ESTABLISHING A WEB BASED EVALUATION PROGRAM FOR BENCHMARKING OUTCOMES OF CARDIAC AND PULMONARY REHABILITATION PROGRAMS IN NEW YORK STATE

Computational and data grids represent an emerging technology that allows geographically and organizationally distributed resources (e.g.computing and storage resources) to be linked and accessed in a fashion that is transparent to the user, presenting an extension of the desktop for users whose computational, data and visualization needs extend beyond their local systems. The New York State Grid is an integrated computational and data grid that provides web-based access for users from around the world to computational, application and data storage resources. This grid is used in a ubiquitous fashion, where the users have virtual access to their data sets and applications, but do not need to be made aware of the details of the data storage or computational devices that are specifically employed. Two of the applications that users worldwide have access to on a variety of grids, including the New York State Grid, are theSnBandBnPprograms, which implement theShake-and-Bakemethod of molecular structure (SnB) and substructure (BnP) determination, respectively. In particular, through our grid portal (i.e.logging on to a web site),SnBhas been run simultaneously on all computational resources on the New York State Grid as well as on more than 1100 of the over 3000 processors available through the Open Science Grid.

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Comparative Initial and Sustained Engagement in Web-Based Training by Behavioral Healthcare Providers in New York State

Journal of Technology in Behavioral Science ◽

10.1007/s41347-017-0027-1 ◽

2017 ◽

Vol 3 (2) ◽

pp. 41-48

Author(s):

Rachel Talley ◽

I-Chin Chiang ◽

Nancy H. Covell ◽

Lisa B. Dixon

Keyword(s):

New York ◽

New York State ◽

Healthcare Providers ◽

Web Based ◽

York State ◽

Behavioral Healthcare

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Genomic Epidemiology of Historical Clostridium perfringens Outbreaks in New York State Using Two Web-based Platforms: National Center for Biotechnology Information-Pathogen Detection and FDA-GalaxyTrakr

Journal of Clinical Microbiology ◽

10.1128/jcm.02200-20 ◽

2020 ◽

Author(s):

Jaclyn Carey ◽

Jocelyn Cole ◽

Sai Laxmi Gubbala Venkata ◽

Hannah Hoyt ◽

Lisa Mingle ◽

...

Keyword(s):

New York ◽

Clostridium Perfringens ◽

Pathogen Detection ◽

New York State ◽

The United States ◽

Food Samples ◽

Whole Genome Sequence ◽

Web Based ◽

Phylogenetic Clustering ◽

York State

Clostridium perfringens is the second-leading cause of bacterial foodborne illness in the United States. The Wadsworth Center (WC) at the New York State Department of Health enumerates infectious dose from primary patient and food samples and until recently, identified C. perfringens to the species level only. We investigated whether whole-genome sequence-based subtyping could benefit epidemiological investigations of this pathogen, as it has with other enteric organisms. We retrospectively sequenced 76 patient and food samples received between May 2010-February 2020, including 52 samples linked epidemiologically to 13 outbreaks and 24 sporadic samples not linked to other samples. Phylogenetic trees were built using two web-based platforms; National Centers for Biotechnology Information Pathogen Detection (NCBI-PD) and GalaxyTrakr (a Galaxy instance supported by the GenomeTrakr initiative). For GalaxyTrakr analyses single nucleotide polymorphism (SNP) matrices and maximum likelihood (ML) trees were generated using 3 different reference genomes. Across the four separate analyses phylogenetic clustering was generally concordant with epidemiologically-identified outbreaks. SNP diversity among phylogenetically-linked samples in an outbreak ranged from 0-20 SNPs, excepting one outbreak ranging from 4-62 SNPs. Importantly, four of the 13 outbreaks harbored one or more samples that were phylogenetic outliers, and for two outbreaks, no samples were closely related. Three specimens were found harboring two distinct genotypes. For samples below CDC enumeration dose threshold, phylogenetic clustering was robust and linked patient and/or food samples. We concluded that WGS phylogenetic clusters are: 1) largely concordant with epidemiologically-defined outbreaks, irrespective of analysis platform or reference genome we employed; 2) have limited pairwise SNP diversity, allowing phylogenetic clusters to be distinguished from sporadic cases; 3) can aid in epidemiological investigations by identifying outlier and polyclonal samples.

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