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We investigate how reliable movement can emerge in aggregates of highly error-prone individuals. The individuals-robotic modules-move stochastically using vibration motors. By coupling them via elastic links, soft-bodied aggregates can be created. We present distributed algorithms that enable the aggregates to move and deform reliably. The concept and algorithms are validated through formal analysis of the elastic couplings and experiments with aggregates comprising up to 49 physical modules-among the biggest soft-bodied aggregates to date made of autonomous modules. The experiments show that aggregates with elastic couplings can shrink and stretch their bodies, move with a precision that increases with the number of modules, and outperform aggregates with no, or rigid, couplings. Our findings demonstrate that mechanical couplings can play a vital role in reaching coherent motion among individuals with exceedingly limited and error-prone abilities, and may pave the way for low-power, stretchable robots for high-resolution monitoring and manipulation.
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http://dx.doi.org/10.1038/s41467-023-39660-6 | DOI Listing |
Mol Carcinog
August 2025
Department of Health Toxicology, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Tobacco smoke is a major risk factor for esophageal squamous cell carcinoma (ESCC), yet only a subset of smokers develop this disease, implicating gene-smoking interactions in modulating individual susceptibility. Through integrative transcriptomic analyses of normal and tumor samples from smokers and nonsmokers, we identify four smoke-responsive genes (CXCL14, HORMAD1, WFDC5, and MPZ) as potential contributors to ESCC carcinogenesis. Among these, HORMAD1 is markedly upregulated in ESCC cells upon exposure to cigarette smoke condensate (10 µg/mL), benzo[a]pyrene (3 µM), or 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (10 µM), correlating with activation of error-prone nonhomologous end joining (NHEJ) in response to DNA damage.
View Article and Find Full Text PDFStat Med
July 2025
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Environmental epidemiologists are often interested in estimating the effect of time-varying functions of the exposure history on health outcomes. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually subject to measurement errors. To obtain unbiased estimates of the effects of such mismeasured functions in longitudinal studies with discrete outcomes, a method applicable to the main study/validation study design is developed.
View Article and Find Full Text PDFSports Med
July 2025
Emeritus Professor, Sports Medicine, Ruhr University Bochum, Bochum, Germany.
Oxidative phosphorylation and glycolysis are the two truly major energy metabolism pathways in humans. While maximal oxygen uptake ( max) has been used for a century as a whole-body measure of maximal oxidative phosphorylation, there is no universally accepted, comparable measure of maximal glycolysis. However, already in 1984, Alois Mader introduced the maximal rate of lactate accumulation in mmol/kg/s related to active muscle weight (vLamax) for his mathematical model of human exercise metabolism.
View Article and Find Full Text PDFbioRxiv
June 2025
Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Center of Digital Health, Charitéplatz 1, 10117 Berlin, Germany.
Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects tissue and organ function. Since 2020, more than 50 spatially aware clustering (SAC) methods have been developed for this purpose.
View Article and Find Full Text PDFbioRxiv
June 2025
Department of Physics, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Advances in fluorescence microscopy have enabled high-resolution tracking of individual biomolecules in living cells. However, accurate estimation of diffusion parameters from single-particle trajectories remains challenging due to static and dynamic localization errors inherent in these measurements. While previous studies have characterized how such errors affect mean-squared displacement (MSD) analysis, practical guidelines for minimizing them during data acquisition and correcting them during analysis are still lacking.
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