Processamento e análise de vídeos utilizando Floresta de Caminhos Ótimos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Global gene expression profiling of oral cavity cancers suggests molecular heterogeneity within anatomic subsites

Abstract Background Oral squamous cell carcinoma (OSCC) is a frequent neoplasm, which is usually aggressive and has unpredictable biological behavior and unfavorable prognosis. The comprehension of the molecular basis of this variability should lead to the development of targeted therapies as well as to improvements in specificity and sensitivity of diagnosis. Results Samples of primary OSCCs and their corresponding surgical margins were obtained from male patients during surgery and their gene expression profiles were screened using whole-genome microarray technology. Hierarchical clustering and Principal Components Analysis were used for data visualization and One-way Analysis of Variance was used to identify differentially expressed genes. Samples clustered mostly according to disease subsite, suggesting molecular heterogeneity within tumor stages. In order to corroborate our results, two publicly available datasets of microarray experiments were assessed. We found significant molecular differences between OSCC anatomic subsites concerning groups of genes presently or potentially important for drug development, including mRNA processing, cytoskeleton organization and biogenesis, metabolic process, cell cycle and apoptosis. Conclusion Our results corroborate literature data on molecular heterogeneity of OSCCs. Differences between disease subsites and among samples belonging to the same TNM class highlight the importance of gene expression-based classification and challenge the development of targeted therapies.

Data Mining in Clinical Practice for the Quantification of Motor Impairment in Parkinson's Disease

Advances in biomedical signal acquisition systems for motion analysis have led to lowcost and ubiquitous wearable sensors which can be used to record movement data in different settings. This implies the potential availability of large amounts of quantitative data. It is then crucial to identify and to extract the information of clinical relevance from the large amount of available data. This quantitative and objective information can be an important aid for clinical decision making. Data mining is the process of discovering such information in databases through data processing, selection of informative data, and identification of relevant patterns. The databases considered in this thesis store motion data from wearable sensors (specifically accelerometers) and clinical information (clinical data, scores, tests). The main goal of this thesis is to develop data mining tools which can provide quantitative information to the clinician in the field of movement disorders. This thesis will focus on motor impairment in Parkinson's disease (PD). Different databases related to Parkinson subjects in different stages of the disease were considered for this thesis. Each database is characterized by the data recorded during a specific motor task performed by different groups of subjects. The data mining techniques that were used in this thesis are feature selection (a technique which was used to find relevant information and to discard useless or redundant data), classification, clustering, and regression. The aims were to identify high risk subjects for PD, characterize the differences between early PD subjects and healthy ones, characterize PD subtypes and automatically assess the severity of symptoms in the home setting.

Forecasts on the dark energy anisotropic stress for the esa euclid survey

The cosmological constant Λ seems to be a not satisfactory explanation of the late-time accelerated expansion of the Universe, for which a number of experimental evidences exist; therefore, it has become necessary in the last years to consider alternative models of dark energy, meant as cause of the accelerated expansion. In the study of dark energy models, it is important to understand which quantities can be determined starting from observational data, without assuming any hypothesis on the cosmological model; such quantities have been determined in Amendola, Kunz et al., 2012. In the same paper it has been further shown that it is possible to estabilish a relation between the model-independent parameters and the anisotropic stress η, which can be also expressed as a combination of the functions appearing in the most general Lagrangian for the scalar-tensor theories, the Horndeski Lagrangian. In the present thesis, the Fisher matrix formalism is used to perform a forecast on the constraints that will be possible to make on the anisotropic stress η in the future, starting from the estimated uncertainties for the galaxy clustering and weak lensing measurements which will be performed by the European Space Agency Euclid mission, to be launched in 2020. Further, constraints coming from supernovae-Ia observations are considered. The forecast is performed for two cases in which (a) η is considered as depending from redshift only and (b) η is constant and equal to one, as in the ΛCDM model.

Attenuation of Eph Receptor Kinase Activation in Cancer Cells by Coexpressed Ephrin Ligands

The Eph receptor tyrosine kinases mediate juxtacrine signals by interacting “in trans” with ligands anchored to the surface of neighboring cells via a GPI-anchor (ephrin-As) or a transmembrane segment (ephrin-Bs), which leads to receptor clustering and increased kinase activity. Additionally, soluble forms of the ephrin-A ligands released from the cell surface by matrix metalloproteases can also activate EphA receptor signaling. Besides these trans interactions, recent studies have revealed that Eph receptors and ephrins coexpressed in neurons can also engage in lateral “cis” associations that attenuate receptor activation by ephrins in trans with critical functional consequences. Despite the importance of the Eph/ephrin system in tumorigenesis, Eph receptor-ephrin cis interactions have not been previously investigated in cancer cells. Here we show that in cancer cells, coexpressed ephrin-A3 can inhibit the ability of EphA2 and EphA3 to bind ephrins in trans and become activated, while ephrin-B2 can inhibit not only EphB4 but also EphA3. The cis-inhibition of EphA3 by ephrin-B2 implies that in some cases ephrins that cannot activate a particular Eph receptor in trans can nevertheless inhibit its signaling ability through cis association. We also found that an EphA3 mutation identified in lung cancer enhances cis interaction with ephrin-A3. These results suggest a novel mechanism that may contribute to cancer pathogenesis by attenuating the tumor suppressing effects of Eph receptor signaling pathways activated by ephrins in trans (Falivelli et al. 2013).

Contributions to exact approaches in combinatorial optimization

The focus of this thesis is to contribute to the development of new, exact solution approaches to different combinatorial optimization problems. In particular, we derive dedicated algorithms for a special class of Traveling Tournament Problems (TTPs), the Dial-A-Ride Problem (DARP), and the Vehicle Routing Problem with Time Windows and Temporal Synchronized Pickup and Delivery (VRPTWTSPD). Furthermore, we extend the concept of using dual-optimal inequalities for stabilized Column Generation (CG) and detail its application to improved CG algorithms for the cutting stock problem, the bin packing problem, the vertex coloring problem, and the bin packing problem with conflicts. In all approaches, we make use of some knowledge about the structure of the problem at hand to individualize and enhance existing algorithms. Specifically, we utilize knowledge about the input data (TTP), problem-specific constraints (DARP and VRPTWTSPD), and the dual solution space (stabilized CG). Extensive computational results proving the usefulness of the proposed methods are reported.

Anosognosia for cerebral achromatopsia--a longitudinal case study

Cerebral achromatopsia is a rare disorder of colour vision caused by bilateral damage to the occipito-temporal cortex. Patients with cerebral achromatopsia are commonly said to suffer due to their disturbed colour sense. Here, we report the case of a patient with cerebral achromatopsia who was initially unaware of his deficit, although three experiments with eye movement recordings demonstrated his severe inability to use colour information in everyday tasks. During two months, the evolution of his colour vision deficit was followed with repeated standardized colour vision tests and eye movement recordings. While his performance continuously improved, he became more and more aware of the deficit. Only after colour vision had almost normalized, his subjective colour sensation was inconspicuous again. The simultaneous occurrence of achromatopsia and the corresponding anosognosia and their parallel recovery suggest that both deficits were due to dysfunction of the same brain region. Consequently, the subjective experience of colour loss in achromatopsia may depend on the residual function of the damaged colour centre.

Distance measures for image segmentation evaluation

The task considered in this paper is performance evaluation of region segmentation algorithms in the ground-truth-based paradigm. Given a machine segmentation and a ground-truth segmentation, performance measures are needed. We propose to consider the image segmentation problem as one of data clustering and, as a consequence, to use measures for comparing clusterings developed in statistics and machine learning. By doing so, we obtain a variety of performance measures which have not been used before in image processing. In particular, some of these measures have the highly desired property of being a metric. Experimental results are reported on both synthetic and real data to validate the measures and compare them with others.

Posttranscriptional regulation of Fas (CD95) ligand killing activity by lipid rafts

Fas (CD95/Apo-1) ligand-mediated apoptosis induction of target cells is one of the major effector mechanisms by which cytotoxic lymphocytes (T cells and natural killer cells) kill their target cells. In T cells, Fas ligand expression is tightly regulated at a transcriptional level through the activation of a distinct set of transcription factors. Increasing evidence, however, supports an important role for posttranscriptional regulation of Fas ligand expression and activity. Lipid rafts are cholesterol- and sphingolipid-rich membrane microdomains, critically involved in the regulation of membrane receptor signaling complexes through the clustering and concentration of signaling molecules. Here, we now provide evidence that Fas ligand is constitutively localized in lipid rafts of FasL transfectants and primary T cells. Importantly, disruption of lipid rafts strongly reduces the apoptosis-inducing activity of Fas ligand. Localization to lipid rafts appears to be predominantly mediated by the characteristic cytoplasmic proline-rich domain of Fas ligand because mutations of this domain result in reduced recruitment to lipid rafts and attenuated Fas ligand killing activity. We conclude that Fas ligand clustering in lipid rafts represents an important control mechanism in the regulation of T cell-mediated cytotoxicity.

Mutations in Uroplakin IIIA are a rare cause of renal hypodysplasia in humans

BACKGROUND: Renal hypodysplasia, characterized by a decrease in nephron number, small overall kidney size, and maldeveloped renal tissue, is a leading cause of chronic renal failure in young children. Familial clustering and renal hypodysplasia phenotypes observed in transgenic animal models suggest a genetic contribution. Uroplakin IIIa (encoded by UPIIIA) is an integral membrane protein present in urothelial plaques, and the murine UPIIIa knockout is associated with urothelial anomalies and vesicoureteral reflux. De novo UPIIIA mutations recently were identified in 4 of 17 patients with severe bilateral renal adysplasia. METHODS: To evaluate the overall role of UPIIIA in human renal hypodysplasia pathogenesis, we performed UPIIIA mutation analysis in a cohort of 170 pediatric patients affected by severe unilateral or bilateral renal hypodysplasia. Eighty-one patients were affected by bilateral nonobstructive renal hypodysplasia; of these, 61 were without vesicoureteral reflux. Eighty-four patients presented with unilateral nonobstructive renal hypodysplasia, including 24 patients with unilateral multicystic dysplastic kidneys. Family history was positive in 11%. RESULTS: Mutation analysis showed 2 heterozygous mutations not observed in 200 race-matched control chromosomes. In only 1 family was distribution of the UPIIIA mutation consistent with a disease-causing effect. This de novo missense mutation (Gly202Asp) was identified in a patient with unilateral multicystic dysplastic kidneys. The second (intronically located) mutation appeared unlikely to be disease causing because it did not segregate with an obvious disease phenotype in the affected family. CONCLUSION: Our results indicate that de novo mutations in UPIIIA can be involved in defective early kidney development, but probably constitute only a rare cause of human renal hypodysplasia in a minor subset of individuals.

Is ITS-2 rDNA suitable marker for genetic characterization of Sarcoptes mites from different wild animals in different geographic areas?

The present study examined the relationship among individual Sarcoptes scabiei mites from 13 wild mammalian populations belonging to nine species in four European countries using the second internal transcribed spacer (ITS-2) of nuclear ribosomal DNA (rDNA) as genetic marker. The ITS-2 plus primer flanking 5.8S and 28S rDNA (ITS-2+) was amplified from individual mites by polymerase chain reaction (PCR) and the amplicons were sequenced directly. A total of 148 ITS-2+ sequences of 404bp in length were obtained and 67 variable sites were identified (16.59%). UPGMA analyses did not show any geographical or host-specific clustering, and a similar outcome was obtained using population pairwise Fst statistics. These results demonstrated that ITS-2 rDNA does not appear to be suitable for examining genetic diversity among mite populations.

Fear-learning deficits in subjects with fibromyalgia syndrome?

BACKGROUND: Fibromyalgia syndrome (FMS) is frequently associated with psychiatric conditions, particularly anxiety. Deficits in contingency learning during fear conditioning have been hypothesized to increase anxiety and, consequently, pain sensation in susceptible individuals. The goal of this study was to examine the relationship between contingency learning and pain experience in subjects with FMS and rheumatoid arthritis (RA). METHODS: Fourteen female FMS subjects, 14 age-matched female RA subjects and 14 age-matched female healthy controls (HCs) were included in a fear-conditioning experiment. The conditioned stimulus (CS) consisted of visual signs, the unconditioned stimulus (US) of thermal stimuli. CS- predicted low-temperature exposure (US), while CS+ was followed by low or high temperature. RESULTS: In the FMS group, only 50% of the subjects were aware of the US-CS contingency, whereas 86% of the RA subjects and all of the HCs were aware of the contingency. CS+ induced more anxiety than CS- in RA subjects and HCs. As expected, low-temperature exposure was experienced as less painful after CS- than after CS+ in these subjects. FMS subjects did not show such adaptive conditioning. The effects of the type of CS on heart rate changes were significant in the HCs and the aware FMS subjects, but not in the unaware FMS subjects. CONCLUSIONS: Contingency learning deficits represent a potentially promising and specific, but largely unstudied, psychopathological factor in FMS. Deficits in contingency learning may increase anxiety and, consequently, pain sensation. These findings have the potential to contribute to the development of novel therapeutic approaches for FMS.

Detecting spatiotemporal clusters of accidental poisoning mortality among Texas counties, U.S., 1980 – 2001

Background Accidental poisoning is one of the leading causes of injury in the United States, second only to motor vehicle accidents. According to the Centers for Disease Control and Prevention, the rates of accidental poisoning mortality have been increasing in the past fourteen years nationally. In Texas, mortality rates from accidental poisoning have mirrored national trends, increasing linearly from 1981 to 2001. The purpose of this study was to determine if there are spatiotemporal clusters of accidental poisoning mortality among Texas counties, and if so, whether there are variations in clustering and risk according to gender and race/ethnicity. The Spatial Scan Statistic in combination with GIS software was used to identify potential clusters between 1980 and 2001 among Texas counties, and Poisson regression was used to evaluate risk differences. Results Several significant (p < 0.05) accidental poisoning mortality clusters were identified in different regions of Texas. The geographic and temporal persistence of clusters was found to vary by racial group, gender, and race/gender combinations, and most of the clusters persisted into the present decade. Poisson regression revealed significant differences in risk according to race and gender. The Black population was found to be at greatest risk of accidental poisoning mortality relative to other race/ethnic groups (Relative Risk (RR) = 1.25, 95% Confidence Interval (CI) = 1.24 – 1.27), and the male population was found to be at elevated risk (RR = 2.47, 95% CI = 2.45 – 2.50) when the female population was used as a reference. Conclusion The findings of the present study provide evidence for the existence of accidental poisoning mortality clusters in Texas, demonstrate the persistence of these clusters into the present decade, and show the spatiotemporal variations in risk and clustering of accidental poisoning deaths by gender and race/ethnicity. By quantifying disparities in accidental poisoning mortality by place, time and person, this study demonstrates the utility of the spatial scan statistic combined with GIS and regression methods in identifying priority areas for public health planning and resource allocation.

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

Functional brain network efficiency predicts intelligence

The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small-world network attributes (high clustering and short path length) and whether these small-world network parameters are associated with intellectual performance. High-density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph-theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small-world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto-Frontal Integration Theory (P-FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for highly productive cognitions.