A Multimodal Transfer Learning Approach for Histopathology and SR-microCT Low-Data Regimes Image Segmentation.

Osteocyte-lacunar bone structures are a discerning marker for bone pathophysiology, given their geometric alterations observed during aging and diseases. Deep Learning (DL) image analysis has showcased the potential to comprehend bone health associated with their mechanisms. However, DL examination requires labeled and multimodal datasets, which is arduous with high-dimensional images.

On the optimization of GWFA algorithm: enabling real-case applications supporting alignment backtracking.

The Human Pangenome Reference Consortium (HPRC) proved that pangenome graphs represent a population's genetic variability more efficiently and accurately than linear references. Graphs can intrinsically encode variations as alternative paths inside a directed set of sequence nodes connected by edges. Despite their higher complexity, graph-based genome analysis pipelines are gaining significant interest, and the first sequence-to-graph aligners have already shown improvements in semi-global alignment.