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TN-CTSI


New UTHSC Tennessee Clinical and Translational Science Institute Established to Improve Human Health for Southerners

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Researchers at the University of Tennessee Health Science Center have formed the Tennessee Clinical and Translational Science Institute (TN-CTSI) to address health inequities in the Southern United States. According to the U.S. Census Bureau, the American South represented the largest population growth by region in 2017. Comprised of 17 states stretching from Texas, Oklahoma, and… Read More

Julia Debugging Basics by Gregory Farage

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This is a practical how-to guide on best practices for debugging code in Julia using the Gallium package. We explore two methods: REPL and Juno-Atom. Installation To use the debugger Gallium in Julia 0.6+, the following packages should be installed: Gallium and ASTInterpreter2. julia> Pkg.add(“Gallium”) julia> Pkg.clone(“https://github.com/Keno/DebuggerFramework.jl”) julia> Pkg.clone(“https://github.com/Keno/ASTInterpreter2.jl”) There are two possible ways to debug with Gallium,… Read More

A Quick Recommendation on SAS Setup at UT by Fridtjof Thomas

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This is a quick guide on the recommended SAS setup for UT users. This is excerpted from a longer guide on software setup and windows 10. SAS 9.4 TSIM3 Large (16GB) download from UT Knoxville This download will take several hours due to its size (zipped file 16GB).  You may want to ask the Biostatistics… Read More

Real Close to the Machine: Floating Point in D | Using Floating Point Without Losing Your Sanity by Dan Clugston [outside article & video]

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An entertaining look at floating point computation and absurdities that you might encounter if you are not careful. The video is entertaining; the details are in the article. Computers were originally conceived as devices for performing mathematics. The earliest computers spent most of their time solving equations. Although the engineering and scientific community now forms… Read More

Missing values in Julia by Milan Bouchet-Valat [outside article]

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Starting from Julia 0.7, missing values are represented using the new missing object. Resulting from intense design discussions, experimentations and language improvements developed over several years, it is the heir of the NA value implemented in the DataArrays package, which used to be the standard way of representing missing data in Julia.