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The highly diverse Colombian Central Collection (CCC) of cultivated potatoes is the most important source of genetic variation for breeding and the agricultural development of this staple crop in Colombia. Potato is the primary source of income for more than 100.000 farming families in Colombia. However, biotic and abiotic challenges limit crop production. Furthermore, climate change, food security, and malnutrition constraints call for adaptive crop development to be urgently addressed. The clonal CCC of potatoes contains 1,255 accessions - an extensive collection size that limits its optimal assessment and use. Our study evaluated different collection sizes from the whole clonal collection to define the best core collection that captures the total genetic diversity of this unique collection, to support a characterization more cost-effectively. Initially, we genotyped 1,141 accessions from the clonal collection and 20 breeding lines using 3,586 genome-wide polymorphic markers to study CCC's genetic diversity. The analysis of molecular variance confirmed the CCC's diversity with a significant population structure (Phi=0.359; =0.001). Three main genetic pools were identified within this collection (CCC_Group_A, CCC_Group_B1, and CCC_Group_B2), and the commercial varieties were located across the pools. The ploidy level was the main driver of pool identification, followed by a robust representation of accessions from Phureja and Andigenum cultivar groups based on former taxonomic classifications. We also found divergent heterozygosity values within genetic groups, with greater diversity in genetic groups with tetraploids (CCC_Group_B1: 0.37, and CCC_Group_B2: 0.53) than in diploid accessions (CCC_Group_A: 0.14). We subsequently generated one mini-core collection size of 3 percent (39 entries) and three further core collections sizes of 10, 15, and 20 percent (i.e., 129, 194, and 258 entries, respectively) from the total samples genotyped. As our results indicated that genetic diversity was similar across the sampled core collection sizes compared to the main collection, we selected the smallest core collection size of 10 percent. We expect this 10 percent core collection to be an optimal tool for discovering and evaluating functional diversity in the genebank to advance potato breeding and agricultural-related studies. This study also lays the foundations for continued CCC curation by evaluating duplicity and admixing between accessions, completing the digitalization of data, and ploidy determination using chloroplast count.
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http://dx.doi.org/10.3389/fpls.2023.1046400 | DOI Listing |
J Nephrol
September 2025
Department of Cardiovascular Sciences, University of Leicester, John Walls' Renal Unit, University Hospitals of Leicester NHS Trust, Leicester, UK.
Background: Individuals with kidney failure experience elevated cardiovascular risk, potentially worsened by the presence of sleep disordered breathing. Despite this association, prevalence of sleep apnoea, and evidence for effective treatments are poorly understood in people with kidney failure. This review examines sleep apnoea prevalence, types of sleep apnoea, and treatment interventions in people with kidney failure receiving dialysis.
View Article and Find Full Text PDFNat Aging
September 2025
Aging Biomarker Consortium (ABC), Beijing, China.
The global surge in the population of people 60 years and older, including that in China, challenges healthcare systems with rising age-related diseases. To address this demographic change, the Aging Biomarker Consortium (ABC) has launched the X-Age Project to develop a comprehensive aging evaluation system tailored to the Chinese population. Our goal is to identify robust biomarkers and construct composite aging clocks that capture biological age, defined as an individual's physiological and molecular state, across diverse Chinese cohorts.
View Article and Find Full Text PDFArch Toxicol
September 2025
Norwegian Scientific Committee for Food and Environment, Norwegian Institute of Public Health, Oslo, Norway.
The transition from traditional animal-based approaches and assessments to New Approach Methodologies (NAMs) marks a scientific revolution in regulatory toxicology, with the potential of enhancing human and environmental protection. However, implementing the effective use of NAMs in regulatory toxicology has proven to be challenging, and so far, efforts to facilitate this change frequently focus on singular technical, psychological or economic inhibitors. This article takes a system-thinking approach to these challenges, a holistic framework for describing interactive relationships between the components of a system of interest.
View Article and Find Full Text PDFMar Environ Res
September 2025
Key Laboratory of Marine Ecosystem Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, 310012, China; State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, 310012, China; Key Laborator
Sri Lanka is uniquely situated at the junction of the Bay of Bengal (BOB) and the Arabian Sea (AS), where phytoplankton community may be strongly influenced by ocean dynamical processes, particularly mesoscale eddies and the East Indian Coastal Current (EICC). Here, to explore these regulatory mechanisms, phytoplankton and physicochemical parameters were collected from the top 200 m water column in the eastern and southern seas of Sri Lanka during the winter monsoon. Results showed higher concentrations of nutrients and phytoplankton abundance within the regions affected by EICC and cyclonic eddy (CE) compared to anticyclonic eddy (ACE).
View Article and Find Full Text PDFJ Environ Manage
September 2025
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
Dissolved oxygen (DO) is a key water quality indicator reflecting river health. Modeling and understanding the spatiotemporal dynamics of DO and its influencing factors are crucial for effective river management. Machine learning (ML) models have gained popularity in water quality prediction; however, their accuracy strongly depends on the predictor variables.
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