Efeito de um primer autocondicionante na resistência ao cisalhamento de bráquetes com compósito pré-incorporado

O uso de primers autocondicionantes e de bráquetes com compósito pré-incorporado tem sido apresentado como uma alternativa para a redução de passos clínicos. O propósito deste estudo foi avaliar o efeito de um primer autocondicionante (Transbond Plus Self-Etching Primer - SEP) na resistência ao cisalhamento de bráquetes com compósito pré-incorporado colados in vivo. A amostra consistiu de 92 dentes obtidos de 23 pacientes com indicação prévia de extração de 4 pré-molares. Os dentes foram divididos em 4 grupos, sendo os bráquetes colados pelo mesmo operador, alternando os quadrantes em cada paciente: Grupo 1 (controle) - Ácido fosfórico à 37% + primer (Transbond XT Primer) + compósito (Transbond XT Adhesive Paste) + bráquete convencional; Grupo 2 - Ácido fosfórico à 37% + primer + bráquete com compósito pré-incorporado; Grupo 3 SEP + compósito + bráquete convencional; Grupo 4 - SEP + bráquete com compósito pré-incorporado. Após 30 dias os pré-molares foram extraídos, sendo submetidos ao teste de resistência ao cisalhamento através da uma Máquina de Ensaios Universal, com velocidade de 0,5mm/min. Os dados obtidos pelos grupos foram analisados com 2-way ANOVA (p<0,05). As forças médias e desvios padrão obtidos foram os seguintes: Grupo 1 = 11,35 (2,36) MPa; Grupo 2 = 9,77 (2,49) MPa; Grupo 3 = 10,89 (2,60) MPa; e Grupo 4 = 10,16 (2,75) MPa. Não foi observada diferença significativa entre o uso do SEP e o de condicionador e primer tradicionais (p = 0,948). De qualquer modo, diferenças significativas na força de adesão foram observadas quando utilizados bráquetes com compósito pré-incorporado (p = 0,032). Pode ser concluído que a combinação do primer autocondicionante com o bráquete com compósito pré-incorporado apresentou valores de força de adesão adequados, sendo promissora para uso clínico.

Advanced Monte Carlo Simulation and Machine Learning for Frequency Domain Optical Coherence Tomography

Optical Coherence Tomography(OCT) is a popular, rapidly growing imaging technique with an increasing number of bio-medical applications due to its noninvasive nature. However, there are three major challenges in understanding and improving an OCT system: (1) Obtaining an OCT image is not easy. It either takes a real medical experiment or requires days of computer simulation. Without much data, it is difficult to study the physical processes underlying OCT imaging of different objects simply because there aren't many imaged objects. (2) Interpretation of an OCT image is also hard. This challenge is more profound than it appears. For instance, it would require a trained expert to tell from an OCT image of human skin whether there is a lesion or not. This is expensive in its own right, but even the expert cannot be sure about the exact size of the lesion or the width of the various skin layers. The take-away message is that analyzing an OCT image even from a high level would usually require a trained expert, and pixel-level interpretation is simply unrealistic. The reason is simple: we have OCT images but not their underlying ground-truth structure, so there is nothing to learn from. (3) The imaging depth of OCT is very limited (millimeter or sub-millimeter on human tissues). While OCT utilizes infrared light for illumination to stay noninvasive, the downside of this is that photons at such long wavelengths can only penetrate a limited depth into the tissue before getting back-scattered. To image a particular region of a tissue, photons first need to reach that region. As a result, OCT signals from deeper regions of the tissue are both weak (since few photons reached there) and distorted (due to multiple scatterings of the contributing photons). This fact alone makes OCT images very hard to interpret.

This thesis addresses the above challenges by successfully developing an advanced Monte Carlo simulation platform which is 10000 times faster than the state-of-the-art simulator in the literature, bringing down the simulation time from 360 hours to a single minute. This powerful simulation tool not only enables us to efficiently generate as many OCT images of objects with arbitrary structure and shape as we want on a common desktop computer, but it also provides us the underlying ground-truth of the simulated images at the same time because we dictate them at the beginning of the simulation. This is one of the key contributions of this thesis. What allows us to build such a powerful simulation tool includes a thorough understanding of the signal formation process, clever implementation of the importance sampling/photon splitting procedure, efficient use of a voxel-based mesh system in determining photon-mesh interception, and a parallel computation of different A-scans that consist a full OCT image, among other programming and mathematical tricks, which will be explained in detail later in the thesis.

Next we aim at the inverse problem: given an OCT image, predict/reconstruct its ground-truth structure on a pixel level. By solving this problem we would be able to interpret an OCT image completely and precisely without the help from a trained expert. It turns out that we can do much better. For simple structures we are able to reconstruct the ground-truth of an OCT image more than 98% correctly, and for more complicated structures (e.g., a multi-layered brain structure) we are looking at 93%. We achieved this through extensive uses of Machine Learning. The success of the Monte Carlo simulation already puts us in a great position by providing us with a great deal of data (effectively unlimited), in the form of (image, truth) pairs. Through a transformation of the high-dimensional response variable, we convert the learning task into a multi-output multi-class classification problem and a multi-output regression problem. We then build a hierarchy architecture of machine learning models (committee of experts) and train different parts of the architecture with specifically designed data sets. In prediction, an unseen OCT image first goes through a classification model to determine its structure (e.g., the number and the types of layers present in the image); then the image is handed to a regression model that is trained specifically for that particular structure to predict the length of the different layers and by doing so reconstruct the ground-truth of the image. We also demonstrate that ideas from Deep Learning can be useful to further improve the performance.

It is worth pointing out that solving the inverse problem automatically improves the imaging depth, since previously the lower half of an OCT image (i.e., greater depth) can be hardly seen but now becomes fully resolved. Interestingly, although OCT signals consisting the lower half of the image are weak, messy, and uninterpretable to human eyes, they still carry enough information which when fed into a well-trained machine learning model spits out precisely the true structure of the object being imaged. This is just another case where Artificial Intelligence (AI) outperforms human. To the best knowledge of the author, this thesis is not only a success but also the first attempt to reconstruct an OCT image at a pixel level. To even give a try on this kind of task, it would require fully annotated OCT images and a lot of them (hundreds or even thousands). This is clearly impossible without a powerful simulation tool like the one developed in this thesis.

Análise molecular vibracional da interface adesivo-dentina após aplicação de múltiplas camadas de sistemas adesivos convencionais de dois passos

O propósito do presente estudo foi analisar o efeito da aplicação de múltiplas camadas consecutivas de dois sistemas adesivos convencionais de dois passos na difusão resinosa e padrão de distribuição dos componentes monoméricos resinosos. Dezesseis terceiros molares humanos hígidos foram tratados com os sistemas adesivos convencionais de dois passos de acordo com as instruções dos fabricantes ou com aplicações em múltiplas camadas consecutivas. Os espécimes foram seccionados paralelamente aos túbulos dentinários e as superfícies submetidas ao polimento com lixas 600, 1200, 1800, 2000 e 4000. Os espectros Raman foram coletados ao longo de uma linha perpendicular a interface adesivo-resina em intervalos de 1 ou 2 m. As medidas de difusão da resina adesiva e distribuição dos componentes monomériccos foram avaliadas pelos picos Raman de 1113 cm-1, 1609 cm-1 e 1454 cm-1. O gradiente de desmineralização usado na determinação da região de hibridização foi avaliado pelo pico de 960 cm-1 da apatita. De acordo com os resultados obtidos, a aplicação de múltiplas camadas apresentou uma tendência de homogeneização dos componentes poliméricos, dependente da composição química da resina adesiva.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

Avaliação das concentrações de BTEX em ambiente indoor: estudo de caso da sala de spinning de uma academia de ginástica Rio de Janeiro / RJ.

Muitos dos locais onde as atividades são realizadas nas academias de ginásticas são salas pequenas e fechadas com sistema de climatização artificial, freqüentados por um grande número de alunos realizando seus exercícios e profissionais auxiliando as atividades. Com isso, há uma intensa transpiração desses indivíduos, uma freqüente rotina de limpeza do piso e de equipamentos com pequenos intervalos, possibilitando a alterações da qualidade do ar indoor. O presente trabalho busca mostrar as tendências de variações nos valores das concentrações dos poluentes atmosféricos BTEX em ambiente indoor, especificamente na sala de spinning de uma academia de ginástica do Rio de Janeiro. Para o monitoramento da qualidade do ar foram utilizados cartuchos de carvão ativado SKC, acoplado a uma bomba KNF com vazão de 1l min. Para a extração de cada amostra foi feita a análise cromatográfica com cromatógrafo a gás modelo 6890 acoplado a um espectrômetro de massa modelo 5973 da marca Agilent. Foram analisadas 34 amostras coletadas na salas de spinning durante as aulas com atividades aeróbicas, o que intensificava a respiração dos indivíduos, possibilitando uma maior inalação destes COVs. Em contrapartida, também foram coletadas 5 amostras outdoor, 4 delas pareadas indoor/ outdoor para uma análise comparativa das concentrações destes poluentes. Dentre os compostos orgânicos voláteis analisados, o tolueno é o BTEX mais abundante obtido neste trabalho, representando 81% destes COVs indoor. Todas as amostras medidas em pares indoor/ outdoor tiveram concentrações maiores no interior, exceto para o benzeno no dia 3/12/2010. Simples atividades usualmente realizadas pelo homem, como a inserção de piso emborrachado, manutenção do sistema de climatização artificial, e limpeza podem alterar o ar indoor. As conclusões alcançadas após as medições das concentrações de BTEX foram de que o ar indoor estava mais poluído do que o outdoor. Este monitoramento da qualidade do ar indoor ainda é escasso no Brasil. Alguns esforços tem sido feito em relação a ambientes confinados como a Portaria n˚3523 do Ministério da Saúde, regulamentando o controle dos ambientes climatizados e a Resolução n˚9 da Agência Nacional de Vigilância Sanitária, além da Resolução CONAMA n ˚3 estabelecendo padrões de qualidade do ar para alguns compostos químicos, porém muitos compostos químicos ainda não são legislados ou não possuem a devida atenção, não sendo suficientes para contemplar a complexidade do assunto

Gaussian processes for machine learning (GPML) toolbox

The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well as others suitable for classification. Finally, a range of inference methods is provided, including exact and variational inference, Expectation Propagation, and Laplace’s method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks.

A chemically inert Rashba split interface electronic structure of C-60, FeOEP and PTCDA on BiAg2/Ag(111) substrates

The fields of organic electronics and spintronics have the potential to revolutionize the electronics industry. Finding the right materials that can retain their electrical and spin properties when combined is a technological and fundamental challenge. We carry out the study of three archetypal organic molecules in intimate contact with the BiAg2 surface alloy. We show that the BiAg2 alloy is an especially suited substrate due to its inertness as support for molecular films, exhibiting an almost complete absence of substrate-molecular interactions. This is inferred from the persistence of a completely unaltered giant spin-orbit split surface state of the BiAg2 substrate, and from the absence of significant metallic screening of charged molecular levels in the organic layer. Spin-orbit split states in BiAg2 turn out to be far more robust to organic overlayers than previously thought.