih-Hsuan Wei, Robert Leaman, Po-Ting Lai, Don Comeau, Shubo Tian, Zhiyong Lu
Supplementary materials accompanying scientific articles are critical components of biomedical research, offering detailed datasets, experimental protocols, and extended analyses that complement the main text. These materials play an important role in enhancing transparency, reproducibility, and scientific impact by providing in depth analyses and the details necessary for reproducing experiments. However, the lack of consistent and standard formats has limited the access to supplementary materials in scientific investigations. In response, we propose a novel system aimed to enhance FAIR access to Supplementary MAterials for Research Transparency (FAIR-SMART). Specifically, we first aggregate supplementary files in a single location, standardize them into structured and machine-readable format, and make them accessible via web APIs. Next, we employ advanced large language models to automatically categorize the tabular data, which represents over 90% of the textual content in supplementary materials, enabling precise and efficient data retrieval. By bridging the gap between diverse file types and automated workflows, this work not only advances biomedical research but also highlights the transformative potential of accessible supplementary materials in shaping the behaviors and decision-making processes of the scientific community. FAIR-SMART is freely available for supplementary materials data retrieval via its APIs: https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/FAIR-SMART/.in PLoS Biology on 2025-10-09 14:00:00 UTC.
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Siblings and nonparental adults provide alternative pathways to cultural inheritance in juvenile great tits
by Sonja Wild, Gustavo Alarcón-Nieto, Lucy M. Aplin
In many animal species, the juvenile period is under strong selection, leading to a concentration of social learning during this stage as an efficient strategy for young individuals to acquire skills essential for survival. However, as social learning is not always adaptive, juveniles need to be strategic in when, who, and what to copy. In species with extended parental care, parents are often preferred sources of information, leading to stable intergenerational transmission of knowledge. However, little is known about transmission pathways in species with limited periods of parental care, and their implication for cultural inheritance. Here, we investigate social learning strategies during development in a model species with a dependence period of a few weeks, the great tit (Parus major). Using fully automated two-option foraging puzzles, we diffused knowledge about the puzzle through breeding populations and then constrained parental individuals’ choices such that parents either (1) both had knowledge of the same option, (2) had conflicting knowledge of the two options, or (3) had no knowledge of how to solve the puzzle. We then tracked solving behavior of 229 newly fledged juveniles over 10 weeks. Parental solving frequency during dependence strongly predicted knowledge acquisition by offspring, suggesting intergenerational cultural inheritance. However, detailed investigation of learning pathways revealed siblings as the most important role models for social learning, followed by nonparental adults and parents. Furthermore, offsprings’ option choices were not predicted by parental choices, but instead influenced by the broader social environment, with evidence for a conformist learning bias. Overall, by using large-scale experimental manipulation of parental behavior, our study offers new insights into social learning pathways and mechanisms of cultural inheritance in r-selected species with limited parental care and multiple offspring. Our findings provide a stark contrast to most previously studied systems exhibiting multigenerational cultures, where cultural transmission overwhelmingly occurs from parents to offspring, and give insights into the more variable transmission routes that might occur across socially learning species.in PLoS Biology on 2025-10-09 14:00:00 UTC.
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Seizure evolution in a mouse model of West syndrome involves complex and time-dependent synapse remodeling, gliosis and alterations in lipid metabolism
by Ruiying Ma, Muwon Kang, Gyu Hyun Kim, Hyojin Kang, Sunho Lee, Yeji Yang, Hea Ji Lee, Seungji Choi, Seungsoo Kim, Seoyeong Kim, Yukyung Jun, Hyewon Kim, Yinhua Zhang, U. Suk Kim, Hyae Rim Kang, Yoonhee Kim, Yulim Lee, Woosuk Chung, Eun Jung Lee, Serk In Park, Eunha Kim, Minji Jeon, Geum-Sook Hwang, Jungmin Choi, Youngae Jung, Jin Young Kim, Eunjoon Kim, Kea Joo Lee, Kihoon Han
Neurodevelopmental disorders can have long-lasting effects, causing not only early pediatric symptoms but also a range of neurological issues throughout adulthood. West syndrome is a severe neurodevelopmental disorder marked by infantile spasms, an early symptom that typically subsides with age. However, many patients progress to other seizure forms, known as seizure evolution, which is closely linked to poor long-term outcomes. Despite its clinical significance, the neurobiological mechanisms behind seizure evolution in West syndrome remain poorly understood. Recent genetic studies have consistently identified the CYFIP2 p.Arg87Cys variant in West syndrome patients, and the Cyfip2+/R87C mouse model carrying this mutation has been shown to recapitulate key symptoms of the disorder, including infantile spasms. In this study, we aimed to gain deeper insight into seizure evolution by conducting longitudinal deep phenotyping of the Cyfip2+/R87C mouse model from the neonatal stage to seven months of age. We tracked seizure activity through behavioral and EEG recordings and employed multi-omic analyses, including tissue and single-cell level transcriptomics, ultrastructural analysis, proteomics, and lipidomics, to capture a comprehensive view of molecular and cellular changes. Our results showed that after an initial period of neonatal spasms, Cyfip2+/R87C mice entered a seizure-free phase, followed by spontaneous recurrent seizures in adulthood, ultimately leading to premature death. This progression was associated with synaptic remodeling, sequential activation of different glial cell types, lipid droplet accumulation in astrocytes, and significant proteomic and lipidomic changes in the brain. These findings suggest that seizure evolution in West syndrome involves complex, time-dependent interactions between neurons and glial cells, along with alterations in lipid metabolism. Our study highlights the potential of longitudinal multi-omic approaches to uncover underlying mechanisms of seizure evolution and suggests that targeting these changes could offer novel therapeutic strategies. Additionally, the dataset generated here may provide valuable insights for other epilepsy and neurodevelopmental disorder models.in PLoS Biology on 2025-10-09 14:00:00 UTC.
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Malaria parasites can optimize transmission investment by sensing two within-host cues
by Avril Wang, Megan Ann Greischar, Nicole Mideo
The timing of investment into reproduction is a key determinant of lifetime reproductive success (fitness). Many organisms exhibit plastic, i.e., environmentally-responsive, investment strategies, raising the questions of what environmental cues trigger responses and why organisms have evolved to respond to those particular cues. For malaria parasites (Plasmodium spp.), investment into the production of specialized transmission stages (versus stages that replicate asexually within the host) is synonymous with reproductive investment and also plastic, responding to host- and parasite-derived factors. Previous theory has identified optimal plastic transmission investment strategies for the rodent malaria parasite, Plasmodium chabaudi, as a function of the time since infection, implicitly assuming that parasites have perfect information about the within-host environment and how it is changing. We extend that theory to ask which cue(s) should parasites use? Put another way, which cue(s) maximize parasite fitness, quantified as host infectiousness during acute infection? Our results show that sensing a parasite-associated cue, e.g., the abundance of infected red blood cells or transmission stages, allows parasites to achieve fitness approaching that of the optimal time-varying strategy, but only when parasites perceive the cue non-linearly, responding more sensitively to changes at low densities. However, no single cue can recreate the best time-varying strategy or allow parasites to adopt terminal investment as the infection ends, a classic expectation for reproductive investment. Sensing two cues—log-transformed infected and uninfected red blood cell abundance—enables parasites to accurately track the progression of the infection, permits terminal investment, and recovers the fitness of the optimal time-varying investment strategy. Importantly, parasites that detect two cues more efficiently exploit hosts, resulting in higher virulence compared with those sensing only one cue. However, parasites sensing two cues also experience larger fitness declines in the face of environmental and developmental fluctuations. Collectively, our results suggest that sensing non-redundant cues enables more optimal transmission investment but trades off against robustness in the face of environmental and developmental noise.in PLoS Biology on 2025-10-09 14:00:00 UTC.
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Geometry of T1 transitions in epithelia
Author(s): Pierre A. Haas
The flows of tissues of epithelial cells often involve T1 transitions. These neighbor exchanges are irreversible rearrangements crossing an energy barrier. Here, by an exact geometric construction, I determine this energy barrier for general, isolated T1 transitions dominated by line tensions. I sho…
[Phys. Rev. E 112, L042401] Published Thu Oct 09, 2025
in Physical Review E: Biological physics on 2025-10-09 10:00:00 UTC.
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Chinese Medicine as First-Line Treatment for Subthreshold Mental Disorders in Primary Care: Opportunities and Challenges [version 2; peer review: 1 approved, 1 approved with reservations]
Subthreshold mental disorders (SMDs), characterized by clusters of psychiatric symptoms that do not meet the criteria for a formal diagnosis yet are sufficiently severe to impair daily functioning. SMDs exhibit a high prevalence and an elevated risk of progression to diagnosed disorders and impose a substantial socioeconomic burden. Despite their significant impact, SMDs often go overlooked and untreated due to a global shortage of mental health professionals and stigmatization associated with conventional psychological and psychiatric treatments. This perspective advocates the integration of Chinese medicine (CM) as a first-line treatment for SMDs, focusing specifically on primary care settings in regions with established CM infrastructure and high public acceptance. Emerging evidence has shown that CM treatments, including acupuncture, herbal medicine, and other modalities, can be effective in managing various mental disorders. Systematic reviews have shown that herbal medicine not only has fewer side effects compared to psychotropic medications but also reduces adverse effects when used as adjunctive therapy. The potential benefits of using CM include mitigating the shortage of mental health professionals by supplementing primary care, preventing the exacerbation of SMDs, and offering a less stigmatized, cost-effective option that could improve help-seeking behaviors. However, challenges such as lack of recognition, insufficient collaboration between CM and mental health specialists, and differing theoretical frameworks hinder its integration into primary care in the mental health care field. Addressing these challenges will require public education, robust research evidence, policy changes, and the development of collaborative frameworks. This study highlights the need for greater recognition and integration of CM as a viable first-line treatment for the management of SMDs within primary care settings.in F1000Research on 2025-10-09 09:58:02 UTC.
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Case Report: Early Detection of Carotid Artery Stenosis with Falck Multifunctional Device (FMD), A Revised Ophthalmodynamometry Method [version 2; peer review: 1 approved, 1 approved with reservations]
Advanced clinical diagnostic tools enable ophthalmologists to diagnose not only ocular pathologies but also identify disorders that extend beyond ocular diseases. Ophthalmodynamometry (ODM), a screening tool that most ophthalmologists do not commonly use, measured reduced mean central retinal artery pressure (MCRAP) in the clinical setting. We describe a 70-year-old female with a reduced MCRAP in the right eye who identified 50% stenosis in her right internal carotid artery (ICA). Early diagnosis facilitated prompt management and potentially prevented future ischemic events.in F1000Research on 2025-10-09 09:46:33 UTC.
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Microcredit and Rural Household Outcomes: Evidence from Habru Woreda’s Smallholder Farmers, Ethiopia [version 2; peer review: awaiting peer review]
Background Most people in third-world countries are impoverished and rely on small-holder farming as a source of income. Due to a lack of working capital to diversify their sources of income and acquire new sources, farmers are highly dependent on financial institutions to access microloans. Method This study utilized logistic regression and propensity score matching methods to analyze the primary data collected from a sample of 385 household heads. Results The Estimation results of the study shows that Gender, age, family size, and education, access to irrigation, extension services, dependency ratio, and distance to credit sources were among the significant determinants of access to microcredit service. Propensity score matching results showed that microloans increased household spending but did not improve household asset accumulations. Conclusion While microloans have increased household spending, it has not significantly improved rural households asset accumulation, largely due to high interest rates and repayment pressures. To enhance long-term welfare impacts, microcredit should be complemented with financial literacy, savings and asset-building programs, and redesigned with lower interest rates and more flexible repayment structures in the study area.in F1000Research on 2025-10-09 09:44:32 UTC.
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General anaesthetics reduce acute lymphoblastic leukaemia malignancies in vitro and in vivo via CXCR4 and osteopontin mediated mechanisms [version 3; peer review: 1 approved, 2 approved with reservations]
Background Acute lymphoblastic leukaemia (ALL) is a common type of cancer in children. General anaesthetics are often used on patients undergoing painful procedures during ALL treatments but their effects on ALL malignancy remain unknown. Herein, we aim to study the effect of propofol and sevoflurane on the migration, homing and chemoresistance of ALL cells. Methods NALM-6 and Reh cells were treated with propofol (5 and 10 μg/ml) or sevoflurane (3.6%) in vitro for six hours. Then, cells were harvested for adhesion assay and migration assay in vitro. In in vivo experiments, GFP-NALM-6 cells were pre-treated with propofol (10 μg/ml) or sevoflurane (3.6%) for six hours. Then, cells were injected intravenously to C57BL/6 female mice followed by intravital microscopy. For chemoresistance study, cells were treated with rising concentrations of Ara-c (0.05-50 nM) plus 10μg/ml of propofol or Ara-C plus 3.6% of sevoflurane for 4 hours, followed by the assessment of cell viability via CCK-8 assay and detection of autophagy via flow cytometry. Results Both anaesthetics reduced in vivo migration and in vivo homing as exemplified by 1) the reduction in the number of cells entering the bone marrow and 2) the disturbance in homing location in relation to endosteal surface. Our results indicated that general anaesthetics reduced the surface CXCR4 expression and the adhesion of leukaemia cells to thrombin cleaved osteopontin (OPN) was reduced. Those changes might result in the alterations in migration and homing. In addition, both anaesthetics sensitised ALL cells to Ara-c possibly through CXCR4 mediated mechanisms. Propofol but not sevoflurane enhanced chemo-related cell death via inducing cytotoxic autophagy. Conclusion Together, our data suggest that both propofol and sevoflurane could reduce ALL migration, and homing in vivo and in vitro via CXCR4 and OPN mediated mechanisms. Both anaesthetics could sensitise ALL cells to chemotherapy possibly via CXCR4 mediated mechanisms.in F1000Research on 2025-10-09 09:11:05 UTC.
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Planet activeness: a new concept to enhance the accuracy of Astromet weather forecast. [version 2; peer review: 1 approved with reservations]
Background Astrometeorology is an ancient science, that deals the relationship between planet position and weather events. Several Indian studies proved that Astrometeorology could be a complementary method to improve numerical weather forecast accuracy. Since 2011, Tamil Nadu Agricultural University is conducting astrometeorological research and devised a novel concept “Planet Activeness Chart”. The principle is that “planets’ influence on a location’s weather varies throughout the day and may be negative, inactive, active, highly active and rule depending on their angle to that location”. Most existing astromet studies used planetary position to predict the occurrence of weather events (yes/no) but failed to capture intensity of the event. The “Planet Activeness Concept” could address this limitation and enhance forecast usability. Methods A study was carried out from 2018 to 2021 with six years data (2011-16) to verify the “Planet activeness” on hourly rainfall and windspeed events in Tamil Nadu. The frequency of planet activeness for a weather event was calculated by dividing the number of times a planet was in the selected activeness during a specific event category by the total number of events. Results The results indicated that negative state of the Sun, active states of the Saturn, Uranus, Venus and Moon were positively associated with rainfall intensity. The windy planet Mercury and Neptune at active state, the Sun and Saturn at rule state, Venus and Uranus at negative state, Jupiter at highly active state had significant influence on the increased wind speed. Conclusion Applying the planet activeness concept with azimuth could enhance the accuracy and usability of Astrometeorological forecasts. This study establishes a mathematical relationship between planet activeness and weather as a first step to understand the science behind this relationship. It is suggested to study different combination of planet activeness during a weather event for more insights.in F1000Research on 2025-10-09 09:08:18 UTC.
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Application of digital health technologies to substance use reduction among students in higher education institutions: A scoping review [version 3; peer review: 1 approved with reservations, 1 not approved]
Psychoactive substances alter perception, mood, cognition, or consciousness and include a wide range of compounds such as alcohol, marijuana, nicotine, and khat. Substance use among college and university students is associated with significant health issues, academic struggles, and premature death. This scoping review examines digital health interventions, including mobile and internet platforms, targeting substance use reduction among college students in low- and middle-income countries (LMICs). A comprehensive search across databases such as PubMed, PsycINFO, Scopus, and Google Scholar identified 8 eligible studies conducted across seven countries between 2013 and 2025. These studies focused primarily on alcohol use and included digital health tools like instant messaging, Telegram applications, text messaging, and web-based interventions. The results suggest that digital health technologies can effectively motivate college students in LMICs to reduce or abstain from psychoactive substance use. However, there is a notable research gap in evaluating the effectiveness and feasibility of these tools, especially mobile text messaging, which remains one of the most widely used methods in LMICs. The review highlights the need for further research, including systematic reviews and meta-analyses, to better understand the impact of digital health interventions on substance use reduction and to develop evidence-based programs for behavior change.in F1000Research on 2025-10-09 09:05:32 UTC.
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Why is it important? Financial literacy in students in entrepreneurship: A systematic literature review [version 2; peer review: 1 approved with reservations, 1 not approved]
Student entrepreneurship is increasingly recognized as a driver of economic growth, innovation, and job creation, particularly in developing countries. However, limited financial literacy remains a major barrier to entrepreneurial success among students. This study aims to systematically review the role of financial literacy in supporting student entrepreneurship. A systematic search using the PRISMA 2020 method was conducted in Scopus for the period 2014–2024 with the following search string: (“financial literacy” AND entrepreneurship AND student*) OR (“financial literacy” AND “youth entrepreneurship”). From an initial 155 records, 36 studies met the inclusion criteria: peer-reviewed, empirical, English language, and focusing on students in the context of entrepreneurship. The review identifies three main themes. First, the financial skills addressed include budgeting, saving and borrowing, cash-flow management, investment and risk assessment, and basic accounting. Second, approaches to improving financial literacy are primarily through integrated curricula, business simulations, student venture projects, and industry mentoring. Third, financial literacy strongly impacts entrepreneurial outcomes, including higher self-efficacy, better financial decision-making, stronger resilience in cash-flow management, and greater sustainability of student ventures. The findings underscore the importance of integrating financial literacy as core human capital within entrepreneurship education, especially in developing countries. Limitations of this review include reliance on Scopus and exclusion of non-English studies. The study provides theoretical, practical, and policy implications for universities, educators, and policymakers.in F1000Research on 2025-10-09 09:03:40 UTC.
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Health Expenditures and Social Sustainability in Nigeria: A Disaggregated Grossman Analysis of Short-Run Gains and Long-Run Paradoxes [version 1; peer review: awaiting peer review]
Background Global health expenditure has increased dramatically in the past decades, yet poor health outcomes in many emerging markets, including Nigeria, pose efficiency and sustainability questions in health financing. Nigeria exemplifies such paradox: with increased health spending, life expectancy has declined, while infant mortality is elevated, jeopardising Sustainable Development Goal 3 (Good Health and Well-being) attainment. This research examines how disaggregated health financing segments: health expenditure per capita, recurrent health expenditure, capital health expenditure, and out-of-pocket health spending (OPHS) impact social sustainability indicators in the form of life expectancy, and infant mortality. Methods By utilising annual time series from 1990-2023 through the use of an Autoregressive Distributed Lag (ARDL) panel to address potential endogeneities, short- as well as long-run impacts are accounted for. Results Results indicate that per capita, recurrent and capital expenditures are significant in enhancing life expectancy in the long run, whereas all the financing segments are absent from having any statistically significant long-run impact on infant mortality. Paradoxical short-run mortality increases are observed in relation to increased recurrent as well as capital expenditures, which is indicative of inefficiencies as well as misappropriation. OPHS has mixed short-run impacts, as well as is insignificant in the long run, which accentuates its regressive burden. Conclusions The study concludes that financing volume alone is insufficient; expenditure composition, governance, and institutional reforms are critical to achieving socially sustainable health outcomes. Policy recommendations include reducing OPHS reliance, prioritising primary healthcare, and embedding sustainability principles in health financing so as to align Nigeria’s health system with SDG 3 targets by 2030.in F1000Research on 2025-10-09 09:01:50 UTC.
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MTAP deficiency confers resistance to cytosolic nucleic acid sensing and STING agonists
Science, Volume 390, Issue 6769, October 2025.
in Science on 2025-10-09 07:00:00 UTC.
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mRNA initiation and termination are spatially coordinated
Science, Volume 390, Issue 6769, October 2025.
in Science on 2025-10-09 07:00:00 UTC.
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A cGAS-mediated mechanism in naked mole-rats potentiates DNA repair and delays aging
Science, Volume 390, Issue 6769, October 2025.
in Science on 2025-10-09 07:00:00 UTC.
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Acidosis orchestrates adaptations of energy metabolism in tumors
Science, Volume 390, Issue 6769, October 2025.
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T cell cholesterol transport links intestinal immune responses to dietary lipid absorption
Science, Volume 390, Issue 6769, October 2025.
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A genome-to-proteome map reveals how natural variants drive proteome diversity and shape fitness
Science, Volume 390, Issue 6769, October 2025.
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Mechanism of DNA targeting by human LINE-1
Science, Volume 390, Issue 6769, October 2025.
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Targeted protein evolution in the gut microbiome by diversity-generating retroelements
Science, Volume 390, Issue 6769, October 2025.
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Constraining exoplanet interiors using observations of their atmospheres
Science, Volume 390, Issue 6769, October 2025.
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Erratum for the Report “Top-down cortical input during NREM sleep consolidates perceptual memory” by D. Miyamoto et al.
Science, Volume 390, Issue 6769, October 2025.
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Can COP30 put investment in research at the heart of adaptation?
Science, Volume 390, Issue 6769, October 2025.
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The complicated legacy of antibiotics
Science, Volume 390, Issue 6769, Page 137-137, October 2025.
in Science on 2025-10-09 06:00:29 UTC.
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The lithium dilemma
Science, Volume 390, Issue 6769, Page 136-136, October 2025.
in Science on 2025-10-09 06:00:29 UTC.
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Greater noctule bats prey on and consume passerines in flight
Science, Volume 390, Issue 6769, Page 178-181, October 2025.
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Ubiquitin-mediated mitophagy regulates the inheritance of mitochondrial DNA mutations
Science, Volume 390, Issue 6769, Page 156-163, October 2025.
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Anion sublattice design enables superionic conductivity in crystalline oxyhalides
Science, Volume 390, Issue 6769, Page 199-204, October 2025.
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Total solar eclipse triggers dawn behavior in birds: Insights from acoustic recordings and community science
Science, Volume 390, Issue 6769, Page 152-155, October 2025.
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Photo-induced nonvolatile rewritable ferroaxial switching
Science, Volume 390, Issue 6769, Page 195-198, October 2025.
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Tropical forest carbon offsets deliver partial gains amid persistent over-crediting
Science, Volume 390, Issue 6769, Page 182-187, October 2025.
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Wavefront shaping enables high-power multimode fiber amplifier with output focus
Science, Volume 390, Issue 6769, Page 173-177, October 2025.
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SALICYLIC ACID SENSOR1 reveals the propagation of an SA hormone surge during plant pathogen advance
Science, Volume 390, Issue 6769, Page 188-194, October 2025.
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Learning from my stutter
Science, Volume 390, Issue 6769, Page 210-210, October 2025.
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In Other Journals
Science, Volume 390, Issue 6769, Page 142-143, October 2025.
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Longevity steps on the cGAS
Science, Volume 390, Issue 6769, Page 126-127, October 2025.
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A light switch for a hidden order
Science, Volume 390, Issue 6769, Page 131-131, October 2025.
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A tripartite alliance for dietary fat absorption
Science, Volume 390, Issue 6769, Page 128-129, October 2025.
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Natural directed evolution in gut microbiota
Science, Volume 390, Issue 6769, Page 129-130, October 2025.
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Science diplomacy is a tool, not a currency
Science, Volume 390, Issue 6769, Page 139-139, October 2025.
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Strategic foundations of science diplomacy
Science, Volume 390, Issue 6769, Page 138-139, October 2025.
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