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Guidelines for public database submission of uncultivated virus genome sequences for taxonomic classification (vol 41, pg 898, 2023)

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NATURE BIOTECHNOLOGY 2023年第9期41卷 1346-1346页

作者： Adriaenssens, Evelien M. M. Roux, Simon Brister, J. Rodney Karsch-Mizrachi, Ilene Kuhn, Jens H. Varsani, Arvind Yigang, Tong Reyes, Alejandro Lood, Cedric Lefkowitz, Elliot J. Sullivan, Matthew B. B. Edwards, Robert A. A. Simmonds, Peter Rubino, Luisa Sabanadzovic, Sead Krupovic, Mart Dutilh, Bas E. E. Quadram Institute Bioscience Norwich Research Park Rosalind Franklin Road Norwich UK United States Department of Energy Joint Genome Institute Lawrence Berkeley National Laboratory Berkeley CA USA National Center for Biotechnology Information National Library of Medicine National Institutes of Health Bethesda MD USA Integrated Research Facility at Fort Detrick National Institute of Allergy and Infectious Diseases National Institutes of Health Fort Detrick Frederick MD USA The Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA Structural Biology Research Unit Department of Clinical Laboratory Sciences University of Cape Town Cape Town South Africa Beijing Advanced Innovation Center for Soft Matter Science and Engineering College of Life Science and Technology Beijing University of Chemical Technology Beijing China Max Planck Tandem Group in Computational Biology Department of Biological Sciences Universidad de los Andes Bogotá Colombia Centre of Microbial and Plant Genetics Department of Microbial and Molecular Systems KU Leuven Leuven Belgium Laboratory of Gene Technology Department of Biosystems KU Leuven Leuven Belgium Institute of Biodiversity Faculty of Biological Sciences Cluster of Excellence Balance of the Microverse Friedrich Schiller University Jena Jena Germany Department of Microbiology University of Alabama at Birmingham Birmingham AL USA Department of Microbiology The Ohio State University Columbus OH USA Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus OH USA Center of Microbiome Science The Ohio State University Columbus OH USA College of Science and Engineering Flinders University Bedford Park South Australia Australia Nuffield Department of Medicine University of Oxford South Parks Road Oxford UK Consiglio Nazionale delle Ricerche Istituto per la Protezione Sostenibile delle Piante Bari Italy Department of Biochemistry Molecular Bio

Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging

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arXiv 2025年

作者： Ayllón, Elena Mulero Mantegna, Massimiliano Shen, Linlin Soda, Paolo Guarrasi, Valerio Tortora, Matteo Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden Department of Naval Electrical Electronics and Telecommunications Engineering University of Genoa Genoa Italy

Accurate lung tumor segmentation is crucial for improving diagnosis, treatment planning, and patient outcomes in oncology. However, the complexity of tumor morphology, size, and location poses significant challenges for automated segmentation. This study presents a comprehensive benchmarking analysis of deep learning-based segmentation models, comparing traditional architectures such as U-Net and DeepLabV3, self-configuring models like nnUNet, and foundation models like MedSAM, and MedSAM 2. Evaluating performance across two lung tumor segmentation datasets, we assess segmentation accuracy and computational efficiency under various learning paradigms, including few-shot learning and fine-tuning. The results reveal that while traditional models struggle with tumor delineation, foundation models, particularly MedSAM 2, outperform them in both accuracy and computational efficiency. These findings underscore the potential of foundation models for lung tumor segmentation, highlighting their applicability in improving clinical workflows and patient outcomes. © 2025, CC BY-NC-SA.

关键词： Image segmentation

Attitudes Toward Gender Parity Initiatives—A Comparative Study

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Sustainable Development 2025年第0期

作者： Furtado, Julia Vasconcelos Moreira, António Carrizo Rodrigues, Ricardo José Mota, Jorge Humberto Fernandes ESTGA—Superior School of Technology and Management of Águeda University of Aveiro Aveiro Portugal NECE UBI—Research Center for Business Sciences Universidade of Beira Interior Covilhã Portugal GOVCOPP–Research Unit on Governance Competitiveness and Public Policies University of Aveiro Aveiro Portugal DEGEIT—Department of Economics Management Industrial Engineering and Tourism University of Aveiro Aveiro Portugal INESCTEC—Institute for Systems and Computer Engineering Technology and Science Porto Portugal Campus Universitário de Santiago Aveiro Portugal

The struggle for gender parity (sustainable development goal 5) sustains a controversial debate among authors, leaders, and organizations. Studies connecting social dominance orientation, status threat, and attitudes towards gender parity initiatives are scant. This article investigates these relationships and their mediators, focusing on gender parity initiatives in three independent studies—Brazil, Canada, and Portugal—comparing data collected from professors and employees in public and private HEIs. Both social dominance orientation (SDO) and status threat influence employees' attitudes towards gender parity initiatives, with SDO being the most important and significant determinant. Canadian employees are more supportive of gender affirmative actions and share a preference for group-based inequality. Employees in all three countries perceive their societies as egalitarian, with a gender parity agenda in place, and no longer requiring efforts for gender equity regardless of the respondents' gender. Intriguing insights on the perceptions and attitudes of those self-identifying as “woman” and “man” were found. © 2025 ERP Environment and John Wiley & Sons Ltd.

关键词： comparative study gender parity social dominance orientation against egalitarianism social dominance orientation towards dominance status threat

Identifying common signatures and potential therapeutic biomarkers in COPD and lung cancer using miRNA-mRNA co-expression networks

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Informatics in Medicine Unlocked 2022年 34卷

作者： Fathinavid, Amirhossein Mousavian, Zaynab Najafi, Ali Nematzadeh, Sajjad Salimi, Mahdieh Masoudi-Nejad, Ali Department of Computer Engineering College of Engineering Hamedan Branch Islamic Azad University Hamedan Iran Laboratory of Systems Biology and Bioinformatics (LBB) Institute of Biochemistry and Biophysics University of Tehran Tehran Iran Department of Computer Science School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran Molecular Biology Research Center System Biology and Poisoning Institute Tehran Iran Computer Engineering Department Architecture and Engineering Faculty Nisantasi University Istanbul Turkey Department of Medical Genetics Institute of Medical Biotechnology National Institute of Genetic Engineering and Biotechnology (NIGEB) Tehran Iran

The association between non-small cell lung cancer (NSCLC) and chronic obstructive pulmonary disease (COPD) is now well established, as people with COPD are more likely to develop lung carcinoma. However, the evidence for this relationship is inconclusive and there is currently little information on the underlying molecular mechanisms. MicroRNAs (miRNAs) are one of the regulatory factors in NSCLC and COPD that their functions are widely studied in many chronic diseases and cancers. In this study, we applied systems biology approaches to identify and predict miRNAs that potentially play regulatory roles between COPD and NSCLC. We performed differential expression analysis on public miRNA and mRNA expression datasets, for both of diseases, and then we reconstructed two miRNA-mRNA co-expression networks and merged these two co-expression networks into a community co-expression network. Results indicated the existence of very common miRNAs (ex. hsa-miR-326 and hsa-miR-1293) and mRNAs (such as FAT2, ALOX5AP, and LDB2) between the two mentioned diseases. Moreover, we discovered specific miRNAs (hsa-miR-574-3p) that targeted common mRNAs. We utilized drug-target interaction networks to identify candidate drugs for common mRNAs, which can be considered in the treatment of two diseases (e.g. iloperidone). Generally, our study highlighted common miRNAs between COPD and NSCLC that as new signatures or biomarkers for therapeutic purposes. Investigating the miRNA biomarkers in this study improves our understanding about the shared mechanisms between COPD and NSCLC. © 2022

关键词： Co-expression networks Community networks COPD miRNA-mRNA network Non-small cell lung cancer

A Graph Neural Network-based Model with Out-of-Distribution Robustness for Enhancing Antiretroviral Therapy Outcome Prediction for HIV-1

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arXiv 2023年

作者： Teodoro, Giulia Di Siciliano, Federico Guarrasi, Valerio Vandamme, Anne-Mieke Ghisetti, Valeria Sönnerborg, Anders Zazzi, Maurizio Silvestri, Fabrizio Palagi, Laura Sapienza University of Rome Department of Computer Control and Management Engineering Antonio Ruberti Rome00185 Italy EuResist Network Rome00152 Italy Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Rome00128 Italy Department of Medical Biotechnologies University of Siena Siena53100 Italy KU Leuven Department of Microbiology Immunology and Transplantation Rega Institute for Medical Research Clinical and Epidemiological Virology Leuven Belgium Center for Global Health and Tropical Medicine Instituto de Higiene e Medicina Tropical Universidade Nova de Lisboa Lisbon1349-008 Portugal Molecular Biology and Microbiology Unit Amedeo di Savoia Hospital ASL Città di Torino Turin10128 Italy Karolinska Institutet Division of Infectious Diseases Department of Medicine Huddinge Stockholm14152 Sweden Karolinska University Hospital Department of Infectious Diseases Stockholm14186 Sweden

Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This scarcity of data can arise either due to the introduction of a new drug to the market or due to limited use in clinical settings, resulting in clinical dataset with highly unbalanced therapy representation. To tackle this issue, we introduce a novel joint fusion model, which combines features from a Fully Connected (FC) Neural Network and a Graph Neural Network (GNN) in a multi-modality fashion. Our model uses both tabular data about genetic sequences and a knowledge base derived from Stanford drug-resistance mutation tables, which serve as benchmark references for deducing in-vivo treatment efficacy based on the viral genetic sequence. By leveraging this knowledge base structured as a graph, the GNN component enables our model to adapt to imbalanced data distributions and account for Out-of-Distribution (OoD) drugs. We evaluated these models’ robustness against OoD drugs in the test set. Our comprehensive analysis demonstrates that the proposed model consistently outperforms the FC model. These results underscore the advantage of integrating Stanford scores in the model, thereby enhancing its generalizability and robustness, but also extending its utility in contributing in more informed clinical decisions with limited data availability. The source code is available at https://***/federicosiciliano/*** Codes 68 © 2023, CC BY-NC-ND.

关键词： Graph neural networks

Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson’s Disease Screening in OCT

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arXiv 2025年

作者： Zhang, Xiaoqing Shi, Hanfeng Li, Xiangyu Ye, Haili Xu, Tao Li, Na Hu, Yan Lv, Fan Chen, Jiangfan Liu, Jiang Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Ophthalmology Optometry and Vision Science Wenzhou Medical University Wenzhou China Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Computer Science University of Nottingham Ningbo China Ningbo315100 China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou325035 China Department of Electronic and Information Engineering Changchun University Changchun130022 China

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain learning techniques can enhance the feature representations of deep neural networks (DNNs) by decomposing frequency components involving rich texture features. Additionally, previous works have not exploited texture features for automated PD screening in OCT. Motivated by the above analysis, we propose a novel Adaptive Wavelet Filter (AWF) that serves as the Practical Texture Feature Amplifier to fully leverage the merits of texture features to boost the PD screening performance of DNNs with the aid of frequency domain learning. Specifically, AWF first enhances texture feature representation diversities via channel mixer, then emphasizes informative texture feature representations with the well-designed adaptive wavelet filtering token mixer. By combining the AWFs with the DNN stem, AWFNet is constructed for automated PD screening. Additionally, we introduce a novel Balanced Confidence (BC) Loss by mining the potential of sample-wise predicted probabilities of all classes and class frequency prior, to further boost the PD screening performance and trustworthiness of AWFNet. The extensive experiments manifest the superiority of our AWFNet and BC over state-of-the-art methods in terms of PD screening performance and trustworthiness. © 2025, CC BY-NC-SA.

关键词： Neurodegenerative diseases

Deep Learning for Human Locomotion Analysis in Lower-Limb Exoskeletons: A Comparative Study

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arXiv 2025年

作者： Coser, Omar Tamantini, Christian Tortora, Matteo Furia, Leonardo Sicilia, Rosa Zollo, Loredana Soda, Paolo Unit of Computer Systems & Bioinformatics Università Campus Bio-Medico di Roma Via Álvaro del Portillo 21 Rome00128 Italy Unit of Advanced Robotics and Human-Centered Technologies Università Campus Bio-Medico di Roma Via Álvaro del Portillo 21 Rome00128 Italy Dipartimento di Ingegneria Navale Elettrica Elettronica e delle Telecomunicazioni Università degli Studi di Genova Via all’Opera Pia 11a Genova16145 Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Universitetstorget 4 Umeå40196 Sweden Institute of Cognitive Sciences and Technologies National Research Council of Italy Rome00196 Italy

Wearable robotics for lower-limb assistance have become a pivotal area of research, aiming to enhance mobility for individuals with physical impairments or augment the performance of able-bodied users. Accurate and adaptive control systems are essential to ensure seamless interaction between the wearer and the robotic device, particularly when navigating diverse and dynamic terrains. Despite the recent advances in neural networks for time series analysis, no attempts have been directed towards the classification of ground conditions, categorized into five classes and subsequently determining the ramp’s slope and stair’s height. In this respect, this paper presents an experimental comparison between eight deep neural network backbones to predict high-level locomotion parameters across diverse terrains. All the models are trained on the publicly available CAMARGO 2021 dataset. IMU-only data equally or outperformed IMU+EMG inputs, promoting a cost-effective and efficient design. Indeeds, using three IMU sensors, the LSTM achieved high terrain classification accuracy (0.94 ± 0.04) and precise ramp slope (1.95 ± 0.58°) and the CNN-LSTM a stair height (15.65 ± 7.40 mm) estimations. As a further contribution, SHAP analysis justified sensor reduction without performance loss, ensuring a lightweight setup. The system operates with 2 ms inference time, supporting real-time applications. The code is code available at https://***/cosbidev/Human-Locomotion-Identification. © 2025, CC BY.

关键词： Adaptive control systems

Quantum Software engineering: A New Genre of Computing

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arXiv 2022年

作者： Akbar, Muhammad Azeem Khan, Arif Ali Mahmood, Sajjad Rafi, Saima Department of Software Engineering LUT University Lappeenranta Finland M3S Empirical Software Engineering Research Unit University of Oulu Oulu90570 Finland Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia University of Murcia Department of Informatics and Systems Murcia Spain

Quantum computing (QC) is no longer only a scientific interest but is rapidly becoming an industrially available technology that can potentially tackle the limitations of classical computing. Over the last few years, major technology giants have invested in developing hardware and programming frameworks to develop quantum-specific applications. QC hardware technologies are gaining momentum, however, operationalizing the QC technologies trigger the need for software-intensive methodologies, techniques, processes, tools, roles, and responsibilities for developing industrial-centric quantum software applications. This paper presents the vision of the quantum software engineering (QSE) life cycle consisting of quantum requirements engineering, quantum software design, quantum software implementation, quantum software testing, and quantum software maintenance. This paper particularly calls for joint contributions of software engineering research and industrial community to present real-world solutions to support the entire quantum software development activities. The proposed vision facilitates the researchers and practitioners to propose new processes, reference architectures, novel tools, and practices to leverage quantum computers and develop emerging and next generations of quantum software. © 2022, CC BY.

关键词： Software design