Pending Review Microbiomes contribute to multiple ecosystem services by transforming organic matter in soil. Extreme shifts in the environment, such as drying-rewetting cycles during drought, can impact microbial metabolism of organic matter by altering their physiology and function. These...
Filter results
Content type
Tags
- (-) Biomarkers (4)
- (-) Whole Genome Sequencing (2)
- Omics (9)
- PerCon SFA (9)
- High Throughput Sequencing (8)
- Genomics (7)
- Machine Learning (6)
- Type 1 Diabetes (6)
- Autoimmunity (5)
- Sequencer System (5)
- Synthetic Biology (5)
- Molecular Profiling (4)
- Mass Spectrometry (3)
- Mass spectrometry-based Omics (3)
- Predictive Modeling (3)
- A. pittii SO1 (2)
- Alternative Splicing (2)
- Amplicon Sequencing (2)
- Biological and Environmental Research (2)
- Imaging (2)
- Long Read Sequencer (2)
- Mass spectrometry data (2)
- Proteomics (2)
- RNA Sequence Analysis (2)
- Sorghum bicolor (2)
- Spectroscopy (2)
- Statistical Expression Analysis (2)
- DOE (1)
- Mass Spectrometer (1)
- Output Databases (1)
Metabolite exchange between plant roots and their associated rhizosphere microbiomes underpins plant growth promotion by microbes. Sorghum bicolor is a cereal crop that feeds animals and humans and is used for bioethanol production. Its root tips exude large amounts of a lipophilic benzoquinone...
The Human Islet Research Network (HIRN) is a large consortia with many research projects focused on understanding how beta cells are lost in type 1 diabetics (T1D) with a goal of finding how to protect against or replace the loss of functional beta cells. The consortia has multiple branches of...
Datasets
0
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...
Datasets
1
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...
Datasets
1
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...
Datasets
3
Last updated on 2023-05-31T16:35:53+00:00 by LN Anderson PerCon SFA: Sequencing of Sorgoleone Promoting Rhizobacteria Isolates Whole genome sequencing (WGS) of sorgoleone utilizing rhizobacteria strains Pseudomonas sorgoleonovorans SO81 , Burkholderia anthina SO82 , and Acinetobacter pittii SO1 , as...