Genetic diversity among natural populations of Ottelia acuminata (Gaghep.) Dandy revealed by ISSR

Ottelia acuminata (Gagnep.) Dandy, an aquatic species of the Hydrocharitaceae, is endemic to China. A performance comparison of genetic diversity of 4 natural populations was conducted to investigatewhether or not water pollution in their habitats has anything to do with this species being endangered. A total number of 120 O. acuminate accessions were analyzed, by amplification of their DNAs with 15 primers (ISSR). Thirteen primers were scored and 214 bands were detected, of which 170 werepolymorphic (79.44%). The results showed that the genetic indices in polluted Jian Lake group were always the smallest ones, when compared with those of the other groups. It indicated that the polluted water did affect the genetic diversity of O. acuminate populations. And ISSRs seemed to be effectivetools for detecting genetic variation among O. acuminate geographical groups

Improvement of the Photoelectrochemical Stability of Cu2O Photocathode by Ph—CΞC—Cu Grafting

As one of the most efficient photocathodes, Cu2O has attracted substantial attention because of its theoretically high solar-to-hydrogen (STH) efficiency. However, its applications in photoelectrochemical (PEC) fields are severely restricted by the poor stability derived from serious photocorrosion. In this work, high-quality phenylethynyl copper (Ph—CΞC—Cu) are successfully self-assembled on the surface of Cu2O photocathode by a novel photothermal method to improve its photostability. With the protection of the Ph—CΞC—Cu layer, 85% of the initial photocurrent density can be remained, while only 28% of initial photocurrent density is left on bare Cu2O photocathode prepared on a copper foam (CF) substrate. The significantly improved photostability of Cu2O photocathode by the Ph—CΞC—Cu protective layer is attributed to its strong hydrophobicity, which can efficiently inhibit the corrosion of Cu2O by the aqueous electrolyte solution due to its special crystal structure. Based on the obtained Ph—CΞC—Cu/Cu2O photocathode, a two-photoelectrode cell with excellent stability (>5 h) has also been successfully constructed for water splitting without the need of an electric bias

Ablation of EIF5A2 induces tumor vasculature remodeling and improves tumor response to chemotherapy via regulation of matrix metalloproteinase 2 expression

Hepatocellular carcinoma (HCC) is a highly vascularized tumor with poor clinical outcome. Our previous work has shown that eukaryotic initiation factor 5A2 (EIF5A2) over-expression enhances HCC cell metastasis. In this study, EIF5A2 was identified to be an independent risk factor for poor disease-specific survival among HCC patients. Both in vitro and in vivo assays indicated that ablation of endogenous EIF5A2 inhibited tumor angiogenesis by reducing matrix metalloproteinase 2 (MMP-2) expression. Given that MMP-2 degrades collagen IV, a main component of the vascular basement membrane (BM), we subsequently investigated the effect of EIF5A2 on tumor vasculature remodeling using complementary approaches, including fluorescent immunostaining, transmission electron microscopy, tumor perfusion assays and tumor hypoxia assays. Taken together, our results indicate that EIF5A2 silencing increases tumor vessel wall continuity, increases blood perfusion and improves tumor oxygenation. Additionally, we found that ablation of EIF5A2 enhanced the chemosensitivity of HCC cells to 5-Fluorouracil (5-FU). Finally, we demonstrated that EIF5A2 might exert these functions by enhancing MMP-2 activity via activation of p38 MAPK and JNK/c-Jun pathways. Conclusion: This study highlights an important role of EIF5A2 in HCC tumor vessel remodeling and indicates that EIF5A2 represents a potential therapeutic target in the treatment of HCC.published_or_final_versio

Myeloid sarcoma with ulnar nerve entrapment: A case report

BACKGROUND: Myeloid sarcoma (MS) is relatively rare, occurring mainly in the skin and lymph nodes, and MS invasion of the ulnar nerve is particularly unusual. The main aim of this article is to present a case of MS invading the brachial plexus, causing ulnar nerve entrapment syndrome, and to further clinical understanding of the possibility of MS invasion of peripheral nerves. CASE SUMMARY: We present the case of a 46-year-old man with a 13-year history of well-treated acute nonlymphocytic leukaemia who was admitted to the hospital after presenting with numbness and pain in his left little finger. The initial diagnosis was considered a simple case of nerve entrapment disease, with magnetic resonance imaging showing slightly abnormal left brachial plexus nerve alignment with local thickening, entrapment, and high signal on compression lipid images. Due to the severity of the ulnar nerve compression, we surgically investigated and cleared the entrapment and nerve tissue hyperplasia; however, subsequent pathological biopsy results revealed evidence of MS. The patient had significant relief from his neurological symptoms, with no postoperative complications, and was referred to the haemato-oncology department for further consultation about the primary disease. This is the first report of safe treatment of ulnar nerve entrapment from MS. It is intended to inform hand surgeons that nerve entrapment may be associated with extramedullary MS, as a rare presenting feature of the disease. CONCLUSION: MS invasion of the brachial plexus and surrounding tissues of the upper arm, resulting in ulnar nerve entrapment and degeneration with significant neurological pain and numbness in the little finger, is uncommon. Surgical treatment significantly relieved the patient’s nerve entrapment symptoms and prevented further neurological impairment. This case is reported to highlight the rare presenting features of MS

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

Incorporating topological information for predicting robust cancer subnetwork markers in human protein-protein interaction network

BACKGROUND: Discovering robust markers for cancer prognosis based on gene expression data is an important yet challenging problem in translational bioinformatics. By integrating additional information in biological pathways or a protein-protein interaction (PPI) network, we can find better biomarkers that lead to more accurate and reproducible prognostic predictions. In fact, recent studies have shown that, “modular markers,” that integrate multiple genes with potential interactions can improve disease classification and also provide better understanding of the disease mechanisms. RESULTS: In this work, we propose a novel algorithm for finding robust and effective subnetwork markers that can accurately predict cancer prognosis. To simultaneously discover multiple synergistic subnetwork markers in a human PPI network, we build on our previous work that uses affinity propagation, an efficient clustering algorithm based on a message-passing scheme. Using affinity propagation, we identify potential subnetwork markers that consist of discriminative genes that display coherent expression patterns and whose protein products are closely located on the PPI network. Furthermore, we incorporate the topological information from the PPI network to evaluate the potential of a given set of proteins to be involved in a functional module. Primarily, we adopt widely made assumptions that densely connected subnetworks may likely be potential functional modules and that proteins that are not directly connected but interact with similar sets of other proteins may share similar functionalities. CONCLUSIONS: Incorporating topological attributes based on these assumptions can enhance the prediction of potential subnetwork markers. We evaluate the performance of the proposed subnetwork marker identification method by performing classification experiments using multiple independent breast cancer gene expression datasets and PPI networks. We show that our method leads to the discovery of robust subnetwork markers that can improve cancer classification. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1224-1) contains supplementary material, which is available to authorized users

In situ epitaxial MgB2 thin films for superconducting electronics

A thin film technology compatible with multilayer device fabrication is critical for exploring the potential of the 39-K superconductor magnesium diboride for superconducting electronics. Using a Hybrid Physical-Chemical Vapor Deposition (HPCVD) process, it is shown that the high Mg vapor pressure necessary to keep the MgB$_2$ phase thermodynamically stable can be achieved for the {\it in situ} growth of MgB$_2$ thin films. The films grow epitaxially on (0001) sapphire and (0001) 4H-SiC substrates and show a bulk-like $T_c$ of 39 K, a $J_c$(4.2K) of $1.2 \times 10^7$ A/cm$^2$ in zero field, and a $H_{c2}(0)$ of 29.2 T in parallel magnetic field. The surface is smooth with a root-mean-square roughness of 2.5 nm for MgB$_2$ films on SiC. This deposition method opens tremendous opportunities for superconducting electronics using MgB$_2$

Identification of differentially expressed subnetworks based on multivariate ANOVA

<p>Abstract</p> <p>Background</p> <p>Since high-throughput protein-protein interaction (PPI) data has recently become available for humans, there has been a growing interest in combining PPI data with other genome-wide data. In particular, the identification of phenotype-related PPI subnetworks using gene expression data has been of great concern. Successful integration for the identification of significant subnetworks requires the use of a search algorithm with a proper scoring method. Here we propose a multivariate analysis of variance (MANOVA)-based scoring method with a greedy search for identifying differentially expressed PPI subnetworks.</p> <p>Results</p> <p>Given the MANOVA-based scoring method, we performed a greedy search to identify the subnetworks with the maximum scores in the PPI network. Our approach was successfully applied to human microarray datasets. Each identified subnetwork was annotated with the Gene Ontology (GO) term, resulting in the phenotype-related functional pathway or complex. We also compared these results with those of other scoring methods such as <it>t </it>statistic- and mutual information-based scoring methods. The MANOVA-based method produced subnetworks with a larger number of proteins than the other methods. Furthermore, the subnetworks identified by the MANOVA-based method tended to consist of highly correlated proteins.</p> <p>Conclusion</p> <p>This article proposes a MANOVA-based scoring method to combine PPI data with expression data using a greedy search. This method is recommended for the highly sensitive detection of large subnetworks.</p

Seasonal variations in carbon, nitrogen and phosphorus concentrations and C:N:P stoichiometry in different organs of a Larix principis-rupprechtii Mayr. plantation in the Qinling Mountains, China

Understanding how concentrations of elements and their stoichiometry change with plant growth and age is critical for predicting plant community responses to environmental change. Weusedlong-term field experiments to explore how the leaf, stem and root carbon (C), nitrogen (N) and phosphorous (P) concentrations and their stoichiometry changed with growth and stand age in a L.principis-rupprechtii Mayr. plantation from 2012–2015 in the Qinling Mountains, China. Our results showed that the C, N and P concentrations and stoichiometric ratios in different tissues of larch stands were affected by stand age, organ type andsampling month and displayed multiple correlations with increased stand age in different growing seasons. Generally, leaf C and N concentrations were greatest in the fast-growing season, but leaf P concentrations were greatest in the early growing season. However, no clear seasonal tendencies in the stem and root C, N and P concentrations were observed with growth. In contrast to N and P, few differences were found in organ-specific C concentrations. Leaf N:P was greatest in the fast-growing season, while C:N and C:P were greatest in the late-growing season. No clear variations were observed in stem and root C:N, C:P andN:Pthroughout the entire growing season, but leaf N:P was less than 14, suggesting that the growth of larch stands was limited by N in our study region. Compared to global plant element concentrations and stoichiometry, the leaves of larch stands had higher C, P, C:NandC:PbutlowerNandN:P,andtherootshadgreater PandC:NbutlowerN,C:Pand N:P. Our study provides baseline information for describing the changes in nutritional elements with plant growth, which will facilitates plantation forest management and restoration, and makes avaluable contribution to the global data pool on leaf nutrition and stoichiometry