The molybdenum isotopic composition in river water: Constraints from small catchments

We report molybdenum isotope compositions and concentrations in water samples from a variety of river catchment profiles in order to investigate the influence of anthropogenic contamination, catchment geology, within-river precipitation, and seasonal river flow variations on riverine molybdenum. Our results show that the observed variations in δ98/95Mo from 0‰ to 1.9‰ are primarily controlled by catchment lithology, particularly by weathering of sulfates and sulfides. Erosion in catchments dominated by wet-based glaciers leads to very high dissolved molybdenum concentrations. In contrast, anthropogenic inputs affect neither the concentration nor the isotopic composition of dissolved molybdenum in the rivers studied here. Seasonal variations are also quite muted. The finding that catchment geology exerts the primary control on the delivery of molybdenum to seawater indicates that the flux and isotope composition of molybdenum to seawater has likely varied in the geologic past.

Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.