Validation of AltiKa Matching Pursuit Rain Flag

The major drawback of Ka band, operating frequency of the AltiKa altimeter on board SARAL, is its sensitivity to atmospheric liquid water. Even light rain or heavy clouds can strongly attenuate the signal and distort the signal leading to erroneous geophysical parameters estimates. A good detection of the samples affected by atmospheric liquid water is crucial. As AltiKa operates at a single frequency, a new technique based on the detection by a Matching Pursuit algorithm of short scale variations of the slope of the echo waveform plateau has been developed and implemented prelaunch in the ground segment. As the parameterization of the detection algorithm was defined using Jason-1 data, the parameters were re-estimated during the cal-val phase, during which the algorithm was also updated. The measured sensor signal-to-noise ratio is significantly better than planned, the data loss due to attenuation by rain is significantly smaller than expected (<0.1%). For cycles 2 to 9, the flag detects about 9% of 1Hz data, 5.5% as rainy and 3.5 % as backscatter bloom (or sigma0 bloom). The results of the flagging process are compared to independent rain data from microwave radiometers to evaluate its performances in term of detection and false alarms.

Desenvolvimento de método para determinação de resíduos de sedativos e B-bloqueadores em rim por LC-MS/MS

O desenvolvimento de métodos adequados que permitam o monitoramento de resíduos e contaminantes em alimentos é de suma importância pois é a única forma de garantir a segurança dos alimentos evitando danos à saúde do consumidor. Para isso, fazse necessário que estes métodos sejam rápidos, fáceis e de baixo custo, capazes de detectar a presença de resíduos em concentrações baixas e em diferentes matrizes. Este trabalho consistiu no desenvolvimento de método para determinação de 5 sedativos e 14 β-bloqueadores em amostras de rim suíno e posterior análise por Cromatografia Líquida Acoplada à Espectrometria de Massas em Série (LC-MS/MS). O procedimento de extração que melhor se adequou para análise destes compostos consistiu na pesagem de 2 g de amostra e adição de 10 mL de acetonitrila seguida de homogeneização com auxílio de Ultra-Turrax e mesa agitadora. Após extração, as amostras foram submetidas a duas técnicas de clean-up, sendo elas, congelamento do extrato à baixa temperatura e extração em fase sólida dispersiva (d-SPE) utilizando como sorvente Celite® 545. Uma etapa de concentração foi realizada com auxílio de concentrador de amostras sob fluxo de N2 e temperatura controlada. As amostras secas foram retomadas com metanol e analisadas utilizando sistema LC-MS/MS com Ionização por Eletrospray (ESI), operando no modo MRM positivo, coluna Poroshell 120 EC-C18 (3,0 x 50 mm, 2,7 μm) para separação dos analitos, e gradiente de fase móvel composta por (A) solução aquosa acidificada com 0,1% de ácido fórmico (v/v) e (B) metanol 0,1% ácido fórmico (v/v). Os parâmetros de validação avaliados foram linearidade, seletividade, efeito matriz, precisão, veracidade, recuperação, limite de decisão, capacidade de detecção, incerteza da medição, robustez, limite de detecção e de quantificação. Além disso foram observados os critérios de desempenho aplicáveis à detecção por espectrometria de massas e estabilidade dos compostos. A recuperação foi avaliada em 10 μg kg-1 e a veracidade em 5, 10 e 15 μg kg-1 apresentando resultados satisfatórios entre 70 - 85% e 90 - 101%, respectivamente. O limite de quantificação determinado foi de 2,5 μg kg-1 , exceto para carazolol que foi de 1,25 μg kg- 1 . O estudo de linearidade foi realizado entre 0 e 20 μg kg-1 apresentando coeficientes de determinação superiores a 0,98. Estes procedimentos foram realizados através de análise de matriz branca fortificada. Além disso, o presente método foi utilizado para analisar carazolol, azaperone e azaperol em amostras de ensaio colaborativo de rim suíno, apresentando resultados muito próximos aos reais. Portanto, é possível concluir que o método desenvolvido é adequado para análise de sedativos e β-bloqueadores através de extração dos compostos e limpeza do extrato eficientes utilizando procedimentos rápidos, fáceis e de baixo custo, garantindo resultados seguros e confiáveis.

Análise das mudanças espaciais e temporais nos padrões de paisagem na região do oeste da Bahia sobre a formação do grupo urucuia (1988-2011) e suas implicações para a conservação do Cerrado

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós Graduação em Geografia, 2015.

Genetic algorithm seeding of idiotypic networks for mobile-robot navigation

Robot-control designers have begun to exploit the properties of the human immune system in order to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne’s idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, preengineered set of behaviours, limiting variability. This paper describes a method for encoding behaviours as a variable set of attributes, and shows that when the encoding is used with a genetic algorithm (GA), multiple sets of diverse behaviours can develop naturally and rapidly, providing much greater scope for flexible behaviour-selection. The algorithm is tested extensively with a simulated e-puck robot that navigates around a maze by tracking colour. Results show that highly successful behaviour sets can be generated within about 25 minutes, and that much greater diversity can be obtained when multiple autonomous populations are used, rather than a single one.

Zika virus damages the human placental barrier and presents marked fetal neurotropism

An unusually high incidence of microcephaly in newborns has recently been observed in Brazil. There is a temporal association between the increase in cases of microcephaly and the Zika virus (ZIKV) epidemic. Viral RNA has been detected in amniotic fluid samples, placental tissues and newborn and fetal brain tissues. However, much remains to be determined concerning the association between ZIKV infection and fetal malformations. In this study, we provide evidence of the transplacental transmission of ZIKV through the detection of viral proteins and viral RNA in placental tissue samples from expectant mothers infected at different stages of gestation. We observed chronic placentitis (TORCH type) with viral protein detection by immunohistochemistry in Hofbauer cells and some histiocytes in the intervillous spaces. We also demonstrated the neurotropism of the virus via the detection of viral proteins in glial cells and in some endothelial cells and the observation of scattered foci of microcalcifications in the brain tissues. Lesions were mainly located in the white matter. ZIKV RNA was also detected in these tissues by real-time-polymerase chain reaction. We believe that these findings will contribute to the body of knowledge of the mechanisms of ZIKV transmission, interactions between the virus and host cells and viral tropism.

Bioprospection of Petit Verdot grape pomace as a source of anti-inflammatory compounds.

The aim of this study was to evaluate the anti-inflammatory activity of Petit Verdot Extract and hexane, chloroform and ethyl acetate fractions obtained from grape pomace, in addition to identifying active compounds. The PVE and EAF reduced significantly paw edema and neutrophil migration when compared with control groups. The PVE reduced levels of TNF-α and IL1-β in the peritoneal fluid, whereas the EAF did not reduce cytokines significantly. Two hydroxybenzoic acids, two proanthocyanidins, three flavan-3-ol monomers and three anthocyanins were identified in the PVE and EAF by LC-MS/MS. The stilbene transresveratrol was identified only in the EAF. The phenolic compounds were quantified using HPLC-DAD analysis, except for the stilbenes, which were not sensible for the detection by the method. Since PVE and EAF showed significantly anti-inflammatory effects and high concentration of phenolic compounds, we concluded that Petit Verdot pomace could be an interesting source of anti-inflammatory bioactives.

Whole-body cortisol increases after direct and visual contact with a predator in zebrafish, Danio rerio

Ornamental fish may be severely affected by a stressful environment. Stressors impair the immune response, reproduction and growth rate; thus, the identification of possible stressors will aid to improve the overall quality of ornamental fish. The aim of this study was to determine whole-body cortisol of adult zebrafish, Danio rerio, following visual or direct contact with a predator species. Zebrafish were distributed in three groups: the first group, which consisted of zebrafish reared completely isolated of the predator, was considered the negative control; the second group, in which the predator, Parachromis managuensis was stocked together with zebrafish, was considered the positive control; the third group consisted of zebrafish stocked in a glass aquarium, with direct visual contact with the predator. The mean whole-body cortisol concentration in zebrafish from the negative control was 6.78 +/- 1.12 ng g(-1), a concentration statistically lower than that found in zebrafish having visual contact with the predator (9.26 +/- 0.88 ng g(-1)) which, in turn, was statistically lower than the mean whole-body cortisol of the positive control group (12.35 +/- 1.59 ng g(-1)). The higher whole-body cortisol concentration found in fish from the positive control can be attributed to the detection, by the zebrafish, of relevant risk situations that may involve a combination of chemical, olfactory and visual cues. One of the functions of elevated cortisol is to mobilize energy from body resources to cope with stress. The elevation of whole-body cortisol in fish subjected to visual contact with the predator involves only the visual cue in the recognition of predation risk. We hypothesized that the zebrafish could recognize predator characteristics in P managuensis, such as length, shape, color and behavior. Nonetheless, the elevation of whole-body cortisol in zebrafish suggested that the visual contact of the predator may elicit a stress response in prey fish. This assertion has a strong practical application concerning the species distribution in ornamental fish markets in which prey species should not be allowed to see predator species. Minimizing visual contact between prey and predator fish may improve the quality, viability and welfare of small fish in ornamental fish markets. (c) 2007 Elsevier B.V. All rights reserved.

Comparison of Computer-Based and Optical Face Recognition Paradigms

The main objectives of this thesis are to validate an improved principal components analysis (IPCA) algorithm on images; designing and simulating a digital model for image compression, face recognition and image detection by using a principal components analysis (PCA) algorithm and the IPCA algorithm; designing and simulating an optical model for face recognition and object detection by using the joint transform correlator (JTC); establishing detection and recognition thresholds for each model; comparing between the performance of the PCA algorithm and the performance of the IPCA algorithm in compression, recognition and, detection; and comparing between the performance of the digital model and the performance of the optical model in recognition and detection. The MATLAB © software was used for simulating the models. PCA is a technique used for identifying patterns in data and representing the data in order to highlight any similarities or differences. The identification of patterns in data of high dimensions (more than three dimensions) is too difficult because the graphical representation of data is impossible. Therefore, PCA is a powerful method for analyzing data. IPCA is another statistical tool for identifying patterns in data. It uses information theory for improving PCA. The joint transform correlator (JTC) is an optical correlator used for synthesizing a frequency plane filter for coherent optical systems. The IPCA algorithm, in general, behaves better than the PCA algorithm in the most of the applications. It is better than the PCA algorithm in image compression because it obtains higher compression, more accurate reconstruction, and faster processing speed with acceptable errors; in addition, it is better than the PCA algorithm in real-time image detection due to the fact that it achieves the smallest error rate as well as remarkable speed. On the other hand, the PCA algorithm performs better than the IPCA algorithm in face recognition because it offers an acceptable error rate, easy calculation, and a reasonable speed. Finally, in detection and recognition, the performance of the digital model is better than the performance of the optical model.

Novel Analytical Methodologies for the Monitoring of Traditional and Non-traditional Pollutants in different Environmental Compartments of South Florida

Routine monitoring of environmental pollution demands simplicity and speed without sacrificing sensitivity or accuracy. The development and application of sensitive, fast and easy to implement analytical methodologies for detecting emerging and traditional water and airborne contaminants in South Florida is presented. A novel method was developed for quantification of the herbicide glyphosate based on lyophilization followed by derivatization and simultaneous detection by fluorescence and mass spectrometry. Samples were analyzed from water canals that will hydrate estuarine wetlands of Biscayne National Park, detecting inputs of glyphosate from both aquatic usage and agricultural runoff from farms. A second study describes a set of fast, automated LC-MS/MS protocols for the analysis of dioctyl sulfosuccinate (DOSS) and 2-butoxyethanol, two components of Corexit®. Around 1.8 million gallons of those dispersant formulations were used in the response efforts for the Gulf of Mexico oil spill in 2010. The methods presented here allow the trace-level detection of these compounds in seawater, crude oil and commercial dispersants formulations. In addition, two methodologies were developed for the analysis of well-known pollutants, namely Polycyclic Aromatic Hydrocarbons (PAHs) and airborne particulate matter (APM). PAHs are ubiquitous environmental contaminants and some are potent carcinogens. Traditional GC-MS analysis is labor-intensive and consumes large amounts of toxic solvents. My study provides an alternative automated SPE-LC-APPI-MS/MS analysis with minimal sample preparation and a lower solvent consumption. The system can inject, extract, clean, separate and detect 28 PAHs and 15 families of alkylated PAHs in 28 minutes. The methodology was tested with environmental samples from Miami. Airborne Particulate Matter is a mixture of particles of chemical and biological origin. Assessment of its elemental composition is critical for the protection of sensitive ecosystems and public health. The APM collected from Port Everglades between 2005 and 2010 was analyzed by ICP-MS after acid digestion of filters. The most abundant elements were Fe and Al, followed by Cu, V and Zn. Enrichment factors show that hazardous elements (Cd, Pb, As, Co, Ni and Cr) are introduced by anthropogenic activities. Data suggest that the major sources of APM were an electricity plant, road dust, industrial emissions and marine vessels.

Use of fluorescence lifetime imaging microscopy (FLIM) as a timer of cell cycle S phase

Incorporation of thymidine analogues in replicating DNA, coupled with antibody and fluorophore staining, allows analysis of cell proliferation, but is currently limited to monolayer cultures, fixed cells and end-point assays. We describe a simple microscopy imaging method for live real-time analysis of cell proliferation, S phase progression over several division cycles, effects of anti-proliferative drugs and other applications. It is based on the prominent (~ 1.7-fold) quenching of fluorescence lifetime of a common cell-permeable nuclear stain, Hoechst 33342 upon the incorporation of 5-bromo-2’-deoxyuridine (BrdU) in genomic DNA and detection by fluorescence lifetime imaging microscopy (FLIM). We show that quantitative and accurate FLIM technique allows high-content, multi-parametric dynamic analyses, far superior to the intensity-based imaging. We demonstrate its uses with monolayer cell cultures, complex 3D tissue models of tumor cell spheroids and intestinal organoids, and in physiological study with metformin treatment.

Authentification biométrique par dynamique de frappe pour évaluation à distance utilisant SVM à une classe

La machine à vecteurs de support à une classe est un algorithme non-supervisé qui est capable d’apprendre une fonction de décision à partir de données d’une seule classe pour la détection d’anomalie. Avec les données d’entraînement d’une seule classe, elle peut identifier si une nouvelle donnée est similaire à l’ensemble d’entraînement. Dans ce mémoire, nous nous intéressons à la reconnaissance de forme de dynamique de frappe par la machine à vecteurs de support à une classe, pour l’authentification d’étudiants dans un système d’évaluation sommative à distance à l’Université Laval. Comme chaque étudiant à l’Université Laval possède un identifiant court, unique qu’il utilise pour tout accès sécurisé aux ressources informatiques, nous avons choisi cette chaîne de caractères comme support à la saisie de dynamique de frappe d’utilisateur pour construire notre propre base de données. Après avoir entraîné un modèle pour chaque étudiant avec ses données de dynamique de frappe, on veut pouvoir l’identifier et éventuellement détecter des imposteurs. Trois méthodes pour la classification ont été testées et discutées. Ainsi, nous avons pu constater les faiblesses de chaque méthode dans ce système. L’évaluation des taux de reconnaissance a permis de mettre en évidence leur dépendance au nombre de signatures ainsi qu’au nombre de caractères utilisés pour construire les signatures. Enfin, nous avons montré qu’il existe des corrélations entre le taux de reconnaissance et la dispersion dans les distributions des caractéristiques des signatures de dynamique de frappe.

La posición socio económica en el campo de la actividad física y su relación con la imagen corporal: Aproximación desde el enfoque determinantes/ determinación social de la salud

El presente trabajo analiza la definición de la categoría posición socioeconómica (PSE) y las variables con las cuales se representa en los productos académicos del campo de la actividad física, además de su relación con la categoría de imagen corporal. Para lograr el objetivo, se rastrean elementos que permiten dar cuenta si los documentos de investigación se abordan desde alguno de los dos contextos: determinantes (DDSS) o determinación social de la salud (DSS). Se inicia con un rastreo global por medio de los motores de búsqueda, las bases de datos y los repositorios institucionales. Posteriormente se parametriza la ruta, desde las categorías imagen corporal (IC) y PSE. Las investigaciones pretenden dar cuenta de la evaluación a 15 años del programa "Salud para Todos" de la ONU de 2001, en el marco de los Objetivos Del Milenio. Se revisaron resúmenes de los productos, descartando aquellos donde la categoría PSE o sus descriptores asociados tuvieran un papel secundario. Se limitó a Latinoamérica y España por su tradición histórica colonizadora; con el ánimo de conocer la postura de esta comunidad frente al proceso globalizado de la salud en el mundo. Al grupo final se le aplican criterios parametrizados a partir de la revisión teórica, para responder los interrogantes basados en las implicaciones que tiene la PSE en el pensamiento actual de la producción científica en el campo de la actividad física; y cómo las otras categorías de análisis se ven o no manifiestas. El índice de calidad científica CASPe, determina la pertinencia de los textos. En el aspecto teórico, se encuentra que la categoría PSE, a pesar de ser muy utilizada, tiene una conceptualización difusa. Por tal motivo, se propone una definición de PSE sustentada en el pensamiento sociológico. En el aspecto empírico, al rastrear las variables con que se reemplaza la PSE en las investigaciones, se encuentran grandes diferencias y el uso de múltiples y disímiles subcategorías.

Machine Learning Unsupervised Methods in the Design of an On-board Health Monitoring System for Satellite Applications

The dissertation starts by providing a description of the phenomena related to the increasing importance recently acquired by satellite applications. The spread of such technology comes with implications, such as an increase in maintenance cost, from which derives the interest in developing advanced techniques that favor an augmented autonomy of spacecrafts in health monitoring. Machine learning techniques are widely employed to lay a foundation for effective systems specialized in fault detection by examining telemetry data. Telemetry consists of a considerable amount of information; therefore, the adopted algorithms must be able to handle multivariate data while facing the limitations imposed by on-board hardware features. In the framework of outlier detection, the dissertation addresses the topic of unsupervised machine learning methods. In the unsupervised scenario, lack of prior knowledge of the data behavior is assumed. In the specific, two models are brought to attention, namely Local Outlier Factor and One-Class Support Vector Machines. Their performances are compared in terms of both the achieved prediction accuracy and the equivalent computational cost. Both models are trained and tested upon the same sets of time series data in a variety of settings, finalized at gaining insights on the effect of the increase in dimensionality. The obtained results allow to claim that both models, combined with a proper tuning of their characteristic parameters, successfully comply with the role of outlier detectors in multivariate time series data. Nevertheless, under this specific context, Local Outlier Factor results to be outperforming One-Class SVM, in that it proves to be more stable over a wider range of input parameter values. This property is especially valuable in unsupervised learning since it suggests that the model is keen to adapting to unforeseen patterns.

Deep learning models for the automatic analysis of volleyball plays

Collecting and analysing data is an important element in any field of human activity and research. Even in sports, collecting and analyzing statistical data is attracting a growing interest. Some exemplar use cases are: improvement of technical/tactical aspects for team coaches, definition of game strategies based on the opposite team play or evaluation of the performance of players. Other advantages are related to taking more precise and impartial judgment in referee decisions: a wrong decision can change the outcomes of important matches. Finally, it can be useful to provide better representations and graphic effects that make the game more engaging for the audience during the match. Nowadays it is possible to delegate this type of task to automatic software systems that can use cameras or even hardware sensors to collect images or data and process them. One of the most efficient methods to collect data is to process the video images of the sporting event through mixed techniques concerning machine learning applied to computer vision. As in other domains in which computer vision can be applied, the main tasks in sports are related to object detection, player tracking, and to the pose estimation of athletes. The goal of the present thesis is to apply different models of CNNs to analyze volleyball matches. Starting from video frames of a volleyball match, we reproduce a bird's eye view of the playing court where all the players are projected, reporting also for each player the type of action she/he is performing.