Skaarup Chapman (gendershrine8)
Auto-detection of cerebral aneurysms via convolutional neural network (CNN) is being increasingly reported. However, few studies to date have accurately predicted the risk, but not the diagnosis itself. We developed a multi-view CNN for the prediction of rupture risk involving small unruptured intracranial aneurysms (UIAs) based on three-dimensional (3D) digital subtraction angiography (DSA). The performance of a multi-view CNN-ResNet50 in accurately predicting the rupture risk (high vs. non-high) of UIAs in the anterior circulation measuring less than 7 mm in size was compared with various CNN architectures (AlexNet and VGG16), with similar type but different layers (ResNet101 and ResNet152), and single image-based CNN (single-view ResNet50). The sensitivity, specificity, and overall accuracy of risk prediction were estimated and compared according to CNN architecture. The study included 364 UIAs in training and 93 in test datasets. A multi-view CNN-ResNet50 exhibited a sensitivity of 81.82 (66.76-91.29)%, a specificity of 81.63 (67.50-90.76)%, and an overall accuracy of 81.72 (66.98-90.92)% for risk prediction. AlexNet, VGG16, ResNet101, ResNet152, and single-view CNN-ResNet50 showed similar specificity. However, the sensitivity and overall accuracy were decreased (AlexNet, 63.64% and 76.34%; VGG16, 68.18% and 74.19%; ResNet101, 68.18% and 73.12%; ResNet152, 54.55% and 72.04%; and single-view CNN-ResNet50, 50.00% and 64.52%) compared with multi-view CNN-ResNet50. Regarding F1 score, it was the highest in multi-view CNN-ResNet50 (80.90 (67.29-91.81)%). Our study suggests that multi-view CNN-ResNet50 may be feasible to assess the rupture risk in small-sized UIAs.Modern plant-breeding practices have narrowed the genetic base of wheat, such that there is a need to introduce new germplasms with underexploited diversity into breeding programs. Wheat landraces are a very valuable resource when searching for genetic variation, which not only possess increased adaptability, but also quality-related traits. Several studies have shown a wide genetic diversity in Spanish wheat landraces compared to other germplasm collections; therefore, the main objective of this study is to analyze the variability in a collection of 189 landraces from the Spanish National Plant Genetic Resources Centre (Centro de Recursos Fitogenéticos, CRF-INIA, Alcalá de Henares), in relation to end-use quality traits. We characterized the whole collection for high-molecular-weight glutenin and puroindoline allelic composition, and for gluten strength. In addition, grain protein content, grains per spike, and thousand kernel weight were evaluated in samples from four-year field trials. The relationship between glutenin composition and quality was evaluated, and some alleles strongly associated with high quality were identified in the collection, some of them specific for Iberian landraces. The results also show the presence of novel variability within high-molecular-weight glutenin and puroindolines, which needs to be characterized further in order to assess its influence on wheat quality. In addition, a set of landraces showing outstanding values for gluten quality and a good agronomic performance was selected for testing in field trials in order to evaluate the suitability of their direct use in cropping systems. The objective of our project was to identify a late recanalization predictor in ruptured intracranial aneurysms treated with coil embolization. This goal was achieved by means of a statistical analysis followed by a computational fluid dynamics (CFD) with porous media modelling approach. Porous media CFD simulated the hemodynamics within the aneurysmal dome after coiling. Firstly, a retrospective single center analysis of 66 aneurysmal subarachnoid hemorrhage patients was conducted. The authors assessed morphometric parameters, packing density, first coil volume packing density (1st VPD) and recanalization rate on digital subtraction a
