Impact 2007: Personalising Learning with Technology

The Impact 2007: Personalising Learning with Technology project was commissioned by the British Educational Communications and Technology Agency (Becta). This report presents the findings from Impact 2007: Phases One and Two. The findings are based on both quantitative and qualitative data collected from the 67 Impact 2007 schools. All of the schools contributed to the teacher and pupil online surveys. This provided 450 teacher and more than 1,300 primary and 2,000 secondary pupil questionnaire responses being available for analysis. In addition, senior managers and ICT co-ordinators were interviewed from 30 schools and 24 case study schools provided illuminative data from observations and researcher/teacher discussions. Quantitative analyses of the data included the use of cluster and factor analysis, analysis of variance and regression, and also multilevel modelling in orde

A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography

Purpose - To develop and validate a deep learning (DL) framework for the detection and quantification of drusen and reticular pseudodrusen (RPD) on optical coherence tomography scans. Design - Development and validation of deep learning models for classification and feature segmentation. Methods - A DL framework was developed consisting of a classification model and an out-of-distribution (OOD) detection model for the identification of ungradable scans; a classification model to identify scans with drusen or RPD; and an image segmentation model to independently segment lesions as RPD or drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with a self-reported diagnosis of age-related macular degeneration (AMD) and 250 UKBB controls. Drusen and RPD were manually delineated by five retina specialists. The main outcome measures were sensitivity, specificity, area under the ROC curve (AUC), kappa, accuracy and intraclass correlation coefficient (ICC). Results - The classification models performed strongly at their respective tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans classifier, the OOD model, and the drusen and RPD classification model). The mean ICC for drusen and RPD area vs. graders was 0.74 and 0.61, respectively, compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that the model's sensitivity was close to human performance. Conclusions - The models achieved high classification and segmentation performance, similar to human performance. Application of this robust framework will further our understanding of RPD as a separate entity from drusen in both research and clinical settings.Comment: 26 pages, 7 figure

Spectroscopy, MOST Photometry, and Interferometry of MWC 314: Is it an LBV or an interacting binary?

MWC 314 is a bright candidate luminous blue variable that resides in a fairly close binary system, with an orbital period of 60.753$\pm$0.003 d. We observed MWC 314 with a combination of optical spectroscopy, broad-band ground- and space-based photometry, as well as with long baseline, near-infrared interferometry. We have revised the single-lined spectroscopic orbit and explored the photometric variability. The orbital light curve displays two minima each orbit that can be partially explained in terms of the tidal distortion of the primary that occurs around the time of periastron. The emission lines in the system are often double-peaked and stationary in their kinematics, indicative of a circumbinary disc. We find that the stellar wind or circumbinary disc is partially resolved in the K\prime-band with the longest baselines of the CHARA Array. From this analysis, we provide a simple, qualitative model in an attempt to explain the observations. From the assumption of Roche Lobe overflow and tidal synchronisation at periastron, we estimate the component masses to be M1 $\approx 5$ M$_\odot$ and M2$\approx 15$ M$_\odot$, which indicates a mass of the LBV that is extremely low. In addition to the orbital modulation, we discovered two pulsational modes with the MOST satellite. These modes are easily supported by a low-mass hydrogen-poor star, but cannot be easily supported by a star with the parameters of an LBV. The combination of these results provides evidence that the primary star was likely never a normal LBV, but rather is the product of binary interactions. As such, this system presents opportunities for studying mass-transfer and binary evolution with many observational techniques.Comment: 26 pages, 7 figures, 5 tables, 2 appendices with 7 additional tables and 2 additional figures. Accepted for publication in MNRA

Framework for a Community Health Observing System for the Gulf of Mexico Region: Preparing for Future Disasters

© Copyright © 2020 Sandifer, Knapp, Lichtveld, Manley, Abramson, Caffey, Cochran, Collier, Ebi, Engel, Farrington, Finucane, Hale, Halpern, Harville, Hart, Hswen, Kirkpatrick, McEwen, Morris, Orbach, Palinkas, Partyka, Porter, Prather, Rowles, Scott, Seeman, Solo-Gabriele, Svendsen, Tincher, Trtanj, Walker, Yehuda, Yip, Yoskowitz and Singer. The Gulf of Mexico (GoM) region is prone to disasters, including recurrent oil spills, hurricanes, floods, industrial accidents, harmful algal blooms, and the current COVID-19 pandemic. The GoM and other regions of the U.S. lack sufficient baseline health information to identify, attribute, mitigate, and facilitate prevention of major health effects of disasters. Developing capacity to assess adverse human health consequences of future disasters requires establishment of a comprehensive, sustained community health observing system, similar to the extensive and well-established environmental observing systems. We propose a system that combines six levels of health data domains, beginning with three existing, national surveys and studies plus three new nested, longitudinal cohort studies. The latter are the unique and most important parts of the system and are focused on the coastal regions of the five GoM States. A statistically representative sample of participants is proposed for the new cohort studies, stratified to ensure proportional inclusion of urban and rural populations and with additional recruitment as necessary to enroll participants from particularly vulnerable or under-represented groups. Secondary data sources such as syndromic surveillance systems, electronic health records, national community surveys, environmental exposure databases, social media, and remote sensing will inform and augment the collection of primary data. Primary data sources will include participant-provided information via questionnaires, clinical measures of mental and physical health, acquisition of biological specimens, and wearable health monitoring devices. A suite of biomarkers may be derived from biological specimens for use in health assessments, including calculation of allostatic load, a measure of cumulative stress. The framework also addresses data management and sharing, participant retention, and system governance. The observing system is designed to continue indefinitely to ensure that essential pre-, during-, and post-disaster health data are collected and maintained. It could also provide a model/vehicle for effective health observation related to infectious disease pandemics such as COVID-19. To our knowledge, there is no comprehensive, disaster-focused health observing system such as the one proposed here currently in existence or planned elsewhere. Significant strengths of the GoM Community Health Observing System (CHOS) are its longitudinal cohorts and ability to adapt rapidly as needs arise and new technologies develop

Framework for a Community Health Observing System for the Gulf of Mexico Region: Preparing for Future Disasters

© Copyright © 2020 Sandifer, Knapp, Lichtveld, Manley, Abramson, Caffey, Cochran, Collier, Ebi, Engel, Farrington, Finucane, Hale, Halpern, Harville, Hart, Hswen, Kirkpatrick, McEwen, Morris, Orbach, Palinkas, Partyka, Porter, Prather, Rowles, Scott, Seeman, Solo-Gabriele, Svendsen, Tincher, Trtanj, Walker, Yehuda, Yip, Yoskowitz and Singer. The Gulf of Mexico (GoM) region is prone to disasters, including recurrent oil spills, hurricanes, floods, industrial accidents, harmful algal blooms, and the current COVID-19 pandemic. The GoM and other regions of the U.S. lack sufficient baseline health information to identify, attribute, mitigate, and facilitate prevention of major health effects of disasters. Developing capacity to assess adverse human health consequences of future disasters requires establishment of a comprehensive, sustained community health observing system, similar to the extensive and well-established environmental observing systems. We propose a system that combines six levels of health data domains, beginning with three existing, national surveys and studies plus three new nested, longitudinal cohort studies. The latter are the unique and most important parts of the system and are focused on the coastal regions of the five GoM States. A statistically representative sample of participants is proposed for the new cohort studies, stratified to ensure proportional inclusion of urban and rural populations and with additional recruitment as necessary to enroll participants from particularly vulnerable or under-represented groups. Secondary data sources such as syndromic surveillance systems, electronic health records, national community surveys, environmental exposure databases, social media, and remote sensing will inform and augment the collection of primary data. Primary data sources will include participant-provided information via questionnaires, clinical measures of mental and physical health, acquisition of biological specimens, and wearable health monitoring devices. A suite of biomarkers may be derived from biological specimens for use in health assessments, including calculation of allostatic load, a measure of cumulative stress. The framework also addresses data management and sharing, participant retention, and system governance. The observing system is designed to continue indefinitely to ensure that essential pre-, during-, and post-disaster health data are collected and maintained. It could also provide a model/vehicle for effective health observation related to infectious disease pandemics such as COVID-19. To our knowledge, there is no comprehensive, disaster-focused health observing system such as the one proposed here currently in existence or planned elsewhere. Significant strengths of the GoM Community Health Observing System (CHOS) are its longitudinal cohorts and ability to adapt rapidly as needs arise and new technologies develop

Researching effective approaches to cleaning in hospitals: protocol of the REACH study, a multi-site stepped-wedge randomised trial

BackgroundThe Researching Effective Approaches to Cleaning in Hospitals (REACH) study will generate evidence about the effectiveness and cost-effectiveness of a novel cleaning initiative that aims to improve the environmental cleanliness of hospitals. The initiative is an environmental cleaning bundle, with five interdependent, evidence-based components (training, technique, product, audit and communication) implemented with environmental services staff to enhance hospital cleaning practices.Methods/designThe REACH study will use a stepped-wedge randomised controlled design to test the study intervention, an environmental cleaning bundle, in 11 Australian hospitals. All trial hospitals will receive the intervention and act as their own control, with analysis undertaken of the change within each hospital based on data collected in the control and intervention periods. Each site will be randomised to one of the 11 intervention timings with staggered commencement dates in 2016 and an intervention period between 20 and 50 weeks. All sites complete the trial at the same time in 2017. The inclusion criteria allow for a purposive sample of both public and private hospitals that have higher-risk patient populations for healthcare-associated infections (HAIs). The primary outcome (objective one) is the monthly number of Staphylococcus aureus bacteraemias (SABs), Clostridium difficile infections (CDIs) and vancomycin resistant enterococci (VRE) infections, per 10,000 bed days. Secondary outcomes for objective one include the thoroughness of hospital cleaning assessed using fluorescent marker technology, the bio-burden of frequent touch surfaces post cleaning and changes in staff knowledge and attitudes about environmental cleaning. A cost-effectiveness analysis will determine the second key outcome (objective two): the incremental cost-effectiveness ratio from implementation of the cleaning bundle.The study uses the integrated Promoting Action on Research Implementation in Health Services (iPARIHS) framework to support the tailored implementation of the environmental cleaning bundle in each hospital.DiscussionEvidence from the REACH trial will contribute to future policy and practice guidelines about hospital environmental cleaning. It will be used by healthcare leaders and clinicians to inform decision-making and implementation of best-practice infection prevention strategies to reduce HAIs in hospitals

The role of radical economic restructuring in truancy from school and engagement in crime

Of late, criminologists have become acutely aware of the relationship between school outcomes and engagement in crime as an adult. This phenomenon – which has come to be known as the ‘school-to-prison-pipeline’ – has been studied in North America and the UK, and requires longitudinal datasets. Typically, these studies approach the phenomenon from an individualist perspective and examine truancy in terms of the truants’ attitudes, academic achievement or their home-life. What remains unclear however is a consideration of a) how macro-level social and economic processes may influence the incidence of truancy, and b) how structural processes fluctuate over time, and in so doing produce variations in truancy rates or the causal processes associated with truancy. Using longitudinal data from two birth cohort studies, we empirically address these blind-spots and test the role of social-structural processes in truancy, and how these may change over timeEconomic and Social Research Counci

2015 Research & Innovation Day Program

A one day showcase of applied research, social innovation, scholarship projects and activities.https://first.fanshawec.ca/cri_cripublications/1002/thumbnail.jp

Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

In the version of this article initially published, the author affiliations incorrectly listed “Candiolo Cancer Institute FPO-IRCCS, Candiolo (TO), Italy” as “Candiolo Cancer Institute, Candiolo, Italy.” The change has been made to the HTML and PDF versions of the article