Abstract
Recent advances in the cost-efficiency of sequencing technologies enabled the combined DNA-and RNA-sequencing of human individuals at the population-scale, making genome-wide investigations of the inter-individual genetic impact on gene expression viable. Employing mRNA-sequencing data from the Geuvadis Project and genome sequencing data from the 1000 Genomes Project we show that the computational analysis of DNA sequences around splice sites and poly-A signals is able to explain several observations in the phenotype data. In contrast to widespread assessments of statistically significant associations between DNA polymorphisms and quantitative traits, we developed a computational tool to pinpoint the molecular mechanisms by which genetic markers drive variation in RNA-processing, cataloguing and classifying alleles that change the affinity of core RNA elements to their recognizing factors. The in silico models we employ further suggest RNA editing can moonlight as a splicing-modulator, albeit less frequently than genomic sequence diversity. Beyond existing annotations, we demonstrate that the ultra-high resolution of RNA-Seq combined from 462 individuals also provides evidence for thousands of bona fide novel elements of RNA processing-alternative splice sites, introns, and cleavage sites-which are often rare and lowly expressed but in other characteristics similar to their annotated counterparts.

Abstract
Firefighting is a hazardous occupation. Firefighters are commonly subjected to high levels of stress and fatigue that could interfere with their ability to take decisions under pressure. Based on this assumption, a laboratory protocol was conducted among 10 Portuguese firefighters in order to address their stress and mental fatigue levels and its impact on cognitive performance. A 2-Choice Reaction Time Task was used to measure cognitive performance (before and after a stress condition) and a psychosocial stress paradigm was used to assess stress. Visual Analogue Scales were also used to measure perceived stress and mental fatigue. Results suggested that firefighters felt more fatigued at the end of the protocol, however their performance only decreased in the first reaction time task. Furthermore, findings suggested that stress improved firefighters reaction times, however when stress levels increased, performance decreased. This suggests the need to better understand firefighters optimal stress levels and fatigue, in order to promote their health and work safety, by improving their performance under demanding situations.

Abstract

Abstract
Optical immersion clearing is a technique that has been widely studied for more than two decades and that is used to originate a temporary transparency effect in biological tissues. If applied in cooperation with clinical methods it provides optimization of diagnosis and treatment procedures. This technique turns biological tissues more transparent through two main mechanisms - tissue dehydration and refractive index (RI) matching between tissue components. Such matching is obtained by partial replacement of interstitial water by a biocompatible agent that presents higher RI and it can be completely reversible by natural rehydration in vivo or by assisted rehydration in ex vivo tissues. Experimental data to characterize and discriminate between the two mechanisms and to find new ones are necessary. Using a simple method, based on collimated transmittance and thickness measurements made from muscle samples under treatment, we have estimated the diffusion properties of glucose, ethylene glycol (EG) and water that were used to perform such characterization and discrimination. Comparing these properties with data from literature that characterize their diffusion in water we have observed that muscle cell membrane permeability limits agent and water diffusion in the muscle. The same experimental data has allowed to calculate the optical clearing (OC) efficiency and make an interpretation of the internal changes that occurred in muscle during the treatments. The same methodology can now be used to perform similar studies with other agents and in other tissues in order to solve engineering problems at design of inexpensive and robust technologies for a considerable improvement of optical tomographic techniques with better contrast and in-depth imaging.

Abstract
A great variety of human gesture recognition methods exist in the literature, yet there is still a lack of solutions to encompass some of the challenges imposed by real life scenarios. In this document, a gesture recognition for robotic search and rescue missions in the high seas is presented. Themethod aims to identify shipwrecked people by recognizing the hand waving gesture sign. We introduce a novelmotion descriptor, through which high recognition accuracy can be achieved even for low resolution images. The method can be simultaneously applied to rigid object characterization, hence object and gesture recognition can be performed simultaneously. The descriptor has a simple implementation and is invariant to scale and gesture speed. Tests, preformed on a maritime dataset of thermal images, proved the descriptor ability to reach a meaningful representation for very low resolution objects. Recognition rates with 96.3% of accuracy were achieved.

Abstract
Iris liveness detection methods have been developed to overcome the vulnerability of iris biometric systems to spoofing attacks. In the literature, it is typically assumed that a known attack modality will be perpetrated. Then liveness models are designed using labelled samples from both real/live and fake/spoof distributions, the latter derived from the assumed attack modality. In this work it is argued that a comprehensive modelling of the spoof samples is not possible in a real-world scenario where the attack modality cannot be known with a high degree of certainty. In fact making this assumption will render the liveness detection system more vulnerable to attacks that were not included in the original training. To provide a more realistic evaluation, this work proposes: a) testing the binary models with unknown spoof samples that were not present in the training step; b) the use of a single-class classification designing the classifier by modelling only the distribution of live samples. The results obtained support the assertion that many evaluation methods from the literature are misleading and may lead to optimistic estimates of the robustness of liveness detection in practical use cases.