Sentiment Analysis of Tweets on Soda Taxes.

CONTEXT: As a primary source of added sugars, sugar-sweetened beverage (SSB) consumption may contribute to the obesity epidemic. A soda tax is an excise tax charged on selling SSBs to reduce consumption. Currently, 8 cities/counties in the United States have imposed soda taxes. OBJECTIVE: This study assessed people's sentiments toward soda taxes in the United States based on social media posts on Twitter. DESIGN: We designed a search algorithm to systematically identify and collect soda tax-related tweets posted on Twitter. We built deep neural network models to classify tweets by sentiments. SETTING: Computer modeling. PARTICIPANTS: Approximately 370 000 soda tax-related tweets posted on Twitter from January 1, 2015, to April 16, 2022. MAIN OUTCOME MEASURE: Sentiment associated with a tweet. RESULTS: Public attention paid to soda taxes, indicated by the number of tweets posted annually, peaked in 2016, but has declined considerably ever since. The decreasing prevalence of tweets quoting soda tax-related news without revealing sentiments coincided with the rapid increase in tweets expressing a neutral sentiment toward soda taxes. The prevalence of tweets expressing a negative sentiment rose steadily from 2015 to 2019 and then slightly leveled off, whereas that of tweets expressing a positive sentiment remained unchanged. Excluding news-quoting tweets, tweets with neutral, negative, and positive sentiments occupied roughly 56%, 29%, and 15%, respectively, during 2015-2022. The authors' total number of tweets posted, followers, and retweets predicted tweet sentiment. The finalized neural network model achieved an accuracy of 88% and an F1 score of 0.87 in predicting tweet sentiments in the test set. CONCLUSIONS: Despite its potential to shape public opinion and catalyze social changes, social media remains an underutilized source of information to inform government decision making. Social media sentiment analysis may inform the design, implementation, and modification of soda tax policies to gain social support while minimizing confusion and misinterpretation.

RST1 Is a FREE1 Suppressor That Negatively Regulates Vacuolar Trafficking in Arabidopsis.

FYVE domain protein required for endosomal sorting1 (FREE1), a plant-specific endosomal sorting complex required for transport-I component, is essential for the biogenesis of multivesicular bodies (MVBs), vacuolar degradation of membrane protein, cargo vacuolar sorting, autophagic degradation, and vacuole biogenesis in Arabidopsis (Arabidopsis thaliana). Here, we report the characterization of RESURRECTION1 (RST1) as a suppressor of free1 that, when mutated as a null mutant, restores the normal MVB and vacuole formation of a FREE1-RNAi knockdown line and consequently allows survival. RST1 encodes an evolutionarily conserved multicellular organism-specific protein, which contains two Domain of Unknown Function 3730 domains, showing no similarity to known proteins, and predominantly localizes in the cytosol. The depletion of FREE1 causes substantial accumulation of RST1, and transgenic Arabidopsis plants overexpressing RST1 display retarded seedling growth with dilated MVBs, and inhibition of endocytosed FM4-64 dye to the tonoplast, suggesting that RST1 has a negative role in vacuolar transport. Consistently, enhanced endocytic degradation of membrane vacuolar cargoes occurs in the rst1 mutant. Further transcriptomic comparison of rst1 with free1 revealed a negative association between gene expression profiles, demonstrating that FREE1 and RST1 have antagonistic functions. Thus, RST1 is a negative regulator controlling membrane protein homeostasis and FREE1-mediated functions in plants.

Thermodynamic coupling of the tandem RRM domains of hnRNP A1 underlie its pleiotropic RNA binding functions.

The functional properties of RNA binding proteins (RBPs) require allosteric regulation through interdomain communication. Despite the importance of allostery to biological regulation, only a few studies have been conducted to describe the biophysical nature by which interdomain communication manifests in RBPs. Here, we show for hnRNP A1 that interdomain communication is vital for the unique stability of its amino-terminal domain, which consists of two RNA recognition motifs (RRMs). These RRMs exhibit drastically different stability under pressure. RRM2 unfolds as an individual domain but remains stable when appended to RRM1. Variants that disrupt interdomain communication between the tandem RRMs show a significant decrease in stability. Carrying these mutations over to the full-length protein for in vivo experiments revealed that the mutations affected the ability of the disordered carboxyl-terminal domain to engage in protein-protein interactions and influenced the protein's RNA binding capacity. Collectively, this work reveals that thermodynamic coupling between the tandem RRMs of hnRNP A1 accounts for its allosteric regulatory functions.

Sentiment Analysis of Tweets on Menu Labeling Regulations in the US.

Menu labeling regulations in the United States mandate chain restaurants to display calorie information for standard menu items, intending to facilitate healthy dietary choices and address obesity concerns. For this study, we utilized machine learning techniques to conduct a novel sentiment analysis of public opinions regarding menu labeling regulations, drawing on Twitter data from 2008 to 2022. Tweets were collected through a systematic search strategy and annotated as positive, negative, neutral, or news. Our temporal analysis revealed that tweeting peaked around major policy announcements, with a majority categorized as neutral or news-related. The prevalence of news tweets declined after 2017, as neutral views became more common over time. Deep neural network models like RoBERTa achieved strong performance (92% accuracy) in classifying sentiments. Key predictors of tweet sentiments identified by the random forest model included the author's followers and tweeting activity. Despite limitations such as Twitter's demographic biases, our analysis provides unique insights into the evolution of perceptions on the regulations since their inception, including the recent rise in negative sentiment. It underscores social media's utility for continuously monitoring public attitudes to inform health policy development, execution, and refinement.