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Submitted by ELISA MATTIAS SARTORI null (elisamsartori@gmail.com) on 2016-06-23T20:05:53Z No. of bitstreams: 1 Elisa Mattias Sartori.pdf: 1436534 bytes, checksum: dd0f5ca15c561bf7bd3853e437fe11ee (MD5) Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2016-06-27T18:58:23Z (GMT) No. of bitstreams: 1 sartori_em_dr_arafo.pdf: 1436534 bytes, checksum: dd0f5ca15c561bf7bd3853e437fe11ee (MD5) Made available in DSpace on 2016-06-27T18:58:23Z (GMT). No. of bitstreams: 1 sartori_em_dr_arafo.pdf: 1436534 bytes, checksum: dd0f5ca15c561bf7bd3853e437fe11ee (MD5) Previous issue date: 2016-04-27 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Proposição: Este estudo teve como objetivo avaliar a modulação dos miRNAs que afetam o potencial osteogênico de CTMs em diferentes topografias de superfície de discos de vidro. Material e Métodos: Células tronco mesenquimais humanas foram plaqueadas nas diferentes superfícies e comparadas após 3, 7 e 14 dias para atividade de fosfatase alcalina, expressão de genes (Osteocalcina, Osteopontina, Sialo Proteína Óssea, Osterix, Runx2, BMP2 e ALP) e expressão de miRNAs. Microscopia eletrônica de varredura das superfícies com células foram obtidas para avaliação de adesão celular. Resultados: Atividade de fosfatase alcalina nas diferentes superfícies foi significantemente maior na superfície com nanotopografia. Do mesmo modo, a expressão de genes relacionados com osteoblastos foi mais elevada na superfície nano. Com 14 dias foi observado um aumento de 3.5 e 9 vezes para os genes Runx2 e Osterix, respectivamente. O gene da BMP2 e ALP também apresentou um aumento de 4 e 7 vezes comparado ao controle. Utilizando a tecnologia de sequenciamento de RNA (RNA-Seq) onde todos os RNAs existentes foram sequenciados, um total de 123 miRNAs com diferença de expressão foram encontrados comparando a superfície controle (dia 7) com a superfície nano (dia 14). 48 miRNAs apresentaram uma redução na expressão e 75 apresentaram um aumento de expressão. Alguns destes apresentaram marcadores para genes osteogênicos já identificados, tais como hsa-miR-135b-5p marcador para OCN, BSP, Runx2, CO15A1 e OSX, hsa-miR-122-5p marcador para OPN, hsa-miR-196a-5p marcador para BMP4, hsa-miR-26b-5p marcador para BMP2 e hsa-miR-148b-3p marcador para OPN. Conclusão: As superfícies com nanotopografia tem o potencial de melhorar a resposta de osseointegração de maneira a reduzir o tempo de osseointegração e também aumentar a produção de tecido ósseo ao redor dos implantes favorecendo assim áreas de qualidade óssea baixa. A utilização de miRNAs para alterar a resposta de diferenciação pode também ajudar a controlar o processo de osseointegração. Purpose: This study aimed to evaluate the modulation of miRNAs that affect the osteogenic potential of MSCs in different surface topographies of glass disks. Material and Methods: Human mesenchymal stem cells (hMSCs) were plated on different surfaces of glass disks and compared at 3,7 and 14 days for alkaline phosphatase (ALP) activity, expression of genes (Osteocalcin, Osteopontin, Bone Sialo Protein, Osterix, Runx2, BMP2 and ALP) and expression of miRNAs. Scanning electron microscopy of surfaces with cells were obtained to analyse the attachment of cells. Results: ALP activity on different surfaces was significantly greater in the nanotopography surface. At day 14 there was a 3.5-fold and a 9-fold increase for Runx2 and Osterix gene, respectively. BMP2 and ALP also increased by 4- and 7-fold compared to control. Using RNA sequencing technology (RNA-Seq) a total of 123 miRNAs were found differently expressed comparing control (day 7) to nano surface (day 14). 48 miRNAs were downregulated and 75 were upregulated. Some of them regulated osteogenic genes such as hsa-miR-135b-5p that targets OCN, BSP, RUNX2, CO15A1 and OSX, hsa-miR-122-5p wich targets OPN, hsa-miR-196a-5p targets BMP4, hsa-miR-26b-5p that targets BMP2 and hsa-miR-148b-3p that targets OPN. Conclusion: Surfaces with nanotopography have the potential to improve osseointegration response in order to reduce the time of osseointegration and also increase the production of bone tissue around the implants improving low bone quality areas. The use of miRNAs to affect differentiation response may also help control the osseointegration process. CAPES: BEX8187/14-2

In this decade, establishing structure-function relationships in human brain has become one of the most influential concepts in modern cognitive neuroscience since interactions among cerebral components are fundamental to explain cortical activities ([1]; [2]; [3]). In literature such relationships have been defined in terms of structural, functional and effective connectivity. This distinction, mainly focused on the theoretic concept, is also related to the different measurement instruments and analytical tools used for acquiring and processing the data. The structural connectivity refers to a pattern of anatomical links among brain regions. Its analysis aims to characterize the architecture of complex networks underlying the cerebral functional organization. Magnetic Resonance Imaging and especially Diffusion Tensor Imaging can be used to convey information concerning the physical connection between neuronal populations. Functional/effective connectivity aims at identifying the presence and the strength of connections in terms of statistically significant dependency. The former is defined as the temporal correlation between neurophysiological events occurring in distributed neuronal groups and areas. The latter describes the causal influence that one neural system exerts over another either directly or indirectly in terms of temporal precedence and physical control ([4];[5]). Functional and effective connectivity can be estimated exploiting both Functional Magnetic Resonance Imaging (fMRI) and electrophysiological signals, such as Electroencephalography (EEG) and Magnetoencephalography (MEG), with different advantages and drawbacks, respectively. fMRI provides high spatial resolution (mm) but poor temporal precision (s) while EEG/MEG has more limited spatial resolution (cm) and higher temporal precision (ms). Because functional and effective connectivity are largely estimated over time, EEG and MEG are more suitable for calculating such connectivity. In literature several methods have been developed to characterize brain connectivity in terms of network topology, connections strength and causality, following two main approaches: the data-driven, where topology, causality and strength are all inferred from data, and the neural model-based, where the model topology is postulated from a priori knowledge and only the connections strength is estimated from the data. - Data driven approach. The data driven approach includes linear, non-linear and information-based techniques. The linear ones provide a battery of indices derived by multivariate autoregressive models (MVAR) based on Granger causality principles ([6]) or MVAR frequency response ([7]). Such are Ordinary Coherence, Partial Coherence, Directed Transfer Function (DTF) and Partial Directed Coherence (PDC). These indexes measure the strength of the linear coupling between two signals; in addition DTF and PDC provide information about causal influence ([8]). o Among the non-linear techniques, phase synchronization has been shown to be very effcient in detecting interactions between oscillators. The phase locking values approach assumes that two dynamic systems may have their phases synchronized even if their amplitude are zero correlates ([9]). o The most representative information-based technique is the cross mutual information that measures the mutual dependence between two signals by quantifying the amount of information gained about one signal from measuring the other, as a function of delay between these two signals ([10]). - Neural model based approach. Representative methods are the Structural Equations Modelling (SEM) and the Dynamic Causal Modelling (DCM) ([11]; [12]). They are multivariate technique used to test hypothesis regarding the influences among interacting variables, but different concepts underlies these two methods. SEM approach assumes that neuronal dynamics are very fast in relation to signals uctuations and, hence, is based on a static neuronal model. This case, the neuronal activity has reached steady-state and changes in connectivity are led directly by changes in the covariance structure of the observed time series ([13]). On the other hand, in DCM the observed time series are modelled as a deterministic dynamical system in which external inputs causes changes in neural activity and therefore in connectivity values ([14]). Most approaches, like those based on Granger causality principles, have been examined in literature to quantify their ability in revealing cerebral connections ([15];[16]; [11]) but their simulation studies do not provide a comprehensive analysis because they use in silico data generated by self-referential linear methods which do not reproduce the complexity of brain. To overcome this issue, an innovative simulation approach has been developed in this work, based on a nonlinear neural mass model ([17]) totally independent of SEM and MVAR linear equation and able to address the complexity of neural networks. This no-self referential approach was exploited to generate in silico network data to be used as a benchmark, to quantitatively compare obtained results with true connections. The main objective of this work was to understand limits and advantages of MVAR indexes and SEM by exploiting the simulation study. Thus, it mainly serves as a proof-of-concept for connectivity measures under ideal conditions. Our purpose was to derive from simulation results some practical procedures in order to classify different brain states to support both cognitive research and clinical activity. First, research activity was focused to address connectivity on simulated data obtained on three regions networks characterized by different strength connections and based on different levels of non linearity. Second, a dataset, made available by Department of Medicine, University of Padova was used to explore application of these methods to real data by applying the simulation study suggestions. This thesis consists of three main section. The ffrst one includes Chapter 1-2-3 describing in detailed the considered connectivity measures, such are those based on Multivariate Autoregressive models and the Structural Equation Modelling, and the simulation study. The second part depicts in silico results and the application to EEG data. Finally, comments are reported in Discussion and Conclusions. Chapter 1 explains how the connecting parameters of MVAR and SEM models are identified on EEG data and describes procedures commonly exploited to analyse connectivity. Chapter 2 reports an overview about the principal models used to generate in silico data, namely the neural mass models, and described the neural mass model exploited in this work. Finally, it characterizes network models adopted to simulate data and lists the procedure followed to generate in silico datasets. Chapter 3 summarizes the computations implemented to have more insights on our data by analysing the output of each methods. It describes the procedure used to evaluate the statistical signiffcance of each index results, such are the F-test for Granger causality index and the null distribution threshold using surrogate data for MVAR frequency indexes. Chapter 4 illustrates the results obtained with the simulation study. First, we reported the complete analysis for a representative subset of experiments, then for all datasets we showed topology and strength estimates. Chapter 6 delineates the procedure followed to study the connectivity in case of hepatic encephalopathy. Chapter 7 covers the Discussion and Conclusions. The Appendix is a parallel work aimed to understand the meaning of connectivity indexes computed via Structural Equation Modelling. By exploiting the neural mass model used to simulate cortical data, the objective is to quantify which measure its estimates represent. We demonstrated that Granger causality is a good estimator with high values both of sensitivity and specificity, while frequency indexes, DTF and PDC, are too much affected by the threshold choice and their interpretation in terms of absolute strength connection is not clear. As regard SEM, we proved the difficulty of its approach to describe just simple situations. Even if SEM is based on linear regression as well as MVAR models, it differently assumes there is no connection with past information, as if brain connectivity could describe time series relationships by the instant we observe it. Hence, it is not sufficiently robust to characterize neuronal dynamic activity.

Made available in DSpace on 2014-06-11T19:23:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-03-13Bitstream added on 2014-06-13T19:30:04Z : No. of bitstreams: 1 sartori_em_me_araca.pdf: 1981097 bytes, checksum: 108176df6308fa34d3c4ef0f7ea4c265 (MD5) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Objetivo: Avaliar comparativamente, em costela bovina, a deformação, a rugosidade e a perda de massa, de três diferentes tipos de tratamento de superfície de fresas, utilizadas em osteotomias, para instalação de implantes osseointegráveis. Materiais e método: Foram utilizadas 25 costelas bovinas e 3 tipos de fresas helicoidais de 2.0 mm e 3.0 mm para osteotomias para instalação de implantes (Fresa de aço (G1), Fresa com revestimento de filme de Carboneto de Tungstênio em Matriz de Carbono (G2) e Fresa de Zircônia (G3)), que foram subdivididas em 5 subgrupos: 1 – fresas sem uso e 2, 3, 4 e 5 correspondente ao número de perfurações 10, 20, 30 e 40, respectivamente. Todas as fresas foram submetidas a medidas de rugosidade (Ra, Rz e Rmáx), massa (gramas) e análise em microscópio eletrônico de varredura (MEV) antes e após uso. Os dados foram tabulados e submetidos a análise estatística através do Teste de Kruskal-Wallis e, quando encontrada diferença estatisticamente significante, ao Teste de Dunn. Resultados: Houve perda de massa em todos os grupos (G1, G2 e G3), sendo essa redução de forma gradual, conforme o número de perfurações realizadas (1, 2, 3, 4 e 5). Mas esta diferença não foi estatisticamente significante (P

In this work we present a novel method to remove the gradient artifact in co-registered EEG/fMRI, of wide applicability and able to preserve EEG integrity. It is based on an iterative subtraction, but it improves upon exiting methods since for each slice the amplitude of the gradient templates to be subtracted is optimally adjusted by linear regression. This avoids the need of adaptive noise cancellation, or principal component analysis. Performance of this algorithm was first evaluated on both in silico and real data. Results indicate that few iterations are needed for a successful gradient removal, without compromising frequency content of EEG.