Imunodeficiência Comum Variável (ICV) é uma imunodeficiência primária de igual distribuição entre os sexos e que afeta crianças e adultos, caracterizada por hipogamaglobulinemia com susceptibilidade aumentada a infecções e ampla variedade de complicações não infecciosas, como autoimunidade, malignidade, hiperplasia linfoide, doenças gastrointestinais, dentre outras. Os objetivos deste estudo foram: avaliar as manifestações clínicas, infecciosas e não infecciosas, mais frequentes em portadores de ICV (antes e após início da terapia com reposição de imunoglobulina humana) acompanhados em ambulatórios de imunologia do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (HCFMRP-USP), além dos níveis séricos de imunoglobulinas (IgG, IgA e IgM) ao diagnóstico, bem como as alterações quantitativas de células CD19+, CD3+CD4+, CD3+CD8+ e CD3-CD16+CD56+ desses pacientes. Neste estudo descritivo foram obtidas informações de pacientes com ICV acompanhados no HCFMRPUSP, através de registros de prontuários médicos. Foram avaliados 32 pacientes: 19 do sexo masculino e 13 do sexo feminino. A mediana da idade de início dos sintomas foi de 8,5 anos, com um pico de incidência precoce. O tempo médio de atraso para o diagnóstico foi de 7,7 anos. Todos os pacientes apresentaram infecções recorrentes, que levaram ao diagnóstico da ICV. As infecções mais frequentes foram as respiratórias, sendo que antes do diagnóstico as pneumonias foram as mais observadas (gerando, inclusive, grande número de internações) e durante o primeiro ano de uso regular da terapia de reposição com imunoglobulina humana as rinossinusites foram as que mais ocorreram. Houve redução na incidência de infecções após início do tratamento. Todos os pacientes apresentaram níveis séricos de IgG, IgA e IgM reduzidos ao diagnóstico, sendo que as medianas dos níveis séricos foram de 158 mg/dL, 10,15 mg/dL e 17 mg/dL, respectivamente. De 30 pacientes que haviam realizado imunofenotipagem, cerca de 73% apresentaram número absoluto reduzido de células CD19+ e 40% apresentaram número absoluto reduzido de linfócitos T CD4+. A relação CD4/CD8 foi invertida em aproximadamente 53% dos pacientes. Em 18 pacientes as células natural killer foram quantificadas e cerca de 56% deles apresentaram número absoluto reduzido. A maioria (97%) dos pacientes manifestou, no mínimo, uma comorbidade não infecciosa no tempo médio de seguimento de 8,2 anos, sendo que hiperplasia linfoide e doença pulmonar crônica foram as mais frequentes, cada uma ocorrendo em cerca de metade dos pacientes. O atraso para o diagnóstico da ICV foi importante, sugerindo que a presença de infecções recorrentes, especialmente do trato respiratório, deveria levar à investigação de deficiências de anticorpos, com dosagem de imunoglobulinas. Complicações não infecciosas foram extremamente comuns nesta casuística, ressaltando o amplo espectro clínico da doença. / Common variable immunodeficiency (CVID) is a primary immunodeficiency that is equally distributed between men and women and affects children and adults, characterized by hypogammaglobulinemia with increased susceptibility to infections and a wide variety of noninfectious complications such as autoimmunity, malignancy, lymphoid hyperplasia, gastrointestinal diseases, among others. The purposes of this study were to evaluate infectious and non-infectious clinical manifestations (before and after immunoglobulin replacement therapy) of CVID patients attended at immunology outpatient clinics of the Clinical Hospital of Ribeirão Preto Medical School - University of São Paulo (HCFMRP-USP), in addition to immunoglobulins (IgG, IgA and IgM) serum levels at diagnosis, as well as quantitative differences in CD19+, CD3+CD4+, CD3+CD8+ and CD3-CD16+CD56+ cells. In this descriptive study, data of CVID patients followed up at HCFMRP-USP were collected through medical records. Thirty-two patients were found: 19 males and 13 females. The median age of onset of symptoms was 8.5 years, with an early peak of incidence. The mean delay for diagnosis was 7.7 years. All patients had recurrent infections, which led to the diagnosis of CVID. The most frequent infections were respiratory tract infections. Pneumonias were more observed before the diagnosis (generating a large number of hospitalizations) and rhinosinusitis were more frequent during the first year under regular use of immunoglobulin replacement therapy. There was a reduction in the incidence of infections after initiation of treatment. All patients had low IgG, IgA and IgM serum levels (lower than the 3th percentile for age) at diagnosis and the median of serum levels were 158 mg/dL, 10.15 mg/dL and 17 mg/dL, respectively. Among 30 patients that had been immunophenotyped, approximately 73% had a reduced absolute number of CD19+ cells and 40% had a reduced absolute number of T CD4+ lymphocytes. The CD4/CD8 ratio was inverted in approximately 53% of the patients. Natural killer cells were quantified in 18 patients and about 56% of them had reduced absolute number. The majority (97%) of patients manifested at least one noninfectious comorbidity at a mean follow-up time of 8.2 years, with lymphoid hyperplasia and chronic lung disease being the most common, each occurring in about half of the patients. The delay for the diagnosis of CVID was important, suggesting that the presence of recurrent infections, especially of the respiratory tract, should lead to the investigation of antibody deficiencies with dosage of immunoglobulins. Noninfectious complications were extremely common in this series, highlighting the broad clinical spectrum of the disease.

3-D computational modelling of the human spine provides a sophisticated and cost-effective medium for bioengineers, researchers, and ergonomics designers in order to study the biomechanical behaviour of the human spine under different loading conditions. Developing a generic parametric computational human spine model to be employed in biomechanical modelling introduces a considerable potential to reduce the complexity of implementing and amending the intricate spinal geometry. The main objective of this research is to develop a 3-D parametric human spine model generation framework based on a command file system, by which the parameters of each vertebra are read from the database system, and then modelled within commercial 3-D CAD software. A novel data acquisition and generation system was developed as a part of the framework for determining the unknown vertebral dimensions, depending on the correlations between the parameters estimated from existing anthropometrical studies in the literature. The data acquisition system embodies a predictive methodology that comprehends the relations between the features of the vertebrae by employing statistical and geometrical techniques. Relations amongst vertebral parameters such as golden ratio were investigated and successfully implemented into the algorithms. The validation of the framework was carried out by comparing the developed 3-D computational human spine models against various real life human spine data, where good agreements were achieved. The constructed versatile framework possesses the capability to be utilised as a basis for quickly and effectively developing biomechanical models of the human spine such as finite element models.

Context. Image searching in historical handwritten documents is a challenging problem in computer vision and pattern recognition. The amount of documents which have been digitalized is increasing each day, and the task to find occurrences of a selected sub-image in a collection of documents has special interest for historians and genealogist. Objectives. This thesis develops a technique for image searching in historical documents. Divided in three phases, first the document is segmented into sub-images according to the words on it. These sub-images are defined by a features vector with measurable attributes of its content. And based on these vectors, a clustering algorithm computes the distance between vectors to decide which images match with the selected by the user. Methods. The research methodology is experimentation. A quasi-experiment is designed based on repeated measures over a single group of data. The image processing, features selection, and clustering approach are the independent variables; whereas the accuracies measurements are the dependent variable. This design provides a measurement net based on a set of outcomes related to each other. Results. The statistical analysis is based on the F1 score to measure the accuracy of the experimental results. This test analyses the accuracy of the experiment regarding to its true positives, false positives, and false negatives detected. The average F-measure for the experiment conducted is F1 = 0.59, whereas the actual performance value of the method is matching ratio of 66.4%. Conclusions. This thesis provides a starting point in order to develop a search engine for historical document collections based on pattern recognition. The main research findings are focused in image enhancement and segmentation for degraded documents, and image matching based on features definition and cluster analysis.

Context. Software evaluation is a research hotspot of both academia and industry. Users as the ultimate beneficiary of software products, their evaluation becomes more and more importance. In the real word, the users’ evaluation outcomes as the reference for end-users selecting products, and for project managers comparing their product with competitive products. A mobile application is a special software, which is facing the same situation. It is necessary to find and test an evaluation method for a mobile application which based on users’ feedback and give more reference for different stakeholders. Objectives. The aim of this thesis is to apply and evaluate AF in mobile application features rating. There are three kinds of people, and three processes are involved in a rating method applying process, rating designers in rating design process, rating providers in the rating process, and end-users in selecting process. Each process has the corresponding research objectives and research questions to test the applicability of AF method and the satisfaction of using AF and using AF rating outcomes. Methods. The research method of this thesis is a mixed method. The thesis combined experiment, questionnaire, and interview to achieve the research aim. The experiment is using for constructing a rating environment to simulate mobile application evaluation in the real world and test the applicability of AF method. Questionnaire as a supporting method utilizing for collecting the ratings from rating providers. And interviews are used for getting the satisfaction feedback of rating providers and end-users. Results. In this thesis, all AF use conditions are met, and AF evaluation system can be built in mobile application features rating. Comparing with existing method rating outcomes, the rating outcomes of AF are correct and complete. Although, the good feelings of end-users using AF rating outcomes to selecting a product, due to the complex rating process and heavy time cost, the satisfaction of rating providers is negative. Conclusions. AF can be used in mobile application features rating. Although there are many obvious advantages likes more scientific features weight, and more rating outcomes for different stakeholders, there are also shortages to improve such as complex rating process, heavy time cost, and bad information presentation. There is no evidence AF can reply the existing rating method in apps stores. However, there is still research value of AF in future work.

This paper performs a corpus-based critical discourse analysis on the terms“vamp”, “slut”, “prude” and “spinster” and how they are used in context fromthe 1920s to the 2000s. They were categorized according to what attitudeswere connected to them, positive, neutral and negative. An interest was alsotaken in what attributive adjectives were used in context with each term. Theresults showed consistent negative attitudes towards “prude” and “spinster”,while the attitudes towards “Vamp” and “slut” were mixed with negative andpositive.

La représentation d’image sous une forme hiérarchique a été largement utilisée dans un contexte de classification. Une telle représentation est capable de modéliser le contenu d’une image à travers une structure arborescente. Dans cette thèse, nous étudions les méthodes à noyaux qui permettent de prendre en entrée des données sous une forme structurée et de tenir compte des informations topologiques présentes dans chaque structure en concevant des noyaux structurés. Nous présentons un noyau structuré dédié aux structures telles que des arbres non ordonnés et des chemins (séquences de noeuds) équipés de caractéristiques numériques. Le noyau proposé, appelé Bag of Subpaths Kernel (BoSK), est formé en sommant les noyaux calculés sur les sous-chemins (un sac de tous les chemins et des noeuds simples) entre deux sacs. Le calcul direct de BoSK amène à une complexité quadratique par rapport à la taille de la structure (nombre de noeuds) et la quantité de données (taille de l’ensemble d’apprentissage). Nous proposons également une version rapide de notre algorithme, appelé Scalable BoSK (SBoSK), qui s’appuie sur la technique des Random Fourier Features pour projeter les données structurées dans un espace euclidien, où le produit scalaire du vecteur transformé est une approximation de BoSK. Cet algorithme bénéficie d’une complexité non plus linéaire mais quadratique par rapport aux tailles de la structure et de l’ensemble d’apprentissage, rendant ainsi le noyau adapté aux situations d’apprentissage à grande échelle. Grâce à (S)BoSK, nous sommes en mesure d’effectuer un apprentissage à partir d’informations présentes à plusieurs échelles dans les représentations hiérarchiques d’image. (S)BoSK fonctionne sur des chemins, permettant ainsi de tenir compte du contexte d’un pixel (feuille de la représentation hiérarchique) par l’intermédiaire de ses régions ancêtres à plusieurs échelles. Un tel modèle est utilisé dans la classification des images au niveau pixel. (S)BoSK fonctionne également sur les arbres, ce qui le rend capable de modéliser la composition d’un objet (racine de la représentation hiérarchique) et les relations topologiques entre ses sous-parties. Cette stratégie permet la classification des tuiles ou parties d’image. En poussant plus loin l’utilisation de (S)BoSK, nous introduisons une nouvelle approche de classification multi-source qui effectue la classification directement à partir d’une représentation hiérarchique construite à partir de deux images de la même scène prises à différentes résolutions, éventuellement selon différentes modalités. Les évaluations sur plusieurs jeux de données de télédétection disponibles dans la communauté illustrent la supériorité de (S)BoSK par rapport à l’état de l’art en termes de précision de classification, et les expériences menées sur une tâche de classification urbaine montrent la pertinence de l’approche de classification multi-source proposée. / Hierarchical image representations have been widely used in the image classification context. Such representations are capable of modeling the content of an image through a tree structure. In this thesis, we investigate kernel-based strategies that make possible taking input data in a structured form and capturing the topological patterns inside each structure through designing structured kernels. We develop a structured kernel dedicated to unordered tree and path (sequence of nodes) structures equipped with numerical features, called Bag of Subpaths Kernel (BoSK). It is formed by summing up kernels computed on subpaths (a bag of all paths and single nodes) between two bags. The direct computation of BoSK yields a quadratic complexity w.r.t. both structure size (number of nodes) and amount of data (training size). We also propose a scalable version of BoSK (SBoSK for short), using Random Fourier Features technique to map the structured data in a randomized finite-dimensional Euclidean space, where inner product of the transformed feature vector approximates BoSK. It brings down the complexity from quadratic to linear w.r.t. structure size and amount of data, making the kernel compliant with the large-scale machine-learning context. Thanks to (S)BoSK, we are able to learn from cross-scale patterns in hierarchical image representations. (S)BoSK operates on paths, thus allowing modeling the context of a pixel (leaf of the hierarchical representation) through its ancestor regions at multiple scales. Such a model is used within pixel-based image classification. (S)BoSK also works on trees, making the kernel able to capture the composition of an object (top of the hierarchical representation) and the topological relationships among its subparts. This strategy allows tile/sub-image classification. Further relying on (S)BoSK, we introduce a novel multi-source classification approach that performs classification directly from a hierarchical image representation built from two images of the same scene taken at different resolutions, possibly with different modalities. Evaluations on several publicly available remote sensing datasets illustrate the superiority of (S)BoSK compared to state-of-the-art methods in terms of classification accuracy, and experiments on an urban classification task show the effectiveness of proposed multi-source classification approach.

Information retrieval systems are playing an important role in our day to day life for getting the required information. Many text retrieval systems are available and are working successfully. Even though internet is full of other media like images, audio and video, retrieval systems for these media are rare and have not achieved success as that of text retrieval systems. Image retrieval systems are useful in many applications; there is a high demand for effective and efficient tool for image organization and retrieval as per users need. Images are classified into text based image retrieval and content based image retrieval, we proposed a text based image retrieval system, which makes use of ontology to make the retrieval process intelligent. We worked on Cricket World Cup 2007. We combined text based image retrieval approach with content based image retrieval, which uses color and texture as basic low level features. / kvishu223@gmail.com, pravs72@yahoo.co.in.

Sentiment analysis concerns the computational study of opinions expressed in text. Social media domains provide a wealth of opinionated data, thus, creating a greater need for sentiment analysis. Typically, sentiment lexicons that capture term-sentiment association knowledge are commonly used to develop sentiment analysis systems. However, the nature of social media content calls for analysis methods and knowledge sources that are better able to adapt to changing vocabulary. Invariably existing sentiment lexicon knowledge cannot usefully handle social media vocabulary which is typically informal and changeable yet rich in sentiment. This, in turn, has implications on the analyser's ability to effectively capture the context therein and to interpret the sentiment polarity from the lexicons. In this thesis we use SentiWordNet, a popular sentiment-rich lexicon with a substantial vocabulary coverage and explore how to adapt it for social media sentiment analysis. Firstly, the thesis identifies a set of strategies to incorporate the effect of modifiers on sentiment-bearing terms (local context). These modifiers include: contextual valence shifters, non-lexical sentiment modifiers typical in social media and discourse structures. Secondly, the thesis introduces an approach in which a domain-specific lexicon is generated using a distant supervision method and integrated with a general-purpose lexicon, using a weighted strategy, to form a hybrid (domain-adapted) lexicon. This has the dual purpose of enriching term coverage of the general purpose lexicon with non-standard but sentiment-rich terms as well as adjusting sentiment semantics of terms. Here, we identified two term-sentiment association metrics based on Term Frequency and Inverse Document Frequency that are able to outperform the state-of-the-art Point-wise Mutual Information on social media data. As distant supervision may not be readily applicable on some social media domains, we explore the cross-domain transferability of a hybrid lexicon. Thirdly, we introduce an approach for improving distant-supervised sentiment classification with knowledge from local context analysis, domain-adapted (hybrid) and emotion lexicons. Finally, we conduct a comprehensive evaluation of all identified approaches using six sentiment-rich social media datasets.

Lung cancer is the leading cause of cancer-related death in the United States and worldwide. Early detection of lung cancer can help improve patient outcomes, and survival prediction can inform plans of treatment. By extracting quantitative features from computed tomography scans of lung cancer, predictive models can be built that can achieve both early detection and survival prediction. To build these predictive models, first a detected lung nodule is segmented, then image features are extracted, and finally a model can be built utilizing image features to make predictions. These predictions can help radiologists improve cancer care. Building predictive models based on medical images is the basis of the budding field of radiomics. The hypothesis is that images contain phenotypic information that can be extracted to aid prediction and that automated methods can detect some things beyond human detection. With improved detection and predictive models radiomics aims to help assist radiologists and oncologists provide personalized care. In this work a model is presented to predict long term survival versus short term survival. Forty adenocarcinoma diagnostic lung computed tomography (CT) scans from Moffitt Cancer Center were analyzed for survival prediction. These forty cases were in the top and bottom quartile for survival. A decision tree classifier was able to predict the survival group with an accuracy of 77.5% using five image features chosen from 219 using relief-f. Another contribution of this work is a model for predicting cancer from suspicious nodules. The national lung screening trial was used to build a training set of 261 screening CTs and a test set of 237 CTs. These images were taken at the initial screening, one and two years before cancer developed. From these precursor images, which nodules developed into cancer, could be predicted at 76.79% accuracy with an area under the receiver operating characteristic curve of 0.82. A risk score was also developed to provide a measure of risk during screening. The developed risk score performed favorably in predictive accuracy compared to Lung-RADS on this data set. The Data Science Bowl was also entered and this work examines the knowledge gained from a large-scale competition to improve imaging. In this competition participants were tasked with predicting cancer from 1397 training cases on 506 test cases. The winning entry performed with a logLoss of 0.39975 while making use of all the training data while our entry scored 1.56555 with a different set of training data. A lower logLoss shows greater accuracy. This work explains our approach and examines the winning entry. An overview of the state of radiomicis as it applies to lung cancer is also provided. These contributions of predictive models will help to provide decision support to medical practitioners. By providing tools to the medical field the goal is to advance automated medical imaging to aid clinicians in creating diagnosis and treatment plans.

A new realm of discourse research has started examining medical interactions in the crowded space – hospitals (Iedema, 2007). Beyond clinical settings and dyadic doctor-patient interactions, scholars have begun investigating doctors’ interactions in various hospital settings including Emergency Rooms and hospitals’ wards (e.g., Eggins & Slade, 2012; Slade & Eggins, 2016; Slade et al., 2015). Other investigations have expanded this scope of discourse research to include other health professionals, such as nurses (e.g., Staples, 2015). Drawing on discourse analytic approaches (Critical Discourse Analysis, Halliday’s Systemic Functional Grammar, and Interactional Sociolinguistics), this study examined nurse-to-nurse handoff interactions in two hospitals in Saudi Arabia. Nursing handoff – the transfer of patient information, professional responsibility, and accountability between departing and incoming nursing teams (Manser et al., 2010; Riesenberg et al., 2010; Slade & Eggins, 2016; Wood et al., 2014) – is a critical communicative practice which ensures the continuity and quality of care provided to hospitalized patients. The aim of this study was to provide detailed analyses of the language used in this type of nursing discourse and its impact on the quality of handoffs. The data included 80 nursing handoff interactions, which were observed and audio-recorded in 7 different wards at two sectors (National Guard Hospital and King Fahad General Hospital) in Saudi Arabia including: Intensive Care Units, General-Adult, General- Pediatric, Oncology-Pediatric, Oncology-Palliative, ENT, Urology and Surgical wards. The nurse participants come from various cultural backgrounds including Philippines, Indonesia, India, Malaysia, Morocco, South Africa, Egypt, Jordan, and Saudi Arabia. The analyses provided a detailed description of this type of nursing discourse including the discourse pragmatic features (i.e., linguistic, interactional, and interpersonal features) which nurses use while delivering and receiving patient information. In addition, the findings provide insights into the various discourse features that contributed either positively (e.g., using discourse markers, presenting complete thoughts, presenting sufficient detailed patient information) or negatively (e.g., producing questions instead of statements, shifting verb tenses, focusing on one patient issue as opposed to providing detailed patient information report) to the nursing handoff practices in this setting. The findings also point to the vital role that head nurses play in this nursing discourse and its impact on enhancing the quality of nursing handoffs. Additionally, a six-stage nursing handoff model was developed from the data, which could be used for nursing training in the National Guard Hospital and its branches in Saudi Arabia. Finally, the findings provide further support for Eggins and Slade’s (2012) claim that communicatively effective handovers are achieved interactionally and with the collaboration of both departing and incoming teams. Furthermore, the use of standardized protocols (like SBAR) alone proved to be insufficient in guaranteeing effective nursing handoff.