A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms

In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen`s Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology. (C) 2010 Elsevier B.V. All rights reserved.

Estimação em modelos de tempo de falha acelerado para dados de sobrevivência correlacionados

We presented in this work two methods of estimation for accelerated failure time models with random e_ects to process grouped survival data. The _rst method, which is implemented in software SAS, by NLMIXED procedure, uses an adapted Gauss-Hermite quadrature to determine marginalized likelihood. The second method, implemented in the free software R, is based on the method of penalized likelihood to estimate the parameters of the model. In the _rst case we describe the main theoretical aspects and, in the second, we briey presented the approach adopted with a simulation study to investigate the performance of the method. We realized implement the models using actual data on the time of operation of oil wells from the Potiguar Basin (RN / CE).

A gene signature in histologically normal surgical margins is predictive of oral carcinoma recurrence

Background: Oral Squamous Cell Carcinoma (OSCC) is a major cause of cancer death worldwide, which is mainly due to recurrence leading to treatment failure and patient death. Histological status of surgical margins is a currently available assessment for recurrence risk in OSCC; however histological status does not predict recurrence, even in patients with histologically negative margins. Therefore, molecular analysis of histologically normal resection margins and the corresponding OSCC may aid in identifying a gene signature predictive of recurrence.Methods: We used a meta-analysis of 199 samples (OSCCs and normal oral tissues) from five public microarray datasets, in addition to our microarray analysis of 96 OSCCs and histologically normal margins from 24 patients, to train a gene signature for recurrence. Validation was performed by quantitative real-time PCR using 136 samples from an independent cohort of 30 patients.Results: We identified 138 significantly over-expressed genes (> 2-fold, false discovery rate of 0.01) in OSCC. By penalized likelihood Cox regression, we identified a 4-gene signature with prognostic value for recurrence in our training set. This signature comprised the invasion-related genes MMP1, COL4A1, P4HA2, and THBS2. Overexpression of this 4-gene signature in histologically normal margins was associated with recurrence in our training cohort (p = 0.0003, logrank test) and in our independent validation cohort (p = 0.04, HR = 6.8, logrank test).Conclusion: Gene expression alterations occur in histologically normal margins in OSCC. Over-expression of the 4-gene signature in histologically normal surgical margins was validated and highly predictive of recurrence in an independent patient cohort. Our findings may be applied to develop a molecular test, which would be clinically useful to help predict which patients are at a higher risk of local recurrence.

Interação genótipo-ambiente em características de crescimento de bovinos da raça Nelore

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliação genético-quantitativa de características reprodutivas em éguas da raça puro-sangue inglês

Pós-graduação em Zootecnia - FMVZ

Adaptação do método de Bott para contraste de densidade variando com a profundidade

Apresentamos um novo método para estimar o relevo do embasamento de bacias sedimentares através da extensão analítica da expressão da placa Bouguer para contraste de densidade entre o pacote sedimentar e o embasamento decrescendo monotonicamente com a profundidade de acordo com uma lei hiperbólica. O método requer ruído contido nos dados gravimétricos tenha desvio padrão inferior a 0,01 mGal. As estimativas do relevo do embasamento são obtidas nas posições espaciais localizadas diretamente abaixo de cada observação. A metodologia foi aplicada a dados sintéticos de bacias sedimentares simuladas apresentando relevo do embasamento suave. O método produziu relevo do embasamento estimado próximo do relevo simulado. O método foi também aplicado a três conjuntos de dados reais: Bacia do Recôncavo (Brasil), Graben do Büyük Menderes (Turquia) e Graben de San Jacinto (Estados Unidos). As soluções produzidas pelo método proposto apresentaram estimativas de profundidades máximas em acordo com as informações geológicas disponíveis: 6 km para Bacia do Recôncavo, 1,6 km para Vale do Büyük Menderes e 2,2 km para Graben de San Jacinto.

Estimação de parâmetros genéticos para características produtivas em búfalos na Amazônia Oriental

Dados de 1.182 registros de produção de fêmeas bubalinas da raça Murrah e seus mestiços, parindo no período de 1967 a 2005, foram utilizados para estimação de parâmetros genéticos utilizando-se o método de máxima verossimilhança restrita. O modelo animal utilizado para estimação de componentes de variância incluiu os efeitos fixos de rebanho, ano e época de parto, ordem de parto e duração da lactação e os efeitos aleatórios do animal, e ambiente permanente e temporário. As estimativas de herdabilidade foram 0,25, 0,18, 0,08 e 0,09, para produção de leite, produção de gordura, duração da lactação e produção de leite por dia de intervalo de parto, respectivamente. As estimativas de repetibilidade foram 0,33, 0,29 e 0,10 para produção de leite, produção de gordura e duração da lactação, respectivamente. As correlações genéticas entre produções de leite e gordura, produção de leite com duração da lactação, produção de leite com produção de leite por dia de intervalo de partos, produção da gordura com duração da lactação, produção de gordura com produção de leite por dia de intervalo de partos e duração da lactação com produção de leite por dia de intervalo de partos foram 0,93; 0,76; 0,99; 0,89; 0,87 e -0,27, respectivamente. Os resultados demonstram que ganhos genéticos podem ser obtidos pela seleção das produções de leite e gordura.

GENETIC VARIABILITY FOR JUVENILE CHARACTERS OF Cedrela fissilis VELL. PROGENIES: A SUBSIDY FOR DEFINITION OF SEED COLLECTION ZONE

The objective of this study was to evaluate the genetic differences among three matrix groups of Cedrela fissilis based on quantitative juvenile variables on a progeny test to define seed collecting zones and use of seeds of this species in the study region as well as to evaluate genetic variability of the sampled material. A progeny test was established in a nursery with seeds from 48 seed trees collected in the municipalities of Rio Negrinho, Mafra and Sao Bento do Sul, state of Santa Catarina, and in the municipalities of Lapa, Rio Negro, Campo do Tenente and Antonio Olinto, state of Parana. Of the collected seed trees, 33 sampled trees were distributed in three sites and 15 trees were dispersed in the studied region. It was used a complete random block design, with 8 replicates and 20 plants per plot. Evaluated data included: emergency rate; seedling base diameter and height (61, 102 and 145 days after the seeds were sowed); seedling survival; number of leaves per seedling; aerial section dry mass and root dry mass; and the foliar area of the third fully expanded leaf measured from the apical meristem. The Maximum Restricted Likelihood Method (REML) was used, using the software SELEGEN for analysis. It was found that the juvenile characters are strongly genetically controlled and they can be used to estimate genetic variability of population samples of Cedrela fissilis. The three groups of trees spatially limited did not significantly differ among each other, allowing to conclude that the three areas are part of the same tree seed transfer zone.

Statistical methods for analysis and processing of medical ultrasound: applications to segmentation and restoration

In this thesis two major topics inherent with medical ultrasound images are addressed: deconvolution and segmentation. In the first case a deconvolution algorithm is described allowing statistically consistent maximum a posteriori estimates of the tissue reflectivity to be restored. These estimates are proven to provide a reliable source of information for achieving an accurate characterization of biological tissues through the ultrasound echo. The second topic involves the definition of a semi automatic algorithm for myocardium segmentation in 2D echocardiographic images. The results show that the proposed method can reduce inter- and intra observer variability in myocardial contours delineation and is feasible and accurate even on clinical data.

Computational Techniques for Spatial Logistic Regression with Large Datasets

In epidemiological work, outcomes are frequently non-normal, sample sizes may be large, and effects are often small. To relate health outcomes to geographic risk factors, fast and powerful methods for fitting spatial models, particularly for non-normal data, are required. We focus on binary outcomes, with the risk surface a smooth function of space. We compare penalized likelihood models, including the penalized quasi-likelihood (PQL) approach, and Bayesian models based on fit, speed, and ease of implementation. A Bayesian model using a spectral basis representation of the spatial surface provides the best tradeoff of sensitivity and specificity in simulations, detecting real spatial features while limiting overfitting and being more efficient computationally than other Bayesian approaches. One of the contributions of this work is further development of this underused representation. The spectral basis model outperforms the penalized likelihood methods, which are prone to overfitting, but is slower to fit and not as easily implemented. Conclusions based on a real dataset of cancer cases in Taiwan are similar albeit less conclusive with respect to comparing the approaches. The success of the spectral basis with binary data and similar results with count data suggest that it may be generally useful in spatial models and more complicated hierarchical models.

The early Eocene global warming events: Implications from ODP Leg 208 Walvis Ridge depth transect

Five sections drilled in multiple holes over a depth transect of more than 2200 m at the Walvis Ridge (SE Atlantic) during Ocean Drilling Program (ODP) Leg 208 resulted in the first complete early Paleogene deep-sea record. Here we present high-resolution stratigraphic records spanning a ~4.3 million yearlong interval of the late Paleocene to early Eocene. This interval includes the Paleocene-Eocene thermal maximum (PETM) as well as the Eocene thermal maximum (ETM) 2 event. A detailed chronology was developed with nondestructive X-ray fluorescence (XRF) core scanning records and shipboard color data. These records were used to refine the shipboard-derived spliced composite depth for each site and with a record from ODP Site 1051 were then used to establish a continuous time series over this interval. Extensive spectral analysis reveals that the early Paleogene sedimentary cyclicity is dominated by precession modulated by the short (100 kyr) and long (405 kyr) eccentricity cycles. Counting of precession-related cycles at multiple sites results in revised estimates for the duration of magnetochrons C24r and C25n. Direct comparison between the amplitude modulation of the precession component derived from XRF data and recent models of Earth's orbital eccentricity suggests that the onset of the PETM and ETM2 are related to a 100-kyr eccentricity maximum. Both events are approximately a quarter of a period offset from a maximum in the 405-kyr eccentricity cycle, with the major difference that the PETM is lagging and ETM2 is leading a 405-kyr eccentricity maximum. Absolute age estimates for the PETM, ETM2, and the magnetochron boundaries that are consistent with recalibrated radiometric ages and recent models of Earth's orbital eccentricity cannot be precisely determined at present because of too large uncertainties in these methods. Nevertheless, we provide two possible tuning options, which demonstrate the potential for the development of a cyclostratigraphic framework based on the stable 405-kyr eccentricity cycle for the entire Paleogene.

Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm

The modelling of inpatient length of stay (LOS) has important implications in health care studies. Finite mixture distributions are usually used to model the heterogeneous LOS distribution, due to a certain proportion of patients sustaining-a longer stay. However, the morbidity data are collected from hospitals, observations clustered within the same hospital are often correlated. The generalized linear mixed model approach is adopted to accommodate the inherent correlation via unobservable random effects. An EM algorithm is developed to obtain residual maximum quasi-likelihood estimation. The proposed hierarchical mixture regression approach enables the identification and assessment of factors influencing the long-stay proportion and the LOS for the long-stay patient subgroup. A neonatal LOS data set is used for illustration, (C) 2003 Elsevier Science Ltd. All rights reserved.

Virtual Clinical Trials in PET Imaging for Improved Diagnosis of and Evaluation of Therapies for Cancer

Thesis (Ph.D.)--University of Washington, 2016-08

Normative reference values for the 20-meter shuttlerun test in a population-based sample of schoolchildren in Bogota, Colombia: The FUPRECOL Study

Our aim was to determine the normative reference values of cardiorespiratory fitness (CRF) and to establish the proportion of subjects with low CRF suggestive of future cardio-metabolic risk.