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Over the past decade, researchers have identified and quantified about a dozen new forms of influence made possible by the internet. Although experts have expressed concern about how users may be harmed by social media content, few studies have sought to quantify the extent to which go-vote reminders on such platforms can impact voting. In the present study, we sent go-vote reminders to US voters on a simulation of Facebook and found (a) that interspersing vote reminders in the message feed significantly increased the magnitude of voting intentions (by over 40% under certain conditions) and (b) that vote reminders also had a significant impact on how people might vote. Because reminders of this sort are ephemeral, social media platforms that send vote reminders to users in a partisan fashion might be able to shift the outcomes of close elections without people's knowledge.

[This corrects the article DOI: 10.1371/journal.pone.0256087.].

A meta-analysis is a quantitative, formal study design in epidemiology and clinical medicine that systematically integrates and quantitatively synthesizes findings from multiple independent studies. This approach not only enhances statistical power but also enables the exploration of effects across diverse populations and helps resolve controversies arising from conflicting studies. This study aims to develop and implement a user-friendly tool for conducting meta-analyses, addressing the need for an accessible platform that simplifies the complex statistical procedures required for evidence synthesis while maintaining methodological rigor. The platform available at MetaAnalysisOnline.com enables comprehensive meta-analyses through an intuitive web interface, requiring no programming expertise or command-line operations. The system accommodates diverse data types including binary (total and event numbers), continuous (mean and SD), and time-to-event data (hazard rates with CIs), while implementing both fixed-effect and random-effect models using established statistical approaches such as DerSimonian-Laird, Mantel-Haenszel, and inverse variance methods for effect size estimation and heterogeneity assessment. In addition to statistical tests, graphical representations including the forest plot, the funnel plot, and the z score plot can be drawn. A forest plot is highly effective in illustrating heterogeneity and pooled results. The risk of publication bias can be revealed by a funnel plot. A z score plot provides a visual assessment of whether more research is needed to establish a reliable conclusion. All the discussed models and visualization options are integrated into the registration-free web-based portal. Leveraging MetaAnalysisOnline.com's capabilities, we examined treatment-related adverse events in patients with cancer receiving perioperative anti-PD-1 immunotherapy through a systematic review encompassing 10 studies with 8099 total participants. Meta-analysis revealed that anti-PD-1 therapy doubled the risk of adverse events (risk ratio 2.15, 95% CI 1.39-3.32), with significant between-study heterogeneity (I =95%) and publication bias detected through the Egger test (P=.02). While these findings suggest increased toxicity associated with anti-PD-1 treatment, the z score analysis indicated that additional studies are needed for definitive conclusions. In summary, the web-based tool aims to bridge the void for clinical and life science researchers by offering a user-friendly alternative for the swift and reproducible meta-analysis of clinical and epidemiological trials.

Purpose The proper validation of prognostic biomarkers is an important clinical issue in breast cancer research. MicroRNAs (miRNAs) have emerged as a new class of promising breast cancer biomarkers. In the present work, we developed an integrated online bioinformatic tool to validate the prognostic relevance of miRNAs in breast cancer. Methods A database was set up by searching the GEO, EGA, TCGA, and PubMed repositories to identify datasets with published miRNA expression and clinical data. Kaplan–Meier survival analysis was performed to validate the prognostic value of a set of 41 previously published survival-associated miRNAs. Results All together 2178 samples from four independent datasets were integrated into the system including the expression of 1052 distinct human miRNAs. In addition, the web-tool allows for the selection of patients, which can be filtered by receptors status, lymph node involvement, histological grade, and treatments. The complete analysis tool can be accessed online at: www.kmplot.com/mirpower . We used this tool to analyze a large number of deregulated miRNAs associated with breast cancer features and outcome, and confirmed the prognostic value of 26 miRNAs. A significant correlation in three out of four datasets was validated only for miR - 29c and miR - 101 . Conclusions In summary, we established an integrated platform capable to mine all available miRNA data to perform a survival analysis for the identification and validation of prognostic miRNA markers in breast cancer.

Background miRNAs regulate the expression of several genes with one miRNA able to target multiple genes and with one gene able to be simultaneously targeted by more than one miRNA. Therefore, it has become indispensable to shorten the long list of miRNA-target interactions to put in the spotlight in order to gain insight into understanding the regulatory mechanism orchestrated by miRNAs in various cellular processes. A reasonable solution is certainly to prioritize miRNA-target interactions to maximize the effectiveness of the downstream analysis. Results We propose a new and easy-to-use web tool MIENTURNET (MicroRNA ENrichment TURned NETwork) that receives in input a list of miRNAs or mRNAs and tackles the problem of prioritizing miRNA-target interactions by performing a statistical analysis followed by a fully featured network-based visualization and analysis. The statistics is used to assess the significance of an over-representation of miRNA-target interactions and then MIENTURNET filters based on the statistical significance associated with each miRNA-target interaction. In addition, the holistic approach of the network theory is used to infer possible evidences of miRNA regulation by capturing emergent properties of the miRNA-target regulatory network that would be not evident through a pairwise analysis of the individual components. Conclusion MIENTURNET offers the possibility to consistently perform both statistical and network-based analyses by using only a single tool leading to a more effective prioritization of the miRNA-target interactions. This has the potential to avoid researchers without computational and informatics skills to navigate multiple websites and thus to independently investigate miRNA activity in every cellular process of interest in an easy and at the same time exhaustive way thanks to the intuitive web interface. The web application along with a well-documented and comprehensive user guide are freely available at http://userver.bio.uniroma1.it/apps/mienturnet/ without any login requirement.

Background DNA sequencing is at the core of many molecular biology laboratories. Despite its long history, there is a lack of user-friendly Sanger sequencing data analysis tools that can be run interactively as a web application or at large-scale in batch from the command-line. Results We present Tracy, an efficient and versatile command-line application that enables basecalling, alignment, assembly and deconvolution of sequencing chromatogram files. Its companion web applications make all functionality of Tracy easily accessible using standard web browser technologies and interactive graphical user interfaces. Tracy can be easily integrated in large-scale pipelines and high-throughput settings, and it uses state-of-the-art file formats such as JSON and BCF for reporting chromatogram sequencing results and variant calls. The software is open-source and freely available at https://github.com/gear-genomics/tracy , the companion web applications are hosted at https://www.gear-genomics.com . Conclusions Tracy can be routinely applied in large-scale validation efforts conducted in clinical genomics studies as well as for high-throughput genome editing techniques that require a fast and rapid method to confirm discovered variants or engineered mutations. Molecular biologists benefit from the companion web applications that enable installation-free Sanger chromatogram analyses using intuitive, graphical user interfaces.

With the growth and prevalence of social media platforms, many companies have been using them to engage with customers and encourage user-generated content about their products and services. In this paper, we analyze user-generated posts from the Facebook business pages of multiple companies across several industries to understand what users post on Facebook business pages and how post valence and content characteristics affect engagement, measured as the number of likes and comments received by a post. Our analysis demonstrates that negative posts are significantly more prevalent than positive posts, and negative posts also tend to attract more likes and more comments than positive posts. Importantly, engagement depends not only on the valence of a post but also on the specific post content. We observe three types of customer complaints respectively related to product and service quality, money issues, and corporate social responsibility issues. We show that social complaints receive more likes, but fewer comments, than quality or money complaints. Our findings reveal the practical challenges of managing Facebook business pages as a new channel of interacting with customers, and they highlight the need to explore effective response strategies to manage customer complaints and other service requests on social media. With the growth and prevalence of social media platforms, many companies have been using them to engage with customers and encourage user-generated content (UGC) about their products and services. However, there has not been much research on the characteristics of UGC on these platforms and, correspondingly, their impact on customer engagement. In this paper, we analyze user-generated posts from Facebook business pages of multiple companies to understand what users post on Facebook business pages and how post valence and content characteristics affect engagement, measured as the number of likes and comments received by a post. We control for a variety of factors, including post linguistic features, poster characteristics, and post context heterogeneity. Our analysis demonstrates that for user-generated posts on Facebook business pages, negative posts are significantly more prevalent than positive posts, which contrasts with the J-shaped valence distribution of online consumer reviews. We also show that engagement depends not only on the valence of a post but also on the specific ways in which a post is positive or negative. We observe three types of customer complaints, respectively, related to product and service quality, money issues, and social and environmental issues. Our analyses show that social complaints receive more likes, but fewer comments, than quality or money complaints. Such nuances can only be uncovered by analyzing the actual post content, going beyond the valence of the posts. Furthermore, we theoretically discuss and empirically demonstrate that liking and commenting are engagement behaviors with different antecedents. For example, positive posts tend to attract more likes yet fewer comments than neutral posts. Overall, our research shows that user-generated posts on Facebook business pages represent a distinctive form of UGC that is conceptually different from online consumer reviews. Our work advances the knowledge on UGC and has practical implications for firms’ social media marketing strategy.

Background Genetic association studies (GAS) aims to evaluate the association between genetic variants and phenotypes. In the last few years, the number of this type of study has increased exponentially, but the results are not always reproducible due to experimental designs, low sample sizes and other methodological errors. In this field, meta-analysis techniques are becoming very popular tools to combine results across studies to increase statistical power and to resolve discrepancies in genetic association studies. A meta-analysis summarizes research findings, increases statistical power and enables the identification of genuine associations between genotypes and phenotypes. Meta-analysis techniques are increasingly used in GAS, but it is also increasing the amount of published meta-analysis containing different errors. Although there are several software packages that implement meta-analysis, none of them are specifically designed for genetic association studies and in most cases their use requires advanced programming or scripting expertise. Results We have developed MetaGenyo, a web tool for meta-analysis in GAS. MetaGenyo implements a complete and comprehensive workflow that can be executed in an easy-to-use environment without programming knowledge. MetaGenyo has been developed to guide users through the main steps of a GAS meta-analysis, covering Hardy-Weinberg test, statistical association for different genetic models, analysis of heterogeneity, testing for publication bias, subgroup analysis and robustness testing of the results. Conclusions MetaGenyo is a useful tool to conduct comprehensive genetic association meta-analysis. The application is freely available at http://bioinfo.genyo.es/metagenyo/ .