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

Panax ginseng (Panax ginseng C. A. Mey.) is a perennial herb of the genus ginseng, which is used as medicine with dried roots and rhizomes. With the deepening of research on ginseng, the chemical components and pharmacological effects of ginseng have gradually been discovered. Endophytes are beneficial to host plants. However, the composition of endophytes in different organs from ginseng is poorly elucidated. The report of ginsenoside production by endophytic microbes isolated from Panax sp., motivated us to explore the endophytic microbial diversity related to the roots, stems, and leaves. In this study, the V5-V7 variable region of endophytic bacteria 16S rRNA gene and V1 variable region of endophytic fungi ITS gene in different organs were analyzed by high-throughput sequencing. The diversity and abundance of endophytic microbes in the three organs are different and are affected by the organs. For example, the most abundant endophytic bacterial genus in roots was Mycobacterium, while, the stems and leaves were Ochrobactrum. Similarly, the fungal endophytes, Coniothyrium and Cladosporium, were also found in high abundance in stems, in comparison to roots and leaves. The Shannon index shows that the diversity of endophytic bacteria in roots is the highest, and the richness of endophytic bacterial was root > stem (p < 0.05). Principal coordinate analysis showed that there were obvious microbial differences among the three groups, and the endophytic bacterial composition of the leaves was closer to that of the roots. This study provides an important reference for the study of endophytic microorganisms in ginseng.

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 1.34 MFPS, which is currently technically impossible. In this paper, we propose the long exposure short pulse synchronous phase lock (LSPL) method, which can capture the dynamic surface shape using a camera working at 10 FPS. In addition, our proposed approach uses a single laser pulse and can achieve the dynamic surface shape measurement on a single frame image.

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 1.33 to 5.02. The soil selenium flow coefficient (mobility factor, MF) increased with higher nitrogen application rates. The sum of the MF values for each soil layer treated with nitrogen application rates of 192 kg hm-2 and 240 kg hm-2 was 0.70, which was 64% higher than that for the control group with no nitrogen application. In the 0-20 cm soil layer, the highest summed water-soluble and exchangeable selenium and relative percentage of total selenium (12.45%) was observed at a nitrogen application rate of 240 kg hm-2. In the 20-40 cm soil layer, the highest relative percentage content of water-soluble and exchangeable selenium and total selenium (12.66%) was observed at a nitrogen application rate of 192 kg hm-2. Experimental treatment of black wheat with various concentrations of sodium selenite showed that selenium treatment at 50 µmol L-1 significantly increased the reduced glutathione (GSH) levels in the leaves and roots of seedlings, where the GSH contents increased by 155.4% in the leaves and by 91.5% in the roots. Further analysis of the soil-black wheat system showed that nitrogen application in selenium-rich areas affected the soil selenium flow coefficient and morphological composition, thereby changing the enrichment coefficient for leaves (0.823), transport capacity from leaves to grains (-0.530), and enrichment coefficient for roots (0.38). These changes ultimately affected the selenium concentration in the grains of black wheat.

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

To explore the ecotoxicity of Cu2O nanoparticles (NPs) on plant roots, the effects of Cu2O-NPs with different concentrations of 10, 50, 100 and 200 mg·L-1 on the seedling growth, root morphology, and cytogenetic toxicity of wheat 'Zhoumai 18' (Triticum aestivum Zhoumai 18) were examined in a hydroponic experiment. The results showed that Cu2O-NPs inhibited the growth of wheat seedlings. Cu2O-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. Furthermore, with the increases of Cu2O-NPs concentrations, the root elongation zone shortened and the root became hard and brittle, while the average diameter of roots increased. Under the concentration of 100 mg·L-1 Cu2O-NPs, the mitotic index significantly decreased, and vacuolization, plasma membrane detachment, chromosomal abnormality occurred in the root tip cell. In conclusion, Cu2O-NPs are genotoxic to wheat seedlings, with consequences on the growth and development of wheat seedlings and root morphology.

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. We computationally extracted phenotype modules and found that the modularity is well correlated with a physiological classification of human diseases. We also found correlations between the modularity and functional genomics as well as its connection to drug-target associations.

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). Validations of prediction performance show that the 23-prognostic gene classifier identified by Method A outperforms other gene classifiers identified by Methods B and C or previously reported for gliomas on 17 of 20 independent sample cohorts across five tumor types. We also find that among the 23 genes are 21 related to cellular proliferation and two related to response to stress/immune response. We further find that the increased expression of the 21 genes and the decreased expression of the other two genes are associated with poorer survival, which is supportive with the notion that cellular proliferation and immune response contribute to a significant portion of predictive power of prognostic classifiers. Our results demonstrate that the systems biology-based approach enables to identify common survival-associated genes.