Common bean (Phaseolus vulgaris L.) is an important legume crop of north-western (NW) Himalayan region and the major disease that causes catastrophic loss to the crop is anthracnose, which is caused by Colletotrichum lindemuthianum.  https://www.selleckchem.com/products/kpt-330.html  The pathogen is highly diverse and most of the commercial cultivars are susceptible to different races prevalent in the region. The lack of information on the genomic regions associated with anthracnose resistance in NW Himalayan common bean population prompted us to dissect Quantitative Resistance Loci (QRLs) against major anthracnose races. In this study, 188 common bean landraces collected from NW region were screened against five important anthracnose races and 113 bean genotypes showed resistance to one or multiple races. Genotyping by sequencing (GBS) was performed on a panel of 192 bean lines (4 controls plus 188 Indian beans) and 22,589 SNPs were obtained that are evenly distributed. Population structure analysis of 192 bean genotypes categorized 188 Indian beans into two major clusters representing Andean and Mesoamerican gene pools with obvious admixtures. Many QRLs associated with anthracnose resistance to Indian C. lindemuthianum virulences (race 3, 87, and 503) are located at Pv04 within the gene models that encode typical resistance gene signatures. The QRLs associated with race 73 are located on Pv08 and overlaps with Co-4 anthracnose resistance gene. A SNP located at distal end of Pv11 in a gene model Phvul.011G202300 which encodes a LRR with a typical NB-ARC domain showed association with race 73 resistance. Common bean genomic regions located at Pv03, Pv09, and Pv11 showed association with resistance to anthracnose race 2047. The present study showed presence of many novel bean genomic regions associated with anthracnose resistance. The presence of Co-4 and Co-2 genes in our material is encouraging for breeding durable anthracnose resistant cultivars for the region.Cuticular wax is closely related to plant resistance to abiotic stress. 3-Ketoacyl-CoA synthase (KCS) catalyzes the biosynthesis of very-long-chain fatty acid (VLCFA) wax precursors. In this study, a novel KCS family gene was isolated from Newhall navel orange and subsequently named CsKCS6. The CsKCS6 protein has two main domains that belong to the thiolase-like superfamily, the FAE1-CUT1-RppA and ACP_syn_III_C domains, which exist at amino acid positions 80-368 and 384-466, respectively. CsKCS6 was expressed in all tissues, with the highest expression detected in the stigma; in addition, the transcription of CsKCS6 was changed in response to drought stress, salt stress and abscisic acid (ABA) treatment. Heterologous expression of CsKCS6 in Arabidopsis significantly increased the amount of VLCFAs in the cuticular wax on the stems and leaves, but there were no significant changes in total wax content. Compared with that of the wild-type (WT) plants, the leaf permeability of the transgenic plants was lower. Further research showed that, compared with the WT plants, the transgenic lines experienced less water loss and ion leakage after dehydration stress, displayed increased survival under drought stress treatment and presented significantly longer root lengths and survival under salt stress treatment. Our results indicate that CsKCS6 not only plays an important role in the synthesis of fatty acid precursors involved in wax synthesis but also enhances the tolerance of transgenic Arabidopsis plants to abiotic stress. Thus, the identification of CsKSC6 could help to increase the abiotic stress tolerance of Citrus in future breeding programs.The yield and quality of fresh lettuce can be determined from the growth rate and color of individual plants. Manual assessment and phenotyping for hundreds of varieties of lettuce is very time consuming and labor intensive. In this study, we utilized a "Sensor-to-Plant" greenhouse phenotyping platform to periodically capture top-view images of lettuce, and datasets of over 2000 plants from 500 lettuce varieties were thus captured at eight time points during vegetative growth. Here, we present a novel object detection-semantic segmentation-phenotyping method based on convolutional neural networks (CNNs) to conduct non-invasive and high-throughput phenotyping of the growth and development status of multiple lettuce varieties. Multistage CNN models for object detection and semantic segmentation were integrated to bridge the gap between image capture and plant phenotyping. An object detection model was used to detect and identify each pot from the sequence of images with 99.82% accuracy, semantic segmentation model was utilized to segment and identify each lettuce plant with a 97.65% F1 score, and a phenotyping pipeline was utilized to extract a total of 15 static traits (related to geometry and color) of each lettuce plant. Furthermore, the dynamic traits (growth and accumulation rates) were calculated based on the changing curves of static traits at eight growth points. The correlation and descriptive ability of these static and dynamic traits were carefully evaluated for the interpretability of traits related to digital biomass and quality of lettuce, and the observed accumulation rates of static straits more accurately reflected the growth status of lettuce plants. Finally, we validated the application of image-based high-throughput phenotyping through geometric measurement and color grading for a wide range of lettuce varieties. The proposed method can be extended to crops such as maize, wheat, and soybean as a non-invasive means of phenotype evaluation and identification.Kernel weight is a key determinant of yield in wheat (Triticum aestivum L.). Starch consists of amylose and amylopectin and is the major constituent of mature grain. Therefore, starch metabolism in the endosperm during grain filling can influence kernel weight. In this study, we sequenced 87 genes involved in starch metabolism from 300 wheat accessions and detected 8,141 polymorphic sites. We also characterized yield-related traits across different years in these accessions. Although the starch contents fluctuated, thousand kernel weight (TKW) showed little variation. Polymorphisms in six genes were significantly associated with TKW. These genes were located on chromosomes 2A, 2B, 4A, and 7A; none were associated with starch content or amylose content. Variations of 15 genes on chromosomes 1A and 7A formed haplotype blocks in 26 accessions. Notably, accessions with higher TKWs had more of the favorable haplotypes. We thus conclude that these haplotypes contribute additive effects to TKW.