Flink: Semantic Web technology for the extraction and analysis of social networks

We present the Flink system for the extraction, aggregation and visualization of online social networks. Flink employs semantic technology for reasoning with personal information extracted from a number of electronic information sources including web pages, emails, publication archives and FOAF profiles. The acquired knowledge is used for the purposes of social network analysis and for generating a webbased presentation of the community. We demonstrate our novel method to social science based on electronic data using the example of the Semantic Web research community

Learning by building: A visual modelling language for psychology students

Cognitive modelling involves building computational models of psychological theories in order to learn more about them, and is a major research area allied to psychology and artificial intelligence. The main problem is that few psychology students have previous programming experience. The course lecturer can avoid the problem by presenting the area only in general terms. This leaves the process of building and testing models, which is central to the methodology, an unknown. Alternatively, students can be introduced to one of the existing cognitive modelling languages, though this can easily be overwhelming, hindering rather than helping their understanding. Our solution was to design and build a programming language for the intended population. The result is Hank, a visual cognitive modelling language for the psychologist. Our informal analyses have investigated the effectiveness of Hank in its intended context of use, both as a paper and pencil exercise for individuals, and as a computer based project to be carried out in groups. The findings largely support the Hank design decisions, and illuminate many of the challenges inherent in designing a programming language for an educational purpose

Risk of COVID-19 after natural infection or vaccinationResearch in context

Summary: Background: While vaccines have established utility against COVID-19, phase 3 efficacy studies have generally not comprehensively evaluated protection provided by previous infection or hybrid immunity (previous infection plus vaccination). Individual patient data from US government-supported harmonized vaccine trials provide an unprecedented sample population to address this issue. We characterized the protective efficacy of previous SARS-CoV-2 infection and hybrid immunity against COVID-19 early in the pandemic over three-to six-month follow-up and compared with vaccine-associated protection. Methods: In this post-hoc cross-protocol analysis of the Moderna, AstraZeneca, Janssen, and Novavax COVID-19 vaccine clinical trials, we allocated participants into four groups based on previous-infection status at enrolment and treatment: no previous infection/placebo; previous infection/placebo; no previous infection/vaccine; and previous infection/vaccine. The main outcome was RT-PCR-confirmed COVID-19 >7–15 days (per original protocols) after final study injection. We calculated crude and adjusted efficacy measures. Findings: Previous infection/placebo participants had a 92% decreased risk of future COVID-19 compared to no previous infection/placebo participants (overall hazard ratio [HR] ratio: 0.08; 95% CI: 0.05–0.13). Among single-dose Janssen participants, hybrid immunity conferred greater protection than vaccine alone (HR: 0.03; 95% CI: 0.01–0.10). Too few infections were observed to draw statistical inferences comparing hybrid immunity to vaccine alone for other trials. Vaccination, previous infection, and hybrid immunity all provided near-complete protection against severe disease. Interpretation: Previous infection, any hybrid immunity, and two-dose vaccination all provided substantial protection against symptomatic and severe COVID-19 through the early Delta period. Thus, as a surrogate for natural infection, vaccination remains the safest approach to protection. Funding: National Institutes of Health