Large Distance Modification of Newtonian Potential and Structure Formation in Universe

In this paper, we study the effects of super-light brane world perturbative modes on structure formation in our universe. As these modes modify the large distance behavior of Newtonian potential, they effect the clustering of a system of galaxies. So, we explicitly calculate the clustering of galaxies interacting through such a modified Newtonian potential. We use a suitable approximation for analyzing this system of galaxies, and discuss the validity of such approximations. We observe that such corrections also modify the virial theorem for such a system of galaxies.Comment: 13 pages, 3 captioned figure

Summary of 3-node and 4-node feed-forward loops based on glioblastoma related data.

a<p>FFL: feed-forward loop.</p>b<p>Definitions of the nodes and links were provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002488#pcbi-1002488-t001" target="_blank">Table 1</a>.</p

Uncovering MicroRNA and Transcription Factor Mediated Regulatory Networks in Glioblastoma

<div><p>Glioblastoma multiforme (GBM) is the most common and lethal brain tumor in humans. Recent studies revealed that patterns of microRNA (miRNA) expression in GBM tissue samples are different from those in normal brain tissues, suggesting that a number of miRNAs play critical roles in the pathogenesis of GBM. However, little is yet known about which miRNAs play central roles in the pathology of GBM and their regulatory mechanisms of action. To address this issue, in this study, we systematically explored the main regulation format (feed-forward loops, FFLs) consisting of miRNAs, transcription factors (TFs) and their impacting GBM-related genes, and developed a computational approach to construct a miRNA-TF regulatory network. First, we compiled GBM-related miRNAs, GBM-related genes, and known human TFs. We then identified 1,128 3-node FFLs and 805 4-node FFLs with statistical significance. By merging these FFLs together, we constructed a comprehensive GBM-specific miRNA-TF mediated regulatory network. Then, from the network, we extracted a composite GBM-specific regulatory network. To illustrate the GBM-specific regulatory network is promising for identification of critical miRNA components, we specifically examined a Notch signaling pathway subnetwork. Our follow up topological and functional analyses of the subnetwork revealed that six miRNAs (miR-124, miR-137, miR-219-5p, miR-34a, miR-9, and miR-92b) might play important roles in GBM, including some results that are supported by previous studies. In this study, we have developed a computational framework to construct a miRNA-TF regulatory network and generated the first miRNA-TF regulatory network for GBM, providing a valuable resource for further understanding the complex regulatory mechanisms in GBM. The observation of critical miRNAs in the Notch signaling pathway, with partial verification from previous studies, demonstrates that our network-based approach is promising for the identification of new and important miRNAs in GBM and, potentially, other cancers.</p> </div

Canonical pathways overrepresented in genes involved in the composite glioblastoma-specific regulatory network.

a<p>Adjusted <i>P</i>-value was calculated by Fisher's exact test following by Benjamini-Hochberg multiple testing correction.</p

Summary of relationships among GBM-related genes, GBM-related miRNAs, and TFs.

a<p>miRNA: microRNA.</p>b<p>TF: transcription factor.</p>c<p>miRNA-gene: miRNA repression of gene expression.</p>d<p>miRNA-TF: miRNA repression of TF expression.</p>e<p>TF-gene: TF regulation of gene expression.</p>f<p>TF-miRNA: TF regulation of to miRNA expression.</p>g<p>Gene-gene: gene-gene coexpression.</p

Graphical representations of the composite glioblastoma-related miRNA-TF regulatory network and its network characteristics.

<p>A) Graphical representation of the composite glioblastoma miRNA-TF regulatory network. The network was generated from 3-node and 4-node composite-FFL motifs. B) Degree distribution of all nodes (genes, miRNAs and TFs) in the network. The Y-axis represents the proportion of nodes with a specific degree. C–E) Three higher-order subnetworks. In each subfigure, nodes in red correspond to GBM-related miRNAs, nodes in green correspond to GBM-related genes, and nodes in blue correspond to transcription factors. The edge colors represent different relationships: red for the repression of miRNAs to genes or TFs, blue for the regulation of TFs to genes or miRNAs, and black for the coexpression of GBM-related genes.</p

Additional file 2 of Investigating the relationship between consultation length and quality of tele-dermatology E-consults in China: a cross-sectional standardized patient study

Additional file 2: Examples of specific consultations in English and Chinese

Additional file 1 of Investigating the relationship between consultation length and quality of tele-dermatology E-consults in China: a cross-sectional standardized patient study

Additional file 1: Selection conditions of SP cases and characteristics of urticaria

Additional file 3 of Investigating the relationship between consultation length and quality of tele-dermatology E-consults in China: a cross-sectional standardized patient study

Additional file 3: Characteristics of the tele-dermatology E-consults services providers

A catalogue of mixed feed-forward regulatory loops (FFLs).

<p>According to the relationship between the transcription factor (TF) and microRNA (miRNA), the mixed FFLs were classified as the TF-FFL model (the TF directly regulates the miRNA), miRNA-FFL model (the miRNA only directly regulates the TF) or composite-FFL model (the TF and the miRNA regulate each other). The relationships represented by solid lines are required while the relationships represented by dot lines are not required. B) Five types of putative regulations involved in these FFLs: miRNA-gene represents that the miRNA represses gene expression; miRNA-TF represents that the miRNA represses the TF gene expression; TF-gene represents the regulation by TF of the expression of the gene; TF-miRNA represents the regulation of TF to expression of miRNAs; and, gene-gene represents gene coexpression.</p