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Predicting Dry Matter Intake in Lactating Crossbred Cows Under Semi-Arid Conditions Using an Error-Based Evolving Takagi-Sugeno Fuzzy Model

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IEEE Access 2025年 13卷 71521-71529页

作者： Leal, Dijair B. Soares, Rafael A. Junior, Vicente R. Rocha Moncao, Flavio P. Camargos, Murilo O. D'Angelo, Marcos F. S. V. Miralles-Pechuan, Luis UNIMONTES Graduate Program in Animal Science Montes Claros39401-089 Brazil UNIMONTES Graduate Program in Computer Modeling and Systems Montes Claros39401-089 Brazil UNIMONTES Department of Agricultural Sciences Minas Gerais Montes Claros39401-089 Brazil UNIMONTES Department of Computer Science Minas Gerais Montes Claros39401-089 Brazil Technological University Dublin School of Computer Science Dublin 8 D07 EWV4 Ireland

Accurately predicting dry matter intake (DMI) in lactating crossbred cows under semi-arid conditions is a critical challenge in dairy farming. Precise DMI predictions are essential for optimizing nutrient utilization and enhancing animal performance, particularly in semi-arid environments, where complex and dynamic conditions prevail. This paper introduces a novel Error-Based Evolving Takagi-Sugeno (EBETS) Fuzzy Model designed to address these challenges by dynamically adapting to real-time data and leveraging both current and historical information to enhance prediction accuracy. The EBETS model employs fuzzy set theory and multivariate Gaussian functions to model complex, nonlinear relationships between variables, offering a more robust and flexible approach to DMI prediction. In experiments involving data from 114 crossbred cows under semi-arid conditions, the EBETS model was compared to traditional models and an artificial neural network. It achieved a mean squared error of 0.25, which is more than 90% lower than that of all the other models, while demonstrating a higher correlation coefficient with the expected values, indicating superior predictive performance and reliability. These results underscore the EBETS model’s substantial superiority over existing methods, highlighting its potential to significantly improve dairy farming productivity by providing precise and reliable DMI predictions in challenging environmental conditions. The model’s ability to continuously learn and adapt to changing conditions makes it a promising tool for various applications in agricultural science. © 2013 IEEE.

关键词： Prediction models

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A Multifunctional Hydrogel with Multimodal Self-Powered Sensing Capability and Stable Direct Current Output for Outdoor Plant Monitoring Systems

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Nano-Micro Letters 2025年第4期17卷 1-24页

作者： Xinge Guo Luwei Wang Zhenyang Jin Chengkuo Lee Department of Electrical&Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117576Singapore Center for Intelligent Sensors and MEMS(CISM) National University of Singapore5 Engineering Drive 1Singapore 117608Singapore NUS Graduate School-Integrative Sciences and Engineering Program(ISEP) National University of SingaporeSingapore 119077Singapore Research Center for Sustainable Urban Farming(SUrF) National University of SingaporeSingapore 117558Singapore

Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability *** systems facilitate informed decisions that enhance efficiency in broader environmental *** outdoor systems equipped with energy harvesters and self-powered sensors often struggle with fluctuating energy sources,low durability under harsh conditions,non-transparent or non-biocompatible materials,and complex ***,a multifunctional hydrogel is developed,which can fulfill all the above requirements and build selfsustainable outdoor monitoring systems solely by *** can serve as a stable energy harvester that continuously generates direct current output with an average power density of 1.9 W m^(-3)for nearly 60 days of operation in normal environments(24℃,60%RH),with an energy density of around 1.36×10^(7)J m^(-3).It also shows good self-recoverability in severe environments(45℃,30%RH)in nearly 40 days of continuous ***,this hydrogel enables noninvasive and self-powered monitoring of leaf relative water content,providing critical data on evaluating plant health,previously obtainable only through invasive or high-power consumption *** potential extends to acting as other self-powered environmental *** multifunctional hydrogel enables self-sustainable outdoor systems with scalable and low-cost production,paving the way for future agriculture.

关键词： Self-powered sensor Hydrogel Energy harvester Outdoor farming Self-sustainable IoT

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ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks

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computers in Biology and Medicine 2025年 185卷 109541-109541页

作者： Le, Van-The Malik, Muhammad-Shahid Lin, Yi-Jing Liu, Yu-Chen Chang, Yan-Yun Ou, Yu-Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan Department of Computer Sciences Karakoram International University Gilgit-Baltistan 15100 Pakistan

Adenosine triphosphate plays a vital role in providing energy and enabling key cellular processes through interactions with binding proteins. The increasing amount of protein sequence data necessitates computational methods for identifying binding sites. However, experimental identification of adenosine triphosphate-binding residues remains challenging. To address the challenge, we developed a multi-window convolutional neural network architecture taking pre-trained protein language model embeddings as input features. In particular, multiple parallel convolutional layers scan for motifs localized to different window sizes. Max pooling extracts salient features concatenated across windows into a final multi-scale representation for residue-level classification. On benchmark datasets, our model achieves an area under the ROC curve of 0.95, significantly improving on prior sequence-based models and outperforming convolutional neural network baselines. This demonstrates the utility of pre-trained language models and multi-window convolutional neural networks for advanced sequence-based prediction of adenosine triphosphate-binding residues. Our approach provides a promising new direction for elucidating binding mechanisms and interactions from primary structure. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study

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Journal of Medical Imaging 2025年第2期12卷

作者： Ohara, Erik Y. Vigneshwaran, Vibujithan Souza, Raissa Vamosi, Finn G. Wilms, Matthias Forkert, Nils D. University of Calgary Biomedical Engineering Graduate Program Calgary AB Canada University of Calgary Department of Radiology Calgary AB Canada University of Calgary Hotchkiss Brain Institute Calgary AB Canada University of Calgary Alberta Children's Hospital Research Institute Calgary AB Canada University of Calgary Department of Computer Science Calgary AB Canada University of Calgary Departments of Pediatrics and Community Health Sciences Calgary AB Canada

Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation. Approach: Five DR techniques [2D principal component analysis (PCA), 2.5D PCA, 3D PCA, autoencoder, and Vector Quantised-Variational AutoEncoder] were applied to 23,692 3D brain images to create low-dimensional representations for causal DL model training. Convolutional neural networks were used to quantitatively evaluate age and sex changes on the counterfactual neuroimages. Age alterations were measured using the mean absolute error (MAE), whereas sex changes were assessed via classification accuracy. Results: The 2.5D PCA technique achieved the lowest MAE of 4.16 when changing the age variable of an original image. When sex was changed, the autoencoder embedding led to the highest classification accuracy of 97.84% while also significantly impacting the age variable predictions, increasing the MAE to 5.24 years. Overall, 3D PCA provided the best balance, with an age prediction MAE of 4.57 years while maintaining 94.01% sex classification accuracy when altering the age variable and 94.73% sex classification accuracy and the lowest age prediction MAE (3.84 years) when altering the sex variable. Conclusions: 3D PCA appears to be the best-suited DR method for causal neuroimage analysis. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： causal artificial intelligence causality deep learning dimensionality reduction generative models normalizing flows

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Simulated prosthetic vision identifies checkerboard as an effective raster pattern for retinal implants

arXiv

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arXiv 2025年

作者： Kasowski, Justin M. Beyeler, Michael Interdepartmental Graduate Program in Dynamical Neuroscience University of California Santa BarbaraCA93106 United States Department of Computer Science University of California Santa BarbaraCA93106 United States Department of Psychological & Brain Sciences University of California Santa BarbaraCA93106 United States

Objective: This study systematically evaluates the impact of raster patterns-specific spatial arrangements of sequential electrode activation-on performance and perceived difficulty in simulated prosthetic vision (SPV). By addressing a critical gap in the literature, we aimed to identify patterns that optimize functional vision in retinal implants. Approach: Sighted participants completed letter recognition and motion discrimination tasks under four raster patterns (horizontal, vertical, checkerboard, and random) using an immersive SPV system. The simulations employed psychophysically validated models of electrode activation, phosphene appearance, and temporal dynamics, ensuring realistic representation of prosthetic vision. Performance accuracy and self-reported difficulty were analyzed to assess the effects of raster patterning. Main Results: The checkerboard pattern consistently outperformed other raster patterns, yielding significantly higher accuracy and lower difficulty ratings across both tasks. The horizontal and vertical patterns introduced biases aligned with apparent motion artifacts, while the checkerboard minimized such effects. Random patterns resulted in the lowest performance, underscoring the importance of structured activation. Significance: These findings provide the first systematic evaluation of raster patterns, suggesting that the checkerboard configuration may enhance usability and perceptual clarity in retinal implants. While based on simulated environments, this study establishes a foundation for optimizing spatial and temporal electrode activation strategies in next-generation devices. © 2025, CC BY.

关键词： Prosthetics

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Survey and Improvement Strategies for Gene Prioritization with Large Language Models

arXiv

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arXiv 2025年

作者： Neeley, Matthew B. Qi, Guantong Wang, Guanchu Tang, Ruixiang Mao, Dongxue Liu, Chaozhong Pasupuleti, Sasidhar Yuan, Bo Xia, Fan Liu, Pengfei Liu, Zhandong Hu, Xia Jan and Dan Duncan Neurological Research Institute Texas Children’s Hospital Houston United States Graduate School of Biomedical Sciences Program in Quantitative and Computational Biosciences Baylor College of Medicine Houston United States Graduate School of Biomedical Sciences Program in Genetics and Genomics Baylor College of Medicine Houston United States Department of Computer Science Rice University Houston United States Department of Computer Science Rutgers University NJ United States Department of Pediatrics Baylor College of Medicine Houston United States Department of Molecular and Human Genetics Baylor College of Medicine Houston United States Human Genome Sequencing Center Baylor College of Medicine Houston United States Baylor Genetics Houston United States

Background Rare diseases can be difficult to diagnose due to limited patient data and broad genetic diversity. Despite the advances in variant prioritization tools, many rare disease cases remain undiagnosed. While large language models (LLMs) have performed well in medical exams, their accuracy in diagnosing rare genetic diseases has not yet been evaluated. Methods To identify causal genes for genetic diseases, we benchmarked various LLMs for gene prioritization. We employed multi-agent and Human Phenotype Ontology (HPO) classification approaches to identify patient case groups based on phenotypes categorizing them into different levels of solvability. To address LLM limitations in ranking large gene sets, we used a divide-and-conquer strategy to break down the ranking task into smaller subsets. Mini-batching inputs and limiting the number of generated tokens improved efficiency. Results In its vanilla form, GPT-4 consistently outperformed the other LLMs with an accuracy around 30%. Multi-agent and HPO classification approaches aided us in distinguishing between confidently-solved and challenging cases. In addition, we observed bias towards well-studied genes and input order sensitivity which are drawbacks of LLMs in disease-causal gene prioritization. Our divide-and-conquer strategy enhanced accuracy by overcoming positional and gene frequency biases in literature. This framework, based on benchmarking insights and novel techniques, significantly optimizes the overall process when identifying disease-causal genes for genetic diseases when compared to our baseline evaluation, and thereby better enables the development of targeted diagnostic and therapeutic interventions. Conclusions Using HPO classification and our novel multi-agent techniques, and our LLM divide-and-conquer strategy (1) highlighted the importance of accounting for differences in patient case solvability and (2) yielded improved performance in identifying causal genes for rare diseases when compare

关键词： Gene Ontology

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Temporal resolution of spike coding in feedforward networks with signal convergence and divergence

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PLoS Computational Biology 2025年第4 APRIL期21卷 e1012971页

作者： Mobille, Zach Sikandar, Usama Bin Sponberg, Simon Choi, Hannah School of Mathematics Georgia Institute of Technology AtlantaGA United States Interdisciplinary Graduate Program in Quantitative Biosciences Georgia Institute of Technology AtlantaGA United States School of Physics Georgia Institute of Technology AtlantaGA United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States School of Biological Sciences Georgia Institute of Technology AtlantaGA United States

Convergent and divergent structures in the networks that make up biological brains are found across many species and brain regions at various spatial scales. Neurons in these networks fire action potentials, or "spikes," whose precise timing is becoming increasingly appreciated as large sources of information about both sensory input and motor output. In this work, we investigate the extent to which feedforward convergent/divergent network structure is related to the gain in information of spike timing representations over spike count representations. While previous theories on coding in convergent and divergent networks have largely neglected the role of precise spike timing, our model and analyses place this aspect at the forefront. For a suite of stimuli with different timescales, we demonstrate that structural bottlenecks–small groups of neurons post-synaptic to network convergence–have a stronger preference for spike timing codes than expansion layers created by structural divergence. We further show that this relationship can be generalized across different spike-generating models and measures of coding capacity, implying a potentially fundamental link between network structure and coding strategy using spikes. Additionally, we found that a simple network model based on convergence and divergence ratios of a hawkmoth (Manduca sexta) nervous system can reproduce the relative contribution of spike timing information in its motor output, providing testable predictions on optimal temporal resolutions of spike coding across the moth sensory-motor pathway at both the single-neuron and population levels. © 2025 Mobille et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Neurons

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Promoting active behaviours in schoolchildren using the socio-ecological model in the physical education class: study protocol of the ACTIBESE project

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BMC Pediatrics 2025年第1期25卷 1-14页

作者： Rodríguez-Rodríguez, Fernando Cristi-Montero, Carlos Zurita-Corvalán, Natalia Brand, Caroline Mota, Jorge Chillón, Palma Gaya, Anelise Reis Brazo-Sayavera, Javier Aguilar-Farias, Nicolás IRyS Group Physical Education School Pontificia Universidad Católica de Valparaíso Valparaíso Chile Laboratory for Integrative and Translational Research in Population Health (ITR) Research Center in Physical Activity Health and Leisure (CIAFEL) Faculty of Sports University of Porto (FADEUP) Porto Portugal Department of Physical Education and Sports Faculty of Sport Sciences Sport and Health University Research Institute (iMUDS) University of Granada Granada Spain Graduate Program in Human Movement Sciences Federal University of Rio Grande do Sul (UFRGS) RS Porto Alegre Brazil Department of Sports and Computer Science Universidad Pablo de Olavide (UPO) Seville 41013 Spain Department of Physical Education Sports and Recreation Universidad de La Frontera Temuco Chile PhD Program in Educational Sciences University of Granada Granada Spain

Background: The correlation between parental and child physical activity levels has been the subject of significant investigation. Existing research has illuminated this association, particularly emphasising its influence during childhood. However, as children transition into adolescence, this relationship appears to weaken. Despite the recognised importance of parental involvement in promoting physical activity among children, scant attention has been directed towards understanding effective strategies and interventions to bolster this relationship. Consequently, there exists a critical gap in the literature concerning actionable measures to enhance parent-child physical activity engagement during the pivotal stage of adolescence. Methods: The ACTIBESE project aims to assess and intervene in Chilean schoolchildren’s active behaviours by examining the influence of personal, interpersonal, and school factors. This study includes a cross-sectional and intervention study involving schools in the Valparaíso and Araucanía Regions, representing urban and rural areas. Participants, divided into children (8–11 years old) and adolescents (12–16 years old), will undergo a comprehensive evaluation, including sociodemographic characteristics, physical activity (PA) levels, parental and peer support, teaching styles of the teachers, and school environment characteristics. In the intervention study, a 5-month continuous training program for teachers will be implemented, focusing on improving teaching styles and enhancing teaching competencies related to physical education and health classes. The research will use mixed models, ANCOVA, and logistic regressions for data analysis, network analysis, exploratory factors analysis, and structural equation modelling. Discussion: Through innovative research methods, interventions involving schoolteachers, and a diverse geographical scope, this project aims to make significant contributions to both the scientific understanding of promoting

关键词： Exercise Health Intervention Physical activity Well-being

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Biomedical data and AI

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Science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Measurement of peripheral nerve magnetostimulation thresholds of a head solenoid coil between 200 Hz and 88.1 kHz

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IEEE Journal of Translational Engineering in Health and Medicine 2025年

作者： Barksdale, Alex C. Ferris, Natalie G. Mattingly, Eli Sliwiak, Monika Guerin, Bastien Wald, Lawrence L. Davids, Mathias Klein, Valerie MIT Department of Electrical Engineering and Computer Science Cambridge MA United States Martinos Center for Biomedical Imaging Charlestown MA United States Harvard University Harvard Graduate Program in Biophysics Cambridge MA United States Harvard-MIT Division of Health Sciences and Technology Boston MA United States Harvard Medical School Boston MA United States

Objective: Magnetic fields switching at kilohertz frequencies induce electric fields in the body, which can cause peripheral nerve stimulation (PNS). Although magnetostimulation has been extensively studied below 10 kHz, the behavior of PNS at higher frequencies remains poorly understood. This study aims to investigate PNS thresholds at frequencies up to 88.1 kHz and to explore deviations from the widely accepted hyperbolic strength-duration curve (SDC). Methods and procedures: PNS thresholds were measured in the head of 8 human volunteers using a solenoidal coil at 16 distinct frequencies, ranging from 200 Hz to 88.1 kHz. A hyperbolic SDC was used as a reference to compare the frequency-dependent behavior of PNS thresholds. Results: Contrary to the predictions of the hyperbolic SDC, PNS thresholds did not decrease monotonically with frequency. Instead, thresholds reached a minimum near 25 kHz, after which they increased by an average of 39% from 25 kHz to 88.1 kHz across subjects. This pattern indicates a significant deviation from previously observed behavior at lower frequencies. Conclusion: Our results suggest that PNS thresholds exhibit a non-monotonic frequency dependence at higher frequencies, diverging from the traditional hyperbolic SDC. These findings offer critical data for refining neurodynamic models and provide insights for setting PNS safety limits in applications like MRI gradient coils and magnetic particle imaging (MPI). Further investigation is needed to understand the biological mechanisms driving these deviations beyond 25 kHz. Clinical impact: These findings call for further basic research into biological mechanisms underlying high frequency PNS threshold trends, and supports refinement of safety guidelines for MRI and MPI systems for clinical implementation. © 2013 IEEE.

关键词： high frequency magnetostimulation magnetic particle imaging (MPI) magnetic resonance imaging (MRI) MRI gradient safety peripheral nerve stimulation

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