PNNL researchers developed a new computational workflow to study day-to-night gene expression dynamics of cyanobacterium in real time.

The ability of a storm-resolving weather model to predict the growth of storms over central Argentina was evaluated with data from the Clouds, Aerosols, and Complex Terrain Interactions (CACTI) field campaign in central Argentina.

Understanding molecular modifications of proteins in the red yeast could help scientists re-engineer the organism’s phenotype.

Researchers have developed the first coastal transport model for microplastics to understand how particles move in waterways and coastal currents.

Researchers from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.

Lakes are important sentinels of climate change and contribute significantly to the emissions of the second most important greenhouse gas—methane.

The rate of conversion of cloud droplets to precipitation, known as the autoconversion rate, remains a major source of uncertainty in characterizing aerosol’s cloud lifetime effects and precipitation in global and regional models.

To assess the impact of observation period and gauge location, model parameters were learned on scenarios using different chunks of streamflow observations.

PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.

Continued studies will deepen scientists’ understanding of virus-host interactions at the molecular level and also pave the way for developing better drugs to fight emerging viruses.

This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.

Flexible, high-throughput proteomics approach identifies protein modifications related to pancreatic stress and insulin production.

Scientists uncover how cyanobacteria's circadian cycle controls carbon and energy metabolism, increasing carbon dioxide-to-bioproducts yields.

Scientists map how transitions from day to night control gene regulatory networks in cyanobacteria, revealing key orchestrators of metabolic switching.

PNNL scientists use proteomics to unveil how high-density lipoproteins protect against cholesterol accumulation in the artery wall.

Computing and artificial intelligence aid scientists who seek to understand function-driven design and control of biological systems.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

The new PTM-Psi workflow opens the door for uncovering molecular insights within proteomics datasets.