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Jia-ming Li

Bio: Jia-ming Li is an academic researcher from Nanjing Agricultural University. The author has contributed to research in topics: PEAR & Gene. The author has an hindex of 1, co-authored 2 publications receiving 2 citations.

Papers
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Journal ArticleDOI
TL;DR: Pearprocess is a new cost-effective web-application for semi-automated quantification of two-dimensional phenotypic traits from digital imagery using an easy imaging protocol and is a promising new tool for use in evaluating future germplasms for crop breeding programs.

5 citations

Journal ArticleDOI
TL;DR: Gen expression analysis revealed that PbrMLO genes were distributed in various pear tissues, suggesting their diverse functions, and evolutionary insight is provided into Pbr MLO and its functional characteristics.

2 citations


Cited by
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Journal ArticleDOI
TL;DR: An automatic method to measure indicators of pear fruit spots based on image recognition was proposed in this article, which can automatically measure fruit spot area, amount, size, and the color difference between fruit spots and epidermis.

5 citations

Journal ArticleDOI
TL;DR: In this paper , the authors highlight key advances in pear genetics, genomics, and breeding driven by the availability of whole-genome sequences, including wholegenome resequencing efforts, pear domestication, and evolution.
Abstract: Abstract Pear, belonging to the genus Pyrus, is one of the most economically important temperate fruit crops. Pyrus is an important genus of the Rosaceae family, subfamily Maloideae, and has at least 22 different species with over 5000 accessions maintained or identified worldwide. With the release of draft whole-genome sequences for Pyrus, opportunities for pursuing studies on the evolution, domestication, and molecular breeding of pear, as well as for conducting comparative genomics analyses within the Rosaceae family, have been greatly expanded. In this review, we highlight key advances in pear genetics, genomics, and breeding driven by the availability of whole-genome sequences, including whole-genome resequencing efforts, pear domestication, and evolution. We cover updates on new resources for undertaking gene identification and molecular breeding, as well as for pursuing functional validation of genes associated with desirable economic traits. We also explore future directions for “pear-omics”.

4 citations

Journal ArticleDOI
TL;DR: In this paper , the authors identified 30 universal stress proteins (USPs) in the grape genome, which could be divided into three classes according to their encoded protein sequences, and this classification was reflected by the distribution of conserved motifs.
Abstract: With the frequent occurrence of extreme natural disasters, unfavorable growth environment is a common phenomenon in the life cycle of plants. In recent years, universal stress proteins (USPs) have attracted extensive attention in the field of plant science for their expression patterns and functional analysis. However, the characterization of the USP family remains unclear in grape. In this study, we identified 30 VvUSPs in the grape genome, which could be divided into three classes according to their encoded protein sequences, and this classification was reflected by the distribution of conserved motifs. Gene duplication analysis demonstrated that segmental duplication was an important pathway in the expansion of the VvUSP family. The expression patterns of 12 VvUSPs were significantly different between tissues, implying they had different functions in various tissues. Cis-acting element and expression analysis showed that most of the promoter regions of VvUSPs contained sequences responsive to hormones and stress elements, especially the promoter region of VIT_16s0013g01920. In conclusion, our findings provide comprehensive information for the further investigation of the genetics and protein functions of the USP gene family in grape.

2 citations

Journal ArticleDOI
01 Aug 2022-Foods
TL;DR: In this article , the particle swarm optimization (PSO) algorithm was used to optimize the combination of penalty factor C and kernel function parameter g for evaluating the stone cell content (SCC) of Korla fragrant pears.
Abstract: Stone cells are a distinctive characteristic of pears and their formation negatively affects the quality of the fruit. To evaluate the stone cell content (SCC) of Korla fragrant pears, we developed a Vis/NIR spectroscopy system that allowed for the adjustment of the illuminating angle. The successive projective algorithm (SPA) and the Monte Carlo uninformative variable elimination (MCUVE) based on the sampling algorithm were used to select characteristic wavelengths. The particle swarm optimization (PSO) algorithm was used to optimize the combination of penalty factor C and kernel function parameter g. Support vector regression (SVR) was used to construct the evaluation model of the SCC. The SCC of the calibration set ranged from 0.240% to 0.657% and that of the validation set ranged from 0.315% to 0.652%. The SPA and MCUVE were used to optimize 57 and 83 characteristic wavelengths, respectively. The combinations of C and g were (6.2561, 0.2643) and (2.5133, 0.1128), respectively, when different characteristic wavelengths were used as inputs of SVR, indicating that the first combination had good generalization ability. The correlation coefficients of the SPA-SVR model after pre-processing the standardized normal variate (SNV) for both sets were 0.966 and 0.951, respectively. These results show that the SNV-SPA-SVR model satisfied the requirements of intelligent evaluation of SCC in Korla fragrant pears.

1 citations