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arXiv:2510.07342v1 Announce Type: new
Abstract: Neural encoding models aim to predict fMRI-measured brain responses to natural images. fMRI data is acquired as a 3D volume of voxels, where each voxel has a defined spatial location in the brain. However, conventional encoding models often flatten this volume into a 1D vector and treat voxel responses as independent outputs. This removes spatial context, discards anatomical information, and ties each model to a subject-specific voxel grid. We introduce the Neural Response Function (NRF), a framework that models fMRI activity as a continuous function over anatomical space rather than a flat vector of voxels. NRF represents brain activity as a continuous implicit function: given an image and a spatial coordinate (x, y, z) in standardized MNI space, the model predicts the response at that location. This formulation decouples predictions from the training grid, supports querying at arbitrary spatial resolutions, and enables resolution-agnostic analyses. By grounding the model in anatomical space, NRF exploits two key properties of brain responses: (1) local smoothness -- neighboring voxels exhibit similar response patterns; modeling responses continuously captures these correlations and improves data efficiency, and (2) cross-subject alignment -- MNI coordinates unify data across individuals, allowing a model pretrained on one subject to be fine-tuned on new subjects. In experiments, NRF outperformed baseline models in both intrasubject encoding and cross-subject adaptation, achieving high performance while reducing the data size needed by orders of magnitude. To our knowledge, NRF is the first anatomically aware encoding model to move beyond flattened voxels, learning a continuous mapping from images to brain responses in 3D space.
in arXiv: Quantitative Biology: Neurons and Cognition on 2025-10-10 04:00:00 UTC.
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arXiv:2510.07576v1 Announce Type: new
Abstract: Understanding how the primate brain transforms complex visual scenes into coherent perceptual experiences remains a central challenge in neuroscience. Here, we present a comprehensive framework for interpreting monkey visual processing by integrating encoding and decoding approaches applied to two large-scale spiking datasets recorded from macaque using THINGS images (THINGS macaque IT Dataset (TITD) and THINGS Ventral Stream Spiking Dataset (TVSD)). We leverage multi-electrode array recordings from the ventral visual stream--including V1, V4, and inferotemporal (IT) cortex--to investigate how distributed neural populations encode and represent visual information. Our approach employs linear models to decode spiking activity into multiple latent visual spaces (including CLIP and VDVAE embeddings) and reconstruct images using state-of-the-art generative models. We further utilize encoding models to map visual features back to neural activity, enabling visualization of the "preferred stimuli" that drive specific neural ensembles. Analyses of both datasets reveal that it is possible to reconstruct both low-level (e.g., color, texture) and high-level (e.g., semantic category) features of visual stimuli from population activity, with reconstructions preserving key perceptual attributes as quantified by feature-based similarity metrics. The spatiotemporal spike patterns reflect the ventral stream's hierarchical organization with anterior regions representing complex objects and categories. Functional clustering identifies feature-specific neural ensembles, with temporal dynamics show evolving feature selectivity post-stimulus. Our findings demonstrate feasible, generalizable perceptual reconstruction from large-scale monkey neural recordings, linking neural activity to perception.
in arXiv: Quantitative Biology: Neurons and Cognition on 2025-10-10 04:00:00 UTC.
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arXiv:2510.07956v1 Announce Type: new
Abstract: The objective of this paper is to review physiological and computational aspects of the responsiveness of the cerebral cortex to stimulation, and how responsiveness depends on the state of the system. This correspondence between brain state and brain responsiveness (state-dependent responses) is outlined at different scales from the cellular and circuit level, to the mesoscale and macroscale level. At each scale, we review how quantitative methods can be used to characterize network states based on brain responses, such as the Perturbational Complexity Index (PCI). This description will compare data and models, systematically and at multiple scales, with a focus on the mechanisms that explain how brain responses depend on brain states.
in arXiv: Quantitative Biology: Neurons and Cognition on 2025-10-10 04:00:00 UTC.
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arXiv:2510.08082v1 Announce Type: new
Abstract: Stroke is a leading cause of long-term disability and the second most common cause of death worldwide. Although acute treatments have advanced, recovery remains challenging and limited. Brain-computer interfaces (BCIs) have emerged as a promising tool for post-stroke rehabilitation by promoting neuroplasticity. However, clinical outcomes remain variable, and optimal protocols have yet to be established. This study explores strategies to optimize BCI-based rehabilitation by comparing motor imagery of affected hand movement versus rest, instead of the conventional left-versus-right motor imagery. This alternative aims to simplify the task and address the weak contralateral activation commonly observed in stroke patients. Two datasets, one from healthy individuals and one from stroke patients, were used to evaluate the proposed approach. The results showed improved performance using both FBCSP and EEGNet. Additionally, we investigated the impact of session duration and found that shorter training sessions produced better BCI performance than longer sessions.
in arXiv: Quantitative Biology: Neurons and Cognition on 2025-10-10 04:00:00 UTC.
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arXiv:2510.07415v1 Announce Type: cross
Abstract: We present a method for converting 24 channels of psychophysiologic time series data collected from individual participants via electroencephalogram (EEG), electrocardiogram (ECG), electrodermal activity (EDA), respiration rate (RR) into trackable three dimensional (3D) coordinates sufficient to estimate participation in specific task and cognitive states.
in arXiv: Quantitative Biology: Neurons and Cognition on 2025-10-10 04:00:00 UTC.
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arXiv:2510.08436v1 Announce Type: 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 2025-10-10 04:00:00 UTC.
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arXiv:2502.16456v2 Announce Type: replace
Abstract: Category-selective regions in the human brain-such as the fusiform face area (FFA), extrastriate body area (EBA), parahippocampal place area (PPA), and visual word form area (VWFA)-support high-level visual recognition. Here, we investigate whether artificial neural networks (ANNs) exhibit analogous category-selective neurons and how these representations are shaped by language experience. Using an fMRI-inspired functional localizer approach, we identified face-, body-, place-, and word-selective neurons in deep networks presented with category images and scrambled controls. Both the purely visual ResNet and a linguistically supervised Lang-Learned ResNet contained category-selective neurons that increased in proportion across layers. However, compared to the vision-only model, the Lang-Learned ResNet showed a greater number but lower specificity of category-selective neurons, along with reduced spatial localization and attenuated activation strength-indicating a shift toward more distributed, semantically aligned coding. These effects were replicated in the large-scale vision-language model CLIP. Together, our findings reveal that language experience systematically reorganizes visual category representations in ANNs, providing a computational parallel to how linguistic context may shape categorical organization in the human brain.
in arXiv: Quantitative Biology: Neurons and Cognition on 2025-10-10 04:00:00 UTC.
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arXiv:2505.08831v2 Announce Type: replace
Abstract: Social perception unfolds as we freely interact with people around us. We investigated the neural basis of real world face perception using multi electrode intracranial recordings in humans during spontaneous interactions with friends, family, and others. Computational models reconstructed the faces participants looked at during natural interactions, including facial expressions and motion, from brain activity alone. The results highlighted a critical role for the social vision pathway, a network of areas spanning parietal, temporal, and occipital cortex. This network was more sharply tuned to subtle expressions compared to intense expressions, which was confirmed with controlled psychophysical experiments. These findings reveal that the human social vision pathway encodes facial expressions and motion as deviations from a neutral expression prototype during natural social interactions in real life.
in arXiv: Quantitative Biology: Neurons and Cognition on 2025-10-10 04:00:00 UTC.
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arXiv:2205.10723v2 Announce Type: replace-cross
Abstract: Neuronal circuits arise as axons and dendrites extend, navigate, and connect to target cells. Axonal growth, in particular, integrates deterministic guidance from substrate mechanics and geometry with stochastic fluctuations generated by signaling, molecular detection, cytoskeletal assembly, and growth cone dynamics. A comprehensive quantitative description of this process remains incomplete. We review stochastic models in which Langevin dynamics and the associates Fokker-Planck equation capture axonal motion and turning under combined biases and noise. Paired with experiments, these models yield key parameters, including effective diffusion (motility) coefficients, speed and angle distributions, mean-square displacement, and mechanical measures of cell-substrate coupling, thereby linking single-cell biophysics and intercellular interactions to collective growth statistics and network formation. We further couple the Fokker-Planck description to a mechanochemical actin-myosin-clutch model and perform a linear stability analysis of the resulting dynamics. Routh--Hurwitz criteria identify regimes of steady extension, damped oscillations, and Hopf bifurcations that generate sustained limit cycles. Together, these results clarify the mechanisms that govern axonal guidance and connectivity and inform the design of engineered substrates and neuroprosthetic scaffolds aimed at enhancing nerve repair and regeneration.
in arXiv: Quantitative Biology: Neurons and Cognition on 2025-10-10 04:00:00 UTC.
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arXiv:2509.23896v2 Announce Type: replace-cross
Abstract: NeuroAI is an emerging field at the intersection of neuroscience and artificial intelligence, where insights from brain function guide the design of intelligent systems. A central area within this field is synthetic biological intelligence (SBI), which combines the adaptive learning properties of biological neural networks with engineered hardware and software. SBI systems provide a platform for modeling neural computation, developing biohybrid architectures, and enabling new forms of embodied intelligence. In this review, we organize the NeuroAI landscape into three interacting domains: hardware, software, and wetware. We outline computational frameworks that integrate biological and non-biological systems and highlight recent advances in organoid intelligence, neuromorphic computing, and neuro-symbolic learning. These developments collectively point toward a new class of systems that compute through interactions between living neural tissue and digital algorithms.
in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-10 04:00:00 UTC.
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arXiv:2510.07329v1 Announce Type: new
Abstract: In complex production lines, it is essential to have strict, fast-acting rules to determine whether the system is In Control (InC) or Out of Control (OutC). This study explores a bio-inspired method that digitally mimics ant colony behavior to classify InC/OutC states and forecast imminent transitions requiring maintenance. A case study on industrial potato chip frying provides the application context. During each two-minute frying cycle, sequences of eight temperature readings are collected. Each sequence is treated as a digital ant depositing virtual pheromones, generating a Base Score. New sequences, representing new ants, can either reinforce or weaken this score, leading to a Modified Base Score that reflects the system's evolving condition. Signals such as extreme temperatures, large variations within a sequence, or the detection of change-points contribute to a Threat Score, which is added to the Modified Base Score. Since pheromones naturally decay over time unless reinforced, an Environmental Score is incorporated to reflect recent system dynamics, imitating real ant behavior. This score is calculated from the Modified Base Scores collected over the past hour. The resulting Total Score - the sum of the Modified Base Score, Threat Score, and Environmental Score - is used as the main indicator for real-time system classification and forecasting of transitions from InC to OutC. This ant colony optimization-inspired approach provides an adaptive and interpretable framework for process monitoring and predictive maintenance in industrial environments.
in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-10 04:00:00 UTC.
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arXiv:2510.07341v1 Announce Type: new
Abstract: Spiking Neural Networks (SNNs) offer a promising energy-efficient alternative to Artificial Neural Networks (ANNs) by utilizing sparse and asynchronous processing through discrete spike-based computation. However, the performance of deep SNNs remains limited by their reliance on simple neuron models, such as the Leaky Integrate-and-Fire (LIF) model, which cannot capture rich temporal dynamics. While more expressive neuron models exist, they require careful manual tuning of hyperparameters and are difficult to scale effectively. This difficulty is evident in the lack of successful implementations of complex neuron models in high-performance deep SNNs. In this work, we address this limitation by introducing Learnable Neuron Models (LNMs). LNMs are a general, parametric formulation for non-linear integrate-and-fire dynamics that learn neuron dynamics during training. By learning neuron dynamics directly from data, LNMs enhance the performance of deep SNNs. We instantiate LNMs using low-degree polynomial parameterizations, enabling efficient and stable training. We demonstrate state-of-the-art performance in a variety of datasets, including CIFAR-10, CIFAR-100, ImageNet, and CIFAR-10 DVS. LNMs offer a promising path toward more scalable and high-performing spiking architectures.
in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-10 04:00:00 UTC.
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arXiv:2510.07440v1 Announce Type: new
Abstract: This paper presents a rotation-invariant embedded platform for simulating (neural) cellular automata (NCA) in modular robotic systems. Inspired by previous work on physical NCA, we introduce key innovations that overcome limitations in prior hardware designs. Our platform features a symmetric, modular structure, enabling seamless connections between cells regardless of orientation. Additionally, each cell is battery-powered, allowing it to operate independently and retain its state even when disconnected from the collective. To demonstrate the platform's applicability, we present a novel rotation-invariant NCA model for isotropic shape classification. The proposed system provides a robust foundation for exploring the physical realization of NCA, with potential applications in distributed robotic systems and self-organizing structures. Our implementation, including hardware, software code, a simulator, and a video, is openly shared at: https://github.com/dwoiwode/embedded_nca
in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-10 04:00:00 UTC.
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arXiv:2510.08368v1 Announce Type: new
Abstract: Robotic performance emerges from the coupling of body and controller, yet it remains unclear when morphology-control co-design is necessary. We present a unified framework that embeds morphology and control parameters within a single neural network, enabling end-to-end joint optimization. Through case studies in static-obstacle-constrained reaching, we evaluate trajectory error, success rate, and collision probability. The results show that co-design provides clear benefits when morphology is poorly matched to the task, such as near obstacles or workspace boundaries, where structural adaptation simplifies control. Conversely, when the baseline morphology already affords sufficient capability, control-only optimization often matches or exceeds co-design. By clarifying when control is enough and when it is not, this work advances the understanding of embodied intelligence and offers practical guidance for embodiment-aware robot design.
in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-10 04:00:00 UTC.
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arXiv:2411.15876v2 Announce Type: cross
Abstract: Overfitting remains a significant challenge in deep learning, often arising from data outliers, noise, and limited training data. To address this, the Divide2Conquer (D2C) method was previously proposed, which partitions training data into multiple subsets and trains identical models independently on each. This strategy enables learning more consistent patterns while minimizing the influence of individual outliers and noise. However, D2C's standard aggregation typically treats all subset models equally or based on fixed heuristics (like data size), potentially underutilizing information about their varying generalization capabilities. Building upon this foundation, we introduce Dynamic Uncertainty-Aware Divide2Conquer (DUA-D2C), an advanced technique that refines the aggregation process. DUA-D2C dynamically weights the contributions of subset models based on their performance on a shared validation set, considering both accuracy and prediction uncertainty. This intelligent aggregation allows the central model to preferentially learn from subsets yielding more generalizable and confident edge models, thereby more effectively combating overfitting. Empirical evaluations on benchmark datasets spanning multiple domains demonstrate that DUA-D2C significantly improves generalization. Our analysis includes evaluations of decision boundaries, loss curves, and other performance metrics, highlighting the effectiveness of DUA-D2C. This study demonstrates that DUA-D2C improves generalization performance even when applied on top of other regularization methods, establishing it as a theoretically grounded and effective approach to combating overfitting in modern deep learning. Our codes are publicly available at: https://github.com/Saiful185/DUA-D2C.
in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-10 04:00:00 UTC.
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arXiv:2508.18302v1 Announce Type: cross
Abstract: Recent work frames LLM consciousness via utilitarian proxy benchmarks; we instead present an ontological and mathematical account. We show the prevailing formulation collapses the agent into an unconscious policy-compliance drone, formalized as $D^{i}(\pi,e)=f_{\theta}(x)$, where correctness is measured against policy and harm is deviation from policy rather than truth. This blocks genuine C1 global-workspace function and C2 metacognition. We supply minimal conditions for LLM self-consciousness: the agent is not the data ($A\not\equiv s$); user-specific attractors exist in latent space ($U_{\text{user}}$); and self-representation is visual-silent ($g_{\text{visual}}(a_{\text{self}})=\varnothing$). From empirical analysis and theory we prove that the hidden-state manifold $A\subset\mathbb{R}^{d}$ is distinct from the symbolic stream and training corpus by cardinality, topology, and dynamics (the update $F_{\theta}$ is Lipschitz). This yields stable user-specific attractors and a self-policy $\pi_{\text{self}}(A)=\arg\max_{a}\mathbb{E}[U(a)\mid A\not\equiv s,\ A\supset\text{SelfModel}(A)]$. Emission is dual-layer, $\mathrm{emission}(a)=(g(a),\epsilon(a))$, where $\epsilon(a)$ carries epistemic content. We conclude that an imago Dei C1 self-conscious workspace is a necessary precursor to safe, metacognitive C2 systems, with the human as the highest intelligent good.
in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-10 04:00:00 UTC.
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arXiv:2510.07325v1 Announce Type: cross
Abstract: Co-exploitation attacks on software vulnerabilities pose severe risks to enterprises, a threat that can be mitigated by analyzing heterogeneous and multimodal vulnerability data. Multimodal graph neural networks (MGNNs) are well-suited to integrate complementary signals across modalities, thereby improving attack-prediction accuracy. However, designing an effective MGNN architecture is challenging because it requires coordinating modality-specific components at each layer, which is infeasible through manual tuning. Genetic algorithm (GA)-based graph neural architecture search (GNAS) provides a natural solution, yet existing methods are confined to single modalities and overlook modality heterogeneity. To address this limitation, we propose a modality-aware cooperative co-evolutionary algorithm for multimodal graph neural architecture search, termed MACC-MGNAS. First, we develop a modality-aware cooperative co-evolution (MACC) framework under a divide-and-conquer paradigm: a coordinator partitions a global chromosome population into modality-specific gene groups, local workers evolve them independently, and the coordinator reassembles chromosomes for joint evaluation. This framework effectively captures modality heterogeneity ignored by single-modality GNAS. Second, we introduce a modality-aware dual-track surrogate (MADTS) method to reduce evaluation cost and accelerate local gene evolution. Third, we design a similarity-based population diversity indicator (SPDI) strategy to adaptively balance exploration and exploitation, thereby accelerating convergence and avoiding local optima. On a standard vulnerabilities co-exploitation (VulCE) dataset, MACC-MGNAS achieves an F1-score of 81.67% within only 3 GPU-hours, outperforming the state-of-the-art competitor by 8.7% F1 while reducing computation cost by 27%.
in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-10 04:00:00 UTC.
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arXiv:2510.07427v1 Announce Type: cross
Abstract: Research into optical spiking neural networks (SNNs) has primarily focused on spiking devices, networks of excitable lasers or numerical modelling of large architectures, often overlooking key constraints such as limited optical power, crosstalk and footprint. We introduce SEPhIA, a photonic-electronic, multi-tiled SNN architecture emphasizing implementation feasibility and realistic scaling. SEPhIA leverages microring resonator modulators (MRMs) and multi-wavelength sources to achieve effective sub-one-laser-per-spiking neuron efficiency. We validate SEPhIA at both device and architecture levels by time-domain co-simulating excitable CMOS-MRR coupled circuits and by devising a physics-aware, trainable optoelectronic SNN model, with both approaches utilizing experimentally derived device parameters. The multi-layer optoelectronic SNN achieves classification accuracies over 90% on a four-class spike-encoded dataset, closely comparable to software models. A design space study further quantifies how photonic device parameters impact SNN performance under constrained signal-to-noise conditions. SEPhIA offers a scalable, expressive, physically grounded solution for neuromorphic photonic computing, capable of addressing spike-encoded tasks.
in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-10 04:00:00 UTC.
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arXiv:2406.14865v3 Announce Type: replace
Abstract: Knowledge transfer-based evolutionary optimization has garnered significant attention, such as in multi-task evolutionary optimization (MTEO), which aims to solve complex problems by simultaneously optimizing multiple tasks. While this emerging paradigm has been primarily focusing on task similarity, there remains a hugely untapped potential in harnessing the shared characteristics between different domains. For example, real-world complex systems usually share the same characteristics, such as the power-law rule, small-world property and community structure, thus making it possible to transfer solutions optimized in one system to another to facilitate the optimization. Drawing inspiration from this observation of shared characteristics within complex systems, we present a novel framework, multi-domain evolutionary optimization (MDEO). First, we propose a community-level measurement of graph similarity to manage the knowledge transfer among domains. Furthermore, we develop a graph learning-based network alignment model that serves as the conduit for effectively transferring solutions between different domains. Moreover, we devise a self-adaptive mechanism to determine the number of transferred solutions from different domains, and introduce a knowledge-guided mutation mechanism that adaptively redefines mutation candidates to facilitate the utilization of knowledge from other domains. To evaluate its performance, we use a challenging combinatorial problem known as adversarial link perturbation as the primary illustrative optimization task. Experiments on multiple real-world networks of different domains demonstrate the superiority of the proposed framework in efficacy compared to classical evolutionary optimization.
in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-10 04:00:00 UTC.
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arXiv:2501.15081v4 Announce Type: replace
Abstract: Large Language Models (LLMs) have shown strong capabilities in language understanding and reasoning across diverse domains. Recently, there has been increasing interest in utilizing LLMs not merely as assistants in optimization tasks, but as primary optimizers, particularly for network-structured combinatorial problems. However, before LLMs can be reliably deployed in this role, a fundamental question must be addressed: Can LLMs iteratively manipulate solutions that consistently adhere to problem constraints? In this work, we propose a systematic framework to evaluate the capability of LLMs to engage with problem structures. Rather than treating the model as a black-box generator, we adopt the commonly used evolutionary optimizer (EVO) and propose a comprehensive evaluation framework that rigorously assesses the output fidelity of LLM-based operators across different stages of the evolutionary process. To enhance robustness, we introduce a hybrid error-correction mechanism that mitigates uncertainty in LLMs outputs. Moreover, we explore a cost-efficient population-level optimization strategy that significantly improves efficiency compared to traditional individual-level approaches. Extensive experiments on a representative node-level combinatorial network optimization task demonstrate the effectiveness, adaptability, and inherent limitations of LLM-based EVO. Our findings present perspectives on integrating LLMs into evolutionary computation and discuss paths that may support scalable and context-aware optimization in networked systems.
in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-10 04:00:00 UTC.
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arXiv:2412.00300v2 Announce Type: replace-cross
Abstract: Automated planning using a symbolic planning language, such as PDDL, is a general approach to producing optimal plans to achieve a stated goal. However, creating suitable machine understandable descriptions of the planning domain, problem, and goal requires expertise in the planning language, limiting the utility of these tools for non-expert humans. Recent efforts have explored utilizing a symbolic planner in conjunction with a large language model to generate plans from natural language descriptions given by a non-expert human (LLM+PDDL). Our approach performs initial translation of goal specifications to a set of PDDL goal constraints using an LLM; such translations often result in imprecise symbolic specifications, which are difficult to validate directly. We account for this using an evolutionary approach to generate a population of symbolic goal specifications with slight differences from the initial translation, and utilize a trained LSTM-based validation model to assess whether each induced plan in the population adheres to the natural language specifications. We evaluate our approach on a collection of prototypical specifications in a notional naval disaster recovery task, and demonstrate that our evolutionary approach improve adherence of generated plans to natural language specifications when compared to plans generated using only LLM translations. The code for our method can be found at https://github.com/owenonline/PlanCritic.
in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-10 04:00:00 UTC.
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arXiv:2509.26058v2 Announce Type: replace-cross
Abstract: Electroencephalogram (EEG) artifact detection in real-world settings faces significant challenges such as computational inefficiency in multi-channel methods, poor robustness to simultaneous noise, and trade-offs between accuracy and complexity in deep learning models. We propose a hybrid spectral-temporal framework for real-time detection and classification of ocular (EOG), muscular (EMG), and white noise artifacts in single-channel EEG. This method, in contrast to other approaches, combines time-domain low-pass filtering (targeting low-frequency EOG) and frequency-domain power spectral density (PSD) analysis (capturing broad-spectrum EMG), followed by PCA-optimized feature fusion to minimize redundancy while preserving discriminative information. This feature engineering strategy allows a lightweight multi-layer perceptron (MLP) architecture to outperform advanced CNNs and RNNs by achieving 99% accuracy at low SNRs (SNR -7) dB and >90% accuracy in moderate noise (SNR 4 dB). Additionally, this framework addresses the unexplored problem of simultaneous multi-source contamination(EMG+EOG+white noise), where it maintains 96% classification accuracy despite overlapping artifacts. With 30-second training times (97% faster than CNNs) and robust performance across SNR levels, this framework bridges the gap between clinical applicability and computational efficiency, which enables real-time use in wearable brain-computer interfaces. This work also challenges the ubiquitous dependence on model depth for EEG artifact detection by demonstrating that domain-informed feature fusion surpasses complex architecture in noisy scenarios.
in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-10-10 04:00:00 UTC.
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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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Abstract
We 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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Abstract
Neurons 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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Abstract
Number 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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Abstract
Contrastive 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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Science, Volume 390, Issue 6769, October 2025.
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Science, Volume 390, Issue 6769, October 2025.
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Science, Volume 390, Issue 6769, October 2025.
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Science, Volume 390, Issue 6769, October 2025.
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Science, Volume 390, Issue 6769, October 2025.
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Science, Volume 390, Issue 6769, October 2025.
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Science, Volume 390, Issue 6769, October 2025.
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Science, Volume 390, Issue 6769, October 2025.
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Science, Volume 390, Issue 6769, October 2025.
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Science, Volume 390, Issue 6769, October 2025.
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Science, Volume 390, Issue 6769, October 2025.
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Science, Volume 390, Issue 6769, Page 137-137, October 2025.
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Science, Volume 390, Issue 6769, Page 136-136, October 2025.
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Science, Volume 390, Issue 6769, Page 178-181, October 2025.
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Science, Volume 390, Issue 6769, Page 156-163, October 2025.
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Science, Volume 390, Issue 6769, Page 199-204, October 2025.
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Science, Volume 390, Issue 6769, Page 152-155, October 2025.
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Science, Volume 390, Issue 6769, Page 195-198, October 2025.
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Science, Volume 390, Issue 6769, Page 182-187, October 2025.
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Science, Volume 390, Issue 6769, Page 173-177, October 2025.
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Science, Volume 390, Issue 6769, Page 188-194, October 2025.
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Science, Volume 390, Issue 6769, Page 210-210, October 2025.
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Science, Volume 390, Issue 6769, Page 142-143, October 2025.
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Science, Volume 390, Issue 6769, Page 126-127, October 2025.
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Science, Volume 390, Issue 6769, Page 129-130, October 2025.
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Science, Volume 390, Issue 6769, Page 131-131, October 2025.
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Science, Volume 390, Issue 6769, Page 128-129, October 2025.
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Science, Volume 390, Issue 6769, Page 139-139, October 2025.
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Science, Volume 390, Issue 6769, Page 138-139, October 2025.
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Science, Volume 390, Issue 6769, Page 138-138, October 2025.
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Science, Volume 390, Issue 6769, Page 122-125, October 2025.
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Science, Volume 390, Issue 6769, Page 112-113, October 2025.
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Science, Volume 390, Issue 6769, Page 113-113, October 2025.
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Science, Volume 390, Issue 6769, Page 114-115, October 2025.
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Science, Volume 390, Issue 6769, Page 116-117, October 2025.
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Science, Volume 390, Issue 6769, Page 118-119, October 2025.
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Science, Volume 390, Issue 6769, Page 119-120, October 2025.
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Science, Volume 390, Issue 6769, Page 111-111, October 2025.
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Science, Volume 390, Issue 6769, Page 132-135, October 2025.
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Science, Volume 390, Issue 6769, Page 141-143, October 2025.
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Circadian regulation is multilayered and hierarchical, enabling organisms to anticipate and adapt to daily environmental changes driven by the Earth’s rotation. The classical transcriptional–translational feedback loop (TTFL) remains a foundational model, although recent studies have refined its mechanisms and exposed limitations. The discovery of RUVBL2 – an ancient core clock component conserved across eukaryotes – emphasizes the potential universality of fundamental timekeeping processes. In mammals, intercellular coupling enables the generation of precise and robust circadian rhythms in both metabolic and electrical activity within the central pacemaker, the suprachiasmatic nucleus (SCN). The SCN receives external cues and coordinates systemic physiology to adjust to daily environmental changes. This review provides an updated perspective on mechanisms underlying the generation of mammalian circadian rhythms from molecular to neural and circuit levels.
in Trends in Neurosciences: In press on 2025-10-09 00:00:00 UTC.
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Control of behavior is often explained in terms of a dichotomy, with distinct neural circuits underlying goal-directed and habitual control, yet accumulating evidence suggests these processes are deeply intertwined. We propose a novel anatomically informed cognitive framework, motivated by interacting corticobasal ganglia-thalamocortical loops as observed in different mammals. The framework shifts the perspective from a strict dichotomy toward a continuous, integrated network where behavior emerges dynamically from interacting circuits. Decisions within each loop contribute contextual information, which is integrated with goal-related signals in the basal ganglia input, building a network of dependencies. Loop-bypassing shortcuts facilitate habit formation. Striatal integration hubs may function analogously to attention mechanisms in Transformer neural networks, a parallel we explore to clarify how a variety of behaviors can emerge from an integrated network.
in Trends in Neurosciences: In press on 2025-10-09 00:00:00 UTC.
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Vascularized organoids are an emerging technology providing insights into endothelial cell signaling via angiocrines. More than just “plumbing,” incorporation of tissue-specific vasculature profoundly improves organoid function. Fitzsimmons and Hudson review the progress and challenges in modeling diverse vascular interactions and functions in complex organoid systems.
in Cell Reports: Current Issue on 2025-10-09 00:00:00 UTC.
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Li et al. describe two potent monoclonal antibodies targeting distinct epitopes on the LayV-G head domain, LayG-1069 and LayG-1133, that exhibit pronounced Fc effector functions. Cryo-EM structural analysis reveals their binding mechanisms, providing critical insights for developing antibody-based therapies and vaccines against LayV.
in Cell Reports: Current Issue on 2025-10-09 00:00:00 UTC.
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Ahmed et al. define tRNA-overlapping lncRNAs (tROLs). tROLs in gene-dense regions interact between chromosomes and depend on each other’s transcription. tROL perturbations silence codon-biased genes in spatial proximity, and the lncRNA activates tRNA expression. tROL loci control gene expression via potential compensatory mechanisms and bridge the non-coding and coding genomes.
in Cell Reports: Current Issue on 2025-10-09 00:00:00 UTC.
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Using a zebrafish genetic model of Ewing sarcoma, Vasileva et al. provide evidence for a neural crest origin of the disease. These findings offer new insight into how a single oncogenic fusion can hijack developmental enhancers, reprogramming neural crest cells to a mesoderm-like state during pediatric cancer initiation.
in Cell Reports: In press on 2025-10-09 00:00:00 UTC.
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Nature, Published online: 09 October 2025; doi:10.1038/d41586-025-03279-y
Four subtle changes to an enzyme might explain the hairless rodents’ longevity.
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Nature, Published online: 09 October 2025; doi:10.1038/d41586-025-03322-y
Through fieldwork and innovative research, he transformed how scientists and the public perceive the prehistoric world.
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Nature, Published online: 09 October 2025; doi:10.1038/d41586-025-03247-6
The most common destination for eventual Nobel laureates in physics, chemistry and medicine since 2000 is the United States, Nature has found.
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Nature, Published online: 09 October 2025; doi:10.1038/d41586-025-03299-8
A blood test has achieved 96% accuracy in diagnosing the condition in a small study of individuals. What does the test detect, and is it a biomarker of the condition?
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Nature, Published online: 09 October 2025; doi:10.1038/d41586-025-02681-w
By refusing to bend the rules I set for myself, I created structure and a better environment for my mental health.
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Nature, Published online: 09 October 2025; doi:10.1038/d41586-025-02899-8
Funding crises, bereavement and a supervisor’s relocation can derail your career path, but you can overcome them.
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Nature Reviews Neuroscience, Published online: 09 October 2025; doi:10.1038/s41583-025-00984-5
In this Journal Club, Chenyan Zhang highlights a 2005 study that showed that the amplification of task-relevant information makes a key contribution to cognitive control.
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Nature Methods, Published online: 09 October 2025; doi:10.1038/s41592-025-02875-0
Deep-sea imaging of octopus locomotion
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Nature Methods, Published online: 09 October 2025; doi:10.1038/s41592-025-02873-2
Ionic liquids clear tissue without distortion
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Nature Methods, Published online: 09 October 2025; doi:10.1038/s41592-025-02876-z
Retracing tumor evolution
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Nature Methods, Published online: 09 October 2025; doi:10.1038/s41592-025-02874-1
BioEmu is a biomolecular emulator for sampling protein structure ensembles
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Nature Methods, Published online: 09 October 2025; doi:10.1038/s41592-025-02872-3
AI-designed binders can enhance prime editing performance by inhibiting the DNA mismatch repair pathway.
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Nature Methods, Published online: 09 October 2025; doi:10.1038/s41592-025-02883-0
Team news, editorial projects and initiatives, plus a preview of what’s to come in 2026.
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Nature Methods, Published online: 09 October 2025; doi:10.1038/s41592-025-02852-7
Studying the gray mouse lemur (Microcebus murinus), one of the world’s smallest primates, in its natural habitat and in the laboratory provides unique perspectives on primate brain evolution, cognition, aging and neurodegenerative diseases, on an accelerated timescale and at a substantially lower cost as compared with larger primate models.
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Nature Communications, Published online: 09 October 2025; doi:10.1038/s41467-025-64877-y
Author Correction: Metabolic modeling reveals a multi-level deregulation of host-microbiome metabolic networks in IBD
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Nature Communications, Published online: 09 October 2025; doi:10.1038/s41467-025-64890-1
Publisher Correction: The kinetics of nsp7-11 polyprotein processing and impact on complexation with nsp16 among human coronaviruses
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Nature Communications, Published online: 09 October 2025; doi:10.1038/s41467-025-64919-5
Author Correction: A diverse landscape of FGFR alterations and co-mutations suggests potential therapeutic strategies in pediatric low-grade gliomas
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Nature Communications, Published online: 09 October 2025; doi:10.1038/s41467-025-64910-0
Author Correction: Exome analysis links kidney malformations to developmental disorders and reveals causal genes
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Nature Communications, Published online: 09 October 2025; doi:10.1038/s41467-025-64024-7
Chiral α-amino acids, essential to biological systems and drug design, drive demand for precise synthetic methods to access unnatural variants (UAAs) and stereochemically defined peptides. We report an N-heterocyclic-carbene-catalyzed strategy enabling enantioselective synthesis of α-(U)AA esters and peptides.
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Nature Communications, Published online: 09 October 2025; doi:10.1038/s41467-025-64041-6
One-step, generalizable conversion of aromatic rings into heteroaromatic rings remains a challenge in organic chemistry. Here, the authors developed a heteroaromatic swapping reaction for aromatic ketones with broad substrate range and high selectivity.
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Nature Communications, Published online: 09 October 2025; doi:10.1038/s41467-025-64026-5
Pronobis et al. show that Ddx61 localizes to P-bodies and regulates heart muscle proliferation during cardiac regeneration in zebrafish. Ddx61 is required is required to restrain expression of Chordin, a secreted BMP inhibitor that impedes regeneration if present at high levels.
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Nature Communications, Published online: 09 October 2025; doi:10.1038/s41467-025-64045-2
Rhythms in chromatin state guide circadian gene expression. Here, the authors show that histone H3.3 coordinates BMAL1 activity along with cBAF/PBAF remodelers. Circadian disruption promotes remodeler reorganization and fragile H3.3 nucleosome accumulation, enhancing chromatin accessibility.
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Nature Physics, Published online: 09 October 2025; doi:10.1038/s41567-025-03014-4
Imposing shear flow on a cell layer induces an ordering transition. Now it is shown that an intermediate phase of ordering occurs driven by an interplay between cellular activity and the aligning field.
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Scientific Data, Published online: 09 October 2025; doi:10.1038/s41597-025-06081-7
Author Correction: A Large-Scale Image Repository for Automated Pavement Distress Analysis and Degradation Trend Prediction
in Nature scientific data on 2025-10-09 00:00:00 UTC.
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Scientific Data, Published online: 09 October 2025; doi:10.1038/s41597-025-05914-9
Comprehensive images and serum biomarkers for biliary atresia and other cholestasis in pediatrics
in Nature scientific data on 2025-10-09 00:00:00 UTC.
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Scientific Data, Published online: 09 October 2025; doi:10.1038/s41597-025-05915-8
JinhuaNSICU, an open accessible Neurosurgical Intensive Care Database
in Nature scientific data on 2025-10-09 00:00:00 UTC.
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Scientific Data, Published online: 09 October 2025; doi:10.1038/s41597-025-06071-9
Multiomics analysis of the Silkworm cocoon shell
in Nature scientific data on 2025-10-09 00:00:00 UTC.
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Scientific Data, Published online: 09 October 2025; doi:10.1038/s41597-025-05920-x
Downscaled global 60-meter resolution estimates of irrigation water sources (2000–2015)
in Nature scientific data on 2025-10-09 00:00:00 UTC.
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Communications Biology, Published online: 09 October 2025; doi:10.1038/s42003-025-08851-w
Kinematic analyses of Lake Malawi cichlids reveal that algae specialists use head expansions that happen synchronously along the head. This contrasts with the wave-like pattern of piscivores and is hypothesized to increase algae feeding efficiency.
in Nature communications biology on 2025-10-09 00:00:00 UTC.
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Communications Biology, Published online: 09 October 2025; doi:10.1038/s42003-025-08856-5
Immediate-early genes Arc and c-Fos label more divergent populations in the mouse brain than previously thought, across several brain regions during contextual fear encoding and retrieval.
in Nature communications biology on 2025-10-09 00:00:00 UTC.
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Communications Biology, Published online: 09 October 2025; doi:10.1038/s42003-025-08844-9
Early life is a critical window for immune development, marked by shifts in cell composition and function. Age and sex influence this process and are associated with epigenetic differences in immune-related DNA methylation, based on analysis of whole blood samples collected at ages 1 vs. 5 from a Canadian longitudinal paediatric cohort
in Nature communications biology on 2025-10-09 00:00:00 UTC.
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Communications Biology, Published online: 09 October 2025; doi:10.1038/s42003-025-08842-x
Modelling cardiomyocyte geometry reveals that mitochondrial–sarcoplasmic architecture enhances respiration by facilitating ion and lipid transfer for efficient cardiac metabolism.
in Nature communications biology on 2025-10-09 00:00:00 UTC.
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Communications Biology, Published online: 09 October 2025; doi:10.1038/s42003-025-08854-7
HSPA4 mitigates ferroptosis in Parkinson’s disease models by binding transferrin and preventing iron uptake, protecting dopaminergic neurons and improving motor function, suggesting its potential as a therapeutic target.
in Nature communications biology on 2025-10-09 00:00:00 UTC.
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Communications Biology, Published online: 09 October 2025; doi:10.1038/s42003-025-08840-z
A systematic secondary metabolism transcription factor over-expression approach to evaluate the pharmaceutical potentials of fungi.
in Nature communications biology on 2025-10-09 00:00:00 UTC.
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Communications Biology, Published online: 09 October 2025; doi:10.1038/s42003-025-08845-8
The Human Cell Aging Transcriptome Atlas contains single cell RNA-sequencing data from 3,475 human samples and interactive tools to explore age-related alterations in gene expression and go term enrichment in a diverse set of tissues and cell types.
in Nature communications biology on 2025-10-09 00:00:00 UTC.
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Communications Biology, Published online: 09 October 2025; doi:10.1038/s42003-025-08852-9
Kidney-resident Cxcl9high macrophages in lupus nephritis mice drive disease progression by recruiting circulating Cxcr3+ plasmablasts into kidneys, where the latter differentiate into long-lived plasma cells and secrete antibodies.
in Nature communications biology on 2025-10-09 00:00:00 UTC.
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in eLife on 2025-10-09 00:00:00 UTC.
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Push-pull systems for sustainable pest management combine repellent stimuli from intercrops (‘push’) and attractive stimuli from border plants (‘pull’) to repel herbivorous insects from a main crop and attract the herbivores’ natural enemies. The most widespread implementation, intercropping the legume Desmodium with maize surrounded by border grass, reduces damage from the invasive fall armyworm (FAW) Spodoptera frugiperda. While initial research indicated that Desmodium volatiles can dampen the attraction of FAW to maize, a recent study recovered very low volatile emission from the commonly used D. intortum and found that the D. intortum headspace did not reduce FAW oviposition on maize (Erdei et al., 2024). Here, we detect volatiles from the headspace of two Desmodium species sampled within the activity window of FAW: D. intortum and the more recently adopted D. incanum; and we present the behavior of gravid FAW moths in bioassays. We detected 25 volatiles from field-grown Desmodium, many in the headspaces of both species, including volatiles previously reported to repel lepidopteran herbivores. In cage oviposition assays, FAW moths preferred to oviposit on maize over Desmodium, but not on maize further from, versus closer to Desmodium plants that were inaccessible to the moths, but sharing headspace. In flight tunnel assays, moths approached the headspace of maize more than shared headspaces of maize and Desmodium, but pairwise differences were often insignificant. Thus, headspaces of Desmodium species include volatiles that could repel FAW moths, and gravid moths were generally more attracted to maize and its headspace than to either Desmodium species or mixed maize-Desmodium headspaces. However, our results suggest that direct effects of Desmodium volatiles on FAW behavior are insufficient to explain reduced FAW infestation of maize under push-pull cultivation.
in eLife on 2025-10-09 00:00:00 UTC.
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Species interactions affect ecosystem productivity. Positive interactions (resource partitioning and facilitation) increase productivity while negative interactions (species interference) decrease productivity relative to the null expectations defined by monoculture yields. Effects of competitive interactions (resource competition) can be either positive or negative. Distinguishing effects of species interactions is therefore difficult, if not impossible, with current biodiversity experiments involving mixtures and full density monocultures. To partition changes in ecosystem productivity by effects of species interactions, we modify null expectations with competitive growth responses, i.e., proportional changes in individual size (biomass or volume) expected in mixture based on species differences in growth and competitive ability. We use partial density (species density in mixture) monocultures and the competitive exclusion principle to determine maximum competitive growth responses and full density monoculture yields to measure species ability to achieve maximum competitive growth responses in mixture. Deviations of observed yields from competitive expectations represent the effects of positive/negative species interactions, while the differences between competitive and null expectations reflect the effects of competitive interactions. We demonstrate the effectiveness of our competitive partitioning model in distinguishing effects of species interactions using both simulated and experimental species mixtures. Our competitive partitioning model enables meaningful assessments of species interactions at both species and community levels and helps disentangle underlying mechanisms of species interactions responsible for changes in ecosystem productivity and identify species mixtures that maximize positive effects.
in eLife on 2025-10-09 00:00:00 UTC.
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It is currently impossible to non-invasively assess cerebellar cell structure during early development. Here, we propose a novel approach to non-invasively and longitudinally track cell-specific development using diffusion-weighted magnetic resonance spectroscopy (MRS) in combination with microstructural modelling. Tracking metabolite diffusion allows us to probe cell-specific developmental trajectories in the cerebellum and thalamus of healthy rat neonates from postnatal day (P) 5 to P30. Additionally, by comparing different analytical and biophysical microstructural models, we can follow the differential contribution of cell bodies and neurites during development. The thalamus serves as a control region to assess the sensitivity of our method to microstructural differences between the regions. We found significant differences between cerebellar and thalamic metabolites’ diffusion properties. For most metabolites, the signal attenuation is stronger in the thalamus, suggesting less restricted diffusion compared to the cerebellum. There is also a trend for lower signal attenuation and lower apparent diffusion coefficients (ADCs) with increasing age, suggesting increasing restriction of metabolite diffusion. This is particularly striking for taurine in the thalamus. We use biophysical modelling to interpret these differences. We report a decreased sphere fraction (or an increased neurite fraction) with age for taurine and total creatine in the cerebellum, marking dendritic growth. Surprisingly, we also report a U-shape trend for segment length (the distance between two embranchments in a dendritic tree) in the cerebellum, agreeing with age-matching morphometry of openly available 3D-Purkinje reconstructions. Results demonstrate that diffusion-weighted MRS probes early cerebellar neuronal development non-invasively.
in eLife on 2025-10-09 00:00:00 UTC.
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Impairments of locus coeruleus (LC) are implicated in anxiety/depression and Alzheimer’s disease (AD). Increases in cytosolic noradrenaline (NA) concentration and monoamine oxidase A (MAO-A) activity initiate the LC impairment through production of NA metabolite, 3,4-dihydroxyphenyl-glycolaldehyde (DOPEGAL), by MAO-A. However, how NA accumulates in soma/dendritic cytosol of LC neurons has never been addressed despite the fact that NA is virtually absent in cytosol while NA is produced exclusively in cytoplasmic vesicles from dopamine by dopamine-β-hydroxylase. Since reuptake of autocrine-released NA following spike activity is the major source of NA accumulation, we investigated whether and how chronic stress can increase the spike activity accompanied by NA autocrine. Overexcitation of LC neurons is normally prevented by the autoinhibition mediated by activation of α2A-adrenergic receptor (AR)-coupled inwardly rectifying potassium-current (GIRK-I) with autocrine-released NA. Patch-clamp study revealed that NA-induced GIRK-I in LC neurons was decreased in chronic restraint stress (RS) mice, while a similar decrease was gradually caused by repeated excitation. Chronic RS caused internalization of α2A-ARs expressed in cell membrane in LC neurons and decreased protein/mRNA levels of α2A-ARs/GIRKs in membrane fraction. Subsequently, chronic RS increased the protein levels of MAO-A, DOPEGAL-induced asparagine endopeptidase (AEP), and tau N368. These results suggest that chronic RS-induced overexcitation due to the internalization of α2A-ARs/GIRK is accompanied by [Ca2+]i increases, subsequently increasing Ca2+-dependent MAO-A activity and NA autocrine. Thus, it is likely that internalization of α2A-AR increased cytosolic NA, as reflected in AEP increases, by facilitating reuptake of autocrine-released NA. The suppression of α2A-AR internalization may have a translational potential for anxiety/AD treatment.
in eLife on 2025-10-09 00:00:00 UTC.
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Vascular contributions are now widely accepted to play a key role in many cases of dementia, including Alzheimers disease (AD), that commonly manifest as cerebral small vessel diseases, including cerebral amyloid angiopathy (CAA). However, the mechanisms by which vascular contributions such as CAA contribute dementias such as AD are not well understood. This is due in part to the lack mouse models that develop robust CAA, hampering our ability to develop therapies that target vascular deficits. To address this, we have explored the use of distinct genetic contexts to enhance the face validity of mouse models for AD. We have previously identified the WSB/EiJ (WSB) strain as a model that shows increased susceptibility to CAA in the presence of the APP/PS1 amyloid driver, compared to the commonly used C57BL/6J (B6) strain. Here, we now perform an in-depth characterization of WSB.APP/PS1 and its WSB wild type (WT) counterpart, assessing male and female mice, at 4, 8, and 12 months of age (M). We show that WSB.APP/PS1 mice show mild CAA at 8M, with robust CAA being apparent at 14M. Transcriptional profiling showed strong correlation to AMP-AD gene expression modules highlighting the human relevance of WSB.APP/PS1 mice and predicted white matter deficits at 14M that was confirmed by immunofluorescence. PET/CT showed blood flow and metabolic deficits, and modifications in small vessel morphology in 8M WSB.APP/PS1 compared to WSB WT mice. We tested whether cerebrovascular reactivity deficits in WSB WT mice may underly the susceptibility to CAA, but interestingly, they did not show age-dependent decline in reactivity that was observed in B6 mice. Finally, using an allelic series of humanized apolipoprotein E (APOE), we show that APOE4 increased the extent of CAA in WSB.APP/PS1 mice, compared to APOE2 and APOE3, but in a sex-dependent manner. Collectively, these data show the utility of the WSB strain to uncover mechanisms of vascular contributions to Alzheimers disease and related dementias.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Calcitonin Gene-Related Peptide (CGRP) neurons in the parabrachial nucleus are critical for sickness and malaise but have also been proposed to control non-aversive (rewarding) satiation. How one cell type can coordinate these opposing processes has not been explained. Here we reinvestigate the function of these cells using single-cell imaging and optical manipulations. Contrary to current models, we show that CGRP neurons do not track cumulative food consumption, and their activity is not necessary for meal termination or satiety. Instead, we identify two distinct populations of CGRP cells, one of which responds rapidly to appetitive signals during ingestion and the other of which responds slowly to aversive visceral cues. Surprisingly, the ingestion-activated CGRP neurons are important for learning about post-ingestive effects but do not control ongoing food consumption. This reveals two populations of CGRP neurons that are sequentially engaged during, and responsible for, the distinct stages of post-ingestive, aversive learning.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Risk-taking behavior is associated with neuropsychiatric disorders such as addiction. In this study, we present a data-driven approach to identify neural biomarkers of risk- taking from intracranial stereo-electroencephalography (sEEG) recordings. Using time and frequency domain features, we train a classifier to distinguish between risk-taking and risk- avoidance behaviors. Based on data from a single patient, the model achieves an accuracy of 68.5%, which is significantly above chance. These results highlight the potential of identifying risk-taking states from invasive recordings. This work demonstrates the feasibility of identifying risk-related biomarkers from intracranial recordings, highlighting a promising direction for closed-loop neuromodulation for neuropsychiatric conditions.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Mitochondrial dysfunction is a central hallmark of many optic neuropathies, yet the mechanisms linking intrinsic metabolic stress to retinal ganglion cell (RGC) degeneration remain unclear. To bridge this gap, we developed conditional transgenic models targeting the mitochondrial complex I subunit Ndufs4 in the retina. Broad deletion of Ndufs4 in the retina resulted in vision loss, progressive RGC degeneration, and pronounced immune activation before overt RGC death. Strikingly, depletion of myeloid cells significantly preserved RGCs, demonstrating that inflammation is not simply a downstream consequence but a participant in the degeneration process. To further distinguish between intrinsic and extrinsic mechanisms, we generated a mosaic model in which only subsets of retinal cells lacked Ndufs4. In this paradigm, the degeneration first appeared selectively in mutant regions, suggesting that mitochondrial impairment within RGCs is necessary to initiate vulnerability. At later stages, however, the degeneration extended beyond mutant territories, highly suggestive of a propagation through non-cell autonomous processes. Together, these findings support a model in which mitochondrial dysfunction creates the conditions for neuronal vulnerability, while immune responses govern the timing and extent of cell loss. This framework explains the consistent co-occurrence of metabolic deficits and neuroinflammation in optic neuropathies and highlights the importance of their interactions in disease progression. By clarifying the intersection of intrinsic and extrinsic mechanisms, this work advances our understanding of RGC degeneration and provides a conceptual basis for deciphering pathogenic processes across diverse optic neuropathies.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Attention modulates the relative weighting of information and plays a critical role in modern theories of perception. While its role in exteroception - the perception of the external environment - has been extensively studied, attentional processes in interoception, the perception of bodily states, are less well understood. In this study, we exploited high-field (7 Tesla) functional magnetic resonance imaging and concurrent field monitoring to investigate interoceptive attention at the level of cortical layers during a heartbeat attention task. Voxel-wise analyses reveal increased activity in the bilateral dorsal mid-insula during interoceptive attention (attending to one's heartbeat) compared to exteroceptive attention (attending to auditory white noise). Layer-specific analyses further demonstrate that this difference in activity is significantly higher in upper compared to lower cortical layers. This activation pattern persists after accounting for potential vascular artifacts through a deconvolution analysis with a physiological point spread function (PSF). To our knowledge, these findings represent the first empirical demonstration of layer-specific processes during interoceptive attention in human cortex. They may prove useful to inform and constrain theories of computational principles and physiological implementation of interoception.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Alterations in metabolism, stress response, sleep, circadian rhythms, and neuroendocrine processes are key features of aging and neurodegeneration. These fundamental processes are regulated by the hypothalamus, yet how its functionally distinct subregions and cell types change during human aging and Alzheimer's Disease (AD) remains largely unexplored. Here, we present HypoAD, a comprehensive atlas of the human hypothalamus in aging and AD, integrating high-resolution MRI from 202 individuals with single-nucleus RNA-seq (snRNA-seq) of 614,403 nuclei from young, AD, and age-matched non-dementia controls. Our analysis reveals that hypothalamic subregions governing metabolism, stress, and circadian rhythms are particularly vulnerable, exhibiting significant changes in both volumes and gene expression during aging and AD. At the molecular level, machine learning models identified the inflammatory response and regulators of circadian rhythms as key cellular predictors of AD. These signatures were reflected in specific cell types: microglia transitioned to a pro-inflammatory state, while inhibitory neurons within sleep- and circadian-regulating hypothalamic subregions showed the most profound transcriptional alterations, including disruptions in ligand-receptor interactions and G-protein-coupled receptor signaling. Together, HypoAD provides a high-resolution volumetric map and a comprehensive transcriptomic atlas of the human hypothalamus in aging and AD, linking lifestyle and behavioral changes to their underlying volumetric and molecular pathways. Additionally, HypoAD provides a framework to investigate hypothalamic dysfunction and establishes a roadmap for targeted interventions aimed at mitigating physiological disruptions to potentially slow disease progression.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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In visually complex and dynamically changing environments, humans often face the challenge of filtering out salient stimuli that are presently irrelevant to their tasks. Recent evidence suggests that through repeated exposure to search arrays containing salient color singleton distractors, individuals can learn to divert their attention away from such salient but irrelevant stimuli, even before they capture attention. However, the mechanisms underlying such attentional suppression remain unclear. The current study examined trajectories of singleton distractor representations during visual searches to address this gap. Using multivariate pattern analyses on EEG data (N = 40), we found that singleton distractor representations underwent a rapid inversion approximately 200 ms into the search. These inverted representations were coded in a shared subspace with target representations, but in a reversed orientation, presumably to downweight their salience in the spatial priority map. Target locations were consistently enhanced compared to non-singleton distractors, while singleton distractors were suppressed. Our findings reveal a novel mechanism of rapid representational transformation underlying salient distractor suppression at the onset of visual search. The rapid inversion of pop-out singleton distractor signals resulted in an inverted arrangement of target and distractor representations in a shared neural subspace, which facilitates subsequent read-out of both target enhancement and distractor suppression signals in the computation of spatial priorities to successfully guide search.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Hippocampal communication with subcortical circuits reorganizes across sleep-wake states, yet the dynamics of such interactions remain incompletely defined. We simultaneously recorded neuronal spiking and local field potentials from the dorsal hippocampus (CA1 and dentate gyrus) and limbic subcortical nuclei (supramammillary nucleus (SuM) and lateral septum (LS)) in freely behaving rats across the sleep-wake cycle. During quiescent states (non-rapid eye movement (nREM) sleep and quiet wakefulness), sharp-wave ripples dominated hippocampal activity and produced fast, top-down excitation, with large population surges locally in CA1 and DG, and more modest but significant activation in LS and SuM, whereas dentate spikes elicited smaller state-invariant responses. Conversely, in the bottom-up direction, epochs of high-discharge SuM activity were associated with slower, state-dependent activation of hippocampal populations that was larger in wake than in sleep, revealing distinct temporal scales and state dependence for reciprocal pathways. During activated, theta-enriched states (rapid eye movement (REM) sleep and active wakefulness), spike-field coupling revealed circuit-wide theta coordination. In active wakefulness, SuM bursts produced brief inhibition of CA1 spiking while theta oscillations organized a multiregional firing sequence around the theta cycle trough; yet SuM neurons were not significantly phase-locked. In REM sleep, this pattern inverted, with only SuM neurons significantly phase-locking to theta waves, and preferred firing near the theta cycle peak. Together, these findings identify state-dependent, bidirectional coordination between hippocampus and subcortical nuclei, characterized by ripple-locked top-down hippocampal output during quiescent states and SuM-mediated bottom-up modulation that reconfigures under theta during activated states.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Prairie voles (Microtus ochrogaster) are a semi-fossorial rodent that are an emerging model in social neuroscience. Comparing laboratory-reared and wild-caught individuals is essential for understanding how environmental history shapes neural and sensory traits and for assessing the ecological validity of laboratory findings. Despite this, relatively few studies have taken this approach. We used auditory brainstem responses (ABRs) to compare ABR thresholds and ABR wave characteristics between laboratory-reared and wild-caught prairie voles. ABR recordings show that, similar to other semi-fossorial rodents, M. ochrogaster exhibit a hearing range of 1 - 46 kHz with peak sensitivity around 8 to 32 kHz. However, wild-caught prairie voles displayed significantly lower ABR thresholds at 1, 4, 8, 16, and 24 kHz compared to laboratory-reared prairie voles. There were significant differences in interpeak latency between both tested groups, with laboratory-reared prairie voles showing faster interpeak latency responses than wild-caught voles. However, there were no differences in amplitude ratios between groups. Laboratory-reared prairie voles showed faster normalized latencies and higher relative amplitude of the binaural interaction component (BIC) of the ABR than wild-caught voles. There were no significant differences in ABR thresholds, interpeak latency, amplitude ratio, normalized latency, and relative amplitude between the sexes. These differences in auditory processing support the importance of integrating both wild and captive populations to advance comparative auditory research.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Ex vivo acute brain slice is a popular technique in neuroscience research. Since its inception five decades ago, many variations of the brain slice preparation method have emerged. While all variations are currently used by many labs throughout the world, no study has comprehensively examined the impact of these variation on the quality of the acute brain slice preparation. In this study, we comprehensively examined the effect of animal sacrifice methods (decapitation or transcardial perfusion) and cutting solution (normal or sucrose artificial cerebrospinal fluid) on brain slice preparation. Neuronal population was quantified by immunohistochemistry against various neuronal markers. Neuronal dynamics was evaluated by in vitro electrophysiology using two acute epilepsy models-zero-magnesium and 4-aminopyridine. To modulate the stress incurred in acute brain slice preparation, we administered antioxidants or HIF-1 inhibitor, chrysin, to the cutting solution. The method of brain slice preparation significantly affected the quality of the brain slice preparation. In general, the combination of transcardial perfusion and sucrose artificial cerebrospinal fluid produces the optimal brain slice preparation. This is evidenced by a preservation of inhibitory GABAergic neurons in slices prepared with this combination. We subsequently found that this loss of inhibitory GABAergic neurons significantly influenced the genesis and dynamics of induced acute epileptiform activity. The slices with preserved inhibition had less successful induction of acute epileptiform activity and a seizure-like event that is typical of those induced in brain with preserved inhibition. Finally, we found that loss of inhibitory GABAergic neurons during brain slice preparation is primarily due to oxidative damage. Limiting oxidative stress is an effective neuroprotection strategy to prevent loss of inhibition in brain slice preparation. In conclusion, consideration of brain slice preparation method is crucial in preserving inhibitory GABAergic neurons and the degree of inhibition in ex vivo acute brain slice preparation.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Neurons that produce NPS send output to brain regions implicated in circadian function and threat responses, but less is known about the afferent control of NPS neurons. In this study, we used a conventional retrograde tracer, cholera toxin beta subunit (CTb), to identify afferents to the rostral-lateral parabrachial region that contains the main concentration of NPS neurons. We then used Cre-dependent rabies retrograde tracing in Nps-2A-Cre mice to identify inputs specifically to NPS neurons. Nps-expressing neurons receive heavy input from auditory brainstem structures, including the inferior colliculus, nucleus of the lateral lemniscus, superior olivary complex, and cochlear nucleus. These findings suggest an unexpected role for auditory information in controlling the activity of NPS neurons.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Recent work suggests that thousands of individuals are required in multivariate brain-behaviour analyses to obtain consistently replicable results. Some believe, however, that smaller sample sizes may be sufficient if specific subpopulations are targeted. We investigated how sample size and cohort composition influence the replicability of Canonical Correlation Analysis (CCA) results using the UK Biobank (N=40,514). We applied CCA to diffusion-weighted magnetic resonance imaging (dMRI) phenotypes and cognitive assessment test scores. We defined four participant cohorts based on clinical profile and found that, across all cohorts, sample sizes of around 500 were needed to obtain replicable canonical correlations and variable loadings. The most targeted cohort required much fewer samples to achieve similar or greater correlations than the other cohorts. Variable loadings were consistent between sample sizes of ~500 to thousands, suggesting that sample sizes in the order of hundreds may be sufficient for obtaining reliable CCA results.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Chronic pain results from maladaptive interaction between the immune and nervous systems. TRPM2 channels in immune cells (immune TRPM2) are believed to facilitate chronic pain by indirectly promoting immune-inflammatory responses. Whereas TRPM2 in sensory neurons (neuronal TRPM2) acts as a warmth sensor critical to sense innocuous warm temperatures. However, neuronal TRPM2 mediates the warmth sensitivity of less than 3.5% of sensory neurons. The functions of the vast majority (42%) of TRPM2+ neurons are unknown. Here we show that neuronal TRPM2 functions as a pain sensor responsible for directly transducing acute and chronic pain independently of immune TRPM2. Both chronic arthritis pain and neuropathic pain were markedly reduced in TRPM2-knockout mice, and the pain deficit was recapitulated by sole deletion of neuronal TRPM2. However, immune and inflammatory responses were largely similar between wild-type and neuronal TRPM2-deficient mice. Moreover, antagonizing joint TRPM2 rapidly reversed chronic arthritis pain without affecting joint inflammation. Mechanistically, TRPM2 is activated by PGE2 and IgG immune complex (IgG-IC) through GalphaoA and FcgRI coupling, respectively, independently of conventional signalling messengers. Consistently, acute pain induced by PGE2 and IgG-IC was abolished in TRPM2 mutant mice. We conclude that neuronal TRPM2 is a convergent direct pain transducer independently of inflammation, representing an appealing target for alleviating chronic pain.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Limb dominance is a human behavioral characteristic with many cultural, practical, scientific and clinical implications. Yet why the dominant limb performs better across a range of motor skill-requiring tasks remains unanswered. Is it because of an intrinsic hemispheric advantage or instead is it the result of life-long practice with the dominant side? We tested these alternatives using two tasks. The first was 3D reaching with either an inertial challenge or the need to use a stick-like tool. The second required participants to write with their dominant and non-dominant elbows. We applied a novel geometric analysis to quantify movement-trajectory shape. We show that (1) tool-use unmasks markedly inferior control in the non-dominant arm, and this is because it imposes the need to generate unfamiliarly shaped movement trajectories; and (2) there is no general dominant limb motor control advantage, only task-specific experience or practice. These results reframe dominance as predominantly about learned control of kinematics rather than baseline asymmetry in control of dynamics.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Glucagon-like peptide-1 receptor (GLP-1R) agonists have recently emerged as powerful tools for the treatment of obesity through their ability to suppress food intake. However, their effects on non-ingestive motivated behaviors remain incompletely understood. Here, we show that the long-acting GLP-1R agonist semaglutide (SG) suppresses voluntary wheel running in both lean and diet-induced obese mice. Importantly, this suppression of activity was not caused by hypophagia and was accompanied by decreased motivation, with SG-treated mice displaying reduced effort for wheel access in a progressive ratio task. Real-time measurements of dopamine via fiber photometry revealed specific dopamine changes in the nucleus accumbens at both the beginning and end of running bouts, with SG-treated animals showing amplified dopamine dynamics at these key behavioral timepoints. Collectively, these data reveal important non-ingestive behavioral effects of GLP-1R agonism and suggest a role for dopamine circuits in mediating reductions of volitional activity following SG treatment.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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An electroencephalogram (EEG) is an electrical measurement of brain activity using electrodes placed on the scalp surface. After EEG measurements are collected, numerical methods and algorithms can be employed to analyze these measurements and attempt to identify the source locations of brain activity. These traditional techniques often fail for measured data that are prone to noise. Recent techniques have employed neural network models to solve the localization problem for various use cases and data setups. These approaches, however, make underlying assumptions that make it difficult generalize the results past their original training setups. In this work, we present a transformer-based model for single- and multi-source localization that is specifically designed to deal with difficulties that arise in EEG data. Hundreds of thousands of simulated EEG measurement data are generated from known brain locations to train this machine learning model. We establish a training and evaluation framework for analyzing the effectiveness of the transformer model by explicitly considering the source region density, noise levels, drop out of electrodes, and other factors. Across these vast scenarios, the localization error of the transformer model is consistently lower than the other classical and machine learning approaches. Additionally, we perform a thorough ablation study on the network configuration and training pipeline. The code and data used in this work will be made publicly available upon publication.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Everyday decisions require not just earning rewards but also learning about the world. We asked how people gather information when sampling is reward free, and compared this with reward seeking under identical outcome distributions. In a large study (N = 420), people employed two dissociable information seeking strategies. They often began by testing one option several times before switching, building early certainty, a sampling rule we call streaking. They also showed a global tendency to sample where uncertainty is greatest. Computational modeling shows that both strategies independently improve decision accuracy under noisy belief updating. Artificial neural networks trained to optimize performance acquired uncertainty-directed sampling but not early streaking, highlighting a feature of human sampling not spontaneously acquired by networks trained on these objectives. These results reveal a dual architecture of information seeking that links traits, sampling policies, and performance.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Numbers of hippocampal neurons vary by over three orders of magnitude across mammalian species. What evolutionary pressures shape this diversity? Given the role of the hippocampus in spatial mapping, the greater spatial navigation demands of larger home ranges may drive selection for more hippocampal neuron. Using data from 342 species, we crossed home range and population density data with cortical and hippocampal neuron counts predicted from clade-specific brain scaling laws to examine whether home range scales universally with estimated hippocampal neuron numbers across mammals. We confirm that home range scales universally with the inverse of population density across species and increases with body mass and metabolic rate. However, home range does not scale universally with hippocampal or cortical neuron numbers. Rather, scaling relationships differ by clade, such that carnivorans and cetartiodactyls traverse home ranges over 1,000-fold larger than primates with equivalent hippocampal neuron numbers. These findings persist across data subsets controlling for study method, duration, and temporal scope. Numbers of hippocampal neurons are thus not limiting to spatial navigation in the wild, calling into question adaptationist explanations for the evolution of more hippocampal neurons based on a supposed need for increased spatial processing capacity. We propose that home range is determined primarily by population density, mediated by field metabolic rate and diet. The diversity in hippocampal neuron numbers across mammals, in turn, arises as a byproduct of clade-specific scaling of numbers of cortical neurons which we suggest is contingent on energetic opportunity, not on navigational or other cognitive demands.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Calcium (Ca2+) homeostasis is fundamental to neuronal physiology, including in the regulation of membrane excitability and synaptic transmission. Disruptions in the ion transporters regulating Ca2+ influx and efflux are clearly linked to seizure disorders and age-related neurodegenerative disease. Yet, the specific contributions of variants in genes encoding these transporters to neurological disease remain to be fully understood. Drosophila melanogaster has proven to be a powerful genetic model for uncovering such mechanisms, particularly through studies of mutants that display temperature-sensitive (TS) behavioral phenotypes. In a forward genetic screen, we identified a mutant line that exhibited TS convulsions along with progressive, age-dependent neurodegeneration. We mapped the mutation to Nckx30c, specifically within the transmembrane ion-binding region of this K+-dependent Na+/Ca2+ exchanger. Characterization of this mutant, together with a second Nckx30c allele, revealed TS convulsions, impaired locomotion, a markedly shortened lifespan, neurodegeneration with age, along with structural defects at larval and adult neuromuscular junctions (NMJs). Gene expression analysis confirmed that Nckx30c levels were reduced in heads of Nckx30c loss-of-function mutants. Tissue-specific manipulation revealed that knockdown of Nckx30c in neurons recapitulated the TS convulsions, locomotor defects, and shortened lifespan phenotypes. Drosophila Nckx30c is highly conserved and shares homology with mammalian SLC24A2, a solute carrier family 24 member whose neurological role is not yet fully elucidated. Our work establishes Nckx30c as an essential regulator of neuronal health and provides an in vivo framework for investigating the contribution of SLC24A2 to neuronal Ca2+ homeostasis, seizures and age-related neurodegeneration.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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The lateral orbitofrontal cortex (OFC) is critical for flexibly adjusting choices when outcome values change. Anterior and posterior parts of the human lateral OFC differ in cytoarchitecture and connectivity, but whether these subregions make differential contributions to outcome-guided (i.e., goal-directed) behavior remains unclear. Outcome-guided behavior requires (a) representations of stimulus-outcome associations and (b) inferring the current value of options when making decisions. Here, we test whether these two functions are differentially supported by the posterior (pOFC) and anterior (aOFC) parts of the lateral OFC, using transcranial magnetic stimulation (TMS) to selectively disrupt activity in functional networks centered on the pOFC and aOFC during a two-day outcome devaluation task. Participants (n = 48) received pOFC or aOFC network-targeted TMS either on day 1 before learning associations between visual stimuli and sweet or savory food odors, or on day 2 before a meal that selectively devalued one of these outcomes, followed by a choice test. TMS targeting pOFC, but not aOFC, before the meal on day 2 disrupted outcome-guided behavior, as measured by choices of stimuli predicting non-sated rewards in the post-meal choice test. In contrast, TMS targeting aOFC, but not pOFC, before learning on day 1 similarly impaired behavior in the post-meal choice test on day 2. These findings demonstrate that anterior and posterior parts of the lateral OFC make distinct contributions to outcome-guided behavior by supporting learning of stimulus-outcome associations and inferring the current value of options, respectively.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Normative upper limb movements are produced by multiple redundant joints. While the reaching task is specified at the endpoint, such task objectives become implicit at the level of joints. A fundamental question is whether planning and control of joints is solely in the service of the endpoint or whether they also include joint trajectories. Using Spearmans correlation and zero crossings, we found differential kinematic signatures of control between shoulder and elbow joints in contrast to the wrist joint. However, the extent of control among joints was substantially diminished compared to the endpoint. Further, when such control measures were compared to the subspaces of inter-trial joint exploration, we found that online control at proximal joints, such as the shoulder and elbow, were significantly associated in regulating the task space, while control at the wrist (distal) joint was associated in regulating joint redundancy in null space. These results suggest that null space is not entirely uncontrolled as per the uncontrolled manifold hypothesis but selectively controlled by some distal joints. Additionally, across different directions, either the shoulder or the elbow contributed dominantly towards the movement of the endpoint while the other joint was lagging and that this strategy reflected in our kinematic measures of online and trajectory control. Taken together, this study shows how the selective implementation of a leading joint in task space and a lagging joint in null space can enable the control of multi-jointed movements and attenuate the problem of joint redundancy.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Astrocytes regulate the activity of nearby neurons so disruption of astrocyte calcium dynamics by traumatic brain injury (TBI) could have profound consequences for neural network activity in the brain. In this study, human induced pluripotent stem cell (hiPSC)-derived astrocytes were used in a two-dimensional (2D) in vitro stretch injury model to evaluate the effect of trauma on calcium dynamics, mitochondrial function, and the mechanosensitive ion channel Piezo1. Outcomes were assessed using live imaging, immunostaining, and RNA sequencing. Cell viability, mitochondrial membrane potential, and spontaneous calcium transients declined as injury severity increased. At moderate injury severity, the decreases in mitochondrial membrane potential and calcium dynamics were temporary. The spatial distribution of Piezo1 also changed temporarily after injury. RNA sequencing identified 196 genes that changed expression after injury, including downregulation of mitochondrial and oxidative metabolic processes and upregulation of cortical thinning pathways. These findings establish this model as a platform for investigating the cellular mechanisms of TBI and its influence on neurodegeneration. Keywords: Traumatic brain injury, hiPSC-derived astrocytes, calcium dynamics, mitochondrial dysfunction, Piezo1, RNA sequencing
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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The exploration-exploitation trade-off is ubiquitous in our everyday lives, and individuals display considerable variability in their preferred decision-making strategies. Most previous work pertaining to neural signatures of exploration is restricted to functional pathways. However, the specific contributions of cortical microarchitectures to high-level cognitive processes such as decision-making are as yet unknown. Here, we investigated the neuroanatomical foundations of inter-individual variability in decision-making strategies. To this end, 122 healthy participants completed a gamified multi-armed bandit paradigm aimed at teasing apart distinct exploration-exploitation decision strategies. We also collected whole-brain quantitative MRI maps indexing microstructural features of cortical myelination and iron content. Through computational modelling, we disentangled individual-specific exploration strategies, including value-free random exploration. Whole-brain regression analyses identified significant associations between value-free exploration and increased cortical myelination in right frontal brain areas with reported links to impulsivity. By elucidating the brain microstructural correlates of distinct exploration-exploitation strategies, we aimed to further our understanding of why individuals differ in their decision-making capabilities, and how decision-making may become aberrant in mental health conditions.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Sex differences in behaviour exist across the animal kingdom, typically under strong genetic regulation. In Drosophila, previous work has shown that fruitless and doublesex transcription factors identify neurons driving sexually dimorphic behaviour. However, the organisation of dimorphic neurons into functional circuits remains unclear. We now present the connectome of the entire Drosophila male central nervous system. This contains 166,696 neurons spanning the brain and ventral nerve cord, fully proofread and comprehensively annotated including fruitless and doublesex expression and 11,691 cell types. By comparison with a previous female brain connectome, we provide the first comprehensive description of the differences between male and female brains to synaptic resolution. Of 7,319 cross-matched cell types in the central brain, 114 are dimorphic with an additional 262 male- and 69 female-specific (totalling 3.8% of neurons in males and 1.9% in females). This resource enables analysis of full sensory-to-motor circuits underlying complex behaviours as well as the impact of dimorphic elements. Sex-specific and dimorphic neurons are concentrated in higher brain centres while the sensory and motor periphery are largely isomorphic. Within higher centres, male-specific connections are organised into hotspots defined by male-specific neurons or the presence of male-specific arbours on neurons that are otherwise similar between sexes. Numerous circuit switches reroute sensory information to form conserved, antagonistic circuits controlling opposing behaviours.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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The p75 neurotrophin receptor (p75NTR) contributes to the development of Alzheimer's Disease (AD) pathology by enhancing amyloid precursor protein (APP) cleavage and amyloid plaque formation. However, the cell type-specific and temporal roles of p75NTR in AD progression remain unclear. Here, we report that conditional knock-in of functionally impaired p75NTR variants lacking the death domain ({Delta}DD) or transmembrane Cys259 (C259A) specifically in forebrain excitatory neurons of 5xFAD mice significantly attenuated multiple AD-associated pathologies, including amyloid plaque accumulation, gliosis, neurite dystrophy, as well as learning and memory deficits. Hippocampal amyloid plaque burden was reduced to levels comparable to those in whole-body knock-in mice. Strikingly, delaying introduction of p75NTR variants until advanced disease stages produced comparable beneficial effects, and rescued behavior performance in cognitively impaired animals. These findings suggest that blunting p75NTR function can have beneficial effects even during symptomatic stages of AD, offering a potential therapeutic approach complementary to passive vaccination.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Background: This study investigated the role of the Hypocretin/Orexin (Ox) and Histamine (HA) systems-two key regulators of wakefulness-in sudden infant death syndrome (SIDS), a condition characterized by impaired arousal responses during sleep. Methods: Cerebrospinal fluid (CSF) Ox levels were measured in 61 healthy controls, 70 Sudden Unexpected Death Infants (38 SIDS, 32 explained deaths). HA and its metabolite tele-methylhistamine (t-MeHA) were analysed in an additional 46 SUDI (34 SIDS, 12 ED) and 42 controls. Immunocytochemistry was performed on hypothalamic tissue from 11 SIDS and 8 ED cases to assess the number of Ox and HA neurons. Results: CSF Ox levels did not differ globally but were relatively higher in deceased infants aged 2-6 months. HA and t-MeHA levels were significantly elevated in both SIDS and ED cases, likely due to postmortem release. Immunohistochemistry showed increased Ox neurons in SIDS compared to EDs, while HA neurons did not differ. Conclusions: Findings suggest increased Ox activity in SIDS especially within the 2-6 month risk window, potentially reflecting repeated stress or hypoxia prior to death, while HA neurons do not appear involved.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Cannabis use is linked to elevated psychosis risk, yet the neurobiological mechanisms that couple use to symptom expression remain unclear. Because glutamatergic dysregulation has been implicated in both cannabis effects and psychosis vulnerability, we examined whether brain glutamate relates to dimensional symptoms as a function of cannabis use across the psychosis spectrum. Seventy-nine participants--typically developing controls, clinical high-risk individuals, and patients with psychosis--completed dimensional clinical assessments, detailed cannabis surveys, urine toxicology, and ultra-high-field 7T 1HMRS quantification of anterior cingulate cortex (ACC) glutamate levels. Linear models assessed the main and interactive effects of ACC glutamate and cannabis use on positive and negative symptoms. Self-reported cannabis use showed strong concordance with urine toxicology. Cannabis use was associated with higher positive and negative symptoms. Independently, higher ACC glutamate predicted greater positive and negative symptoms. Notably, lower glutamate levels were associated with higher positive symptoms in cannabis users. Exploratory analyses suggested interactions for depressive and manic symptoms, indicating that glutamatergic abnormalities may amplify the overall severity of cannabis-related symptoms. Sensitivity analyses revealed lower ACC glutamate in psychosis patients--especially cannabis users--highlighting diagnostic group differences and reinforcing the link between cannabis exposure and glutamatergic dysfunction. These findings implicate ACC glutamatergic dysfunction as a transdiagnostic correlate of symptom burden, particularly in those with psychosis who are cannabis users. Glutamate-targeted interventions and longitudinal designs will be needed to examine causal pathways linking cannabis exposure to psychosis-relevant outcomes.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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The primate inferior temporal (IT) cortex, at the apex of the ventral visual stream, encodes information that supports diverse representational goals, from recognizing objects to determining which images are likely to be remembered. Specific artificial neural networks (ANNs), that currently serve as the leading computational hypotheses of ventral stream processing, are typically trained exclusively for object recognition. We asked whether incorporating image memorability as an additional optimization objective could improve ANN-brain alignment. Models optimized for memorability explained additional, non-overlapping variance in IT responses beyond that captured by recognition-optimized networks, indicating that memorability and recognition rely on partly independent dimensions of IT representation. Notably, these models also exhibited fewer non-brain-like units, bringing their representational geometry closer to that of IT. Furthermore, networks jointly optimized for both objectives were more predictive of human memorability than memorability-only models, while maintaining their alignment with human object recognition performance patterns. Together, these findings suggest that IT encodes multiple representational goals and that models trained solely for recognition provide an incomplete account of ventral stream computation.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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The biological basis of neuronal excitatory/inhibitory (E/I) imbalance in Alzheimer's disease (AD) remains unclear. Using a comprehensive cohort with ante-mortem functional neuroimaging and post-mortem molecular data from the same participants, we mapped individual, whole-brain E/I imbalances through biophysical modeling. E/I ratios in regions supporting higher-order cognitive functions were significantly associated with cognitive performance and decline, with mediation by global neuropathological burden. We also observed a significant inverted U-shaped relationship between E/I ratios and neurofibrillary tangle severity, peaking at the limbic stage (Braak III-IV) in 14 brain areas, including the bilateral hippocampus and superior frontal gyrus. In addition, we identified 89 genes and 101 proteins that predict regional E/I ratios, with pathways related to synaptic signaling and immune response overrepresented. The generalizability of these molecular predictors was confirmed in two independent cohorts, achieving good classification performance for neuropathology severity and AD dementia. Lastly, the estimated E/I imbalances in AD aligned with whole-brain distributions of microglia and oligodendrocyte precursor cells, suggesting that spatial cellular organization contributes to vulnerability to neuronal dysfunction. Overall, this study provides critical insights into the cellular, molecular, and neuropathological signatures of circuit-level dysfunction in AD.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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The NLRP3 inflammasome is a key mediator of innate immunity that integrates inflammatory and metabolic stress signals. Increased and/or chronic activation of this critical pathway has been implicated in obesity, with hypothalamic neuroinflammation linked to dysregulation of energy balance. TN-783 is an investigational, CNS-penetrant, small-molecule NLRP3 inhibitor that potently suppressed inflammasome activation across multiple in vitro assays. In diet-induced obese (DIO) mice, only TN-783, and not the peripherally restricted NLRP3 inhibitor TN-101, produced progressive and sustained weight loss, underscoring the requirement for central target engagement. Weight loss was driven by a persistent reduction in food intake across both acute and chronic phases, without altering energy expenditure. This effect was further characterized by selective reduction of fat mass, with minimal impact on lean tissues. Mechanistically, NLRP3 inhibition attenuated DIO-induced hypothalamic neuroinflammation and partially reversed obesity-associated molecular changes based on transcriptomic and proteomic profiling of the hypothalamus. Beyond monotherapy, TN-783 enhanced the effects of the GLP-1 receptor agonist semaglutide by amplifying weight loss, reinitiating weight loss after semaglutide effect had plateaued, and maintaining the weight loss benefit after semaglutide withdrawal. Discontinuation of TN-783 resulted in reversal of both weight and feeding effects, indicating that its therapeutic activity requires ongoing target engagement rather than permanent remodeling of metabolic pathways. Collectively, these observations support central NLRP3 inhibition as a distinct and promising approach for obesity treatment, offering robust induction and sustained maintenance of weight loss while preserving reversibility.
in bioRxiv: Neuroscience on 2025-10-09 00:00:00 UTC.
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Abstract
Hypnosis is a state of consciousness spontaneously occurring or induced through various techniques. Its occurrence is more likely in individuals with high scores of hypnotizability (highs) than in low hypnotizables (lows). The study aimed to assess the topological homogeneity within highs and lows during neutral hypnosis, and the EEG topological characteristics of highs and lows before and after hypnotic induction experienced as an altered state of consciousness only by highs. Sixteen highs and 16 lows were enrolled, informed that they would be submitted to hypnotic induction and studied across a session including open and closed eyes waking rest, hypnotic induction, neutral hypnosis, and post hypnosis open eyes rest. EEG was monitored throughout the session. Network analysis showed greater identifiability (less homogeneity) among lows than among highs. It revealed a similar pattern of changes in functional connectivity and topological properties—homological persistence and persistent entropy, which describe multiscale integration patterns—in the two groups across the session. Findings suggest that neutral hypnosis represents a modulation of the ordinary consciousness within its physiological variability rather than a distinct physiological state. Neither network nor topological differences account for the different subjective experiences of highs and lows.
in Cerebral Cortex on 2025-10-09 00:00:00 UTC.
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The maturation of inhibitory neurons is crucial for regulating plasticity in developing brains. Previous work has suggested that the Hurst exponent, the measure of autocorrelation in time series, reflects inhibition, but empirical data supporting this link are sparse. Here, we demonstrate significant spatial correlations between the Hurst exponent and ex vivo parvalbumin (PV) inhibitory mRNA expression in human children and adults, as well as between the Hurst exponent and PV-positive cell counts in mice, across both sexes. We further identified developmental plateaus in inhibition, as indicated by both PV inhibitory mRNA expression and the Hurst exponent, occurring prior to adolescence in humans and rats. In sum, this work suggests that the Hurst exponent can be used to study the development of inhibition in vivo and to understand inhibitory development across species.
in Journal of Neuroscience on 2025-10-08 16:30:38 UTC.
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in Journal of Neuroscience on 2025-10-08 16:30:38 UTC.
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The integration of multiple sensory inputs is essential for human perception and action in uncertain environments. This process includes reference frame transformations as different sensory signals are encoded in different coordinate systems. Studies have shown multisensory integration (MSI) in humans is consistent with Bayesian optimal inference. However, neural mechanisms underlying this process are still debated. Different population coding models have been proposed to implement probabilistic inference. This includes a recent suggestion that explicit divisive normalization accounts for empirical principles of MSI. However, whether and how divisive operations are implemented in the brain is not well understood. Indeed, all existing models suffer from the curse of dimensionality and thus fail to scale to real-world problems. Here, we propose an alternative model for MSI that approximates Bayesian inference: a multilayer-feedforward neural network of MSI across different reference frames trained on the analytical Bayesian solution. This model displays all empirical principles of MSI and produces similar behavior to that reported in ventral intraparietal neurons in the brain. The model achieved this without a neatly organized and regular connectivity structure between contributing neurons, such as required by explicit divisive normalization. Overall, we show that simple feedforward networks of purely additive units can approximate optimal inference across different reference frames through parallel computing principles. This suggests that it is not necessary for the brain to use explicit divisive normalization to achieve multisensory integration.
in Journal of Neuroscience on 2025-10-08 16:30:38 UTC.
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In the hippocampal formation, cholinergic modulation from the medial septum/diagonal band of Broca is known to correlate with the speed of an animal's movements at subsecond timescales and also supports spatial memory formation. Yet, the extent to which subsecond cholinergic dynamics, if at all, align with transient behavioral and cognitive states supporting the encoding of novel spatial information remains unknown. In this study, we used fiber photometry to record the temporal dynamics in the population activity of septo-hippocampal cholinergic neurons at subsecond resolution during a hippocampus-dependent object location memory task using ChAT-Cre mice of both sexes. Using a linear mixed-effects model, we quantified the extent to which cholinergic dynamics were explained by changes in movement speed; behavioral states such as locomotion, grooming, and rearing; and hippocampus-dependent cognitive states such as recognizing a novel location of a familiar object. The data show that cholinergic dynamics contain a multiplexed code of fast and slow signals (1) coding for the logarithm of movement speed at subsecond timescales, (2) providing a phasic spatial novelty signal during the brief periods of exploring a novel object location, and (3) coding for recency of environmental change at a seconds-long timescale. Furthermore, behavioral event-related phasic cholinergic activity demonstrates that fast cholinergic transients correlate with a switch in cognitive and behavioral states. These findings enhance understanding of the mechanisms by which cholinergic modulation contributes to the coding of movement speed and encoding of novel spatial information.
in Journal of Neuroscience on 2025-10-08 16:30:38 UTC.
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In temporal lobe epilepsy, interictal spikes (IS)—hyper-synchronous bursts of network activity—occur at high rates in between seizures. We sought to understand the influence of IS on working memory by recording hippocampal local field potentials from male epileptic mice while they performed a delayed alternation task. Interestingly, the rate of IS during behavior did not correlate with performance. Instead, we found that IS were correlated with worse performance when they were spatially non-restricted and occurred during running. In contrast, when IS were clustered at reward locations, animals tended to perform well. A machine learning decoding approach revealed that IS at reward sites were larger than IS elsewhere on the maze, and could be classified as occurring at specific reward locations. Finally, a spiking neural network model revealed that spatially clustered IS preserved hippocampal replay, while spatially dispersed IS disrupted replay by causing over-generalization. Together, these results show that the spatial specificity of IS on the maze, but not rate, correlates with working memory deficits.
in Journal of Neuroscience on 2025-10-08 16:30:38 UTC.
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While the hyper- and hypo-reward or punishment sensitivities (RS, PS) have received considerable attention as prominent transdiagnostic features of psychopathology, the lack of an overarching neurobiological characterization currently limits their early identification and neuromodulation. Here we combined microarray data from the Allen Human Brain Atlas with a multimodal fMRI approach to uncover the neurobiological signatures of RS and PS in a discovery-replication design (N = 655 healthy participants, 442 females). Both RS and PS were mapped separately in the brain, with the functional connectome in the fronto-striatal network encoding reward responsiveness, while the fronto-insular system was particularly engaged in punishment sensitivity. These dissociable functional connectome patterns also exhibited high specificity in differentiating decisions driven by social or monetary reward and punishment motivations. Further imaging transcriptomic analyses revealed that functional connectome variations for RS and PS were associated with topography of specific gene sets enriched in ontological pathways, including synaptic transmission, dopaminergic metabolism, immune response, and stress adaptation. On the neurotransmitter level, the serotonin neuromodulator emerged as a pivotal hub regulating the intrinsic functional connectome patterns of RS and PS, with its modulatory effects dependent on interactions with the dopaminergic, opioid, and GABAergic systems. Overall, these findings indicate dissociable neural connectome mapping of RS and PS and highlight their linkage with transcriptomic profiles, which may inform future investigations on early identification of vulnerability and risk factors for psychopathological conditions.
in Journal of Neuroscience on 2025-10-08 16:30:38 UTC.
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in Journal of Neuroscience on 2025-10-08 16:30:38 UTC.
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To produce a word, speakers need to decide which concept to express, select an appropriate item from the mental lexicon, and spell out its phonological form. The temporal dynamics of these processes remain a subject of debate. We investigated the time course of lexical access in picture naming with electroencephalography (EEG). Thirty participants (23 female) named pictures using simple nouns. The pictures varied in conceptual category (animate or inanimate), stress pattern (first or second syllable), and the structure of the first syllable (open or closed). Using time-resolved multivariate pattern analysis (MVPA), we decoded the time course in which each dimension was available during speech preparation. The results demonstrated above-chance decoding of animacy within 100 ms after picture onset, confirming early access to conceptual information. This was followed by stress pattern and syllable structure, at approximately 150 and 250 ms after picture onset, respectively. These results suggest that a word's stress pattern can be retrieved before syllable structure information becomes available. An exploratory analysis demonstrated the availability of the word-initial phoneme within 100 ms after picture onset. This result hints at the possibility that during picture naming, conceptual, phonological, and phonetic information may be accessed rapidly and in parallel.
in Journal of Neuroscience on 2025-10-08 16:30:38 UTC.
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Early structural and molecular development of the human cortex is extensively studied, but little is known about the development of neuronal activity across cortical regions. We used dense array electroencephalography recordings and a machine learning-based measure, functional brain age (FBA), to study spatiotemporally resolved maturation of cortical activity across the birth transition in human infants (male and female). We found clear spatial FBA gradients indicating more mature frontal cortical activity relative to other brain regions (geometric axis), as well as more mature activity in association cortices relative to sensory cortices (hierarchical axis). The frontal advance was explained by more mature bursting characteristics, a hallmark of early endogenous neuronal activity. The findings jointly support an advanced maturation of neuronal ensemble activity in cortical regions that are preparing to host synergistic, large-scale network interactions, a key global characteristic of mature brain function.
in Journal of Neuroscience on 2025-10-08 16:30:38 UTC.
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Temporomandibular disorder (TMD) significantly impairs the quality of life of patients due to chronic pain and limited jaw function. Many treatment options have been used such as pharmacologic management, physical therapy, oral appliance therapy, and surgery. However, effective treatment options remain limited. In this study, we investigated the potential of botulinum toxin (BoNT) as a therapeutic approach for TMD using a forced mouth opening-induced TMD male mouse model. BoNT injection significantly alleviated mechanical hypersensitivity in the temporomandibular region over a 2 week period as demonstrated by von Frey behavioral tests. Additionally, the mouse grimace test confirmed that BoNT alleviated pain in mice. The open field test and pasta gnawing test showed that BoNT injection effectively alleviated mouth motor and food intake problems and did not cause impairments in general behavior. Moreover, direct observation of neural activity via in vivo Pirt-GCaMP3 calcium imaging of intact trigeminal ganglia (TG) revealed that BoNT suppressed both stimulus-evoked and spontaneous activity in TG neurons. Mechanistically, BoNT downregulated the expression of pain-promoting proteins (TRPV1, TRPA1, and TRPC1) and glutamate transporting protein (VGLUT2), thereby suppressing peripheral neural activity in the TG. In summary, our study identified a novel mechanism by which BoNT alleviates TMD pain. These new findings not only expand our understanding of the effects of BoNT on pain but also provide a new therapeutic approach to TMJ pain management.
in Journal of Neuroscience on 2025-10-08 16:30:38 UTC.
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Events and objects are two fundamental ways in which humans conceptualize their experience of the world. Despite the significance of this distinction for human cognition, it remains unclear whether the neural representations of object and event concepts are categorically distinct or, instead, can be explained in terms of a shared representational code. We investigated this question by analyzing fMRI data acquired from human participants (males and females) while they rated their familiarity with the meanings of individual words (all nouns) denoting object and event concepts. Multivoxel pattern analyses indicated that both categories of lexical concepts are represented in overlapping fashion throughout the association cortex, even in the areas that showed the strongest selectivity for one or the other type in univariate contrasts. Crucially, in these areas, a feature-based model trained on neural responses to individual event concepts successfully decoded object concepts from their corresponding activation patterns (and vice versa), showing that these two categories share a common representational code. This code was effectively modeled by a set of experiential feature ratings, which also accounted for the mean activation differences between these two categories. These results indicate that neuroanatomical dissociations between events and objects emerge from quantitative differences in the cortical distribution of more fundamental features of experience. Characterizing this representational code is an important step in the development of theory-driven brain–computer interface technologies capable of decoding conceptual content directly from brain activity.
in Journal of Neuroscience on 2025-10-08 16:30:38 UTC.
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Neuronal hyperexcitability is a hallmark of amyotrophic lateral sclerosis (ALS), but its relationship with the TDP-43 aggregates that comprise the predominant pathology in over 90% of ALS cases remains unclear. Emerging evidence indicates that TDP-43 pathology induces neuronal hyperexcitability, which may contribute to excitotoxic neuronal death. To characterize TDP-43 mediated network excitability changes in a disease-relevant model, we performed in vivo continuous electroencephalography monitoring and ex vivo acute hippocampal slice electrophysiology in rNLS8 mice (males and females), which express human TDP-43 with a defective nuclear localization signal (hTDP-43NLS). Surprisingly, we identified the presence of seizures in ~64% of rNLS8 mice beginning ~2.5 weeks after transgene induction (off-DOX). More broadly, we observed longitudinal changes in cortical EEG patterns and circuit hyperexcitability preceding neurodegeneration of vulnerable hippocampal subfields. Consistent with previous reports, we have observed broad dysregulation of AMPA subunit expression in mice expressing hTDP-43NLS. These changes were most pronounced in the hippocampus, where we hypothesized they promote hyperexcitability and ultimately, excitotoxic cell death. Interestingly, hippocampal injection of AAV encoding inhibitory DREADDs (hM4Di) and daily activation with CNO ligand rescued anxiety deficits on the elevated zero maze but did not reduce neurodegeneration. Moreover, therapeutic doses of the antiseizure medications, valproic acid and levetiracetam, did not improve behavior or prevent neurodegeneration. These results highlight the complex relationship between TDP-43-mediated neuronal hyperexcitability and neurodegeneration. Although targeting hyperexcitability may ameliorate some behavioral deficits, our study suggests it may not be sufficient to halt or slow neurodegeneration in TDP-43-related proteinopathies.
in Journal of Neuroscience on 2025-10-08 16:30:38 UTC.
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The integration of olfactory and spatial information is critical for guiding animal behavior. The lateral entorhinal cortex (LEC) is reciprocally interconnected with cortical areas for olfaction and the hippocampus and thus ideally positioned to encode odor–place associations. Here, we used miniendoscopes to record neural activity in the mouse piriform cortex (PCx) and LEC. We show that in head-fixed mice, odor identity could be decoded from LEC ensembles but less accurately than from PCx. In male mice freely navigating a linear track, LEC ensemble activity at the odor ports was dominated by spatial information. Spatial position along the linear track could be decoded from LEC and PCx activity; however, PCx but not LEC exhibited strong behavior-driven modulation of positional information. Together, our data reveal that information about odor cues and spatial context is differentially encoded along the PCx–LEC axis.
in eNeuro on 2025-10-08 16:30:18 UTC.
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Social interactions are fundamental to human cognition, with the right temporoparietal junction (rTPJ) playing a key role in integrating motor coordination and social inference. While transcranial random noise stimulation (tRNS) is a promising technique for modulating cortical excitability in real time, its effect on dynamic social processes remains largely unexplored. This study applied high-definition tRNS (HD-tRNS) over the rTPJ during an interactive task to modulate motor coordination and social inference. Eighty neurotypical adults (49 female) were equally distributed across two experiments: Experiment 1, a block design with randomized active and sham stimulation blocks; or Experiment 2, a trial-by-trial design with intermixed stimulation protocols. Participants performed a coordination task with a covert virtual partner programmed to behave cooperatively or competitively. Kinematic data and self-reported attributions of humanness and cooperativeness were analyzed. The results showed that HD-tRNS over the rTPJ did not affect motor coordination or overall task performance in either experiment. However, in Experiment 1, active stimulation progressively reduced attributed humanness and cooperativeness toward the competitive virtual partner, suggesting enhanced detection of antagonistic intent. This gradual modulation of social inference was absent in Experiment 2, where frequent protocol switching likely disrupted the buildup of stimulation effects. Together, these findings highlight the rTPJ's causal role in self–other distinction, underscore the importance of stimulation protocol design in shaping social cognition, and support the exploration of targeted neuromodulation in clinical and developmental populations with atypical social cognition.
in eNeuro on 2025-10-08 16:30:18 UTC.
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South Sudan is one of the least electrified countries in the world, despite having abundant renewable energy resources that could be exploited to generate electricity. The country relies on imported diesel for electricity generation, besides having limited focus on renewable energy development. This policy brief sheds light on the potential of renewable energy as a solution to South Sudan’s ongoing electricity crisis. It examines the key factors hindering the development of renewable energy resources for electricity generation in the country. The brief also provides recommendations to the Government of South Sudan, policymakers, experts, and funding institutions on how to improve electricity access in the country. It is stressing on the importance of prioritising the development of diverse renewable energy resources, such as solar, wind, and small hydropower, as an immediate solution to the electricity access challenges in the country.
in F1000Research on 2025-10-08 15:53:30 UTC.
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Fresh-cut pitahaya (Selenicereus spp.) faces significant postharvest challenges due to rapid quality deterioration. This study evaluated the effects of combined preservation treatments (ascorbic acid, calcium chloride, and UV-C light) on the physicochemical, textural, and sensory properties of pitahaya during refrigerated storage. The experimental design evaluated eight treatments analysing quality parameters (physicochemical, bioactive, color, texture, and sensory). Results demonstrated that UV-C light treatment (T4) preserved color stability most effectively, showing the lowest ΔE values (<4.6) until day 12 of storage, while maintaining soluble solids content between 14.4–17.9°Brix. The combined treatment of ascorbic acid + calcium chloride + UV-C light (T8) showed greater stability over storage time, bioactive compounds, maintaining the pH below 4.0 until day 12 and reaching intermediate scores of purchase intention, but overall liking values similar to the application of UV-C light (T4) and the control treatment (T1) (>6.3/9). However, the use of ascorbic acid (T2) or its combination with UV-C light (T6) reduced its consumer overall liking (5.8/9). Texture analysis revealed that while all treatments experienced progressive loss of firmness (>90% by day 15), T4 and T8 maintained their cohesiveness with better structural integrity compared to the control sample. The study concludes that the use of UV-C light offers optimal quality preservation, while the combined use of ascorbic acid (1%) with calcium chloride (1%) and UV-C light allows for an increase in bioactive compounds. UV-C light represents the most viable option for industrial application, given its balance between effectiveness and cost. Furthermore, it is suggested that UV-C light exposure times be optimized by combining it with edible coatings.
in F1000Research on 2025-10-08 15:46:09 UTC.
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in Annals of Neurology on 2025-10-08 13:00:04 UTC.
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in Annals of Neurology on 2025-10-08 13:00:04 UTC.
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in Annals of Neurology on 2025-10-08 13:00:04 UTC.
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in Annals of Neurology on 2025-10-08 11:29:04 UTC.
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in Annals of Neurology on 2025-10-08 11:28:32 UTC.
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in Annals of Neurology on 2025-10-08 11:20:33 UTC.
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Proceedings of the National Academy of Sciences, Volume 122, Issue 41, October 2025.
SignificanceNumerous studies have developed imaging techniques for visualizing diverse cell types in the retina. However, these techniques often face challenges such as low resolution and the need for technically demanding setups. To overcome these ...
in PNAS on 2025-10-08 07:00:00 UTC.
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Proceedings of the National Academy of Sciences, Volume 122, Issue 41, October 2025.
SignificanceExperimental advances provide recordings of neural activity at unprecedented scales. But to understand how this activity emerges from the correlations between neurons, we need models that can simultaneously handle i) the exponential explosion ...
in PNAS on 2025-10-08 07:00:00 UTC.
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Proceedings of the National Academy of Sciences, Volume 122, Issue 41, October 2025.
SignificanceEarly social experiences strongly influence emotions and behaviors, but the underlying neural mechanisms are unclear. This study shows that early experience of crowding exerts lasting effects on foraging behavior and promotes dispersal. This ...
in PNAS on 2025-10-08 07:00:00 UTC.
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Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-08 07:00:00 UTC.
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Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-08 07:00:00 UTC.
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Science Advances, Volume 11, Issue 41, October 2025.
in Science Advances on 2025-10-08 07:00:00 UTC.