Evaluating the use of lecture capture using a revealed preference approach

This article discusses the introduction of lecture capture technology on a large undergraduate module with diverse student cohorts. Literature has so far relied on surveying students to discover their use of the technology or attempted to quantify the impact of watching lecture recordings on assessment performance. Alternatively, the principal contribution of this article is an evaluation of the use of the recorded lectures using a revealed preference approach. Specifically we identify to what extent students watched lecture recordings, rather than simply claimed to watch them when asked to provide comments on the technology. Data indicates the number of distinct students who watched recordings, the frequency with which they watched recordings, the average length of viewings as well as the time of day when lectures were viewed. We monitored viewings over two academic years, identifying ‘spikes’ in the number of viewings in the days before tests, as well as regularities in the viewing patterns across the two years. We analyse the data to assess the extent to which students used the recordings, how and when they watched the recordings. We conclude that the students value lecture recordings, making more extensive use of the recordings than has been identified in the literature to date. Ultimately, lecture recordings are suggested to offer valuable support for students’ independent study

F12RS SGR No. 3 (Athletics)

A RESOLUTION To acknowledge and thank the Athletic Department at Louisiana State University for its ongoing contributions to the University

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a \u27clock\u27 of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal \u27clock\u27 of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman\u27s), indicating that our model assigns a more advanced GA when an individual\u27s daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs

Extreme 15N Depletion in Seagrasses

Seagrass beds form an important part of the coastal ecosystem in many parts of the world but are very sensitive to anthropogenic nutrient increases. In the last decades, stable isotopes have been used as tracers of anthropogenic nutrient sources and to distinguish these impacts from natural environmental change, as well as in the identification of food sources in isotopic food web reconstruction. Thus, it is important to establish the extent of natural variations on the stable isotope composition of seagrass, validating their ability to act as both tracers�of nutrients and food sources. Around the world, depending on the seagrass species and ecosystem, values of seagrass N normally vary from 0 to 8 ? ?15N. In this study, highly unusual seagrass N isotope values were observed on the east coast of Qatar, with significant spatial variation over a scale of a few metres, and with ?15N values ranging from +2.95 to ?12.39 ? within a single bay during March 2012. This pattern of variation was consistent over a period of a year although there was a seasonal effect on the seagrass ?15N values. Seagrass, water column and sediment nutrient profiles were not correlated with seagrass ?15N values and neither were longer-term indicators of nutrient limitation such as seagrass biomass and height. Sediment ?15N values were correlated with Halodule uninervis ?15N values and this, together with the small spatial scale of variation, suggest that localised sediment processes may be responsible for the extreme isotopic values. Consistent differences in sediment to plant 15N discrimination between seagrass species also suggest that species-specific nutrient uptake mechanisms contribute to the observed ?15N values. This study reports some of the most extreme, negative ?15N values ever noted for seagrass (as low as ?12.4 ?) and some of the most highly spatially variable (values varied over 15.4 ? in a relatively small area of only 655�ha). These results are widely relevant, as they demonstrate the need for adequate spatial and temporal sampling when working with N stable isotopes to identify food sources in food web studies or as tracers of anthropogenic nutrients.Scopu

Are GPs under-investigating older patients presenting with symptoms of ovarian cancer? Observational study using General Practice Research Database

Background: Recent studies suggest that older patients in the United Kingdom are not benefiting as much from improvements in cancer treatments as their younger counterparts. We investigate whether this might be partly due to differential referral rates using ovarian cancer as an example. Methods: From the General Practice Research Database (GPRD), we identified all women aged 40–80 years on 1 June 2002 with a Read code for ovarian cancer between 1 June 2002 and 31 May 2007. Using these records, we compared the GPRD incidence of ovarian cancer with rates compiled from the UK cancer registries and investigated the relationship between age and coded investigations for suspected ovarian cancer. Results: The GPRD rates peaked earlier, at 70–74, and were lower than registry rates for nearly all ages particularly for patients over 59. The proportion investigated or referred by the GP decreased significantly with age and delays between first coded symptom and investigation showed a U-shaped distribution by age. Conclusions: GPs appear to be less likely to recognise and to refer patients presenting with ovarian cancer as they get older. If our findings extend to other cancers, lack of or delays in referral to secondary care may partly explain poor UK cancer mortality rates of older people

National First Peoples Gathering on Climate Change

Our purpose in hosting the National First Peoples Gathering on Climate Change (the Gathering) was to celebrate, learn from and enhance First Peoples-led climate action. We set out to strengthen kinships, cultural identity and well-being, and to strengthen caring for Country by using both Indigenous and scientific knowledge. The Gathering supported this overall purpose through five aims:• Bring Traditional Owners together to share with one another about climate change • Share scientific information in a form useful for Traditional Owners• Identify options for policy to respond to climate change • Provide tangible information to take back to communities• Highlight First Peoples’ climate change actions. 110 Traditional Owners from across Australia attended the Gathering