Volume 4 Issue 4, April 2024

Wildfires have increased in frequency and intensity due to climate change and have had severe impacts on the built environment worldwide. Moving forward, models should take inspiration from epidemic network modeling to predict damage to individual buildings and understand the impact of different mitigations on the community vulnerability in a network setting.

Cooperation is crucial for human prosperity, and population structure fosters it through pairwise interactions and coordinated behavior in larger groups. A recent study explores the evolution of behavioral strategies in higher-order population structures, including pairwise and multi-way interactions to reveal that higher-order interactions promote cooperation across networks, especially when they are formed by conjoined communities.

Approaches are needed to accelerate the discovery of transition metal complexes (TMCs), which is challenging owing to their vast chemical space. A large dataset of diverse ligands is now introduced and leveraged in a multiobjective genetic algorithm that enables the efficient optimization of TMCs in chemical spaces containing billions of them.

SANGO efficiently removed batch effects between the query and reference single-cell ATAC signals through the underlying genome sequences, to enable cell type assignment according to the reference data. The method achieved superior performance on diverse datasets and could detect unknown tumor cells, providing valuable functional biological signals.

A method is developed for the directional optimization of multiple properties without prior knowledge on their nature. Using a large ligand dataset, diverse metal complexes are found along the Pareto front of vast chemical spaces.

Cooperation is not merely a dyadic phenomenon, it also includes multi-way social interactions. A mathematical framework is developed to study how the structure of higher-order interactions influences cooperative behavior.

This study introduces SANGO, a method for accurate single-cell annotation leveraging genomic sequences around accessibility peaks within single-cell ATAC sequencing data. SANGO consistently outperforms existing methods across diverse datasets for identification of cell type and detection of unknown tumor cells. SANGO enables the discovery of cell-type-specific functional insights through expression enrichment, cis-regulatory chromatin interactions and motif enrichment analyses.

A fast and versatile three-dimensional cell-based model, called SimuCell3D, is developed for high-resolution simulations of large and complex biological tissues. SimuCell3D natively integrates intra- and extracellular entities, including extracellular matrix, nuclei and polarized cell surfaces.