Complex signal vectors, particularly spectra, are integral to many scientific domains. Interpreting these signals often involves decomposing them into contributions from independent components and subtraction or deconvolution of the channel and instrument noise. Despite the fundamental nature of this task, researchers frequently rely on costly commercial tools.
View Article and Find Full Text PDFThe GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.
View Article and Find Full Text PDFStigma and discrimination are fundamental causes of health inequities, and reflect privilege, power, and disadvantage within society. Experiences and impacts of stigma and discrimination are well-documented, but a critical gap remains on effective strategies to reduce stigma and discrimination in sexual and reproductive healthcare settings. We aimed to address this gap by conducting a mixed-methods systematic review and narrative synthesis to describe strategy types and characteristics, assess effectiveness, and synthesize key stakeholder experiences.
View Article and Find Full Text PDFIn order to improve the accuracy of molecular dynamics simulations, classical forcefields are supplemented with a kernel-based machine learning method trained on quantum-mechanical fragment energies. As an example application, a potential-energy surface is generalized for a small DNA duplex, taking into account explicit solvation and long-range electron exchange-correlation effects. A long-standing problem in molecular science is that experimental studies of the structural and thermodynamic behavior of DNA under tension are not well confirmed by simulation; study of the potential energy vs extension taking into account a novel correction shows that leading classical DNA models have excessive stiffness with respect to stretching.
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