Planet Neuroscience

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Subject-Independent Imagined Speech Detection via Cross-Subject Generalization and Calibration

arXiv:2511.13739v1 Announce Type: new Abstract: Achieving robust generalization across individuals remains a major challenge in electroencephalogram based imagined speech decoding due to substantial variability in neural activity patterns. This study examined how training dynamics and lightweight subject specific adaptation influence cross subject performance in a neural decoding framework. A cyclic inter subject training approach, involving shorter per subject training segments and frequent alternation among subjects, led to modest yet consistent improvements in decoding performance across unseen target data. Furthermore, under the subject calibrated leave one subject out scheme, incorporating only 10 % of the target subjects data for calibration achieved an accuracy of 0.781 and an AUC of 0.801, demonstrating the effectiveness of few shot adaptation. These findings suggest that integrating cyclic training with minimal calibration provides a simple and effective strategy for developing scalable, user adaptive brain computer interface systems that balance generalization and personalization.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-11-19 05:00:00 UTC.

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A Disentangled Low-Rank RNN Framework for Uncovering Neural Connectivity and Dynamics

arXiv:2511.13899v1 Announce Type: new Abstract: Low-rank recurrent neural networks (lrRNNs) are a class of models that uncover low-dimensional latent dynamics underlying neural population activity. Although their functional connectivity is low-rank, it lacks disentanglement interpretations, making it difficult to assign distinct computational roles to different latent dimensions. To address this, we propose the Disentangled Recurrent Neural Network (DisRNN), a generative lrRNN framework that assumes group-wise independence among latent dynamics while allowing flexible within-group entanglement. These independent latent groups allow latent dynamics to evolve separately, but are internally rich for complex computation. We reformulate the lrRNN under a variational autoencoder (VAE) framework, enabling us to introduce a partial correlation penalty that encourages disentanglement between groups of latent dimensions. Experiments on synthetic, monkey M1, and mouse voltage imaging data show that DisRNN consistently improves the disentanglement and interpretability of learned neural latent trajectories in low-dimensional space and low-rank connectivity over baseline lrRNNs that do not encourage partial disentanglement.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-11-19 05:00:00 UTC.

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A Brain Wave Encodes a Thousand Tokens: Modeling Inter-Cortical Neural Interactions for Effective EEG-based Emotion Recognition

arXiv:2511.13954v1 Announce Type: new Abstract: Human emotions are difficult to convey through words and are often abstracted in the process; however, electroencephalogram (EEG) signals can offer a more direct lens into emotional brain activity. Recent studies show that deep learning models can process these signals to perform emotion recognition with high accuracy. However, many existing approaches overlook the dynamic interplay between distinct brain regions, which can be crucial to understanding how emotions unfold and evolve over time, potentially aiding in more accurate emotion recognition. To address this, we propose RBTransformer, a Transformer-based neural network architecture that models inter-cortical neural dynamics of the brain in latent space to better capture structured neural interactions for effective EEG-based emotion recognition. First, the EEG signals are converted into Band Differential Entropy (BDE) tokens, which are then passed through Electrode Identity embeddings to retain spatial provenance. These tokens are processed through successive inter-cortical multi-head attention blocks that construct an electrode x electrode attention matrix, allowing the model to learn the inter-cortical neural dependencies. The resulting features are then passed through a classification head to obtain the final prediction. We conducted extensive experiments, specifically under subject-dependent settings, on the SEED, DEAP, and DREAMER datasets, over all three dimensions, Valence, Arousal, and Dominance (for DEAP and DREAMER), under both binary and multi-class classification settings. The results demonstrate that the proposed RBTransformer outperforms all previous state-of-the-art methods across all three datasets, over all three dimensions under both classification settings. The source code is available at: https://github.com/nnilayy/RBTransformer.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-11-19 05:00:00 UTC.

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Intrinsic Resonance depends on Network Size of Coupled-Delayed Interacting Oscillators

arXiv:2511.14065v1 Announce Type: new Abstract: The collective frequency that emerges from synchronized neuronal populations--the network resonance--shows a systematic relationship with brain size: whole-brain's large networks oscillate slowly, whereas finer parcellations of fixed volume exhibit faster rhythms. This resonance-size scaling has been reported in delayed neural mass models and human neuroimaging, yet the physical mechanism remained unresolved. Here we show that size-dependent resonance follows directly from propagation delays in delay-coupled phase oscillators. Starting from a Kuramoto model with heterogeneous delays, we linearize around the near-synchronous solution and obtain a closed-form approximation linking the resonance $\Omega$ to the mean delay and the effective coupling field. The analysis predicts a generic scaling law: $\Omega \approx (\sum_j c_{ij} \tau)^{-1}$, so resonance is delay-limited and therefore depends systematically on geometric size or parcellation density. We evaluate four growth scenarios--expanding geometry, fixed-volume parcellation, constant geometry, and an unphysical reference case--and show that only geometry-consistent scaling satisfies the analytical prediction. Numerical simulations with heterogeneous delays validate the law and quantify its error as a function of delay dispersion. These results identify a minimal physical mechanism for size-dependent cortical resonance and provide an analytical framework that unifies numeric simulation outputs.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-11-19 05:00:00 UTC.

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A region-specific brain dysfunction underlies cognitive impairment in long COVID brain fog

arXiv:2511.14188v1 Announce Type: new Abstract: Long COVID "brain fog" is a common and debilitating subjective syndrome often associated with persistent cognitive impairment after COVID-19 infection. Here we identify a specific regional brain dysfunction that mediates this cognitive impairment and provide evidence that targeted neuromodulation improves this deficit. In 120 patients with long COVID brain fog, we found an aberrant perceptual processing pattern. Patients with more severe brain fog committed significantly more false alarms (impulsive responses to non-signals) despite preserved overall accuracy. Both high-density (128-channel) EEG and structural MRI analyses provided converging evidence of a right inferior insula deficit, characterized by a blunted neural monitoring signal and cortical atrophy. We confirmed this deficit in a separate 796-participant UK Biobank longitudinal COVID re-imaging cohort, where COVID-19 survivors also showed selective impairment on a perceptual processing task and corresponding longitudinal atrophy of the right inferior insula compared with healthy controls. Finally, in a proof-of-principle randomized, sham-controlled trial (n = 40), a non-invasive, excitatory theta-burst ultrasound stimulation protocol targeting the right inferior insula rescued the perceptual deficit by reducing false alarms. These findings provide evidence of a causal role for right inferior insula dysfunction in long COVID-related perceptual impairment and show that modulation of this region can rescue the deficit, establishing it as a novel therapeutic target for long COVID cognitive impairment.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-11-19 05:00:00 UTC.

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Multi-network Topology Underlying Individual Language Learning Success

arXiv:2511.14453v1 Announce Type: new Abstract: Adult language learning varies greatly among individuals. Traditionally associated with frontotemporal language regions, this variability is increasingly seen as stemming from distributed brain networks. However, the role of these networks and their topological organization in explaining these differences remains unclear. We hypothesize that graph-theory-based network analysis of intrinsic multimodal connectivities across multiple networks explains overall and component-specific variations in language learning. We tested this in 101 healthy adults who underwent resting-state fMRI, structural MRI, and diffusion tensor imaging before seven days of six artificial language training tasks. We identified one dominant general learning component shared across tasks and five task-specific ones. Cross-validated predictive models used multimodal multi-network graph-theoretic metrics to predict final learning outcomes (LO) and rates (LR). We significantly predicted the LO and LR of the general component, which were primarily contributed by dorsal attention and frontoparietal networks. Nodal local efficiency was the most consistent predictor, with additional contributions from node clustering coefficient and network centrality for LR, highlighting local robustness, mesoscale network segregation, and global influence in explaining individual differences. Only task-specific word learning LO was predictable, relying on default mode and frontoparietal hubs with high betweenness centrality and efficiency. These findings demonstrate that intrinsic network topologies underlie differences in language learning success, supporting a multiple-systems hypothesis in which attentional-control networks interact with default and subcortical systems to shape learning trajectories. This advances mechanistic understanding and paves the way for personalized language education.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-11-19 05:00:00 UTC.

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Effect of Dopamine in Enhancement of SNR of Cortico-Striatal-Thalamo-Cortical Loop Spiking

arXiv:2511.14466v1 Announce Type: new Abstract: In this work, the effects of dopamine neurotransmitter within the Cortico-Striatal-Thalamo-Cortical (CSTC) loop. Simulations confirmed dopamine facilitates movement via thalamic disinhibition. Analysis of its impact on the signal-to-noise ratio (SNR) revealed a complex, region-specific outcome: SNR increased in some regions (e.g., D2 Striatum: 3.41 dB to 6.25 dB), decreased in others (e.g., Thalamus VL: 6.24 dB to 3.93 dB), and remained stable elsewhere (e.g., M1: 3.16 dB to 3.13 dB). This heterogeneity stems from dopamine increasing the excitability of D1-receptor-expressing neurons, which amplifies channel conductance noise and reduces SNR in specific circuits. Thus, dopamine acts not as a uniform signal enhancer, but as a complex modulator that critically balances facilitation and noise within the CSTC loop.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-11-19 05:00:00 UTC.

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DecNefLab: A Modular and Interpretable Simulation Framework for Decoded Neurofeedback

arXiv:2511.14555v1 Announce Type: new Abstract: Decoded Neurofeedback (DecNef) is a flourishing non-invasive approach to brain modulation with wide-ranging applications in neuromedicine and cognitive neuroscience. However, progress in DecNef research remains constrained by subject-dependent learning variability, reliance on indirect measures to quantify progress, and the high cost and time demands of experimentation. We present DecNefLab, a modular and interpretable simulation framework that formalizes DecNef as a machine learning problem. Beyond providing a virtual laboratory, DecNefLab enables researchers to model, analyze and understand neurofeedback dynamics. Using latent variable generative models as simulated participants, DecNefLab allows direct observation of internal cognitive states and systematic evaluation of how different protocol designs and subject characteristics influence learning. We demonstrate how this approach can (i) reproduce empirical phenomena of DecNef learning, (ii) identify conditions under which DecNef feedback fails to induce learning, and (iii) guide the design of more robust and reliable DecNef protocols in silico before human implementation. In summary, DecNefLab bridges computational modeling and cognitive neuroscience, offering a principled foundation for methodological innovation, robust protocol design, and ultimately, a deeper understanding of DecNef-based brain modulation.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-11-19 05:00:00 UTC.

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THD-BAR: Topology Hierarchical Derived Brain Autoregressive Modeling for EEG Generic Representations

arXiv:2511.13733v1 Announce Type: cross Abstract: Large-scale pre-trained models hold significant potential for learning universal EEG representations. However, most existing methods, particularly autoregressive (AR) frameworks, primarily rely on straightforward temporal sequencing of multi-channel EEG data, which fails to capture the rich physiological characteristics inherent to EEG signals. Moreover, their time-centered modeling approach also limits the effective representation of the dynamic spatial topology of brain activity. To address these challenges and fully exploit the potential of large-scale EEG models, we propose a novel Topology Hierarchical Derived Brain Autoregressive Modeling (THD-BAR) for EEG generic representations. The core innovation of THD-BAR lies in the introduction of the Brain Topology Hierarchy (BTH), which establishes a multi-scale spatial order for EEG channels. This hierarchical structure enables a redefinition of autoregressive learning as a "next-scale-time prediction" problem, effectively capturing both spatial and temporal dynamics. Based on BTH, we design a Topology-Hierarchical Vector Quantized-Variational Autoencoder (THVQ-VAE) for multi-scale tokenization and develop an enhanced Brain Autoregressive (BAR) module with specialized masking strategies for prediction. Through extensive large-scale pre-training on 17 datasets, followed by rigorous validation on 10 downstream datasets spanning 5 distinct tasks, THD-BAR consistently outperforms existing methods. These results highlight the superior generalization and modeling capabilities of our proposed approach.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-11-19 05:00:00 UTC.

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

arXiv:2403.20239v4 Announce Type: replace Abstract: Objective 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. Methods 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.gouv (NCT05114070). Results 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 and one moderate case erroneously flagged for treatment. Conclusions Quantized probability densities of delta spectral features from early EEG (within 6 hours of birth) revealed significant differences between mild and moderate/severe HIE, enabling accurate discrimination of candidates for TH. Significance 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 2025-11-19 05:00:00 UTC.

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Quantum Models of Consciousness from a Quantum Information Science Perspective

arXiv:2501.03241v3 Announce Type: replace Abstract: This perspective explores various quantum models of consciousness from the viewpoint of quantum information science, offering potential ideas and insights. The models under consideration can be categorized into three distinct groups based on the level at which quantum mechanics might operate within the brain: those suggesting that consciousness arises from electron delocalization within microtubules inside neurons, those proposing it emerges from the electromagnetic field surrounding the entire neural network, and those positing it originates from the interactions between individual neurons governed by neurotransmitter molecules. Our focus is particularly on the Posner model of cognition, for which we provide preliminary calculations on the preservation of entanglement of phosphate molecules within the geometric structure of Posner clusters. These findings provide valuable insights into how quantum information theory can enhance our understanding of brain functions.

in arXiv: Quantitative Biology: Neurons and Cognition on 2025-11-19 05:00:00 UTC.

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MS2Edge: Towards Energy-Efficient and Crisp Edge Detection with Multi-Scale Residual Learning in SNNs

arXiv:2511.13735v1 Announce Type: new Abstract: Edge detection with Artificial Neural Networks (ANNs) has achieved remarkable prog\-ress but faces two major challenges. First, it requires pre-training on large-scale extra data and complex designs for prior knowledge, leading to high energy consumption. Second, the predicted edges perform poorly in crispness and heavily rely on post-processing. Spiking Neural Networks (SNNs), as third generation neural networks, feature quantization and spike-driven computation mechanisms. They inherently provide a strong prior for edge detection in an energy-efficient manner, while its quantization mechanism helps suppress texture artifact interference around true edges, improving prediction crispness. However, the resulting quantization error inevitably introduces sparse edge discontinuities, compromising further enhancement of crispness. To address these challenges, we propose MS2Edge, the first SNN-based model for edge detection. At its core, we build a novel spiking backbone named MS2ResNet that integrates multi-scale residual learning to recover missing boundary lines and generate crisp edges, while combining I-LIF neurons with Membrane-based Deformed Shortcut (MDS) to mitigate quantization errors. The model is complemented by a Spiking Multi-Scale Upsample Block (SMSUB) for detail reconstruction during upsampling and a Membrane Average Decoding (MAD) method for effective integration of edge maps across multiple time steps. Experimental results demonstrate that MS2Edge outperforms ANN-based methods and achieves state-of-the-art performance on the BSDS500, NYUDv2, BIPED, PLDU, and PLDM datasets without pre-trained backbones, while maintaining ultralow energy consumption and generating crisp edge maps without post-processing.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-11-19 05:00:00 UTC.

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Towards a Comprehensive Theory of Reservoir Computing

arXiv:2511.14484v1 Announce Type: new Abstract: In reservoir computing, an input sequence is processed by a recurrent neural network, the reservoir, which transforms it into a spatial pattern that a shallow readout network can then exploit for tasks such as memorization and time-series prediction or classification. Echo state networks (ESN) are a model class in which the reservoir is a traditional artificial neural network. This class contains many model types, each with sets of hyperparameters. Selecting models and parameter settings for particular applications requires a theory for predicting and comparing performances. Here, we demonstrate that recent developments of perceptron theory can be used to predict the memory capacity and accuracy of a wide variety of ESN models, including reservoirs with linear neurons, sigmoid nonlinear neurons, different types of recurrent matrices, and different types of readout networks. Across thirty variants of ESNs, we show that empirical results consistently confirm the theory's predictions. As a practical demonstration, the theory is used to optimize memory capacity of an ESN in the entire joint parameter space. Further, guided by the theory, we propose a novel ESN model with a readout network that does not require training, and which outperforms earlier ESN models without training. Finally, we characterize the geometry of the readout networks in ESNs, which reveals that many ESN models exhibit a similar regular simplex geometry as has been observed in the output weights of deep neural networks.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-11-19 05:00:00 UTC.

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Attention via Synaptic Plasticity is All You Need: A Biologically Inspired Spiking Neuromorphic Transformer

arXiv:2511.14691v1 Announce Type: new Abstract: Attention is the brain's ability to selectively focus on a few specific aspects while ignoring irrelevant ones. This biological principle inspired the attention mechanism in modern Transformers. Transformers now underpin large language models (LLMs) such as GPT, but at the cost of massive training and inference energy, leading to a large carbon footprint. While brain attention emerges from neural circuits, Transformer attention relies on dot-product similarity to weight elements in the input sequence. Neuromorphic computing, especially spiking neural networks (SNNs), offers a brain-inspired path to energy-efficient intelligence. Despite recent work on attention-based spiking Transformers, the core attention layer remains non-neuromorphic. Current spiking attention (i) relies on dot-product or element-wise similarity suited to floating-point operations, not event-driven spikes; (ii) keeps attention matrices that suffer from the von Neumann bottleneck, limiting in-memory computing; and (iii) still diverges from brain-like computation. To address these issues, we propose the Spiking STDP Transformer (S$^{2}$TDPT), a neuromorphic Transformer that implements self-attention through spike-timing-dependent plasticity (STDP), embedding query--key correlations in synaptic weights. STDP, a core mechanism of memory and learning in the brain and widely studied in neuromorphic devices, naturally enables in-memory computing and supports non-von Neumann hardware. On CIFAR-10 and CIFAR-100, our model achieves 94.35\% and 78.08\% accuracy with only four timesteps and 0.49 mJ on CIFAR-100, an 88.47\% energy reduction compared to a standard ANN Transformer. Grad-CAM shows that the model attends to semantically relevant regions, enhancing interpretability. Overall, S$^{2}$TDPT illustrates how biologically inspired attention can yield energy-efficient, hardware-friendly, and explainable neuromorphic models.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-11-19 05:00:00 UTC.

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QUASAR: An Evolutionary Algorithm to Accelerate High-Dimensional Optimization

arXiv:2511.13843v1 Announce Type: cross Abstract: High-dimensional numerical optimization presents a persistent challenge. This paper introduces Quasi-Adaptive Search with Asymptotic Reinitialization (QUASAR), an evolutionary algorithm to accelerate convergence in complex, non-differentiable problems afflicted by the curse of dimensionality. Evaluated on the notoriously difficult CEC2017 benchmark suite of 29 functions, QUASAR achieved the lowest overall rank sum (150) using the Friedman test, significantly outperforming L-SHADE (229) and standard DE (305) in the dimension-variant trials. QUASAR also proves computationally efficient, with run times averaging $1.4 \text{x}$ faster than DE and $7.8 \text{x}$ faster than L-SHADE ($p \ll 0.001$) in the population-variant trials. Building upon Differential Evolution (DE), QUASAR introduces a highly stochastic architecture to dynamically balance exploration and exploitation. Inspired by the probabilistic behavior of quantum particles in a stellar core, the algorithm implements three primary components that augment standard DE mechanisms: 1) probabilistically selected mutation strategies and scaling factors; 2) rank-based crossover rates; 3) asymptotically decaying reinitialization that leverages a covariance matrix of the best solutions to introduce high-quality genetic diversity. QUASAR's performance establishes it as an effective, user-friendly optimizer for complex high-dimensional problems.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-11-19 05:00:00 UTC.

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Evolving Generalizable Parallel Algorithm Portfolios for Binary Optimization Problems via Domain-Agnostic Instance Generation

arXiv:2501.02906v4 Announce Type: replace Abstract: Generalization is the core objective when training optimizers from data. However, limited training instances often constrain the generalization capability of the trained optimizers. Co-evolutionary approaches address this challenge by simultaneously evolving a parallel algorithm portfolio (PAP) and an instance population to eventually obtain PAPs with good generalization. Yet, when applied to a specific problem class, these approaches have a major limitation. They require practitioners to provide instance generators specially tailored to the problem class, which is often non-trivial to design. This work proposes a general-purpose, off-the-shelf PAP construction approach, named domain-agnostic co-evolution of parameterized search (DACE), for binary optimization problems where decision variables take values of 0 or 1. The key novelty of DACE lies in its neural network-based domain-agnostic instance representation and generation mechanism that eliminates the need for domain-specific instance generators. The strong generality of DACE is validated across three real-world binary optimization problems: the complementary influence maximization problem (CIMP), the compiler arguments optimization problem (CAOP), and the contamination control problem (CCP). Given only a small set of training instances from these problem classes, DACE, without requiring domain knowledge, constructs PAPs with even better generalization performance than existing approaches on all three classes, despite their use of domain-specific instance generators.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-11-19 05:00:00 UTC.

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Self-Organization of Attractor Landscapes in High-Capacity Kernel Logistic Regression Hopfield Networks

arXiv:2511.13053v2 Announce Type: replace-cross Abstract: Kernel-based learning methods can dramatically increase the storage capacity of Hopfield networks, yet the dynamical mechanism behind this enhancement remains poorly understood. We address this gap by conducting a geometric analysis of the network's energy landscape. We introduce a novel metric, "Pinnacle Sharpness," to quantify the local stability of attractors. By systematically varying the kernel width and storage load, we uncover a rich phase diagram of attractor shapes. Our central finding is the emergence of a "ridge of optimization," where the network maximizes attractor stability under challenging high-load and global-kernel conditions. Through a theoretical decomposition of the landscape gradient into a direct "driving" force and an indirect "feedback" force, we reveal the origin of this phenomenon. The optimization ridge corresponds to a regime of strong anti-correlation between the two forces, where the direct force, amplified by the high storage load, dominates the opposing collective feedback force. This demonstrates a sophisticated self-organization mechanism: the network adaptively harnesses inter-pattern interactions as a cooperative feedback control system to sculpt a robust energy landscape. Our findings provide a new physical picture for the stability of high-capacity associative memories and offer principles for their design.

in arXiv: Computer Science: Neural and Evolutionary Computing on 2025-11-19 05:00:00 UTC.

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Author Correction: Confocal Airy beam oblique light-sheet tomography for brain-wide cell type distribution and morphology

Nature Methods, Published online: 19 November 2025; doi:10.1038/s41592-025-02982-y

Author Correction: Confocal Airy beam oblique light-sheet tomography for brain-wide cell type distribution and morphology

in Nature Methods on 2025-11-19 00:00:00 UTC.

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Development and Internal Validation of a Novel Risk Prediction Score for Endometrial Malignancy in Premenopausal Women with Abnormal Uterine Bleeding [version 1; peer review: awaiting peer review]

Background Endometrial malignancy in premenopausal women with abnormal uterine bleeding (AUB) is rare but clinically challenging. Current diagnostic strategies rely on endometrial sampling, which is invasive and often unnecessary. This study aimed to develop and internally validate a novel risk prediction score for endometrial malignancy in premenopausal women with AUB. Methods A retrospective longitudinal analytical study was conducted over 8 years and 11 months (January 2016–November 2024) at Charles Nicolle Hospital, Tunis, Tunisia. Premenopausal women with AUB who underwent endometrial biopsy followed by hysterectomy were included. Comparative analyses, logistic regression, and ROC curve analysis were performed. Significant variables were weighted according to adjusted odds ratios to construct a risk prediction score. Results Among 209 patients, 13 (6.2%) had endometrial malignancy. Independent predictors of endometrial malignancy were: oral contraceptive use (OR 29.9, 95% CI 1.5–587.1, p = 0.025), endometrial thickness >9 mm (OR 25.3, 95% CI 4.3–147.6, p < 0.001), vascularization (OR 98.3, 95% CI 3.7–2594.8, p = 0.006). Protective factors included hemorrhage episode ≤1 (OR 0.20, 95% CI 0.08–0.52, p = 0.001) and lower bleeding abundance (OR 0.30, 95% CI 0.13–0.65, p = 0.002). The final score allocated points as follows: endometrial thickness >9 mm (+3), oral contraceptive use (+3), vascularization (+4), hemorrhage episodes ≤1 (−2), and lower bleeding abundance (−1). A score ≥7 defined high risk. Model discrimination was excellent (AUC 0.901, 95% CI 0.825–0.976, p < 0.001). At a cutoff ≥7, sensitivity was 77%, specificity 90%, positive predictive value 34%, and negative predictive value 98%. Conclusions We developed and internally validated a novel risk prediction score for endometrial malignancy in premenopausal women with AUB. With strong diagnostic performance and high negative predictive value, this score may help clinicians better identify women who truly require invasive sampling.

in F1000Research on 2025-11-18 17:02:21 UTC.

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Values Deduced to Apply the Pavement Condition Index Methodology to Ecuador [version 1; peer review: awaiting peer review]

Road maintenance is essential for preserving the useful life of the roads. The evaluation methodology used in Ecuador is the Pavement Condition Index (PCI) method. It is the most complete method based on the analysis of the functional and structural damages that the pavement presents, the same ones that depend on its class, severity, and quantity. The PCI methodology uses data based on experiments made in the United States, so when evaluating pavements in Ecuador using the said methodology, it is not a correct adaptation to our reality because of the differences in deterioration of climatic factors and construction aspects. The PCI methodology has a study of values deduced from each of the damages present on the road, which are very strict in their qualification because the pavement service index in the United States looks for roads with high-quality standards compared to the Ecuadorian reality, where economic factors such as restrictions on usual road maintenance. The research proposes to generate changes in the abacus that generate the deduced values of the PCI methodology and adapt them to our country. Resulting in a 20% variability in the Ecuadorian Pavement Serviceability Index (PSI), which was applied to the Pavement Condition Index (PCI) method, commonly used for pavement evaluation.

in F1000Research on 2025-11-18 16:37:28 UTC.

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Family and Community Dynamics of Substance Abuse Among Youths in Bushenyi District, Western Uganda [version 1; peer review: awaiting peer review]

Background Youth substance abuse is an escalating public health challenge in Uganda, undermining individual well-being, family stability, and community development. Understanding the influence of family and community dynamics is critical for designing effective prevention strategies. Aim This study investigated the relationship between family and community factors and substance abuse among youths in Bushenyi District, Western Uganda. Methods A cross-sectional study design was employed. Structured questionnaires were administered to 381 youths aged 18–30 years, collecting sociodemographic information and data on family and community influences (parental drug use, home rules, peer influence, and law enforcement). Descriptive statistics summarised the data, chi-square tests assessed associations, and logistic regression identified predictors at a 5% significance level. Results Early initiation of drug use was common, particularly in bars and among peer groups. Substance abuse was significantly associated with parental drug use (χ2 = 102.68, p < 0.001), living arrangements (χ2 = 21.46, p = 0.001), peer influence (χ2 = 13.40, p = 0.001), and weak law enforcement (χ2 = 12.95, p = 0.001). Regression analysis showed that strict home rules were protective (Exp(B) = 0.325, p < 0.001), while weak law enforcement increased the risk of substance abuse (Exp(B) = 0.357, p = 0.003). Conclusion Family and community dynamics, particularly parental behaviour, peer pressure, and ineffective institutional regulation, are central drivers of youth substance abuse. Strengthening parental involvement, enforcing household rules, and improving law enforcement and community-based interventions are important in reducing the burden of substance use. The study highlights the need for multisectoral action involving families, schools, religious institutions, and law enforcement, while providing evidence to inform prevention strategies and future research on contextual risk pathways.

in F1000Research on 2025-11-18 16:23:07 UTC.

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Survey about Barriers and Solutions for Enhancing Computational Reproducibility in Scientific Research [version 1; peer review: awaiting peer review]

Background Rapid development and adoption of digital technologies across all research disciplines underlines the need for accessible and reusable computational data and code. Methods An anonymous, multidisciplinary survey covering open science, data publishing and reuse, as well as code publishing and reuse was conducted to gather insights into researchers’ practices, needs, and barriers. Results A total of 254 people initiated the survey, with 133 complete responses (mostly from Europe, equally distributed among scientific fields). Survey revealed that registered reports, replication studies and pre-registration are the least applied practices (52%, 38% and 42%), while open software and OA publishing demonstrated widespread adoption (83% and 69%) of the respondents, respectively. Data sharing is hindered mostly by lack of time (60%) and sufficient funding (44%). Among the predefined obstacles of code sharing, again, the lack of time to build proper documentation (65%), pressure to publish (51%), and the insufficient funding (42%) are the most mentioned reasons. On the other hand, most stimulating factors are the requirement of journals to share data/codes (score: 482), followed by incentives and rewards by institutions (score: 439). The survey showed that 28% of researchers never tried to reproduce a study, and when replication was attempted, researchers often found that open data (70%), open code (71%), and metadata (86%) were missing or incomplete. The analysis of open-ended responses highlighted the need for training, career-stage guidelines, and basic programming skills for researchers. Conclusions Although the likely low response rate may limit its representativeness, this study provides an up-to-date snapshot. A recurrent theme throughout the responses is the need for structural incentives and institutional support. Researchers claim that making work reproducible requires time, resources, and expertise; however, these efforts are rarely rewarded in conventional academic evaluation systems, highlighting the need for a systemic cultural shift.

in F1000Research on 2025-11-18 16:17:11 UTC.

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BISCUIT: An Open-Source Platform for Visual Comparison of Segmentation Models in Bioimage Analysis [version 1; peer review: awaiting peer review]

Background Segmentation in microscopy images is a critical task in bioimage analysis, with many deep learning models available (e.g., Cellpose, Omnipose, StarDist, SAM-based models). However, researchers often face challenges in choosing the most suitable model for their data, as quantitative metrics do not always reflect the biological relevance of segmentation results. Methods We developed BISCUIT (BioImage Segmentation Comparison Utility and Interactive Tool), an open-source platform that enables users to run multiple state-of-the-art segmentation algorithms on the same images and visually compare their outputs side-by-side. BISCUIT is implemented as an interactive Jupyter Notebook pipeline, leveraging existing segmentation libraries, and can be executed either via a zero-installation cloud environment (Google Colab) or on local high-performance computing resources. Results Using BISCUIT, we demonstrate how visual inspection of segmentation outputs can reveal qualitative differences between algorithms that may be overlooked by abstract performance metrics. For example, in a fluorescence microscopy image dataset, BISCUIT allowed direct comparison of segmentations from Cellpose, Omnipose, and StarDist, highlighting differences in how each algorithm delineated cell boundaries. This visual approach helped identify the model that produced the most biologically plausible segmentation for the dataset. Conclusions BISCUIT provides an intuitive platform for bioimage analysts and life scientists to evaluate and “see what really works” on their data. The platform is openly available and extensible, lowering the barrier for researchers to perform rapid, interactive benchmarking of segmentation models on their own microscopy data.

in F1000Research on 2025-11-18 16:15:25 UTC.

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A Review of Deep Learning Techniques for EEG-Based Emotion Recognition: Models, Methods, and Datasets [version 1; peer review: awaiting peer review]

Emotion Recognition (ER) with Electroencephalography (EEG) has become a major area of focus in affective computing due to its direct measurement of the activity of the brain. ER based on EEG has also advanced with the popularity of Deep Learning (DL) and its advancements related to classification accuracy and model efficiency. This systematic review is conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and aims to provide an overview of DL-based EEG emotion recognition approaches. A comprehensive literature search was conducted across five major databases covering the publications from 2020 to 2025. The studies with EEG signals for ER using DL architectures were included in the present review. Finally, a total of 233 articles were considered after eligibility screening. To enhance the diversity of investigation, we assessed the public datasets utilized for ER based on EEG in terms of their stimulation procedures and emotional representation. Further, the provided analysis attempts to direct future research toward EEG-based emotion identification systems that are more interpretable, generalizable, and data-efficient. This systematic review aims to provide a roadmap for developing EEG-driven ER, guiding researchers toward more reliable, scalable, and practically useful systems.

in F1000Research on 2025-11-18 15:52:37 UTC.

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A Novel Particle Marine Predator Optimizer for Gene Selection Health Problem [version 1; peer review: awaiting peer review]

Background High-dimensional microarray data complicates reliable cancer classification. Compact, informative gene panels are needed to maintain predictive power while improving interpretability and cost. Methods We propose a two-stage feature-selection pipeline. Stage 1 ranks genes via an ensemble of filters—ReliefF, chi-square, and Kullback–Leibler divergence—tempered by minimum redundancy–maximum relevance to promote diversity. Stage 2 performs wrapper-based subset search using a Particle Marine Predator Optimizer that fuses Marine Predators Algorithm for global exploration with Particle Swarm Optimization for local refinement. The objective maximizes cross-validated SVM accuracy while penalizing subset size. Results Across seven benchmarks (Breast, CNS, Leukemia, Leukemia-3c, Leukemia-4c, Lymphoma, Ovarian), we compare against Bat Algorithm, Grey Wolf Optimizer, Marine Predators Algorithm, White Shark Optimizer, and recent representatives using accuracy, F1, precision, sensitivity, Matthews correlation coefficient, selected-gene count, and convergence behavior. The method frequently matches or exceeds alternatives while selecting few genes, achieving perfect accuracy on several datasets (Leukemia, Leukemia-3c, Lymphoma, Ovarian) and stable, strong performance on the remainder. Typical subset sizes are 2–5 genes for Leukemia variants, 7–8 for CNS, and ~20 for Breast. Optimization traces show rapid, steady improvement. Conclusions The pipeline achieves an effective exploration–exploitation balance, yielding compact gene panels without sacrificing classification performance. Its modular design supports straightforward extension to larger cohorts and other omics modalities.

in F1000Research on 2025-11-18 15:24:48 UTC.

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An evolutionary approach to predict the orientation of CRISPR arrays

by Axel Fehrenbach, Alexander Mitrofanov, Omer S. Alkhnbashi, Rolf Backofen, Franz Baumdicker

CRISPR-Cas is a defense system of bacteria and archaea against phages. Parts of the foreign DNA, called spacers, are incorporated into the CRISPR array which constitutes the immune memory. The orientation of CRISPR arrays is crucial for analyzing and understanding the functionality of CRISPR systems and their targets. Several methods have been developed to identify the orientation of a CRISPR array. To predict the orientation, different methods use different features such as the repeat sequences between the spacers, the location of the leader sequence, the Cas genes, or PAMs. However, those features are often not sufficient to predict the orientation with certainty, or different methods disagree. Remarkably, almost all CRISPR systems have been found to insert spacers in a polarized manner at the leader end of the array. We introduce CRISPR-evOr, a method that leverages the resulting patterns to predict the acquisition orientation for (a group of) CRISPR arrays by reconstructing and comparing the likelihood of their evolutionary history with respect to both possible acquisition orientations. The new method is independent of Cas type, leader existence and location, and transcription orientation. CRISPR-evOr is thus particularly useful for arrays that other CRISPR orientation tools cannot predict confidently and to verify or resolve conflicting predictions from existing tools. CRISPR-evOr currently confidently predicts the orientation of 28.3% of the arrays in the considered subset of CRISPRCasdb, which other tools like CRISPRDirection and CRISPRstrand cannot reliably orient. As our tool leverages evolutionary information we expect this percentage to grow in the future when more closely related arrays will be available. Additionally, CRISPR-evOr provides confident decisions for rare subtypes of CRISPR arrays, where knowledge about repeats and leaders and their orientation is limited.

in PLoS Computational Biology on 2025-11-18 14:00:00 UTC.

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Ten simple rules for early-career researchers supervising short-term student projects

by Rebecca M. Crossley, Philip K. Maini

Supervising a short-term research project at undergraduate or master’s level is a rewarding yet complex responsibility that extends far beyond subject expertise. It offers supervisors the opportunity to inspire the next generation of scientists, while providing students with a crucial platform to develop research skills, academic identity, and resilience. However, short-term research projects often come with challenges, including varying levels of student motivation, limited time frames, and the need for intensive skill development. Effective supervision can enhance student learning, foster independent thinking, and improve both the quality and impact of student work, while also contributing to a positive and inclusive research culture. In this article, we present ten simple rules to guide supervisors, particularly those with limited supervision experience, such as early-career researchers, in supporting undergraduate and master’s students through their research journeys. These rules emphasise balancing project requirements with student interests, managing scope, fostering community integration, promoting open science practices, and providing structured yet flexible guidance. By adopting these practical strategies, supervisors can create a more productive, supportive, and enriching research experience for both students and themselves.

in PLoS Computational Biology on 2025-11-18 14:00:00 UTC.

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The blood–brain barrier regulates brain tumor growth through the SLC36 amino acid transporter Pathetic in Drosophila

by Qian Dong, Edel Alvarez-Ochoa, Phuong-Khanh Nguyen, Paul Orih, Natasha Fahey-Lozano, Hina Kosakamoto, Fumiaki Obata, Cyrille Alexandre, Louise Y. Cheng

Tumors adapt their metabolism to sustain increased proliferation, rendering them particularly vulnerable to fluctuations in nutrient availability. However, the role of the tumor microenvironment in modulating sensitivity to nutrient restriction (NR) remains poorly understood. Using a Drosophila brain dedifferentiation neural stem cell (NSC) tumor model induced by Prospero (Pros) inhibition, we show that tumor sensitivity to NR is governed by the blood–brain barrier (BBB) glia. We found that the SLC36 amino acid transporter Pathetic (Path) regulates brain branched-chain amino acids (BCAAs) levels. Under NR, while wild-type buffers against low nutrient levels by upregulating Path, tumor glia down-regulate Path. Furthermore, Path is specifically required by the tumor (but not wildtype) BBB; its downregulation causes reduced cell cycle progression of BBB glial cells and, in turn, restricts NSC tumor growth. Path influences BBB glial cell cycle via the BCAA-mTor-S6K pathway, and its expression is controlled by Ilp6 levels and the Insulin/PI3K pathway. Overexpression of Path is sufficient to counteract the inhibitory effects of NR on tumor growth. These findings suggest that Path levels at the glial niche BBB play a key role in determining tumor sensitivity to NR.

in PLoS Biology on 2025-11-18 14:00:00 UTC.

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Transient frontopolar cortex stimulation induces prolonged disruption to counterfactual processing

by Matthew Ainsworth, Juan M. Galeazzi, Carlos Pedreira, Mark G. Stokes, Mark J. Buckley

Frontopolar cortex (FPC) contains area 10, an anterior sub-region of prefrontal cortex exclusive to humans and nonhuman primates (NHPs) which is thought to support monitoring the value of switching between alternative goals. However, the neuronal mechanisms underlying this function are unclear. Here, we used multielectrode arrays to record the local field potentials (LFPs) in the FPC of two macaques performing a Wisconsin Card Sorting Test analogue and found that bursts of gamma and beta in FPC tracked counterfactual not current rule value. Moreover, we show that brief high-frequency microstimulation to a single trial causally affects both LFP activity in FPC, as well as rule-guided decision-making across successive trials. Following stimulation of FPC, we observed reduced exploration of the counterfactual rule prerule-change, as well as a delayed adaptation to the newly relevant rule postrule-change. A similar, multi-trial time-course disturbance to beta and gamma activity within FPC was also induced following single-trial microstimulation. These findings link neuronal activity in FPC with behavioral monitoring of the value of counterfactual rules and provide neural mechanistic insights into how FPC supports rule-based decision-making.

in PLoS Biology on 2025-11-18 14:00:00 UTC.

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Eye blinks synchronize with musical beats during music listening

by Yiyang Wu, Xiangbin Teng, Yi Du

Auditory-motor synchronization, the alignment of body movements with rhythmic patterns in music, is a universal human behavior, yet its full scope remains incompletely understood. Through four experiments with 123 young nonmusicians, integrating eye-tracking, neurophysiological recordings, white matter structural imaging, and behavioral analysis, we reveal a previously unrecognized form of synchronization: spontaneous eye blinks synchronize with musical beats. Blinks robustly synchronized with beats across a range of tempi and independently of melodic cues. Electroencephalogram recordings revealed a dynamic correspondence between blink timing and neural beat tracking. Blink synchronization performance was linked to white matter microstructure variation in the left superior longitudinal fasciculus, a key sensorimotor pathway. Additionally, the strength of blink synchronization reflected the modulation of dynamic auditory attention. These findings establish blink synchronization as a novel behavioral paradigm, expanding the auditory-motor synchronization repertoire and highlighting the intricate interplay between music rhythms and oculomotor activity. This discovery underscores a cross-modal active sensing mechanism, offering new insights into embodied music perception, rhythm processing, and their potential clinical applications.

in PLoS Biology on 2025-11-18 14:00:00 UTC.

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Modernizing Commercial Agency Regulations in Saudi Arabia: Legal Reforms and Comparative Insights [version 2; peer review: 2 not approved]

Background This study analyzes Saudi Arabia’s 2022–2023 reforms to the Commercial Agency Law through legal, economic, and comparative lenses. The pre-reform regime—marked by rigid nationality limits, procedural burdens, and litigation-prone termination rules—constrained market entry and investor confidence. Methods Using a doctrinal approach to statutes and implementing regulations, triangulated with Saudi judicial practice and policy instruments, we benchmark Saudi reforms against the UAE and UK. A Shariah lens clarifies how gharar (uncertainty), fasakh (rescission), and unjust enrichment (akl al-māl bil-bāṭil) shape agency disputes. Results Key changes include more flexible nationality rules, end-to-end digital registration and renewal, clearer termination/compensation standards, and formal recognition of arbitration (including SCCA). Together these measures enhance contractual predictability and lower compliance frictions, with early indications of improved market transparency and investor sentiment. Conclusions The reforms signal a hybrid model—liberalization aligned with global practice while preserving a Shariah-grounded identity. Remaining priorities include consistent judicial application, practitioner training, clearer guidance, and GCC coordination. We propose thematically grouped policy steps to consolidate gains and support Vision 2030’s diversification agenda.

in F1000Research on 2025-11-18 11:26:49 UTC.

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Beyond Classrooms: Community-Based Learning for Personal, Social, and Vocational Empowerment [version 1; peer review: awaiting peer review]

Community-Based Learning (CBL) situates learning with and within communities to build capabilities that extend beyond classrooms. This systematic review synthesizes evidence on the effectiveness of CBL for personal, social, and vocational empowerment. Following PRISMA 2020, we searched Scopus, Web of Science, ERIC, ProQuest, and Google Scholar (2010–2025) and conducted thematic synthesis complemented by bibliometric mapping. Of 8,395 records identified, 122 studies met inclusion criteria. Findings show consistent, positive effects on personal outcomes (e.g., self-confidence, agency, self-regulation) and social outcomes (e.g., teamwork, leadership, civic engagement), with vocational outcomes (e.g., employability, entrepreneurship, financial capability) also positive but more contingent on program design and market linkages. Effectiveness is strengthened by co-design with communities, cultural relevance, facilitator capability, cross-sector partnerships, and inclusive digital mediation; it is hindered by short funding cycles, heterogenous indicators, uneven governance, and equity/reporting gaps. The review integrates global evidence with local practices and SDG priorities (4, 8, 11) and proposes a unifying indicator logic to improve comparability across settings. We conclude that CBL functions as a systems intervention: when pedagogy is aligned with culture, governance, digital infrastructure, and labor-market connections, empowerment effects are stronger and more durable. Materials and extraction sheets are openly available on Zenodo to support transparency and reuse.

in F1000Research on 2025-11-18 10:53:51 UTC.

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The efficacy of High Intensity Interval Training (HIIT) versus aerobic exercise in the early stage of Parkinson’s Disease: A Systematic Review [version 1; peer review: awaiting peer review]

Purpose Exercise is shown to be effective in Parkinson’s Disease (PD), but there is still controversy over which type of exercise is most effective. This study aimed to evaluate the efficacy of High-intensity interval training (HIIT) versus aerobic training in motor and non-motor symptoms in PD. Materials and Methods A literature search was performed since February 2024 to July 2025 in MEDLINE, EMBASE, Discovery @Bolton, PsycINFO, Google Scholar, PubMed, CINAHL, and SPORTDiscus. Data extracted by a single reviewer and cross checked by another. The risk of bias was assessed using the Revised Cochrane risk of bias tool, and quality of reporting was assessed using the Template for Intervention Description and Replication (TiDieR) checklist. Results The search yielded 9 studies involving a total of 263 people with PD in the early stages. The results from the risk of bias showed that 88.8% of studies were judged to be of moderate to high quality. The completeness of intervention reporting showed overall moderate (66.1%) quality of reporting. Conclusion HIIT showed improvement in physiological, cognitive, and overall quality of life (QoL) outcomes when compared to aerobic exercise. HIIT also showed significant improvement in cognitive function. PROSPERO Registration Number: CRD42023478541, 02 November 2023.

in F1000Research on 2025-11-18 10:52:11 UTC.

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Does the Home-death Rate Reflect Medically Attended Community Deaths in Japan? [version 1; peer review: awaiting peer review]

Background The home-death rate is widely used in Japan as an indicator of community-based end-of-life care; however, this rate includes deaths without professional involvement. This study examines whether the home-death rate reflects medically attended home-based deaths across municipalities. Methods An ecological panel study was conducted using data from 1,741 municipalities obtained in 2017, 2020, and 2023 (5,222 municipality-year observations). National datasets from the Ministry of Health, Labor, and Welfare include data on medical institutions, home-visit nursing services, and clinic-certified home deaths. The dependent variable was the proportion of deaths occurring at home. Explanatory variables included home deaths certified by home-care support clinics and general clinics as well as deaths under home-visit nursing reimbursed by medical or long-term care insurance. Fixed-effects panel regression models with municipality and year effects were applied, using cluster-robust standard errors. Finally, correlations between the model residuals and prefectural rates of coroner-investigated deaths were examined to explore the influence of non-medical deaths. Results Home deaths certified by general clinics and deaths under medical-insurance-reimbursed visiting nursing were significantly associated with higher home-death rates (β=4.06, 95% confidence interval (95%CI), 1.28–6.85; β=31.75, 95%CI 8.14–55.37, respectively). The associations between home-care support clinics and long-term care insurance-reimbursed visiting nursing were not significant. No correlation was found between residuals and prefectural rates of coroner-investigated deaths (r=0.09, 95%CI, –0.20–0.37). Conclusions Japan’s home-death rate partly reflects medically attended home deaths, particularly those involving general clinics and medical-insurance-reimbursed visiting nursing. However, because this rate also includes deaths without professional care, it should be interpreted as a contextual rather than a quality indicator of end-of-life care and complemented by measures capturing care involvement and coordination.

in F1000Research on 2025-11-18 10:50:24 UTC.

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Learning Science Beyond Sight: Conceptual Engagement of Elementary School Students with Visual Impairment through Hands-On Activities [version 1; peer review: awaiting peer review]

Background A pressing challenge in science education is that visually impaired students continue to lack access to appropriate instructional media, making it difficult for them to engage with abstract concepts and resulting in limited opportunities for equitable education. Responding to this challenge, the present study investigates the design and implementation of hands-on, multisensory instructional strategies aimed at improving conceptual understanding of magnetism among elementary students with visual impairments. Method The research involved four elementary school students with varying degrees of visual acuity. Employing the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) framework, the research was divided into 3 stages: needs analysis (A) using explanatory case study, design and develop (DD) instructional media and validate using Aiken’s V, implement and evaluate (IE) the media into classroom activity using evaluative case study. Result The findings indicate that tactile models, auditory explanations, and adapted activity kits significantly improved students’ engagement and comprehension of key magnetism concepts such as magnetic properties, forces, and polarity. Through an evaluative case study, the research demonstrates that instructional materials tailored to the sensory and cognitive profiles of visually impaired learners not only foster deeper conceptual understanding but also promote greater participation and confidence in science learning. Conclusions Multisensory science kits help students access abstract science material so that it can be felt and helps achieve conceptual understanding. Moreover, the results may inform teachers, curriculum designers, and science education professionals on how to design and adapt science content in ways that are both accessible and pedagogically sound.

in F1000Research on 2025-11-18 10:08:24 UTC.

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White paper: standards for handling and analyzing plant pan-genomes [version 2; peer review: 2 approved with reservations]

Plant pan-genomes, which aggregate genomic sequences and annotations from multiple individuals of a species, have emerged as transformative tools for understanding genetic diversity, adaptation, and evolutionary dynamics. Super-pan-genomes, extending across species boundaries, further enable comparative analyses of clades or genera, bridging breeding applications with evolutionary insights (Shang et al., 2022; Li et al., 2023a). However, the absence of standardized practices for data generation, analysis, and sharing hinders reproducibility and interoperability. This white paper presents a harmonized framework developed by the ELIXIR E-PAN consortium, addressing nomenclature, quality control (QC), data formats, visualization, and community practices. By adopting these guidelines, researchers can enhance FAIR (Findable, Accessible, Interoperable, Reusable) compliance, foster collaboration, and accelerate translational applications in crop improvement and evolutionary biology.

in F1000Research on 2025-11-18 09:19:00 UTC.

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First photographic evidence of the Egyptian fruit bat, Rousettus aegyptiacus (Pteropodidae) in the King Salman Bin Abdulaziz Royal Nature Reserve, Hail Region, Saudi Arabia [version 2; peer review: 2 approved with reservations]

Background This study aimed to document the occurrence of the Egyptian fruit bat Rousettus aegyptiacus in King Salman Bin Abdulaziz Royal Nature Reserve (KSRNR), Hail region, Saudi Arabia, where its presence had not previously been confirmed. Methods Field surveys were conducted in the Hail region, and direct observations were documented using photographic and video evidence. The colony was visually counted in the field during a single daytime visit from an estimated distance of 15 meters. Results More than 50 individuals, including adults and juveniles, were observed roosting in a rock crevice. The photographic documentation confirms the presence of a colony of R. aegyptiacus in this area, extending the known distribution of the species into a previously unrecorded region of Saudi Arabia. Conclusions These findings provide the first photographic record of R. aegyptiacus in KSRNR and highlight the species’ ecological use of arid rock crevices. The results emphasize the need for targeted surveys and long-term monitoring to better understand the distribution and conservation of this species in desert environments.

in F1000Research on 2025-11-18 09:10:44 UTC.

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Transformando la experiencia turística: Realidad Virtual para la fidelización de clientes en la industria turística [version 2; peer review: 1 approved with reservations]

Background Today, the tourism sector in particular is becoming increasingly competitive, forcing companies to adopt innovative approaches to attract the attention of consumers. In this context, a virtual reality application is presented with the aim of strengthening brand loyalty through immersive experiences in tourism companies. Methods The development was divided into three phases: planning, implementation and operation, which emphasized close collaboration with stakeholders to adapt to changing requirements; implementation, which focused on a simple design with emphasis on creating a scalable and secure backend architecture; and finally, in the operation phase, two iterations were carried out. The first focused on creating an immersive virtual environment with intuitive interfaces and basic interactive elements. The second iteration enhanced these basic features and incorporated advanced features such as greater interactivity with virtual objects for a more realistic experience. Results A 66.36% increase in brand loyalty was achieved, demonstrating increased customer engagement and sustainable revenue generation. There was also a 65.48% increase in customer retention, indicating a continued willingness to purchase services from this brand. In addition, 78.15% of participants expressed a high level of satisfaction with the virtual reality experience. Conclusions This research contributes to the process of inclusive and sustainable industrialization by improving infrastructure and making industries more sustainable. This will be achieved by making more efficient use of resources and promoting the adoption of technologies.

in F1000Research on 2025-11-18 08:54:33 UTC.

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Scoping review for physiotherapy interventions for people living with long COVID [version 3; peer review: 1 approved, 1 not approved]

Background A diverse spectrum of long COVID symptoms (LCS) have the scope of physical rehabilitation. Due to limited resources, very little is known about the physiotherapy and rehabilitation interventions for LCS and their clinical application. This study aims to enlist the physiotherapy and rehabilitation interventions in the management of musculoskeletal, neurological, cognitive, cardiorespiratory, mental health, and functional impairments of LCS. Methods The study was a scoping review of the literature published between April 2020 and July 2022. Results 87 records were extracted followed by a standard process of The Preferred Reporting Items for Systematic reviews and meta-analysis (PRISMA) extension for Scoping reviews (PRISMA-ScR). The included records had a 3223 LCS population. All types of primary and secondary evidences were retrieved except for qualitative studies. The evidence was evaluated by an appraisal scoring tool followed by the guidelines of the “Enhancing the Quality and Transparency of Health Research (EQUATOR) network”. The included papers had a mean appraisal score of 0.7807 on a 0 to 1 scale (SD 0.08), the minimum score was for study protocols (0.5870), and the maximum score was for Cohort studies (.8977). Sixty seven (67) evidence-based interventions were documented from 17 clinical categories. The most weighted interventions were treating underlying symptoms of long COVID (Adjusted score 1/1), management of fatigue (Adjusted score 0.963/1), aerobic exercise and balance training (Adjusted score 0.951/1), multidisciplinary rehabilitation (Adjusted score 0.926/1), and low resistance training and aerobic exercise (Adjusted score 0.889/1). Conclusion We recommend Long COVID rehabilitation in a multidisciplinary approach by treating the individual symptoms, especially fatigue. Physiotherapy interventions include were exercise, modalities, patient education, respiratory rehabilitation, and telerehabilitation. Scoping reviews do not require protocol registration from PROSPERO.

in F1000Research on 2025-11-18 08:30:27 UTC.

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Building the connectome of a small brain with a simple stochastic developmental generative model

Proceedings of the National Academy of Sciences, Volume 122, Issue 47, November 2025.
SignificanceUnderstanding how neural circuits form during development is key to unraveling the principles that govern brain structure and function. We use statistical generative models to study the well-characterized nervous system ofCaenorhabditis ...

in PNAS on 2025-11-18 08:00:00 UTC.

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Alleviation of negative emotional and pain responses through amygdala-hypothalamic social circuit activation

Proceedings of the National Academy of Sciences, Volume 122, Issue 47, November 2025.
Pain is a multifaceted experience that not only impacts individuals physiologically but also reshapes their emotional well-being and social dynamics. The expression of pain and postpain emotional states is profoundly influenced by social contexts. Recent ...

in PNAS on 2025-11-18 08:00:00 UTC.

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Gluten affects behaviors related to activity and anxiety in mice fed high fat diets

Journal of Neurophysiology, Ahead of Print.

in Journal of Neurophysiology on 2025-11-18 06:20:18 UTC.

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Role of BK channels and Kv4.2 channels in the electrophysiological properties of Merkel cells in rat whisker hair follicles

Journal of Neurophysiology, Volume 134, Issue 5, Page 1762-1773, November 2025.

in Journal of Neurophysiology on 2025-11-18 03:49:58 UTC.

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Conditional mGluR5 knockout in glutamatergic pathways disrupts the development of excitatory synaptic transmission onto mouse MNTB neurons

Journal of Neurophysiology, Volume 134, Issue 5, Page 1749-1761, November 2025.

in Journal of Neurophysiology on 2025-11-18 03:49:57 UTC.

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High resolution detection of stationary and evolving two-dimensional current source density within neuronal microcircuits

Journal of Neurophysiology, Volume 134, Issue 5, Page 1733-1748, November 2025.

in Journal of Neurophysiology on 2025-11-18 03:49:55 UTC.

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Can pupillometry reveal perturbation detection in sensorimotor adaptation during grasping?

Journal of Neurophysiology, Volume 134, Issue 5, Page 1804-1817, November 2025.

in Journal of Neurophysiology on 2025-11-18 03:49:54 UTC.

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Bidirectional locomotion induces asymmetric limb adaptations

Journal of Neurophysiology, Volume 134, Issue 5, Page 1774-1784, November 2025.

in Journal of Neurophysiology on 2025-11-18 03:49:52 UTC.

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Visual context affects the perceived timing of tactile sensations elicited through intracortical microstimulation: a case study of two participants

Journal of Neurophysiology, Volume 134, Issue 5, Page 1785-1803, November 2025.

in Journal of Neurophysiology on 2025-11-18 03:49:50 UTC.

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Non-canonical roles of lysosomes in neurons

Neurons are highly polarised and compartmentalised cells with organelles that are specialised to support their spatial and functional demands. This includes lysosomes, which are single-membrane-bound organelles enveloping acidic contents enriched with hydrolytic enzymes. While classically thought to be localised at the soma where they degrade waste, lysosomes have a range of dynamic nondegradative functions throughout neurons. Here, we review lysosomal dynamics and non-canonical functions in neurons, including axonal mRNA transport, mammalian target of rapamycin (mTOR) and Ca2+ signalling, neuronal remodelling, and interorganellar contact sites. We synthesise work across a range of model systems and species, providing insights from neurological diseases, where previous lysosomal research has focussed on proteostatic failure. This perspective highlights the need to better define lysosomal heterogeneity, compartmentalisation and specialisation in neurons.

in Trends in Neurosciences: In press on 2025-11-18 00:00:00 UTC.

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C5aR1+ microglia exacerbate neuroinflammation and cerebral edema in acute brain injury

Zhou et al. identify a pro-inflammatory C5aR1⁺ microglial subtype enriched in human cerebral edema tissues after TBI and ICH. C5aR1⁺ microglia amplify neuroinflammation, promote neurotoxic astrocyte polarization via LGALS9-CD44, and recruit neutrophils via CCL3/CCL5. Pharmacological C5aR1 inhibition reduces cerebral edema, highlighting a potential therapeutic target.

in Neuron: In press on 2025-11-18 00:00:00 UTC.

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Distinct populations of vascular smooth muscle and sinoatrial progenitors are localized adjacent to pro-epicardium

The pro-epicardium, once considered a uniform structure, generates diverse cardiac lineages. Miao et al. reveal that it contains distinct progenitor pools for vascular smooth muscle and pacemaker cells, in addition to epicardial cells, thereby providing an early road map for cardiac development and cell fate specification.

in Cell Reports: Current Issue on 2025-11-18 00:00:00 UTC.

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Modulation function of sphingomyelin molecular species in TLR4 signaling and cell death

Huang et al. demonstrate that sphingomyelin species regulate macrophage pyroptosis and cytokine release via TLR4/MD-2 and caspase signaling, revealing lipid-mediated control of innate immunity.

in Cell Reports: Current Issue on 2025-11-18 00:00:00 UTC.

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Blocking glycogen synthase 1 in white adipose tissue alleviates hypermetabolism following severe burn injury through inhibition of JAK2 by UDPG

Zhuo et al. find that glycogen metabolism in white adipose tissue is involved in thermogenesis and hypermetabolism after burn injury. Suppression of glycogen synthesis elevates UDPG, which interacts with JAK2 and blocks interleukin-6 signaling. Inhibition of glycogen synthesis by a small chemical inhibitor alleviates hypermetabolism and improves survival after burn.

in Cell Reports: Current Issue on 2025-11-18 00:00:00 UTC.

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Herpes simplex virus type 1 disseminates from the cornea to the CNS in mice by thwarting type III interferon immune defenses

Jian Zhou et al. reported that corneal HSV-1 infection upregulates SOCS1/3 expression, thereby suppressing STING-IFN-λ signaling to facilitate viral CNS invasion. Enhancing IFN-λ responses represents a therapeutic strategy to block viral spread from the cornea to the CNS.

in Cell Reports: Current Issue on 2025-11-18 00:00:00 UTC.

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MIAT-DHX9 spatiotemporal expression drives venous neointimal hyperplasia through nucleolar homeostasis and mitotic progression

Long non-coding RNAs (lncRNAs) are key regulators in disease, but their role in subnuclear structure and mitosis remains unclear. This study reveals that lncRNA MIAT directs the helicase DHX9 to the nucleolus, driving smooth muscle cell proliferation and vascular disease. Knockdown of MIAT or DHX9 disrupts nucleolar homeostasis and mitotic progression. The MIAT-DHX9 axis also contributes to neuroblastoma pathogenesis, offering a therapeutic target for treating hyperproliferative disorders.

in Cell Reports: Current Issue on 2025-11-18 00:00:00 UTC.

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Structural basis of DNA-dependent coactivator recruitment by the tuft cell master regulator POU2F3

Crystal structures and deep mutational scanning experiments by Alpsoy et al. reveal how the transcriptional coactivators POU2F3 and OCA-T form a DNA-dependent complex required for tuft cell lineage specification. Protein features essential for the function of this complex are identified, uncovering a structural vulnerability in tuft cell-like lung cancers.

in Cell Reports: In press on 2025-11-18 00:00:00 UTC.

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