Data package for Lipids represent a dynamic, yet stable pool of microbially-derived soil carbon This data is published under a CC0 license . The authors encourage data reuse and request attribution by referencing the below citations for the data packages and associated manuscript. Please cite as...

Machine learning is a core technology that is rapidly advancing within type 1 diabetes (T1D) research. Our Human Islet Research Network (HIRN) grant is studying early cellular response initiating β cell stress in T1D through the generation of heterogenous low- and high-throughput molecular...

The Environmental Determinants of Diabetes in the Young (TEDDY) study is searching for factors influencing the development of type 1 diabetes (T1D) in children. Research has shown that there are certain genes that correlate to higher risk of developing T1D, but not all children with these genes...

The Diabetes Autoimmunity Study in the Young (DAISY) seeks to find environmental factors that can trigger the development of type 1 diabetes (T1D) in children. DAISY follows children with high-risk of developing T1D based on family history or genetic markers. Genes, diets, infections, and...

Last updated on 2024-03-21T18:47:20+00:00 by LN Anderson MPLEx Method Protocol for Integrative Single-Sample Processing for Subsequent Proteomic, Metabolomic, and Lipidomic Analyses Method Source Contributions LN Anderson , Data Curation The Metabolite, Protein and Lipid Extraction (MPLEx) method...