A figura humana: Comparação entre crianças saudáveis e crianças com cancro

Dissertação de Mestrado apresentada ao Instituto Superior de Psicologia Aplicada para obtenção de grau de Mestre na especialidade de Psicologia Clínica.

Experimental study of temperatures during drilling of ex-vivo bovine bones

The thermal bone necrosis induced during a drilling process is a frequent and potential phenomenon, which contributes to post-operative problems. The frictional heat generated from the contact between the drill bit and the hole wall is unavoidable. However, understanding advanced techniques for acquiring reliable thermal data on bone drilling is important to ensure the quality of the drilled hole. The purpose of this study is to present two different experimental methods to analyse the drilling conditions that generate the lower temperatures, avoiding the occurrence of thermal bone necrosis. Ex-vivo bovine bones were used to simulate the drilling process considering the effect of drill bit diameter, drill speed and feed-rate. Different experiments were performed to assess the repeatability of the tests. The results identified the drill bit diameter as the most critical parameter for inducing higher temperatures in bone drilling.

Experimental study of temperatures during drilling of ex-vivo bovine bones

he thermal bone necrosis induced during a drilling process is a frequent and potential phenomenon, which contributes to post-operative problems. The frictional heat generated from the contact between the drill bit and the hole wall is unavoidable. However, understanding advanced techniques for acquiring reliable thermal data on bone drilling is important to ensure the quality of the drilled hole.

Explora: Infrastructure for Scaling Up Software Visualisation to Corpora

Visualisation provides good support for software analysis. It copes with the intangible nature of software by providing concrete representations of it. By reducing the complexity of software, visualisations are especially useful when dealing with large amounts of code. One domain that usually deals with large amounts of source code data is empirical analysis. Although there are many tools for analysis and visualisation, they do not cope well software corpora. In this paper we present Explora, an infrastructure that is specifically targeted at visualising corpora. We report on early results when conducting a sample analysis on Smalltalk and Java corpora.

Machine Learning Unsupervised Methods in the Design of an On-board Health Monitoring System for Satellite Applications

The dissertation starts by providing a description of the phenomena related to the increasing importance recently acquired by satellite applications. The spread of such technology comes with implications, such as an increase in maintenance cost, from which derives the interest in developing advanced techniques that favor an augmented autonomy of spacecrafts in health monitoring. Machine learning techniques are widely employed to lay a foundation for effective systems specialized in fault detection by examining telemetry data. Telemetry consists of a considerable amount of information; therefore, the adopted algorithms must be able to handle multivariate data while facing the limitations imposed by on-board hardware features. In the framework of outlier detection, the dissertation addresses the topic of unsupervised machine learning methods. In the unsupervised scenario, lack of prior knowledge of the data behavior is assumed. In the specific, two models are brought to attention, namely Local Outlier Factor and One-Class Support Vector Machines. Their performances are compared in terms of both the achieved prediction accuracy and the equivalent computational cost. Both models are trained and tested upon the same sets of time series data in a variety of settings, finalized at gaining insights on the effect of the increase in dimensionality. The obtained results allow to claim that both models, combined with a proper tuning of their characteristic parameters, successfully comply with the role of outlier detectors in multivariate time series data. Nevertheless, under this specific context, Local Outlier Factor results to be outperforming One-Class SVM, in that it proves to be more stable over a wider range of input parameter values. This property is especially valuable in unsupervised learning since it suggests that the model is keen to adapting to unforeseen patterns.

Inside standard and hyperspectral low-dose CT variational imaging: parameter identification and material decomposition

The main contribution of this thesis is the proposal of novel strategies for the selection of parameters arising in variational models employed for the solution of inverse problems with data corrupted by Poisson noise. In light of the importance of using a significantly small dose of X-rays in Computed Tomography (CT), and its need of using advanced techniques to reconstruct the objects due to the high level of noise in the data, we will focus on parameter selection principles especially for low photon-counts, i.e. low dose Computed Tomography. For completeness, since such strategies can be adopted for various scenarios where the noise in the data typically follows a Poisson distribution, we will show their performance for other applications such as photography, astronomical and microscopy imaging. More specifically, in the first part of the thesis we will focus on low dose CT data corrupted only by Poisson noise by extending automatic selection strategies designed for Gaussian noise and improving the few existing ones for Poisson. The new approaches will show to outperform the state-of-the-art competitors especially in the low-counting regime. Moreover, we will propose to extend the best performing strategy to the hard task of multi-parameter selection showing promising results. Finally, in the last part of the thesis, we will introduce the problem of material decomposition for hyperspectral CT, which data encodes information of how different materials in the target attenuate X-rays in different ways according to the specific energy. We will conduct a preliminary comparative study to obtain accurate material decomposition starting from few noisy projection data.

Advanced side information creation techniques and framework for Wyner-Ziv video coding

Recently, several distributed video coding (DVC) solutions based on the distributed source coding (DSC) paradigm have appeared in the literature. Wyner-Ziv (WZ) video coding, a particular case of DVC where side information is made available at the decoder, enable to achieve a flexible distribution of the computational complexity between the encoder and decoder, promising to fulfill novel requirements from applications such as video surveillance, sensor networks and mobile camera phones. The quality of the side information at the decoder has a critical role in determining the WZ video coding rate-distortion (RD) performance, notably to raise it to a level as close as possible to the RD performance of standard predictive video coding schemes. Towards this target, efficient motion search algorithms for powerful frame interpolation are much needed at the decoder. In this paper, the RD performance of a Wyner-Ziv video codec is improved by using novel, advanced motion compensated frame interpolation techniques to generate the side information. The development of these type of side information estimators is a difficult problem in WZ video coding, especially because the decoder only has available some reference, decoded frames. Based on the regularization of the motion field, novel side information creation techniques are proposed in this paper along with a new frame interpolation framework able to generate higher quality side information at the decoder. To illustrate the RD performance improvements, this novel side information creation framework has been integrated in a transform domain turbo coding based Wyner-Ziv video codec. Experimental results show that the novel side information creation solution leads to better RD performance than available state-of-the-art side information estimators, with improvements up to 2 dB: moreover, it allows outperforming H.264/AVC Intra by up to 3 dB with a lower encoding complexity.

Advanced image processing techniques for detection and quantification of drusen

Dissertation presented to obtain the degree of Doctor of Philosophy in Electrical Engineering, speciality on Perceptional Systems, by the Universidade Nova de Lisboa, Faculty of Sciences and Technology

The promise of advanced imaging techniques for the detection of hepatitis C virus antigens in the infected liver.

Injection drug use before and after liver transplantation: a retrospective multicenter analysis on incidence and outcome. Clin Transplant 2009 DOI: 10.1111/j.1399-0012.2009.01121.x. Background and aims: Injecting drug use (IDU) before and after liver transplantation (LT) is poorly described. The aim of this study was to quantify relapse and survival in this population and to describe the causes of mortality after LT. Methods: Past injection drug users were identified from the LT listing protocols from four centers in Switzerland and France. Data on survival and relapse were collected and used for uni- and multivariate analysis. Results: Between 1988 and 2006, we identified 59 patients with a past history of IDU. The mean age at transplantation was 42.4 yr and the majority of patients were men (84.7%). The indication for LT was for the vast majority viral cirrhosis accounting for 91.5% of cases, while alcoholic cirrhosis was 5.1%. There were 16.9% of patients who had a substitution therapy before and 6.8% who continued after LT. Two patients (3.4%) relapsed into IDU after LT and died at 18 and 41 months. The mean follow-up was 51 months. Overall survival was 84%, 66%, and 61% at 1, 5, and 10 yr after transplantation. Conclusions: Documented IDU was rare in liver transplanted patients. Past IDU was not associated with poorer survival after LT, and relapse after LT occurred in 3.4%.

Advanced light microscopy techniques

This article summarizes the basic principles of light microscopy, with examples of applications in biomedicine that illustrate the capabilities of thetechnique.

Cloud clearing techniques over land for land surface temperature retrieval from the Advanced Along Track Scanning Radiometer

We present five new cloud detection algorithms over land based on dynamic threshold or Bayesian techniques, applicable to the Advanced Along Track Scanning Radiometer (AATSR) instrument and compare these with the standard threshold based SADIST cloud detection scheme. We use a manually classified dataset as a reference to assess algorithm performance and quantify the impact of each cloud detection scheme on land surface temperature (LST) retrieval. The use of probabilistic Bayesian cloud detection methods improves algorithm true skill scores by 8-9 % over SADIST (maximum score of 77.93 % compared to 69.27 %). We present an assessment of the impact of imperfect cloud masking, in relation to the reference cloud mask, on the retrieved AATSR LST imposing a 2 K tolerance over a 3x3 pixel domain. We find an increase of 5-7 % in the observations falling within this tolerance when using Bayesian methods (maximum of 92.02 % compared to 85.69 %). We also demonstrate that the use of dynamic thresholds in the tests employed by SADIST can significantly improve performance, applicable to cloud-test data to provided by the Sea and Land Surface Temperature Radiometer (SLSTR) due to be launched on the Sentinel 3 mission (estimated 2014).

Nicholas Colon, Jr, Proprietor Tele-FM Television and Advanced Television Techniques graduate, 1954.

This is a photograph showing Nicholas Colon, Jr. a New York Trade School, Advanced Television Techniques graduate with an unnamed worker at his Tele-FM Television company. Original caption reads, "Nicholas Colon, Jr. - Advanced Television Techniques 1954, operates and up-to-date and successful television service shop. He is on the executive board of CETA (Certified Electronic Technicians Association). He prefers to employ graduate technicians of the New York Trade School Advanced Television Techniques course." Black and white photograph with caption glued to reverse.

Feasibility of oncoplastic techniques in the surgical management of locally advanced breast cancer

Background: Locally advanced breast cancer (LABC) is still common in developing countries. The association between neoadjuvant chemotherapy (NC) and oncoplastic surgery (OS) might provide an oncological treatment with satisfactory aesthetic results.Purpose: The goal was to demonstrate if oncoplastic surgical techniques can be utilized to treat LABC which was submitted to neoadjuvant chemotherapy.Methods: This prospective clinical trial included breast cancer patients, clinical stage III, who underwent established NC regimen. All patients underwent preoperative planning to control the tumor size and to define the surgical technique. A detailed analysis of the pathological specimen was performed.Results: 50 patients were assessed and surgically treated. Tumor size ranged from 3.0 to 14.0 cm (median 6.5 cm). Pathologic response was rated as stable, progressive, partial response, and complete response in 10%, 8%, 80% and 2% of the cases, respectively. Seventeen (34%) patients were submitted to OS. No patient had positive margins. Skin involvement was presented in 36% of pathologic specimen.Conclusions: Oncoplastic surgical techniques for selected patients decrease the rates of radical surgery despite large tumors. (www.clinicaltrials.gov, NCT00820690). (C) 2012 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

Advanced spectroscopic techniques and chemometric analysis for atmospheric organic aerosol characterization and source apportionment

Atmospheric aerosol particles directly impact air quality and participate in controlling the climate system. Organic Aerosol (OA) in general accounts for a large fraction (10–90%) of the global submicron (PM1) particulate mass. Chemometric methods for source identification are used in many disciplines, but methods relying on the analysis of NMR datasets are rarely used in atmospheric sciences. This thesis provides an original application of NMR-based chemometric methods to atmospheric OA source apportionment. The method was tested on chemical composition databases obtained from samples collected at different environments in Europe, hence exploring the impact of a great diversity of natural and anthropogenic sources. We focused on sources of water-soluble OA (WSOA), for which NMR analysis provides substantial advantages compared to alternative methods. Different factor analysis techniques are applied independently to NMR datasets from nine field campaigns of the project EUCAARI and allowed the identification of recurrent source contributions to WSOA in European background troposphere: 1) Marine SOA; 2) Aliphatic amines from ground sources (agricultural activities, etc.); 3) Biomass burning POA; 4) Biogenic SOA from terpene oxidation; 5) “Aged” SOAs, including humic-like substances (HULIS); 6) Other factors possibly including contributions from Primary Biological Aerosol Particles, and products of cooking activities. Biomass burning POA accounted for more than 50% of WSOC in winter months. Aged SOA associated with HULIS was predominant (> 75%) during the spring-summer, suggesting that secondary sources and transboundary transport become more important in spring and summer. Complex aerosol measurements carried out, involving several foreign research groups, provided the opportunity to compare source apportionment results obtained by NMR analysis with those provided by more widespread Aerodyne aerosol mass spectrometers (AMS) techniques that now provided categorization schemes of OA which are becoming a standard for atmospheric chemists. Results emerging from this thesis partly confirm AMS classification and partly challenge it.