Nitrogen Application Can Optimize Form of Selenium in Soil in Selenium-Rich Areas to Affect Selenium Absorption and Accumulation in Black Wheat.

The composition and form of selenium in the soil have significant effects on the selenium content of crops. In this study, we investigated the selenium absorption pathway in plants by studying the interaction between nitrogen fertilizer and soil selenium. Our results showed that the selenium concentration enrichment factors (CEF) varied within the same region due to nitrogen fertilizer application, where they ranged from 1.

Endophytic bacterial and fungal community compositions in different organs of ginseng (Panax ginseng).

Panax ginseng (Panax ginseng C. A. Mey.

Impacts of cuprous oxide nanoparticles on wheat root morphology and genotoxicity.

To explore the ecotoxicity of CuO nanoparticles (NPs) on plant roots, the effects of CuO-NPs with different concentrations of 10, 50, 100 and 200 mg·L on the seedling growth, root morphology, and cytogenetic toxicity of wheat 'Zhoumai 18' ( Zhoumai 18) were examined in a hydroponic experiment. The results showed that CuO-NPs inhibited the growth of wheat seedlings. CuO-NPs reduced root and shoot lengths, fresh weights of shoot and root, root relative activity and ratio of root to shoot of wheat seedlings, but increased primary root number.

Long Exposure Short Pulse Synchronous Phase Lock Method for Capturing High Dynamic Surface Shape.

In infrared weak target detection systems, high-frequency vibrating mirrors (VMs) are a core component. The dynamic surface shape of the VM has a direct impact on imaging quality and the optical modulation effect, so its measurement is necessary but also very difficult. Measurement of the dynamic surface shape of VMs requires a transiently acquired image series, but traditional methods cannot perform this task, as, when the VM is vibrating at a frequency of 3033 Hz, using high-speed cameras to acquire the images would result in frame rates exceeding 1.

A systems biology-based gene expression classifier of glioblastoma predicts survival with solid tumors.

Accurate prediction of survival of cancer patients is still a key open problem in clinical research. Recently, many large-scale gene expression clusterings have identified sets of genes reportedly predictive of prognosis; however, those gene sets shared few genes in common and were poorly validated using independent data. We have developed a systems biology-based approach by using either combined gene sets and the protein interaction network (Method A) or the protein network alone (Method B) to identify common prognostic genes based on microarray gene expression data of glioblastoma multiforme and compared with differential gene expression clustering (Method C).

Modularity in the genetic disease-phenotype network.

Similar disease phenotypes are engendered as a result of the modular nature of gene networks; thus we hypothesized that all human genetic disease phenotypes appear in similar modular styles. Network representations of phenotypes make it possible to explore this hypothesis. We investigated the modularity of a network of genetic disease phenotypes.