University of Phoenix College of Doctoral Studies scholars Pamayla E. Darbyshire, DHA, MSN/CNS, and Carl Beitsayadeh, MS, have published the article, “Enhancing Student Success through GAI and Predictive Analytics,” in International Journal for Educational Media and Technology. The conceptual paper proposes a theoretically grounded 16-stage framework for integrating generative artificial intelligence and predictive analytics into a unified, closed-loop support system for online higher education.

DENVER - Researchers at National Jewish Health have uncovered a previously unknown mechanism that helps drive inflammation in allergic asthma, offering new insight into how the disease develops and potentially revealing new targets for future therapies.

Colorectal cancer histopathological grading relies on the accurate segmentation of glandular structures. Current deep learning–based methods depend heavily on large-scale pixel-level annotations that are labor-intensive and not amenable to clinical practice. Weakly supervised semantic segmentation offers a promising alternative; yet, existing class activation map–based weakly supervised semantic segmentation approaches often produce incomplete, low-quality pseudo-masks that overemphasize discriminative regions and fail to provide reliable supervision for unannotated glandular structures, limiting their suitability for dense histopathology segmentation under sparse supervision. We propose a novel weakly supervised teacher–student framework that leverages sparse pathologists’ annotations and an Exponential Moving Average–stabilized teacher network to generate refined pseudo-masks.

Normalization can standardize and improve machine learning (ML) performance on omics data. However, it is unclear whether normalization is associated with overfitting (i.e., worse cross-dataset performance than intra-dataset performance). Therefore, we aimed to examine associations of normalization and regularization with overfitting of ML on omics data.