Characterization of lncRNA–miRNA–mRNA Network to Reveal Potential Functional ceRNAs in Bovine Skeletal Muscle

There is growing evidence that non-coding RNAs are emerging as critical regulators of skeletal muscle development. In order to reveal their functional roles and regulatory mechanisms, we constructed a lncRNA–miRNA–mRNA network according to the ceRNA (competitive endogenous RNA) theory, using our high-throughput sequencing data. Subsequently, the network analysis, GO (Gene Ontology) analysis, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis were performed for functional annotation and exploration of lncRNA ceRNAs. The results uncovered a scale-free characteristics network which exhibited high functional specificity for bovine skeletal muscle development: co-expression lncRNAs were significantly enriched in muscle development related biological processes and the Wnt signaling pathway. Furthermore, GSEA (Gene Set Enrichment Analysis) indicated that the risk score has a tendency to associate with myogenesis, and differentially expressed RNAs were validated by qPCR, further confirming the credibility of our network. In summary, this study provides insights into lncRNA-mediated ceRNA function and mechanisms in bovine skeletal muscle development and will expand our understanding of lncRNA biology in mammals

Growth Evaluation of Listed Companies of the Agricultural Processing Industry in China

By 2019, agricultural products processing and production enterprises have grown into an large industry for farmers to make a better living and for consumers to enjoy safer food. More importantly, it has become a vital supporting force to safeguard China’s growing modernization. However, the agricultural Processing industry is in an era of great challenges and opportunities. Therefore, this study is dedicated to provide a unique and sustainable-development perspective of this issue. A comprehensive scoring system is constructed to obtain the outstanding listed agricultural product processing enterprises with excellent scores and ratings. The excellent characteristics of high-growth listed companies are summarized to give suggestions and inspirations to sustainable economy of the agricultural processing industry in China

Study on Monte Carlo Simulation of Intelligent Traffic Lights Based on Fuzzy Control Theory

Abstract: Based on the traffic flow pattern in single intersection, this paper advances a design method for intelligent traffic lights in the state of stochastic dynamic change of traffic flow by employing the Fuzzy Control Theory. The application of fuzzy control technology to achieve the traffic lights control system not only breaks the traditional mechanical models but also manages to simulate intelligence control system in which the traffic is directed by the police, making possible signal conversion for different situations and optimum delay time of green light. It is proved that the fuzzy control system can effectively reduce the delay time length of waiting vehicles. In consideration of the randomness and robustness of traffic flow, this paper employs the Monte Carlo method in combination with MATLAB to simulate, which proved to be more effective than the traditional control method. It significantly increases the utilization rate of traffic, thereby generating considerable economic benefits. Copyright © 2013 IFSA

UAV-Based High-Throughput Approach for Fast Growing Cunninghamia lanceolata (Lamb.) Cultivar Screening by Machine Learning

Obtaining accurate measurements of tree height and diameter at breast height (DBH) in forests to evaluate the growth rate of cultivars is still a significant challenge, even when using light detection and ranging (LiDAR) and three-dimensional (3-D) modeling. As an alternative, we provide a novel high-throughput strategy for predicting the biomass of forests in the field by vegetation indices. This study proposes an integrated pipeline methodology to measure the biomass of different tree cultivars in plantation forests with high crown density, which combines unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Using a planation of Cunninghamia lanceolate, which is commonly known as Chinese fir, in Fujian, China, images were collected while using a hyperspectral camera. Vegetation indices and modeling were processed in Python using decision trees, random forests, support vector machine, and eXtreme Gradient Boosting (XGBoost) third-party libraries. The tree height and DBH of 2880 samples were manually measured and clustered into three groups—“Fast”, “median”, and “normal” growth groups—and 19 vegetation indices from 12,000 pixels were abstracted as the input of features for the modeling. After modeling and cross-validation, the classifier that was generated by random forests had the best prediction accuracy when compared to other algorithms (75%). This framework can be applied to other tree species to make management and business decisions

Two Novel SNPs in RET Gene Are Associated with Cattle Body Measurement Traits

The rearrangement of the transfection (RET) gene, which mediates the functions of the ganglion in the gastrointestinal tract, plays an important role in the development of the gastrointestinal nervous system. Therefore, the RET gene is a potential factor influencing animal body measurement. The aim of this study was to reveal the significant genetic variations in the bovine RET gene and investigate the relationship between genotypes and body measurement in two Chinese cattle breeds (Qinchuan and Nanyang cattle). In this study, two SNPs (c.1407A>G and c.1425C>G) were detected in the exon 7 of RET gene by sequencing. For the SNP1 and SNP2, the GG genotype was significantly associated with body height, hip height, and chest circumference in Qinchuan cattle (p < 0.05). Individuals with an AG-CC genotype showed the lowest value of all body measurement in both breeds. Our results demonstrate that the polymorphisms in the bovine RET gene were significantly associated with body measurement, which could be used as DNA marker on the marker-assisted selection (MAS) and improve the performance of beef cattle

Disruption of PD-1 Enhanced the Anti-tumor Activity of Chimeric Antigen Receptor T Cells Against Hepatocellular Carcinoma

Cancer immunotherapy has made unprecedented breakthrough in the fields of chimeric antigen receptor-redirected T (CAR T) cell therapy and immune modulation. Combination of CAR modification and the disruption of endogenous inhibitory immune checkpoints on T cells represent a promising immunotherapeutic modality for cancer treatment. However, the potential for the treatment of hepatocellular carcinoma (HCC) has not been explored. In this study, the gene expressing the programmed death 1 receptor (PD-1) on the Glypican-3 (GPC3)-targeted second-generation CAR T cells employing CD28 as the co-stimulatory domain was disrupted using the CRISPR/Cas9 gene-editing system. It was found that, in vitro, the CAR T cells with the deficient PD-1 showed the stronger CAR-dependent anti-tumor activity against native programmed death 1 ligand 1-expressing HCC cell PLC/PRF/5 compared with the wild-type CAR T cells, and meanwhile, the CD4 and CD8 subsets, and activation status of CAR T cells were stable with the disruption of endogenous PD-1. Additionally, the disruption of PD-1 could protect the GPC3-CAR T cells from exhaustion when combating with native PD-L1-expressing HCC, as the levels of Akt phosphorylation and anti-apoptotic protein Bcl-xL expression in PD-1 deficient GPC3-CAR T cells were significantly higher than those in wild-type GPC3-CAR T cells after coculturing with PLC/PRF/5. Furthermore, the in vivo anti-tumor activity of the CAR T cells with the deficient PD-1 was investigated using the subcutaneous xenograft tumor model established by the injection of PLC/PRF/5 into NOD-scid-IL-2Rγ-/- (NSG) mice. The results indicated that the disruption of PD-1 enhanced the in vivo anti-tumor activity of CAR T cells against HCC, improved the persistence and infiltration of CAR T cells in the NSG mice bearing the tumor, and strengthened the inhibition of tumor-related genes expression in the xenograft tumors caused by the GPC3-CAR T cells. This study indicates the enhanced anti-tumor efficacy of PD-1-deficient CAR T cells against HCC and suggests the potential of precision gene editing on the immune checkpoints to enhance the CAR T cell therapies against HCC

EGFR modulates monounsaturated fatty acid synthesis through phosphorylation of SCD1 in lung cancer

Abstract Background Epidermal growth factor receptor (EGFR), a well-known oncogenic driver, contributes to the initiation and progression of a wide range of cancer types. Aberrant lipid metabolism including highly produced monounsaturated fatty acids (MUFA) is recognized as a hallmark of cancer. However, how EGFR regulates MUFA synthesis in cancer remains elusive. This is the focus of our study. Methods The interaction between EGFR and stearoyl-CoA desaturase-1 (SCD1) was detected byco-immunoprecipitation. SCD1 protein expression, stability and phosphorylation were tested by western blot. The synthesis of MUFA was determined by liquid chromatography-mass spectrometry. The growth of lung cancer was detected by CCK-8 assay, Annexin V/PI staining, colony formation assay and subcutaneous xenograft assay. The expression of activated EGFR, phosphorylated and total SCD1 was tested by immunohistochemistry in 90 non-small cell lung cancersamples. The clinical correlations were analyzed by Chi-square test, Kaplan-Meier survival curve analysis and Cox regression. Results EGFR binds to and phosphorylates SCD1 at Y55. Phosphorylation of Y55 is required for maintaining SCD1 protein stability and thus increases MUFA level to facilitate lung cancer growth. Moreover, EGFR-stimulated cancer growth depends on SCD1 activity. Evaluation of non-small cell lung cancersamples reveals a positive correlation among EGFR activation, SCD1 Y55 phosphorylation and SCD1 protein expression. Furthermore, phospho-SCD1 Y55 can serve as an independent prognostic factor for poor patient survival. Conclusions Ourstudy demonstrates that EGFR stabilizes SCD1 through Y55 phosphorylation, thereby up-regulating MUFA synthesis to promote lung cancer growth. Thus, we provide the first evidence that SCD1 can be subtly controlled by tyrosine phosphorylation and uncover a previously unknown direct linkage between oncogenic receptor tyrosine kinase and lipid metabolism in lung cancer. We also propose SCD1 Y55 phosphorylation as a potential diagnostic marker for lung cancer

Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature

High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding. However, manual measurements within breeding programs are time consuming and have high cost and low throughput, which limit the application in large-scale field phenotyping. In this study, we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders. In total, 13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield. Using an Unmanned Aerial Vehicle (UAV) platform equipped with a hyperspectral camera to capture images over multiple time series, a rice yield classification model based on the XGBoost algorithm was proposed. Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not. The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice. Thus, we developed a low-cost, high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency