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CIMMYT maize lines (CMLs), which represent the tropical maize germplasm, are freely available worldwide. All currently released 615 CMLs and fourteen temperate maize inbred lines were genotyped with 180 kompetitive allele-specific PCR single nucleotide polymorphisms to develop a reference fingerprinting SNP dataset that can be used to perform quality control (QC) and genetic diversity analyses. The QC analysis identified 25 CMLs with purity, identity, or mislabeling issues. Further field observation, purification, and re-genotyping of these CMLs are required. The reference fingerprinting SNP dataset was developed for all of the currently released CMLs with 152 high-quality SNPs. The results of principal component analysis and average genetic distances between subgroups showed a clear genetic divergence between temperate and tropical maize, whereas the three tropical subgroups partially overlapped with one another. More than 99% of the pairs of CMLs had genetic distances greater than 0.30, showing their high genetic diversity, and most CMLs are distantly related. The heterotic patterns, estimated with the molecular markers, are consistent with those estimated using pedigree information in two major maize breeding programs at CIMMYT. These research findings are helpful for ensuring the regeneration and distribution of the true CMLs, via QC analysis, and for facilitating the effective utilization of the CMLs, globally.
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http://dx.doi.org/10.3390/plants11223092 | DOI Listing |
J Phys Chem Lett
September 2025
Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
In this work, we present a machine learning (ML) approach for predicting the optimal range separation parameters in transition metal complexes (TMCs), aiming to reduce the computational cost associated with optimally tuned range-separated hybrid (OT-RSH) functionals while preserving their accuracy. A data set containing 4380 TMCs was constructed by screening the tmQM database, with each TMC represented by a 62 087-dimensional multiple-fingerprint feature (MFF) vector and labeled with its optimally tuned range separation parameter. Multiple regression models were applied to train the prediction model, and the support vector machine (SVM) model yielded the best performance.
View Article and Find Full Text PDFMicrob Genom
September 2025
Department of Biology, Science for Life, Plant-Microbe Interactions, Utrecht University, Netherlands, 3584CH Utrecht.
The plant root microbiome is a complex community shaped by interactions among bacteria, the plant host and the environment. Synthetic community (SynCom) experiments help disentangle these interactions by inoculating host plants with a representative set of culturable microbial isolates from the natural root microbiome. Studying these simplified communities provides valuable insights into microbiome assembly and function.
View Article and Find Full Text PDFAvian Pathol
September 2025
The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, St. Lucia, QLD 4072, Australia.
This study examined the genetic diversity in a collection of field isolates of and compared that with the diversity in the serovars A to D and F to I reference strains and available whole genome sequences. Phylogenetic analysis of the 16S rRNA gene of the nine Australian isolates and twelve sequences of overseas isolates resulted in six clusters with the Australian isolates not closely related to either the type strain or the serovar reference strains. The suitability of low cost finger-printing techniques, ERIC-PCR and rep-PCR, when applied to were also evaluated.
View Article and Find Full Text PDFZhongguo Zhong Yao Za Zhi
July 2025
Engineering Center of State Ministry of Education for Standardization of Chinese Medicine Processing,College of Pharmacy, Nanjing University of Chinese Medicine Nanjing 210023, China.
Traditional Chinese medicine(TCM) decoction pieces are a core carrier for the inheritance and innovation of TCM, and their quality and safety are critical to public health and the sustainable development of the industry. Conventional quality control models, while having established a well-developed system through long-term practice, still face challenges such as relatively long inspection cycles, insufficient objectivity in characterizing complex traits, and urgent needs for improving the efficiency of integrating multidimensional quality information when confronted with the dual demands of large-scale production and precision quality control. With the rapid development of artificial intelligence, machine learning can deeply analyze multidimensional data of the morphology, spectroscopy, and chemical fingerprints of decoction pieces by constructing high-dimensional feature space analysis models, significantly improving the standardization level and decision-making efficiency of quality evaluation.
View Article and Find Full Text PDFZhongguo Zhong Yao Za Zhi
July 2025
Shandong University of Traditional Chinese Medicine Ji'nan 250355, China State Key Laboratory of Integration and Innovation of Classic Formula and Modern Chinese Medicine,Lunan Pharmaceutical Group Co., Ltd. Linyi 276005, China.
This study established the HPLC fingerprints and a multi-component content determination method for Bidens pilosa var. radiata and B. pilosa and conducted comprehensive evaluation by integrating fingerprint similarity comparison, cluster analysis(CA), and principal component analysis(PCA), aiming to provide a reference for the establishment of quality standards for Bidentis Herba.
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