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The 2020 Vice-Presidential debate afforded the opportunity to examine online reactions toward a woman of color, Kamala Harris, and a white man, Mike Pence, as they vied for the same position. We collected tweets from the Twitter API related to Harris and Pence, mainly using neutral hashtags. We examined keywords for gender and race slurs and conducted a multivariate analysis of tweet sentiment. Gender and racial slurs surface in both Harris and Pence datasets, showcasing the insidious nature of sexist and racist stereotypes that seep into online conversations regarding a high-status job debate. As anticipated, tweets regarding Harris contained a higher proportion of racist and sexist curse words, highlighting greater levels of harassment and “intersectional,” multi-ethnic/gender attacks. Racial insults targeting Blacks or Asians were more negative than those associated with Whites. Unexpectedly, tweets related to Harris were more positive in average sentiment than those regarding Pence. Yet, there were significantly more retweets, and more negativity of retweets, relating to Harris than to Pence, underscoring the relatively widespread broadcasting of derogatory messages about Harris. Overall, we found that harassing messages toward the candidates reinforced traditional race and gender stereotypes and bolstered the status of those who posted negative content by attaining more retweets. Harassers routinely invoked well-worn, stereotypical insults in their attacks, especially when targeting a multiracial woman.

While there has been much discussion in recent decades on the nature of social capital and its importance in online interactions, it is my contention that these discussions have been dominated by the American Communitarian tradition. In this article, I begin with an overview of American Communitarianism to identify the key elements therein that are found in contemporary theories of social capital. Following this, I expose some of the weaknesses of this tradition and apply Bourdieu's distinctive theoretical framework to online interactions to demonstrate the fecundity of Bourdieu's sociological perspective when applied to contemporary online interactions. To do this, I examine interactions online that involve 'internet memes', as digital inhabitants themselves colloquially define them. It is my contention that an agonistic model, rather than a communitarian one, best describes the online interactions of digital inhabitants.

In this paper, I argue that unique locations of digital interactions constitute distinct information-technological media. I first apply a close reading of Couch’s (1995a) work to digital sociology, and then analyze three popular social media networks: www.Imgur.com, www.Reddit.com, and www.Twitter.com. I close with a theoretical application of Couch’s paradigm for analyzing the qualities of information technologies.

In this project, I scrape and curate a novel dataset consisting of all posts and comments written in 58 subreddits belonging to the “Manosphere”. This is a diffuse social movement concerned with what they believe is the rampant misandry of contemporary feminism. As such, it represents the first study to examine such a breadth of Manosphere discourse and analyze the themes therein, advancing our understanding of the prevalence and ubiquity of the themes of their discourse. This project uniquely captures the main forums in which the Manosphere congregated for over a decade and analyzes the texts therein with machine learning techniques. The aim of this project is to increase our knowledge about the Manosphere, with particular attention paid to: 1) the role of misogynist terrorist attacks and their influence on the Manosphere’s discourse and collective identity, 2) the optimal machine learning techniques for analyzing nascent text-based communities, and 3) rhythms of sexual coercion that are normalized in a specific kind of Manosphere post, the “Field Report.”In Chapter 2 of this project, I examine the discourse of the Manosphere on Reddit in the aftermath of violent misogynist attacks. Drawing on scholarship related to terrorist studies, collective identity, and precarious masculinity, I show how the main issues of the Manosphere, masculinity, and perceived misandry influence the beliefs and behaviors of the movement’s members. I find that varying kinds of posts in the Manosphere forums are influenced differently by the violent attacks. Rather than one uniform pattern of influence following violent attacks, posts with relational topics, where members share interpersonal concerns, are stymied, while posts with ideological topics, where members refine and reiterate their core beliefs, receive a boost in frequency. I discuss the implications of this finding in light of the movement’s collective identity.In Chapter 3, I evaluate several different machine learning techniques in the task of predicting popular and controversial content on Reddit. These data contain a class imbalance, common for many text datasets. As such, it advances our understanding of best practices for evaluating small textual datasets with a class imbalance. This is relevant as new communities take root in digital spaces and forums akin to subreddits. I also overview several common metrics for evaluating machine learners for class imbalance prediction tasks, finding balanced accuracy to be most successful.In Chapter 4, I analyze a specific kind of post from the Pick-Up Artist and Seduction communities within the Manosphere: “Field Reports”. While many field reports detail consensual interactions, some describe an ebb and flow of resistance to sexual escalation and subsequent persistence even in spite of that resistance. These patterns that are legitimated through the encouraging comments of Manosphere members reify hegemonic masculinity and reproduce gender inequality in contemporary society. I analyze these findings in light of the Traditional Sexual Script, which undergirds the beliefs of these subsets of the Manosphere.

The early diagnosis of active hepatitis C virus (HCV) infection remains a significant barrier to the treatment of the disease and to preventing the associated significant morbidity and mortality seen, worldwide. Current testing is delayed due to the high cost, long turnaround times and high expertise needed in centralised diagnostic laboratories. Here we demonstrate a user-friendly, low-cost pan-genotypic assay, based upon reverse transcriptase loop mediated isothermal amplification (RT-LAMP). We developed a prototype device for point-of-care use, comprising a LAMP amplification chamber and lateral flow nucleic acid detection strips, giving a visually-read, user-friendly result in <40 min. The developed assay fulfils the current guidelines recommended by World Health Organisation and is manufactured at minimal cost using simple, portable equipment. Further development of the diagnostic test will facilitate linkage between disease diagnosis and treatment, greatly improving patient care pathways and reducing loss to follow-up, so assisting in the global elimination strategy. Current HCV nucleic acid-based diagnosis is largely performed in centralised laboratories. Here, the authors present a pan-genotypic RNA assay, based on reverse transcriptase loop mediated isothermal amplification and develop a low-cost prototype paper-based lateral flow device for point-of-care use, providing a visually read result within 40 min.

Sialic acid-binding immunoglobulin-type lectins (Siglecs) are immunomodulatory receptors that are regulated by their glycan ligands. The connections between Siglecs and human disease motivate improved methods to detect Siglec ligands. Here, we describe a new versatile set of Siglec-Fc proteins for glycan ligand detection. Enhanced sensitivity and selectivity are enabled through multimerization and avoiding Fc receptors, respectively. Using these Siglec-Fc proteins, Siglec ligands are systematically profiled on healthy and cancerous cells and tissues, revealing many unique patterns. Additional features enable the production of small, homogenous Siglec fragments and development of a quantitative ligand-binding mass spectrometry assay. Using this assay, the ligand specificities of several Siglecs are clarified. For CD33 (Siglec-3), we demonstrate that it recognizes both α2-3 and α2-6 sialosides in solution and on cells, which has implications for its link to Alzheimer’s disease susceptibility. These soluble Siglecs reveal the abundance of their glycan ligands on host cells as self-associated molecular patterns. Sialic acid-binding immunoglobulin-type lectins (Siglecs) are a family of immunomodulatory receptors expressed on cells of the hematopoietic lineage. Here the authors demonstrate an approach for the identification of the glycan ligands of Siglecs, which is also applicable to other families of glycan-binding proteins.

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks. We present a practical workflow describing how to use deep learning to develop continuous-risk models that can predict various adverse outcomes from structured electronic health records.

The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients 1 . To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records 2 – 17 and using acute kidney injury—a common and potentially life-threatening condition 18 —as an exemplar. Our model was developed on a large, longitudinal dataset of electronic health records that cover diverse clinical environments, comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert. In addition to predicting future acute kidney injury, our model provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests 9 . Although the recognition and prompt treatment of acute kidney injury is known to be challenging, our approach may offer opportunities for identifying patients at risk within a time window that enables early treatment. A deep learning approach that predicts the risk of acute kidney injury may help to identify patients at risk of health deterioration within a time window that enables early treatment.

To determine the impact of the Covid-19 pandemic on the number of occupational health consultations and to highlight influencing factors. Retrospective observational study of consultations from an inter-company occupational health service. Data were retrieved during three consecutive years: 2019 (baseline), and 2020-2021. For comparisons purposes, we used the number of occupational health consultations per day and per full-time equivalent occupational healthcare worker (n consultations/d/FTE). Multivariate analysis was performed using logistic regression, for each lockdown vs the same period one year before. A total of 103,351 consultations were included. The number of consultations decreased by 14.3% in 2020 compared to 2019 but increased by 33.7% in 2021 compared to 2020. There were 4.9 consultations/d/FTE, 4.69 to 5.12 in 2019; 4.07, 3.81 to 4.34 in 2020; and 5.35, 5.16 to 5.55 in 2021. The first lockdown had a massive impact on the number of consultations, whereas the activity returned to normal from August 2020 with an increase in 2021. Age was associated with a decrease in the propension of consulting for the three lockdown periods (p < 0.001). The proportion of consultations for return-to-work was multiplied by 2.44 (2.02 to 2.95, p < 0.001) during the first lockdown, associated with a reduced risk of being declared unfit to work (OR = 0.48, 95 CI 0.27 to 0.84, p = 0.010). The Covid-19 pandemic had a huge impact on the medical activity of occupational health departments, with a massive decrease in 2020 followed by an increase in 2021 compared to 2019.

Last year, three Earth-sized planets were discovered to be orbiting the nearby Jupiter-sized star TRAPPIST-1; now, follow-up photometric observations from the ground and from space show that there are at least seven Earth-sized planets in this star system, and that they might be the right temperature to harbour liquid water on their surfaces. Seven Earth-like planets around a nearby dwarf star Michaël Gillon et al . report the results of a photometric monitoring campaign of the star TRAPPIST-1 from the ground and space. They reveal that at least seven planets with sizes and masses similar to Earth revolve around this Jupiter-sized star. These planets all have equilibrium temperatures low enough to make it possible for liquid water to exist on their surfaces. One aim of modern astronomy is to detect temperate, Earth-like exoplanets that are well suited for atmospheric characterization. Recently, three Earth-sized planets were detected that transit (that is, pass in front of) a star with a mass just eight per cent that of the Sun, located 12 parsecs away 1 . The transiting configuration of these planets, combined with the Jupiter-like size of their host star—named TRAPPIST-1—makes possible in-depth studies of their atmospheric properties with present-day and future astronomical facilities 1 , 2 , 3 . Here we report the results of a photometric monitoring campaign of that star from the ground and space. Our observations reveal that at least seven planets with sizes and masses similar to those of Earth revolve around TRAPPIST-1. The six inner planets form a near-resonant chain, such that their orbital periods (1.51, 2.42, 4.04, 6.06, 9.1 and 12.35 days) are near-ratios of small integers. This architecture suggests that the planets formed farther from the star and migrated inwards 4 , 5 . Moreover, the seven planets have equilibrium temperatures low enough to make possible the presence of liquid water on their surfaces 6 , 7 , 8 .