Highly laborious plating of cultures onto solid media is inevitable in conventional yeast two-hybrid systems. We have successfully developed Liquid Y2H-Seq, a method which replaces solid media with liquid media for culturing and that displaces visual inspection of colonies with sequencing. Soybean (Glycine max) is a typical photoperiod-sensitive crop, meaning that specific duration light regime has a huge impact on soybean flowering and production. We obtained a lot of putatively interactions for soybean flowering genes. Finally, these reports introduce a labor-reducing and time-saving method for identifying protein-protein interactions, based on simple modifications to a ubiquitous protocol in life science research.

Soybean ( Glycine max [L.] Merr.) has been grown in diverse environments in a wide range of latitudes in the world. However, the adaptation of soybean to low-latitude, high-altitude environments characterized by short day and low temperature is still in its early stages. To understand the genetic basis of adaptation in this region, we conducted cultivar screening in low-latitude, high-altitude mountainous regions and carried out genome-wide association studies (GWAS) using 200 diverse soybean cultivars spanning maturity groups 000 to VIII. Evaluation of flowering time (DTF), maturity time (DTM), and other agronomic traits including node number on main stem (NNM), plant height (PH), effective number of pods per plant (EPN) and 100-seed weight (HGW) were performed in Bamei with the altitude of 3460 m (30°29’4”N and 101°28’50”E) and Xianshui with the altitude of 2946 m (30°57’48”N and 101°9’26”E), in Daofu county, Sichuan province, southwest China, in 2019 and 2023, respectively. We screened 5 MG I-II adapted cultivars for Bamei and 17 MG II-V cultivars, for Xianshui, exhibiting late flowering and maturity, along with tall plant height, and high number of nodes and pods per plant. The allelic combinations E1/e2-ns/e3-tr/E4 and E1/e2-ns/E3/E4, and only E1/e2-ns/E3/E4 predominated in the adaptive cultivars in Bamei and Xianshui, respectively. Thus, the cultivars adaptive to low-latitude and high-altitude regions are featured with relatively late flowering and maturing in MG I-V mostly from mid- and low-latitude region with allelic combination mostly of E1/e2-ns/E3/E4. Additionally, our results revealed 9, 6, and 2 genomic regions significantly associated with DTF, NNM and PH, respectively, with two of these genomic regions having QTNs significantly associated with both DTF and PH. Most significant QTNs of the genomic regions associated with DTF were located near previously identified quantitative trait loci, and their alternative loci caused significant differences in flowering time. Furthermore, 7 genomic regions were located near homologs of Arabidopsis flowering time genes FD-1, bZIP29, SEC, PA2, PIE1, FY, IAA31, AS1 and MBD9. Non-synonymous mutations within the candidate genes Glyma.09g112200 ( GmPIE1) , Glyma.13g198200 ( GmFY) and Glyma.17g042800 ( GmIAA31) were found to cause later flowering and taller plant height in both environments. Collection of adaptive cultivars, molecular markers, and candidate genes identified in this study holds substantial promise within the realm of soybean adaptation in low-latitude and high-altitude regions.

In order to count soybean seeds quickly and accurately, improve the speed of seed test and the level of soybean breeding , this dissertation developed a method of soybean seed counting based on VGG-Two (VGG-T). Firstly, in view of the lack of available image dataset in the field of soybean seed counting, a fast target point labeling method of combining pre-annotation based on digital image processing technology with manual correction annotation is proposed to speed up the establishment of publicly available soybean seed image dataset with annotation. This method only takes 197 minutes to mark 37,563 seeds, which saves 1,592 minutes than ordinary manual marking and replaces 96% of manual workload. Secondly, a method that would combine the density estimation-based methods and the convolution neural network (CNN)-based methods is developed to accurately estimate the seed count from an individual threshed seed image with a single perspective. Finally, the model is tested, and verify the effectiveness of the algorithm through three comparative experiments (with and without data enhancement, VGG16 and VGG-T, multiple sets of test set), which respectively provided 0.6 and 0.2 mean absolute error (MAE) in the original image and patch cases, while mean squared error (MSE) is 0.6 and 0.3. Compared with traditional image image morphology operations, ResNet18, ResNet18-T and vgg16, this method improves the accuracy of soybean seed counting.