DNA Barcoding as a tool for Zoological Taxonomy: Identification of bony fish in the Mediterranean Sea

The description of all the species present in nature is a vast task to be fulfilled by using the classical approach of morphological description of the organisms. In recent years, the traditional taxonomy, based primarily on identification keys of species, has shown a number of limitations in the use of the distinctive features in many animal taxa and inconsistencies with the genetic data. Furthermore, the increasing need to get a true estimate of biodiversity has led Zoological Taxonomy to seek new approaches and methodologies to support the traditional methods. The classification procedure has added modern criteriasuch as the evolutionary relationships and the genetic, biochemical and morphological characteristics of the organisms.Until now the Linnean binomial was the only abbreviated code associated with the description of the morphology of a species. The new technologies aim to achieve a short nucleotide sequence of the DNA to be used as an unique and solely label for a particular species, a specific genetic barcode. For both morphological and genetic approaches, skills and experience are required. Taxonomy is one of zoological disciplines that has been benefited from the achievements reached by modern molecular biotechnology. Using a molecular approach it is possible to identify cryptic species, to establish a family relationship between species and their membership of taxonomic categories or to reconstruct the evolutionary history of a taxon.

Combining Leaf Shape and Texture for Costa Rican Plant Species Identification

In the last decade, research in Computer Vision has developed several algorithms to help botanists and non-experts to classify plants based on images of their leaves. LeafSnap is a mobile application that uses a multiscale curvature model of the leaf margin to classify leaf images into species. It has achieved high levels of accuracy on 184 tree species from Northeast US. We extend the research that led to the development of LeafSnap along two lines. First, LeafSnap’s underlying algorithms are applied to a set of 66 tree species from Costa Rica. Then, texture is used as an additional criterion to measure the level of improvement achieved in the automatic identification of Costa Rica tree species. A 25.6% improvement was achieved for a Costa Rican clean image dataset and 42.5% for a Costa Rican noisy image dataset. In both cases, our results show this increment as statistically significant. Further statistical analysis of visual noise impact, best algorithm combinations per species, and best value of , the minimal cardinality of the set of candidate species that the tested algorithms render as best matches is also presented in this research

Pseudoneglect and visual attention networks

Pseudoneglect represents the tendency for healthy individuals to show a slight but consistent bias in favour of stimuli appearing in the left visual field. The bias is often measured using variants of the line bisection task. An accurate model of the functional architecture of the visuospatial attention system must account for this widely observed phenomenon, as well as for modulation of the direction and magnitude of the bias within individuals by a variety of factors relating to the state of the participant and/or stimulus characteristics. To date, the neural correlates of pseudoneglect remain relatively unmapped. In the current thesis, I employed a combination of psychophysical measurements, electroencephalography (EEG) recording and transcranial direct current stimulation (tDCS) in an attempt to probe the neural generator(s) of pseudoneglect. In particular, I wished to utilise and investigate some of the factors known to modulate the bias (including age, time-on-task and the length of the to-be-bisected line) in order to identify neural processes and activity that are necessary and sufficient for the lateralized bias to arise. Across four experiments utilising a computerized version of a perceptual line bisection task, pseudoneglect was consistently observed at baseline in healthy young participants. However, decreased line length (experiments 1, 2 and 3), time-on-task (experiment 1) and healthy aging (experiment 3) were all found to modulate the bias. Specifically, all three modulations induced a rightward shift in subjective midpoint estimation. Additionally, the line length and time-on-task effects (experiment 1) and the line length and aging effects (experiment 3) were found to have additive relationships. In experiment 2, EEG measurements revealed the line length effect to be reflected in neural activity 100 – 200ms post-stimulus onset over source estimated posterior regions of the right hemisphere (RH: temporo-parietal junction (TPJ)). Long lines induced a hemispheric asymmetry in processing (in favour of the RH) during this period that was absent in short lines. In experiment 4, bi-parietal tDCS (Left Anodal/Right Cathodal) induced a polarity-specific rightward shift in bias, highlighting the crucial role played by parietal cortex in the genesis of pseudoneglect. The opposite polarity (Left Cathodal/Right Anodal) did not induce a change in bias. The combined results from the four experiments of the current thesis provide converging evidence as to the crucial role played by the RH in the genesis of pseudoneglect and in the processing of visual input more generally. The reduction in pseudoneglect with decreased line length, increased time-on-task and healthy aging may be explained by a reduction in RH function, and hence contribution to task processing, induced by each of these modulations. I discuss how behavioural and neuroimaging studies of pseudoneglect (and its various modulators) can provide empirical data upon which accurate formal models of visuospatial attention networks may be based and further tested.

RICH AND EFFICIENT VISUAL DATA REPRESENTATION

Increasing the size of training data in many computer vision tasks has shown to be very effective. Using large scale image datasets (e.g. ImageNet) with simple learning techniques (e.g. linear classifiers) one can achieve state-of-the-art performance in object recognition compared to sophisticated learning techniques on smaller image sets. Semantic search on visual data has become very popular. There are billions of images on the internet and the number is increasing every day. Dealing with large scale image sets is intense per se. They take a significant amount of memory that makes it impossible to process the images with complex algorithms on single CPU machines. Finding an efficient image representation can be a key to attack this problem. A representation being efficient is not enough for image understanding. It should be comprehensive and rich in carrying semantic information. In this proposal we develop an approach to computing binary codes that provide a rich and efficient image representation. We demonstrate several tasks in which binary features can be very effective. We show how binary features can speed up large scale image classification. We present learning techniques to learn the binary features from supervised image set (With different types of semantic supervision; class labels, textual descriptions). We propose several problems that are very important in finding and using efficient image representation.

Task-based approach used on surfaces sampling strategy definition: the case of antineoplastic occupational exposure

Task-based approach implicates identifying all the tasks developed in each workplace aiming to refine the exposure characterization. The starting point of this approach is the recognition that only through a more detailed and comprehensive understanding of tasks is possible to understand, in more detail, the exposure scenario. In addition allows also the most suitable risk management measures identification. This approach can be also used when there is a need of identifying the workplace surfaces for sampling chemicals that have the dermal exposure route as the most important. In this case is possible to identify, through detail observation of tasks performance, the surfaces that involves higher contact (frequency) by the workers and can be contaminated. Identify the surfaces to sample when performing occupational exposure assessment to antineoplasic agents. Surfaces selection done based on the task-based approach.

Machine for visual inspection

The project was made during the Erasmus+ Program in Instituto Superior de Engenharia do Porto, Portugal. I had a pleasure to do this in Gislotica Mechanical Solution, Lda. This document presents a process of design a vertical inspection station for truck tires. The first part contains an introduction. There are information about Gislotica Company and also first analysis of problem. In next part is presented way to figured out the task and described all issues connected with designed machine. In last part were made some conclusions about problems and results. There is a place not only for sum up design process but also my develop during the project. I repeatedly pointed out which issues were new for me. A lot of times I focus on myself and gained experience and information about design process.

Identification and characterisation of telomere regulatory and signalling pathways after induction of telomere dysfunction

Telomeres are DNA-protein complexes which cap the ends of eukaryotic linear chromosomes. In normal somatic cells telomeres shorten and become dysfunctional during ageing due to the DNA end replication problem. This leads to activation of signalling pathways that lead to cellular senescence and apoptosis. However, cancer cells typically bypass this barrier to immortalisation in order to proliferate indefinitely. Therefore enhancing our understanding of telomere dysfunction and pathways involved in regulation of the process is essential. However, the pathways involved are highly complex and involve interaction between a wide range of biological processes. Therefore understanding how telomerase dysfunction is regulated is a challenging task and requires a systems biology approach. In this study I have developed a novel methodology for visualisation and analysis of gene lists focusing on the network level rather than individual or small lists of genes. Application of this methodology to an expression data set and a gene methylation data set allowed me to enhance my understanding of the biology underlying a senescence inducing drug and the process of immortalisation respectively. I then used the methodology to compare the effect of genetic background on induction of telomere uncapping. Telomere uncapping was induced in HCT116 WT, p21-/- and p53-/- cells using a viral vector expressing a mutant variant of hTR, the telomerase RNA template. p21-/- cells showed enhanced sensitivity to telomere uncapping. Analysis of a candidate pathway, Mismatch Repair, revealed a role for the process in response to telomere uncapping and that induction of the pathway was p21 dependent. The methodology was then applied to analysis of the telomerase inhibitor GRN163L and synergistic effects of hypoglycaemia with this drug. HCT116 cells were resistant to GRN163L treatment. However, under hypoglycaemic conditions the dose required for ablation of telomerase activity was reduced significantly and telomere shortening was enhanced. Overall this new methodology has allowed our group and collaborators to identify new biology and improve our understanding of processes regulating telomere dysfunction.

A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.

Marqueurs électrophysiologiques des dysfonctions attentionnelles et de leur récupération suite à un traumatisme craniocérébral léger

L’objectif principal de cette thèse était d’obtenir, via l’électrophysiologie cognitive, des indices de fonctionnement post-traumatisme craniocérébral léger (TCCL) pour différents niveaux de traitement de l’information, soit l’attention sélective, les processus décisionnels visuoattentionnels et les processus associés à l’exécution d’une réponse volontaire. L’hypothèse centrale était que les mécanismes de production des lésions de même que la pathophysiologie caractérisant le TCCL engendrent des dysfonctions visuoattentionnelles, du moins pendant la période aiguë suivant le TCCL (i.e. entre 1 et 3 mois post-accident), telles que mesurées à l’aide d’un nouveau paradigme électrophysiologique conçu à cet effet. Cette thèse présente deux articles qui décrivent le travail effectué afin de rencontrer ces objectifs et ainsi vérifier les hypothèses émises. Le premier article présente la démarche réalisée afin de créer une nouvelle tâche d’attention visuospatiale permettant d’obtenir les indices électrophysiologiques (amplitude, latence) et comportementaux (temps de réaction) liés aux processus de traitement visuel et attentionnel précoce (P1, N1, N2-nogo, P2, Ptc) à l’attention visuelle sélective (N2pc, SPCN) et aux processus décisionnels (P3b, P3a) chez un groupe de participants sains (i.e. sans atteinte neurologique). Le deuxième article présente l’étude des effets persistants d’un TCCL sur les fonctions visuoattentionelles via l’obtention des indices électrophysiologiques ciblés (amplitude, latence) et de données comportementales (temps de réaction à la tâche et résultats aux tests neuropsychologiques) chez deux cohortes d’individus TCCL symptomatiques, l’une en phase subaigüe (3 premiers mois post-accident), l’autre en phase chronique (6 mois à 1 an post-accident), en comparaison à un groupe de participants témoins sains. Les résultats des articles présentés dans cette thèse montrent qu’il a été possible de créer une tâche simple qui permet d’étudier de façon rapide et peu coûteuse les différents niveaux de traitement de l’information impliqués dans le déploiement de l’attention visuospatiale. Par la suite, l’utilisation de cette tâche auprès d’individus atteints d’un TCCL testés en phase sub-aiguë ou en phase chronique a permis d’objectiver des profils d’atteintes et de récupération différentiels pour chacune des composantes étudiées. En effet, alors que les composantes associées au traitement précoce de l’information visuelle (P1, N1, N2) étaient intactes, certaines composantes attentionnelles (P2) et cognitivo-attentionnelles (P3a, P3b) étaient altérées, suggérant une dysfonction au niveau des dynamiques spatio-temporelles de l’attention, de l’orientation de l’attention et de la mémoire de travail, à court et/ou à long terme après le TCCL, ceci en présence de déficits neuropsychologiques en phase subaiguë surtout et d’une symptomatologie post-TCCL persistante. Cette thèse souligne l’importance de développer des outils diagnostics sensibles et exhaustifs permettant d’objectiver les divers processus et sous-processus cognitifs susceptible d’être atteints après un TCCL.

A busca visual da cor, como medida avaliadora do funcionamento do sistema visual: um estudo exploratório a propósito da objetividade da felicidade em crianças

Este estudo, procura explicar a modularidade da mente humana, como um conjunto de módulos, permitindo desta forma contribuir para o estudo das ciências cognitivas. Estes módulos da arquitetura mental, permitem que a nossa mente interprete a cor resultante do sistema visual e das longitudes de ondas do espetro eletromagnético refratado dos objetos. Tendo por base o estudo do sistema visual, as células sensíveis, designadas por fotorrecetores percorrem o nervo ótico até atingir o encéfalo, localizando-se aí o sistema percetivo, permitindo desta forma realizar o estudo sobre busca visual da cor, como medida avaliadora do funcionamento do sistema visual, um estudo exploratório a propósito da objetividade da felicidade em crianças, que visa explorar a busca visual disjuntiva da cor como medida objetiva do bom funcionamento mental, do bem-estar subjetivo, como construto da felicidade. A amostra foi constituída por um grupo de 49 crianças não institucionalizadas e por um grupo de 16 crianças institucionalizadas, de ambos os sexos. Para a concretização deste estudo, foi necessária a utilização de uma tarefa de busca visual disjuntiva, que utilizou as simetrias de cores pertencentes ao mesmo par oponente e cores pertencentes a diferentes pares oponentes. Os resultados sugerem que não há qualquer interferência da institucionalização no funcionamento mental, logo no bem-estar subjetivo nas crianças; ABSTRACT: This study seeks to explain the modularity of the human mind, as a set of modules, giving this way a contribution to the study of the cognitive sciences. These modules of the mental architecture, allow our mind to interpret the resulting color of the visual system and the wavelengths of the electromagnetic spectrum refracted from the objects. Based on the study of our visual system, sensitive cells known as photoreceptors, which run along the optic nerve to the encephalon, being the perceptive system located there, allowing in this way to carry out the study on visual search of colour, as an assessment measure of the functioning of the visual system, an exploratory study concerning the objectivity of happiness in children, which aims to explore the disjunctive visual search of color as an objective measure of good mental functioning, of subjective well-being, as a construct of happiness. The sample consisted of a group of 49 non institutionalized children and of a group of 16 institutionalized children from both sexes. For the implementation of this study it was necessary to use a disjunctive visual search task, which used the Symmetry of colours belonging to the same opponent pair, and colours belonging to different opponent pairs. The results suggest that there is no interference from the institutionalization in mental functioning, therefore in the children’s subjective well being.

Seeing it my way: a case of a selective deficit in inhibiting self-perspective

Little is known about the functional and neural architecture of social reasoning, one major obstacle being that we crucially lack the relevant tools to test potentially different social reasoning components. In the case of belief reasoning, previous studies tried to separate the processes involved in belief reasoning per se from those involved in the processing of the high incidental demands such as the working memory demands of typical belief tasks (e.g., Stone et al., 1998; Samson et al., 2004). In this study, we developed new belief tasks in order to disentangle, for the first time, two perspective taking components involved in belief reasoning: (1) the ability to inhibit one’s own perspective (self-perspective inhibition) and (2) the ability to infer someone else’s perspective as such (other-perspective taking). The two tasks had similar demands in other-perspective taking as they both required the participant to infer that a character has a false belief about an object’s location. However, the tasks varied in the self-perspective inhibition demands. In the task with the lowest self-perspective inhibition demands, at the time the participant had to infer the character’s false belief, he or she had no idea what the new object’s location was. In contrast, in the task with the highest self-perspective inhibition demands, at the time the participant had to infer the character’s false belief, he or she knew where the object was actually located (and this knowledge had thus to be inhibited). The two tasks were presented to a stroke patient, WBA, with right prefrontal and temporal damage. WBA performed well in the low-inhibition false belief task but showed striking difficulty in the task placing high self-perspective inhibition demands, showing a selective deficit in inhibiting self-perspective. WBA also made egocentric errors in other social and visual perspective taking tasks, indicating a difficulty with belief attribution extending to the attribution of emotions, desires and visual experiences to other people. The case of WBA, together with the recent report of three patients impaired in belief reasoning even when self-perspective inhibition demands were reduced (Samson et al., 2004), provide the first neuropsychological evidence that (a) the inhibition of one’s own point of view and (b) the ability to infer someone else’ s point of view, rely on distinct neural and functional processes.

A Visual Analytics Approach to Comparing Cohorts of Event Sequences

Sequences of timestamped events are currently being generated across nearly every domain of data analytics, from e-commerce web logging to electronic health records used by doctors and medical researchers. Every day, this data type is reviewed by humans who apply statistical tests, hoping to learn everything they can about how these processes work, why they break, and how they can be improved upon. To further uncover how these processes work the way they do, researchers often compare two groups, or cohorts, of event sequences to find the differences and similarities between outcomes and processes. With temporal event sequence data, this task is complex because of the variety of ways single events and sequences of events can differ between the two cohorts of records: the structure of the event sequences (e.g., event order, co-occurring events, or frequencies of events), the attributes about the events and records (e.g., gender of a patient), or metrics about the timestamps themselves (e.g., duration of an event). Running statistical tests to cover all these cases and determining which results are significant becomes cumbersome. Current visual analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. Visual analytics tools leverage humans' ability to easily see patterns and anomalies that they were not expecting, but is limited by uncertainty in findings. Statistical tools emphasize finding significant differences in the data, but often requires researchers have a concrete question and doesn't facilitate more general exploration of the data. Combining visual analytics tools with statistical methods leverages the benefits of both approaches for quicker and easier insight discovery. Integrating statistics into a visualization tool presents many challenges on the frontend (e.g., displaying the results of many different metrics concisely) and in the backend (e.g., scalability challenges with running various metrics on multi-dimensional data at once). I begin by exploring the problem of comparing cohorts of event sequences and understanding the questions that analysts commonly ask in this task. From there, I demonstrate that combining automated statistics with an interactive user interface amplifies the benefits of both types of tools, thereby enabling analysts to conduct quicker and easier data exploration, hypothesis generation, and insight discovery. The direct contributions of this dissertation are: (1) a taxonomy of metrics for comparing cohorts of temporal event sequences, (2) a statistical framework for exploratory data analysis with a method I refer to as high-volume hypothesis testing (HVHT), (3) a family of visualizations and guidelines for interaction techniques that are useful for understanding and parsing the results, and (4) a user study, five long-term case studies, and five short-term case studies which demonstrate the utility and impact of these methods in various domains: four in the medical domain, one in web log analysis, two in education, and one each in social networks, sports analytics, and security. My dissertation contributes an understanding of how cohorts of temporal event sequences are commonly compared and the difficulties associated with applying and parsing the results of these metrics. It also contributes a set of visualizations, algorithms, and design guidelines for balancing automated statistics with user-driven analysis to guide users to significant, distinguishing features between cohorts. This work opens avenues for future research in comparing two or more groups of temporal event sequences, opening traditional machine learning and data mining techniques to user interaction, and extending the principles found in this dissertation to data types beyond temporal event sequences.

Report of the Stock Identification Methods Working Group (SIMWG) 2016

The Stock Identification Methods Working Group (SIMWG) worked by correspondence in 2016. The working group was chaired by Lisa Kerr (USA). The work plan for SIMWG in 2016 comprised four Terms of Reference (ToR), some of which are continuing goals for SIMWG: a ) Review recent advances in stock identification methods; b ) Build a reference database with updated information on known biological stocks for species of ICES interest; c ) Provide technical reviews and expert opinions on matters of stock identifica-tion, as requested by specific Working Groups and SCICOM; d ) Review and report on advances in mixed stock analysis, and assess their po-tential role in improving precision of stock assessment. ToR a) is an ongoing task of SIMWG in which we provide a comprehensive update on recent applications of stock identification techniques to ICES species of interest, summa-rize new approaches in stock identification, and novel combinations of existing applica-tions. ToR b) is a multi-annual ToR in which SIMWG has taking steps to build a reference data-base consisting of SIMWG reviews of issues of stock identity for ICES species. ToR c) is a key ongoing task by SIMWG in which we addresses specific requests by ICES working groups for technical advice on issues of stock identity. This year we provided advice on mackerel in the Northeast Atlantic as requested by WGWIDE. ToR d) is a multi-annual ToR that is focused on tracking developments in the application of mixed stock analysis and the integration of this information into assessment and management.

Shifting development in mid-childhood: the influence of between-task interference

Performance on the task-switching paradigm is greatly affected by the amount of conflict between tasks. Compared to adults, children appear to be particularly influenced by this conflict, suggesting that the ability to resolve interference between tasks improves with age. We used the task-switching paradigm to investigate how this ability develops in mid-childhood. Experiment 1 compared 5- to 8-year-olds’ and 9- to 11-year-olds’ ability to switch between decisions about the colour of an object and its shape. The 5- to 8-year-olds were slower to switch task and experienced more interference from the irrelevant task than the 9-to 11-year-olds, suggesting a developmental improvement in resolving conflict between tasks during mid-childhood. Experiment 2 explored this further, examining the influence of stimulus and response interference at different ages. This was done by separating the colour and shape dimensions of the stimulus and reducing overlap between responses. The results supported the development of conflict resolution in task-switching during mid-childhood. They also revealed that a complex interplay of factors, including the tasks used and previous experience with the task, affected children’s shifting performance.

Generating Discriminative Objective Proposals via Submodular Ranking

Object recognition has long been a core problem in computer vision. To improve object spatial support and speed up object localization for object recognition, generating high-quality category-independent object proposals as the input for object recognition system has drawn attention recently. Given an image, we generate a limited number of high-quality and category-independent object proposals in advance and used as inputs for many computer vision tasks. We present an efficient dictionary-based model for image classification task. We further extend the work to a discriminative dictionary learning method for tensor sparse coding. In the first part, a multi-scale greedy-based object proposal generation approach is presented. Based on the multi-scale nature of objects in images, our approach is built on top of a hierarchical segmentation. We first identify the representative and diverse exemplar clusters within each scale. Object proposals are obtained by selecting a subset from the multi-scale segment pool via maximizing a submodular objective function, which consists of a weighted coverage term, a single-scale diversity term and a multi-scale reward term. The weighted coverage term forces the selected set of object proposals to be representative and compact; the single-scale diversity term encourages choosing segments from different exemplar clusters so that they will cover as many object patterns as possible; the multi-scale reward term encourages the selected proposals to be discriminative and selected from multiple layers generated by the hierarchical image segmentation. The experimental results on the Berkeley Segmentation Dataset and PASCAL VOC2012 segmentation dataset demonstrate the accuracy and efficiency of our object proposal model. Additionally, we validate our object proposals in simultaneous segmentation and detection and outperform the state-of-art performance. To classify the object in the image, we design a discriminative, structural low-rank framework for image classification. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categories simultaneously without losing structural information. A discriminative low-rank representation for images with respect to the constructed dictionary is obtained. With semantic structure information and strong identification capability, this representation is good for classification tasks even using a simple linear multi-classifier.