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This study investigates the behaviour of consumers regarding four cuts of Iberian meat with greater presence in the market: tenderloin, secreto, presa and pluma. A sample of 1501 consumers responded to an online survey about their consumption habits for these four cuts, sociodemographic characteristics and lifestyle. From this information, three homogeneous segments of consumers were identified: "unmotivated and indifferent to Iberian meat", "innovators and stakeholders" and "traditional with frequent consumption". The Iberian tenderloin and the secreto were the most consumed cuts in all consumption segments, while the main reason for the lower consumption of presa and pluma was "I don't like it", especially among "unmotivated" consumers.
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http://dx.doi.org/10.1016/j.meatsci.2020.108373 | DOI Listing |
J Magn Reson Imaging
September 2025
Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan.
Background: Automated cardiac MR segmentation enables accurate and reproducible ventricular function assessment in Tetralogy of Fallot (ToF), whereas manual segmentation remains time-consuming and variable.
Purpose: To evaluate the deep learning (DL)-based models for automatic left ventricle (LV), right ventricle (RV), and LV myocardium segmentation in ToF, compared with manual reference standard annotations.
Study Type: Retrospective.
J Am Coll Cardiol
August 2025
Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Cardiology, Kaiser Permanente Santa Clara Medical Center, Santa Clara, California, USA. Electronic address:
Background: Accurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular disease and tracking of change over time; however, manual assessment requires time-consuming effort and can be imprecise. Artificial intelligence has the potential to reduce clinician burden by automating the time-intensive task of comprehensive measurement of echocardiographic parameters.
Objectives: The purpose of this study was to develop and validate open-sourced deep learning semantic segmentation models for the automated measurement of 18 anatomic and Doppler measurements in echocardiography.
J Neurosci Methods
September 2025
Department of CSE, Indian Institute of Information Technology Vadodara- International Campus Diu (IIITV-ICD), 362520, Diu, India. Electronic address:
The Electroencephalogram (EEG) is a vital physiological signal for monitoring brain activity and understanding neurological capacities, disabilities, and cognitive processes. Analyzing and classifying EEG signals are key to assessing an individual's reactions to various stimuli. Manual EEG analysis is time-consuming and labor-intensive, necessitating automated tools for efficiency.
View Article and Find Full Text PDFAlcohol Clin Exp Res (Hoboken)
September 2025
Alcohol Research Group, Public Health Institute, Emeryville, California, USA.
Background: Individuals who consume alcohol often use other drugs as well. Little is known about the clustering of heavy and binge drinking with the use of other substances (tobacco, cannabis, illicit drugs, and nonmedical prescription drugs). Overweight/obesity, highly prevalent in the United States (US) and an established health risk factor, may also cluster with them.
View Article and Find Full Text PDFSci Adv
September 2025
Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
Image-based modeling is essential for understanding cardiovascular hemodynamics and advancing the diagnosis and treatment of cardiovascular diseases. Constructing patient-specific vascular models remains labor-intensive, error-prone, and time-consuming, limiting their clinical applications. This study introduces a deep-learning framework that automates the creation of simulation-ready vascular models from medical images.
View Article and Find Full Text PDF