Pixian Doubanjiang (PXDB)'s distinctive umami profile is primarily attributed to its unique peptides; however, their structural characteristics, sensory mechanisms, and biosynthetic pathways during aging remain poorly understood. This study employed a machine learning-based approach to investigate umami peptides in 1-2 year aged PXDB. We identified 117 peptides, predicting 69 with umami potential. Sensory analysis confirmed VEGGLR's remarkably low umami threshold (0.22 mmol/L). Molecular docking further elucidated VEGGLR's interaction with T1R1/T1R3 receptors via salt bridges and hydrogen bonds, enhancing umami perception. Observed post-translational modifications, including phosphorylation and acetylation on protein N6U2M1/N6UWT4, suggest a potential regulatory role in umami peptide biosynthesis. These findings offer key molecular insights into PXDB umami development, enhancing our understanding of its flavor chemistry.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.foodchem.2025.143672 | DOI Listing |
PLoS One
March 2025
Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Coyoacán, Ciudad de México, México.
The 2030 Agenda for Sustainable Development of the United Nations outlines 17 goals for countries of the world to address global challenges in their development. However, the progress of countries towards these goal has been slower than expected and, consequently, there is a need to investigate the reasons behind this fact. In this study, we have used a novel data-driven methodology to analyze time-series data for over 20 years (2000-2022) from 107 countries using unsupervised machine learning (ML) techniques.
View Article and Find Full Text PDFNanomaterials (Basel)
February 2025
National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba 305-8568, Japan.
Ultrafast laser processing is a critical technology for micro- and nano-fabrication due to its ability to minimize heat-affected zones. The effects of intensity variation on the ultrafast laser ablation of fused silica were investigated to gain fundamental insights into the dynamic modulation of pulse intensity. This study revealed significant enhancement in ablation efficiency for downward ramp intensity modulation compared to the upward ramp.
View Article and Find Full Text PDFJ Comput Chem
March 2025
Department of Mathematics, Michigan State University, East Lansing, Michigan, USA.
Protein structural fluctuations, measured by Debye-Waller factors or B-factors, are known to be closely associated with protein flexibility and function. Theoretical approaches have also been developed to predict B-factor values, which reflect protein flexibility. Previous models have made significant strides in analyzing B-factors by fitting experimental data.
View Article and Find Full Text PDFFront Oncol
February 2025
Centre de Recherche du CHU de Québec, Université Laval, Québec, QC, Canada.
Purpose: In the context of lung cancer screening, the scarcity of well-labeled medical images poses a significant challenge to implement supervised learning-based deep learning methods. While data augmentation is an effective technique for countering the difficulties caused by insufficient data, it has not been fully explored in the context of lung cancer screening. In this research study, we analyzed the state-of-the-art (SOTA) data augmentation techniques for lung cancer binary prediction.
View Article and Find Full Text PDFFront Artif Intell
February 2025
Department of Surgery, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia.
Heart disease is a leading cause of mortality worldwide, making accurate early detection essential for effective treatment and management. This study introduces a novel hybrid machine-learning approach that combines transfer learning using the VGG16 convolutional neural network (CNN) with various machine-learning classifiers for heart disease detection. A conditional tabular generative adversarial network (CTGAN) was employed to generate synthetic data samples from actual datasets; these were evaluated using statistical metrics, correlation analysis, and domain expert assessments to ensure the quality of the synthetic datasets.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!