Evolutionary genomics of the emergence of brown algae as key components of coastal ecosystems.

Brown seaweeds are keystone species of coastal ecosystems, often forming extensive underwater forests, and are under considerable threat from climate change. In this study, analysis of multiple genomes has provided insights across the entire evolutionary history of this lineage, from initial emergence, through later diversification of the brown algal orders, down to microevolutionary events at the genus level. Emergence of the brown algal lineage was associated with a marked gain of new orthologous gene families, enhanced protein domain rearrangement, increased horizontal gene transfer events, and the acquisition of novel signaling molecules and key metabolic pathways, the latter notably related to biosynthesis of the alginate-based extracellular matrix, and halogen and phlorotannin biosynthesis.

SPARTA: Interpretable functional classification of microbiomes and detection of hidden cumulative effects.

The composition of the gut microbiota is a known factor in various diseases and has proven to be a strong basis for automatic classification of disease state. A need for a better understanding of microbiota data on the functional scale has since been voiced, as it would enhance these approaches' biological interpretability. In this paper, we have developed a computational pipeline for integrating the functional annotation of the gut microbiota into an automatic classification process and facilitating downstream interpretation of its results.

A Deep Learning-Based Approach to Characterize Skull Physical Properties: A Phantom Study.

Transcranial ultrasound imaging is a popular method to study cerebral functionality and diagnose brain injuries. However, the detected ultrasound signal is greatly distorted due to the aberration caused by the skull bone. The aberration mechanism mainly depends on thickness and porosity, two important skull physical characteristics.

Machine Learning Assessment of Background Parenchymal Enhancement in Breast Cancer and Clinical Applications: A Literature Review.

Background Parenchymal Enhancement (BPE) on breast MRI holds promise as an imaging biomarker for breast cancer risk and prognosis. The ability to identify those at greatest risk can inform clinical decisions, promoting early diagnosis and potentially guiding strategies for prevention such as risk-reduction interventions with the use of selective estrogen receptor modulators and aromatase inhibitors. Currently, the standard method of assessing BPE is based on the Breast Imaging-Reporting and Data System (BI-RADS), which involves a radiologist's qualitative categorization of BPE as minimal, mild, moderate, or marked on contrast-enhanced MRI.

Optimizing Thermoplastic Starch Film with Heteroscedastic Gaussian Processes in Bayesian Experimental Design Framework.

The development of thermoplastic starch (TPS) films is crucial for fabricating sustainable and compostable plastics with desirable mechanical properties. However, traditional design of experiments (DOE) methods used in TPS development are often inefficient. They require extensive time and resources while frequently failing to identify optimal material formulations.