Characterizing the Role of International Graduates of the Epidemic Intelligence Service in Increasing the Epidemiological Capacity and Diversity of the United States Public Health Workforce.

CONTEXT: A trained and diverse public health workforce is needed to respond to public health threats. The Epidemic Intelligence Service (EIS) is an applied epidemiology training program. Most EIS officers are from the United States, but some are from other countries and bring unique perspectives and skills. OBJECTIVES/EVALUATION: To characterize international officers who participated in the EIS program and describe their employment settings after training completion. DESIGN: International officers were people who participated in EIS and who were not US citizens or permanent residents. We analyzed data from EIS's application database during 2009-2017 to describe officers' characteristics. We used data from the Centers for Disease Control and Prevention's (CDC's) workforce database for civil servants and EIS exit surveys to describe jobs taken after program completion. MAIN OUTCOME MEASURES: We described the characteristics of the international officers, jobs taken immediately after program completion, and duration of employment at CDC. RESULTS: Among 715 officers accepted in EIS classes of 2009-2017, 85 (12%) were international applicants, with citizenships from 40 different countries. Forty (47%) had 1 or more US postgraduate degrees, and 65 (76%) were physicians. Of 78 (92%) international officers with available employment data, 65 (83%) reported taking a job at CDC after program completion. The remaining took a public health job with an international entity (6%), academia (5%), or other jobs (5%). Among 65 international officers who remained working at CDC after graduation, the median employment duration was 5.2 years, including their 2 years in EIS. CONCLUSIONS: Most international EIS graduates remain at CDC after program completion, which strengthens the diversity and capacity of CDC's epidemiological workforce. Further evaluations are needed to determine the effects of pulling away crucial talent from other countries needing experienced epidemiologists and to what extent retaining those persons can benefit public health globally.

Fast bootstrapping-based estimation of confidence intervals of expression levels and differential expression from RNA-Seq data.

SUMMARY: This note presents IsoEM2 and IsoDE2, new versions with enhanced features and faster runtime of the IsoEM and IsoDE packages for expression level estimation and differential expression. IsoEM2 estimates fragments per kilobase million (FPKM) and transcript per million (TPM) levels for genes and isoforms with confidence intervals through bootstrapping, while IsoDE2 performs differential expression analysis using the bootstrap samples generated by IsoEM2. Both tools are available with a command line interface as well as a graphical user interface (GUI) through wrappers for the Galaxy platform. AVAILABILITY AND IMPLEMENTATION: The source code of this software suite is available at https://github.com/mandricigor/isoem2. The Galaxy wrappers are available at https://toolshed.g2.bx.psu.edu/view/saharlcc/isoem2_isode2/. CONTACT: imandric1@student.gsu.edu or ion@engr.uconn.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Inferring metabolic pathway activity levels from RNA-Seq data.

BACKGROUND: Assessing pathway activity levels is a plausible way to quantify metabolic differences between various conditions. This is usually inferred from microarray expression data. Wide availability of NGS technology has triggered a demand for bioinformatics tools capable of analyzing pathway activity directly from RNA-Seq data. In this paper we introduce XPathway, a set of tools that compares pathway activity analyzing mapping of contigs assembled from RNA-Seq reads to KEGG pathways. The XPathway analysis of pathway activity is based on expectation maximization and topological properties of pathway graphs. RESULTS: XPathway tools have been applied to RNA-Seq data from the marine bryozoan Bugula neritina with and without its symbiotic bacterium "Candidatus Endobugula sertula". We successfully identified several metabolic pathways with differential activity levels. The expression of enzymes from the identified pathways has been further validated through quantitative PCR (qPCR). CONCLUSIONS: Our results show that XPathway is able to detect and quantify the metabolic difference in two samples. The software is implemented in C, Python and shell scripting and is capable of running on Linux/Unix platforms. The source code and installation instructions are available at http://alan.cs.gsu.edu/NGS/?q=content/xpathway .