eLife

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1. We couldn't get details of this article at this time:
   10.1101/2024.01.10.575073

2. We couldn't get details of this article at this time:
   10.1101/2024.03.01.582981

3. We couldn't get details of this article at this time:
   10.1101/2024.02.26.582181

4. The RNA-binding activity of the TRIM-NHL protein NHL-2 is essential for miRNA-mediated gene regulation

   This article has 10 authors:

   1. Nasim Saadat
   2. Rhys N. Colson
   3. Acadia L. Grimme
   4. Uri Seroussi
   5. Joshua W. Anderson
   6. Julie M. Claycomb
   7. Matthew C. J. Wilce
   8. Katherine McJunkin
   9. Jacqueline A. Wilce
   10. Peter R. Boag

   This article has been curated by 1 group:

   • Curated by eLife

     eLife assessment

     This useful study identifies amino acid residues in the C. elegans RNA-binding protein NHL-2 that are required for RNA binding in vitro and NHL-2 function in vivo. The evidence in support of the authors' mechanistic model is currently incomplete, as data implicating specific NHL-2 amino acids in RNA binding per se in vivo are not presented. This manuscript will be of interest to scientists working in the area of gene regulation.

   Reviewed by eLife

5. We couldn't get details of this article at this time:
   10.1101/2023.10.24.563722

6. We couldn't get details of this article at this time:
   10.21203/rs.3.rs-3400169/v2

7. Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning

   This article has 4 authors:

   1. Sarah Jo C Venditto
   2. Kevin J Miller
   3. Carlos D Brody
   4. Nathaniel D Daw

   This article has been curated by 1 group:

   • Curated by eLife

     eLife Assessment

     This important work by Veneditto and colleagues developed a new modeling approach, called a mixture-of-agent hidden Markov model (MoA-HMM), in which choice behaviors are modeled as transitions between discrete states defined by different weighting of several reinforcement learning and decision strategies. The authors apply this approach to their previous data collected from rats performing the two-step task, and show that this method predicts fluctuations in neural and other behavioral data and provides better fits to the data than previous methods. The revision has greatly improved the manuscript, the evidence supporting the conclusions is convincing, and the method is of general interest to the field.

   Reviewed by eLife

8. We couldn't get details of this article at this time:
   10.1101/2024.02.27.582239

9. We couldn't get details of this article at this time:
   10.1101/2023.12.12.571272

10. Deconstructing Complexity: A Computational Topology Approach to Trajectory Inference in the Human Thymus with tviblindi

    This article has 14 authors:

    1. Jan Stuchly
    2. David Novak
    3. Nadezda Brdickova
    4. Petra Hadlova
    5. Vojen Sadilek
    6. Ahmad Iksi
    7. Daniela Kuzilkova
    8. Michael Svaton
    9. George Alehandro Saad
    10. Pablo Engel
    11. Herve Luche
    12. Ana E Sousa
    13. Afonso RM Almeida
    14. Tomas Kalina

    This article has been curated by 1 group:

    • Curated by eLife

      eLife Assessment

      The authors present an algorithm and workflow for the inference of developmental trajectories from single-cell data, including a mathematical approach to increase computational efficiency. In this latest version, the authors addressed the benchmarking of the novel method, but the absence of quantitative comparisons to state-of-the-art methods still make this study incomplete. Based on the shown validation approaches, one can neither ultimately judge if the shown method will be an advance over previous work nor whether the approach will be of general useful applicability.

    Reviewed by eLife