sj-docx-1-pus-10.1177_09636625221138357 – Supplemental material for Intention of health experts to counter health misinformation in social media: Effects of perceived threat to online users, correction efficacy, and self-affirmation

Supplemental material, sj-docx-1-pus-10.1177_09636625221138357 for Intention of health experts to counter health misinformation in social media: Effects of perceived threat to online users, correction efficacy, and self-affirmation by Liang Chen and Hongjie Tang in Public Understanding of Science</p

Party Preferences and Voting Model in January 2017

In January survey Public Opinion Research Centre investigated how people trust to political Parties and whether they are willing to take part in elections to Chamber of Deputies. In the press release there are two different types of information: Party preference and a model of voting behaviour.Party preferences give us information about public sympathy with political Parties in the group of citizens who have voting right and there is also included a part of citizens who will not take part in elections or they do not knot who to vote for.Voting model indicate anticipated result of elections to Chamber of Deputies in the time of the survey. Voting model comes out of Party preferences but it includes only those who want to take part in elections and they answered us a Party they would vote for – in short this group does not include undecided people and non – voters

DataSheet_1_Venous thromboembolism and severe COVID-19: a Mendelian randomization trial and transcriptomic analysis.pdf

IntroductionVenous thromboembolism (VTE) is known to be intricately linked to severe COVID-19 (sCOVID-19) occurrence. Herein, we employed univariable Mendelian randomization (MR) and transcriptome analysis to predict the causal association and associated signaling networks between VTE and sCOVID-19.MethodsPotential VTE and sCOVID-19 association was assessed using MR-Egger, weighted median, simple mode, weighted mode, and inverse variance weighted (IVW) regression. We conducted independent univariable analyses involving VTE and sCOVID-19. Using heterogeneity, pleiotropy, and the Leave-One-Out examinations, we performed sensitivity analyses. Thereafter, we performed transcriptome analysis of the GSE164805 dataset to identify differentially expressed genes (DEGs) linked to single nucleotide polymorphisms (SNPs). Lastly, we conducted immune analyses.ResultsBased on our univariable analysis, VTE was a strong indicator of sCOVID-19 development, and it was intricately linked to sCOVID-19. We further conducted sensitivity analysis to demonstrate the reliability of our results. Using differential analysis, we identified 15 major genes, namely, ACSS2, CEP250, CYP4V2, DDB2, EIF6, GBGT1, GSS, MADD, MAPK8IP1, MMP24, YBPC3, NT5DC3, PROCR, SURF6, and YIPF2, which were strongly connected to suppressive adaptive immune as well as augmented inflammatory cells. In addition, we uncovered strong associations with most differential immunologic gene sets, such as, the Major Histocompatibility Complex (MHC), immunoactivators, and immunosuppressors.ConclusionHerein, we demonstrated we strong association between VTE and enhanced sCOVID-19 risk. We also identified 15 DEGs which potentially contribute to the shared immunologic pathogenesis between VTE and sCOVID-19.</p

Effect of active ingredients, and their dosing to CHRL 28,90 days of age

Subject of this work is to design a batch of concrete with additives, which are to replace the cement of various doses so as to not adversely affect the properties of fresh and hardened concrete and simultaneously withstand the environment XF

Additional file 9: Table S6. of Inference of kinship using spatial distributions of SNPs for genome-wide association studies

Average (standard deviation) of kinship coefficient estimates of KIND and KING for all valid pairs, and the estimated values of the unknown parameter p for KIND. Data: 1000 genomes. Note: Kinship coefficient estimates by REAP are not available because frappe did not finish within the 300 hour walltime. (DOC 29 kb

Additional file 14: Table S11. of Inference of kinship using spatial distributions of SNPs for genome-wide association studies

Kinship estimation using SNPs with MAF >0.4. Data are from the CEU population of 1000 genomes data. The averages (and standard deviation) of kinship coefficient estimates are shown. (DOC 28 kb

China’s energy intensity under BAU, EEI, LC and ELC scenarios. (Scenarios are defined in Table 2).

<p>China’s energy intensity under BAU, EEI, LC and ELC scenarios. (Scenarios are defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone-0077699-t002" target="_blank">Table 2</a>).</p

China’s total energy consumption under BAU, EEI, LC and ELC scenarios. (Scenarios are defined in Table 2).

<p>China’s total energy consumption under BAU, EEI, LC and ELC scenarios. (Scenarios are defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone-0077699-t002" target="_blank">Table 2</a>).</p

Scenario description and parameter definition.

a<p>Parameters definition: <i>m,</i> growth rate of GDP per capita (%); <i>r</i>, population growth rate (%); <i>k</i>, technology progress rate (%); <i>f</i>, energy structure optimization rate (%).</p>b<p>Data sources: The values of parameters are calculated or assumed based on the references from CCAP <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone.0077699-Center1" target="_blank">[44]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone.0077699-Center2" target="_blank">[45]</a>, CAS <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone.0077699-Study1" target="_blank">[46]</a>, SCPRC <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone.0077699-State1" target="_blank">[47]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone.0077699-State2" target="_blank">[48]</a>.</p

China’s GDP growth under BAU, EEI, LC and ELC scenarios (Scenarios are defined in Table 2).

<p>China’s GDP growth under BAU, EEI, LC and ELC scenarios (Scenarios are defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone-0077699-t002" target="_blank">Table 2</a>).</p