COVID and Translational Science supercomputer (CATS)

新冠肺炎和转化科学超级计算机 (CATS)

• 批准号: 10177277
• 负责人: Patricia Kovatch
• 金额: $ 200万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10177277
• 关键词: Area; Asthma; COVID-19 pandemic; COVID-19 prevention; Complex; Computerized Medical Record; Data; Dengue Fever; Diagnosis; Disease; Down Syndrome; Image; Long COVID; Malignant neoplasm of brain; Memory; Mental disorders; Molecular; Myocarditis; Performance; Research; Research Project Grants; Time; Translational Research; Translations; body system; coronavirus disease; drug discovery; improved; instrument; pandemic disease; petabyte; shared memory; simulation; supercomputer; translational pipeline

Abstract: To enable new kinds of scientific discovery and translation related to the newly emerged COVID-19 pandemic, we are requesting a new high-performance instrument with large shared memory nodes. The COVID and Translational Science (CATS) supercomputer, will support over $120 million in 50 research projects for 46 PIs. Due to the urgent need for improved understanding, diagnosis, treatment and prevention of COVID-19, related projects are very time-sensitive in the near-term. CATS will also be utilized for a spectrum of translational science applications from molecular dynamic simulations leading towards drug discovery and multi-scale analyses incorporating omic, electronic medical record data and images. Even after the pandemic is better controlled, research will continue for years on the post-COVID-19 syndrome due to its complex invasion on multiple organ systems. Other disease areas needing CATS include psychiatric disease, asthma, Down syndrome, myocarditis, Dengue fever and brain cancer. In total, CATS will contain with 2,640 Intel cores, 82 TB of memory and 16 petabytes of storage. The enhanced capabilities of CATS will shorten the time to solution and enable more complex biomedical analyses.

摘要： 使与新出现的新冠肺炎相关的新的科学发现和翻译成为可能 在大流行期间，我们需要一种新的具有大容量共享内存节点的高性能仪器。这个 COVID和翻译科学(CATS)超级计算机将在50项研究中支持超过1.2亿美元 46项私人投资策略的项目。由于迫切需要改善了解、诊断、治疗和 预防新冠肺炎，相关项目在短期内非常具有时效性。猫也将被利用 从分子动力学模拟到 结合基因组、电子病历数据和图像的药物发现和多尺度分析。 即使在疫情得到更好的控制之后，对后新冠肺炎的研究仍将持续多年 因其复杂侵袭多个器官系统而引起的综合征。其他需要猫的疾病领域包括 精神疾病、哮喘、唐氏综合症、心肌炎、登革热和脑癌。总体而言，猫 将包含2,640个英特尔核心、82 TB内存和16 PB存储空间。增强的功能 CATS的出现将缩短解决问题的时间，并使更复杂的生物医学分析成为可能。

项目成果

Anticoagulation in Patients With COVID-19: JACC Review Topic of the Week.

• DOI: 10.1016/j.jacc.2021.12.023
• 发表时间: 2022-03-08
• 期刊: Journal of the American College of Cardiology
• 影响因子: 24
• 作者: Farkouh ME;Stone GW;Lala A;Bagiella E;Moreno PR;Nadkarni GN;Ben-Yehuda O;Granada JF;Dressler O;Tinuoye EO;Granada C;Bustamante J;Peyra C;Godoy LC;Palacios IF;Fuster V
• 通讯作者: Fuster V

Sex disparities in influenza: A multiscale network analysis.

• DOI: 10.1016/j.isci.2022.104192
• 发表时间: 2022-05-20
• 期刊: ISCIENCE
• 影响因子: 5.8
• 作者: Wang, Chang;Lashua, Lauren P.;Carter, Chalise E.;Johnson, Scott K.;Wang, Minghui;Ross, Ted M.;Ghedin, Elodie;Zhang, Bin;Forst, Christian V.
• 通讯作者: Forst, Christian V.

Sequential intrahost evolution and onward transmission of SARS-CoV-2 variants.

• DOI: 10.1038/s41467-023-38867-x
• 发表时间: 2023-06-03
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Gonzalez-Reiche, Ana;Alshammary, Hala;Schaefer, Sarah;Patel, Gopi F.;Polanco, Jose C.;Carreno, Juan Manuel;Amoako, Angela;Rooker, Aria;Cognigni, Christian;Floda, Daniel;van de Guchte, Adriana;Khalil, Zain;Farrugia, Keith;Assad, Nima;Zhang, Jian;Alburquerque, Bremy A.;Sominsky, Levy;Gleason, Charles;Srivastava, Komal;Sebra, Robert;Ramirez, Juan David;Banu, Radhika;Shrestha, Paras;Krammer, Florian;Paniz-Mondolfi, Alberto;Sordillo, Emilia Mia;Simon, Viviana;van Bakel, Harm
• 通讯作者: van Bakel, Harm

Everybody nose: molecular and clinical characteristics of nasal colonization during active methicillin-resistant Staphylococcus aureus bloodstream infection.

• DOI: 10.1186/s12879-022-07371-w
• 发表时间: 2022-04-24
• 期刊: BMC INFECTIOUS DISEASES
• 影响因子: 3.7
• 作者: Schwarz, Erika Reategui;van de Guchte, Adriana;Dupper, Amy C.;Caban, Ana Berbel;Nadkarni, Devika;Fox, Lindsey;Mills, Alexandra;Obla, Ajay;Chacko, Kieran, I;Oussenko, Irina;Samaroo, Flora;Polanco, Jose;Silvera, Richard;Smith, Melissa L.;Patel, Gopi;Gitman, Melissa;Alburquerque, Bremy;Chung, Marilyn;Sullivan, Mitchell J.;van Bakel, Harm;Altman, Deena R.
• 通讯作者: Altman, Deena R.

Analyzing Spatial Transcriptomics Data Using Giotto.

• DOI: 10.1002/cpz1.405
• 发表时间: 2022-04
• 期刊: Current protocols
• 作者: Del Rossi, Natalie;Chen, Jiaji G;Yuan, Guo-Cheng;Dries, Ruben
• 通讯作者: Dries, Ruben