Abstract

Abstract
Assessment of retinal vessels is fundamental for the diagnosis of many disorders such as heart diseases, diabetes and hypertension. The imaging of retina using advanced fundus camera has become a standard in computer-assisted diagnosis of opthalmic disorders. Modern cameras produce high quality color digital images, but during the acquisition process the light reflected by the retinal surface generates a luminosity and contrast variation. Irregular illumination can introduce severe distortions in the resulting images, decreasing the visibility of anatomical structures and consequently demoting the performance of the automated segmentation of these structures. In this paper, a novel approach for illumination correction of color fundus images is proposed and applied as preprocessing step for retinal vessel segmentation. Our method builds on the connection between two different phenomena, shadows and haze, and works by removing the haze from the image in the inverted intensity domain. This is shown to be equivalent to correct the nonuniform illumination in the original intensity domain. We tested the proposed method as preprocessing stage of two vessel segmentation methods, one unsupervised based on mathematical morphology, and one supervised based on deep learning Convolutional Neural Networks (CNN). Experiments were performed on the publicly available retinal image database DRIVE. Statistically significantly better vessel segmentation performance was achieved in both test cases when illumination correction was applied.

Abstract
Peripheral nerve injury and regeneration is a challenging scientific field with relevant clinical implications. Most peripheral nerve regeneration studies have been mainly carried out on rodents. However, it is important to note that the validity of the rodent as a model to study nerve injury and regeneration and translate these results into clinical practice has been questioned by several researchers. To overcome this problem, some investigators have used companion animals and large animal species as models for experimental peripheral nerve regeneration studies. Live sheep are often used in biomedical research because of availability, simplicity of care and housing, cost and body weight similar to humans and acceptance by society as a research animal. Despite these advantages, studies on nerve regeneration and repair in sheep have only been undertaken a few decades ago and compared to rat and mice experimental studies, there are much fewer investigations. The authors have compiled and sorted the available literature on experimental ovine nerve studies in order to guide the peripheral nerve investigator in choosing clinically relevant and interpretable models for studies on neural regeneration that are much needed in order to make progress towards new surgical and medical treatment of peripheral nerves.

Abstract
Museums promote cultural experiences through the exhibits and the stories behind them. Nevertheless, museums are not always designed to engage and interest young audiences, particularly teenagers. This Ph.D. proposal in Digital Media explores how digital technologies can facilitate Natural History and Science Museums in fostering and creating immersive museum experiences for teenagers. Especially by using digital storytelling along with location-based gaming. The overall objectives of the work are to establish guidelines, design, develop and study interactive storytelling and gamification experiences in those type of museums focusing in particular on delivering pleasurable and engaging experiences for teens of 15-17 years old.

Abstract
This paper presents a novel multi-stage stochastic distributed generation investment planning model for making investment decisions under uncertainty. The problem, formulated from a coordinated system planning viewpoint, simultaneously minimizes the net present value of costs rated to losses, emission, operation, and maintenance, as well as the cost of unserved energy. The formulation is anchored on a two-period planning horizon, each having multiple stages. The first period is a short-term horizon in which robust decisions are pursued in the face of uncertainty; whereas, the second one spans over a medium to long-term horizon involving exploratory and/or flexible investment decisions. The operational variability and uncertainty introduced by intermittent generation sources, electricity demand, emission prices, demand growth, and others are accounted for via probabilistic and stochastic methods, respectively. Metrics such as cost of ignoring uncertainty and value of perfect information are used to clearly demonstrate the benefits of the proposed stochastic model. A real-life distribution network system is used as a case study and the results show the effectiveness of the proposed model.

Abstract
AbstractDiscovery of cancer drivers has traditionally focused on the identification of protein-coding genes. Here we present a comprehensive analysis of putative cancer driver mutations in both protein-coding and non-coding genomic regions across >2,500 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We developed a statistically rigorous strategy for combining significance levels from multiple driver discovery methods and demonstrate that the integrated results overcome limitations of individual methods. We combined this strategy with careful filtering and applied it to protein-coding genes, promoters, untranslated regions (UTRs), distal enhancers and non-coding RNAs. These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types. Novel recurrent events were found in the promoters or 5’UTRs ofTP53, RFTN1, RNF34,andMTG2,in the 3’UTRs ofNFKBIZandTOB1,and in the non-coding RNARMRP.We provide evidence that the previously reported non-coding RNAsNEAT1andMALAT1may be subject to a localized mutational process. Perhaps the most striking finding is the relative paucity of point mutations driving cancer in non-coding genes and regulatory elements. Though we have limited power to discover infrequent non-coding drivers in individual cohorts, combined analysis of promoters of known cancer genes show little excess of mutations beyondTERT.