E-learning: you don't always get what you hope for

Despite substantial growth in the use of information and communication technologies (ICT) throughout western societies, there is much evidence of technology-led innovations within Higher Education (HE) failing to achieve the anticipated transformations in learning and teaching. This paper reviews evidence from research and evaluation studies relating not only to e-learning, but also to wider HE practices. It argues that the use of ICT does not, in itself, result in improved educational outcomes and ways of working. It considers contextual factors that are of greater significance in determining how and why e-learning is used in HE. Students' engagement with e-learning relates to their expectations and conceptions of learning and to assessment demands. Academics need to re-assess their own beliefs and practices concerning teaching and assessment and their impact on the experience of learners. Both teachers and learners need to understand why e-learning activities are to be undertaken and the rewards expected to be derived

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

The temporal pattern of immune and inflammatory proteins prior to a recurrent coronary event in post-acute coronary syndrome patients

Purpose: We assessed the temporal pattern of 29 immune and inflammatory proteins in post-acute coronary syndrome (ACS) patients, prior to the development of recurrent ACS. Methods: High-frequency blood sampling was performed in 844 patients admitted for ACS during one-year follow-up. We conducted a case-control study on the 45 patients who experienced reACS (cases) and two matched event-free patients (controls) per case. Olink Proteomics’ immunoassay was used to obtain serum levels of the 29 proteins, expressed in an arbitrary unit on the log2-scale (Normalized Protein eXpression, NPX). Linear mixed-effects models were applied to examine the temporal pattern of the proteins, and to illustrate differences between cases and controls. Results: Mean age was 66 ± 12 years and 80% were men. Cases and controls had similar baseline clinical characteristics. During the first 30 days, and after multiple testing correction, cases had significantly higher serum levels of CXCL1 (difference of 1.00 NPX, p ¼ 0.002), CD84 (difference of 0.64 NPX, p ¼ 0.002) and TNFRSF10A (difference of 0.41 NPX, p < 0.001) than controls. After 30 days, serum levels of all 29 proteins were similar in cases and controls. In particular, no increase was observed prior to reACS. Conclusions: Among 29 immune and inflammatory proteins, CXCL1, CD84 and TNFRSF10A were associated with early reACS after initial ACS-admission

Technical summary

Human interference with the climate system is occurring. Climate change poses risks for human and natural systems. The assessment of impacts, adaptation, and vulnerability in the Working Group II contribution to the IPCC's Fifth Assessment Report (WGII AR5) evaluates how patterns of risks and potential benefits are shifting due to climate change and how risks can be reduced through mitigation and adaptation. It recognizes that risks of climate change will vary across regions and populations, through space and time, dependent on myriad factors including the extent of mitigation and adaptation

The temporal pattern of immune and inflammatory proteins prior to a recurrent coronary event in post-acute coronary syndrome patients

Purpose: We assessed the temporal pattern of 29 immune and inflammatory proteins in post-acute coronary syndrome (ACS) patients, prior to the development of recurrent ACS. Methods: High-frequency blood sampling was performed in 844 patients admitted for ACS during one-year follow-up. We conducted a case-control study on the 45 patients who experienced reACS (cases) and two matched event-free patients (controls) per case. Olink Proteomics’ immunoassay was used to obtain serum levels of the 29 proteins, expressed in an arbitrary unit on the log2-scale (Normalized Protein eXpression, NPX). Linear mixed-effects models were applied to examine the temporal pattern of the proteins, and to illustrate differences between cases and controls. Results: Mean age was 66 ± 12 years and 80% were men. Cases and controls had similar baseline clinical characteristics. During the first 30 days, and after multiple testing correction, cases had significantly higher serum levels of CXCL1 (difference of 1.00 NPX, p = 0.002), CD84 (difference of 0.64 NPX, p = 0.002) and TNFRSF10A (difference of 0.41 NPX, p < 0.001) than controls. After 30 days, serum levels of all 29 proteins were similar in cases and controls. In particular, no increase was observed prior to reACS. Conclusions: Among 29 immune and inflammatory proteins, CXCL1, CD84 and TNFRSF10A were associated with early reACS after initial ACS-admission

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

Association of Brain Age, Lesion Volume, and Functional Outcome in Patients With Stroke

BACKGROUND AND OBJECTIVES: Functional outcomes after stroke are strongly related to focal injury measures. However, the role of global brain health is less clear. In this study, we examined the impact of brain age, a measure of neurobiological aging derived from whole-brain structural neuroimaging, on poststroke outcomes, with a focus on sensorimotor performance. We hypothesized that more lesion damage would result in older brain age, which would in turn be associated with poorer outcomes. Related, we expected that brain age would mediate the relationship between lesion damage and outcomes. Finally, we hypothesized that structural brain resilience, which we define in the context of stroke as younger brain age given matched lesion damage, would differentiate people with good vs poor outcomes. METHODS: We conducted a cross-sectional observational study using a multisite dataset of 3-dimensional brain structural MRIs and clinical measures from the ENIGMA Stroke Recovery. Brain age was calculated from 77 neuroanatomical features using a ridge regression model trained and validated on 4,314 healthy controls. We performed a 3-step mediation analysis with robust mixed-effects linear regression models to examine relationships between brain age, lesion damage, and stroke outcomes. We used propensity score matching and logistic regression to examine whether brain resilience predicts good vs poor outcomes in patients with matched lesion damage. RESULTS: We examined 963 patients across 38 cohorts. Greater lesion damage was associated with older brain age (β = 0.21; 95% CI 0.04-0.38, DISCUSSION: We provide evidence that younger brain age is associated with superior poststroke outcomes and modifies the impact of focal damage. The inclusion of imaging-based assessments of brain age and brain resilience may improve the prediction of poststroke outcomes compared with focal injury measures alone, opening new possibilities for potential therapeutic targets