causes charcoal rot, which can significantly reduce yield and seed quality of soybean and dry bean resulting from primarily environmental stressors. Although charcoal rot has been recognized as a warm climate-driven disease of increasing concern under global climate change, knowledge regarding population genetics and climatic variables contributing to the genetic diversity of is limited This study conducted genome sequencing for 95 isolates from soybean and dry bean across the continental United States, Puerto Rico, and Colombia. Inference on the population structure using 76,981 single nucleotide polymorphisms (SNPs) revealed that the isolates exhibited a discrete genetic clustering at the continental level and a continuous genetic differentiation regionally. A majority of isolates from the United States (96%) grouped in a clade with a predominantly clonal genetic structure, while 88% of Puerto Rican and Colombian isolates from dry bean were assigned to a separate clade with higher genetic diversity. A redundancy analysis (RDA) was used to estimate the contributions of climate and spatial structure to genomic variation (11,421 unlinked SNPs). Climate significantly contributed to genomic variation at a continental level with temperature seasonality explaining the most variation while precipitation of warmest quarter explaining the most when spatial structure was accounted for. The loci significantly associated with multivariate climate were found closely to the genes related to fungal stress responses, including transmembrane transport, glycoside hydrolase activity and a heat-shock protein, which may mediate climatic adaptation for . On the contrary, limited genome-wide differentiation among populations by hosts was observed. These findings highlight the importance of population genetics and identify candidate genes of that can be used to elucidate the molecular mechanisms that underly climatic adaptation to the changing climate.
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http://dx.doi.org/10.3389/fgene.2023.1103969 | DOI Listing |
Food Res Int
January 2025
College of Food Science and Engineering, Changchun University, Changchun, Jilin 130022, China. Electronic address:
The presence of exogenous protein can delay starch digestion. However, systematic studies on the effects of protein on starch digestion under various heat treatments still need to be completed. In this study, the effects of exogenous protein and heat treatments on corn starch digestibility were investigated.
View Article and Find Full Text PDFToxics
December 2024
Department of Industrial Engineering, University of Applied Sciences Technikum Wien, 17 Hoechstaedtplatz 6, 1200 Vienna, Austria.
Cadmium (Cd) is one of the foremost phytotoxic elements. Its proportion in agricultural soil is increasing critically due to anthropogenic activities. Cd stress is a major crop production threat affecting food security globally.
View Article and Find Full Text PDFInt J Biol Macromol
December 2024
Hainan University-HSF/LWL Collaborative Innovation Laboratory, School of Food Science and Engineering, Hainan University, Haikou 570228, China. Electronic address:
The research on the combination of starch and galactomannans (GM) with dry heat treatment (DHT) is currently insufficient, which hinders the starch application. In this study, the impacts of dry heat treatment and GM complex on the structural, gelatinization properties, and digestibility of pea starch (PS) were investigated. The gelatinization viscosity and gel hardness of dry heated-PS were decreased.
View Article and Find Full Text PDFCurr Res Food Sci
December 2024
Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA.
Some yellow-colored market classes of dry bean ( L.) are valued by consumers as an easy-to-digest, fast cooking alternative to darker colored red and black beans, which in comparison generally have longer cooking times and reduced iron bioavailability. There is evidence that the cooking time of yellow beans is linked to the dietary fiber content and may also contribute to nutrient digestibility and bioavailability.
View Article and Find Full Text PDFSci Rep
December 2024
School of Big Data, Fuzhou University of International Studies and Trade, Fuzhou, 350202, China.
The traditional machine learning methods such as decision tree (DT), random forest (RF), and support vector machine (SVM) have low classification performance. This paper proposes an algorithm for the dry bean dataset and obesity levels dataset that can balance the minority class and the majority class and has a clustering function to improve the traditional machine learning classification accuracy and various performance indicators such as precision, recall, f1-score, and area under curve (AUC) for imbalanced data. The key idea is to use the advantages of borderline-synthetic minority oversampling technique (BLSMOTE) to generate new samples using samples on the boundary of minority class samples to reduce the impact of noise on model building, and the advantages of K-means clustering to divide data into different groups according to similarities or common features.
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