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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.
A machine learning-assisted directed evolution method is developed, combining hierarchical unsupervised clustering and supervised learning, to guide protein engineering by iteratively exploring the large mutational sequence space.
Climate data are often stored at higher precision than is needed. The proposed compression automatically determines the precision from the data’s bitwise real information, removing any false information and leading to a more efficient compression.
The authors present a framework for modeling cell interactions using rigid bodies, which can used to represent cells as free moving polygons, to allow epithelial layers to smoothly interact, to model bacteria and to robustly represent membranes.
The authors propose a molecular modeling approach to simulating the galvanostatic charge–discharge process of supercapacitors under constant-potential conditions. This model can accurately predict supercapacitor dynamics when compared with experimental observations.
Combining bioinformatics data and atomistic simulations, this study develops a sequence-dependent coarse-grained model for biomolecular phase separation. This model achieves a quantitative agreement with experimental observations. Extensive benchmarks exemplify its performance.
A plurality of epidemiological models are analyzed using physics-informed neural networks to identify time-dependent parameters and data-driven fractional models. The results are reported for different geographical locations by inferring unknown parameters and unobserved dynamics.
A method for comorbidity discovery informed by each patient’s demographic and medical history is introduced. Statistics for 4,623,841 pairs of potentially comorbid medical terms are provided as a searchable web resource.
Using a statistical method for transient correlations, the waxing and waning in levels of population infection by SARS-CoV-2 are shown to respond to temperature and absolute humidity, across geographical locations and for different temporal and spatial resolutions.
An analysis of GPS pedestrian traces shows that (1) people increasingly deviate from the shortest path when the distance between origin and destination increases and that (2) chosen paths are statistically different when origin and destination are swapped. Ultimately, this can explain the observed human attitude in selecting different paths upon return trips.
A variational autoencoder-based order parameter is proposed and demonstrated on various high-entropy alloys, providing a computational technique for understanding chemical ordering in alloys. Ultimately, this has the potential to facilitate the development of rational alloy design strategies.