Aprendizado ativo com aplicações ao diagnóstico de parasitos

Orientadores: Alexandre Xavier Falcão, Pedro Jussieu de RezendeTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Conjuntos de imagens têm crescido consideravelmente com o rápido avanço de inúmeras tecnologias de imagens, demandando soluções urgentes para o processamento, organização e recuperação da informação. O processamento, neste caso, objetiva anotar uma dada imagem atribuindo-na um rótulo que representa seu conteúdo semântico. A anotação é crucial para a organizaçao e recuperação efetiva da informação relacionada às imagens. No entanto, a anotação manual é inviável em grandes conjuntos de dados. Além disso, a anotação automática bem sucedida por um classificador de padrões depende fortemente da qualidade de um conjunto de treinamento reduzido. Técnicas de aprendizado ativo têm sido propostas para selecionar, a partir de um grande conjunto, amostras de treinamento representativas, com uma sugestão de rótulo que pode ser confirmado ou corrigido pelo especialista. Apesar disso, essas técnicas muitas vezes ignoram a necessidade de tempos de resposta interativos durante o processo de aprendizado ativo. Portanto, esta tese de doutorado apresenta métodos de aprendizado ativo que podem reduzir e/ou organizar um grande conjunto de dados, tal que a fase de seleção não requer reprocessá-lo inteiramente a cada iteração do aprendizado. Além disso, tal seleção pode ser interrompida quando o número de amostras desejadas, a partir do conjunto de dados reduzido e organizado, é identificado. Os métodos propostos mostram um progresso cada vez maior, primeiro apenas com a redução de dados, e em seguida com a subsequente organização do conjunto reduzido. Esta tese também aborda um problema real --- o diagnóstico de parasitos --- em que a existência de uma classe diversa (isto é, uma classe de impureza), com tamanho muito maior e amostras que são similares a alguns tipos de parasitos, torna a redução de dados consideravelmente menos eficaz. Este problema é finalmente contornado com um tipo de organização de dados diferente, que ainda permite tempos de resposta interativos e produz uma abordagem de aprendizado ativo melhor e robusta para o diagnóstico de parasitos. Os métodos desenvolvidos foram extensivamente avaliados com diferentes tipos de classificadores supervisionados e não-supervisionados utilizando conjunto de dados a partir de aplicações distintas e abordagens baselines que baseiam-se em seleção aleatória de amostras e/ou reprocessamento de todo o conjunto de dados a cada iteração do aprendizado. Por fim, esta tese demonstra que outras melhorias são obtidas com o aprendizado semi-supervisionadoAbstract: Image datasets have grown large with the fast advances and varieties of the imaging technologies, demanding urgent solutions for information processing, organization, and retrieval. Processing here aims to annotate the image by assigning to it a label that represents its semantic content. Annotation is crucial for the effective organization and retrieval of the information related to the images. However, manual annotation is unfeasible in large datasets and successful automatic annotation by a pattern classifier strongly depends on the quality of a much smaller training set. Active learning techniques have been proposed to select those representative training samples from the large dataset with a label suggestion, which can be either confirmed or corrected by the expert. Nevertheless, these techniques very often ignore the need for interactive response times during the active learning process. Therefore, this PhD thesis presents active learning methods that can reduce and/or organize the large dataset such that sample selection does not require to reprocess it entirely at every learning iteration. Moreover, it can be interrupted as soon as a desired number of samples from the reduced and organized dataset is identified. These methods show an increasing progress, first with data reduction only, and then with subsequent organization of the reduced dataset. However, the thesis also addresses a real problem --- the diagnosis of parasites --- in which the existence of a diverse class (i.e., the impurity class), with much larger size and samples that are similar to some types of parasites, makes data reduction considerably less effective. The problem is finally circumvented with a different type of data organization, which still allows interactive response times and yields a better and robust active learning approach for the diagnosis of parasites. The methods have been extensively assessed with different types of unsupervised and supervised classifiers using datasets from distinct applications and baseline approaches that rely on random sample selection and/or reprocess the entire dataset at each learning iteration. Finally, the thesis demonstrates that further improvements are obtained with semi-supervised learningDoutoradoCiência da ComputaçãoDoutora em Ciência da Computaçã

Choosing the most effective pattern classification model under learning-time constraint

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Nowadays, large datasets are common and demand faster and more effective pattern analysis techniques. However, methodologies to compare classifiers usually do not take into account the learning-time constraints required by applications. This work presents a methodology to compare classifiers with respect to their ability to learn from classification errors on a large learning set, within a given time limit. Faster techniques may acquire more training samples, but only when they are more effective will they achieve higher performance on unseen testing sets. We demonstrate this result using several techniques, multiple datasets, and typical learning-time limits required by applications.Nowadays, large datasets are common and demand faster and more effective pattern analysis techniques. However, methodologies to compare classifiers usually do not take into account the learning-time constraints required by applications. This work presentsCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOFUNDECT - FUNDAÇÃO DE APOIO AO DESENVOLVIMENTO DConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)CNPq [303182/2011-3, 477692/2012-5, 552559/2010-5, 481556/2009-5, 303673/2010-9, 470571/2013-6, 306166/2014-3, 311140/2014-9]CAPES [01-P-01965/2012]FAPESP [2011/14058-5, 2012/18768-0, 2007/52015-0, 2013/20387-7, 2014/16250-9]311140/2014-9; 303182/2011-3; 477692/2012-5; 552559/2010-5; 481556/2009-5; 303673/2010-9; 303182/2011-3; 470571/2013-6; 306166/2014-301-P-01965/20122011/14058-5, 2012/18768-0; 2007/52015-0; 2013/20387-7; 2014/16250-9sem informaçã

Desconfortos osteomusculares e alterações da qualidade de vida em gestantes

Objective: To verify the main musculoskeletal discomfort and the quality of life of pregnant women. Methodology: The research was conducted at the Municipal Health Unit. The target audience was pregnant women, aged between 18 and 35 years, from the 4th month of pregnancy. The evaluation instruments used were the Nordic Questionnaire, the Brazilian Version of the SF-6 Quality of Life Questionnaire and also a physical therapy evaluation. Results: 30 pregnant women with a mean age of 24.4 ± 3.89 years participated. The most reported body regions with pain were the lower back, hip and lower limbs. The general health and vitality were altered, especially in pregnant women with low back pain. Conclusions: The pregnant women in this study were young, who presented discomfort in the lower back, hip and lower limbs. Such discomfort was shown to influence the vitality and general health of the participants in this study.Objetivo: Verificar quais os principais desconfortos osteomusculares e a qualidade de vida das gestantes. Metodologia: A pesquisa foi realizada na Unidade Municipal de Saúde. O público alvo foi mulheres grávidas, com idade entre 18 e 35 anos, a partir do 4º mês de gestação. Foram usados como instrumentos de avaliação o Questionário Nórdico, a Versão Brasileira do Questionário de Qualidade de Vida SF-6 e também uma avaliação fisioterapêutica. Resultados: Participaram 30 gestantes com média de idade de 24,4 ± 3,89 anos. As regiões do corpo mais relatadas com presença de dores foram a lombar, quadril e membros inferiores. O estado geral de saúde e a vitalidade mostraram-se alteradas, principalmente nas gestantes com dor lombar. Conclusões: As gravidas deste estudo eram jovens, que apresentaram desconforto nas regiões lombar, quadril e membros inferiores. Tal desconforto mostrou influenciar na vitalidade e no estado geral de saúde das participantes deste estudo

Novel Nanotechnology of TiO 2

The aim of this study was to assess the performance of glass ionomer cement (GIC) added with TiO2 nanotubes. TiO2 nanotubes [3%, 5%, and 7% (w/w)] were incorporated into GIC’s (Ketac Molar EasyMix™) powder component, whereas unblended powder was used as control. Physical-chemical-biological analysis included energy dispersive spectroscopy (EDS), surface roughness (SR), Knoop hardness (SH), fluoride-releasing analysis, cytotoxicity, cell morphology, and extracellular matrix (ECM) composition. Parametric or nonparametric ANOVA were used for statistical comparisons (α≤0.05). Data analysis revealed that EDS only detected Ti at the 5% and 7% groups and that GIC’s physical-chemical properties were significantly improved by the addition of 5% TiO2 as compared to 3% and GIC alone. Furthermore, regardless of TiO2 concentration, no significant effect was found on SR, whereas GIC-containing 7% TiO2 presented decreased SH values. Fluoride release lasted longer for the 5% and 7% TiO2 groups, and cell morphology/spreading and ECM composition were found to be positively affected by TiO2 at 5%. In conclusion, in the current study, nanotechnology incorporated in GIC affected ECM composition and was important for the superior microhardness and fluoride release, suggesting its potential for higher stress-bearing site restorations

The Lipid Mediator Resolvin D1 Reduces the Skin Inflammation and Oxidative Stress Induced by UV Irradiation in Hairless Mice

UV irradiation-induced oxidative stress and inflammation contribute to the development of skin diseases. Therefore, targeting oxidative stress and inflammation might contribute to reduce skin diseases. Resolvin D1 (RvD1) is a bioactive metabolite generated during inflammation to actively orchestrate the resolution of inflammation. However, the therapeutic potential of RvD1 in UVB skin inflammation remains undetermined, which was, therefore, the aim of the present study. The intraperitoneal treatment with RvD1 (3-100 ng/mouse) reduced UVB irradiation-induced skin edema, myeloperoxidase activity, matrix metalloproteinase 9 activity, and reduced glutathione depletion with consistent effects observed with the dose of 30 ng/mouse, which was selected to the following experiments. RvD1 inhibited UVB reduction of catalase activity, and hydroperoxide formation, superoxide anion production, and gp91phox mRNA expression. RvD1 also increased the Nrf2 and its downstream targets NQO1 and HO-1 mRNA expression. Regarding cytokines, RvD1 inhibited UVB-induced production of IL-1β, IL-6, IL-33, TNF-α, TGF-β, and IL-10. These immuno-biochemical alterations by RvD1 treatment had as consequence the reduction of UVB-induced epidermal thickness, sunburn and mast cell counts, and collagen degradation. Therefore, RvD1 inhibited UVB-induced skin oxidative stress and inflammation, rendering this resolving lipid mediator as a promising therapeutic agent

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

DROP : a Data Reduction and Organization Paradigm and its Application in Image Analysis

Advisors: Alexandre X. Falcao, Pedro J. de Rezende. Data and location of the PhD thesis defense: 28th April 2014, Institute of Computing, University of Campinas, Campinas, BrazilIn this paper, we deal with the problem of the annotation process in image analysis. This problem refers to the trade-off, wherein the human knowledge is indispensable for the success of the process and human's time and effort are precious resources. Can the human annotate a minimum number of images and the classifier label the remaining ones with high accuracy? Active learning techniques have been investigated to answer this question. However, these techniques very often ignore the need for interactive response times during the active learning process. They usually adopt a common paradigm which is impractical considering large datasets. We propose an effective and efficient Data Reduction and Organization Paradigm for image analysis. In our paradigm, the proposed active learning methods should be able to reduce and/or organize the large dataset such that sample selection does not require to reprocess it entirely at each learning iteration. Moreover, it can be interrupted as soon as a desired number of samples from the reduced and organized dataset is identified. These methods show an increasing progress, first with data reduction only, and then with subsequent organization of the reduced dataset. Experimental results have demonstrated the robustness of the proposed paradigm using datasets from distinct applications and baseline approaches

Integrated provision of relative and absolute QoS in interative computer services with real-time responsiveness requirements

Aplicações de sistemas computacionais emergentes atribuindo requisitos de resposta na forma de tempo de resposta requerem uma abordagem de sistemas de tempo real. Nesses sistemas, a qualidade de serviço é expressa como garantia das restrições temporais. Um amplo leque de técnicas para provisão de QoS encontram-se na literatura. Estas técnicas são baseadas tanto na diferenciação de serviço (QoS relativa), quanto na especificação de garantia de desempenho (QoS absoluta). Porém, a integração de QoS relativa e absoluta em nível de aplicação não tem sido tão explorada. Este trabalho realiza o estudo, a análise e a proposta de um método de escalonamento de tempo real em um ambiente simulado, baseado em contratos virtuais adaptativos e modelo re-alimentado. O objetivo é relaxar as restrições temporais dos usuários menos exigentes e priorizar usuários mais exigentes, sem degradar a qualidade do sistema como um todo. Para tanto, estratégias são exploradas em nível de escalonamento para o cumprimento dos contratos especificados por requisitos de tempo médio de resposta. Os resultados alcançados com o emprego do método proposto sinalizam uma melhoria em termos de qualidade de serviço relativa e absoluta e uma melhor satisfação dos usuários. Este trabalho também propõe uma extensão para os modelos convencionalmente estudados nesse contexto, ampliando a formulação original de duas classes para n classes de serviçosEmerging computer system application posing responsiveness requirement in the form of response time demand a real-time system approach. In these systems, the quality of service is expressed as guarantees on time constraints. A wide range of techniques for QoS provision is found in the literature. These techniques are based both on either service differentiation (relative QoS) or specification of performance guaranteeS (absolute QoS). However, integrated provision of both relative and absolute QoS at application level is not as well explored. This work conducts the study, analysis and proposal of a real time scheduling method in a simulated environment. This method is based on adaptive virtual contracts and feedback model. The goal is to relax the time constraints of less demanding users and prioritize the time constraints of most demanding users, without degrading the quality of the system as a whole. Strategies toward this goal are exploited in the system scheduling level and are aimed at the problem of fulfulling service-level agreements specifying average response times requirements. The results achieved with the proposed method indicate an improvement in relative and absolute QoS and a better user satisfaction. This work also proposes an extension to the conventional models studied in this context, extending the original formulation of two classes for n classes of service

Active semi-supervised learning for biological data classification.

Due to datasets have continuously grown, efforts have been performed in the attempt to solve the problem related to the large amount of unlabeled data in disproportion to the scarcity of labeled data. Another important issue is related to the trade-off between the difficulty in obtaining annotations provided by a specialist and the need for a significant amount of annotated data to obtain a robust classifier. In this context, active learning techniques jointly with semi-supervised learning are interesting. A smaller number of more informative samples previously selected (by the active learning strategy) and labeled by a specialist can propagate the labels to a set of unlabeled data (through the semi-supervised one). However, most of the literature works neglect the need for interactive response times that can be required by certain real applications. We propose a more effective and efficient active semi-supervised learning framework, including a new active learning method. An extensive experimental evaluation was performed in the biological context (using the ALL-AML, Escherichia coli and PlantLeaves II datasets), comparing our proposals with state-of-the-art literature works and different supervised (SVM, RF, OPF) and semi-supervised (YATSI-SVM, YATSI-RF and YATSI-OPF) classifiers. From the obtained results, we can observe the benefits of our framework, which allows the classifier to achieve higher accuracies more quickly with a reduced number of annotated samples. Moreover, the selection criterion adopted by our active learning method, based on diversity and uncertainty, enables the prioritization of the most informative boundary samples for the learning process. We obtained a gain of up to 20% against other learning techniques. The active semi-supervised learning approaches presented a better trade-off (accuracies and competitive and viable computational times) when compared with the active supervised learning ones