eLife

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1. We couldn't get details of this article at this time:
   10.64898/2026.05.27.728092

2. mTOR regulates longevity through a bile-acid like hormonal mechanism and DHS-26/DHRS1

   This article has 8 authors:

   1. Klara Schilling
   2. Alex Zaufel
   3. Kaylee M. Morris
   4. Anna Löhrke
   5. Ratni Saini
   6. Hans-Joachim Knölker
   7. Tarek Moustafa
   8. Adam Antebi

   This article has been curated by 1 group:

   • Curated by eLife

     eLife Assessment

     This fundamental study reveals a new downstream mechanism that mediates mTOR's effect on lifespan in C. elegans. Using a combination of genetic, genomic, and functional analyses, the authors uncovered that a bile acid-like hormone, dafachronic acid (DA), acts downstream of mTOR to modulate lifespan. The reviewers found the evidence provided to be compelling.

   Reviewed by eLife

3. We couldn't get details of this article at this time:
   10.1101/2024.10.29.620866

4. We couldn't get details of this article at this time:
   10.64898/2026.04.27.721040

5. We couldn't get details of this article at this time:
   10.1101/2025.11.26.690816

6. A single evidence accumulation process informs perceptual choices and subsequent confidence reports

   This article has 7 authors:

   1. J.P. Grogan
   2. L. Vermeylen
   3. S.L. Mannion
   4. C. McCabe
   5. D. Monakhovych
   6. K. Desender
   7. R.G. O’Connell

   This article has been curated by 1 group:

   • Curated by eLife

     eLife Assessment

     This valuable study addresses two debates about how confidence is computed: whether it continues the accumulation process that produced the initial choice or reflects a separate one, and whether that accumulation stops at a time limit or an evidence boundary. The evidence is solid, combining systematic model comparison with an independent neural marker of evidence accumulation to adjudicate between models that behaviour alone cannot separate. Support for the boundary-based stopping rule is stronger than for the single-process account, as the neural comparison is qualitative rather than quantified and the alternatives tested cover only a narrow range of plausible two-stage architectures. The work will be of interest to researchers studying decision-making and metacognition.

   Reviewed by eLife

7. We couldn't get details of this article at this time:
   10.64898/2026.05.19.726328

8. We couldn't get details of this article at this time:
   10.64898/2026.06.08.730780

9. The digital sphinx: Can a worm brain control a fly body?

   This article has 4 authors:

   1. Bingni W. Brunton
   2. Elliott T.T. Abe
   3. Lawrence Jianqiao Hu
   4. John C. Tuthill

   This article has been curated by 1 group:

   • Curated by eLife

     eLife Assessment

     What can a neural network trained to imitate animal behavior tell us about biology? This valuable work uses deep reinforcement learning to train an artificial neural network to transform the dynamics of a recurrent neural network based on the C. elegans connectome into an adult Drosophila walking program in a physical model of the fly body, demonstrating that achieving plausible output dynamics does not in and of itself imply biologically meaningful simulation. Evidence for this basic claim is solid, but more extensive analyses, better methodological description, and a discussion of deeper challenges in the undertaking of biological brain modeling would strengthen the study. This result demands the attention of the practitioners of the growing field of connectome simulation for the purpose of gaining mechanistic understanding of nervous system function.

   Reviewed by eLife

10. cuBNM: GPU-Accelerated Brain Network Modeling

    This article has 10 authors:

    1. Amin Saberi
    2. Bin Wan
    3. Kevin J Wischnewski
    4. Kyesam Jung
    5. Leonard Sasse
    6. Felix Hoffstaedter
    7. Boris C Bernhardt
    8. Simon B Eickhoff
    9. Oleksandr V Popovych
    10. Sofie L Valk

    This article has been curated by 1 group:

    • Curated by eLife

      eLife Assessment

      This important study introduces an open-source software package that improves the computational efficiency of whole-brain modelling and facilitates individualised model fitting in large neuroimaging cohorts. The evidence is convincing for the main computational and practical claims, supported by evaluations across multiple optimisation strategies, speed benchmarks, and demonstrations using human neuroimaging data. Its modular design and documentation should make it a resource that is of value to researchers interested in scalable and individualised brain network modelling.

    Reviewed by eLife