Combination analysis of genome-wide association and transcriptome sequencing of residual feed intake in quality chickens

GO mapping for genes within the 50 bp flanking regions of the significant RFI-related SNPs. (XLS 31 kb

Edge Detection from RGB-D Image Based on Structured Forests

This paper looks into the fundamental problem in computer vision: edge detection. We propose a new edge detector using structured random forests as the classifier, which can make full use of RGB-D image information from Kinect. Before classification, the adaptive bilateral filter is used for the denoising processing of the depth image. As data sources, information of 13 channels from RGB-D image is computed. In order to train the random forest classifier, the approximation measurement of the information gain is used. All the structured labels at a given node are mapped to a discrete set of labels using the Principal Component Analysis (PCA) method. NYUD2 dataset is used to train our structured random forests. The random forest algorithm is used to classify the RGB-D image information for extracting the edge of the image. In addition to the proposed methodology, the quantitative comparisons of different algorithms are presented. The results of the experiments demonstrate the significant improvements of our algorithm over the state of the art

Vascular niche IL-6 induces alternative macrophage activation in glioblastoma through HIF-2α.

Spatiotemporal regulation of tumor immunity remains largely unexplored. Here we identify a vascular niche that controls alternative macrophage activation in glioblastoma (GBM). We show that tumor-promoting macrophages are spatially proximate to GBM-associated endothelial cells (ECs), permissive for angiocrine-induced macrophage polarization. We identify ECs as one of the major sources for interleukin-6 (IL-6) expression in GBM microenvironment. Furthermore, we reveal that colony-stimulating factor-1 and angiocrine IL-6 induce robust arginase-1 expression and macrophage alternative activation, mediated through peroxisome proliferator-activated receptor-γ-dependent transcriptional activation of hypoxia-inducible factor-2α. Finally, utilizing a genetic murine GBM model, we show that EC-specific knockout of IL-6 inhibits macrophage alternative activation and improves survival in the GBM-bearing mice. These findings illustrate a vascular niche-dependent mechanism for alternative macrophage activation and cancer progression, and suggest that targeting endothelial IL-6 may offer a selective and efficient therapeutic strategy for GBM, and possibly other solid malignant tumors

Constructing a robust protein-protein interaction network by integrating multiple public databases

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) are a critical component for many underlying biological processes. A PPI network can provide insight into the mechanisms of these processes, as well as the relationships among different proteins and toxicants that are potentially involved in the processes. There are many PPI databases publicly available, each with a specific focus. The challenge is how to effectively combine their contents to generate a robust and biologically relevant PPI network.</p> <p>Methods</p> <p>In this study, seven public PPI databases, BioGRID, DIP, HPRD, IntAct, MINT, REACTOME, and SPIKE, were used to explore a powerful approach to combine multiple PPI databases for an integrated PPI network. We developed a novel method called <it>k</it>-votes to create seven different integrated networks by using values of <it>k</it> ranging from 1-7. Functional modules were mined by using SCAN, a Structural Clustering Algorithm for Networks. Overall module qualities were evaluated for each integrated network using the following statistical and biological measures: (1) modularity, (2) similarity-based modularity, (3) clustering score, and (4) enrichment.</p> <p>Results</p> <p>Each integrated human PPI network was constructed based on the number of votes (<it>k</it>) for a particular interaction from the committee of the original seven PPI databases. The performance of functional modules obtained by SCAN from each integrated network was evaluated. The optimal value for <it>k</it> was determined by the functional module analysis. Our results demonstrate that the <it>k</it>-votes method outperforms the traditional union approach in terms of both statistical significance and biological meaning. The best network is achieved at <it>k</it>=2, which is composed of interactions that are confirmed in at least two PPI databases. In contrast, the traditional union approach yields an integrated network that consists of all interactions of seven PPI databases, which might be subject to high false positives.</p> <p>Conclusions</p> <p>We determined that the k-votes method for constructing a robust PPI network by integrating multiple public databases outperforms previously reported approaches and that a value of k=2 provides the best results. The developed strategies for combining databases show promise in the advancement of network construction and modeling.</p

Portland Cement Hydration Behavior at Low Temperatures: Views from Calculation and Experimental Study

Environmental condition affects the property of construction materials. This study gives an initial understanding of Portland cement hydration under low temperatures from the views of laboratory experiments (including electrical resistivity, degree of hydration (DoH), and maturity) as well as thermodynamic calculation. The hydrates of Portland cement at the given period were detected with X-ray diffraction (XRD), and their microstructure was observed by scanning electron microscope (SEM). Experiment result (i.e., DoH and electrical resistivity) indicated that the hydration of Portland cement was delayed by low temperature without hydration stopping at −5°C. Based on a basic kinetics model, the thermodynamic calculation predicted that the final hydrate differs in dependence on environmental temperatures. The mechanical behavior trend of Portland cement paste affected by low temperatures potentially is linked to the appearing of aluminate compounds and reduction of portlandite

Geochronology, geochemistry, and tectonic significance of the Shirenshan gneiss in the southern margin of the North China Block

The Shirenshan Block is a complex geological body located in the southern margin of the North China Block (NCB). From south to north, it can be divided into the Taihua Group migmatite, and the Shirenshan gneiss and magmatic rocks. The petrographic features, tectonic setting, provenance, and geological age of the Shirenshan gneiss using comprehensive field investigations, microstructural analysis, zircon U-Pb radioactive dating, and geochemical analyses were investigated for this study. The petrology, geochemistry, and geochronology of the Shirenshan gneiss suggests that it is mainly a felsic rock and its protolith was a high-K calc-alkaline series A-type granite. The protolith is high in SiO2, Al2O3, K2O, Na2O, and low in CaO and MgO. Overall, the Sr-Nd isotope composition of the samples showed no significant difference, indicating that the Taihua Group migmatite and the Shirenshan gneiss have the same source material. The Shirenshan block may be partially melted from the Taihua group and formed during activity of the Luo-Luan Fault. By the method of zircon dating analysis, the protolith age of the Shirenshan block was determined as 1559±16Ma (Early Proterozoic). Then, the crystallization age of the syntectonic migmatite is 439.2±7.6Ma, which was formed by subduction of the Taihua Group. During the early Cretaceous (119.5±1.3Ma), the Shirenshan gneiss may have experienced regional migmatization and formed the zircon rims age of the Yanshanian period. Litho-geochemical features of the Shirenshan block are similar to A1-type granites indicating that they are post-orogenic. Therefore, the metamorphic deformation of the Shirenshan gneiss reflects the tectonics in the southern margin of the NCB.</p