Approximated gene expression trajectories for gene regulatory network inference on cell tracks.

The study of pattern formation has benefited from our ability to reverse-engineer gene regulatory network (GRN) structure from spatiotemporal quantitative gene expression data. Traditional approaches have focused on systems where the timescales of pattern formation and morphogenesis can be separated. Unfortunately, this is not the case in most animal patterning systems, where pattern formation and morphogenesis are co-occurring and tightly linked.

Chainsaw: protein domain segmentation with fully convolutional neural networks.

Motivation: Protein domains are fundamental units of protein structure and play a pivotal role in understanding folding, function, evolution, and design. The advent of accurate structure prediction techniques has resulted in an influx of new structural data, making the partitioning of these structures into domains essential for inferring evolutionary relationships and functional classification.

Results: This article presents Chainsaw, a supervised learning approach to domain parsing that achieves accuracy that surpasses current state-of-the-art methods.

The Dangers of Short-Term Confinement: Indicators of Safety Risks Among Individuals in Jail.

Guided by the premise that an individual's safety may be more at risk when their routines, personal attributes, or lack of guardianship influence their opportunity for exposure to violence, we examined the factors that influenced victimization risks and safety perceptions among a representative sample of respondents incarcerated at a large Midwestern jail. Results showed that vulnerable individuals such as those who were victimized prior to their incarceration, and those who antagonized others such as those who perpetrated assault, were threatened more often, were more at risk of assault victimization, and perceived more dangerous conditions. Conversely, females and individuals with greater self-control were less likely to have experienced victimization and generally felt safer in jail.

Seasonal Arctic sea ice forecasting with probabilistic deep learning.

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times.

Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores.

Some organizations such as 23andMe and the UK Biobank have large genomic databases that they re-use for multiple different genome-wide association studies. Even research studies that compile smaller genomic databases often utilize these databases to investigate many related traits. It is common for the study to report a genetic risk score (GRS) model for each trait within the publication.