Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation

We propose a new deep learning method for tumour segmentation when dealing with missing imaging modalities. Instead of producing one network for each possible subset of observed modalities or using arithmetic operations to combine feature maps, our hetero-modal variational 3D encoder-decoder independently embeds all observed modalities into a shared latent representation. Missing data and tumour segmentation can be then generated from this embedding. In our scenario, the input is a random subset of modalities. We demonstrate that the optimisation problem can be seen as a mixture sampling. In addition to this, we introduce a new network architecture building upon both the 3D U-Net and the Multi-Modal Variational Auto-Encoder (MVAE). Finally, we evaluate our method on BraTS2018 using subsets of the imaging modalities as input. Our model outperforms the current state-of-the-art method for dealing with missing modalities and achieves similar performance to the subset-specific equivalent networks.Comment: Accepted at MICCAI 201

Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app

Understanding the geographical distribution of COVID-19 through the general population is key to the provision of adequate healthcare services. Using self-reported data from 1 960 242 unique users in Great Britain (GB) of the COVID-19 Symptom Study app, we estimated that, concurrent to the GB government sanctioning lockdown, COVID-19 was distributed across GB, with evidence of ’urban hotspots’. We found a geo-social gradient associated with predicted disease prevalence suggesting urban areas and areas of higher deprivation are most affected. Our results demonstrate use of self-reported symptoms data to provide focus on geographical areas with identified risk factors

Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app

Understanding the geographical distribution of COVID-19 through the general population is key to the provision of adequate healthcare services. Using self-reported data from 1 960 242 unique users in Great Britain (GB) of the COVID-19 Symptom Study app, we estimated that, concurrent to the GB government sanctioning lockdown, COVID-19 was distributed across GB, with evidence of ’urban hotspots’. We found a geo-social gradient associated with predicted disease prevalence suggesting urban areas and areas of higher deprivation are most affected. Our results demonstrate use of self-reported symptoms data to provide focus on geographical areas with identified risk factors

Leading change beyond your classroom – Capacity building in SoTL and leadership by SaMnet

The issue: The introduction of quality standards can place delegates to the ACSME conference in the forefront of reflection on, and changes to, teaching in their school, faculty, and university. How do you make the transition from being someone who experiments and implements strategies to teach more effectively into someone who leads colleagues in doing so? Furthermore, what support can you gain in that process, both support from within your institution, as you work to help others to satisfy externally imposed standards, as well as outside your university? Approach: Development of the capability of academic staff in science and mathematics to lead change is a chief aim of the ALTC/OLT project developing the Science and Mathematics network of Australian university educators – SaMnet. We have engaged more than 100 university staff (‘SaMnet Scholars’) across 19 institutions in teams of four to pursue action-learning projects. SaMnet are supporting these teams with guidance in the scholarship of teaching and learning and in providing insight into leading organisational change. SaMnet also provides mentoring and contact with other academics who share an interest in the area of a particular project, e.g., standards, inquiry learning, or new ICTs. The effort is meant to change an individual innovation in teaching into a formally recorded experiment. A well documented experiment can not only be published; it can provide data sufficient to convince colleagues, heads of school, deans, and others that the innovation addresses their key performance indicators, such as those that result from national quality standards. This approach builds on an uneven foundation: (1) the growth in SoTL in universities; (2) a history of initiatives to improve teaching in science and mathematics that is recognised has having left little widespread impact; and (3) literature on the nature of change in organisations, in general, and on change in universities, in particular. The first set of SaMnet’s action-learning projects is approaching a mid-point, and the projects have another year to run. The ACSME conference provides an opportune moment to reflect and gain additional perspectives on the strategies being pursued. Progress: Work in progress - initial. Key questions: 1. How might insights into organisational change be relevant to your efforts in your university, particularly in relation to coming standards? 2. What do you feel that you could learn from SaMnet Scholars at this session about tailoring your efforts to gain support from your head of school and dean? 3. What are we missing? What could a national network of science and mathematics academics do for you that we might not have thought of? 4. What aspects of leading change would you like to learn about – steps and transitions in change processes (Kotter and Bridges), aspects of a new idea that spur adoption (Rogers), or surface issues and underlying issues that need to be addressed (Wilber)

Attributes and predictors of Long-COVID: analysis of COVID cases and their symptoms collected by the Covid Symptoms Study App

Reports of “Long-COVID”, are rising but little is known about prevalence, risk factors, or whether it is possible to predict a protracted course early in the disease. We analysed data from 4182 incident cases of COVID-19 who logged their symptoms prospectively in the COVID Symptom Study app. 558 (13.3%) had symptoms lasting >=28 days, 189 (4.5%) for >=8 weeks and 95 (2.3%) for >=12 weeks. Long-COVID was characterised by symptoms of fatigue, headache, dyspnoea and anosmia and was more likely with increasing age, BMI and female sex. Experiencing more than five symptoms during the first week of illness was associated with Long-COVID, OR=3.53 [2.76;4.50]. A simple model to distinguish between short and long-COVID at 7 days, which gained a ROC-AUC of 76%, was replicated in an independent sample of 2472 antibody positive individuals. This model could be used to identify individuals for clinical trials to reduce long-term symptoms and target education and rehabilitation services

Attributes and predictors of long COVID

Reports of long-lasting coronavirus disease 2019 (COVID-19) symptoms, the so-called ‘long COVID’, are rising but little is known about prevalence, risk factors or whether it is possible to predict a protracted course early in the disease. We analyzed data from 4,182 incident cases of COVID-19 in which individuals self-reported their symptoms prospectively in the COVID Symptom Study app. A total of 558 (13.3%) participants reported symptoms lasting ≥28 days, 189 (4.5%) for ≥8 weeks and 95 (2.3%) for ≥12 weeks. Long COVID was characterized by symptoms of fatigue, headache, dyspnea and anosmia and was more likely with increasing age and body mass index and female sex. Experiencing more than five symptoms during the first week of illness was associated with long COVID (odds ratio = 3.53 (2.76–4.50)). A simple model to distinguish between short COVID and long COVID at 7 days (total sample size, n = 2,149) showed an area under the curve of the receiver operating characteristic curve of 76%, with replication in an independent sample of 2,472 individuals who were positive for severe acute respiratory syndrome coronavirus 2. This model could be used to identify individuals at risk of long COVID for trials of prevention or treatment and to plan education and rehabilitation services

Changes in symptomatology, reinfection, and transmissibility associated with the SARS-CoV-2 variant B.1.1.7: an ecological study

Background The SARS-CoV-2 variant B.1.1.7 was first identified in December, 2020, in England. We aimed to investigate whether increases in the proportion of infections with this variant are associated with differences in symptoms or disease course, reinfection rates, or transmissibility. Methods We did an ecological study to examine the association between the regional proportion of infections with the SARS-CoV-2 B.1.1.7 variant and reported symptoms, disease course, rates of reinfection, and transmissibility. Data on types and duration of symptoms were obtained from longitudinal reports from users of the COVID Symptom Study app who reported a positive test for COVID-19 between Sept 28 and Dec 27, 2020 (during which the prevalence of B.1.1.7 increased most notably in parts of the UK). From this dataset, we also estimated the frequency of possible reinfection, defined as the presence of two reported positive tests separated by more than 90 days with a period of reporting no symptoms for more than 7 days before the second positive test. The proportion of SARS-CoV-2 infections with the B.1.1.7 variant across the UK was estimated with use of genomic data from the COVID-19 Genomics UK Consortium and data from Public Health England on spike-gene target failure (a non-specific indicator of the B.1.1.7 variant) in community cases in England. We used linear regression to examine the association between reported symptoms and proportion of B.1.1.7. We assessed the Spearman correlation between the proportion of B.1.1.7 cases and number of reinfections over time, and between the number of positive tests and reinfections. We estimated incidence for B.1.1.7 and previous variants, and compared the effective reproduction number, Rt, for the two incidence estimates. Findings From Sept 28 to Dec 27, 2020, positive COVID-19 tests were reported by 36 920 COVID Symptom Study app users whose region was known and who reported as healthy on app sign-up. We found no changes in reported symptoms or disease duration associated with B.1.1.7. For the same period, possible reinfections were identified in 249 (0·7% [95% CI 0·6–0·8]) of 36 509 app users who reported a positive swab test before Oct 1, 2020, but there was no evidence that the frequency of reinfections was higher for the B.1.1.7 variant than for pre-existing variants. Reinfection occurrences were more positively correlated with the overall regional rise in cases (Spearman correlation 0·56–0·69 for South East, London, and East of England) than with the regional increase in the proportion of infections with the B.1.1.7 variant (Spearman correlation 0·38–0·56 in the same regions), suggesting B.1.1.7 does not substantially alter the risk of reinfection. We found a multiplicative increase in the Rt of B.1.1.7 by a factor of 1·35 (95% CI 1·02–1·69) relative to pre-existing variants. However, Rt fell below 1 during regional and national lockdowns, even in regions with high proportions of infections with the B.1.1.7 variant. Interpretation The lack of change in symptoms identified in this study indicates that existing testing and surveillance infrastructure do not need to change specifically for the B.1.1.7 variant. In addition, given that there was no apparent increase in the reinfection rate, vaccines are likely to remain effective against the B.1.1.7 variant. Funding Zoe Global, Department of Health (UK), Wellcome Trust, Engineering and Physical Sciences Research Council (UK), National Institute for Health Research (UK), Medical Research Council (UK), Alzheimer's Society

Factors influencing students’ perceptions of their quantitative skills

There is international agreement that quantitative skills (QS) are an essential graduate competence in science. QS refer to the application of mathematical and statistical thinking and reasoning in science. This study reports on the use of the Science Students Skills Inventory to capture final year science students' perceptions of their QS across multiple indicators, at two Australian research-intensive universities. Statistical analysis reveals several variables predicting higher levels of self-rated competence in QS: students' grade point average, students' perceptions of inclusion of QS in the science degree programme, their confidence in QS, and their belief that QS will be useful in the future. The findings are discussed in terms of implications for designing science curricula more effectively to build students' QS throughout science degree programmes. Suggestions for further research are offered

Perceptions of science graduating students on their learning gains

In this study, the Science Student Skills Inventory was used to gain understanding of student perceptions about their science skills set developed throughout their programme (scientific content knowledge, communication, scientific writing, teamwork, quantitative skills, and ethical thinking). The study involved 400 responses from undergraduate science students about to graduate from two Australian research-intensive institutions. For each skill, students rated on a four-point Likert scale their perception of the importance of developing the skill within the programme, how much they improved it throughout their undergraduate science programme, how much they saw the skill included in the programme, how confident they were about the skill, and how much they will use the skill in the future. Descriptive statistics indicate that overall, student perception of importance of these skills was greater than perceptions of improvement, inclusion in the programme, confidence, and future use. Quantitative skills and ethical thinking were perceived by more students to be less important. t-Test analyses revealed some differences in perception across different demographic groups (gender, age, graduate plans, and research experience). Most notably, gender showed significant differences across most skills. Implications for curriculum development are discussed, and lines for further research are given