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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Soeial Media Produet Reviews 33

Using Valence and Arousal-infused Bi-LSTM for Sentiment Anal...

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33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021

作者： Cheng, Yu-Ya Yeh, Wen-Chao Chen, Yan-Ming Chang, Yung-Chun Professional Master Program in Data Science Taipei Medical University Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Graduate Institute of Data Science Taipei Medical University Taipei Taiwan

ISBN: (纸本)9789869576949

With the popularity of the current Internet age, online social platforms have provided a bridge for communication between private companies, public organizations, and the public. The purpose of this research is to understand the user's experience of the product by analyzing product review data in different fields. We propose a BiLSTM-based neural network which infused rich emotional information. In addition to consider Valence and Arousal which is the smallest morpheme of emotional information, the dependence relationship between texts is also integrated into the deep learning model to analyze the sentiment. The experimental results show that this research can achieve good performance in predicting the vocabulary Valence and Arousal. In addition, the integration of VA and dependency information into the BiLSTM model can have excellent performance for social text sentiment analysis, which verifies that this model is effective in emotion recognition of social medial short text. © 2021 ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. All rights reserved.

关键词： Social networking (online)

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Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysis

arXiv

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arXiv 2023年

作者： Nguyen, Thanh-Huy Kha, Quang Hien Truong, Thai Ngoc Toan Lam, Ba Thinh Ngo, Ba Hung Dinh, Quang Vinh Le, Nguyen Quoc Khanh Department of Biomedical Engineering National Cheng Kung University Taiwan International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taiwan Viet Nam Ho Chi Minh City International University Viet Nam Graduate School of Data Science Chonnam National University Korea Republic of Vietnamese-German University Viet Nam Research Center for Artificial Intelligence in Medicine Taipei Medical University Taiwan Professional Master Program in Artificial Intelligence in Medicine Taiwan

In recent years, many mammographic image analysis methods have been introduced for improving cancer classification tasks. Two major issues of mammogram classification tasks are leveraging multi-view mammographic information and class-imbalance handling. In the first problem, many multi-view methods have been released for concatenating features of two or more views for the training and inference stage. Having said that, most multi-view existing methods are not explainable in the meaning of feature fusion, and treat many views equally for diagnosing. Our work aims to propose a simple but novel method for enhancing examined view (main view) by leveraging low-level feature information from the auxiliary view (ipsilateral view) before learning the high-level feature that contains the cancerous features. For the second issue, we also propose a simple but novel malignant mammogram synthesis framework for upsampling minor class samples. Our easy-to-implement and no-training framework has eliminated the current limitation of the CutMix algorithm which are unreliable synthesized images with random pasted patches, hard-contour problems, and domain shift problems. Our results on VinDr-Mammo and CMMD datasets show the effectiveness of our two new frameworks for both multi-view training and synthesizing mammographic images, outperforming the previous conventional methods in our experimental settings. © 2023, CC BY-NC-SA.

关键词： Mammography

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Gender Disparities in Parkinson's Disease: Exploring Quality of Life Among Mexican Men and Women (2011-2019)

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Parkinsonism and Related Disorders 2024年 122卷 106832-106832页

作者： Hernández-Medrano, A.J. Medina-Pérez, M.F. Romero-Terán, D.P. Abundes-Corona, A. Cervantes-Arriaga, A. Rodríguez-Violante, M. Instituto Nacional de Neurología y Neurocirugía Clinical Laboratory of Neurodegenerative Diseases Mexico City Mexico University of Maryland Baltimore County Master of Professional Studies (MPS) in Data Science Program Baltimore United States

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Frailty and Other Factors Associated With Early Outcomes in Middle-to Older Age Trauma Patients: A Prospective Cohort Study

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American Journal of Geriatric Psychiatry 2024年第2期32卷 244-255页

作者： Yeh, Tian-Shin Kang, Jiunn-Horng Littlejohns, Thomas J. Wu, Chia-Chieh Chen, Jin-Hua Piravej, Krisna Chiu, Wen-Ta Lam, Carlos Department of Physical Medicine and Rehabilitation School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Physical Medicine and Rehabilitation Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Epidemiology and Nutrition Harvard T. H. Chan School of Public Health Harvard University Boston MA United States Department of Physical Medicine and Rehabilitation National Taiwan University Hospital Taipei Taiwan Department of Physical Medicine and Rehabilitation College of Medicine National Taiwan University Taipei Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taipei Taiwan Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei Taiwan International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Nuffield Department of Population Health University of Oxford Oxford United Kingdom Emergency Department Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Emergency School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Graduate Institute of Data Science College of Management Taipei Medical University Taipei Taiwan Institutional Research Center Office of Data Science Taipei Medical University Taipei Taiwan Biostatistics Center Department of Medical Research Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok Thailand Department of Chula Neuroscience Center King Chulalongkorn Memorial Hospital Bangkok Thailand Graduate Institute of Injury Prevention and Control College of Public Health Taipei Medical University Taipei Taiwan AHMC Health System Alhambra CA United States

Objectives: To prospectively investigate associations of frailty and other predictor variables with functional recovery and health outcomes in middle-aged and older patients with trauma. Design: Single-center prospective cohort study. Setting: Emergency department of Wan Fang Hospital in Taiwan. Participants: Trauma patients aged 45 and older. Measurements: Frailty was assessed with the Clinical Frailty Scale (CFS). Injury mechanisms, pre-existing diseases, and fracture locations were recorded at baseline. The primary outcome was functional recovery assessed using the Barthel Index (BI). Secondary outcomes were new care needs, unscheduled return visits, and falls 3 months postinjury. Results: A total of 588 participants were included in the final analysis. For every one-point increase in the CFS, the multivariable-adjusted odds ratio (OR, 95% confidence interval [CI]) of failure to retain the preinjury BI was 1.34 (1.16–1.55);associations were consistent across levels of age and injury severities. Significant joint associations of frailty and age with poor functional recovery were observed. CFS was also associated with new care needs (OR for every one-point increase, 1.36, 95% CI, 1.17–1.58), unscheduled return visits (OR 1.26, 95% CI, 1.04–1.51), and falls (OR 1.23, 95% CI, 1.01–1.51). Other variables associated with failure to retain preinjury BI included road traffic accident and presence of hip fracture. Conclusion: Frailty was significantly associated with poor functional and health outcomes regardless of injury severity in middle-aged and older patients with trauma. Injury mechanisms and fracture locations were also significant predictors of functional recovery postinjury. © 2023 American Association for Geriatric Psychiatry

关键词： Clinical Frailty Scale Frailty functional recovery injury middle-to older age

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Morphological and metabolic asymmetries of the thalamic subregions in temporal lobe epilepsy predict cognitive functions

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Scientific Reports 2023年第1期13卷 1-9页

作者： Tung, Hsin Tsai, Shih-Chuan Huang, Pu-Rong Hsieh, Peiyuan F. Lin, Yi-Ching Peng, Syu-Jyun Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan Center of Faculty Development Taichung Veterans General Hospital Taichung Taiwan Neurological Institute Taichung Veterans General Hospital Taichung Taiwan Institute of Clinical Medicine National Yang Ming Chiao Tung University Taipei Taiwan Department of Nuclear Medicine Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging and Radiological Technology Institute of Radiological Science Central Taiwan University of Science and Technology Taichung Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University No.250 Wuxing St. Xinyi Dist. Taipei City 110 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Medical University Taipei Taiwan

Both morphological and metabolic imaging were used to determine how asymmetrical changes of thalamic subregions are involved in cognition in temporal lobe epilepsy (TLE). We retrospectively recruited 24 left-TLE and 15 right-TLE patients. Six thalamic subnuclei were segmented by magnetic resonance imaging, and then co-registered onto Positron emission tomography images. We calculated the asymmetrical indexes of the volumes and normalized standard uptake value ratio (SUVR) of the entire and individual thalamic subnuclei. The SUVR of ipsilateral subnuclei were extensively and prominently decreased compared with the volume loss. The posterior and medial subnuclei had persistently lower SUVR in both TLE cases. Processing speed is the cognitive function most related to the metabolic asymmetry. It negatively correlated with the metabolic asymmetrical indexes of subregions in left-TLE, while positively correlated with the subnuclei volume asymmetrical indexes in right-TLE. Epilepsy duration negatively correlated with the volume asymmetry of most thalamic subregions in left-TLE and the SUVR asymmetry of ventral and intralaminar subnuclei in right-TLE. Preserved metabolic activity of contralateral thalamic subregions is the key to maintain the processing speed in both TLEs. R-TLE had relatively preserved volume of the ipsilateral thalamic volume, while L-TLE had relatively decline of volume and metabolism in posterior subnucleus. © 2023, The Author(s).

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Free Energy Minimization Using the 2-D Cluster Variation Method: Initial Code Verification and Validation

arXiv

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arXiv 2018年

作者： Maren, Alianna J. Northwestern University School of Professional Studies Master of Science in Data Science Program

A new approach for artificial general intelligence (AGI), building on neural network deep learning architectures, can make use of one or more hidden layers that have the ability to continuously reach a free energy minimum even after input stimulus is removed, allowing for a variety of possible behaviors. One reason that this approach has not been developed until now has been the lack of a suitable free energy equation;one that would avoid some of the difficulties known in Hopfield-style neural networks. The cluster variation method (CVM) offers a means for characterizing 2-D local pattern distributions, or configuration variables, and provides a free energy formalism in terms of these configuration variables. The equilibrium distribution of these configuration variables is defined in terms of a single interaction enthalpy parameter, h, for the case of equiprobable distribution of bistate (neural/neural ensemble) units. For non-equiprobable distributions, the equilibrium distribution can be characterized by providing a fixed value for the fraction of units in the active state (x1), corresponding to the influence of a per-unit activation enthalpy, together with the pairwise interaction enthalpy parameter h. This paper provides verification and validation (V&V) for code that computes the configuration variable and thermodynamic values for 2-D CVM grids characterized by different interaction enthalpy parameters, or h-values. This means that there is now a working foundation for experimenting with a 2-D CVM-based hidden layer that can, as an alternative to responding strictly to inputs, also now independently come to its own free energy minimum. Such a system can also return to a free energy-minimized state after it has been perturbed, which will enable a range of input-independent behaviors that have not been hitherto available. A further use of this 2-D CVM grid is that by characterizing different kinds of patterns in terms of their corresponding h-values (together wit

关键词： Free energy

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Corrigendum to "Investigating nurses' acceptance of patients' bring your own device implementation in a clinical setting: A pilot study" [Asia-Pacific J Oncol Nurs 10 (2023) 100195]

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Asia-Pacific journal of oncology nursing 2024年第5期11卷 100437页

作者： Shuo-Chen Chien Chun-You Chen Chia-Hui Chien Usman Iqbal Hsuan-Chia Yang Huei-Chia Hsueh Shuen-Fu Weng Wen-Shan Jian Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Artificial Intelligence Research and Development Center Wan Fang Hospital Taipei Medical University Taipei Taiwan. International Center for Health Information and Technology College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Radiation Oncology Wan Fang Hospital Taipei Medical University Taipei Taiwan. Office of Public Affairs Taipei Medical University Taipei Taiwan. Health ICT Department of Health Tasmania Australia. Global Health and Health Security Department College of Public Health Taipei Medical University Taipei Taiwan. Department of Artificial Intelligence in Medicine Professional Master Program Taipei Medical University Taipei Taiwan. Department of Pharmacy Taipei Veterans General Hospital Taipei Taiwan. Division of Endocrinology and Metabolism Department of Internal Medicine Taipei Medical University Hospital Taipei Taiwan. Division of Endocrinology and Metabolism Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. School of Health Care Administration Taipei Medical University Taipei Taiwan. School of Gerontology Health Management College of Nursing Taipei Medical University Taipei Taiwan. Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan. Graduate Institute of Data Science Taipei Medical University Taipei Taiwan.

[This corrects the article DOI: 10.1016/***.2023.100195.].

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Correction: A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical data Acquisition: Development and Implementation Study

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Journal of medical Internet research 2021年第7期23卷 e31085页

作者： Cheng-Sheng Yu Shy-Shin Chang Tzu-Hao Chang Jenny L Wu Yu-Jiun Lin Hsiung-Fei Chien Ray-Jade Chen Department of Family Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. Department of Family Medicine Taipei Medical University Hospital Taipei Taiwan. Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan. Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan. Division of Plastic Surgery Department of Surgery Taipei Medical University Hospital and School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. Department of Surgery School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. Division of General Surgery Department of Surgery Taipei Medical University Hospital Taipei Taiwan.

[This corrects the article DOI: 10.2196/27806.].

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Trust in government moderates the association between fear of COVID-19 as well as empathic concern and preventive behaviour

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Communications Psychology 2023年第1期1卷 1-16页

作者： Arzu Karakulak Beyza Tepe Radosveta Dimitrova Mohamed Abdelrahman Azzam Amin Plamen Akaliyski Rana Alaseel Batoul Bakkar Wassim Gharz Edine Fatema Mohsen Yousuf Abdulqader Alkamali Danny A. Lizarzaburu Aguinaga Andrii Andres John Jamir Benzon R. Aruta Marios Assiotis Olga Solomontos-Kountouri Hrant Avanesyan Maria Muradyan Norzihan Ayub Maria Bacikova-Sleskova Raushan Baikanova Aidos Bolatov Telman Seisembekov Sunčica Bartoluci Ksenija Bosnar Anna Janovská Darko Katović Franjo Prot David Benitez Rita Rivera Ivanna Bodnar Iuliia Pavlova Judyta Borchet Maria Kazmierczak Aleksandra Lewandowska-Walter Justyna Michalek-Kwiecien Katarzyna Skrzypińska Yunier Broche-Pérez Carmen Buzea Rosalinda Cassibba Pasquale Musso Fabiola Silletti Marta Martín Carbonell Bin-Bin Chen Gordana Ristevska Dimitrovska Dương Công Doanh Alejandra del Carmen Dominguez Espinosa Nelli Ferenczi Regina Fernández-Morales Jorge Gaete Yiqun Gan Suely Giolo Rubia Carla Formighieri Giordani Camila Muhl José Rasia Maria-Therese Friehs Shahar Gindi Biljana Gjoneska Juan Carlos Godoy Lorena Cecilia López Steinmetz Maria del Pilar Grazioso Marinés Mejía Camellia Hancheva Given Hapunda Shogo Hihara Russell Sarwar Kabir Tinka Delakorda Kawashima Kazumi Sugimura Brett Walter Mohd Saiful Husain Md Saiful Islam Sadia Sultana Nino Javakhishvili Ana Makashvili Veljko Jovanović Nor Ba’yah Abdul Kadir Johannes Karl Zhumaly Kauyzbay Richa Khanna Meetu Khosla Martina Klicperová-Baker Iva Poláčková Šolcová Ana Kozina Steven Eric Krauss Rodrigo Landabur Katharina Lefringhausen Yun-Hsia Liang Sadia Malik Denisse Manrique-Millones Stefanos Mastrotheodoros Breeda McGrath Enkeleint A. Mechili Samson Mhizha Diana Miconi Rodrigo Moreta-Herrera Andrej Naterer Arash Nemat Felix Neto Joana Neto Luz Marina Alonso Palacio Hassan Okati-Aliabad Carlos Iván Orellana Ligia María Orellana Sushanta Kumar Mishra Joonha Park Eddy Peralta Petro Petrytsa Saša Pišot Benedicta Prihatin Dwi Riyanti Laura Francisca Sudarnoto Angela Oktavia Suryani Adil Samekin Danielius Serapinas Rasa Pilkaus Istanbul Policy Center Sabanci University Istanbul Turkey Department of Psychology MEF University Istanbul Turkey Department of Psychology Stockholm University Stockholm Sweden Social Psychology Department Doha Institute for Graduate Studies Doha Qatar Mokhtass for Consultations and Research Doha Qatar Department of Sociology and Social Policy Lingnan University Hong Kong SAR China Faculty of Medicine Syrian Private University Damascus Syrian Arab Republic Ipswich Hospital Ipswich UK United Private School Athaiba Oman Universidad César Vallejo Trujillo Peru Department of Physical Education Lviv Polytechnic National University Lviv Ukraine Department of Psychology De La Salle University Manila Philippines Theological School Church of Cyprus Nicosia Cyprus General Psychology Chair Yerevan State University Yerevan Armenia Faculty of Psychology and Education University of Malaysia Sabah Sabah Malaysia Department of Educational Psychology and Health Psychology Pavol Jozef Šafárik University Košice Slovakia Astana Medical University Astana Kazakhstan University of Zagreb Zagreb Croatia Clinical Psychology Department Albizu University Miami FL USA Counseling & Psychological Services Duke University Durham NC USA Lviv State University of Physical Culture Lviv Ukraine Institute of Psychology University of Gdansk Gdansk Poland Polish Society for the Psychology of Religion University of Gdańsk Gdańsk Poland Department of Psychology Universidad Central Marta Abreu de Las Villas Santa Clara Cuba Department of Social Sciences and Communication Transilvania University of Brasov Brasov Romania Department of Education Psychology Communication University of Bari Bari Italy Faculty of Psychology Cooperative University of Colombia Santa Marta Colombia Psychology Department Fudan University Shanghai China Higher Medical School University St. Kliment Ohridski Bitola North Macedonia PHI Psihomedika Bitola North Macedonia National Economics University

With the COVID-19 pandemic, behavioural scientists aimed to illuminate reasons why people comply with (or not) large-scale cooperative activities. Here we investigated the motives that underlie support for COVID-19 preventive behaviours in a sample of 12,758 individuals from 34 countries. We hypothesized that the associations of empathic prosocial concern and fear of disease with support towards preventive COVID-19 behaviours would be moderated by trust in the government. Results suggest that the association between fear of disease and support for COVID-19 preventive behaviours was strongest when trust in the government was weak (both at individual- and country-level). Conversely, the association with empathic prosocial concern was strongest when trust in the government was high, but this moderation was only found at individual-level scores of governmental trust. We discuss how motivations may be shaped by socio-cultural context, and outline how findings may contribute to a better understanding of collective action during global crises.

关键词： Human behaviour Infectious diseases

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