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Cryogel Addition Effect on Ultrasound-Assisted Thawing of Pork Meat.
Int J Food Sci
December 2024
Laboratory 13 Thermal and Structural Analysis of Materials and Foods, Multidisciplinary Research Unit, National Autonomous University of Mexico-Superior Studies Faculty at Cuautitlan (UNAM-FESC), Cuautitlan Izcalli, Mexico State, Mexico.
The use of new technologies that allow for improving conventional food preservation processes is what the industry has been adopting in recent decades, with high-intensity ultrasound (US) and the application of cryoprotectant agents (cryogels) being those that have become more relevant today. For this reason, in this study, cuts of pork frozen in liquid nitrogen with and without waxy starch cryogel and thawed under controlled conditions in water immersion and with US were used, evaluating thermal parameters such as the initial zone and the melting rate of ice crystals and quality parameters such as pH, water holding capacity (WHC), microstructure, color profile, shear force, and surface changes. It was shown that the addition of cryogel modifies the initial fusion zone, that US-assisted thawing increases the fusion rate, and that both factors influence the quality parameters.
View Article and Find Full Text PDFThe impact of team cohesion on athlete engagement in collegiate basketball leagues: the moderating role of paternalistic leadership.
Front Psychol
December 2024
School of Physical Education and Sport Science, Fujian Normal University, Fuzhou, Fujian, China.
Objective: This study investigates the impact of team cohesion on the engagement of college basketball players, and examines the moderating effects of authoritarian, benevolent, and moral leadership styles within paternalistic leadership on this influence. This provides theoretical insights for improving athlete engagement.
Methods: Data from 404 athletes participating in the 26th China Collegiate Basketball League were collected through a questionnaire survey and analyzed statistically.
An explainable predictive model for anxiety symptoms risk among Chinese older adults with abdominal obesity using a machine learning and SHapley Additive exPlanations approach.
Front Psychiatry
December 2024
Operations Management and External Communications Department, The Thirteenth People's Hospital of Chongqing, Chongqing, China.
Background: Early detection of anxiety symptoms can support early intervention and may help reduce the burden of disease in later life in the elderly with abdominal obesity, thereby increasing the chances of healthy aging. The objective of this research is to formulate and validate a predictive model that forecasts the probability of developing anxiety symptoms in elderly Chinese individuals with abdominal obesity.
Method: This research's model development and internal validation encompassed 2,427 participants from the 2017-2018 Study of the Chinese Longitudinal Healthy Longevity Survey (CLHLS).
Dynamic response to short-pulsed U-ion beams of material candidates for vacuum beam windows manufacturing.
Heliyon
December 2024
University of Münster, Schlossplatz 2, Münster, 48149, Germany.
The introduction of next-generation extremely energetic particle accelerator facilities, such as the High-Luminosity upgrade of the LHC (HL-LHC) or the proposed future circular collider (FCC), will dramatically increase the energy stored in the circulating particle beams. This will critically affect the thermo-physical and mechanical properties of the materials adopted, possibly compromising their reliability during the operating lifetime. In this scenario, it is paramount to assess the dynamic thermo-mechanical response of materials presently used, or being developed for future use, in beam intercepting devices exposed to potentially destructive events caused by the impact of energetic particle beams.
View Article and Find Full Text PDFProteomics and machine learning: Leveraging domain knowledge for feature selection in a skeletal muscle tissue meta-analysis.
Heliyon
December 2024
Evolved.Bio, 280 Joseph Street, Kitchener, Ontario, Canada.
Omics techniques, such as proteomics, contain crucial data for understanding biological processes, but they remain underutilized due to their high dimensionality. Typically, proteomics research focuses narrowly on using a limited number of datasets, hindering cross-study comparisons, a problem that can potentially be addressed by machine learning. Despite this potential, machine learning has seen limited adoption in the field of proteomics.
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