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P-012 Investigating the potential role of microRNAs as biomarkers in idiopathic non-obstructive azoospermia: A systematic review and in-silico analysis of the affected pathways

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human Reproduction 2022年第SUPPLEMENT_1期37卷

作者： Pantou, A Grigoriadis, S Karagkouni, D Pantos, K Maziotis, E Tomara, P Hatzigeorgiou, A Agarwal, A Sfakianoudis, K Simopoulou, M Genesis Genoma Lab Department of Genetic Diagnosis- Clinical Genetics & Research Athens Greece National and Kapodistrian University of Athens Laboratory of Physiology- Medical School Athens Greece University of Thessaly DIANA-Lab- Department of Computer Science and Biomedical Informatics Lamia Greece Genesis Athens Clinic Centre for Human Reproduction Athens Greece Cleveland Clinic American Center for Reproductive Medicine Cleveland Greece

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Revisiting metric learning for few-shot image classification

arXiv

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arXiv 2019年

作者： Li, Xiaomeng Yu, Lequan Fu, Chi-Wing Fang, Meng Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Tencent AI Lab United States

The goal of few-shot learning is to recognize new visual concepts with just a few amount of labeled samples in each class. Recent effective metric-based few-shot approaches employ neural networks to learn a feature similarity comparison between query and support examples. However, the importance of feature embedding, i.e., exploring the relationship among training samples, is neglected. In this work, we present a simple yet powerful baseline for few-shot classification by emphasizing the importance of feature embedding. Specifically, we revisit the classical triplet network from deep metric learning, and extend it into a deep K-tuplet network for few-shot learning, utilizing the relationship among the input samples to learn a general representation learning via episode-training. Once trained, our network is able to extract discriminative features for unseen novel categories and can be seamlessly incorporated with a non-linear distance metric function to facilitate the few-shot classification. Our result on the miniImageNet benchmark outperforms other metric-based few-shot classification methods. More importantly, when evaluated on completely different datasets (Caltech-101, CUB-200, Stanford Dogs and Cars) using the model trained with miniImageNet, our method significantly outperforms prior methods, demonstrating its superior capability to generalize to unseen classes. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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Correction: Exergame (ExerG)-Based Physical-Cognitive Training for Rehabilitation in Adults With Motor and Balance Impairments: Usability Study

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JMIR serious games 2025年 13卷 e73405页

作者： Silvia Herren Barbara Seebacher Sarah Mildner Yanick Riederer Ulrike Pachmann Nija Sonja Böckler Stephan Niedecken Sabrina Alicia Sgandurra Leo Bonati Isabella Hotz Alexandra Schättin Roman Jurt Christian Brenneis Katharina Lenfert Frank Behrendt Stefan Schmidlin Lennart Nacke Corina Schuster-Amft Anna Lisa Martin-Niedecken Research Department Reha Rheinfelden Rheinfelden Switzerland. Department of Rehabilitation Science Clinic for Rehabilitation Muenster Gröben 700 Muenster 6232 Austria 43 533720004 ext 6205. Clinical Department of Neurology Medical University of Innsbruck Innsbruck Austria. Karl Landsteiner Institute for Interdisciplinary Rehabilitation Research Muenster Austria. Sphery Ltd Zurich Switzerland. VASCage - Centre on Clinical Stroke Research Innsbruck Austria. Department of Design Institute for Design Research Zurich University of the Arts Zurich Switzerland. English Language and Literature Faculty of Arts University of Waterloo Waterloo ON Canada. Stroke Center and Department of Neurology University Hospital Basel Basel Switzerland. Department of Clinical Research University of Basel Basel Switzerland. Department of Neurology Clinic for Rehabilitation Muenster Muenster Austria. School of Engineering and Computer Science Bern University of Applied Sciences Burgdorf Switzerland. HCI Games Group Stratford School of Interaction Design and Business University of Waterloo Waterloo ON Canada. Department for Sport Exercise and Health University of Basel Basel Switzerland.

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In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate?

arXiv

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arXiv 2025年

作者： Bucknall, Ben Siddiqui, Saad Thurnherr, Lara McGurk, Conor Harack, Ben Reuel, Anka Paskov, Patricia Mahoney, Casey Mindermann, Sören Singer, Scott Hiremath, Vinay Segerie, Charbel-Raphaël Delaney, Oscar Abate, Alessandro Barez, Fazl Cohen, Michael K. Torr, Philip Huszár, Ferenc Calinescu, Anisoara Jones, Gabriel Davis Bengio, Yoshua Trager, Robert Department of Engineering Science University of Oxford Oxford Martin AI Governance Initiative United Kingdom Safe AI Forum Oxford Martin AI Governance Initiative United Kingdom King’s College London United Kingdom Safe AI Forum United Kingdom Oxford Martin AI Governance Initiative United Kingdom Stanford University The Belfer Center for Science and International Affairs Harvard Kennedy School United States RAND Mila - Quebec AI Institute Canada Carnegie Endowment for International Peace Oxford Martin AI Governance Initiative United Kingdom Centre for the Governance of AI United Kingdom France Institute for AI Policy and Strategy Department of Computer Science University of Oxford United Kingdom Department of Engineering Science University of Oxford United Kingdom UC Berkeley Center for Human Compatible AI United States University of Cambridge United Kingdom Oxford Digital Health Labs University of Oxford United Kingdom Oxford Martin AI Governance Initiative Blavatnik School of Government University of Oxford United Kingdom

International cooperation is common in AI research, including between geopolitical rivals. While many experts advocate for greater international cooperation on AI safety to address shared global risks, some view cooperation on AI with suspicion, arguing that it can pose unacceptable risks to national security. However, the extent to which cooperation on AI safety poses such risks, as well as provides benefits, depends on the specific area of cooperation. In this paper, we consider technical factors that impact the risks of international cooperation on AI safety research, focusing on the degree to which such cooperation can advance dangerous capabilities, result in the sharing of sensitive information, or provide opportunities for harm. We begin by why nations historically cooperate on strategic technologies and analyse current US-China cooperation in AI as a case study. We further argue that existing frameworks for managing associated risks can be supplemented with consideration of key risks specific to cooperation on technical AI safety research. Through our analysis, we find that research into AI verification mechanisms and shared protocols may be suitable areas for such cooperation. Through this analysis we aim to help researchers and governments identify and mitigate the risks of international cooperation on AI safety research, so that the benefits of cooperation can be fully realised. © 2025, CC BY.

关键词： Safety engineering

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How End Users Express Conditionals in Programming by Demonstration for Mobile Apps

How End Users Express Conditionals in Programming by Demonst...

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IEEE Symposium on Visual Languages and human Centric Computing (VL/HCC)

作者： Marissa Radensky Toby Jia-Jun Li Brad A. Myers Computer Science Department Amherst College Amherst MA Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh PA

Though conditionals are an integral component of programming, providing an easy means of creating conditionals remains a challenge for programming-by-demonstration (PBD) systems for task automation. We hypothesize that a promising method for implementing conditionals in such systems is to incorporate the use of verbal instructions. Verbal instructions supplied concurrently with demonstrations have been shown to improve the generalizability of PBD. However, the challenge of supporting conditional creation using this multi-modal approach has not been addressed. In this extended abstract, we present our study on understanding how end users describe conditionals in natural language for mobile app tasks. We conducted a formative study of 56 participants asking them to verbally describe conditionals in different settings for 9 sample tasks and to invent conditional tasks. Participant responses were analyzed using open coding and revealed that, in the context of mobile apps, end users often omit desired else statements when explaining conditionals, sometimes use ambiguous concepts in expressing conditionals, and often desire to implement complex conditionals. Based on these findings, we discuss the implications for designing a multimodal PBD interface to support the creation of conditionals.

关键词： Task analysis Programming Natural languages Automation Encoding Public transportation Visualization

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Predicting risk of dyslexia with an online gamified test

arXiv

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arXiv 2019年

作者： Rello, Luz Baeza-Yates, Ricardo Ali, Abdullah Bigham, Jeffrey P. Serra, Miquel Department of Information Systems and Technology IE University Madrid Spain KCCIS Northeastern University at Silicon Valley San JoseCA United States School of Computer Science University of Washington SeattleWA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Department of Cognition and Development University of Barcelona Barcelona Spain

Dyslexia is a specific learning disorder related to school failure. Detection is both crucial and challenging, especially in languages with transparent orthographies, such as Spanish. To make detecting dyslexia easier, we designed an online gamified test and associated predictive machine learning model. In a study with more than 4,300 participants, our model correctly detected nearly 80% of participants with dyslexia. Our work shows that dyslexia can be screened using a computational approach. An online screening tool based on our methods has already been used by more than 200,000 people. Copyright © 2019, The Authors. All rights reserved.

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Genomic Surveillance of SARS-CoV-2 in Taiwan: A Perspective on Evolutionary Data Interpretation and Sequencing Issues

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Biomedical journal 2024年 100820页

作者： Yu-Nong Gong Nai-Yu Kuo Ting-Syuan Yeh Shin-Ru Shih Guang-Wu Chen Research Center for Emerging Viral Infections College of Medicine Chang Gung University Taoyuan Taiwan International Master Degree Program for Molecular Medicine in Emerging Viral Infections College of Medicine Chang Gung University Taoyuan Taiwan Department of Laboratory Medicine Linkou Chang Gung Memorial Hospital Taoyuan Taiwan. Medical Education Department Linkou Chang Gung Memorial Hospital Taoyuan Taiwan. National Institute of Infectious Diseases and Vaccinology National Health Research Institutes Taiwan. Department of Laboratory Medicine Linkou Chang Gung Memorial Hospital Taoyuan Taiwan Department of Medical Biotechnology and Laboratory Science College of Medicine Chang Gung University Taoyuan Taiwan Research Center for Chinese Herbal Medicine Research Center for Food and Cosmetic Safety and Graduate Institute of Health Industry Technology College of Human Ecology Chang Gung University of Science and Technology Taoyuan Taiwan. Department of Computer Science and Information Engineering College of Engineering Chang Gung University Taoyuan Taiwan. Electronic address: gwchen@mail.cgu.edu.tw.

This review presents a comprehensive perspective on the genomic surveillance of SARS-CoV-2 in Taiwan, with a focus on next-generation sequencing and phylogenetic interpretation. This article aimed to explore how Taiwan has utilized genomic sequencing technologies and surveillance to monitor and mitigate the spread of COVID-19. We examined databases and sources of genomic sequences and highlighted the role of data science methodologies in the explanation and analyses of evolutionary data. This review addressed the challenges and limitations inherent in genomic surveillance, such as concerns regarding data quality and the necessity for interdisciplinary expertise for accurate data interpretation. Special attention was given to the unique challenges faced by Taiwan, including its high population density and major transit destination for international travelers. We underscored the far-reaching implications of genomic surveillance data for public health policy, particularly in influencing decisions regarding travel restrictions, vaccine administration, and public health decision-making. Studies were examined to demonstrate the effectiveness of using genomic data to implement public health measures. Future research should prioritize the integration of methodologies and technologies in evolutionary data science, particularly focusing on phylodynamic analytics. This integration is crucial to enhance the precision and applicability of genomic data. Overall, we have provided an overview of the significance of genomic surveillance in tracking SARS-CoV-2 variants globally and the pivotal role of data science methodologies in interpreting these data for effective public health interventions.

关键词： SARS-CoV-2 data science genomic surveillance next-generation sequencing phylodynamics

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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A molecular single-cell lung atlas of lethal COVID-19 (vol 595, pg 114, 2021)

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NATURE 2021年第7882期598卷 E2-E2页

作者： Melms, Johannes C. Biermann, Jana Huang, Huachao Wang, Yiping Nair, Ajay Tagore, Somnath Katsyv, Igor Rendeiro, Andre F. Amin, Amit Dipak Schapiro, Denis Frangieh, Chris J. Luoma, Adrienne M. Filliol, Aveline Fang, Yinshan Ravichandran, Hiranmayi Clausi, Mariano G. Alba, George A. Rogava, Meri Chen, Sean W. Ho, Patricia Montoro, Daniel T. Kornberg, Adam E. Han, Arnold S. Bakhoum, Mathieu F. Anandasabapathy, Niroshana Suarez-Farinas, Mayte Bakhoum, Samuel F. Bram, Yaron Borczuk, Alain Guo, Xinzheng V. Lefkowitch, Jay H. Marboe, Charles Lagana, Stephen M. Del Portillo, Armando Tsai, Emily J. Zorn, Emmanuel Markowitz, Glen S. Schwabe, Robert F. Schwartz, Robert E. Elemento, Olivier Saqi, Anjali Hibshoosh, Hanina Que, Jianwen Izar, Benjamin Department of Medicine Division of Hematology/Oncology Columbia University Irving Medical Center New York NY USA Columbia Center for Translational Immunology Columbia University Irving Medical Center New York NY USA Herbert Irving Comprehensive Cancer Center Columbia University Irving Medical Center New York NY USA Program for Mathematical Genomics Columbia University New York NY USA Columbia Center for Human Development Columbia University Irving Medical Center New York NY USA Division of Digestive and Liver Diseases Columbia University Irving Medical Center New York NY USA Department of Medicine Columbia University Irving Medical Center New York NY USA Institute of Human Nutrition Columbia University New York NY USA Department of Systems Biology Columbia University Irving Medical Center New York NY USA Department of Pathology and Cell Biology Columbia University Irving Medical Center New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Caryl and Israel Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA WorldQuant Initiative for Quantitative Prediction Weill Cornell Medicine New York NY USA Department of Physiology and Biophysics Weill Cornell Medical College New York NY USA Department of Dermatology Weill Cornell Medical College New York NY USA Meyer Cancer Center Weill Cornell Medical College New York NY USA Laboratory of Systems Pharmacology Harvard Medical School Boston MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Cancer Immunology and Virology Dana-Farber Cancer Center Boston MA USA Human Immune Monitoring Core Columbia University Irving Medical Center New York NY USA Department of Medicine Division of Pulmonary and Critical Care Massachusetts General Hospital Boston MA USA Cell Circu

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MODL-41. LOSS OF DISTAL CHROMOSOME 6Q AND ASSOCIATEDPDE10AINDUCES AN AGGRESSIVE GLIOMA PHENOTYPE BY FACILITATING PRONEURAL TO MESENCHYMAL TRANSITION

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Neuro-Oncology 2023年第SUPPLEMENT_5期25卷 v307–v308页

作者： Nuechterlein, Nicholas Shelbourn, Allison Alvinez, Neriah Szulzewsky, Frank Arora, Sonali Paddison, Patrick Merino-Galan, Leyre Pattwell, Siobhan Sulman, Erik Jung, Mi-Yeon Brown, Desmond Banasavadi-Siddegowda, Yeshavanth Kumar Tang, Kayen Jackson, Sadhana Stoica, Stefan Chittaboina, Prashant Patel, Anoop Holland, Eric Cimino, Patrick Shapiro, Linda Surgical Neurology Branch National Institutes of Neurological Disorders and Stroke NIH Bethesda MD USA Division of Human Biology Fred Hutchinson Cancer Center Seattle WA USA Ben Towne Center for Childhood Cancer Research Seattle Children's Research Institute Seattle WA USA New York University Grossman School of Medicine Laura and Isaac Perlmutter Cancer Center at NYU Langone Health New York NY USA Neurosurgical Oncology Unit Surgical Neurology Branch NINDS NIH Bethesda MD USA Department of Neurosurgery Duke University Durham NC USA Department of Computer Science and Engineering University of Washington Seattle WA USA

Somatic copy number alterations (SCNAs) are common in glioblastoma, with some SCNAs having prognostic implications. Identifying and characterizing SCNA-related genes acting as oncogenes or tumor suppressors may potentially provide new therapeutic avenues for gliomas. In this study, we sought to uncover novel SCNA-related genes by employing an unbiased log rank analysis screen of genomic regions associated with glioblastoma survival. We identified 6q27 loss as an independent poorly-prognostic SCNA across three separate glioblastoma cohorts. Anin vitroCRISPR screen using human neural stem cells identified five genes in the 6q27 region that caused increased cellular proliferation. One of these genes, phosphodiesterase 10A (PDE10A), had a gene dosage effect on expression and decreased expression in human and mouse glioma tumor tissue compared to normal brain, which are both common tumor suppressor phenotypes. Knockdown of Pde10a in mouse neural stem cellsin vitrovalidated the human cell CRISPR screen phenotype. Suppression of Pde10a in RCAS/Tva mouse gliomain vivocaused decreased survival with increased tumor grade, mitotic activity, necrosis, Vegfa expression, and AKT/pS6 signaling. Single cell RNA-sequencing of mouse gliomas revealed that Pde10a suppression results in a shift from proneural to mesenchymal-like state. In human glioma tissue, PDE10A loss is also associated with an increased mesenchymal signature. Top activated pathways in Pde10a suppressed gliomas involved cell-cell interaction, including upregulation of cell adhesion molecules and downregulation of focal adhesion complex disassembly genes. Functional analysisin vitroconfirmed that Pde10a suppression results in increased extracellular matrix adhesion and decreased migration. Relevant to standard glioblastoma treatment, PDE10A loss was associated with decreasedMGMTpromoter methylation, decreased DNA methyltransferase gene expression, and it potentiated radiation resistancein vitro. In summary, we leverag

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