Volume 2 Issue 4, April 2022

The rapidly growing demand to share data more openly creates a need for secure and privacy-preserving sharing technologies. However, there are multiple challenges associated with the development of a universal privacy-preserving data sharing mechanism, and existing solutions still fall short of their promises.

Dr Jack Dongarra, Distinguished Professor at the University of Tennessee and recipient of the 2021 A. M. Turing Award, spoke with Nature Computational Science about his contributions to high-performance computing (HPC) and his insights into the future of this field.

A dynamic model of SARS-CoV-2 transmission is integrated with a 63-sector economic model to identify control strategies for optimizing economic production while keeping schools and universities operational, and for constraining infections such that emergency hospital capacity is not exceeded.

A robust and reliable codec is the backbone for any digital DNA storage. A recent work introduces a codec based on ancient Chinese philosophy, yin–yang, that outperforms other codecs in terms of reliability and physical information density.

Stochastic modeling of antibody binding dynamics on patterned antigen substrates suggests the separation distance between adjacent antigens could be a control mechanism for the directed bipedal migration of bound antibodies.

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.

The yin–yang codec transcoding algorithm is proposed to improve the practicality and robustness of DNA data storage.

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.

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.