sj-docx-1-gaz-10.1177_17480485231206364 - Supplemental material for Personalization of Trump and Xi in the U.S.–China trade conflict news: Comparison between the U.S. and China

Supplemental material, sj-docx-1-gaz-10.1177_17480485231206364 for Personalization of Trump and Xi in the U.S.–China trade conflict news: Comparison between the U.S. and China by Shujun Liu in International Communication Gazette</p

Descriptive statistics and bivariate correlation.

Previous studies mainly focused on individual-level factors that influence the adoption and usage of mobile technology and social networking sites, with little emphasis paid to the influences of household situations. Using multilevel modelling approach, this study merges household- (n1 = 1,455) and individual-level (n2 = 2,570) data in the U.K. context to investigate (a) whether a household economic capital (HEC) can affect its members’ Twitter adoption, (b) whether the influences are mediated by the member’s activity variety and self-reported efficacy with mobile technology, and (c) whether the members’ traits, including educational level, gross income and residential area, moderate the relationship between HEC and Twitter adoption. Significant direct and indirect associations were discovered between HEC and its members’ Twitter adoption. The educational level and gross income of household members moderated the influence of HEC on individuals’ Twitter adoption.</div

Individual income as moderator on the association between HEC and Twitter adoption.

Individual income as moderator on the association between HEC and Twitter adoption.</p

Direct and indirect effects summary.

Previous studies mainly focused on individual-level factors that influence the adoption and usage of mobile technology and social networking sites, with little emphasis paid to the influences of household situations. Using multilevel modelling approach, this study merges household- (n1 = 1,455) and individual-level (n2 = 2,570) data in the U.K. context to investigate (a) whether a household economic capital (HEC) can affect its members’ Twitter adoption, (b) whether the influences are mediated by the member’s activity variety and self-reported efficacy with mobile technology, and (c) whether the members’ traits, including educational level, gross income and residential area, moderate the relationship between HEC and Twitter adoption. Significant direct and indirect associations were discovered between HEC and its members’ Twitter adoption. The educational level and gross income of household members moderated the influence of HEC on individuals’ Twitter adoption.</div

Individual educational level as moderator on the association between HEC and Twitter adoption.

Individual educational level as moderator on the association between HEC and Twitter adoption.</p

Summary of hypotheses and proposed model.

Previous studies mainly focused on individual-level factors that influence the adoption and usage of mobile technology and social networking sites, with little emphasis paid to the influences of household situations. Using multilevel modelling approach, this study merges household- (n1 = 1,455) and individual-level (n2 = 2,570) data in the U.K. context to investigate (a) whether a household economic capital (HEC) can affect its members’ Twitter adoption, (b) whether the influences are mediated by the member’s activity variety and self-reported efficacy with mobile technology, and (c) whether the members’ traits, including educational level, gross income and residential area, moderate the relationship between HEC and Twitter adoption. Significant direct and indirect associations were discovered between HEC and its members’ Twitter adoption. The educational level and gross income of household members moderated the influence of HEC on individuals’ Twitter adoption.</div

Results of multilevel mediation analysis.

Previous studies mainly focused on individual-level factors that influence the adoption and usage of mobile technology and social networking sites, with little emphasis paid to the influences of household situations. Using multilevel modelling approach, this study merges household- (n1 = 1,455) and individual-level (n2 = 2,570) data in the U.K. context to investigate (a) whether a household economic capital (HEC) can affect its members’ Twitter adoption, (b) whether the influences are mediated by the member’s activity variety and self-reported efficacy with mobile technology, and (c) whether the members’ traits, including educational level, gross income and residential area, moderate the relationship between HEC and Twitter adoption. Significant direct and indirect associations were discovered between HEC and its members’ Twitter adoption. The educational level and gross income of household members moderated the influence of HEC on individuals’ Twitter adoption.</div

PD1/PD-L1 signaling sustains the survival and proliferation of cisplatin resistant cells.

<p>A, Resistant and parental H69 cells were treated with indicated doses of cisplatin and subjected to CCK8 assays. Note: CR, Cisplatin resistance. B, The H69R and H82R cells were growing in drug-free medium for 72 hours. The qPCR or Western blot was used to measure the RNA (left panel) or protein (right panel) expression of PD1 or PD-L1. Data represent three independent experiments, and are the mean ±SD, *<i>P</i> < 0.05. Note: PA, Parental; CR, Cisplatin resistance. C, H69R and H82R cells were transfected with PD-L1 shRNA or control vectors for 24 hours, and then exposed to 3 μM of cisplatin for additional 24 hours. Western blot (left panel) was used to measure PD-L1 protein expression, but CCK8 assays for the cell proliferation. Note: Con, Control vectors; shP, PD-L1 shRNA; Cis, Cisplatin. In CCK8 assays, the experiments are done two times independently with 8 replicates. *<i>P</i> < 0.05, **<i>P</i> < 0.01.</p

PD1 and PD-L1 are highly expressed in lung cancer cells.

<p>A, qPCR measuring PD1 and PD-L1 expression in fresh-frozen human lung normal or cancer tissues (n = 30/group). Data represent the mean + SD, *<i>P</i> < 0.05. B and C, qPCR (B) and Western blot (C) analysis assessing PD1 and PD-L1 expression in human normal and lung cancer cell lines. Data represent three independent experiments.</p

Additional file 1 of Characteristics of human papillomavirus prevalence and infection patterns among women aged 25–64 according to age groups and cytology results in Ordos City, China

Additional file 1.  eTable 1. Prevalence of different HPV genotypes infection by age group. eTable 2. Prevalence of different HPV genotypes infection by cytology results