Scientific Computing and Data announcements, in publications, and in the news
Scientific Computing and Data News
High Performance Computing (HPC), Research Data Services (RDS), and Mount Sinai Data Warehouse (MSDW) impact the research community in contributions to science
Events, updates, and news from HPC, RDS, and MSDW
March 3, 2023 - HPC - Annual User Surveys Available
February 28, 2023 - RDS - REDCap Training Materials Now Available
February 28, 2023 – Thank you for attending the REDCap Introductory Training on Friday, February 24. Session materials, including the session recording, are now available! Click here for more.
February 23, 2023 - MSDW - Leaf and ATLAS Training Materials Now Available
February 3, 2023 - RDS - REDCap Training on February 24
February 3, 2023 – An introductory training session for new users of REDCap to set up and begin using REDCap will be on Friday, February 24, from 1:00-2:00 pm. Click here for more information and to register.
February 3, 2023 - MSDW - Leaf & ATLAS Training on February 23
February 3, 2023 – An introductory training session for new users of Leaf and ATLAS cohort query tools will be on Thursday, February 23, from 12:00-1:00 pm. Click here for more information and to register.
February 2, 2023 - HPC - Minerva Spring Training Sessions
February 2, 2023 – Minerva Spring Training sessions are scheduled! Join us for Introduction to Minerva, Load Sharing Facility (LSF) Job Scheduler, and Running Jupyter Notebook and RStudio on Minerva. Click here for details and registration information.
February 1, 2023 - RDS - MarketScan Now Available on Data Ark
February 1, 2023 – A new Data Set–MarketScan–is now available on the Data Ark: Data Commons. Click here to read more.
February 1, 2023 - RDS - eRAP Login Transition
February 1, 2023 – eRAP login credentials are now synced to Mount Sinai’s Active Directory. Use your Mount Sinai School or Hospital login credentials to log in to eRAP. If you have difficulty logging in, please submit a ticket. As a reminder, all users must be connected to the Mount Sinai network or MSSM Campus by either being on site or connected with VPN tunnel to log in to eRAP.
In The News
Reports and news events for Scientific Computing and Data
July 26, 2022: The Mount Sinai Hospital Is Recognized in U.S. News & World Report “Best Hospitals” Rankings
The Mount Sinai Hospital was ranked in the top 20 in 10 specialties and improved its ranking in seven of those specialties. Specialties with significant rank improvements include:
- Orthopedics, which had the biggest gain, rising to No. 8 from No. 14. Two years ago, the hospital ranked No. 24.
- Pulmonology & Lung Surgery, which rose to No. 17 from No. 20. Two years ago, it ranked No. 27.
- Cancer, which rose to No. 28 from No. 32. Two years ago, the hospital ranked No. 49.
Other departments to achieve top national honors include:
- Cardiology/Heart Surgery, ranked No. 6
- Neurology/Neurosurgery, ranked No. 9
- Rehabilitation, ranked No. 13
- Gastroenterology/GI Surgery, ranked No. 13
- Diabetes/Endocrinology, ranked No. 14
- Urology, ranked No. 14 (tie)
- Ear, Nose & Throat, ranked No. 35 (tie)
Click here to read more.
Dec 1, 2021: Mount Sinai Named a Lead Site for Enrollment in Nationwide Study on the Long-Term Effects of COVID-19
Mount Sinai Named a Lead Site for Enrollment in Nationwide Study on the Long-Term Effects of COVID-19 (MountSinai.org). Click here to read more.
Scientific Computing and Data resources utilized in publications
Abstract: Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients
by P. Kovatch, S. Nirenberg, et al. 2022 August | Background: Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest computed tomography (CT) in combination with plasma cytokines using a machine learning and k-fold cross-validation approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n = 152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within five days from admission were included. doi: 10.1016/j.heliyon.2022.e10166.
Science Stories: Pharmacology - Computer-Aided Structural Biology and Drug Discovery, Dr. Marta Filizola
Preserving Data: Who’s Got Your Back(up)? by Patricia Kovatch
Data management has become an essential part of research. Scientists need to be able to rely on their data infrastructures to recover data in case of disaster or to assist with reproducibility of their results. Ensuring a reliable data infrastructure and backup processes may not be the most exciting part of research, but consequences… Read more
Abstract: Before the Surge: Molecular Evidence of SARS-CoV-2 in New York City Prior to the First Report
by Hernandez MM. et al. 2021 February | New York City (NYC) emerged as a coronavirus disease 2019 (COVID-19) epicenter in March 2020, but there is limited information regarding potentially unrecognized severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections before the first reported case. We utilized a sample pooling strategy to screen for SARS-CoV-2 RNA in de-identified, respiratory pathogen-negative nasopharyngeal specimens from 3,040 patients across our NYC health system who were evaluated for respiratory symptoms or influenza-like illness during the first 10 weeks of 2020. We obtained complete SARS-CoV-2 genome sequences from samples collected between late February and early March. Additionally, we detected SARS-CoV-2 RNA in pooled specimens collected in the week ending 25 January 2020, indicating that SARS-CoV-2 caused sporadic infections in NYC a full month before the first officially documented case. medRxiv:2021.02.08.21251303
See other publications and research made possible through resources from Scientific Computing and Data
Supported by grant UL1TR004419 from the National Center for Advancing Translational Sciences, National Institutes of Health.