Learn More
Until now oligonephropathy to indicate "too few nephrons" has been associated with intrauterine growth restriction and experimentally induced abnormalities of renal development. The purpose of this study was to determine whether there is evidence of abnormal postnatal glomerulogenesis in extremely low birth weight preterm infants. Renal autopsy tissue was(More)
Children born with very low birth weight have a decreased nephron number. Low nephron mass is associated with adult hypertension, proteinuria, and diabetes mellitus. The histomorphometry and radial glomerular count (RGC) of a total nephrectomy from a child with renal disease associated with extreme prematurity was compared with the kidney from a full-term(More)
Computer modelling of the heart has emerged over the past decade as a powerful technique to explore the cardiovascular pathophysiology and inform clinical diagnosis. The current state-of-the-art in biophysical modelling requires a wealth of, potentially invasive, clinical data for the parametrisation and validation of the models, a process that is still too(More)
Non intrusive monitoring of animals in the wild is possible using camera trapping framework, which uses cameras triggered by sensors to take a burst of images of animals in their habitat. However camera trapping framework produces a high volume of data (in the order on thousands or millions of images), which must be analyzed by a human expert. In this work,(More)
1 A fructose-enriched diet induces hypertension, metabolic alterations and insulin resistance in rats, resembling human metabolic syndrome. Previously, we found that prostanoid production was altered in fructose-fed rats. 2 This study analysed the effects of incubation with noradrenaline (NA) and angiotensin II (Ang II) on prostanoid release in mesenteric(More)
The segmentation and classification of animals from camera-trap images is due to the conditions under which the images are taken, a difficult task. This work presents a method for classifying and segmenting mammal genera from camera-trap images. Our method uses Multi-Layer Robust Principal Component Analysis (RPCA) for segmenting, Convolutional Neural(More)
Camera trapping is a technique to study wildlife using automatic triggered cameras. However, camera trapping collects a lot of false positives (images without animals), which must be segmented before the classification step. This paper presents a Multi-Layer Robust Principal Component Analysis (RPCA) for camera-trap images segmentation. Our Multi-Layer RPCA(More)