Research articles

This study reports a chiral instability topography in highly deformed core–shell spheres. A core–shell model and a scaling law are developed to understand its morphoelastic mechanism, which helps the design of a nature-inspired smart topographic gripper based on chiral localization.

A strategy for cooperation in repeated games, called cumulative reciprocity, is proposed. This strategy is robust with respect to errors, enforces fair outcomes, and evolves in environments that are usually hostile to cooperation.

Conduction of neural impulses along axons in the brain is sped up by a substance called myelin, which changes during development and learning. This study reveals that myelin remodelling coordinates and optimizes neuronal communication.

Designing efficient bike path networks requires balancing multiple constraints. In this study, a demand-driven inverse percolation approach is proposed to generate families of efficient bike path networks taking into account cyclist demand and safety preferences.

A framework for measuring how noise in different outbreak data limits the reliability of estimates of epidemic spread is developed and used to show that death time series are rarely better than case data for inferring COVID-19 transmissibility.

A dynamic network model using data from ten European countries indicates that differences in micro-level social interactions can explain a substantial part of variations in the success of national pandemic policies.

A computational method is introduced for mutational intratumor heterogeneity inference from noisy genotype matrices derived from single-cell sequencing data. The proposed method is shown to be accurate and faster than available alternatives.

A machine learning approach is presented to identify dominant patterns in disease progression in amyotrophic lateral sclerosis (ALS), Alzheimer’s disease and Parkinson’s disease. The nonlinearity of ALS progression has important clinical implications.

This study suggests that a lack of co-location hinders the formation of ‘weak ties’—which are crucial for information spread—in communication networks on the basis of an analysis of an email network of more than 2,800 university researchers.

A systematic framework is introduced to calculate the effective carrier lifetime in semiconductor crystals under realistic conditions that are comparable with experiments. It helps explain the discrepancy between the calculated and experimental lifetimes in hybrid perovskites.

A spectrally accurate numerical method for solving partial differential equations (PDEs) on non-uniformly curved surfaces is developed. The method is applied to a PDE model of cell polarization to show that geometric effects allow the existence of unexpected multidomain solutions.

A dimensionality reduction framework, delayed latents across groups (DLAG), is proposed for disentangling the concurrent flow of signals between populations of neurons. DLAG reveals bidirectional communication between visual cortical areas.

The determination of state variables to describe physical systems is a challenging task. A data-driven approach is proposed to automatically identify state variables for unknown systems from high-dimensional observational data.

A machine learning method that can predict how complex molecules bind to the surfaces of transition metals and alloys is developed, which will be useful for catalyst screening, and for understanding and improving catalyst performance.

A deep learning method for protein sequence design on given backbones, ABACUS-R, is proposed in this study. ABACUS-R shows an improved performance when compared with conventional energy function-based methods in wet experiments.

ActiveSVM is introduced as a feature selection procedure for discovering minimal gene sets in large single-cell mRNA-seq datasets.

A graph neural network-based cell clustering model for spatial transcripts obtains cell embeddings from global cell interactions across tissue samples and identifies cell types and subpopulations.

A deep neural network method is developed to learn the mapping function from atomic structure to density functional theory (DFT) Hamiltonian, which helps address the accuracy–efficiency dilemma of DFT and is useful for studying large-scale materials.

A probabilistic generative model for aptamers called RaptGen is introduced, which accelerates the process of aptamer development by generating new aptamer sequences.

Accurate structural brain connectivity estimation is key to uncovering brain–behavior relationships. ReAl-LiFE, a GPU-accelerated approach, is applied for fast and reliable evaluation of individualized brain connectomes at scale.