Design and Optimization of Planting Process Parameters for 2ZYX-2 Type Green Onion Ditching and Transplanting Machine

Considering the current low level of mechanization for domestic green onion planting and the high labor intensity of artificial planting, a 2ZYX-2 green onion ditching and transplanting machine, which can complete ditching, ridging, transplanting, repression, soil covering and other operations, is designed in this study. The Central Composite test design method was carried out with the speed of the transplanting machine, the depth of the opener and the horizontal position of the opener as the experimental factors and with the qualification ratio of perpendicularity, the variation coefficient of the plant spacing and the qualification ratio of the planting depth as the test index. Through the analysis of the model interaction and response surface, the change laws that the influence the machine’s forward speed, the depth of the opener and the horizontal position of the opener were studied. The regression model was optimized by Design-Expert 8.0.6 software, and the accuracy of the predicted results was verified by experiments. The optimal working parameters showed that the forward speed of the machine was 0.06 m/s, the depth of the opener was 102 mm, and the horizontal position of the opener was 29 mm. Under conditions of optimal working parameters, the qualification rate of the verticality was 86.83%, the coefficient of variation for the plant spacing was 2.77, and the pass rate of planting depth was 88.26%. The research related to the thesis can provide a reference for the mechanized planting of green onion, which is of great significance to the cost-effectiveness of the green onion industry.     

A novel peroxiredoxin from the antagonistic endophytic bacterium Enterobacter sp. V1 contributes to cotton resistance against Verticillium dahliae

Plant and Soil - [Aims] To reveal the molecular mechanism of endophytic bacteria in Verticillium wilt-resistant cottons and deeply understand the interaction between soil and plants. [Methods]...     

Effect of Dietary Selenium Supplementation on Growth and Reproduction of Silkworm Bombyx mori L.

Biological Trace Element Research - The effects of selenium (Se) on the growth and reproduction of the Lepidoptera insect, the silkworm, Bombyx mori L were investigated. Initially, the silkworms...     

Theoretical insight into the multiple interactions of quinazoline inhibitors with breast cancer resistance protein (BCRP/ABCG2)

Communicated by Ramaswamy H. Sarma     

Three Meta-Analyses Define a Set of Commonly Overexpressed Genes from Microarray Datasets on Astrocytomas

Glioma is one of the most common tumors of the central nervous system, and one of its main types is astrocytoma. Microarray technology has been widely used to explore the molecular mechanism of cancer. It is universally accepted that meta-analysis considerably improves the statistical robustness of results, particularly in clinical research. To obtain the maximum reliability, we used three different meta-analyses to integrate the four microarray datasets, GSE16011, GSE4290, GSE2223, and GSE19728 (local), and defined the common differentially expressed genes (DEGs) in astrocytomas compared with normal brain tissue. Four DEGs, PCNA, CDC2, CDK2 and CCNB2, which are components of the cell cycle pathway, were chosen for Real-Time Polymerase Chain Reaction (RT-PCR) and immunohistochemistry validation. PCNA is similar to the P53 gene and has been widely implicated in various cancers including gliomas. Therefore, the expression status of PCNA in our study was considered as a reference to test our whole experimental scheme, and the results indicate that our methodology is valid. Although a few studies have reported the overexpression of the CDC2, CDK2 and CCNB2 genes in glioma cell lines, we are the first to identify the statuses of these genes in human astrocytoma tissues at the mRNA and protein levels. The results of the gene validations strongly suggested that the genes play an important role in astrocytomas and could potentially be valuable in the diagnosis and treatment of astrocytoma.     

Estimating soil respiration using spectral vegetation indices and abiotic factors in irrigated and rainfed agroecosystems

Aims

Our aims were to identify the primary factors involved in soil respiration (R_s) variability and the role that spectral vegetation indices played in R_s estimation in irrigated and rainfed agroecosystems during the growing season.

Methods

We employed three vegetation indices [i.e., normalized difference vegetation index (NDVI), green edge chlorophyll index (CI_{green edge}) and enhanced vegetation index (EVI)] derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product as approximations of crop gross primary production (GPP) for R_s estimation. Different statistical models were used to analyze the dependencies of R_s on soil temperature, soil water content and plant photosynthesis, and accuracy of these models were compared in the irrigated and rainfed agroecosystems.

Results

The results demonstrated that a model based only on abiotic factors (e.g., soil temperature and soil water content) failed to describe part of the growing-season variability in R_s. Residual analysis indicated that R_s was influenced by a short-term gross primary production (GPP) and a longer-term (≥3 days) accumulated GPP in the irrigated and rainfed agroecosystems. Therefore, photosynthesis dependency of R_s should be included in the R_s model to describe the growing-season dynamics of R_s. Among the three VIs, CI_{green edge} showed generally better correlations with GPP at different cumulative times and canopy green leaf area index than EVI and NDVI. Adding the CI_{green edge} into the model considering only soil temperature and soil water content significantly improved the simulation accuracy of R_s.

Conclusions

Our results suggest that spectral vegetation index from remote sensing could be used to estimate R_s, which will be helpful for the development of a future R_s model over a large spatial scale.