Three immunoregulatory signatures define non-productive HIV infection in CD4+ T memory stem cells

The persistent HIV reservoir constitutes the main obstacle to curing HIV/AIDS disease. Our understanding of how non-productive HIV infections are established in primary human CD4+

Dispersal, adaptation and persistence of H5N1 in the sub-Antarctic and Antarctica

High pathogenicity avian influenza virus (HPAIV) H5N1 reached the sub-Antarctic and Antarctica in 2023, subsequently spreading to remote locations within this region where it had

ApeA cleaves genomic RNA to defend against RNA phage infection

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The increase in size of metabolic network models especially with the advent of single-cell data calls for scalable reconstruction and analysis tools. Such models, often

Acceleration and Velocity Dissociate Temporal Phases of Postural Control in Rhesus Macaques

Maintaining balance requires the nervous system to transform sensory signals about unexpected postural perturbations into precisely timed motor commands. Although human studies have established that

Machine intelligence supports the full chain of 2D dendrite synthesis

March 19, 2026

arXiv:2603.16959v1 Announce Type: cross
Abstract: Exemplified by the chemical vapor deposition growth of two-dimensional dendrites, which has potential applications in catalysis and presents a parameter-intensive, data-scarce and reaction process-complex model problem, we devise a machine intelligence-empowered framework for the full chain support of material synthesis, encompassing rapid process optimization, accurate customized synthesis, and comprehensive mechanism deciphering.First, active learning is integrated into the experimental workflow, identifying an optimal recipe for the growth of highly-branched, electrocatalytically-active ReSe2 dendrites through 60 experiments (4 iterations), which account for less than 1.3% of the numerous possible parameter combinations.Then, a prediction accuracy-guided data augmentation strategy is developed combined with a tree-based machine learning (ML) algorithm, unveiling a non-linear correlation between 5 process variables and fractal dimension (DF) of ReSe2 dendrites with only 9 experiment additions, which guides the synthesis of various user-defined DF. Finally, we construct a data-knowledge dual-driven mechanism model by integration of cross-scale characterizations, interpretable ML models, and domain knowledge in thermodynamics and kinetics, unraveling synergistic contributions of multiple process parameters to the product morphology. This work demonstrates the ML potential to transform the research paradigm and is adaptable to broader material synthesis.

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