LUMIC: Latent diffUsion for Multiplexed Images of Cells.

The rapid advancement of high-content, single-cell technologies like robotic confocal microscopy with multiplexed dyes (morphological profiling) can be leveraged to reveal fundamental biology, ranging from microbial and abiotic stress to organ development. Specifically, heterogeneous cell systems can be perturbed genetically or with chemical treatments to allow for inference of causal mechanisms. An exciting strategy to navigate the high-dimensional space of possible perturbation and cell type combinations is to use generative models as priors to anticipate high-content outcomes in order to design informative experiments.

Pediatric Intubations in a Semiurban Helicopter Emergency Medicine Service: A Retrospective Review.

Objective: Although a small proportion of helicopter emergency medical service (HEMS) missions are for pediatric patients, it is recognized that children do present unique challenges. This case series aims to evaluate the intubation first-pass success rate in HEMS pediatric patients for both medical and trauma patients in a UK semiurban environment.

Methods: A retrospective review of the computerized records system was performed from January 1, 2015, to July 31, 2022, at 1 UK HEMS.

CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair.

Elucidating gene function is a major goal in biology, especially among non-model organisms. However, doing so is complicated by the fact that molecular conservation does not always mirror functional conservation, and that complex relationships among genes are responsible for encoding pathways and higher-order biological processes. Co-expression, a promising approach for predicting gene function, relies on the general principal that genes with similar expression patterns across multiple conditions will likely be involved in the same biological process.

Predicting the Structural Impact of Human Alternative Splicing.

Protein structure prediction with neural networks is a powerful new method for linking protein sequence, structure, and function, but structures have generally been predicted for only a single isoform of each gene, neglecting splice variants. To investigate the structural implications of alternative splicing, we used AlphaFold2 to predict the structures of more than 11,000 human isoforms. We employed multiple metrics to identify splicing-induced structural alterations, including template matching score, secondary structure composition, surface charge distribution, radius of gyration, accessibility of post-translational modification sites, and structure-based function prediction.