The role of Ancestral MicroRNAs in grass inflorescence development.

Plant inflorescences are complex, highly diverse structures whose morphology is determined in meristems that form during reproductive development. Inflorescence structure influences flower formation, and consequently grain number, and yield in crops. Correct inflorescence and flower development require tight control of gene expression via complex interplay between regulatory networks.

Memory updating in dreams.

Robert Stickgold's research was among the earliest to rigorously quantify the effect of learning on dream content. As a result, we learned that dreaming is influenced by the activation of newly formed memory traces in the sleeping brain. Exactly how this happens is an ongoing area of investigation.

Unraveling Hydration Shell Dynamics and Viscosity Effects Around Cyanamide Probes via 2D IR Spectroscopy.

Hydration dynamics and solvent viscosity play critical roles in the structure and function of biomolecules. An overwhelming body of evidence suggests that protein and membrane fluctuations are closely linked to solvent fluctuations. While extensive research exists on the use of vibrational probes to detect local interactions and solvent dynamics, fewer studies have explored how the behavior of these reporters changes in response to bulk viscosity.

Characterizing Emergency Department Disposition Conversations for Persons Living With Dementia: Protocol for an Ethnographic Study.

Background: Almost 40% of persons living with dementia make an emergency department (ED) visit each year. One of the most impactful and costly elements of their ED care is the decision to discharge or admit them to the hospital-the "disposition" decision. When more than one reasonable option exists regarding a health care decision, such as the decision to admit or not, it often requires a complex conversation between patients, care partners, and ED providers, ideally involving shared decision-making.

Integrating machine learning interatomic potentials with hybrid reverse Monte Carlo structure refinements in .

Structure refinement with reverse Monte Carlo (RMC) is a powerful tool for interpreting experimental diffraction data. To ensure that the under-constrained RMC algorithm yields reasonable results, the hybrid RMC approach applies interatomic potentials to obtain solutions that are both physically sensible and in agreement with experiment. To expand the range of materials that can be studied with hybrid RMC, we have implemented a new interatomic potential constraint in that grants flexibility to apply potentials supported by the () molecular dynamics code.