A semi-automatic approach to code smells detection

Eradication of code smells is often pointed out as a way to improve readability, extensibility and design in existing software. However, code smell detection remains time consuming and error-prone, partly due to the inherent subjectivity of the detection processes presently available. In view of mitigating the subjectivity problem, this dissertation presents a tool that automates a technique for the detection and assessment of code smells in Java source code, developed as an Eclipse plugin. The technique is based upon a Binary Logistic Regression model that uses complexity metrics as independent variables and is calibrated by expert‟s knowledge. An overview of the technique is provided, the tool is described and validated by an example case study.

Gold nanoparticles for the detection of DNA adducts as biomarkers of exposure to acrylamide

The main objective of this thesis was the development of a gold nanoparticle-based methodology for detection of DNA adducts as biomarkers, to try and overcome existing drawbacks in currently employed techniques. For this objective to be achieved, the experimental work was divided in three components: sample preparation, method of detection and development of a model for exposure to acrylamide. Different techniques were employed and combined for de-complexation and purification of DNA samples (including ultrasonic energy, nuclease digestion and chromatography), resulting in a complete protocol for sample treatment, prior to detection. The detection of alkylated nucleotides using gold nanoparticles was performed by two distinct methodologies: mass spectrometry and colorimetric detection. In mass spectrometry, gold nanoparticles were employed for laser desorption/ionisation instead of the organic matrix. Identification of nucleotides was possible by fingerprint, however no specific mass signals were denoted when using gold nanoparticles to analyse biological samples. An alternate method using the colorimetric properties of gold nanoparticles was employed for detection. This method inspired in the non-cross-linking assay allowed the identification of glycidamide-guanine adducts and DNA adducts generated in vitro. For the development of a model of exposure, two different aquatic organisms were studies: a goldfish and a mussel. Organisms were exposed to waterborne acrylamide, after which mortality was recorded and effect concentrations were estimated. In goldfish, both genotoxicity and metabolic alterations were assessed and revealed dose-effect relationships of acrylamide. Histopathological alterations were verified primarily in pancreatic cells, but also in hepatocytes. Mussels showed higher effect concentrations than goldfish. Biomarkers of oxidative stress, biotransformation and neurotoxicity were analysed after prolonged exposure, showing mild oxidative stress in mussel cells, and induction of enzymes involved in detoxification of oxygen radicals. A qualitative histopathological screening revealed gonadotoxicity in female mussels, which may present some risk to population equilibrium.

Automatic extraction of concepts from texts and applications

The extraction of relevant terms from texts is an extensively researched task in Text- Mining. Relevant terms have been applied in areas such as Information Retrieval or document clustering and classification. However, relevance has a rather fuzzy nature since the classification of some terms as relevant or not relevant is not consensual. For instance, while words such as "president" and "republic" are generally considered relevant by human evaluators, and words like "the" and "or" are not, terms such as "read" and "finish" gather no consensus about their semantic and informativeness. Concepts, on the other hand, have a less fuzzy nature. Therefore, instead of deciding on the relevance of a term during the extraction phase, as most extractors do, I propose to first extract, from texts, what I have called generic concepts (all concepts) and postpone the decision about relevance for downstream applications, accordingly to their needs. For instance, a keyword extractor may assume that the most relevant keywords are the most frequent concepts on the documents. Moreover, most statistical extractors are incapable of extracting single-word and multi-word expressions using the same methodology. These factors led to the development of the ConceptExtractor, a statistical and language-independent methodology which is explained in Part I of this thesis. In Part II, I will show that the automatic extraction of concepts has great applicability. For instance, for the extraction of keywords from documents, using the Tf-Idf metric only on concepts yields better results than using Tf-Idf without concepts, specially for multi-words. In addition, since concepts can be semantically related to other concepts, this allows us to build implicit document descriptors. These applications led to published work. Finally, I will present some work that, although not published yet, is briefly discussed in this document.

Improving tuberculosis control through the partnership between university and the health system

INTRODUCTION: Tuberculosis (TB) control is linked to the availability of qualified methods for microbiological diagnostics; however, microscopy with limited sensitivity is the only method available in many locations. The objective of this study was to evaluate the introduction of culture, drug susceptibility testing (DST), and genotyping in the routine of a Municipal Program of Tuberculosis Control. METHODS: Direct microscopy of sputum and culture in Ogawa-Kudoh were performed on 1,636 samples from 787 patients. DST of positive cultures was performed by resazurin microtiter assay and genotyping by mycobacterial interspersed repetitive units-variable number tandem repeat. RESULTS: A total 91 patients with TB were identified. The culture increased case detection by 32% compared with the microscopy; acquired resistance was 3.3% and the genotyping showed high genetic diversity. CONCLUSIONS: Ogawa-Kudoh contributed significantly to the increase in case detection and is suitable for implementation in poor-resource locations. The acquired resistance rate was lower than that reported in a recent Brazilian survey. The high genetic diversity is possibly related to the high TB prevalence in the population, as well as to early detection and suitable treatment of patients. The interaction between research and health care is important for reorienting the practice, transferring technology, and improving TB control.

Involvement and interactions of different immune cells and their cytokines in human visceral leishmaniasis

Visceral leishmaniasis (VL) or kala-azar, a disseminated infection of the lymphoreticular system of the body, is marked by severe defect in immune system of the host. Successful cure of VL depends on the immune status of the host in combination with the effects of the antileishmanial drugs. The rationale approach towards eradication of this disease would be to potentiate the immune functioning of the host in addition to parasite killing. This review deals with different aspects of adaptive and innate immune responses and explores their role in protection or pathogenesis of VL. IL-10 has emerged as the principal cytokine responsible for disease pathogenesis, although evidences regarding its source during active VL remain inconclusive. On the other hand, IFNγ, under the influence of IL-12, is mostly correlated with healing of the disease. Chemokines are important in mounting cell-mediated immune response as they can prevent parasite invasion in association with cytokines. Different types of T cells like CD4, CD8 and NK T cells also contribute to the immunology of this disease. In spite of conflicting reports, the role of regulatory T cells in VL pathogenesis is important. Recently discovered Th17 subset and its different members have been reported to perform diverse functions in the course of VL and leishmaniasis as a whole. Innate immune responses, depending on the cell types, are essential in early parasite detection and subsequent development of an efficient NK cell response. Immunotherapy targeting IL-10 could be looked upon as an interesting option for the treatment of VL.

Factors associated with seropositivity for APGL-Iamong household contacts of leprosy patients

Abstract: INTRODUCTION: Leprosy is mainly transmitted among family members who share genetic and ambient factors. The clinical form of leprosy in the index case and kinship could be risk factors for leprosy transmission. High antibody levels in household contacts (HC) in the absence of neural or skin lesions may characterize latent infection. This study aimed to evaluate the association between seropositivity for anti-phenolic glycolipid-I immunoglobulin M antibodies (APGL-I) in HC and the clinical classification of the index case and to analyze the association between APGL-I positivity with other factors such as age, kinship, and gender. METHODS: We performed a survey among 320 HC of 120 leprosy patients who were evaluated and followed-up in a leprosy outpatient clinic of a university hospital. All HC underwent complete skin examination, peripheral nerve palpation, skin sensory tests, and serologic tests for the detection and quantification of APGL-I. RESULTS: The overall seropositivity rate was 20%, and was greatly affected by kinship. APGL-I seropositivity was higher in siblings (41%), followed by parents (28%), spouses (26%), other (19%), and offspring (14%). Independent risk factors for seropositivity were being siblings (OR 3.3) and being a HC of an index case with indeterminate leprosy (OR 5.3). APGL-I seropositivity was associated with index cases with a bacillary index of 4 (88%; p<.001). Seropositivity among HC was not significantly associated with their gender and age. There was no statistical difference in the seropositivity rates of HC of index patients with paucibacillary and multibacillary leprosy. CONCLUSIONS: Strict evaluation and follow-up of HC with positive results for APGL-I is recommended. Special attention should be paid during the screening of siblings of the index cases, HC of patients with a high bacillary index, and HC of patients with indeterminate leprosy.

Implementation of membrane processes for improvement of the detection of food contaminants

The EM3E Master is an Education Programme supported by the European Commission, the European Membrane Society (EMS), the European Membrane House (EMH), and a large international network of industrial companies, research centres and universities (http://www.em3e.eu)

Intestinal intussusception and occlusion caused by small bowel polyps in the Peutz-Jeghers syndrome. Management by combined intraoperative enteroscopy and resection through minimal enterostomy: case report

The Peutz-Jeghers syndrome is a hereditary disease that requires frequent endoscopic and surgical intervention, leading to secondary complications such as short bowel syndrome. CASE REPORT: This paper reports on a 15-year-old male patient with a family history of the disease, who underwent surgery for treatment of an intestinal occlusion due to a small intestine intussusception. DISCUSSION: An intra-operative fiberscopic procedure was included for the detection and treatment of numerous polyps distributed along the small intestine. Enterotomy was performed to treat only the larger polyps, therefore limiting the intestinal resection to smaller segments. The postoperative follow-up was uneventful. CONCLUSION: We point out the importance of conservative treatment for patients with this syndrome, especially those who will undergo repeated surgical interventions because of clinical manifestation while they are still young.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Vision-based portuguese sign language recognition system

Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

A spectrum-driven damage Identification technique : application and validation through the numerical simulation of the Z24 Bridge

The present paper focuses on a damage identification method based on the use of the second order spectral properties of the nodal response processes. The explicit dependence on the frequency content of the outputs power spectral densities makes them suitable for damage detection and localization. The well-known case study of the Z24 Bridge in Switzerland is chosen to apply and further investigate this technique with the aim of validating its reliability. Numerical simulations of the dynamic response of the structure subjected to different types of excitation are carried out to assess the variability of the spectrum-driven method with respect to both type and position of the excitation sources. The simulated data obtained from random vibrations, impulse, ramp and shaking forces, allowed to build the power spectrum matrix from which the main eigenparameters of reference and damage scenarios are extracted. Afterwards, complex eigenvectors and real eigenvalues are properly weighed and combined and a damage index based on the difference between spectral modes is computed to pinpoint the damage. Finally, a group of vibration-based damage identification methods are selected from the literature to compare the results obtained and to evaluate the performance of the spectral index.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Segmentação do tecido tumoral cerebral em imagens de ressonância magnética

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores

Modelação, simulação e implementação de agitação acústica para aplicações em microfluídica

Tese de Doutoramento (Programa Doutoral em Engenharia Biomédica)