Assessing the Potential of Brewer's Spent Grain to Enhance Cookie Physicochemical and Nutritional Profiles.

Brewers' spent grain (BSG), the major by-product of the brewery industry, has high nutritional value, making it suitable for upcycling into products such as healthy, and sustainable cookies. Nonetheless, the incorporation of BSG in cookies can impact their quality, given the increased fiber and protein content. This work explored the effect of replacing wheat flour with BSG at 50% and 75% in cookie formulations, focusing on physical, chemical, and sensory properties.

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning.

Integrating diverse types of biological data is essential for a holistic understanding of cancer biology, yet it remains challenging due to data heterogeneity, complexity, and sparsity. Addressing this, our study introduces an unsupervised deep learning model, MOSA (Multi-Omic Synthetic Augmentation), specifically designed to integrate and augment the Cancer Dependency Map (DepMap). Harnessing orthogonal multi-omic information, this model successfully generates molecular and phenotypic profiles, resulting in an increase of 32.

Sprint and jump performance are determined by localized BIA - an ecological study in track and field adolescent athletes.

Background: Raw data obtained through bioelectrical impedance analysis (BIA) have been applied in different populations to assess body fluids and cell integrity. Assessing raw BIA parameters in specific muscles is an emerging method for evaluating muscle function. We investigated the associations of the BIA-derived variables of resistance (R), reactance (Xc) and phase angle (PhA) measured through whole-body (WB) and muscle-localized (ML) methods with performance in the countermovement jump (CMJ) and 50-meter (m) sprint.