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Leveraging the motion and force of individual molecular motors in a controlled manner to perform macroscopic tasks can provide substantial benefits to many applications, including robotics. Nonetheless, although millimetre-scale movement has been demonstrated with synthetic and biological molecular motors, their efficient integration into engineered systems that perform macroscopic tasks remains challenging. Here, we describe an active network capable of macroscopic actuation that is hierarchically assembled from an engineered kinesin, a biomolecular motor, and microtubules, resembling the contractile units in muscles. These contracting materials can be formed in desired areas using patterned ultraviolet illumination, allowing their incorporation into mechanically engineered systems, being also compatible with printing technologies. Due to the designed filamentous assembly of kinesins, the generated forces reach the micronewton range, enabling actuation of millimetre-scale mechanical components. These properties may be useful for the fabrication of soft robotic systems with advanced functionalities.
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http://dx.doi.org/10.1038/s41563-021-00969-6 | DOI Listing |
Sci Signal
September 2025
Department of Pathology, Microbiology and Immunology, New York Medical College, Valhalla, NY, USA.
Replication of HIV-1 requires the coordinated action of host and viral transcription factors, most critically the viral transactivator Tat and the host nuclear factor κB (NF-κB). This activity is disrupted in infected cells that are cultured with extracellular vesicles (EVs) present in human semen, suggesting that they contain factors that could inform the development of new therapeutics. Here, we explored the contents of semen-derived EVs (SEVs) from uninfected donors and individuals with HIV-1 and identified host proteins that interacted with HIV Tat and the NF-κB subunit p65.
View Article and Find Full Text PDFPLoS One
September 2025
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China.
MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology, yet their spatial dynamics within tumor microenvironments (TMEs) remain underexplored due to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity prediction. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving high predictive accuracy (Spearman's ρ > 0.
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September 2025
School of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, Hunan, China.
Knowledge tracing can reveal students' level of knowledge in relation to their learning performance. Recently, plenty of machine learning algorithms have been proposed to exploit to implement knowledge tracing and have achieved promising outcomes. However, most of the previous approaches were unable to cope with long sequence time-series prediction, which is more valuable than short sequence prediction that is extensively utilized in current knowledge-tracing studies.
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September 2025
Centre for Experimental Pathogen Host Research, School of Medicine, University College Dublin, Dublin, Ireland.
Background: Acute viral respiratory infections (AVRIs) rank among the most common causes of hospitalisation worldwide, imposing significant healthcare burdens and driving the development of pharmacological treatments. However, inconsistent outcome reporting across clinical trials limits evidence synthesis and its translation into clinical practice. A core outcome set (COS) for pharmacological treatments in hospitalised adults with AVRIs is essential to standardise trial outcomes and improve research comparability.
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September 2025
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Objective: This study employs integrated network toxicology and molecular docking to investigate the molecular basis underlying 4-nonylphenol (4-NP)-mediated enhancement of breast cancer susceptibility.
Methods: We integrated data from multiple databases, including ChEMBL, STITCH, Swiss Target Prediction, GeneCards, OMIM and TTD. Core compound-disease-associated target genes were identified through Protein-Protein Interaction (PPI) network analysis.