Srr1/Ber1 containing the SRR1-like domain and Skb1 the human protein arginine methyltransferase 5 (PRMT5) homolog cause gross chromosomal rearrangements at centromeres in the absence of Rad51 recombinase.

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

ImmuneBuilder is a set of deep learning models trained to predict the structure of antibodies, nanobodies, and T-Cell receptors with state-of-the-art accuracy while being much faster than AlphaFold2 and AlphaFoldMultimer.

This Perspective looks at the choice of label-free optical microscopes available and discusses applications as well as scope for further improvement of labelfree imaging.

Intracellular structures are mapped in human multiple myeloma cells through label-free chemical mapping at nanoscale resolution, yielding sub-diffraction images of the ER, mitochondria and nucleoli.

The collective schooling behaviour of rummy-nose tetra fish is associated with light intensity, moving from a state of disorder through polarization and then into rotational milling as light intensity increases.

Electrophysiological recordings in rats reveal that the hippocampal CA1 region represents learning-related action and reward events faster than the lateral entorhinal cortex.

Analysis of individual-specific functional connectivity can differentiate between normal ageing and Alzheimer Disease and individual-specific between-network connectivity constitute a major contributor for assessing cognitive symptoms in both APOE ε4 carriers and non-carriers.

The differential binding of NLRP3 to non-oxidized and Ox-mtDNA is characterized, which provides insight into the mechanisms of inflammasome assembly

ß-glucans, potential prebiotic fibers derived from mycoprotein, can be degraded by human gut Bacteroides via extracellular glycoside hydrolases; produced oligosaccharides can in turn feed Bifidobacterium species as shown by a cross-feeding approach.

Dr. Chris Wallace is a Wellcome Trust Senior Research Fellow, Director of Research, and Principal Investigator in the Department of Medicine and a Programme Leader in the MRC Biostatistics Unit (BSU) at the University of Cambridge. Dr. Guillermo Reales is a postdoctoral researcher in Dr. Wallace’s group, where he recently co-authored a Comment evaluating the impact of sharing summary statistics on average citation rates of genome-wide association studies (GWASs). In this Q&A, we discussed the inspiration for their recent analysis on GWAS summary statistics, the importance of open data, and potential barriers or paths to data sharing in genomics.

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.