Evaluated articles

eLife Assessment

This paper addresses the significant question of quantifying epistasis patterns, which affect the predictability of evolution, by reanalyzing a recently published combinatorial deep mutational scan experiment. The findings are useful, showing that epistasis is fluid, i.e. strongly background dependent, but that fitness effects of mutations are statistically predictable based on the background fitness. While the general approach appears solid, some claims remain incompletely supported by the analysis, as arbitrary cutoffs are used and the description of methods lacks specifics. This analysis should be of interest to the community working on fitness landscapes.

eLife Assessment

This study makes a valuable contribution to understanding Bayesian inference in dynamic environments by demonstrating how humans integrate prior beliefs with sensory evidence, revealing an overestimation of environmental volatility while accurately tracking noise. The evidence is solid, supported by robust model fitting and principled factorial model set analyses, though limitations in sample size and inconclusive findings on memory capacity tradeoffs reduce the overall impact. Future work should expand validation across datasets, enhance model comparisons, and explore the generalizability of reduced Bayesian frameworks to strengthen the conclusions and broader relevance of the study.

eLife Assessment

This important study uses diffusion magnetic resonance imaging to non-invasively map the white matter fibres connecting the zona incerta and cortex in humans. The authors present convincing evidence to indicate that these connections are organized along a rostro-caudal axis. The findings will be of interest to researchers interested in neuroanatomy and cortico-subcortical connectivity.

eLife Assessment

This manuscript presents a useful mean-field model for a network of Hodgkin-Huxley neurons retaining the equations for ion exchange between the intracellular and extracellular space. The mean-field model derived in this work relies on approximations and heuristic arguments that, on the one hand, allow a closed-form derivation of the mean-field equations, but also raise questions about their justifications and the degree to which the results agree with experiments as well as direct numerical simulations. While the revised manuscript is much improved, reviewers continue to question the methodology for reducing model dimensionality and therefore the evidence for the utility of this approach remains incomplete at present.

eLife Assessment

The study conducted by Hurtado et al. offers important insights and solid evidence regarding the prediction of drug combinations for cancer treatment. By leveraging disease-specific drug response profiles and single-cell transcriptional signatures, this research not only demonstrates a novel and effective approach to identifying potential drug synergies but it also enhances our understanding of the underlying mechanisms of drug response prediction.

eLife Assessment

This study examined the important question of how neurons code temporal information across the hippocampus, dorsal striatum, and orbitofrontal cortex. Using a behavioral task in the rat that requires discrimination between short and long time intervals, the authors conclude that time intervals are represented in all three regions and that synchronized activity of time-coding cells across the brain regions is coordinated by theta rhythms. However, several weaknesses are noted, and in its current form, the study provides incomplete evidence for understanding how temporal information is processed and coordinated throughout these brain networks.

eLife Assessment

This important study provides new evidence on the role of norepinephrine (NE) release in the hippocampus in response to environmental transitions (event boundaries), providing a potential link between NE signaling and the segmentation of episodic memories. The work is solid, employing innovative techniques such as fiber photometry with the GRAB-NE sensor for NE measurement, the analysis of public electrophysiology hippocampal datasets, and well-controlled experiments. While further analysis could strengthen some claims, this work offers insights into memory, neuromodulation, and hippocampal function.