Nearshore Monitoring with X-Band Radar: Maximising Utility in Dynamic and Complex Environments

Coastal management and engineering applications require data that quantify the nature and magnitude of changes in nearshore bathymetry. However, bathymetric surveys are usually infrequent due to high costs and complex logistics. This study demonstrates that ground‐based X‐band radar offers a cost‐effective means to monitor nearshore changes at relatively high frequency and over large areas. A new data quality and processing framework was developed to reduce uncertainties in the estimates of radar‐derived bathymetry and tested using data from an 18‐month installation at Thorpeness (UK). In addition to data calibration and validation, two new elements are integrated to reduce the influence of data scatter and outliers: (a) an automated selection of periods of ‘good data’ and (b) the application of a depth‐memory stabilisation. For conditions when the wave height is >1 m, the accuracy of the radar‐derived depths is shown to be ±0.5 m (95% confidence interval) at 40x40 m spatial resolution. At Thorpeness, radar‐derived bathymetry changes exceeding this error were observed at timescales ranging from three weeks to six months. These data enabled quantification of changes in nearshore sediment volume at frequencies and spatial cover that would be difficult and/or expensive to obtain by other methods. It is shown that the volume of nearshore sediment movement occurring at timescale as short as few weeks are comparable with the annual longshore transport rates reported in this area. The use of radar can provide an early warning of changes in offshore bathymetry likely to impact vulnerable coastal locations

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Apoptosis of CD4+CD25high T Cells in Type 1 Diabetes May Be Partially Mediated by IL-2 Deprivation

from T1D subjects. in T1D may be caught up in a relatively deficient cytokine milieu. function in subjects predisposed to T1D