AI Powers Ability to Represent Microbial Processes in Ecosystem Models
Using an AI approach to combine outputs from microbial models and apply them as inputs to reactive-transport models reduces computational time by several orders of magnitude.
Functional Chemogeography Helps Explain Organic Matter Persistence in Ecosystems
Dynamic organic matter traits tied to reactions can surpass static descriptors in explaining decomposition rates and responses to environmental change.
A Machine Learning Approach to Replace both Manual Methods and “Top Down” Estimates of Riverbed Grain Size Distributions
Artificial intelligence helps analyze images and reduce uncertainty for a key measure of watershed function.
Advancing Energy Innovation
Redox-Driven Protein Complexes Signal Metabolic Modulation in Cyanobacteria
PNNL researchers developed a new computational workflow to study day-to-night gene expression dynamics of cyanobacterium in real time.
Disturbed Sleep Cycle Propels Cyanobacteria Into Surprising Burst of Productivity
Circadian rhythms of blue-green microbes offer new window into predictive phenomics—how to predict and control the traits of an organism.
Ion Filtering Improves Detection of Proteins in Single Cells
PNNL scientists have developed a new mass spectrometry method for in-depth analysis of proteins in individual cells.
PNNL-Led Study Delivers Road Map of Lung Development
Researchers map nearly 9,000 proteins in the human lungs, providing a clearer picture of development.
Nested Nanowells Speed Single Cell Studies
The nested nanoPOTS chip is the next generation of technology developed at PNNL to prepare single cells for proteomics.