Continuous Optimization of a Hierarchical Bayesian Network for Friedreich's Ataxia Severity Classification.

Machine learning algorithms for rare disorders, such as Friedreich's Ataxia (FRDA), often suffer from a lack of data. Therefore, the ability for continuous optimization of an objective assessment model would be very useful as a clinical decision support system. In this study, we propose a Bayesian Network(BN) system for FRDA severity estimation that incorporates a Bayesian Statistical updating system to continuously improve the predictive ability while providing an easily interpretable graphical model.

Antimicrobial hybrid coatings: A review on applications of nano ZnO based materials for biomedical applications.

The extreme survivability of infectious microorganisms on various surfaces prompts for the risk of disease transmissions, posing a perilous concern for global health. Thus, the treatment of these pathogenic microorganisms using the nanomaterials functionalized with antimicrobial coatings reaps relevant scope in the ongoing trend of research. Driven by their admirable biocompatibility, cost-effectiveness, and minimal toxicity, ZnO nanoparticles (ZnO-NPs) based antimicrobial hybrid coatings have emerged as a robust material to prevent the growth of infectious microorganisms on various surfaces, which in turn boosted their applications in the area of biomedical sciences.

STEAM: Spatial Transcriptomics Evaluation Algorithm and Metric for clustering performance.

Motivation: Spatial transcriptomic technologies allow researchers to explore the diversity and specificity of gene expression within their original tissue structure. Accurately identifying regions that are spatially coherent in both gene expression and physical tissue structures is an emerging topic, but challenging due to the lack of ground truth labels which renders complicating validation of clustering consistency and reproducibility. This highlights a need for a computational evaluation framework to rigorously and unbiasedly assess clustering performance.

Regulating the Dynamics of Interpenetrated Porous Frameworks for Inverse CH/CH Separation at Elevated Temperature.

Selective adsorption of ethane (CH) from mixtures containing ethylene (CH) is of interest for the direct production of high purity CH. However, the extremely similar molecular properties of these gases make this process challenging, particularly at elevated temperatures, an implication of saved energy consumption. To address such challenge, we present a new approach for regulating the temperature-dependent dynamics in hydrogen-bonded interpenetrated frameworks.

CellPhenoX: An eXplainable Cell-specific machine learning method to predict clinical Phenotypes using single-cell multi-omics.

Single-cell technologies have enhanced our knowledge of molecular and cellular heterogeneity underlying disease. As the scale of single-cell datasets expands, linking cell-level phenotypic alterations with clinical outcomes becomes increasingly challenging. To address this, we introduce CellPhenoX, an eXplainable machine learning method to identify cell-specific phenotypes that influence clinical outcomes.