The role of liquid-based cytology and ancillary techniques in pleural and pericardic effusions: An institutional experience

BACKGROUND: Fine-needle aspiration cytology (FNAC) of serous membrane effusions may fulfil a challenging role in the diagnostic analysis of both primary and metastatic disease. From this perspective, liquid-based cytology (LBC) represents a feasible and reliable method for empowering the performance of ancillary techniques (ie, immunocytochemistry and molecular testing) with high diagnostic accuracy. METHODS: In total, 3171 LBC pleural and pericardic effusions were appraised between January 2000 and December 2013. They were classified as negative for malignancy (NM), suspicious for malignancy (SM), or positive for malignancy (PM). RESULTS: The cytologic diagnoses included 2721 NM effusions (2505 pleural and 216 pericardic), 104 SM effusions (93 pleural and 11 pericardic), and 346 PM effusions (321 pleural and 25 pericardic). The malignant pleural series included 76 unknown malignancies (36 SM and 40 PM effusions), 174 metastatic lesions (85 SM and 89 PM effusions), 14 lymphomas (3 SM and 11 PM effusions), 16 mesotheliomas (5 SM and 11 SM effusions), and 3 myelomas (all SM effusions). The malignant pericardic category included 20 unknown malignancies (5 SM and 15 PM effusions), 15 metastatic lesions (1 SM and 14 PM effusions), and 1 lymphoma (1 PM effusion). There were 411 conclusive immunocytochemical analyses and 47 molecular analyses, and the authors documented 88% sensitivity, 100% specificity, 98% diagnostic accuracy, 98% negative predictive value, and 100% positive predictive value for FNAC. CONCLUSIONS: FNAC represents a primary diagnostic tool for effusions and a reliable approach with which to determine the correct follow-up. Furthermore, LBC is useful for ancillary techniques, such as immunocytochemistry and molecular analysis, with feasible diagnostic and predictive utility.

Comparing different methods to measure biofilm thickness: the techniques are: optical coherence tomography, confocal laser scanning microscopy and low load compression test

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Engenharia Clínica)

Modelling tyre-road noise with data mining techniques

The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and uneven- ness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

Poly lactic acid based biodegradable stents and functionalization techniques: brief review

Commercial stents, especially metallic ones, present several disadvantages, and this gives rise to the necessity of producing or coating stents with different materials, like natural polymers, in order to improve their biocompatibility and minimize the disadvantages of metallic ones. This review paper discusses some applications of natural-based polymers in stents, namely polylactic acid (PLA) for stent development and chitosan for biocompatible coatings of stents . Furthermore, some effective stent functionalization techniques will be discussed, namely Layer by Layer (LBL) technique.

Big data e data analysis: visualização de informação

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Optimization of filling systems for low pressure by Flow 3D

Dissertação de mestrado integrado em Engenharia Mecânica

Optimization of magnetoelectric composites based on electroactive polymers for energy harvesting and sensor application

Tese de Doutoramento em Engenharia de Materiais.

Retroanálise de parâmetros geomecânicos baseada em algoritmos de otimização

Dissertação de mestrado integrado em Engenharia Civil

Avaliação de vigas de madeira de Castanho tendo por base a classificação visual e ensaios não destrutivos

Dissertação de mestrado Internacional em Sustentabilidade do Ambiente Construído

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Modelação numérica de túneis com consideração explícita das heterogeneidades do maciço

Dissertação de mestrado integrado em Engenharia Civil

O efeito da deposição salina nas características das escorrências rodoviárias em zonas costeiras

Tese de Doutoramento em Engenharia Civil.

Development of an intelligent earthwork optimization system

Tese de Doutoramento em Engenharia Civil.