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6th International Conference on Biomedical and Health Informatics, ICBHI 2024

作者： Chen, Yun-Chien Jiang, Ching-Fen Graduate Degree Program of Smart Healthcare and Bioinformatics I-Shou University Kaohsiung City Taiwan

ISBN: (纸本)9783031863226

Respiratory rate is an important vital sign that reflects health status. In hospital, traditional methods of measuring respiratory rate include manual counting and using a respiratory belt, both of which may pose a risk of contact infection. This study conducted clinical trial in medical ward of hospital, presented a non-contact method based on a 24 GHz continuous-wave radar to detect the displacement of the chest wall during breathing. By processing the radar signal, we were able to obtain the respiratory rate. However, during clinical validation, uncertainty in the daily measurement time of vital sign, which serve as the gold standard, can lead to comparison error. To overcome the interference caused by the issue, this study proposed a validation method that calculates the average of radar-detected respiratory rate over specific time interval and uses the standard deviation of these time interval as an indicator of the stability of the radar-detected respiratory rate value. Statistical results show that the radar-detected respiratory rate derived in this study is consistent with the hospital-measured respiratory rate (p = 0.17) and exhibits significant differences across different subjects (p © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Noninvasive medical procedures

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Analysing protein complexes in plant science: insights and limitation with AlphaFold 3

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Botanical Studies 2025年第1期66卷 1-12页

作者： Lin, Pei-Yu Huang, Shiang-Chin Chen, Kuan-Lin Huang, Yu-Chun Liao, Chia-Yu Lin, Guan-Jun Lee, HueyTyng Chen, Pao-Yang Institute of Plant and Microbial Biology Academia Sinica Taipei Taiwan Institute of Plant Biology National Taiwan University Taipei Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program National Taiwan University Taipei Taiwan Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei Taiwan

AlphaFold 3 (AF3), an artificial intelligence (AI)-based software for protein complex structure prediction, represents a significant advancement in structural biology. Its flexibility and enhanced scalability have unlocked new applications in various fields, specifically in plant science, including improving crop resilience and predicting the structures of plant-specific proteins involved in stress responses, signalling pathways, and immune responses. Comparisons with existing tools, such as ClusPro and AlphaPulldown, highlight AF3’s unique strengths in sequence-based interaction predictions and its greater adaptability to various biomolecular structures. However, limitations persist, including challenges in modelling large complexes, protein dynamics, and structures from underrepresented plant proteins with limited evolutionary data. Additionally, AF3 encounters difficulties in predicting mutation effects on protein interactions and DNA binding, which can be improved with molecular dynamics and experimental validation. This review presents an overview of AF3’s advancements, using examples in plant and fungal research, and comparisons with existing tools. It also discusses current limitations and offers perspectives on integrating molecular dynamics and experimental validation to enhance its capabilities.

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Methacrylic acid/Tween 80 engineered amorphous calcium phosphate as an effective bioactive reinforcing nanofillers in dental adhesive

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Dental Materials Journal 2025年第2期44卷 190-200页

作者： Maimansomsuk, Siripatsorn Teangrom, Paremai Teanchai, Chayada Sinthuvanich, Chomdao Ruengket, Pakorn Attarataya, Jakrada Akkarachaneeyakorn, Khrongkhwan Department of Chemistry Faculty of Science Kasetsart University Bangkok10900 Thailand Center of Excellence for Innovation in Chemistry Faculty of Science Kasetsart University Bangkok10900 Thailand Research Center Faculty of Dentistry Mahidol University Bangkok10400 Thailand Department of Biochemistry Faculty of Science Kasetsart University Bangkok Thailand Faculty of Science Kasetsart University Bangkok Thailand Genetic Engineering and Bioinformatics Program Graduate School Kasetsart University Bangkok Thailand Nakhon Ratchasima30000 Thailand

Bioactive fillers in dental adhesives are designed to release beneficial ions, such as calcium and phosphate, to help in remineralization process, and preventing secondary caries. Their incorporation also aims to improve the longevity of dental restorations. Herein, CaP nanoparticles were synthesized through a water-in-oil emulsion method using Tween80 as a surfactant and methacrylic acid (MAA). MAA officiated as a co-surfactant and reduced the size of CaP nanoparticle, including improved compatibility with the components of dental adhesive. The incorporation of 2% T80/CaP/MAA nanofillers into dental adhesive demonstrated increased efficiency, as evidenced by a higher degree of conversion (%DC) and greater micro-tensile bond strength (μTBS). More importantly, T80/CaP/ MAA nanofillers were able to induce remineralization process in the dental structure, which was confirmed by EDX mapping and IR imaging. Finally, a cytotoxicity assessment of the dental adhesive containing T80/CaP/MAA showed no significant adverse effects on human fibroblast cells. © 2025, Japanese Society for Dental Materials and Devices. All rights reserved.

关键词： Stem cells

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Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics

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PLoS Computational Biology 2025年第4期21卷 e1012962页

作者： Morin, Alexander Chu, Ching Pan Pavlidis, Paul Michael Smith Laboratories University of British Columbia VancouverBC Canada Department of Psychiatry University of British Columbia VancouverBC Canada Graduate Program in Bioinformatics University of British Columbia VancouverBC Canada

The proliferation of single cell transcriptomics has potentiated our ability to unveil patterns that reflect dynamic cellular processes such as the regulation of gene transcription. In this study, we leverage a broad collection of single cell RNA-seq data to identify the gene partners whose expression is most coordinated with each human and mouse transcription regulator (TR). We assembled 120 human and 103 mouse scRNA-seq datasets from the literature (>28 million cells), constructing a single cell coexpression network for each. We aimed to understand the consistency of TR coexpression profiles across a broad sampling of biological contexts, rather than examine the preservation of context-specific signals. Our workflow therefore explicitly prioritizes the patterns that are most reproducible across cell types. Towards this goal, we characterize the similarity of each TR’s coexpression within and across species. We create single cell coexpression rankings for each TR, demonstrating that this aggregated information recovers literature curated targets on par with ChIP-seq data. We then combine the coexpression and ChIP-seq information to identify candidate regulatory interactions supported across methods and species. Finally, we highlight interactions for the important neural TR ASCL1 to demonstrate how our compiled information can be adopted for community use. © 2025 Morin 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： Mammals

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Multi-modal Multitask Learning Model for Simultaneous Classification of Two Epilepsy Biomarkers 27th

Multi-modal Multitask Learning Model for Simultaneous Classi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Broti, Nawara Mahmood Sawada, Masaki Takayama, Yutaro Iijima, Keiya Iwasaki, Masaki Ono, Yumie Electrical Engineering Program Graduate School of Science and Technology Meiji University Kanagawa Kawasaki Japan Department of Neurosurgery Yokohama City University Hospital Kanagawa Yokohama Japan Department of Neurosurgery National Center Hospital National Center of Neurology and Psychiatry Tokyo Kodaira Japan Department of Electronics and Bioinformatics School of Science and Technology Meiji University Kanagawa Kawasaki Japan

ISBN: (纸本)9783031781971

Accurate estimation of the epileptogenic zone is critical for surgical treatment of drug-resistant epilepsy. While epilepsy biomarkers detection for epileptogenic zone localization has traditionally relied on expert clinician knowledge, the use of deep learning in this context has recently gained appeal for objective diagnosis and reducing clinician burden. However, previously proposed classifiers for electrocorticogram data focused on only single biomarkers among many types, requiring separate models and large datasets for each. To minimize clinicians’ workload and patients’ sufferings, innovative methods to classify multiple biomarkers using small training datasets and minimal computation time is required. This research marks the first implementation of multi-modal multitask learning in epilepsy biomarker classification, proposing a model to automatically classify two biomarkers—high-frequency oscillations (HFO) and interictal epileptiform discharges (IED)—from separate datasets simultaneously. We validated the proposed model on 1500 annotated HFO and IED candidate signals each, obtained from 5 patients. Our proposed model can perform both HFO and IED classification simultaneously, with cross-validation accuracies of 90.99% and 93.99%;F1 scores were reported as 0.80 and 0.82, respectively. Our leave-one-patient-out experiments achieved 78.03% and 81.19% accuracy for HFO and IED classification, respectively. Compared to existing state-of-the-art classification architectures, our model is shown to be efficient and robust, yet simple and lightweight. The usefulness of sharing parameters between the two classifiers was confirmed in this practical clinical setting of epileptic biomarker detection, potentially contributing to minimizing the number of annotated datasets and realizing robust, accurate, and simultaneous identification of two biomarkers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Exploring the genomic and transcriptomic profiles of glycemic traits and drug repurposing

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Journal of Biomedical Science 2025年第1期32卷 1-14页

作者： Lin, Min-Rou Tsai, Cheng-Lin Liao, Cai-Sian Wei, Chun-Yu Chou, Wan-Hsuan Hsiao, Tzu-Hung Chang, Wei-Chiao Department of Clinical Pharmacy School of Pharmacy Taipei Medical University Taipei Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program National Taiwan University Taipei Taiwan Core Laboratory of Neoantigen Analysis for Personalized Cancer Vaccine Office of R&D Taipei Medical University Taipei Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Public Health Fu Jen Catholic University New Taipei City Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Master Program in Clinical Genomics and Proteomics School of Pharmacy Taipei Medical University Taipei Taiwan Integrative Research Center in Critical Care Wan Fang Hospital Taipei Medical University Taipei Taiwan

Type 2 diabetes is an increasingly prevalent metabolic disorder with moderate to high heritability. Glycemic indices are crucial for diagnosing and monitoring the disease. Previous genome-wide association study (GWAS) have identified several risk loci associated with type 2 diabetes, but data from the Taiwanese population remain relatively sparse and primarily focus on type 2 diabetes status rather than glycemic trait levels. We conducted a comprehensive genome-wide meta-analysis to explore the genetics of glycemic traits. The study incorporated a community-based cohort of 145,468 individuals and a hospital-based cohort of 35,395 individuals. The study integrated genetics, transcriptomics, biological pathway analyses, polygenic risk score calculation, and drug repurposing for type 2 diabetes. This study assessed hemoglobin A1c and fasting glucose levels, validating known loci (FN3K, SPC25, MTNR1B, and FOXA2) and discovering new genes, including MAEA and PRC1. Additionally, we found that diabetes, blood lipids, and liver- and kidney-related traits share genetic foundations with glycemic traits. A higher PRS was associated with an increased risk of type 2 diabetes. Finally, eight repurposed drugs were identified with evidence to regulate blood glucose levels, offering new avenues for the management and treatment of type 2 diabetes. This research illuminates the unique genetic landscape of glucose regulation in Taiwanese Han population, providing valuable insights to guide future treatment strategies for type 2 diabetes.

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Development of a Smartphone-Linked Immunosensing System for Oxytocin Determination

Biosensors

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Biosensors 2025年第4期15卷 261-261页

作者： Sarubo, Miku Suzuki, Yoka Numazaki, Yuka Kudo, Hiroyuki Electrical Engineering Program Graduate School of Science and Technology Meiji University Kanagawa Kawasaki City214-8571 Japan Department of Electronics and Bioinformatics School of Science and Technology Meiji University Kanagawa Kawasaki City214-8571 Japan

We report an optical immunosensing system for oxytocin (OXT) based on image analysis of color reactions in an enzyme-linked immunosorbent assay (ELISA). We employed a miniaturized optical immunosensing unit that was functionally connected to an LED and a smartphone camera. Our system measures OXT levels using a metric called the RGBscore, which is derived from the red, green, and blue (RGB) information in the captured images. By calculating the RGBscore regressively using the brute-force method, this approach can be applied to smartphones with various CMOS image sensors and firmware. The lower detection limit was determined to be 5.26 pg/mL, and the measurement results showed a higher correlation (r = 0.972) with those obtained from conventional ELISA. These results suggest the potential for its application in a simplified health management system for individuals. © 2025 by the authors.

关键词： Firmware

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Alignment-Free Estimation of Read to Genome Distances and Its Applications

Alignment-Free Estimation of Read to Genome Distances and...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Şapcı, Ali Osman Berk Mirarab, Siavash Bioinformatics and Systems Biology Graduate Program UC San Diego San DiegoCA92093 United States Department of Electrical and Computer Engineering UC San Diego San DiegoCA92093 United States

ISBN: (纸本)9783031902512

Searching reads from unknown origins in a reference database and finding evolutionarily similar genomes is central to many applications. Quantifying the similarity by estimating the distance between each read and matching references could further help downstream analyses such as taxonomic characterization or even placing the read on a reference phylogeny. Such distances can be computed using alignment. Since alignment becomes impractical for ultra-large reference databases, many use k-mer-based tools for downstream tasks, but these methods do not compute distances. We introduce krepp to compute the distance between a read and a genome using a maximum likelihood framework built around k-mers. We further introduce algorithms to place reads on a reference tree. Empirical evaluations show that krepp accurately estimates distances, scales well with large databases, and can place short reads coming from any part of the genome on a reference phylogeny. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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Deep learning-based denoising for unbiased analysis of morphology and stiffness in amyloid fibrils

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Computers in Biology and Medicine 2025年 184卷 109410-109410页

作者： Park, Jaehee Cheong, Da Yeon Lee, Gyudo Han, Cheol E. Department of Electronics and Information Engineering Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of

Understanding the morphology of amyloid fibrils is crucial for comprehending the aggregation and degradation mechanisms of abnormal proteins implicated in various diseases, such as Alzheimer's disease, Parkinson's disease, type II diabetes, and various forms of amyloidosis. Atomic force microscopy (AFM) stands as the most representative method for studying amyloid fibril morphology. However, obstacles in AFM images, including noise, salt, and amorphous aggregates, often impede accurate sample quantification. In this study, we developed denoising software employing a U-Net deep learning architecture to address this issue. The software efficiently eliminated various impediments that interfere with fibril analysis in noisy AFM images, thereby facilitating precise quantification of amyloid fibrils. We also developed automated fibril analysis technologies using the denoised AFM images, leading to quicker, more precise, and more objective assessments of fibril morphology. Furthermore, we presented a method for fibril stiffness extraction from a modulus image through mask creation based on a denoised height image. Our approach secures time efficiency and precision in analyzing amyloid morphology, and we believe it will significantly advance the currently stagnant research on amyloid-related diseases. © 2024 The Authors

关键词： Neurodegenerative diseases

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer Science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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