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Large circuit models: opportunities and challenges

Science China(Information Sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN yiqi CHEN Zhufei CHU Wenji fANG Tsung-yi HO Ru HUANG yu HUANG Sadaf KHAN Min LI Xingquan LI yu LI yun LIANG Jinwei LIU yi LIU yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi yAN Jun yANG Bei yU Mingxuan yUAN evangeline f.y.y.ung Xuan ZENG Haoyi ZHANG Zuodong ZHANG yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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Contribution of the Advanced Research Laboratories at the Noguchi Memorial Institute for Medical Research (NMIMR) to SARS-CoV-2 Testing in Ghana

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Health 2022年第1期14卷 125-136页

作者： Darius N. K. Quansah Ivy A. Asante Evelyn y. Bonney evangeline Obodai Mildred A. Adusei-Poku James Aboagye Susan Adu-Amankwah Samuel Adjei Richard Akuffo Helena Lamptey George B. Kyei John K. Odoom William K. Ampofo Joseph H. K. Bonney Department of Virology Noguchi Memorial Institute for Medical Research University of Ghana Legon Accra Ghana West African Centre for Cell Biology of Infectious Pathogens College of Basic and Applied Sciences University of Ghana Legon Accra Ghana Department of Medical Microbiology University of Ghana Medical School Accra Ghana Medical and Scientific Research Directorate University of Ghana Medical Centre Legon Accra Ghana Department of Epidemiology Noguchi Memorial Institute for Medical Research University of Ghana Legon Accra Ghana Department of Animal Experimentation Noguchi Memorial Institute for Medical Research University of Ghana Legon Accra Ghana Department of Immunology Noguchi Memorial Institute for Medical Research University of Ghana Legon Accra Ghana

In March 2020, the first cases of SARS-CoV-2 were reported in Accra, Ghana. These initial cases were diagnosed at the Advanced Research Laboratories (ARL) of the Noguchi Memorial Institute for Medical Research (NMIMR), University of Ghana. The ARL which hitherto was used for routine clinical research in viral, bacteria and immunological studies has since been the facility of choice for testing for all suspected cases of COVID-19 submitted from across Ghana and beyond. The success of testing at the ARL hinged on the availability of several laboratory spaces furnished with state-of-the-art diagnostic equipment and working aids. During the “peak season” where overwhelming numbers of clinical specimens were received, the ARL processed and got results for close to four thousand samples daily. After general disinfection and re-bagging into smaller numbers, at the entrance of the ARL, the samples are taken to a central receiving laboratory, where they are received and entered in a database with accompanying case investigation forms. All samples that are successfully sorted and matched are sent to general laboratories for nucleic acid extraction and then referred to the Instrumentation laboratory for real time reverse-transcription polymerase chain reaction (RT-PCR). When the RT-PCRs were completed, results were analysed and transmitted via email and/or local network to the data reporting office. The data managers then reported results to the investigators and the Ghana Health Service (GHS). Additionally, the ARL provided a next-generation Genome Sequencing platform in partnership with the West African Centre for Cell Biology of Infectious Pathogens at the University of Ghana, which was essential in reporting the genome data of the circulating variants of SARS-CoV-2 in Ghana. Conclusively, it is worth noting, that the NMIMR fulfilled its mandate of supporting the country with specialized diagnostics through the judicious use of the ARL for SARS-CoV-2 testing, from sample re

关键词： Advanced Research Laboratories Polymerase Chain Reaction Severe Acute Respiratory Syndrome—Corona Virus 2 Corona Virus Disease 2019

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Cancer-associated histone mutation H2BG53D disrupts DNA–histone octamer interaction and promotes oncogenic phenotypes

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Signal Transduction and Targeted Therapy 2020年第1期5卷 2230-2233页

作者： yi Ching Esther Wan Tsz Chui Sophia Leung Dongbo Ding Xulun Sun Jiaxian Liu Lina Zhu Tze Zhen evangeline Kang Du yang yuchen Zhang Jitian Zhang Chengmin Qian Michael Shing yan Huen Qing Li Maggie Zi ying Chow Zongli Zheng Junhong Han Ajay Goel Xin Wang Toyotaka Ishibashi Kui Ming Chan Department of Biomedical Sciences City University of Hong KongHong KongChina Key Laboratory of Biochip Technology Biotech and Health CentreShenzhen Research Institute of City University of Hong KongShenzhenChina Division of Life Science Hong Kong University of Science and TechnologyHong KongChina State Key Laboratory of Protein and Plant Gene Research School of Life Sciences and Peking-Tsinghua Center for Life SciencesPeking UniversityPekingChina School of Biomedical Sciences The University of Hong KongHong KongChina Ming Wai Lau Centre for Reparative Medicine Karolinska InstitutetHong KongChina State Key Laboratory of Biotherapy and Cancer Center West China HospitalWest China Medical SchoolSichuan UniversitySichuanChina Department of Molecular Diagnostics and Experimental Therapeutics Beckman Research Institute at City of Hope Comprehensive Cancer CenterDuarteCAUSA

Dear Editor,Recent studies from us and others have deciphered the clinical relevance of a variety of oncohistones1 in different *** H3K27M and H3K36M mutant histones exert dominant-negative effects on methylation levels of histone H3K27 and H3K36 on wildtype histone protein in pediatric brain cancers2–4 and chondroblastoma,5,6 *** contrast to H3K27M and H3K36M that act in trans to cause global reduction of methylation at the respective residues,the H3G34 mutations,including H3G34V/R and H3G34W/L found in pediatric high-grade glioma and giant cell tumor of the bone,affect the H3K36 methylation in cis and alter the H3K36 methylation on the nucleosome containing the H3G34 mutation(s).

关键词： Cancer clinical pediatric

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2019年全球、地区和国家5岁以下儿童呼吸道合胞病毒引发急性下呼吸道感染疾病负担研究的系统分析

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中国病毒病杂志 2023年第2期13卷 81-95页

作者： you Li Xin Wang Dianna M.Blau Mauricio T.Caballero Daniel R.feikin Christopher J.Gill Shabir A.Madhi Saad B.Omer Eric A.f Simoes Harry Campbell Ana Bermejo Pariente Darmaa Bardach Quique Bassat Jean-Sebastien Casalegno Giorgi Chakhunashvili Nigel Crawford Daria Danilenko Lien Anh Ha Do Marcela Echavarria Angela Gentile Aubree Gordon Terho Heikkinen QSue Huang Sophie Jullien Anand Krishnan Eduardo Luis Lopez Josko Markic Ainara Mira-Iglesias Hannah CMoore Jocelyn Moyes Lawrence Mwananyanda DJames Nokes faseeha Noordeen evangeline Obodai Nandhini Palani Candice Romero Vahid Salimi Ashish Satav Euri Seo Zakhar Shchomak Rosalyn Singleton Kirill Stolyarov Sonia K Stoszek Anne von Gottberg Danielle Wurzel Lay-Myint yoshida Chee fu yung Heather J Zar Harish Nair 张甜甜从兵兵邓舒予不详南京医科大学公共卫生学院江苏南京211166

背景呼吸道合胞病毒(respiratory syncytial virus,RSV)是引起婴幼儿急性下呼吸道感染最常见的病原体。此前估计,2015年全球60月龄以下儿童共发生3 310万次RSV相关急性下呼吸道感染,导致118 200人死亡。此后,几项社区监测研究获得了对RS... 详细信息

背景呼吸道合胞病毒(respiratory syncytial virus,RSV)是引起婴幼儿急性下呼吸道感染最常见的病原体。此前估计,2015年全球60月龄以下儿童共发生3 310万次RSV相关急性下呼吸道感染,导致118 200人死亡。此后,几项社区监测研究获得了对RSV相关社区死亡的更精确估计。本文旨在更新2019年全球、地区和国家层面60月龄以下儿童RSV相关急性下呼吸道感染的发病率和死亡率,重点关注总体死亡率和在研RSV免疫制剂针对的更窄的婴儿年龄组。方法检索获得MEDLINE、Embase、Global Health、CINAHL、Web of Science、LILACS、OpenGrey、中国知网、万方和维普数据库2017—2020年发表文献中的新数据,并纳入RSV GEN合作者提供的未发表数据,以扩展全球RSV疾病负担数据集。纳入的研究符合以下条件:报告了社区中或需要住院治疗的60月龄以下儿童RSV原发性急性下呼吸道感染的数据;报告了至少连续12个月的数据,但院内病死率或RSV季节性定义明确的地区除外;报告了RSV相关急性下呼吸道感染的发病率、住院率、院内病死率或急性下呼吸道感染住院的RSV阳性比例。如果病例定义不明或应用不一致、RSV感染未经实验室确认或仅基于血清学检测、报告的急性下呼吸道感染病例少于50例,则排除该研究。本研究应用广义线性混合效应模型(generalized linear mixed-effects model,GLMM)估计了2019年全球和地区(按国家发展状况和世界银行收入分类)RSV相关急性下呼吸道感染的发病率、住院率和院内病死率;通过基于危险因素的模型估计了国家层面RSV相关急性下呼吸道感染的发病率;通过GLMM开发了新模型,纳入最新的RSV社区死亡率数据,用于估计RSV的总体死亡率。本综述已在PROSPERO注册(CRD42021252400)。结果除了先前综述中纳入的317项研究外,还纳入了113项符合条件的新研究以及51项未发表的研究,共计481项研究。据估计,2019年全球60月龄以下儿童中,共发生3 300万(置信区间为2 540万~4 460万)次RSV相关急性下呼吸道感染,导致360万(290万~460万)例住院、26 300(15 100~49 100)例院内死亡以及101 400(84 500~125 200)例可归因于RSV的总死亡数。在0~6个月的婴儿中,共发生660万(460万~970万)次RSV相关急性下呼吸道感染,导致140万(100万~200万)例住院、13 300(6 800~28 100)例院内死亡以及45 700(38 400~55 900)例可归因于RSV的总死亡数。60月龄以下和28 d~6月龄儿童的死亡中分别有2.0%(置信区间为1.6%~2.4%)和3.6%(3.0%~4.4%)归因于RSV。在各年龄组中,95%以上的RSV相关急性下呼吸道感染事件和97%以上的可归因于RSV的死亡事件都发生在中低收入国家。结论 RSV显著增加了全球60月龄以下儿童的发病和死亡负担,尤其是在出生后前6个月和中低收入国家。本研究强调了全球RSV疾病的总体死亡率负担,60月龄以下儿童中,每50例死亡就有1例可归因于RSV;28 d至6个月大的婴儿中,每28例死亡就有1例可归因于RSV。每1例RSV相关急性下呼吸道感染院内死亡,对应社区中约3例可归因于RSV的死亡。对6月龄内婴儿提供保护的RSV被动免疫计划有望对减轻RSV疾病负担,但实施计划时,仍需要更多数据来了解RSV疾病负担高峰可能向大龄转移的影响。

关键词：呼吸道合胞病毒下呼吸道感染儿童疾病负担住院发病率死亡率病死率模型估计系统分析

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