A hybrid physics-informed neural network and differentiable learning algorithm integrates a recurrent neural network model of 3D continuum soft tissue with a differentiable fluid solver to infer 3D tissue dynamics from sparse 2D images.

Cross-species analysis of brain mass and neuron number suggest that larger-brained species tend to shift towards more right-lateralized individuals, which should be considered in the context of the evolution of hemispheric asymmetries moving forward.

The Ethiopian wolf is one of Africa’s most endangered carnivores. A new fossil specimen indicates the presence of this species in Ethiopia since the Early Pleistocene, with climate modelling indicating variable past and future ranges for the species.

Cryo-EM and XL-MS analyses reveal the conformational landscape of TRiCmediated tubulin folding along TRiC’s ATPase cycle and the interaction sites between tubulin and the closed TRiC chamber.

A model of radiation injury in mice reveals that the dynamics of inflammatory cells control neovascularization and tissue healing.

KLF11 inhibits lung adenocarcinoma cell proliferation and promotes chemotherapy sensitivity by repressing transcription of GPX4 and promoting ferroptosis.

A high-resolution imaging apparatus that allows functional retinal imaging in awake and free moving mice was developed, allowing the study of the effect of anaesthesia on eye motion, blood flow and retinal thickness.

An fMRI study in 47 participants reveals that stimulus-independent and stimulus-dependent neural networks centered around the midbrain periaqueductal gray underpin an individual’s ability to mount placebo analgesia.

JAZF1 regulates human endometrial stromal cells survival and decidualization by restricting the activity of Purβ, which induces the expression of G0S2, a factor that is involved in the mitochondrial apoptotic pathway in recurrent spontaneous abortion.

Establishment of multiple split selectable marker systems that utilize inteins to enable selection of transgenic plants stably transformed with two T-DNAs via Agrobacterium-mediated transformation.

Although liquid-liquid phase separation (LLPS) has been extensively studied in various cellular and organismal contexts, the link between functional influence of a genetic mutation and LLPS with respect to human diseases is poorly understood. A recent article by Mensah et al. looks at a rare genetic disease to identify a frameshift mutation, which triggered aberrant phase separation and nucleolar dysregulation, linking genetic variants to a dysregulation of biomolecular condensates.

March 31st marks Transgender Day of Visibility, an opportunity to celebrate and elevate the achievements of the transgender community. As part of our annual celebration of Transgender Day of Visibility, we reached out to Leland Graber and Dr. Ezra Kottler, two early-career transgender biologists who shared their own experiences and perspectives on improving support systems for the transgender research community.

We present a checklist to improve the reliability and reproducibility of molecular dynamics simulations and related methods.

Although we have come a long way in breaking down the stigma surrounding mental health conditions, we still have a long way to go. Here we speak to Dr Anna Schueth - a postdoc and passionate advocate whose blogs and other efforts are leading the way in changing academia towards a direction that will allow everyone to thrive as their authentic selves and to get the support they need.

Behavioral results suggest that learning by trial-and-error (i.e., reinforcement learning) relies on a teaching signal, the prediction error, which quantifies the difference between the obtained and the expected reward. Evidence suggests that distinct cortico-striatal circuits are recruited to encode better-than-expected (positive prediction error) and worst-than-expected (negative prediction error) outcomes. A recent study by Villano et al.¹ provides evidence for differential networks that underlie learning from positive and negative prediction errors in humans using real-life behavioral data. More specifically, they found that university students are more likely to update beliefs concerning grade expectations following positive rather than negative prediction errors.

A review of citation rates from genomic studies in the GWAS Catalog suggests that sharing summary statistics results, on average, in ~81.8% more citations, highlighting a benefit of publicly sharing GWAS summary statistics.