QTL Mapping of Soybean (Glycine max) Vine Growth Habit Trait.

The vine growth habit (VGH) is a notable property of wild soybean plants that also holds a high degree of importance in domestication as it can preclude using these wild cultivars for breeding and improving domesticated soybeans. Here, a bulked segregant analysis (BSA) approach was employed to study the genetic etiology of the VGH in soybean plants by integrating linkage mapping and population sequencing approaches. To develop a recombinant inbred line (RIL) population, the cultivated Zhongdou41 (ZD41) soybean cultivar was bred with ZYD02787, a wild soybean accession. The VGH status of each line in the resultant population was assessed, ultimately leading to the identification of six and nine QTLs from the BSA sequencing of the F4 population and F6-F8 population sequence mapping, respectively. One QTL shared across these analyzed generations was detected on chromosome 19. Three other QTLs detected by BSA-seq were validated and localized to the 90.93 kb, 2.9 Mb, and 602.08 kb regions of chromosomes 6 and 13, harboring 14, 53, and 4 genes, respectively. Three consistent VGH-related QTLs located on chromosomes 2 and 19 were detected in a minimum of three environments, while an additional six loci on chromosomes 2, 10, 13, and 18 were detected in at least two environments via ICIM mapping. Of all the detected loci, five had been reported previously whereas seven represent novel QTLs. Together, these data offer new insights into the genetic basis of the VGH in soybean plants, providing a rational basis to inform the use of wild accessions in future breeding efforts.

Testing facility location and dynamic capacity planning for pandemics with demand uncertainty.

The outbreak of coronavirus disease 2019 (COVID-19) has seriously affected the whole world, and epidemic research has attracted increasing amounts of scholarly attention. Critical facilities such as warehouses to store emergency supplies and testing or vaccination sites could help to control the spread of COVID-19. This paper focuses on how to locate the testing facilities to satisfy the varying demand, i.e., test kits, caused by pandemics. We propose a two-phase optimization framework to locate facilities and adjust capacity during large-scale emergencies. During the first phase, the initial prepositioning strategies are determined to meet predetermined fill-rate requirements using the sample average approximation formulation. We develop an online convex optimization-based Lagrangian relaxation approach to solve the problem. Specifically, to overcome the difficulty that all scenarios should be addressed simultaneously in each iteration, we adopt an online gradient descent algorithm, in which a near-optimal approximation for a given Lagrangian dual multiplier is constructed. During the second phase, the capacity to deal with varying demand is adjusted dynamically. To overcome the inaccuracy of long-term prediction, we design a dynamic allocation policy and adaptive dynamic allocation policy to adjust the policy to meet the varying demand with only one day's prediction. A comprehensive case study with the threat of COVID-19 is conducted. Numerical results have verified that the proposed two-phase framework is effective in meeting the varying demand caused by pandemics. Specifically, our adaptive policy can achieve a solution with only a 3.3% gap from the optimal solution with perfect information.

QTL mapping and genomic selection of stem and branch diameter in soybean (Glycine max L.).

Introduction: Soybean stem diameter (SD) and branch diameter (BD) are closely related traits, and genetic clarification of SD and BD is crucial for soybean breeding. Methods: SD and BD were genetically analyzed by a population of 363 RIL derived from the cross between Zhongdou41 (ZD41) and ZYD02878 using restricted two-stage multi-locus genome-wide association, inclusive composite interval mapping, and three-variance component multi-locus random SNP effect mixed linear modeling. Then candidate genes of major QTLs were selected and genetic selection model of SD and BD were constructed respectively. Results and discussion: The results showed that SD and BD were significantly correlated (r = 0.74, P < 0.001). A total of 93 and 84 unique quantitative trait loci (QTL) were detected for SD and BD, respectively by three different methods. There were two and ten major QTLs for SD and BD, respectively, with phenotypic variance explained (PVE) by more than 10%. Within these loci, seven genes involved in the regulation of phytohormones (IAA and GA) and cell proliferation and showing extensive expression of shoot apical meristematic genes were selected as candidate genes. Genomic selection (GS) analysis showed that the trait-associated markers identified in this study reached 0.47-0.73 in terms of prediction accuracy, which was enhanced by 6.56-23.69% compared with genome-wide markers. These results clarify the genetic basis of SD and BD, which laid solid foundation in regulation gene cloning, and GS models constructed could be potentially applied in future breeding programs.

The Effect of Welding Defects on the Long-Term Performance of HDPE Pipes.

High-density polyethylene (HDPE) pipes are the preferred pipes of water systems in nuclear power plants because they are durable, corrosion-free, easy to install, and not subject to fouling. However, their long-term performance can be affected by welding defects. In this paper, the effect of welding defects on the long-term performance of HDPE pipe butt fusion joints was studied using a creep test. A welding defect with a hole or inclusion in the joint was simulated by artificially inserting a copper ball during butt fusion welding. The test results showed that the creep life of the joint decreased with increased defect size. An expression describing the creep life and the defect ratio was obtained according to the test results. In addition, the test results showed that the creep life of the joint without a welding bead was about 50% of that in a joint with a welding bead.

Mechanical Properties Study of Miniature Steel Specimens Based on the Small Punch Test and Simulation Methods.

The small punch test (SPT) can be very convenient to obtain mechanical properties due to its unique advantages from small-volume samples, and has gained wide popularity and appreciation among researchers. In this paper, the SPT test and finite element (FE) simulations were performed for three alloys, and the yield stresses (σYS) and ultimate tensile strengths (σUTS) from the uniaxial tensile test (UTT) were correlated with the yield force (Fy) and maximum force (Fm) of the small punch test (SPT) before and after compliance calibration. Finally, the effect of specimen size on the SPT curves was discussed. The results showed that the deviation between SPT test and FE simulation was due to the loading system stiffness, which was confirmed by the loading system compliance calibration test. The SPT curves before and after calibration have less influence on the empirical correlation results for σUTS, while the correlation results for σYS depend on the method used to determine Fy in the SPT curve. Finally, the simulation results indicated that the effect of specimen size on the force-displacement curve in the SPT is slight. This work also provides a reference for subsequent researchers to conduct empirical correlation studies using different specimen sizes.

LINC02154 promotes the proliferation and metastasis of hepatocellular carcinoma by enhancing SPC24 promoter activity and activating the PI3K-AKT signaling pathway.

BACKGROUND: Abnormal expression of long non-coding RNAs (lncRNAs) has been associated with the initiation and progression of hepatocellular carcinoma but, as yet, the clinicopathologic significance and potential role of Linc02154 in HCC remains to be determined. Here, we aimed to investigate the potential role and mode of action of Linc02154 in HCC. METHODS: The expression of Linc02154 in 20 pairs of HCC/normal tissues and 7 HCC cell lines was detected by qRT-PCR. The localization of Linc02154 in HCC cells was detected using fluorescence in situ hybridization and nuclear-plasma separation assays. MTS, EdU incorporation, colony formation, flow cytometry, scratch wound-healing and transwell assays were performed to assess the role of Linc02154 in HCC cell proliferation, migration and invasion in vitro, and BALB/c nude mice xenografts were used to evaluate its role in vivo. RNA sequencing and Western blotting were used to evaluate the regulatory effect of Linc02154 on SPC24 gene expression. A dual-luciferase reporter assay was used to assess a putative interaction of Linc02154 with the SPC24 promoter. RESULTS: We identified a new lncRNA, Linc02154, that is highly expressed in HCC cells and tissues of patients with a poor overall survival. Functional experiments revealed that exogenous Linc02154 expression in MHCC-97H and SK-Hep1 cells promoted their proliferation, migration and invasion in vitro and their tumorigenesis in vivo. Using a dual luciferase reporter assay we found that Linc02154 can enhance SPC24 promoter (-500 bp ~ -1000 region) activity. Exogenous over-expression of Linc02154 led to up-regulation of SPC24 by activating PI3K/AKT and its downstream signals, including cell cycle progression and EMT-associated gene expression. CONCLUSION: Our data suggest that Linc02154 may serve as a valuable biomarker of HCC and as a potential therapeutic target.

Towards Disentangling Latent Space for Unsupervised Semantic Face Editing.

Facial attributes in StyleGAN generated images are entangled in the latent space which makes it very difficult to independently control a specific attribute without affecting the others. Supervised attribute editing requires annotated training data which is difficult to obtain and limits the editable attributes to those with labels. Therefore, unsupervised attribute editing in an disentangled latent space is key to performing neat and versatile semantic face editing. In this paper, we present a new technique termed Structure-Texture Independent Architecture with Weight Decomposition and Orthogonal Regularization (STIA-WO) to disentangle the latent space for unsupervised semantic face editing. By applying STIA-WO to GAN, we have developed a StyleGAN termed STGAN-WO which performs weight decomposition through utilizing the style vector to construct a fully controllable weight matrix to regulate image synthesis, and employs orthogonal regularization to ensure each entry of the style vector only controls one independent feature matrix. To further disentangle the facial attributes, STGAN-WO introduces a structure-texture independent architecture which utilizes two independently and identically distributed (i.i.d.) latent vectors to control the synthesis of the texture and structure components in a disentangled way. Unsupervised semantic editing is achieved by moving the latent code in the coarse layers along its orthogonal directions to change texture related attributes or changing the latent code in the fine layers to manipulate structure related ones. We present experimental results which show that our new STGAN-WO can achieve better attribute editing than state of the art methods.