Key upstream regulator networks modulated by arsenic exposure.

<p>The upstream network was generated by IPA and the networks indicate predicted upstream regulators and their downstream target genes presented in the differentially expressed gene set. Up-regulated genes are in red and down-regulated in green. Solid arrow lines represent direct interaction while dotted lines indirect intereaction.</p

Preliminary identification of potential biomarker genes for arsenic exposure.

<p>Selected up-regulated genes by arsenic exposure were examined by RT-qPCR in individual zebrafish (A) and medaka (B) after treatment with arsenic. (A), Fold changes (log2 ratio) of 14 up-regulated genes measured by RT-qPCR. S1-S9, 9 individual zebrafish treated with 15 ppm sodium; 15 ppm, average of the 9 individual fish; 20 ppm, RT-qPCR measurement from the pooled RNA sample used for RNA-SAGE sequencing; SAGE, RNA-SAGE data for comparison (Log2 fold change). The 9 genes displayed dosage-dependent effect between 15 ppm and 20 ppm are indicated with asterisks. Zebrafish gene symbols and names are shown based on NCBI and underlined genes are annotated manually. (B), Average of fold changes (log2 ratio) of four validated medaka genes in 4 individual medaka fish. (C) Comparison of the expression of arsenic biomarker genes in other chemical treatments by hierarchical clustering heatmap. RNA-SAGE data from the current study (Arsenic) were compared with hepatic RNA-SAGE data from zebrafish treated with 5 µg/L 17β-estradiol (E2), 5 µg/L 11-keto testosterone (KT11) or 10 nM 2,3,7,8-tetrachlorodibenzo-<i>p</i>-dioxin (TCDD). The left clustering is based on the 14 genes identified from zebrafish and the right based on the four genes from both zebrafish and medaka.</p

Significantly affected gene ontology terms by arsenic exposure.

<p>Note: Count is number of genes involved. Fold Enrichment is (m/n)/(M/N), where <b>N</b> = all genes in zebrafish, <b>M</b> = all genes belonging to a GO term, <b>n</b> = genes from the differentially expressed gene set, <b>m</b> = genes from the differentially expressed gene set belonging to a GO term.</p

Coliquefaction of coal and polystyrene in supercritical water

<p>The coliquefaction of coal and polystyrene (PS) in supercritical water (SCW) was carried out in a 50-mL batch stainless steel autoclave reactor, and the effects of the polymer ratio by weight (10–40%), reaction temperature (633.5–703.5 K), and reaction time (30–120 min) were investigated. The main products were analyzed qualitatively by Fourier transform infrared spectroscopy and high-performance liquid chromatography. The results show that polystyrene stimulates coal liquefaction as a hydrogen donor, and the synergistic effects during coliquefaction in SCW were confirmed. The conversion reached a maximum of 62.26% after 60 min at 673.5 K. The phase behavior during coliquefaction was observed in a fused silica capillary reactor using a combined microscope and video recorder system.</p

Additional file 1: Figure S1. of CCL11 promotes migration and proliferation of mouse neural progenitor cells

The time-lapse movie of NPC migration. Images were captured every 15 minutes for 24 hours. (A) NPCs did not migrate toward the PBS-injected side. (B) After CCL11 injection, NPCs actively proliferated and migrated toward the CCL11-injected side. (ZIP 144651 kb

Additional file 2: Figure S2. of CCL11 promotes migration and proliferation of mouse neural progenitor cells

The immunostaining in the model mice of neonatal hypoxic-ischemic brain injury and the sham-operated mice. (A) PSA-NCAM immunostaining image of SVZ in the model mouse. Red: PSA-NCAM; blue: DAPI. Scale bar = 50 μm. (B) Number of PSA-NCAM-positive cells per slice. N = 8. The data are presented as the mean numbers of PSA-NCAM-positive cells in the injury side and the intact side ± SD. * P < 0.05. (C) Dcx immunostaining image of SVZ in the sham-operated mouse. N = 6. Green: Dcx; blue: DAPI. Scale bar = 50 μm. (D) Number of Dcx-positive cells per slice. N = 8. The data are presented as the mean numbers of Dcx-positive cells in the sham-operated side and the intact side ± SD. n.s. not significant. (PDF 655 kb

Is the Best Evidence Good Enough: Quality Assessment and Factor Analysis of Meta-Analyses on Depression

<div><p>Background</p><p>The quality of meta-analyses (MAs) on depression remains uninvestigated.</p><p>Objective</p><p>To assess the overall reporting and methodological qualities of MAs on depression and to explore potential factors influencing both qualities.</p><p>Methods</p><p>MAs investigating epidemiology and interventions for depression published in the most recent year (2014–2015) were selected from PubMed, EMBASE, PsycINFO and Cochrane Library. The characteristics of the included studies were collected and the total and per-item quality scores of the included studies were calculated based on the two checklists. Univariate and multivariate linear regression analyses were used to explore the potential factors influencing the quality of the articles.</p><p>Results</p><p>A total of 217 MAs from 74 peer-reviewed journals were included. The mean score of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was 23.0 of 27 and mean score of Assessment of Multiple Systematic Reviews (AMSTAR) was 8.3 of 11. Items assessing registration and protocol (14.2%, 37/217) in PRISMA and item requiring a full list of included and excluded studies (16.1%, 40/217) in AMSTAR had poorer adherences than other items. The MAs that included only RCTs, pre-registered, had five more authors or authors from Cochrane groups and the MAs found negative results had better reporting and methodological qualities.</p><p>Conclusions</p><p>The reporting and methodological qualities of MAs on depression remained to be improved. Design of included studies, characteristics of authors and pre-registration in PROSPERO database are important factors influencing quality of MAs in the field of depression.</p></div