Complex tissue regeneration in reveals a phylogenetic signal for enhanced regenerative ability in deomyine rodents.

Identifying why complex tissue regeneration is present or absent in specific vertebrate lineages has remained elusive. One also wonders whether the isolated examples where regeneration is observed represent cases of convergent evolution or are instead the product of phylogenetic inertia from a common ancestral program. Testing alternative hypotheses to identify genetic regulation, cell states, and tissue physiology that explain how regenerative healing emerges in some species requires sampling multiple species among which there is variation in regenerative ability across a phylogenetic framework.

PRECIS-2 used as an implementation science tool for global environmental health: A cross-sectional evaluation of the Ecolectivos study protocol to reduce burning of household plastic waste in rural Guatemala.

Background: Randomized controlled trials (RCTs) that evaluate the efficacy of an intervention remain underutilized in community-based environmental health research. RCTs that use a pragmatic design emphasize the effectiveness of interventions in complex, real world settings. Pragmatic trials may be especially relevant when community-based interventions address social and environmental determinants that threaten health equity.

FlowBack: A Generalized Flow-Matching Approach for Biomolecular Backmapping.

Coarse-grained models have become ubiquitous in biomolecular modeling tasks aimed at studying slow dynamical processes such as protein folding and DNA hybridization. These models can considerably accelerate sampling but it remains challenging to accurately and efficiently restore all-atom detail to the coarse-grained trajectory, which can be vital for detailed understanding of molecular mechanisms and calculation of observables contingent on all-atom coordinates. In this work, we introduce FlowBack as a deep generative model employing a flow-matching objective to map samples from a coarse-grained prior distribution to an all-atom data distribution.

Spatial mapping of the HCC landscape identifies unique intratumoral perivascular-immune neighborhoods.

Background: HCC develops in the context of chronic inflammation; however, the opposing roles the immune system plays in both the development and control of tumors are not fully understood. Mapping immune cell interactions across the distinct tissue regions could provide greater insight into the role individual immune populations have within tumors.

Methods: A 39-parameter imaging mass cytometry panel was optimized with markers targeting immune cells, stromal cells, endothelial cells, hepatocytes, and tumor cells.

Old strategies, new environments: Reinforcement Learning on social media.

The rise of social media has profoundly altered the social world - introducing new behaviours which can satisfy our social needs. However, it is yet unknown whether human social strategies, which are well-adapted to the offline world we developed in, operate as effectively within this new social environment. Here, we describe how the computational framework of Reinforcement Learning can help us to precisely frame this problem and diagnose where behaviour-environment mismatches emerge.