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  • A mathematical framework that allows computing the input–output function of neurons with active dendrites reveals how dendrites readily and potently control the response variability, a result that is experimentally confirmed.

    • Zachary Friedenberger
    • Richard Naud
    Brief Communication
  • Designing accessible, interoperable and reusable software for applying deep learning to the study of gene regulation has been a challenge in genomics research. EUGENe is a toolkit that addresses this gap and streamlines end-to-end analyses.

    • Adam Klie
    • David Laub
    • Hannah Carter
    Brief CommunicationOpen Access
  • A deep-learning model, DetaNet, is proposed to efficiently and precisely predict molecular scalars, vectorial and tensorial properties, as well as the infrared, Raman, ultraviolet–visible and nuclear magnetic resonance spectra.

    • Zihan Zou
    • Yujin Zhang
    • Wei Hu
    Brief Communication
  • The reasoning capabilities of OpenAI’s generative pre-trained transformer family were tested using semantic illusions and cognitive reflection tests that are typically used in human studies. While early models were prone to human-like cognitive errors, ChatGPT decisively outperformed humans, avoiding the cognitive traps embedded in the tasks.

    • Thilo Hagendorff
    • Sarah Fabi
    • Michal Kosinski
    Brief CommunicationOpen Access
  • A platform for single-cell meta-analysis of inflammatory bowel disease, named scIBD, enables identification of rare or less-characterized cell types and the dissection of the commonalities and differences between ulcerative colitis and Crohn’s disease.

    • Hu Nie
    • Peilu Lin
    • Lei Zhang
    Brief Communication
  • A microscopic moiré spin model that enables the description of moiré magnetic exchange interactions via a sliding-mapping method is proposed. The twist-angle and substrate-influenced magnetic phase diagram addresses disagreements between theories and experiments.

    • Baishun Yang
    • Yang Li
    • Bing Huang
    Brief Communication
  • A deep learning ab initio method for studying magnetic materials is developed, reducing the computational cost and opening opportunities to predict the electronic properties of magnetic superstructures, such as magnetic skyrmions.

    • He Li
    • Zechen Tang
    • Yong Xu
    Brief CommunicationOpen Access
  • A method to compute the quantum harmonic free energy contributions in large materials and biomolecular simulations at a reasonable cost is proposed, making quantum mechanical estimates of thermodynamic quantities possible for complex systems.

    • Alec F. White
    • Chenghan Li
    • Garnet Kin-Lic Chan
    Brief Communication
  • A computational method is introduced for mutational intratumor heterogeneity inference from noisy genotype matrices derived from single-cell sequencing data. The proposed method is shown to be accurate and faster than available alternatives.

    • Can Kızılkale
    • Farid Rashidi Mehrabadi
    • Salem Malikić
    Brief Communication
  • Accurate structural brain connectivity estimation is key to uncovering brain–behavior relationships. ReAl-LiFE, a GPU-accelerated approach, is applied for fast and reliable evaluation of individualized brain connectomes at scale.

    • Varsha Sreenivasan
    • Sawan Kumar
    • Devarajan Sridharan
    Brief CommunicationOpen Access
  • Scallop2 enables a more accurate assembly of transcripts at both single-cell resolution and sample level through a suite of algorithms that leverage the multi-end and paired-end information in Smart-seq3 and Illumina RNA-seq data.

    • Qimin Zhang
    • Qian Shi
    • Mingfu Shao
    Brief Communication