Observer reliability in counting erythrocyte ghost cells and impact of short-term storage of canine and feline blood samples.

The presence of erythrocyte ghost cells (EG) in blood smears indicates intravascular haemolysis or in-vitro haemolysis. However, observer reliability in detection of EG has not been documented. Immediate blood smear preparation is advised but may not always be practical.

multiDGD: A versatile deep generative model for multi-omics data.

Recent technological advancements in single-cell genomics have enabled joint profiling of gene expression and alternative modalities at unprecedented scale. Consequently, the complexity of multi-omics data sets is increasing massively. Existing models for multi-modal data are typically limited in functionality or scalability, making data integration and downstream analysis cumbersome.

MOSAIC: An Artificial Intelligence-Based Framework for Multimodal Analysis, Classification, and Personalized Prognostic Assessment in Rare Cancers.

Purpose: Rare cancers constitute over 20% of human neoplasms, often affecting patients with unmet medical needs. The development of effective classification and prognostication systems is crucial to improve the decision-making process and drive innovative treatment strategies. We have created and implemented MOSAIC, an artificial intelligence (AI)-based framework designed for multimodal analysis, classification, and personalized prognostic assessment in rare cancers.

Climate change impacts of bioenergy technologies: A comparative consequential LCA of sustainable fuels production with CCUS.

The use of sustainable biomass can be a cost-effective way of reducing the greenhouse gas emissions in the maritime and aviation sectors. Biomass, however, is a limited resource, and therefore, it is important to use the biomass where it creates the highest value, not only economically, but also in terms of GHG reductions. This study comprehensively evaluates the GHG reduction potential of utilising forestry residue in different bioenergy technologies using a consequential LCA approach.

N-of-one differential gene expression without control samples using a deep generative model.

Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representation and can identify the closest normal representation for a given disease sample.