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Associating biotechnology to its lab of origin is a challenging task. A deep learning approach is proposed to find distances between engineered plasmids, which allows the ranking of their most probable labs of origin with high accuracy.
A protocol is developed to construct multi-domain protein structures from cryo-electron microscopy density maps. The results demonstrate the effectiveness of deep-learning-guided inter-domain structure assembly and refinement simulations.
Smart pandemic mitigation strategies are proposed to strategically close higher-risk economic sectors, while allowing dozens of other economic sectors to continue. This would enable schools to remain open and keep hospitalizations within capacity.
Unified structural descriptors of geometrical and graph-theoretical features are developed, allowing knowledge about protein lock-and-key complexes to be utilized to predict the formation of and interaction sites in protein–nanoparticle pairs.
Cascaded gated-recurrent-unit networks trained through a physics-informed multi-fidelity approach can accurately forecast long time sequences and capture their dynamics in a wide range of optical resonance structures and features.
A modeling pipeline for the stochastic binding behavior of antibodies on patterned antigen substrates predicts programmable walking behavior that can be manipulated and directed through pattern geometry.
A Bayesian method is presented for unbiased estimation of timescales from different types of experimental data; the method quantifies the estimation uncertainty and allows for comparing the alternative hypotheses on the underlying dynamics.
The authors propose a two-phase approach to solve the inverse problem of inferring dynamical principles of complex systems from incomplete and noisy data, and apply it to infer the spreading dynamics of H1N1, SARS, and COVID-19.
A fully automated, high-throughput computational framework is presented to predict stable species in liquid solutions. This framework combines density functional theory with classical molecular dynamics simulations to compute the NMR chemical shifts.
The authors demonstrate a robust and rigorous framework that can enumerate up to 100 fluorescent labels in a diffraction limited spot using Bayesian nonparametrics.
To help determine how life history traits of individuals result in emergent properties of a population, laboratory studies of Caenorhabditis elegans were combined with an individual-based simulation, pointing out to potential factors that influence old age as a cause of death.
A multiscale model is presented to quantitatively predict COVID-19 vaccine efficacies by describing the generation, activity and diversity of neutralizing antibodies.
deepManReg uses deep neural networks to map various data types onto a topological space (manifolds) and unfold unseen data connections, thus improving prediction of phenotypes from multi-modal data.
Tensor networks exploit the structure of turbulence to offer a compressed description of flows, which leads to efficient fluid simulation algorithms that can be implemented on both classical and quantum computers.
The authors have developed an adaptive reinforced dynamics approach, which improves the efficiency when exploring the configurational space and free energy landscape of large biomolecules, such as proteins.
A data-driven solution of partial differential equations is developed with conditional generative adversarial networks, which could be used in both forward and inverse problems.
The authors demonstrate an effective approach to lower the computing time required to accurately reach the thermodynamic limit in quantum many-body calculations. This method can be applied to solve problems in a wide range of material systems, including metals, insulators and semiconductors.
The authors present a full-scale model of the entorhinal cortex–dentate gyrus–CA3 network based on experimental data to show that fast lateral inhibition plays a key role in pattern separation.
The study shows that a memory-aware and socially coupled human movement model can reproduce urban growth patterns at the macro level, providing a bottom-up approach to understand urban growth and to reveal its connection to human mobility behavior.
