Molecular marker technology is used widely in plant variety discrimination, molecular breeding, and other fields. To lower the cost of testing and improve the efficiency of data analysis, molecular marker screening is very important. Screening usually involves two phases: the first to control loci quality and the second to reduce loci quantity. To reduce loci quantity, an appraisal index that is very sensitive to a specific scenario is necessary to select loci combinations. In this study, we focused on loci combination screening for plant variety discrimination. A loci combination appraisal index, variety discrimination power (VDP), is proposed, and three statistical methods, probability-based VDP (P-VDP), comparison-based VDP (C-VDP), and ratio-based VDP (R-VDP), are described and compared. The results using the simulated data showed that VDP was sensitive to statistical populations with convergence toward the same variety, and the total probability of discrimination power (TDP) method was effective only for partial populations. R-VDP was more sensitive to statistical populations with convergence toward various varieties than P-VDP and C-VDP, which both had the same sensitivity; TDP was not sensitive at all. With the real data, R-VDP values for sorghum, wheat, maize and rice data begin to show downward tendency when the number of loci is 20, 7, 100, 100 respectively, while in the case of P-VDP and C-VDP (which have the same results), the number is 6, 4, 9, 19 respectively and in the case of TDP, the number is 6, 4, 4, 11 respectively. For the variety threshold setting, R-VDP values of loci combinations with different numbers of loci responded evenly to different thresholds. C-VDP values responded unevenly to different thresholds, and the extent of the response increased as the number of loci decreased. All the methods gave underestimations when data were missing, with systematic errors for TDP, C-VDP, and R-VDP going from smallest to biggest. We concluded that VDP was a better loci combination appraisal index than TDP for plant variety discrimination and the three VDP methods have different applications. We developed the software called VDPtools, which can calculate the values of TDP, P-VDP, C-VDP, and R-VDP. VDPtools is publicly available at https://github.com/caurwx1/VDPtools.git.
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http://dx.doi.org/10.3389/fpls.2021.566796 | DOI Listing |
Sci Rep
January 2025
World Vegetable Center, 60 Yi-Min Liao, Shanhua, Tainan, 74151, Taiwan.
Wild tomato species exhibit natural insect resistance, yet the specific secondary metabolites and underlying mechanisms governing the resistance remain unclear. Moreover, defense expression dynamically adapts to insect herbivory, causing significant metabolic changes and species-specific secondary metabolite accumulation. The present study aims to identify the resistance-related metabolites in wild tomato accessions that influence the defense mechanism against whitefly (Bemisia tabaci Asia II 7) and leafminer (Phthorimaea absoluta).
View Article and Find Full Text PDFMicroPubl Biol
January 2025
Department of Neuroscience, Pomona College, Claremont, California, United States of America.
uses chemosensation to recognize a variety of odors, many of which are released by bacteria, the major food source of . Specific amphid sensory neurons are known to detect different odorants. Here we show that the AWC neuron detects the attractive odorant 1-butanol.
View Article and Find Full Text PDFAnn Fam Med
January 2025
University of Saskatchewan, School of Rehabilitation Sciences, Saskatoon, Saskatchewan, Canada
Purpose: People who are transgender or gender diverse (PTGD) often experience difficulties navigating the health care system due to a variety of factors such as lack of knowledgeable and/or culturally competent clinicians, discrimination, and structural and/or socioeconomic barriers. We sought to determine whether a peer health navigator service in the Canadian province of Saskatchewan helped connect transgender and gender-diverse clients and health care practitioners (HCPs) to resources, and how this service changed their health care experiences.
Methods: Semistructured interviews were conducted with 9 clients and 9 HCPs.
Mar Pollut Bull
January 2025
JK Laxmipat University, Jaipur, Rajasthan, India.
Marine pollution due to oil spills presents major risks to coastal areas and aquatic life, leading to serious environmental health concerns. Oil Spill detection using SAR data has transitioned from traditional segmentation to a variety of machine learning & deep learning models like UNET proving its efficiency for the task. This research paper proposes a GSCAT-UNET model for efficient oil spill detection and discrimination from lookalikes.
View Article and Find Full Text PDFPlants (Basel)
January 2025
Maize Research Institute Zemun Polje, Slobodana Bajića 1, 11185 Belgrade, Serbia.
Driven by the growing demands for plant-based protein in Europe and attempts of soybean breeding programs to improve the productivity of created varieties, this study aimed to enhance genetic resource utilization efficiency by providing information relevant to well-focused breeding targets. A set of 90 accessions was subjected to a comprehensive assessment of genetic diversity in a soybean working collection using three marker types: morphological descriptors, agronomic traits, and SSRs. Genotype grouping patterns varied among the markers, displaying the best congruence with pedigree data and maturity for SSRs and agronomic traits, respectively.
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