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science Bulletin 2025年第6期 934-942页

作者：李灏萱江泽铧管洲榆包玉倩刘月星胡婷婷李佳佳刘茹涵吴量程棣纪宏伟王勇王亚星 Carol Y.Cheung 郑颖丰王继红李震吴卫兵 Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan 程景煜黄天荫李华婷蔡淳毛丽娟 Lee-Ling Lim 覃宇宗盛斌贾伟平 Shanghai University of Sport School of Clinical Medicine Beijing Tsinghua Changgung Hospital Tsinghua Medicine Tsinghua University Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua Medicine Tsinghua University Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Department of Cardiology the Affiliated Hospital of Qingdao University CEMS NCMIS HCMS MADIS Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematics University of Chinese Academy of Sciences Chinese Academy of Sciences Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Beijing Institute of Ophthalmology Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular Diseases Department of Renal Medicine Singapore General Hospital Sing Health-Duke Academic Medical Centre Department of Endocrinology Singapore General Hospital Department of Endocrinology Austin Health Department of Medicine The University of Melbourne(Austin Health) Australian Centre for Accelerating Diabetes Innovations(ACADI) The Univ

Diabetes poses a considerable global health challenge, with varying levels of diabetes knowledge among healthcare professionals, highlighting the importance of diabetes training. Large Language Models(LLMs)provide new insights into diabetes training, but their performance in diabetes-related queries remains uncertain, especially outside the English language like Chinese. We first evaluated the performance of ten LLMs: Chat GPT-3.5, Chat GPT-4.0, Google Bard, Lla MA-7B, Lla MA2-7B, Baidu ERNIE Bot, Ali Tongyi Qianwen, Med GPT, Huatuo GPT, and Chinese Lla MA2-7B on diabetes-related queries, based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC) and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United Kingdom. Second, we assessed the training of primary care physicians(PCPs) without and with the assistance of Chat GPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical assistants. We found that Chat GPT-4.0 outperformed other LLMs in the English examination, achieving a passing accuracy of 62.50%, which was significantly higher than that of Google Bard, Lla MA-7B, and Lla MA2-7B. For the NCE-CPFC examination, Chat GPT-4.0, Ali Tongyi Qianwen, Baidu ERNIE Bot, Google Bard, Med GPT, and Chat GPT-3.5 successfully passed, whereas Lla MA2-7B, Huatuo GPT,Chinese LLa MA2-7B, and Lla MA-7B failed. Chat GPT-4.0(84.82%) surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%). In summary, LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language, and hold great potential to assist future diabetes training for physicians globally.

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Analysis of a reduced model of epithelialmesenchymal fate determination in cancer metastasis as a singularly-perturbed monotone system

arXiv

引用

arXiv 2019年

作者： Al-Radhawi, M. Ali Sontag, Eduardo D. Departments of Bioengineering and Electrical and Computer Engineering Northeastern University 360 Huntington Avenue BostonMA02115 United States Laboratory of Systems Pharmacology Program in Therapeutic Science Harvard Medical School BostonMA02115

Tumor metastasis is one of the main factors responsible for the high fatality rate of cancer. Metastasis can occur after malignant cells transition from the epithelial phenotype to the mesenchymal phenotype. This transformation allows cells to migrate via the circulatory system and subsequently settle in distant organs after undergoing the reverse transition. The core gene regulatory network controlling these transitions consists of a system made up of coupled SNAIL/miRNA-34 and ZEB1/miRNA-200 subsystems. In this work, we formulate a mathematical model and analyze its long-term behavior. We start by developing a detailed reaction network with 24 state variables. Assuming fast promoter and mRNA kinetics, we then show how to reduce our model to a monotone four-dimensional system. For the reduced system, monotone dynamical systems theory can be used to prove generic convergence to the set of equilibria for all bounded trajectories. The theory does not apply to the full model, which is not monotone, but we briefly discuss results for singularly-perturbed monotone systems that provide a tool to extend convergence results from reduced to full systems, under appropriate time separation assumptions. Copyright © 2019, The Authors. All rights reserved.

关键词： Diseases

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Perspectives for Selecting Cloud Microservices

Perspectives for Selecting Cloud Microservices

引用

Software Architecture Companion (ICSA-C)

作者： Marcelo França Claudia Werner Systems Engineering and Computer Science Program IBM Rio de Janeiro Brazil

In the era of Cloud platforms, microservices have multiplied, becoming a de facto standard for software reuse. With so many alternatives, a challenge still open is to find the most adequate one, given a set of requirements and priorities. In this context, we present DIRECTOR, a Cloud microservice selection framework based on different perspectives: technical, social and semantical. Emerging results prove that it can support software acquisition via discovery and comparison of microservices, being able to recommend the fittest among hundreds of candidates.

关键词： Quality of service Cloud computing Artificial intelligence Service-oriented architecture Metadata Standards

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Aperture-Coupled Koch Fractal on-Chip Antennas for 60 GHz ISM Band 2

Aperture-Coupled Koch Fractal on-Chip Antennas for 60 GHz IS...

引用

2nd IEEE MTT-S Latin America Microwave Conference, LAMC 2018

作者： Morais, Samuel Medeiros Araújo Serres, Alexandre Jean Rene Guarany, Ivana Soares Da Silverio Freire, Raimundo Carlos Da Silva, Paulo Fernandes Santana, Ewaldo Elder Carvalho Da Fonseca Silva, Paulo Henrique Post-Graduation Program in Electrical Engineering Federal University of Campina Grande Campina Grande Brazil Post-Graduation Program in Computer Engineering and Systems State University of Maranhão São Luis Brazil Post-Graduation Program in Electrical Engineering Federal Institute of Education Science and Technology of Paraíba João Pessoa Brazil

ISBN: (纸本)9781538673331

This paper presents Koch fractal aperture-coupled patch antennas operating in industrial scientific and medical band at 60 GHz (57 GHz to 64 GHz), for on-chip application. This work is the continuation of the development of aperture-coupled antennas with aperture and patch element built in 2 μm-thick aluminum lying on two layers of silicon with a height of 200 μm. The use of Koch fractal allowed the development of aperture-coupled antennas of greater perimeter with more metal in the patch elements, which promoted an increase in the gain. The observed gain for Koch fractal level 2 was of 10.55 dBi, with a bandwidth of 7.23 GHz, covering the ISM 60 GHz Band, and an increase of less than 10% in total patch dimension. For Koch fractal levels 0 (square), a polarization close to circular was achieved, with an axial ratio less than 6 at resonant frequencies. © 2018 IEEE.

关键词： Natural frequencies

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Automatic text-based personality recognition on monologues and multiparty dialogues using attentive networks and contextual embeddings

arXiv

引用

arXiv 2019年

作者： Jiang, Hang Zhang, Xianzhe Choi, Jinho D. Symbolic Systems Program Stanford University CA94305 United States Department of Electrical Engineering Stanford University CA94305 United States Department of Computer Science Emory University AtlantaGA30322 United States

Previous works related to automatic personality recognition focus on using traditional classification models with linguistic features. However, attentive neural networks with contextual embeddings, which have achieved huge success in text classification, are rarely explored for this task. In this project, we have two major contributions. First, we create the first dialogue-based personality dataset, FRIENDSPERSONA, by annotating 5 personality traits of speakers from Friends TV Show through crowdsourcing. Second, we present a novel approach to automatic personality recognition using pre-trained contextual embeddings (BERT and RoBERTa) and attentive neural networks. Our models largely improve the state-of-art results on the monologue Essays dataset by 2.49%, and establish a solid benchmark on our FRIENDSPERSONA. By comparing results in two datasets, we demonstrate the challenges of modeling personality in multi-party dialogue. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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Reconfigurable Hyperbolic Polaritonics with Correlated Oxide Metasurfaces

Research Square

引用

Research Square 2021年

作者： Aghamiri, Neda Alsadat Hu, Guangwei Fali, Alireza Zhang, Zhen Li, Jiahan Balendhran, Sivacarendran Walia, Sumeet Sriram, Sharath Edgar, James H. Ramanathan, Shriram Alù, Andrea Abate, Yohannes Department of Physics and Astronomy University of Georgia AthensGA30602 United States Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Department of Electrical and Computer Engineering National University of Singapore Kent Ridge Singapore117583 Singapore School of Materials Engineering Purdue University West LafayetteIN47907 United States Tim Taylor Department of Chemical Engineering Kansas State University ManhattanKN 66506 United States School of Physics University of Melbourne ParkvilleVIC3010 Australia School of Engineering RMIT University MelbourneVIC Australia Functional Materials and Microsystems Research Group Micro Nano Research Facility RMIT University MelbourneVIC Australia ARC Centre of Excellence for Transformative Meta-Optical Systems RMIT University MelbourneVIC Australia Physics Program Graduate Center City University of New York New YorkNY10016 United States

Polaritons enable subwavelength confinement and highly anisotropic flows of light over a wide spectral range, holding the promise for applications in modern nanophotonic and optoelectronic devices. However, to fully realize their practical application potential, facile methods enabling nanoscale active control of polaritons are needed. Here, we introduce a hybrid polaritonic-oxide heterostructure platform consisting of van der Waals crystals, such as hexagonal boron nitride (hBN) or alpha-phase molybdenum trioxide (α-MoO3), transferred on nanoscale oxygen vacancy patterns on the surface of prototypical correlated perovskite oxide SmNiO3 (SNO). Using a combination of scanning probe microscopy and infrared nanoimaging techniques, we demonstrate nanoscale real-time reconfigurability of complex hyperbolic phonon polaritons patterned at the nanoscale with unmatched resolution. Hydrogenation and temperature modulation allow spatially localized conductivity modulation of SNO nanoscale patterns, enabling robust real-time modulation and nanoscale reconfiguration of hyperbolic polaritons. Our work paves the way towards nanoscale programmable metasurface engineering as a new paradigm for reconfigurable nanophotonic applications facilitated by a hybrid material platform exploiting extreme light-matter interactions in polaritonic systems. © 2021, CC BY.

关键词： Polariton

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Revised JNLPBA corpus: A revised version of biomedical NER corpus for relation extraction task

arXiv

引用

arXiv 2019年

作者： Huang, Ming-Siang Lai, Po-Ting Tsai, Richard Tzong-Han Hsu, Wen-Lian Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang Ming University Taipei Taiwan Department of Computer Science National Tsing-Hua University Hsinchu Taiwan Intelligent Agent Systems Laboratory Institute of Information Science Academia Sinica Taipei Taiwan Intelligent Information Service Research Laboratory Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The advancement of biomedical named entity recognition (BNER) and biomedical relation extraction (BRE) researches promotes the development of text mining in biological domains. As a cornerstone of BRE, robust BNER system is required to identify the mentioned NEs in plain texts for further relation extraction stage. However, the current BNER corpora, which play important roles in these tasks, paid less attention to achieve the criteria for BRE task. In this study, we present Revised JNLPBA corpus, the revision of JNLPBA corpus, to broaden the applicability of a NER corpus from BNER to BRE task. We preserve the original entity types including protein, DNA, RNA, cell line and cell type while all the abstracts in JNLPBA corpus are manually curated by domain experts again basis on the new annotation guideline focusing on the specific NEs instead of general terms. Simultaneously, several imperfection issues in JNLPBA are pointed out and made up in the new corpus. To compare the adaptability of different NER systems in Revised JNLPBA and JNLPBA corpora, the F1-measure was measured in three open sources NER systems including BANNER, Gimli and NERSuite. In the same circumstance, all the systems perform average 10% better in Revised JNLPBA than in JNLPBA. Moreover, the cross-validation test is carried out which we train the NER systems on JNLPBA/Revised JNLPBA corpora and access the performance in both protein-protein interaction extraction (PPIE) and biomedical event extraction (BEE) corpora to confirm that the newly refined Revised JNLPBA is a competent NER corpus in biomedical relation application. The revised JNLPBA corpus is freely available at ***/BNER/Content/Revised_***. Copyright © 2019, The Authors. All rights reserved.

关键词： Proteins

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Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes (vol 12, pg 631, 2020)

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GENETICS IN MEDICINE 2020年第8期22卷 1427-1427页

作者： Chang, Willie H. Mashouri, Pouria Lozano, Alexander X. Johnstone, Brittney Husic, Mia Olry, Annie Maiella, Sylvie Balci, Tugce B. Sawyer, Sarah L. Robinson, Peter N. Rath, Ana Brudno, Michael Centre for Computational Medicine The Hospital For Sick Children Toronto ON Canada Department of Computer Science Princeton University Princeton NJ USA Faculty of Medicine University of Toronto Toronto ON Canada Department of Materials Science & Engineering Stanford University Stanford CA USA Sunnybrook Health Sciences Centre Toronto ON Canada Orphanet Institut national de la santé et de la recherche médicale Paris France Medical Genetics Program of Southwestern Ontario London Health Sciences Centre London ON Canada Department of Genetics Children’s Hospital of Eastern Ontario and Children’s Hospital of Eastern Ontario Research Institute University of Ottawa Ottawa ON Canada The Jackson Laboratory for Genomic Medicine Farmington CT USA Institute for Systems Genomics University of Connecticut Farmington CT USA Department of Computer Science University of Toronto Toronto ON Canada Genetics and Genome Biology Program The Hospital for Sick Children Toronto ON Canada University Health Network Toronto ON Canada

PurposeComputational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student *** developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated *** Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research *** enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase.

关键词： rare diseases phenotype crowdsourcing medical education machine learning

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Exploring the effect of image enhancement techniques on COVID‐19 detection using chest X‐rays images

arXiv

引用

arXiv 2020年

作者： Rahman, Tawsifur Khandakar, Amith Qiblawey, Yazan Tahir, Anas Kiranyaz, Serkan Kashem, Saad Bin Abul Islam, Mohammad Tariqul Maadeed, Somaya Al Zughaier, Susu M. Khan, Muhammad Salman Chowdhury, Muhammad E.H. Department of Biomedical Physics & Technology University of Dhaka Dhaka 1000 Bangladesh Department of Electrical Engineering Qatar University Doha2713 Qatar Faculty of Robotics and Advanced Computing Qatar Armed Forces‐Academic Bridge Program Qatar Foundation Doha24404 Qatar Dept. of Electrical Electronics and Systems Engineering Universiti Kebangsaan Malaysia BangiSelangor43600 Malaysia Department of Computer Science and Engineering Qatar University Doha2713 Qatar Department of Basic Medical Sciences College of Medicine Biomedical and Pharmaceutical Research Unit QU Health Qatar University Doha2713 Qatar University of Engineering and Technology Peshawar Pakistan

The use of computer-aided diagnosis in the reliable and fast detection of corona virus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the medical infrastructure. Chest X-ray (CXR) imaging has several advantages over other imaging techniques as it is cheap, easily accessible, fast and portable. CXR images are sometimes of poor quality and so image enhancement techniques can help the machine learning models to extract valuable discriminating features from the image. Numerous works have been reported on COVID-19 detection from smaller set of original X-ray images. However, the effect of image enhancement in COVID-19 detection was not reported in the literature. This paper explores the effect of various popular image enhancement techniques and states the © 2020, CC BY.

关键词： Image enhancement

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