Evaluated articles

eLife Assessment

The focus of this manuscript is a computational procedure to reveal signatures of selection on transcription factor binding sites through assessing changes in predicted binding affinity, setting out to avoid biases inherent in previous tests. The general approach could become a valuable resource for the community that can also be used for a broader range of questions. However, in its current implementation, the methods are inadequate to sufficiently support the primary claims.

eLife Assessment

This paper presents important findings on how the shapes of leaves might be biased towards simpler shapes due to biases in how variation is generated by developmental processes rather than selection. The authors present solid evidence that combines image analysis of a herbarium dataset and computational analysis of a model of leaf development. The paper should be of interest to diverse researchers, ranging from plant development to the evolution of complexity more broadly.

eLife Assessment

This useful study introduces a statistical model and accompanying software for jointly analysing how an organism's own genotype, and those of its neighbors, shape its traits (assessing both direct and indirect genetic effects), based on simulations and three datasets from plants. The implementation and its behavior on simulated data are solid, but the evidence that the approach is more powerful, more interpretable, or more novel than established alternatives is incomplete, because the authors do not benchmark against existing methods, nor validate the candidate genes they identify, nor test realistic scenarios in which neighbor effects are weaker than direct effects. The work will be of interest to quantitative geneticists and plant breeders studying competition among neighboring genotypes.

eLife Assessment

The framework of this potentially important study – with the integration of multiple levels of analysis, glymphatic MRI, transcriptomics, functional MRI, and public amyloid maps, in one framework – is clever. The assertion that regional amyloid vulnerability may depend not just on neural activity alone, but on whether clearance is appropriately matched to activity, is an interesting and novel concept. However, the chosen approach to imaging glymphatic clearance relies on indirect inferences from a small subgroup. In its current form, the main conclusions of this study are therefore incompletely supported.

eLife Assessment

This valuable study advances our understanding of genes contributing to Drosophila resistance to octanoic acid, a primary toxin present in Morinda fruit, which is the natural host plant for Drosophila sechellia, a species that has become a model for understanding evolutionary specialization. The authors provide solid results from an original combination of experimental evolution and cell-based CRISPR screens. This work will be of interest to the Drosophila community and researchers interested in the genetic basis of polygenic traits.

eLife Assessment

This study of BDNF signaling in heterogeneous spinal cord cultures provides a fundamental conceptual advance by demonstrating that cell identity and maturation state, rather than receptor stoichiometry alone, ultimately determine how a trophic message is interpreted, in a framework the authors call "prepared competence." The evidence is compelling, with the discrete subpopulation behavior, the maturation-dependent acquisition of signaling competence, and the dissociation between receptor abundance and signaling output emerging clearly from the high-dimensional dataset. This study will be of interest to neurobiologists as well as cell biologists who study the molecular basis of cell signaling.

eLife Assessment

This study presents an important finding that loss or blockade of key integrins unexpectedly enhances central nervous system accumulation of T-cell acute lymphoblastic leukemia cells and may increase their sensitivity to chemotherapy. The evidence is convincing, supported by well-designed in vivo models, CRISPR-based perturbations, competitive assays, imaging, and complementary therapeutic experiments. However, the mechanistic basis linking integrin loss, altered spatial distribution, and increased proliferation remains incompletely defined, and the translational implications would be strengthened by additional survival studies and validation in more clinically relevant models.

eLife Assessment

This study provides important findings regarding the efficacy of a chronotherapeutic protocol (termed LiFE), combining timed light, food, and exercise exposure in improving several physiological and health metrics in a rodent model. The evidence advanced in wild-type mice is solid but inconclusive and underpowered when applied to two transgenic mouse models of Alzheimer's Disease. Additionally, the potential of such protocols in clinical human studies is an open question. Overall, the study suggests that LiFE intervention may have positive effects on metabolic and brain health.

eLife Assessment

This valuable study shows that macaque monkeys preferentially fixate regions in natural scenes that are classified as "meaningful" by a computational model - an earlier model that was developed to identify locations that are semantically informative to humans - suggesting that overt attention to structured visual content is shared across primates. However, support is incomplete for the stronger claim that macaques are guided by semantic meaning, which is confounded by lower-level visual features that co-vary with it and by methodological limitations that complicate interpretation. If the semantic interpretation were more reliably established, the significance of the findings would increase, as they would connect the human cognitive process of scene understanding to neural circuit mechanisms accessible in non-human primates.

eLife Assessment

This important study identifies inhibitory cerebellar nuclei neurons as drivers of dystonic crisis and shows that their modulation can both induce and alleviate severe motor symptoms, proposing a cerebello-thalamic circuit mechanism with clear therapeutic relevance. The evidence is convincing, supported by rigorous bidirectional optogenetic manipulations, iCNN-to-CL thalamic monosynaptic tracing, and deep brain stimulation experiments, although the specificity of the genetic strategy remains to be fully resolved. The study will be of broad interest to neuroscientists and clinicians working on movement disorders and circuit-based therapies.

eLife Assessment

This valuable study rigorously examines how motor learning is influenced by the feedback response to a previous movement error. Using a series of well-conducted experiments, the authors provide solid evidence that the learning response following a cursor jump does not depend on the timing of the perturbation and is influenced by the tonic component of the feedback responses. Further work is needed to determine whether this generalizes to other perturbation paradigms and to more fully understand the relationship between learning and the tonic and phasic components of the feedback response.

eLife Assessment

This study leverages publicly available datasets to confirm, validate and extend the knowledge of the transcriptional profile of beta cells that resist destruction in Type 1 diabetes. The significance of the findings is considered valuable as they could be used for engineering stem cell-derived islets and for identifying therapeutic targets to preserve beta cell survival. The strength of the evidence is solid, in that the findings are supported by a sophisticated bioinformatic analysis pipeline and are largely consistent with and extend the existing literature.

eLife Assessment

This important study uses a feedback-driven recurrent neural network framework to explore the dynamics underlying learning of BCI decoder perturbations. With convincing evidence, the authors demonstrate that behavioral learning trajectories that match those of primates learning within-manifold and outside-manifold perturbations are likely tied to the dynamical controllability of the network and input-driven learning. This work is likely to motivate a new generation of BCI and learning experiments combining large-scale neural recordings with latent dynamical systems analyses.

eLife Assessment

This study provides conditionally useful evidence that amino acid starvation and other stresses induce RNF25-dependent ubiquitination of RPS27A/eS31, extending this pathway beyond A-site-trapping conditions and implicating GCN1. However, incomplete and largely indirect evidence was provided to support key mechanistic claims-notably competition between RNF25 and GCN2 for GCN1 and a role in resolving ribosome collisions. Additional direct and orthogonal evidence is required to substantiate these conclusions.

eLife Assessment

This study presents a valuable finding that coordinated changes in epigenetic modifications and three-dimensional chromatin architecture may drive primary trastuzumab resistance in HER2+ breast cancer. Moreover, this manuscript identifies SGK1 as a potential therapeutic target. The evidence supporting the claims of the authors is solid, although the inclusion of a more direct validation of the key findings using tumor samples from patients with clinical trastuzumab resistance would have strengthened the study. The work will be of interest to scientists or clinicians working in the field of BCs.

eLife Assessment

This manuscript provides a timely and important statistical re-evaluation of a paper by Epp et al., on the discordance of BOLD and CMRO2 measures. The authors present a convincing case based on rigorous re-analysis of the data that these previous results arise predominantly from uncertainty in measurement, rather than physiological features. These findings have implications that are of importance to all studies of brain function using BOLD FMRI.

eLife Assessment

This valuable paper uses a mathematical model applied to a dataset of E coli / ESBL carriage and transmission to infer drivers of drug resistance in France. The strength of support for the study findings is incomplete. While the research question is of importance, and the mathematical model has structural and methodological integrity, numerous issues are noted: insufficient description of the data, lack of included equations and code, definitions of antibiotic use that are not complete, low sensitivity of assays for carriage, technical issues with statistical prior selection and parameter identification, and application of non-regional ECDC surveillance data to France.

eLife Assessment

This potentially valuable study describes the development of protein binders targeting DELE1, a protein involved in activating the integrated stress response when mitochondria are perturbed (the mitoISR pathway. The strategy appears to be successful, as several designed proteins were shown to bind DELE1, disrupt DELE1 oligomerization, and attenuate ISR activation. However, the demonstration of the utility of these inhibitory binders is incomplete, particularly given the limited biological outcomes examined in the current study, thus limiting the significance of the paper in its current form.

eLife Assessment

This useful study employs longitudinal widefield cortical imaging to investigate how bilateral vision loss reshapes spontaneous activity across the mouse cortex over time, revealing a state-dependent alteration in the locomotion-related modulation of visual cortical activity. The work provides solid support for its main findings and offers a thorough characterization of the large-scale reorganization of cortical dynamics following adult vision loss. However, the mechanistic interpretation remains limited, as the conclusions are based on a single abrupt and irreversible manipulation without sham controls and on a recording approach that cannot resolve the cell-type-specific mechanisms invoked in the discussion.

eLife Assessment

This is a valuable paper that compares various deep learning models, trained with different objective functions, on their ability to predict fMRI data collected during naturalistic video gameplay. The data and analysis provide solid within-distribution evidence that models trained with PPO and imitation learning outperform untrained models and standard convolutional networks. However, the evidence for brittleness in out-of-distribution encoding remains incomplete, as the claim that this stems from the networks' training rather than from alternative causes-like overfitting of ridge regression parameters-is not yet fully supported.