Factors Associated with Participation in a Multidomain Web-Based Dementia Prevention Trial: Evidence from Maintain Your Brain (MYB)

Background: The Maintain Your Brain (MYB) trial aims to prevent cognitive decline and dementia through multidomain, web-based risk-reduction. To facilitate translation, it is important to understand drivers of participation. Objective: To describe characteristics associated with participation in MYB. Methods: This was an observational ancillary study of MYB, a randomized controlled trial nested within the 45 and Up Study in New South Wales, Australia. We linked 45 and Up Study survey and MYB participation data. The study cohort comprised 45 and Up Study participants, aged 55-77 years at 1 January 2018, who were invited to participate in MYB. 45 and Up Study participant characteristics and subsequent MYB consent and participation were examined. Results: Of 98,836 invited, 13,882 (14%) consented to participate and 6,190 participated (6%). Adjusting for age and sex, a wide range of factors were related to participation. Higher educational attainment had the strongest relationship with increased MYB participation (university versus school non-completion; AdjOR = 5.15; 95% CI:4.70-5.64) and lower self-rated quality of life with reduced participation (Poor versus Excellent: AdjOR = 0.19; 95% CI:0.11-0.32). A family history of Alzheimer's disease was related to increased participation but most other dementia risk factors such as diabetes, obesity, stroke, high blood pressure, and current smoking were associated with reduced participation. Conclusion: Higher socio-economic status, particularly educational attainment, is strongly associated with engagement in online dementia prevention research. Increasing population awareness of dementia risk factors, and better understanding the participation barriers in at-risk groups, is necessary to ensure online interventions are optimally designed to promote maximum participation

The Sudden Dominance of blaCTX–M Harbouring Plasmids in Shigella spp. Circulating in Southern Vietnam

Shigellosis is a disease caused by bacteria belonging to Shigella spp. and is a leading cause of bacterial gastrointestinal infections in infants in unindustrialized countries. The Shigellae are dynamic and capable of rapid change when placed under selective pressure in a human population. Extended spectrum beta lactamases (ESBLs) are enzymes capable of degrading cephalosporins (a group of antimicrobial agents) and the genes that encode them are common in pathogenic E. coli and other related organisms in industrialized countries. In southern Vietnam, we have isolated multiple cephalosporin-resistant Shigella that express ESBLs. Furthermore, over two years these strains have replaced strains isolated from patients with shigellosis that cannot express ESBLs. Our work describes the genes responsible for this characteristic and we investigate one of the elements carrying one of these genes. These finding have implications for treatment of shigellosis and support the growing necessity for vaccine development. Our findings also may be pertinent for other countries undergoing a similar economic transition to Vietnam's and the corresponding effect on bacterial populations

Environmental sensing and response genes in cnidaria : the chemical defensome in the sea anemone Nematostella vectensis

Author Posting. © The Author(s), 2008. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Cell Biology and Toxicology 24 (2008): 483-502, doi:10.1007/s10565-008-9107-5.The starlet sea anemone Nematostella vectensis has been recently established as a new model system for the study of the evolution of developmental processes, as cnidaria occupy a key evolutionary position at the base of the bilateria. Cnidaria play important roles in estuarine and reef communities, but are exposed to many environmental stressors. Here I describe the genetic components of a ‘chemical defensome’ in the genome of N. vectensis, and review cnidarian molecular toxicology. Gene families that defend against chemical stressors and the transcription factors that regulate these genes have been termed a ‘chemical defensome,’ and include the cytochromes P450 and other oxidases, various conjugating enyzymes, the ATP-dependent efflux transporters, oxidative detoxification proteins, as well as various transcription factors. These genes account for about 1% (266/27200) of the predicted genes in the sea anemone genome, similar to the proportion observed in tunicates and humans, but lower than that observed in sea urchins. While there are comparable numbers of stress-response genes, the stress sensor genes appear to be reduced in N. vectensis relative to many model protostomes and deuterostomes. Cnidarian toxicology is understudied, especially given the important ecological roles of many cnidarian species. New genomic resources should stimulate the study of chemical stress sensing and response mechanisms in cnidaria, and allow us to further illuminate the evolution of chemical defense gene networks.WHOI Ocean Life Institute and NIH R01-ES01591

moocRP: Enabling Open Learning Analytics with an Open Source Platform for Data Distribution, Analysis, and Visualization

In this paper, we address issues of transparency, modularity, and privacy with the introduction of an open source, web-based data repository and analysis tool tailored to the Massive Open Online Course community. The tool integrates data request/authorization and distribution workflow features as well as provides a simple analytics module upload format to enable reuse and replication of analytics results among instructors and researchers. We survey the evolving landscape of competing established and emerging data models, all of which are accommodated in the platform. Data model descriptions are provided to analytics authors who choose, much like with smartphone app stores, to write for any number of data models depending on their needs and the proliferation of the particular data model. Two case study examples of analytics and responsive visualizations based on different data models are described in the paper. The result is a simple but effective approach to learning analytics immediately applicable to X consortium MOOCs and beyond

Modelling learners in crowdsourcing educational systems

Traditionally, learner models estimate a student’s knowledge state solely based on their performance on attempting assessment items. This can be attributed to the fact that in many traditional educational systems, students are primarily involved in just answering assessment items. In recent years, the use of crowdsourcing to support learning at scale has received significant attention. In crowdsourcing educational systems, in addition to attempting assessment items, students are engaged with other various tasks such as creating resources, creating solutions, rating the quality of resources, and giving feedback. Past studies have demonstrated that engaging students in meaningful crowdsourcing tasks, also referred to as learningsourcing, has pedagogical benefits that can enhance student learning. In this paper, we present a learner model that leverages data from students’ learnersourcing contributions alongside attempting assessment items towards modelling of students’ knowledge state. Results from an empirical study suggest that indeed crowdsourced contributions from students can effectively be used in modelling learners

Strategic support of algebraic expression writing

Abstract. The examination of user data as a basis for developing production models of user behavior has been a major focus in the PAT Algebra I Tutor's development. In recent work, we have investigated relationships between related tasks and the solution strategies displayed by students. To solve a PAT Algebra I problem, students must complete several related arithmetic and algebraic tasks. The sequences in which these tasks are completed suggest problem-solving strategies of students. We have observed a characteristic pattern of students ' success rates on related tasks. We have also observed that students ' success on specific skills (e.g. constructing a symbolic representation) may differ depending on whether students previously carried out related tasks in the same problem (e.g. solving an analogous arithmetic question). This information has important implications for our user model and our modeling approach.

Utilising learnersourcing to inform design loop adaptivity

Design-loop adaptivity refers to data-driven decisions that inform the design of learning materials to improve learning for student populations within adaptive educational systems (AES). Commonly in AESs, decisions on the quality of learning material are based on students’ performance, i.e., whether engaging with the material led to learning gains. This paper investigates an alternative approach for design adaptivity, which utilises students’ subjective ratings and comments to infer the quality of the learning material. This approach is in line with the recent shift towards learner-centred learning and learnersourcing, that aim to transform the role of students from passive recipients of content to active participants that engage with various higher-order learning tasks including evaluating the quality of resources. In this paper, we present a suite of aggregation-based and reliability-based methods that can be used to infer the quality of learning material based on student ratings and comments. We investigate the feasibility and accuracy of the methods in a live learnersourcing educational platform called RiPPLE that provides the capacity to capture subjective ratings and comments from students. Empirical data from the use of RiPPLE in a first-year course on information systems are used to evaluate the presented methods. Results indicate that the use of a combination of reliability-based methods provides an acceptable level of accuracy in determining the quality of learning resources