PNNL scientists have developed a new mass spectrometry method for in-depth analysis of proteins in individual cells.

New data analysis approach gives researchers deeper understanding of microbial ecology and evolution.

Data from increasingly complex mass spectrometry instruments met with adaptable data analysis solution.

Proteomics revealed molecular mechanisms by which the drug Verapamil could prolong beta cell function in early-onset type 1 diabetes patients.

Measurements of forest after an anomalous seawater exposure event reveals carbon starvation as dominant process underlying deaths.

Study highlights microbiome’s role in regulating xenobiotic metabolism.

Soil pores are reorganized when newly thawed permafrost soils experience freeze-thaw cycling.

New work developed a methodology to generate building temperature setpoint schedules for use by modelers in large-scale building energy simulations.

Development of a one-meter path length miniature ion mobility spectrometer based on multilevel structures for lossless ion manipulations.

Storage Targets and Release Function Inference Tool uses data to infer the operating policies of large reservoirs on a national scale.

A multisectoral analysis that combines land use data with hydrological simulations reveals wide disparities in potential source water contamination.

Study reveals importance of accounting for micrometer scale distribution of substrates to predict carbon emissions from soil.

COVID-19 had multiple impacts on the electric sector and the nature of those impacts varied on the scale analyzed.

Improving predictability of water quality and nutrient cycling in large river systems.

Developing conceptual models for microbial-environmental–ecosystem interactions is key to enhancing the ability of models to predict future ecosystem function.

Researchers characterize protein signatures that indicate Acute Kidney Injury in COVID-19 patients.

Simulations reveal advantages of using new model vs. allometric scaling for risk assessment.

Research from the PNNL-led WHONDRS consortium reveals chemistry and spatial gradients of metabolomes from river water and sediments around the globe.

Using a novel settlement-related dataset produced more accurate population downscaling than using other, more traditional datasets.

A multi-institutional team has developed a deep learning framework to identify novel molecules as drug candidates.