Institutional and Student Transitions Into Enhanced Blended Learning

This presentation provides an overview of the ‘Transitions into blended learning’ project, which has focused on three areas: developing an institutional transition framework, researching student experiences, and identifying interventions to support effective transitions. The framework identified external drivers for blended learning, a set of considerations for institutions, and a set of processes to facilitate change involving three stakeholder groups at the heart of the model. The work included learner experience research with students newly engaged in blended learning. This work identified support needs around access (to technology and learning materials), attitudes (towards learning online) and attributes (skills) needed to engage autonomously in blended learning. The institution-wide Enhancement themes team identified a set of interventions or ‘anchor points’ to prevent the institution ‘drifting back’ into purely traditional approaches to learning and teaching. These included the recognition and promotion of good practice through case studies, development of an institutional e-learning framework, and an event to encourage staff and students to share good practice in blended learning. This three-year project was largely led by a PhD student (JA), working with the principal investigator (VHD) and the institutional representative (KG)

The Role of Gender, Race and Racial Identity in Relation to Attitudes Toward Interracial Dating

The purpose of this study was to examine whether an individual's gender, race, and racial identity significantly relate to interracial dating attitudes. Two hundred subjects (101 Blacks, 99 Whites) were administered an interracial dating questionnaire and a racial identity measure. Findings indicated that there were no significant main effects for gender, but race was significantly related to interracial dating attitudes with blacks having more positive attitudes. As hypothesized, racial identity was also found to be Significantly related to both Blacks' and Whites' attitudes toward interracial dating

Moving epidemic method (MEM) applied to virology data as a novel real time tool to predict peak in seasonal influenza healthcare utilisation. The Scottish experience of the 2017/18 season to date

Scotland observed an unusual influenza A(H3N2)- dominated 2017/18 influenza season with healthcare services under significant pressure. We report the application of the moving epidemic method (MEM) to virology data as a tool to predict the influenza peak activity period and peak week of swab positivity in the current season. This novel MEM application has been successful locally and is believed to be of potential use to other countries for healthcare planning and building wider community resilience

Clustering of under-five mortality in Rufiji Health and Demographic Surveillance System in rural Tanzania

BACKGROUND\ud \ud Less than 5 years remain before the 2015 mark when countries will be evaluated on their achievements for the Millennium Development Goals (MDGs). The MDG 4 and 6 call for a reduction of child mortality by two-thirds and combating malaria, HIV/AIDS, TB, and other diseases, respectively. To accelerate the achievement of these goals, focused allocation of resources and high deployment of cost-effective interventions is paramount. The knowledge of spatial and temporal distribution of diseases is important for health authorities to prioritize and allocate resources.\ud \ud METHODS\ud \ud To identify possible significant clusters, we used SatTScan software, and analyzed 2,745 cases of under-five with 134,099 person-years for the period between 1999and 2008. Mortality rates for every year were calculated, likewise a spatial scan statistic was used to test for clusters of total under-five mortalities in both space and time.\ud \ud RESULTS\ud \ud A number of significant clusters from space, time, and space-time analysis were identified in several locations for a period of 10 years in the Rufiji Demographic Surveillance Site (RDSS). These locations show that villages within the clusters have an elevated risk of under-five deaths. The spatial analysis identified three significant clusters. The first cluster had only one village, Kibiti A (p < 0.05, the second cluster involved five villages (Mtawanya, Pagae, Kibiti A, Machepe, and Kibiti B; p < 0.05), the third cluster involved one village, Jaribu Mpakani (p < 0.05). A space-time cluster of 10 villages for the period between 1999 and 2002 with a radius of 14.73 km was discovered with the highest risk (RR 1.6, p < 0.001). The mortality rates were very high for the years 1999-2002 according to the analysis. The death rates were 33.5, 26.4, 24.1, and 24.9, respectively. Total childhood mortality rates calculated for the period of 10 years were 21.0 per 1,000 person-years.\ud \ud CONCLUSION\ud \ud During the 10 years of analysis, mortality seemed to decrease in RDSS. The mortality decline should be taken with caution because the Demographic Surveillance System is not statistically representative of the whole population; therefore, inference should not be made to the general population of Tanzania. The pattern observed could be attributed to demographic and weather characteristics of RDSS. This should provide new insights for further studies and interventions toward reducing under-five mortality

JPAAP 9(2) Editorial: Transitions to remote and blended learning

No abstract available

BNT162b2 and ChAdOx1 nCoV-19 vaccinations, incidence of SARS-CoV-2 infections and COVID-19 hospitalisations in Scotland in the Delta era

EAVE II is supported by the Medical Research Council (MR/R008345/1) with the support of BREATHE – The Health Data Research Hub for Respiratory Health, which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund [MC_PC_19004] and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and Scottish Government DG Health and Social Care, the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058) and the Lifelong Health and Well-being study as part of the National Core Studies (MC_PC_20030).Peer reviewedPublisher PD

Doa10/MARCH6 architecture interconnects E3 ligase activity with lipid-binding transmembrane channel to regulate SQLE

Transmembrane E3 ligases play crucial roles in homeostasis. Much protein and organelle quality control, and metabolic regulation, are determined by ER-resident MARCH6 E3 ligases, including Doa10 in yeast. Here, we present Doa10/MARCH6 structural analysis by cryo-EM and AlphaFold predictions, and a structure-based mutagenesis campaign. The majority of Doa10/MARCH6 adopts a unique circular structure within the membrane. This channel is established by a lipid-binding scaffold, and gated by a flexible helical bundle. The ubiquitylation active site is positioned over the channel by connections between the cytosolic E3 ligase RING domain and the membrane-spanning scaffold and gate. Here, by assaying 95 MARCH6 variants for effects on stability of the well-characterized substrate SQLE, which regulates cholesterol levels, we reveal crucial roles of the gated channel and RING domain consistent with AlphaFold-models of substrate-engaged and ubiquitylation complexes. SQLE degradation further depends on connections between the channel and RING domain, and lipid binding sites, revealing how interconnected Doa10/MARCH6 elements could orchestrate metabolic signals, substrate binding, and E3 ligase activity

The Iowa Homemaker vol.3, no.10

Table of Contents Activities of the Merrill Palmer School by Edna E. Walls, page 1 Specific Helps on Everyday Teaching Problems by Florence E. Busse, page 2 Winter Diets and the Elusive Meal by Lucile Barta, page 2 Modern Women and Floriculture by E. C. Volz, page 3 Historic Costume the Mother of Modern Vogue by Clara Jordan, page 4 Feeding the Multitude by Gertrude E. Murray, page 5 Our Travels in France by Josephine Arnquist, page 6 The Evolution of Home Economics at Iowa State by Ruth Elaine Wilson, page 8 The Power of Music by Oscar Hatch Hawley, page 9 To Judge of a Bargain by Mildred Briggs, page 9 Who is Responsible for the Child? by Louise Crawford, page 1

Temporal trends and forecasting of COVID-19 hospitalisations and deaths in Scotland using a national real-time patient-level data platform: a statistical modelling study

This study is part of the EAVE II project. EAVE II is funded by the MRC (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and Scottish Government Director General Health and Social Care. The original EAVE project was funded by the NIHR Health Technology Assessment programme (11/46/23).Background   As the COVID-19 pandemic continues, national-level surveillance platforms with real-time individual person-level data are required to monitor and predict the epidemiological and clinical profile of COVID-19 and inform public health policy. We aimed to create a national dataset of patient-level data in Scotland to identify temporal trends and COVID-19 risk factors, and to develop a novel statistical prediction model to forecast COVID-19-related deaths and hospitalisations during the second wave.  Methods   We established a surveillance platform to monitor COVID-19 temporal trends using person-level primary care data (including age, sex, socioeconomic status, urban or rural residence, care home residence, and clinical risk factors) linked to data on SARS-CoV-2 RT-PCR tests, hospitalisations, and deaths for all individuals resident in Scotland who were registered with a general practice on Feb 23, 2020. A Cox proportional hazards model was used to estimate the association between clinical risk groups and time to hospitalisation and death. A survival prediction model derived from data from March 1 to June 23, 2020, was created to forecast hospital admissions and deaths from October to December, 2020. We fitted a generalised additive spline model to daily SARS-CoV-2 cases over the previous 10 weeks and used this to create a 28-day forecast of the number of daily cases. The age and risk group pattern of cases in the previous 3 weeks was then used to select a stratified sample of individuals from our cohort who had not previously tested positive, with future cases in each group sampled from a multinomial distribution. We then used their patient characteristics (including age, sex, comorbidities, and socioeconomic status) to predict their probability of hospitalisation or death.  Findings   Our cohort included 5 384 819 people, representing 98·6% of the entire estimated population residing in Scotland during 2020. Hospitalisation and death among those testing positive for SARS-CoV-2 between March 1 and June 23, 2020, were associated with several patient characteristics, including male sex (hospitalisation hazard ratio [HR] 1·47, 95% CI 1·38–1·57; death HR 1·62, 1·49–1·76) and various comorbidities, with the highest hospitalisation HR found for transplantation (4·53, 1·87–10·98) and the highest death HR for myoneural disease (2·33, 1·46–3·71). For those testing positive, there were decreasing temporal trends in hospitalisation and death rates. The proportion of positive tests among older age groups (>40 years) and those with at-risk comorbidities increased during October, 2020. On Nov 10, 2020, the projected number of hospitalisations for Dec 8, 2020 (28 days later) was 90 per day (95% prediction interval 55–125) and the projected number of deaths was 21 per day (12–29). Interpretation The estimated incidence of SARS-CoV-2 infection based on positive tests recorded in this unique data resource has provided forecasts of hospitalisation and death rates for the whole of Scotland. These findings were used by the Scottish Government to inform their response to reduce COVID-19-related morbidity and mortality.Publisher PDFPeer reviewe