Does wealth equate to happiness? an 11-year panel data analysis exploring socio-economic indicators and social media metrics.

The Easterlin paradox questions the link between economic growth and national well-being, emphasizing the necessity to explore the impact of economic elasticity, income inequality, and their temporal and spatial heterogeneity on subjective happiness. Despite the importance of these factors, few studies have examined them together, thus ongoing debates about the impact of economics on well-being persist. To fill this gap, our analysis utilizes 11 years of panel data from 31 provinces in China, integrating macroeconomic indicators and social media content to reassess the Easterlin paradox. We use GDP per capita and the Gini coefficient as proxies for economic growth and income inequality, respectively, to study their effects on the subjective well-being expressed by citizens on social media in mainland China. Our approach combines machine learning and fixed effects models to evaluate these relationships. Key findings include: (1) In temporal relationships, a 46.70% increase in GDP per capita implies a 0.38 increase in subjective well-being, while a 0.09 increase in the Gini coefficient means a 1.47 decrease in subjective well-being. (2) In spatial relationships, for every 46.70% increase in GDP per capita, subjective well-being rises by 0.51; however, this relationship is buffered by unfair distribution, and GDP per capita no longer significantly affects subjective well-being when the Gini index exceeds 0.609. This study makes a synthetic contribution to the debate on the Easterlin paradox, indicating that economic growth can enhance well-being if income inequality is kept below a certain level. Although these results are theoretically enlightening for the relationship between economics and national well-being globally, this study's sample comes from mainland China. Due to differences in cultural, economic, and political factors, further research is suggested to explore these dynamics globally.

A reverse transcriptase-mediated ribosomal RNA depletion (RTR2D) strategy for the cost-effective construction of RNA sequencing libraries.

RNA sequencing (RNA-seq)-based whole transcriptome analysis (WTA) using ever-evolving next-generation sequencing technologies has become a primary tool for coding and/or noncoding transcriptome profiling. As WTA requires RNA-seq data for both coding and noncoding RNAs, one key step for obtaining high-quality RNA-seq data is to remove ribosomal RNAs, which can be accomplished by using various commercial kits. Nonetheless, an ideal rRNA removal method should be efficient, user-friendly and cost-effective so it can be adapted for homemade RNA-seq library construction. Here, we developed a novel reverse transcriptase-mediated ribosomal RNA depletion (RTR2D) method. We demonstrated that RTR2D was simple and efficient, and depleted human or mouse rRNAs with high specificity without affecting coding and noncoding transcripts. RNA-seq data analysis indicated that RTR2D yielded highly correlative transcriptome landscape with that of NEBNext rRNA Depletion Kit at both mRNA and lncRNA levels. In a proof-of-principle study, we found that RNA-seq dataset from RTR2D-depleted rRNA samples identified more differentially expressed mRNAs and lncRNAs regulated by Nutlin3A in human osteosarcoma cells than that from NEBNext rRNA Depletion samples, suggesting that RTR2D may have lower off-target depletion of non-rRNA transcripts. Collectively, our results have demonstrated that the RTR2D methodology should be a valuable tool for rRNA depletion.