Asimilaciones y rechazos: presencias del romanticismo en el realismo español del siglo XIX

Contains fulltext : 142936.pdf (publisher's version ) (Closed access)134 p

Automated Segmentation of Abdominal Aortic Aneurysms in Multi-spectral MR Images

An automated method for segmenting the outer boundary of abdominal aortic aneurysms in MR images is presented. The method is based on the well known Active Shape Models (ASM), which fit a global landmark-based shape model on the basis of local boundary appearance models

Adapting Active Shape Models for 3D Segmentation of Tubular Structures in Medical Images

Active Shape Models (ASM) have proven to be an effective approach for image segmentation. In some applications, however, the linear model of gray level appearance around a contour that is used in ASM is not sufficient for accurate boundary localization. Furthermore, the statistical shape model may be too restricted if the training set is limited

Structural basis for integration of GluD receptors within synaptic organizer complexes

Ionotropic glutamate receptor (iGluR) family members are integrated into supramolecular complexes that modulate their location and function at excitatory synapses. However, a lack of structural information beyond isolated receptors or fragments thereof currently limits the mechanistic understanding of physiological iGluR signaling. Here, we report structural and functional analyses of the prototypical molecular bridge linking postsynaptic iGluR δ2 (GluD2) and presynaptic β-neurexin 1 (β-NRX1) via Cbln1, a C1q-like synaptic organizer. We show how Cbln1 hexamers "anchor" GluD2 amino-terminal domain dimers to monomeric β-NRX1. This arrangement promotes synaptogenesis and is essential for D: -serine-dependent GluD2 signaling in vivo, which underlies long-term depression of cerebellar parallel fiber-Purkinje cell (PF-PC) synapses and motor coordination in developing mice. These results lead to a model where protein and small-molecule ligands synergistically control synaptic iGluR function

Automated Selection of Trabecular Bone Regions in Knee Radiographs

Osteoarthritic (OA) changes in knee joints can be assessed by analyzing the structure of trabecular bone (TB) in the tibia. This analysis is performed on TB regions selected manually by a human operator on x-ray images. Manual selection is time-consuming, tedious, and expensive. Even if a radiologist expert or highly trained person is available to select regions, high inter- and intraobserver variabilities are still possible. A fully automated image segmentation method was, therefore, developed to select the bone regions for numerical analyses of changes in bone structures. The newly developed method consists of image preprocessing, delineation of cortical bone plates (active shape model), and location of regions of interest (ROI). The method was trained on an independent set of 40 x-ray images. Automatically selected regions were compared to the "gold standard" that contains ROIs selected manually by a radiologist expert on 132 x-ray images. All images were acquired from subjects locked in a standardized standing position using a radiography rig. The size of each ROI is 12.8×12.8 mm. The automated method results showed a good agreement with the gold standard [similarity index (SI) =0.83 (medial) and 0.81 (lateral) and the offset= [-1.78, 1.27] × [-0.65,0.26] mm (medial) and [-2.15, 1.59] × [-0.58, 0.52] mm (lateral)]. Bland and Altman plots were constructed for fractal signatures, and changes of fractal dimensions (FD) to region offsets calculated between the gold standard and automatically selected regions were calculated. The plots showed a random scatter and the 95% confidence intervals were (-0.006, 0.008) and (-0.001, 0.011). The changes of FDs to region offsets were less than 0.035. Previous studies showed that differences in FDs between non-OA and OA bone regions were greater than 0.05. ROIs were also selected by a second radiologist and then evaluated. Results indicated that the newly developed method could replace a human operator and produces bone regions with an accuracy that is sufficient for fractal analyses of bone texture