Planet Neuroscience

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BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identification

arXiv:2504.16096v1 Announce Type: new Abstract: Neurological conditions, such as Alzheimer's Disease, are challenging to diagnose, particularly in the early stages where symptoms closely resemble healthy controls. Existing brain network analysis methods primarily focus on graph-based models that rely solely on imaging data, which may overlook important non-imaging factors and limit the model's predictive power and interpretability. In this paper, we present BrainPrompt, an innovative framework that enhances Graph Neural Networks (GNNs) by integrating Large Language Models (LLMs) with knowledge-driven prompts, enabling more effective capture of complex, non-imaging information and external knowledge for neurological disease identification. BrainPrompt integrates three types of knowledge-driven prompts: (1) ROI-level prompts to encode the identity and function of each brain region, (2) subject-level prompts that incorporate demographic information, and (3) disease-level prompts to capture the temporal progression of disease. By leveraging these multi-level prompts, BrainPrompt effectively harnesses knowledge-enhanced multi-modal information from LLMs, enhancing the model's capability to predict neurological disease stages and meanwhile offers more interpretable results. We evaluate BrainPrompt on two resting-state functional Magnetic Resonance Imaging (fMRI) datasets from neurological disorders, showing its superiority over state-of-the-art methods. Additionally, a biomarker study demonstrates the framework's ability to extract valuable and interpretable information aligned with domain knowledge in neuroscience.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-04-24 04:00:00 UTC.

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Intelligent Depression Prevention via LLM-Based Dialogue Analysis: Overcoming the Limitations of Scale-Dependent Diagnosis through Precise Emotional Pattern Recognition

arXiv:2504.16504v1 Announce Type: new Abstract: Existing depression screening predominantly relies on standardized questionnaires (e.g., PHQ-9, BDI), which suffer from high misdiagnosis rates (18-34% in clinical studies) due to their static, symptom-counting nature and susceptibility to patient recall bias. This paper presents an AI-powered depression prevention system that leverages large language models (LLMs) to analyze real-time conversational cues--including subtle emotional expressions (e.g., micro-sentiment shifts, self-referential language patterns)--for more accurate and dynamic mental state assessment. Our system achieves three key innovations: (1) Continuous monitoring through natural dialogue, detecting depression-indicative linguistic features (anhedonia markers, hopelessness semantics) with 89% precision (vs. 72% for PHQ-9); (2) Adaptive risk stratification that updates severity levels based on conversational context, reducing false positives by 41% compared to scale-based thresholds; and (3) Personalized intervention strategies tailored to users' emotional granularity, demonstrating 2.3x higher adherence rates than generic advice. Clinical validation with 450 participants shows the system identifies 92% of at-risk cases missed by traditional scales, while its explainable AI interface bridges the gap between automated analysis and clinician judgment. This work establishes conversational AI as a paradigm shift from episodic scale-dependent diagnosis to continuous, emotionally intelligent mental health monitoring.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-04-24 04:00:00 UTC.

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Geometry of Cells Sensible to Curvature and Their Receptive Profiles

arXiv:2504.16869v1 Announce Type: new Abstract: We propose a model of the functional architecture of curvature sensible cells in the visual cortex that associates curvature with scale. The feature space of orientation and position is naturally enhanced via its oriented prolongation, yielding a 4-dimensional manifold endowed with a canonical Engel structure. This structure encodes position, orientation, signed curvature, and scale. We associate an open submanifold of the prolongation with the quasi-regular representation of the similitude group SIM (2), and find left-invariant generators for the Engel structure. Finally, we use the generators of the Engel structure to characterize curvature-sensitive receptive profiles .

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-04-24 04:00:00 UTC.

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Application of an attention-based CNN-BiLSTM framework for in vivo two-photon calcium imaging of neuronal ensembles: decoding complex bilateral forelimb movements from unilateral M1

arXiv:2504.16917v1 Announce Type: new Abstract: Decoding behavior, such as movement, from multiscale brain networks remains a central objective in neuroscience. Over the past decades, artificial intelligence and machine learning have played an increasingly significant role in elucidating the neural mechanisms underlying motor function. The advancement of brain-monitoring technologies, capable of capturing complex neuronal signals with high spatial and temporal resolution, necessitates the development and application of more sophisticated machine learning models for behavioral decoding. In this study, we employ a hybrid deep learning framework, an attention-based CNN-BiLSTM model, to decode skilled and complex forelimb movements using signals obtained from in vivo two-photon calcium imaging. Our findings demonstrate that the intricate movements of both ipsilateral and contralateral forelimbs can be accurately decoded from unilateral M1 neuronal ensembles. These results highlight the efficacy of advanced hybrid deep learning models in capturing the spatiotemporal dependencies of neuronal networks activity linked to complex movement execution.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-04-24 04:00:00 UTC.

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Summary statistics of learning link changing neural representations to behavior

arXiv:2504.16920v1 Announce Type: new Abstract: How can we make sense of large-scale recordings of neural activity across learning? Theories of neural network learning with their origins in statistical physics offer a potential answer: for a given task, there are often a small set of summary statistics that are sufficient to predict performance as the network learns. Here, we review recent advances in how summary statistics can be used to build theoretical understanding of neural network learning. We then argue for how this perspective can inform the analysis of neural data, enabling better understanding of learning in biological and artificial neural networks.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-04-24 04:00:00 UTC.

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Spot solutions to a neural field equation on oblate spheroids

arXiv:2504.16342v1 Announce Type: cross Abstract: Understanding the dynamics of excitation patterns in neural fields is an important topic in neuroscience. Neural field equations are mathematical models that describe the excitation dynamics of interacting neurons to perform the theoretical analysis. Although many analyses of neural field equations focus on the effect of neuronal interactions on the flat surface, the geometric constraint of the dynamics is also an attractive topic when modeling organs such as the brain. This paper reports pattern dynamics in a neural field equation defined on spheroids as model curved surfaces. We treat spot solutions as localized patterns and discuss how the geometric properties of the curved surface change their properties. To analyze spot patterns on spheroids with small flattening, we first construct exact stationary spot solutions on the spherical surface and reveal their stability. We then extend the analysis to show the existence and stability of stationary spot solutions in the spheroidal case. One of our theoretical results is the derivation of a stability criterion for stationary spot solutions localized at poles on oblate spheroids. The criterion determines whether a spot solution remains at a pole or moves away. Finally, we conduct numerical simulations to discuss the dynamics of spot solutions with the insight of our theoretical predictions. Our results show that the dynamics of spot solutions depend on the curved surface and the coordination of neural interactions.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-04-24 04:00:00 UTC.

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A Few-Shot Metric Learning Method with Dual-Channel Attention for Cross-Modal Same-Neuron Identification

arXiv:2504.16520v1 Announce Type: cross Abstract: In neuroscience research, achieving single-neuron matching across different imaging modalities is critical for understanding the relationship between neuronal structure and function. However, modality gaps and limited annotations present significant challenges. We propose a few-shot metric learning method with a dual-channel attention mechanism and a pretrained vision transformer to enable robust cross-modal neuron identification. The local and global channels extract soma morphology and fiber context, respectively, and a gating mechanism fuses their outputs. To enhance the model's fine-grained discrimination capability, we introduce a hard sample mining strategy based on the MultiSimilarityMiner algorithm, along with the Circle Loss function. Experiments on two-photon and fMOST datasets demonstrate superior Top-K accuracy and recall compared to existing methods. Ablation studies and t-SNE visualizations validate the effectiveness of each module. The method also achieves a favorable trade-off between accuracy and training efficiency under different fine-tuning strategies. These results suggest that the proposed approach offers a promising technical solution for accurate single-cell level matching and multimodal neuroimaging integration.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-04-24 04:00:00 UTC.

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A simple EEG-based decision tool for neonatal therapeutic hypothermia in hypoxic-ischemic encephalopathy

arXiv:2403.20239v3 Announce Type: replace Abstract: Accurate identification of hypoxic-ischemic brain injury in the early neonatal period is essential for initiating therapeutic hypothermia (TH) within 6 hours of birth to optimize neurodevelopmental outcomes. We aimed to develop a simple decision-making tool for identifying term neonates with hypoxic-ischemic encephalopathy (HIE) based on features of conventional electroencephalograms (EEG) recorded within 6 hours of birth. EEG recordings from 100 full-term neonates with HIE were graded by pediatric neurologists for severity. Amplitude in slow frequency bands was analyzed, focusing on delta (0.5-4 Hz) spectral power. Temporal fluctuations of delta power characterized each HIE grade, with joint level and duration probability densities estimated for delta oscillation power. This study is registered on clinicaltrials.gov (NCT05114070). These 2D EEG representations effectively distinguish mild HIE cases from those requiring hypothermia, achieving 98% accuracy, 99% sensitivity, 99% positive predictive value, 94% negative predictive value, an F1 score of 99%, and a false alarm rate of only 6%. This system accurately discriminates mild from moderate or severe HIE, with only one mild case mistakenly identified as requiring hypothermia. Quantized probability densities of delta spectral features from early EEG (within 6 hours of birth) revealed significant differences between mild and moderate or severe HIE, enabling accurate discrimination of candidates for TH. Simple, interpretable biomarkers from early EEG can provide an efficient visual clinical decision support tool to identify full-term neonates with HIE eligible for therapeutic hypothermia.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-04-24 04:00:00 UTC.

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DATETIME: A new benchmark to measure LLM translation and reasoning capabilities

arXiv:2504.16155v1 Announce Type: new Abstract: This paper introduces DATETIME, a new high-quality benchmark designed to evaluate the translation and reasoning abilities of a Large Language Model (LLM) on datetimes. A datetime is simply a date and a time, for example '11th.february.2023 ,1:12:31'. Datetimes are an interesting domain because they are intuitive and straightforward for humans to process but present significant challenges for LLMs. At the time of writing, no publicly available benchmark exists for systematically evaluating LLMs on datetime processing. Our experiments show that state-of-the-art models exhibit significant difficulty with tasks involving reasoning on datetimes, and that General Artificial Intelligence is still a distant aspiration. We hypothesize that working with datetimes necessitates translation and/or computation capabilities, and the tasks of the benchmark are organized accordingly. Significant dispersion in performance across models is observed with surprisingly poor performance even on apparently trivial tasks. Whilst frontier models such as ChatGPT, Claude and Llama3.1 have evidently been built and trained with datetime reasoning abilities, significant improvement is required for the open-source models.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-04-24 04:00:00 UTC.

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Regularizing Differentiable Architecture Search with Smooth Activation

arXiv:2504.16306v1 Announce Type: new Abstract: Differentiable Architecture Search (DARTS) is an efficient Neural Architecture Search (NAS) method but suffers from robustness, generalization, and discrepancy issues. Many efforts have been made towards the performance collapse issue caused by skip dominance with various regularization techniques towards operation weights, path weights, noise injection, and super-network redesign. It had become questionable at a certain point if there could exist a better and more elegant way to retract the search to its intended goal -- NAS is a selection problem. In this paper, we undertake a simple but effective approach, named Smooth Activation DARTS (SA-DARTS), to overcome skip dominance and discretization discrepancy challenges. By leveraging a smooth activation function on architecture weights as an auxiliary loss, our SA-DARTS mitigates the unfair advantage of weight-free operations, converging to fanned-out architecture weight values, and can recover the search process from skip-dominance initialization. Through theoretical and empirical analysis, we demonstrate that the SA-DARTS can yield new state-of-the-art (SOTA) results on NAS-Bench-201, classification, and super-resolution. Further, we show that SA-DARTS can help improve the performance of SOTA models with fewer parameters, such as Information Multi-distillation Network on the super-resolution task.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-04-24 04:00:00 UTC.

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Neuro-Evolutionary Approach to Physics-Aware Symbolic Regression

arXiv:2504.16503v1 Announce Type: new Abstract: Symbolic regression is a technique that can automatically derive analytic models from data. Traditionally, symbolic regression has been implemented primarily through genetic programming that evolves populations of candidate solutions sampled by genetic operators, crossover and mutation. More recently, neural networks have been employed to learn the entire analytical model, i.e., its structure and coefficients, using regularized gradient-based optimization. Although this approach tunes the model's coefficients better, it is prone to premature convergence to suboptimal model structures. Here, we propose a neuro-evolutionary symbolic regression method that combines the strengths of evolutionary-based search for optimal neural network (NN) topologies with gradient-based tuning of the network's parameters. Due to the inherent high computational demand of evolutionary algorithms, it is not feasible to learn the parameters of every candidate NN topology to full convergence. Thus, our method employs a memory-based strategy and population perturbations to enhance exploitation and reduce the risk of being trapped in suboptimal NNs. In this way, each NN topology can be trained using only a short sequence of backpropagation iterations. The proposed method was experimentally evaluated on three real-world test problems and has been shown to outperform other NN-based approaches regarding the quality of the models obtained.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-04-24 04:00:00 UTC.

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Introduction to Quantum Machine Learning and Quantum Architecture Search

arXiv:2504.16131v1 Announce Type: cross Abstract: Recent advancements in quantum computing (QC) and machine learning (ML) have fueled significant research efforts aimed at integrating these two transformative technologies. Quantum machine learning (QML), an emerging interdisciplinary field, leverages quantum principles to enhance the performance of ML algorithms. Concurrently, the exploration of systematic and automated approaches for designing high-performance quantum circuit architectures for QML tasks has gained prominence, as these methods empower researchers outside the quantum computing domain to effectively utilize quantum-enhanced tools. This tutorial will provide an in-depth overview of recent breakthroughs in both areas, highlighting their potential to expand the application landscape of QML across diverse fields.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-04-24 04:00:00 UTC.

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Towards a Distributed Federated Learning Aggregation Placement using Particle Swarm Intelligence

arXiv:2504.16227v1 Announce Type: cross Abstract: Federated learning has become a promising distributed learning concept with extra insurance on data privacy. Extensive studies on various models of Federated learning have been done since the coinage of its term. One of the important derivatives of federated learning is hierarchical semi-decentralized federated learning, which distributes the load of the aggregation task over multiple nodes and parallelizes the aggregation workload at the breadth of each level of the hierarchy. Various methods have also been proposed to perform inter-cluster and intra-cluster aggregation optimally. Most of the solutions, nonetheless, require monitoring the nodes' performance and resource consumption at each round, which necessitates frequently exchanging systematic data. To optimally perform distributed aggregation in SDFL with minimal reliance on systematic data, we propose Flag-Swap, a Particle Swarm Optimization (PSO) method that optimizes the aggregation placement according only to the processing delay. Our simulation results show that PSO-based placement can find the optimal placement relatively fast, even in scenarios with many clients as candidates for aggregation. Our real-world docker-based implementation of Flag-Swap over the recently emerged FL framework shows superior performance compared to black-box-based deterministic placement strategies, with about 43% minutes faster than random placement, and 32% minutes faster than uniform placement, in terms of total processing time.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-04-24 04:00:00 UTC.

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Efficient Design of Compliant Mechanisms Using Multi-Objective Optimization

arXiv:2504.16451v1 Announce Type: cross Abstract: Compliant mechanisms achieve motion through elastic deformation. In this work, we address the synthesis of a compliant cross-hinge mechanism capable of large angular strokes while approximating the behavior of an ideal revolute joint. To capture the competing demands of kinematic fidelity, rotational stiffness, and resistance to parasitic motion, we formulate a multi-objective optimization problem based on kinetostatic performance measures. A hybrid design strategy is employed: an efficient beam-based structural model enables extensive exploration of a high-dimensional design space using evolutionary algorithms, followed by fine-tuning with high-fidelity three-dimensional finite element analysis. The resulting Pareto-optimal designs reveal diverse geometric configurations and performance trade-offs.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-04-24 04:00:00 UTC.

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Noise-Tolerant Coreset-Based Class Incremental Continual Learning

arXiv:2504.16763v1 Announce Type: cross Abstract: Many applications of computer vision require the ability to adapt to novel data distributions after deployment. Adaptation requires algorithms capable of continual learning (CL). Continual learners must be plastic to adapt to novel tasks while minimizing forgetting of previous tasks.However, CL opens up avenues for noise to enter the training pipeline and disrupt the CL. This work focuses on label noise and instance noise in the context of class-incremental learning (CIL), where new classes are added to a classifier over time, and there is no access to external data from past classes. We aim to understand the sensitivity of CL methods that work by replaying items from a memory constructed using the idea of Coresets. We derive a new bound for the robustness of such a method to uncorrelated instance noise under a general additive noise threat model, revealing several insights. Putting the theory into practice, we create two continual learning algorithms to construct noise-tolerant replay buffers. We empirically compare the effectiveness of prior memory-based continual learners and the proposed algorithms under label and uncorrelated instance noise on five diverse datasets. We show that existing memory-based CL are not robust whereas the proposed methods exhibit significant improvements in maximizing classification accuracy and minimizing forgetting in the noisy CIL setting.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-04-24 04:00:00 UTC.

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Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin Effect

arXiv:2312.03243v3 Announce Type: replace Abstract: Physics-informed neural networks (PINNs) are at the forefront of scientific machine learning, making possible the creation of machine intelligence that is cognizant of physical laws and able to accurately simulate them. However, today's PINNs are often trained for a single physics task and require computationally expensive re-training for each new task, even for tasks from similar physics domains. To address this limitation, this paper proposes a pioneering approach to advance the generalizability of PINNs through the framework of Baldwinian evolution. Drawing inspiration from the neurodevelopment of precocial species that have evolved to learn, predict and react quickly to their environment, we envision PINNs that are pre-wired with connection strengths inducing strong biases towards efficient learning of physics. A novel two-stage stochastic programming formulation coupling evolutionary selection pressure (based on proficiency over a distribution of physics tasks) with lifetime learning (to specialize on a sampled subset of those tasks) is proposed to instantiate the Baldwin effect. The evolved Baldwinian-PINNs demonstrate fast and physics-compliant prediction capabilities across a range of empirically challenging problem instances with more than an order of magnitude improvement in prediction accuracy at a fraction of the computation cost compared to state-of-the-art gradient-based meta-learning methods. For example, when solving the diffusion-reaction equation, a 70x improvement in accuracy was obtained while taking 700x less computational time. This paper thus marks a leap forward in the meta-learning of PINNs as generalizable physics solvers. Sample codes are available at https://github.com/chiuph/Baldwinian-PINN.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-04-24 04:00:00 UTC.

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SNN-Based Online Learning of Concepts and Action Laws in an Open World

arXiv:2411.12308v3 Announce Type: replace-cross Abstract: We present the architecture of a fully autonomous, bio-inspired cognitive agent built around a spiking neural network (SNN) implementing the agent's semantic memory. This agent explores its universe and learns concepts of objects/situations and of its own actions in a one-shot manner. While object/situation concepts are unary, action concepts are triples made up of an initial situation, a motor activity, and an outcome. They embody the agent's knowledge of its universe's action laws. Both kinds of concepts have different degrees of generality. To make decisions the agent queries its semantic memory for the expected outcomes of envisaged actions and chooses the action to take on the basis of these predictions. Our experiments show that the agent handles new situations by appealing to previously learned general concepts and rapidly modifies its concepts to adapt to environment changes.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-04-24 04:00:00 UTC.

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A Quarter of a Century of Neuromorphic Architectures on FPGAs -- an Overview

arXiv:2502.20415v3 Announce Type: replace-cross Abstract: Neuromorphic computing is a relatively new discipline of computer science, where the principles of biological brain's computation and memory are used to create a new way of processing information, based on networks of spiking neurons. Those networks can be implemented as both analog and digital implementations, where for the latter, the Field Programmable Gate Arrays (FPGAs) are a frequent choice, due to their inherent flexibility, allowing the researchers to easily design hardware neuromorphic architecture (NMAs). Moreover, digital NMAs show good promise in simulating various spiking neural networks because of their inherent accuracy and resilience to noise, as opposed to analog implementations. This paper presents an overview of digital NMAs implemented on FPGAs, with a goal of providing useful references to various architectural design choices to the researchers interested in digital neuromorphic systems. We present a taxonomy of NMAs that highlights groups of distinct architectural features, their advantages and disadvantages and identify trends and predictions for the future of those architectures.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-04-24 04:00:00 UTC.

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Dynamic hashtag recommendation in social media with trend shift detection and adaptation

arXiv:2504.00044v2 Announce Type: replace-cross Abstract: Hashtag recommendation systems have emerged as a key tool for automatically suggesting relevant hashtags and enhancing content categorization and search. However, existing static models struggle to adapt to the highly dynamic nature of social media conversations, where new hashtags constantly emerge and existing ones undergo semantic shifts. To address these challenges, this paper introduces H-ADAPTS (Hashtag recommendAtion by Detecting and adAPting to Trend Shifts), a dynamic hashtag recommendation methodology that employs a trend-aware mechanism to detect shifts in hashtag usage-reflecting evolving trends and topics within social media conversations-and triggers efficient model adaptation based on a (small) set of recent posts. Additionally, the Apache Storm framework is leveraged to support scalable and fault-tolerant analysis of high-velocity social data, enabling the timely detection of trend shifts. Experimental results from two real-world case studies, including the COVID-19 pandemic and the 2020 US presidential election, demonstrate the effectiveness of H-ADAPTS in providing timely and relevant hashtag recommendations by adapting to emerging trends, significantly outperforming existing solutions.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-04-24 04:00:00 UTC.

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CAPO: Cost-Aware Prompt Optimization

arXiv:2504.16005v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have revolutionized natural language processing by solving a wide range of tasks simply guided by a prompt. Yet their performance is highly sensitive to prompt formulation. While automated prompt optimization addresses this challenge by finding optimal prompts, current methods require a substantial number of LLM calls and input tokens, making prompt optimization expensive. We introduce CAPO (Cost-Aware Prompt Optimization), an algorithm that enhances prompt optimization efficiency by integrating AutoML techniques. CAPO is an evolutionary approach with LLMs as operators, incorporating racing to save evaluations and multi-objective optimization to balance performance with prompt length. It jointly optimizes instructions and few-shot examples while leveraging task descriptions for improved robustness. Our extensive experiments across diverse datasets and LLMs demonstrate that CAPO outperforms state-of-the-art discrete prompt optimization methods in 11/15 cases with improvements up to 21%p. Our algorithm achieves better performances already with smaller budgets, saves evaluations through racing, and decreases average prompt length via a length penalty, making it both cost-efficient and cost-aware. Even without few-shot examples, CAPO outperforms its competitors and generally remains robust to initial prompts. CAPO represents an important step toward making prompt optimization more powerful and accessible by improving cost-efficiency.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-04-24 04:00:00 UTC.

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AlphaGrad: Non-Linear Gradient Normalization Optimizer

arXiv:2504.16020v2 Announce Type: replace-cross Abstract: We introduce AlphaGrad, a memory-efficient, conditionally stateless optimizer addressing the memory overhead and hyperparameter complexity of adaptive methods like Adam. AlphaGrad enforces scale invariance via tensor-wise L2 gradient normalization followed by a smooth hyperbolic tangent transformation, $g' = \tanh(\alpha \cdot \tilde{g})$, controlled by a single steepness parameter $\alpha$. Our contributions include: (1) the AlphaGrad algorithm formulation; (2) a formal non-convex convergence analysis guaranteeing stationarity; (3) extensive empirical evaluation on diverse RL benchmarks (DQN, TD3, PPO). Compared to Adam, AlphaGrad demonstrates a highly context-dependent performance profile. While exhibiting instability in off-policy DQN, it provides enhanced training stability with competitive results in TD3 (requiring careful $\alpha$ tuning) and achieves substantially superior performance in on-policy PPO. These results underscore the critical importance of empirical $\alpha$ selection, revealing strong interactions between the optimizer's dynamics and the underlying RL algorithm. AlphaGrad presents a compelling alternative optimizer for memory-constrained scenarios and shows significant promise for on-policy learning regimes where its stability and efficiency advantages can be particularly impactful.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-04-24 04:00:00 UTC.

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Q&A Europa Clipper mission

Nature Communications, Published online: 24 April 2025; doi:10.1038/s41467-025-59094-6

NASA’s latest space probe Europa Clipper, was launched on October 14, 2024, and will study Jupiter’s moon Europa. Europa is covered by an icy crust and is thought to host a subsurface ocean of liquid water. Europa Clipper’s main mission objectives are to study the moon’s ice crust and its surface features, confirm the presence of a subsurface ocean, and determine its chemical composition. Ultimately, this mission will further our understanding of the potential habitability of icy moons in our solar system, such as Europa. In this Q&A we are talking with three scientists (Dr. Christopher Glein, Dr. Elodie Lesage and Dr. Annie Marinan) involved with Clipper, and what particular research questions they hope to answer during the mission.

in Nature Communications on 2025-04-24 00:00:00 UTC.

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Conformational changes in the motor ATPase CpaF facilitate a rotary mechanism of Tad pilus assembly

Nature Communications, Published online: 24 April 2025; doi:10.1038/s41467-025-59009-5

The bacterial Tad pilus extends and retracts using a single bifunctional ATPase CpaF. Here, the authors employ cryo-EM, fluorescent microscopy, and AlphaFold modelling to propose how a rotary mechanism of CpaF catalysis drives Tad pilus assembly.

in Nature Communications on 2025-04-24 00:00:00 UTC.

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JointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation

Nature Communications, Published online: 24 April 2025; doi:10.1038/s41467-025-59243-x

Polygenic risk prediction in non-European populations is limited by small sample sizes and tuning sets. Here, the authors show that JointPRS improves cross-population prediction accuracy by leveraging genetic correlations without requiring individual-level data.

in Nature Communications on 2025-04-24 00:00:00 UTC.

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Neural representation of cytokines by vagal sensory neurons

Nature Communications, Published online: 24 April 2025; doi:10.1038/s41467-025-59248-6

This study showed that vagal sensory neurons in the nodose ganglia selectively encode specific cytokines, enabling real-time body-brain communication of immune signals. This neural encoding of cytokines is disrupted during inflammation associated with a colitis model.

in Nature Communications on 2025-04-24 00:00:00 UTC.

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In-sensor compressing via programmable optoelectronic sensors based on van der Waals heterostructures for intelligent machine vision

Nature Communications, Published online: 24 April 2025; doi:10.1038/s41467-025-59104-7

In-sensor computing offers a promising solution for image processing with reduced data transfer. Here, the authors report programmable and multifunctional van der Waals optoelectronic sensors, showing their application for snapshot compression and recognition of dynamic videos and 3D spectral data.

in Nature Communications on 2025-04-24 00:00:00 UTC.

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Symmetric adenine methylation is an essential DNA modification in the early-diverging fungus Rhizopus microsporus

Nature Communications, Published online: 24 April 2025; doi:10.1038/s41467-025-59170-x

Here, the authors characterize the epigenetic landscape of the human fungal pathogen Rhizopus microsporus with a focus on symmetric DNA N6-methyladenine, revealing its regulatory role in gene expression and its essentiality for viability.

in Nature Communications on 2025-04-24 00:00:00 UTC.

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Local mapping of the nanoscale viscoelastic properties of fluid membranes by AFM nanorheology

Nature Communications, Published online: 24 April 2025; doi:10.1038/s41467-025-59260-w

The viscoelasticity of fluid membranes is challenging to quantify with nanometre precision. Here, the authors develop an AFM-based nanorheology platform to measure local molecular mobilities and spatial correlations in lateral diffusion within the membrane.

in Nature Communications on 2025-04-24 00:00:00 UTC.

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Regulating Na content and Mn defects in birnessite for high-voltage aqueous sodium-ion batteries

Nature Communications, Published online: 24 April 2025; doi:10.1038/s41467-025-59223-1

Instability is a key challenge in aqueous sodium-ion batteries. Here, authors propose a Na-rich birnessite with high Na content and minimal Mn defects, suppressing Mn migration and its accompanying domino degradation effect, which enables a wide potential window and improved structural stability.

in Nature Communications on 2025-04-24 00:00:00 UTC.

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Investigation of hepatitis B virus mutations associated with immune escape and drug resistance in human immunodeficiency virus-infected patients [version 3; peer review: 1 approved with reservations, 1 not approved]

Background Co-infection of hepatitis B virus (HBV) and human immunodeficiency virus (HIV) has an impact on high HBV replication and progression to liver cancer. These may lead to cross-resistance of drugs due to natural mutations or therapeutic pressure. These require continuous monitoring of HBV variants for better diagnosis and treatment strategies. Methods Convenience sampling was used to collect fifty archival sera from Inkosi Albert Luthuli Central Hospital. Sera were subjected to HBsAg screening using ELISA, DNA extraction, PCR amplification, Sanger sequencing, genotype prediction and mutation analysis. Results Of the 50 samples, 86% (N= 43/50) were HBsAg positive; 82% (N=41/50) PCR positive with 92% (N=38/41) sequenced and only 26 sequences were subjected to molecular characterization. The HBV sequences showed similarity to genotype A (73% [N=19/26]), genotypes G (5% [N=3/26]) and genotype c (15% [N=4/26]). Prevalence of the mutations in the surface region was (47% [N=18/38]); including diagnostic failure (K122R and T143S) and immune escape mutations (P127T, G145R, S207N, Y200T, E164D, Y206H and L209V). The mutations in the RT were at (36% [N=14/38]) with drug resistance mutations (DRM) at (50% [7/14]). Mutations showed resistance to lamivudine (LMV) at (35% [5/14]), telbivudine (LdT) at (29% [4/14]), (14% [2/14]) for entecavir (ETV) and (21% [3/14]) for adefovir (ADV). One sample had a combination of L180M, M204V, S202K, and M250I mutations. Conclusions Our findings highlight the prevalence of HBV genotype A in HIV-infected patients in South Africa. The study provides evidence of mutations linked to immune evasion and drug resistance; this infers that these mutations may have clinical implications for the diagnosis and treatment of HBV in HBV/HIV co-infected individuals. Further in vitro studies must be conducted to explore the impact of the identified mutation on the surface protein expression during diagnosis; phenotype impact of the mutant virus towards the antiviral drugs.

in F1000Research on 2025-04-23 16:32:27 UTC.

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This Week in The Journal

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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A Novel APP Knock-In Mouse Model to Study the Protective Effects of the Icelandic Mutation In Vivo

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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Reducing Tinnitus via Inhibitory Influence of the Sensorimotor System on Auditory Cortical Activity

Tinnitus is the subjective perception of a sound without corresponding external acoustic stimuli. Research highlights the influence of the sensorimotor system on tinnitus perception. Associated neuronal processes, however, are insufficiently understood, and it remains unclear how and at which hierarchical level the sensorimotor system interacts with the tinnitus-processing auditory system. We therefore asked 23 patients suffering from chronic tinnitus (11 males) to perform specific exercises, aimed at relaxing or tensing the jaw area, which temporarily modulated tinnitus perception. Associated neuronal processes were assessed using magnetencephalography. Results show that chronic tinnitus patients experienced their tinnitus as weaker and less annoying after completion of relaxing compared with tensing exercises. Furthermore, (1) sensorimotor alpha power and alpha-band connectivity directed from the somatosensory to the auditory cortex increased and (2) gamma power in the auditory cortex reduced, which (3) related to reduced tinnitus annoyance perception on a trial-by-trial basis in the relaxed state. No effects were revealed for 23 control participants without tinnitus (six males) performing the same experiment. We conclude that the increase in directed alpha-band connectivity from the somatosensory to the auditory cortex most likely reflects the transmission of inhibition from the somatosensory to the auditory cortex during relaxation, where concurrently tinnitus-related gamma power reduces. We suggest that revealed neuronal processes are transferable to other tinnitus-modulating systems beyond the sensorimotor one that is involved in attentional or emotional tinnitus modulation and provides deeper mechanistic insights into how and through which channels phantom sound perception might be modulated on a neuronal level.

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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Feedback Scales the Spatial Tuning of Cortical Responses during Both Visual Working Memory and Long-Term Memory

Perception, working memory, and long-term memory each evoke neural responses in the visual cortex. While previous neuroimaging research on the role of the visual cortex in memory has largely emphasized similarities between perception and memory, we hypothesized that responses in the visual cortex would differ depending on the origins of the inputs. Using fMRI, we quantified spatial tuning in the visual cortex while participants (both sexes) viewed, maintained in working memory, or retrieved from long-term memory a peripheral target. In each condition, BOLD responses were spatially tuned and aligned with the target's polar angle in all measured visual field maps including V1. As expected given the increasing sizes of receptive fields, polar angle tuning during perception increased in width up the visual hierarchy from V1 to V2, V3, hV4, and beyond. In stark contrast, the tuned responses were broad across the visual hierarchy during long-term memory (replicating a prior result) and during working memory. This pattern is consistent with the idea that mnemonic responses in V1 stem from top-down sources, even when the stimulus was recently viewed and is held in working memory. Moreover, in long-term memory, trial-to-trial biases in these tuned responses (clockwise or counterclockwise of target) predicted matched biases in memory, suggesting that the reinstated cortical responses influence memory-guided behavior. We conclude that feedback widens spatial tuning in the visual cortex during memory, where earlier visual maps inherit broader tuning from later maps thereby impacting the precision of memory.

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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Association of Bidirectional Network Cores in the Brain with Perceptual Awareness and Cognition

The brain comprises a complex network of interacting regions. To understand the roles and mechanisms of this intricate network, it is crucial to elucidate its structural features related to cognitive functions. Recent empirical evidence suggests that both feedforward and feedback signals are necessary for conscious perception, emphasizing the importance of subnetworks with bidirectional interactions. However, the link between such subnetworks and conscious perception remains unclear due to the complexity of brain networks. In this study, we propose a framework for extracting subnetworks with strong bidirectional interactions—termed the "cores" of a network—from brain activity. We applied this framework to resting-state and task-based human fMRI data from participants of both sexes to identify regions forming strongly bidirectional cores. We then explored the association of these cores with conscious perception and cognitive functions. We found that the extracted central cores predominantly included cerebral cortical regions rather than subcortical regions. Additionally, regarding their relation to conscious perception, we demonstrated that the cores tend to include regions previously reported to be affected by electrical stimulation that altered conscious perception, although the results are not statistically robust due to the small sample size. Furthermore, in relation to cognitive functions, based on a meta-analysis and comparison of the core structure with a cortical functional connectivity gradient, we found that the central cores were related to unimodal sensorimotor functions. The proposed framework provides novel insights into the roles of network cores with strong bidirectional interactions in conscious perception and unimodal sensorimotor functions.

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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Esrp1 and Esrp2 Regulate the Stability of tmc1/2a mRNAs in Zebrafish Sensory Hair Cells

RNA-binding proteins (RBPs) are important for posttranscriptional RNA processing, including pre-mRNA alternative splicing, mRNA stability, and translation. Several RBPs have been shown to play pivotal roles in the inner ear, whose dysfunction leads to auditory and/or balance impairments. Epithelial splicing-regulatory protein 1 (ESRP1) regulates alternative splicing and mRNA stability, and mutations in ESRP1 gene have been associated with sensorineural hearing loss in humans. In Esrp1 knock-out mouse embryos, alternative splicing of its target genes such as Fgfr2 is impaired, which eventually result in cochlear development deficits. However, Esrp1 knock-out mice die soon after birth because of complications from cleft-lip and palate defects, impeding further investigations at later postnatal ages. In the present study, we explored the role of ESRP1 in hearing using zebrafish as a model. We showed that esrp1 and its paralog esrp2 are expressed in the inner ear and certain anterior lateral line (ALL) neuromasts. Furthermore, our data suggested that Esrp1 and Esrp2 are required for the mechano-electrical transduction (MET) function of hair cells. RNA sequencing results indicated a significant decrease in the levels of several mRNAs in esrp1/2 double knock-out larvae. Among the dysregulated genes are tmc1 and tmc2a, which encode essential subunits of the MET complex. Further investigations demonstrated that Esrp1/2 could directly bind to tmc1 and tmc2a mRNAs and affect their stability. Taken together, we showed here that Esrp1 and Esrp2 regulate the MET function of zebrafish sensory hair cells by modulating the stability of tmc1 and tmc2a mRNAs.

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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Toward a Mechanistic Understanding of Reading Difficulties: Deviant Audiovisual Learning Dynamics and Network Connectivity in Children with Poor Reading Skills

Mastering the associations between letters and their corresponding speech sounds (LSS) is pivotal in the early stages of reading development, requiring an effective reorganization of brain networks. Children with poor reading skills often show difficulties in LSS learning. To date, however, it remains unclear how the interaction of brain regions integral to the processing and integration of letters and speech sounds changes with LSS learning. Characterizing these changes and potential differences between children with typical (TR) or poor (PR) reading skills on both behavioral and neural levels is essential for a more comprehensive mechanistic understanding of reading impairments. In this study, we investigated brain network alterations underlying LSS learning and their association with reading skills using functional magnetic resonance imaging in 80 schoolchildren (6.9–10.8 years, 36 females, 27 PR) with a wide range of reading skills. We applied a reinforcement learning drift-diffusion model to LSS learning data and analyzed the corresponding effective connectivity and activation measures in the brain. While both groups learned well, PR showed slower adaptation of responses than TR as trials progressed. This could be explained by a slower adjustment of the drift rate and decision boundary while learning and longer nondecision times. Alongside deviant connectivity in the network of visual, auditory, and associative brain regions, PR also showed reduced striatal modulation of connectivity from visual to audiovisual association areas throughout learning. These findings indicate impaired information transfer to integrative areas, which aids to explain the difficulties in achieving proficient reading skills from a neuroscientific perspective.

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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Striatal Dopamine Represents Valence on Dynamic Regional Scales

Adaptive decision-making relies on flexible updating of learned associations where environmental cues come to predict valenced stimuli, such as food or threat. Cue-guided behavior depends on a network of brain systems, including dopaminergic projections to the striatum. Critically, it remains unclear how dopamine signaling across the striatum encodes multivalent, dynamic learning contexts, where positive and negative associations must be rapidly disambiguated. To understand this, we used a pavlovian discrimination paradigm, where cues predicting food or threat were intermingled during conditioning sessions and their meaning was serially reversed across training. We found that male and female rats readily distinguished these cues and updated their behavior rapidly upon valence reversal. Using fiber photometry, we recorded dopamine signaling in three major striatal subregions—the dorsolateral striatum (DLS), the nucleus accumbens (NAc) core, and the NAc medial shell—finding that valence was represented uniquely across all three regions, indicative of local signals biased for value and salience. Furthermore, ambiguity introduced by cue reversals reshaped striatal dopamine on different timelines: NAc signals updated more readily than those in the DLS. Together, these results indicate that striatal dopamine flexibly encodes stimulus valence according to region-specific rules, and these signals are dynamically modulated by changing contingencies in the resolution of ambiguity about the meaning of environmental cues.

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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Concentration-Dependent Bidirectional Modification of Evoked Synaptic Transmission by Gadolinium and Adverse Effects of Gadolinium-Based Contrast Agent

Gadolinium-based contrast agents (GBCAs) for magnetic resonance imaging (MRI) are gadolinium chelates and can leave gadolinium in brain regions after administration, causing damage to brain tissues. However, the exact effects of gadolinium on synaptic function and the underlying mechanisms have not yet been elucidated. Here, we report that gadolinium differentially modulates evoked and spontaneous synaptic transmission and induces bidirectional changes in the efficacy of evoked synaptic transmission in the mouse hippocampus in a concentration-dependent manner. Low-concentration gadolinium (100 μM) modestly potentiated evoked field excitatory postsynaptic potentials (fEPSPs), while high-concentration gadolinium induced group 1 metabotropic glutamate receptor (mGluR)-, endocannabinoid (eCB)-, and purinergic P2Y1 receptor (P2Y1R)-dependent, presynaptically expressed long-term depression (LTD). Higher concentration of gadolinium (1,000 μM) also induced NMDAR- and mGluR-independent, partially P2Y13R-dependent, postsynaptically expressed LTD. Low-concentration gadolinium greatly increased miniature excitatory postsynaptic current (mEPSC) frequency, while high-concentration gadolinium much more robustly increased its frequency and amplitude. Finally, we found that evoked EPSCs were not affected by a macrocyclic GBCA, gadoterate meglumine (Gd-GOTA, Magnescope). However, evoked EPSCs were enhanced by a linear GBCA, gadopentetate dimeglumine (Gd-DTPA, Magnevist), at 100 μM, a clinically relevant concentration in the human brain after repeated clinical GBCA administration and in the cerebrospinal fluid in the rodent brain during experimental GBCA administration. Thus, evoked and spontaneous synaptic transmissions are independently modulated by gadolinium. Furthermore, Gd-GOTA effectively chelated gadolinium; however, Gd-DTPA had side effects on the evoked synaptic transmission, presumably because it did not completely chelate gadolinium.

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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Motor Preparation Tracks Decision Boundary Crossing Rather Than Accumulated Evidence in Temporal Decision-Making

Interval timing, the ability of animals to estimate the passage of time, is thought to involve diverse neural processes rather than a single central "clock" (Paton and Buonomano, 2018). Each of the different processes engaged in interval timing follows a different dynamic path, according to its specific function. For example, attention tracks anticipated events, such as offsets of intervals (Rohenkohl and Nobre, 2011), while motor processes control the timing of the behavioral output (De Lafuente et al., 2024). However, which processes are involved and how they are orchestrated over time to produce a temporal decision remains unknown. Here, we study motor preparation in the temporal bisection task, in which human (female and male) participants categorized intervals as "long" or "short." In contrast to typical perceptual decisions, where motor plans for all response alternatives are prepared simultaneously (Shadlen and Kiani, 2013), we find that temporal bisection decisions develop sequentially. While preparation for "long" responses was already underway before interval offset, no preparation was found for "short" responses. Furthermore, within intervals categorized as "long," motor preparation was stronger at interval offset for faster responses. Our findings support the two-stage model of temporal decisions, where "long" decisions are considered during the interval itself, while "short" decisions are only considered after the interval is over. Viewed from a wider perspective, our study offers methods to study the neural mechanisms of temporal decisions, by studying the multiple processes that produce them.

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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Methylphenidate Promotes a Frontoparietal-Dominant Brain State Improving Cognitive Performance: A Randomized Trial

Methylphenidate (MP) is a widely used stimulant medication for the treatment of attention-deficit hyperactivity disorder that enhances brain dopamine signaling and improves attention. However, how dopamine stimulation alters brain state dynamics to support improved attention during task performance is still unclear. To address this, we employed a multimodal neuroimaging approach combining positron emission tomography, functional magnetic resonance imaging, and behavioral tasks, to discover associations between dopamine signaling, brain dynamics, and cognition. Multimodal images were collected from 37 healthy adults under a single-blind, counterbalanced, placebo-controlled crossover study. Dynamic functional analysis was used to compare the alterations in dynamic features of brain states before and after MP. Subsequently, we analyzed the correlation between these brain state changes and baseline striatal D₁ and D₂ dopamine receptor (D₁R, D₂R) availability. We also examined alterations in dynamic brain states and their effects on visuospatial tasks. The results showed that MP primarily affected frontoparietal-dominant activated (FPN+), somatomotor-dominant activated (SOM+), and visual-dominant suppressed (VIS–) brain states. Specifically, the dwell time and fractional occupancy exhibited significant increases within the FPN+ and VIS– and an opposite trend within the SOM+. Furthermore, the increase of dwell time in FPN+, which was positively correlated with baseline striatal D₁R availability, was also associated with quicker response in the 2-ball-track task, but not significantly for the 3-ball-track task. The findings suggest that MP's enhancement of brain states with FPN+ and VIS– while decreasing SOM+, in part through D₁R signaling might underlie MP's improvement of attention for low demanding tasks in healthy populations.

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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A Hippocampal-Parietal Network for Reference Frame Coordination

Navigating space and forming memories based on spatial experience are crucial for survival, including storing memories in an allocentric (map-like) framework and conversion into egocentric (body-centered) action. The hippocampus and parietal cortex (PC) comprise a network for coordinating these reference frames, though the mechanism remains unclear. We used a task requiring remembering previous spatial locations to make correct future action and observed that hippocampus can encode the allocentric place, while PC encodes upcoming actions and relays this to hippocampus. Transformation from location to action unfolds gradually, with "Came From" signals diminishing and future action representations strengthening. PC sometimes encodes previous spatial locations in a route-based reference frame and conveys this to hippocampus. The signal for the future location appears first in PC, and then in hippocampus, in the form of an egocentric direction of future goal locations, suggesting egocentric encoding recently observed in hippocampus may originate in PC (or another "upstream" structure). Bidirectional signaling is apparent between PC and hippocampus and suggests a coordinated mechanism for integrating allocentric, route-centered, and egocentric spatial reference frames at the network level during navigation.

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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Visuomotor Memory Is Not Bound to Visual Motion

The motor system adapts its output in response to experienced errors to maintain effective movement in a dynamic environment. This learning is thought to utilize sensory prediction errors, the discrepancy between predicted and observed sensory feedback, to update internal models that map motor outputs to sensory states. However, it remains unclear what sensory information is relevant (e.g., the extent to which sensory predictions depend on visual feedback features). We explored this topic by measuring the transfer of visuomotor adaptation across two contexts where input movements created visual motion in opposite directions by either (1) translating a cursor across a static environment or (2) causing the environment to move toward a static cursor (272 participants: 94 male, 175 female). We hypothesized that this difference in visual feedback should engage distinct internal models, resulting in poor transfer of learning between contexts. Instead, we found nearly complete transfer of learning across contexts, with evidence that the motor memory was bound to the planned displacement of the hand rather than visual features of the task space. Our results suggest that internal model adaptation is not tied to the exact nature of the sensory feedback that results from movement. Instead, adaptation relies on representations of planned movements, allowing a common internal model to be employed across different visual contexts.

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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Visual Statistical Learning Alters Low-Dimensional Cortical Architecture

Our brains are in a constant state of generating predictions, implicitly extracting environmental regularities to support later cognition and behavior, a process known as statistical learning (SL). While prior work investigating the neural basis of SL has focused on the activity of single brain regions in isolation, much less is known about how distributed brain areas coordinate their activity to support such learning. Using fMRI and a classic visual SL task, we investigated changes in whole-brain functional architecture as human female and male participants implicitly learned to associate pairs of images, and later, when predictions generated from learning were violated. By projecting individuals’ patterns of cortical and subcortical functional connectivity onto a low-dimensional manifold space, we found that SL was associated with changes along a single neural dimension describing covariance across the visual-parietal and perirhinal cortex (PRC). During learning, we found regions within the visual cortex expanded along this dimension, reflecting their decreased communication with other networks, whereas regions within the dorsal attention network (DAN) contracted, reflecting their increased connectivity with higher-order cortex. Notably, when SL was interrupted, we found the PRC and entorhinal cortex, which did not initially show learning-related effects, now contracted along this dimension, reflecting their increased connectivity with the default mode and DAN, and decreased covariance with visual cortex. While prior research has linked SL to either broad cortical or medial temporal lobe changes, our findings suggest an integrative view, whereby cortical regions reorganize during association formation, while medial temporal lobe regions respond to their violation.

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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Novel Verbal Instructions Recruit Abstract Neural Patterns of Time-Variable Information Dimensionality

Human performance is endowed by neural representations of information that is relevant for behavior, some of which are also activated in a preparatory fashion to optimize later execution. Most studies to date have focused on highly practiced actions, leaving largely unaddressed the novel reconfiguration of information to generate unique whole task sets. Using electroencephalography, this study investigated the dynamics of the content and geometry reflected on the neural patterns of control representations during reconfiguration of information. We designed a verbal instruction paradigm where each trial involved novel combinations of multicomponent task information. By manipulating three task-relevant factors in a sample of 40 participants (26 females, 14 males), we observed complex coding schemes throughout the trial, during both preparation and implementation stages. The temporal profiles were consistent with a hierarchical structure: whereas task information was active in a sustained manner, the coding of more concrete stimulus features was more transient. Data showed both high dimensionality and abstraction, particularly during instruction encoding and target processing. Our results suggest that whenever task content could be recovered from neural patterns of activity, there was evidence of abstract coding, with an underlying geometry that favored generalization. During target processing, where potential interference across stimulus and response factors increased, orthogonal configurations also appeared. Overall, our findings uncover the dynamic manner with which control representations operate during novel recombination unique scenarios, with changes in dimensionality and abstraction adjusting along processing stages.

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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Decoding the Neural Dynamics of Headed Syntactic Structure Building

The brain builds hierarchical phrases during language comprehension; however, the details and dynamics of the phrase-building process remain underspecified. This study directly probes whether the neural code of verb phrases involves reactivating the syntactic property of a key subcomponent (the "head" verb). To this end, we train a part-of-speech sliding–window verb/adverb decoder on EEG signals recorded while 30 participants read sentences in a controlled experiment. The decoder reaches above-chance performance that is spatiotemporally consistent and generalizes to unseen data across sentence positions. Applying the decoder to held-out data yields predicted activation levels for the verbal "head" of a verb phrase at a distant nonhead word (adverb); the critical adverb appeared either at the end of a verb phrase or at a sequentially and lexically matched position with no verb phrase boundary. There is stronger verb activation beginning at ~600 milliseconds at the critical adverb when it appears at a verb phrase boundary; this effect is not modulated by the strength of conceptual association nor does it reflect word predictability. Time-locked analyses additionally reveal a negativity waveform component and increased beta-delta inter-trial phase coherence, both previously linked to linguistic composition, in a similar time window. With a novel application of neural decoding, our findings delineate the dynamics by which the brain encodes phrasal representations by, in part, reactivating the representation of key subcomponents. We thus establish a link between cognitive accounts of phrasal representations and electrophysiological dynamics.

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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CART Neurons in the Hypothalamic Ventral Premammillary Nucleus (PMv) in Rats Mediate Maternal, But Not Inter-male Aggression

Compared to males, aggression is less frequently noticed in females. Fierce maternal-aggression to thwart the attack/threat of a male conspecific/intruder is transiently expressed as she defends her pups. The odor cues emanated by the intruder provoke aggressive behavior by robustly activating the ventral-premammillary nucleus (PMv) in the hypothalamic-attack area (HAA). But, how PMv activation triggers aggression is unclear. In view of neuropeptide cocaine- and amphetamine-regulated transcript (CART)'s potential to reconfigure neural circuits for behavioral demands, occurrence throughout aggression circuitry, and abundance particularly in PMv, we test the role of PMv^CART in maternal and inter-male aggression in rats. Males/dams actively attacked the intruder; virgin females did not. The dams/males without intruder showed isolated c-Fos cells in PMv, but the intruder's presence triggered c-Fos-activation in different PMv-subdivisions in dams/males. Compared to dams without intruder, confrontation with intruder robustly activated PMv^CART-neurons and augmented CART-ir in ventral-PMv and cart-mRNA in PMv-containing tissues in dams. Conversely, in males, the intruder's presence activated lateral-PMv^CART neurons, but CART-levels remained unaltered. Intra-PMv CART-siRNA administration suppressed maternal-aggression, but male aggression was unaffected. Since PMv is strongly connected with the ventrolateral–ventromedial hypothalamus (VMHvl) and medial preoptic nucleus (MPN), we test whether CART signaling to these nuclei triggers maternal aggression. While VMHvl showed stronger CARTergic-axonal input than MPN, immunoneutralization of CART in VMHvl, but not MPN, blocked maternal-aggression. CART may drive the circuit beyond HAA since VMHvl neurons contacted by CART-axons project to periaqueductal gray. We identify the engagement of vPMv and lPMv during maternal and inter-male aggression, respectively, and CART as a key mediator in the PMv–VMHvl pathway to express maternal-aggression in rats.

in Journal of Neuroscience on 2025-04-23 16:30:27 UTC.

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The Auditory Brainstem Response Diagnoses Alzheimer-Like Disease in the 5xFAD Mouse Model

Early and accurate diagnosis of Alzheimer's disease (AD) will be key for effective personalized treatment plans ( Cummings, 2023). Significant difficulties in auditory processing have been frequently reported in many patients with mild cognitive impairment, the prodromal form of AD ( Tarawneh et al., 2022), making it an outstanding candidate as AD diagnostic biomarker. However, the efficiency of diagnosis with this parameter has not been explored. Here we show that when male mice with amyloidosis begin to show memory decline, changes in the auditory brainstem response (ABR) to clicks enable the reliable diagnosis of disease using a machine learning algorithm. Interpretation of the machine learning diagnosis revealed that the upper levels of the auditory pathway, including the inferior colliculus, were the probable sources of the defects. Histological analyses show that in these locations, neuroinflammation and plaque deposition temporally correlate with behavioral changes consistent with memory loss. While these findings are tempered by the caveat that they derive from amyloidosis mice, we propose that ABR measurements be evaluated as an additional rapid, low-cost, noninvasive biomarker to assist the diagnostic testing of early-stage AD.

in eNeuro on 2025-04-23 16:30:22 UTC.

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Recurrent Interneuron Connectivity Does Not Support Synchrony in a Biophysical Dentate Gyrus Model

Synchronous activity of neuronal networks is found in many brain areas and correlates with cognition and behavior. Gamma synchrony is particularly strong in the dentate gyrus, which is thought to process contextual information in the hippocampus. Several network mechanisms for synchrony generation have been proposed and studied computationally. One such mechanism relies solely on recurrent inhibitory interneuron connectivity, but it requires a large enough number of synapses. Here, we incorporate previously published connectivity data of the dentate gyrus from mice of either sex into a biophysical computational model to test its ability to generate synchronous activity. We find that recurrent interneuron connectivity is insufficient to induce synchronous activity. This applies to an interneuron ring network and the broader dentate gyrus circuitry. Despite asynchronous input, recurrent interneuron connectivity can have small synchronizing effects but can also desynchronize the network for some types of synaptic input. Our results suggest that biologically plausible recurrent inhibitory connectivity alone is likely insufficient to synchronize the dentate gyrus.

in eNeuro on 2025-04-23 16:30:22 UTC.

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Hydrodynamic Performance Enhancement of Torpedo-Shaped Underwater Gliders Using Numerical Techniques [version 2; peer review: 1 approved with reservations, 1 not approved]

Background Underwater gliders are widely used in marine applications for monitoring purposes. These gliders must withstand hydrodynamic forces and maintain its body stability. The underwater environments are highly unpredictable, and small changes in the environment can lead to significant instability in underwater vehicles. Methods This study uses different numerical techniques to investigate the hydrodynamic characteristics of a torpedo-shaped glider. A symmetric torpedo-shaped glider model was created and analyzed using a licensed version of ANSYS 20.1 Fluent tool. The behavior of the torpedo glider under various flow conditions was examined such as variation of grid test, change of turbulent models, the variation in the inflow boundary conditions involves varying the velocity from 10.16 m/s to 15.16 m/s in 1m/s increment and from 10.16 m/s to 7.66 m/s in 0.5 m/s, also six different models were analyzed. Results Research was also attempted with different turbulent models and the Spalart-Allmara model was producing least validation error of 1.28 % with a primary focus on nose optimization. By varying the nose length, the study aimed to identify the best-suited nose geometry to minimize drag force. The nose lengths were varied to 0.205 m and 0.19m, resulting in validation errors of 2.81% and 1.16%, respectively, the results are clearly explained in the sub sequent sections of this article. Conclusion In conclusion, this study has evaluated various modifications and their impact on drag force reduction. The application of Spallart-Allmara model resulted in an improvement of 1.28%. Decrease in velocity lead to a significant reduction in the drag force, with an improvement of 37.3%. The nose optimization also contributed to drag force; a nose length of 0.205m yielded a 3.37% improvement. While a 0.19m nose length resulted in a 1.67% reduction. This study helps researchers in hydrodynamics by optimizing geometry for drag reduction.

in F1000Research on 2025-04-23 16:05:23 UTC.

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Evidence of SARS-CoV-2 bacteriophage potential in human gut microbiota [version 2; peer review: 1 approved, 1 approved with reservations]

Background In previous studies we have shown that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replicates in vitro in bacterial growth medium, that the viral replication follows bacterial growth, and it is influenced by the administration of specific antibiotics. These observations are compatible with a ‘bacteriophage-like’ behaviour of SARS-CoV-2. Methods We have further elaborated on these unusual findings and here we present the results of three different supplementary experiments: (1) an electron-microscope analysis of samples of bacteria obtained from a faecal sample of a subject positive to SARS-CoV-2; (2) mass spectrometric analysis of these cultures to assess the eventual de novo synthesis of SARS-CoV-2 spike protein; (3) sequencing of SARS-CoV-2 collected from plaques obtained from two different gut microbial bacteria inoculated with supernatant from faecal microbiota of an individual positive to SARS-CoV-2. Results Immuno-labelling with Anti-SARS-CoV-2 nucleocapsid protein antibody confirmed presence of SARS-CoV-2 both outside and inside bacteria. De novo synthesis of SARS-CoV-2 spike protein was observed, as evidence that SARS-CoV-2 RNA is translated in the bacterial cultures. In addition, phage-like plaques were spotted on faecal bacteria cultures after inoculation with supernatant from faecal microbiota of an individual positive to SARS-CoV-2. Bioinformatic analyses on the reads obtained by sequencing RNA extracted from the plaques revealed nucleic acid polymorphisms, suggesting different replication environment in the two bacterial cultures. Conclusions Based on these results we conclude that, in addition to its well-documented interactions with eukaryotic cells, SARS-CoV-2 may act as a bacteriophage when interacting with at least two bacterial species known to be present in the human microbiota. If the hypothesis proposed, i.e., that under certain conditions SARS-CoV-2 may multiply at the expense of human gut bacteria, is further substantiated, it would drastically change the model of acting and infecting of SARS-CoV-2, and most likely that of other human pathogenic viruses.

in F1000Research on 2025-04-23 15:15:19 UTC.

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An evolutionary model of rhythmic accelerando in animal vocal signalling

by Yannick Jadoul, Taylor A. Hersh, Elias Fernández Domingos, Marco Gamba, Livio Favaro, Andrea Ravignani

Animal acoustic communication contains many structural features. Among these, temporal structure, or rhythmicity, is increasingly tested empirically and modelled quantitatively. Accelerando is a rhythmic structure which consists of temporal intervals increasing in rate over a sequence. Why this particular vocal behaviour is widespread in many different animal lineages, and how it evolved, is so far unknown. Here, we use evolutionary game theory and computer simulations to link two rhythmic aspects of animal communication, synchronization and overlap: We test whether rhythmic accelerando could evolve under a pressure for acoustic overlap in time. Our models show that higher acceleration values result in a higher payoff, driven by the higher relative overlap between sequences. The addition of a cost to the payoff matrix models a physiological disadvantage to high acceleration rates and introduces a divergence between an individual’s incentive and the overall payoff of the population. Analysis of the invasion dynamics of acceleration strategies shows a stable, non-invadable range of strategies for moderate acceleration levels. Our computational simulations confirm these results: A simple selective pressure to maximise the expected overlap, while minimising the associated physiological cost, causes an initially isochronous population to evolve towards producing increasingly accelerating sequences until a population-wide equilibrium of rhythmic accelerando is reached. These results are robust to a broad range of parameter values. Overall, our analyses show that if overlap is beneficial, emergent evolutionary dynamics allow a population to gradually start producing accelerating sequences and reach a stable state of moderate acceleration. Finally, our modelling results closely match empirical data recorded from an avian species showing rhythmic accelerando, the African penguin. This shows the productive interplay between theoretical and empirical biology.

in PLoS Computational Biology on 2025-04-23 14:00:00 UTC.

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Resource competition-driven bistability and stochastic switching amplify gene expression noise

by Hanah Goetz, Rong Zhang, Xiao Wang, Xiao-Jun Tian

Although the impact of resource competition on the deterministic behavior of synthetic gene circuits has been studied, its effects on gene expression noise remain obscure. In this work, we systematically analyze the role of resource competition in noise propagation within a genetic inhibition cascade circuit. We found that resource competition amplifies gene expression noise by introducing unexpected bistability and stochastic switching between the two stable states. This emergent bistability, driven by resource competition-mediated double negative feedback, allows one gene to dominate expression while suppressing the other in a “winner-takes-all” behavior. Our findings highlight the critical role of resource competition in shaping the noise dynamics and its propagation, underscoring the importance of considering these effects when designing and controlling synthetic circuits.

in PLoS Computational Biology on 2025-04-23 14:00:00 UTC.

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Cell-type-specific firing patterns in a V1 cortical column model depend on feedforward and feedback-driven states

by Giulia Moreni, Rares A. Dorcioman, Cyriel M. A. Pennartz, Jorge F. Mejias

Stimulation of specific cell groups under different network regimes (e.g., spontaneous activity or sensory-evoked activity) can provide insights into the neural dynamics of cortical columns. While these protocols are challenging to perform experimentally, modelling can serve as a powerful tool for such explorations. Using detailed electrophysiological and anatomical data from mouse V1, we built a novel spiking network model of a cortical column, which incorporates pyramidal cells and three distinct interneuron types (PV, SST, and VIP cells, specified per lamina), as well as the dynamic and voltage-dependent properties of AMPA, GABA, and NMDA receptors. We first demonstrate that thalamocortical feedforward (FF) and feedback (FB) stimuli arriving in the column have opposite effects, leading to net columnar excitation and inhibition respectively and revealing translaminar gain control via full-column inhibition by layer 6. We then perturb one cell group (i.e., a cell type in a specific layer) at a time and observe the effects on other cell groups under distinct network states: spontaneous, feedforward-driven, feedback-driven, and a combination of feedforward and feedback. Our findings reveal that when a given group is perturbed, the columnar response varies significantly based on its state, with strong sensory feedforward input decreasing columnar sensitivity to all perturbations and feedback input serving as modulator of intra columnar interactions. Given that activity changes within specific neuronal populations are difficult to predict a priori in experiments, our model may constitute a useful tool to predict outcomes of perturbations and assist in experimental design.

in PLoS Computational Biology on 2025-04-23 14:00:00 UTC.

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Reactivation of previous decisions repulsively biases sensory encoding but attractively biases decision-making

by Minghao Luo, Huihui Zhang, Fang Fang, Huan Luo

Automatic shaping of perception by past experiences is common in many cognitive functions, reflecting the exploitation of temporal regularities in environments. A striking example is serial dependence, i.e., current perception is biased by previous trials. However, the neural implementation of its operational circle in human brains remains unclear. In two experiments with electroencephalography (EEG)/magnetoencephalography (MEG) recordings and delayed-response tasks, we demonstrate a two-stage ‘repulsive-then-attractive’ past–present interaction mechanism underlying serial dependence. First, past-trial reports, instead of past stimuli, serve as a prior to be reactivated during both encoding and decision-making. Crucially, past reactivation interacts with current information processing in a two-stage manner: repelling and attracting the present during encoding and decision-making, and arising in the sensory cortex and prefrontal cortex, respectively. Finally, while the early stage occurs automatically, the late stage is modulated by task and predicts bias behavior. These findings might also illustrate general mechanisms of past–present influences in neural operations.

in PLoS Biology on 2025-04-23 14:00:00 UTC.

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The dorsal and ventral hippocampus contribute differentially to spatial working memory and spatial coding in the prefrontal cortex

by Susanne S. Babl, Torfi Sigurdsson

The hippocampus (HPC) supports spatial working memory (SWM) through its interactions with the prefrontal cortex (PFC). However, it is not clear whether and how the dorsal (dHPC) and ventral (vHPC) poles of the HPC make distinct contributions to SWM and whether they differentially influence the PFC. To address this question, we optogenetically silenced the dHPC or the vHPC while simultaneously recording from the PFC of mice performing a SWM task. We found that whereas both HPC subregions were necessary during the encoding phase of the task, only the dHPC was necessary during the choice phase. Unexpectedly, silencing of either subregion did not affect PFC neurons’ ability to represent the animal’s position, but did alter how it was represented. In contrast, only silencing of the vHPC affected their coding of spatial goals. These results thus reveal distinct contributions of the dorsal and ventral HPC poles to SWM and the coding of behaviorally relevant spatial information by PFC neurons.

in PLoS Biology on 2025-04-23 14:00:00 UTC.

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SGO2 does not play an essential role in separase inhibition during meiosis I in mouse oocytes

by Benjamin Wetherall, David Bulmer, Alexandra Sarginson, Christopher Thomas, Suzanne Madgwick

During meiosis I in oocytes, anaphase is triggered by deactivation of cyclin B1-CDK1 and activation of separase. Active separase plays an essential role in cleaving cohesin rings that hold homologous chromosomes together. Critically, separase must be inhibited until all chromosomes are aligned and the cell is prepared for anaphase I. Inhibition can be mediated through the binding of separase to either securin or cyclin B1-CDK1. The relative contribution of each inhibitory pathway varies depending on cell type. Recently, shugoshin-2 (SGO2) has also been shown to inhibit separase in mitotic cells. Here, we used a separase biosensor and perturbed the three inhibitory pathways during meiosis I in mouse oocytes. We show that inhibition mediated by either securin or cyclin B1-CDK1, but not SGO2, is independently sufficient to suppress separase activity. However, when both the securin and cyclin B1-CDK1 inhibitory pathways are perturbed together, separase activity begins prematurely, resulting in gross segregation defects. Furthermore, we characterized SGO2 destruction dynamics and concluded that it is not an essential separase inhibitor in mouse oocytes. The existence of multiple separase inhibitory pathways highlights the critical importance of tightly regulated separase activity during this unique and challenging cell division.

in PLoS Biology on 2025-04-23 14:00:00 UTC.

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A Novel Mutation in CNTNAP1 Gene Causes Disorganization of Axonal Domains, Hypomyelination and Severe Neurological Deficits

Sell et al. report that a single amino acid change at glycine 350 to valine in mouse Cntnap1 disrupts protein function and compromises the paranodal domain organization and axo-glial junction formation, leading to neurological deficits, which are all rescued by neuronal expression of the wild-type Cntnap1 gene. Our studies open opportunities for potential gene therapy for the CNTNAP1-associated neuropathologies.

ABSTRACT

CNTNAP1 encodes the contactin-associated protein 1 (Cntnap1) which localizes to the paranodal region in all myelinated axons and is essential for axonal domain organization and the propagation of action potentials. To date, close to 45 reported human CNTNAP1 variants have been identified that are associated with dysregulation and disorganization of the axonal domains, resulting in various forms of congenital hypomyelinating neuropathies in children. Currently, no treatments are available for neuropathies caused by CNTNAP1 variants, highlighting the importance of fully characterizing these mutations and their impact on Cntnap1 functions. To understand the importance of a novel human CNTNAP1 likely pathogenic variant that changes glycine at position 349 to valine in a child who also carries a CNTNAP1 truncation and displayed severe neurological deficits, we used CRISPR/Cas9 methodology and introduced a single nucleotide substitution in the mouse Cntnap1 gene, resulting in glycine at 350 to valine (Cntnap1 ^G350V). Trans-allelic combination of Cntnap1 ^G350V with a Cntnap1 null allele (Cntnap1 ^G350V/−) mimics human pathologies, recapitulating hypomyelination neuropathies associated with CNTNAP1 mutations as well as loss of paranodal junctions and disorganization of axonal domains in myelinated axons. Expression of the wild type Cntnap1 transgene in Cntnap1 ^G350V/− mice rescued the mutant phenotypes and restored all neurological deficits. Our studies demonstrate that GGT (glycine) to GTT (valine) change in human CNTNAP1 creates a recessive loss of function allele and lays the foundation for potential gene therapy studies aimed at treating CNTNAP1-associated hypomyelinating neuropathies in children.

in Journal of Neuroscience Research on 2025-04-23 12:31:41 UTC.

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Nitrilase 1 Associated Cerebrovascular Small Vessel Disease

in Annals of Neurology on 2025-04-23 10:28:40 UTC.

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Association Between Vascular NOTCH3 Aggregation and Disease Severity in a CADASIL Cohort – Implications for NOTCH3 Variant‐Specific Disease Prediction

Objective

Vascular NOTCH3 protein ectodomain aggregation is a pathological hallmark of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), a monogenic small vessel disease typically caused by cysteine-altering variants in NOTCH3. Given their high population frequency, these NOTCH3 variants are an important genetic contributor to stroke and vascular dementia worldwide. Disease severity in CADASIL is highly variable and is mainly determined by the position of the pathogenic NOTCH3 variant in the NOTCH3 ectodomain. Here, we aimed to investigate the association between NOTCH3 aggregation load in skin vessels, cysteine-altering NOTCH3 variants, and disease severity in a prospective cohort study of 212 patients with CADASIL with 39 distinct cysteine-altering NOTCH3 variants.

Methods

NOTCH3 aggregation load in skin vessels was determined by calculating the NOTCH3 score; the fraction of skin vessel wall area positive for NOTCH3 staining. Variant-specific NOTCH3 scores were calculated for variants present in 10 or more participants, by averaging the NOTCH3 scores of individuals with that distinct variant. The associations between the NOTCH3 score, NOTCH3 variants, and neuroimaging and clinical outcomes were investigated using multivariable linear mixed models, Cox regression, and mediation analyses.

Results

The NOTCH3 score was significantly associated with lifetime stroke probability and small vessel disease neuroimaging outcomes, but not with age. Variant-specific NOTCH3 scores reflected differences in disease severity associated with distinct NOTCH3 variants.

Interpretation

These findings suggest that differences in NOTCH3 aggregation propensity underlie the differences in disease severity associated with NOTCH3 cysteine-altering variants, and show that NOTCH3-variant specific NOTCH3 scores can contribute to improved individualized disease prediction in CADASIL. ANN NEUROL 2025

in Annals of Neurology on 2025-04-23 10:19:14 UTC.

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Women and nature: Ecofeminist study in social media [version 1; peer review: awaiting peer review]

Background Social media has turned into an important way for ecofeminists to raise awareness, create supportive communities, and push for fairness in environmental matters. Women activists, influencers, and groups are using social media sites like Instagram, Facebook, and YouTube to bring attention to topics like climate change, sustainability, and the link between gender and environmental issues. Methods By taking Ecofeminism as a theory, the paper examines selected accounts from three different social media platforms viz, Facebook, Instagram, and YouTube to explore how they reflect the theme of ecofeminism. Ecofeminism is a theory that highlights the connection between women and nature, advocating for their protection. Results Our findings establish that the principles of ecofeminist theory and the works of ecofeminists have moved beyond physical propagation and are now being actively disseminated on digital media platforms. Through various forms of media such as reels, videos, pictures, and animations, many social media accounts vividly demonstrate the close relationship between women and nature, the exploitation they face from patriarchal society and how they can be protected. Conclusion Since the people of the current age are highly engaged with social media, these platforms effectively communicate the concept of ecofeminism and its importance, motivating global audiences to treat, protect, and preserve both nature and women. This helps in creating an inclusive society and advancing a sustainable world, which is critical in today’s context of environmental degradation and social unrest. The paper contributes to the fields of ecofeminism, media studies, and the sustainable development goals established by the UN in 2015.

in F1000Research on 2025-04-23 09:58:18 UTC.

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Neuronal autophagy controls excitability via ryanodine receptor–mediated regulation of calcium-activated potassium channel function

Proceedings of the National Academy of Sciences, Volume 122, Issue 17, April 2025.
SignificanceAutophagy is a cellular self-degradation process that is of importance for the health of nerve cells and the brain, in particular during aging. How precisely autophagy contributes to brain health remains debated. We show that impairment of ...

in PNAS on 2025-04-23 07:00:00 UTC.

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The tilt illusion arises from an efficient reallocation of neural coding resources at the contextual boundary

Proceedings of the National Academy of Sciences, Volume 122, Issue 17, April 2025.
SignificanceThe tilt illusion is a prominent example of how vision is influenced by sensory context, in which the perceived orientation of a central stimulus is altered by its surround. Previous research has focused on how context affects the response ...

in PNAS on 2025-04-23 07:00:00 UTC.

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Distinct oxytocin signaling pathways synergistically mediate rescue-like behavior in mice

Proceedings of the National Academy of Sciences, Volume 122, Issue 17, April 2025.
SignificanceThis study reveals that mice exhibit spontaneous rescue-like behavior, facilitating the recovery of anesthetized conspecifics without prior training or external rewards. Observer mice, distressed by their anesthetized peers, instinctively ...

in PNAS on 2025-04-23 07:00:00 UTC.

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Bridging life satisfaction data and neurobiological measures would elucidate human well-being

Proceedings of the National Academy of Sciences, Volume 122, Issue 17, April 2025.

in PNAS on 2025-04-23 07:00:00 UTC.

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Functional tendon regeneration is driven by regulatory T cells and IL-33 signaling

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Genetics- and age-driven neuroimmune and disc changes underscore herniation susceptibility and pain-associated behaviors in SM/J mice

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Task-specific regional circuit adaptations in distinct mouse retinal ganglion cells

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Loss of intracellular ATP affects axoplasmic viscosity and pathological protein aggregation in mammalian neurons

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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In vivo functional screens reveal KEAP1 loss as a driver of chemoresistance in small cell lung cancer

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Conservation impacts and hidden actions in a randomized controlled trial of a marine pay-to-release program

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Hydrogen isotopic evidence for a core component in Baffin Island lavas

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Synaptophysin accelerates synaptic vesicle fusion by expanding the membrane upon neurotransmitter loading

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Giant switchable ferroelectric photovoltage in double-perovskite epitaxial films through chemical negative strain

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Heavy boron isotopes in intraplate basalts reveal recycled carbonate in the mantle

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Climate-induced shifts in sulfate dynamics regulate anaerobic methane oxidation in a coastal wetland

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Pharmacologic degradation of WDR5 suppresses oncogenic activities of SS18::SSX and provides a therapeutic of synovial sarcoma

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Collapse of fragile Chinese Swamp Cypress forest

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Multidimensional multiplexing metalens for STED microscopy

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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C. elegans transgenerational avoidance of P. fluorescens is mediated by the Pfs1 sRNA and vab-1

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Reducing racial ingroup biases in empathy and altruistic decision-making by shifting racial identification

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Structure-based discovery of highly bioavailable, covalent, broad-spectrum coronavirus MPro inhibitors with potent in vivo efficacy

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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A molecularly defined mPFC-BLA circuit specifically regulates social novelty preference

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Acute BRCAness induction and AR pathway blockage through CDK12/7/9 degradation enhances PARP inhibitor sensitivity in prostate cancer

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Biomimetic rigid-soft finger design for highly dexterous and adaptive robotic hands

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Reprogrammable curved-straight origami: Multimorphability and volumetric tunability

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Neural representations of noncentral events during narrative encoding predict subsequent story ending originality

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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3D bioprinting of collagen-based high-resolution internally perfusable scaffolds for engineering fully biologic tissue systems

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Magnetically driven triboelectric nanogenerator for a wireless, versatile energy transfer system

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Input-driven dynamics for robust memory retrieval in Hopfield networks

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Structural dissection of ergosterol metabolism reveals a pathway optimized for membrane phase separation

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Conserved sites on the influenza H1 and H3 hemagglutinin recognized by human antibodies

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Conformational plasticity of mitochondrial VDAC2 controls the kinetics of its interaction with cytosolic proteins

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Intratumoral self-assembly of renal-clearable gold nanoparticles as precise photothermal nanomedicine for liver tumor therapy

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Spatial distribution of fishmeal and fish oil factories around the globe

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Encoding mechanical intelligence using ultraprogrammable joints

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Paleozoic carbonatites controlled rare-earth-elements mineralization at Bayan Obo

Science Advances, Volume 11, Issue 17, April 2025.

in Science Advances on 2025-04-23 07:00:00 UTC.

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Reverse Correlation of Natural Statistics for Ecologically-Relevant Characterization of Human Perceptual Templates

Journal of Neurophysiology, Ahead of Print.

in Journal of Neurophysiology on 2025-04-23 03:50:58 UTC.

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LncRNA SLNCR phenocopies the E2F1 DNA binding site to promote melanoma progression

Shah et al. show that the SLNCR-E2F1 complex drives melanoma progression by binding to the E2F1 DNA-binding domain. Blocking SLNCR-E2F1 complex formation without changing the levels of either SLNCR or E2F1 reduces melanoma invasion and extravasation, suggesting this RNA-protein interaction is a potential target for melanoma therapy.

in Cell Reports: Current Issue on 2025-04-23 00:00:00 UTC.

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PI3K/AKT signaling mediates stress-inducible amyloid formation through c-Myc

Lacroix et al. demonstrate that heat-induced activation of the PI3K/AKT signaling cascade drives the formation of functional amyloid aggregates that protect cells from harsh environmental conditions. Here, activation of PI3K and AKT represses GSK3-mediated degradation of c-Myc, leading to upregulation of nucleating noncoding RNAs that mediate this physiological amyloidogenic event.

in Cell Reports: Current Issue on 2025-04-23 00:00:00 UTC.

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Pathological α-synuclein dysregulates epitranscriptomic writer METTL3 to drive neuroinflammation in microglia

Miller et al. demonstrate the m6A epitranscriptomic writer METTL3 and other components of the m6A-METTL-complex (MAC) is uniquely dysregulated in microglial cells during neuroinflammatory processes. Collectively, this work demonstrates the MAC may function as a major regulator of chronic neuroinflammation in neurodegenerative disease.

in Cell Reports: Current Issue on 2025-04-23 00:00:00 UTC.

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Metabolite-dependent m6A methylation driven by mechanotransduction-metabolism-epitranscriptomics axis promotes bone development and regeneration

Li et al. develop a structurally adaptable hydrogel enhancing the cell-cell interactions via gathered E-cadherin, which further promotes glucose uptake, succinate biosynthesis, and m6A methylation. These findings reveal a mechanotransduction-metabolism-epitranscriptomics axis to boost the osteoblastic differentiation of hMSCs.

in Cell Reports: Current Issue on 2025-04-23 00:00:00 UTC.

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