Network signatures based on gene pair expression ratios improve classification and the analysis of muscle-invasive urothelial cancer

Urothelial cancer (UC) is highly recurrent and can progress from non-invasive (NMIUC) to a more aggressive muscle-invasive (MIUC) subtype that invades the muscle tissue layer of the bladder. We present a proof of principle study that network-based features of gene pairs can be used to improve classifier performance and the functional analysis of urothelial cancer gene expression data. In the first step of our procedure each individual sample of a UC gene expression dataset is inflated by gene pair expression ratios that are defined based on a given network structure. In the second step an elastic net feature selection procedure for network-based signatures is applied to discriminate between NMIUC and MIUC samples. We performed a repeated random subsampling cross validation in three independent datasets. The network signatures were characterized by a functional enrichment analysis and studied for the enrichment of known cancer genes. We observed that the network-based gene signatures from meta collections of proteinprotein interaction (PPI) databases such as CPDB and the PPI databases HPRD and BioGrid improved the classification performance compared to single gene based signatures. The network based signatures that were derived from PPI databases showed a prominent enrichment of cancer genes (e.g., TP53, TRIM27 and HNRNPA2Bl). We provide a novel integrative approach for large-scale gene expression analysis for the identification and development of novel diagnostical targets in bladder cancer. Further, our method allowed to link cancer gene associations to network-based expression signatures that are not observed in gene-based expression signatures.

Patient classification for intelligent wheelchair adaptation

A importância e preocupação dedicadas à autonomia e independência das pessoas idosas e dos pacientes que sofrem de algum tipo de deficiência tem vindo a aumentar significativamente ao longo das últimas décadas. As cadeiras de rodas inteligentes (CRI) são tecnologias que podem ajudar este tipo de população a aumentar a sua autonomia, sendo atualmente uma área de investigação bastante ativa. Contudo, a adaptação das CRIs a pacientes específicos e a realização de experiências com utilizadores reais são assuntos de estudo ainda muito pouco aprofundados. A cadeira de rodas inteligente, desenvolvida no âmbito do Projeto IntellWheels, é controlada a alto nível utilizando uma interface multimodal flexível, recorrendo a comandos de voz, expressões faciais, movimentos de cabeça e através de joystick. Este trabalho teve como finalidade a adaptação automática da CRI atendendo às características dos potenciais utilizadores. Foi desenvolvida uma metodologia capaz de criar um modelo do utilizador. A investigação foi baseada num sistema de recolha de dados que permite obter e armazenar dados de voz, expressões faciais, movimentos de cabeça e do corpo dos pacientes. A utilização da CRI pode ser efetuada em diferentes situações em ambiente real e simulado e um jogo sério foi desenvolvido permitindo especificar um conjunto de tarefas a ser realizado pelos utilizadores. Os dados foram analisados recorrendo a métodos de extração de conhecimento, de modo a obter o modelo dos utilizadores. Usando os resultados obtidos pelo sistema de classificação, foi criada uma metodologia que permite selecionar a melhor interface e linguagem de comando da cadeira para cada utilizador. A avaliação para validação da abordagem foi realizada no âmbito do Projeto FCT/RIPD/ADA/109636/2009 - "IntellWheels - Intelligent Wheelchair with Flexible Multimodal Interface". As experiências envolveram um vasto conjunto de indivíduos que sofrem de diversos níveis de deficiência, em estreita colaboração com a Escola Superior de Tecnologia de Saúde do Porto e a Associação do Porto de Paralisia Cerebral. Os dados recolhidos através das experiências de navegação na CRI foram acompanhados por questionários preenchidos pelos utilizadores. Estes dados foram analisados estatisticamente, a fim de provar a eficácia e usabilidade na adequação da interface da CRI ao utilizador. Os resultados mostraram, em ambiente simulado, um valor de usabilidade do sistema de 67, baseado na opinião de uma amostra de pacientes que apresentam os graus IV e V (os mais severos) de Paralisia Cerebral. Foi também demonstrado estatisticamente que a interface atribuída automaticamente pela ferramenta tem uma avaliação superior à sugerida pelos técnicos de Terapia Ocupacional, mostrando a possibilidade de atribuir automaticamente uma linguagem de comando adaptada a cada utilizador. Experiências realizadas com distintos modos de controlo revelaram a preferência dos utilizadores por um controlo compartilhado com um nível de ajuda associado ao nível de constrangimento do paciente. Em conclusão, este trabalho demonstra que é possível adaptar automaticamente uma CRI ao utilizador com claros benefícios a nível de usabilidade e segurança.

Metabolic signature of lung cancer: a metabolomic study of human tissues and biofluids

This thesis reports the application of metabolomics to human tissues and biofluids (blood plasma and urine) to unveil the metabolic signature of primary lung cancer. In Chapter 1, a brief introduction on lung cancer epidemiology and pathogenesis, together with a review of the main metabolic dysregulations known to be associated with cancer, is presented. The metabolomics approach is also described, addressing the analytical and statistical methods employed, as well as the current state of the art on its application to clinical lung cancer studies. Chapter 2 provides the experimental details of this work, in regard to the subjects enrolled, sample collection and analysis, and data processing. In Chapter 3, the metabolic characterization of intact lung tissues (from 56 patients) by proton High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) spectroscopy is described. After careful assessment of acquisition conditions and thorough spectral assignment (over 50 metabolites identified), the metabolic profiles of tumour and adjacent control tissues were compared through multivariate analysis. The two tissue classes could be discriminated with 97% accuracy, with 13 metabolites significantly accounting for this discrimination: glucose and acetate (depleted in tumours), together with lactate, alanine, glutamate, GSH, taurine, creatine, phosphocholine, glycerophosphocholine, phosphoethanolamine, uracil nucleotides and peptides (increased in tumours). Some of these variations corroborated typical features of cancer metabolism (e.g., upregulated glycolysis and glutaminolysis), while others suggested less known pathways (e.g., antioxidant protection, protein degradation) to play important roles. Another major and novel finding described in this chapter was the dependence of this metabolic signature on tumour histological subtype. While main alterations in adenocarcinomas (AdC) related to phospholipid and protein metabolisms, squamous cell carcinomas (SqCC) were found to have stronger glycolytic and glutaminolytic profiles, making it possible to build a valid classification model to discriminate these two subtypes. Chapter 4 reports the NMR metabolomic study of blood plasma from over 100 patients and near 100 healthy controls, the multivariate model built having afforded a classification rate of 87%. The two groups were found to differ significantly in the levels of lactate, pyruvate, acetoacetate, LDL+VLDL lipoproteins and glycoproteins (increased in patients), together with glutamine, histidine, valine, methanol, HDL lipoproteins and two unassigned compounds (decreased in patients). Interestingly, these variations were detected from initial disease stages and the magnitude of some of them depended on the histological type, although not allowing AdC vs. SqCC discrimination. Moreover, it is shown in this chapter that age mismatch between control and cancer groups could not be ruled out as a possible confounding factor, and exploratory external validation afforded a classification rate of 85%. The NMR profiling of urine from lung cancer patients and healthy controls is presented in Chapter 5. Compared to plasma, the classification model built with urinary profiles resulted in a superior classification rate (97%). After careful assessment of possible bias from gender, age and smoking habits, a set of 19 metabolites was proposed to be cancer-related (out of which 3 were unknowns and 6 were partially identified as N-acetylated metabolites). As for plasma, these variations were detected regardless of disease stage and showed some dependency on histological subtype, the AdC vs. SqCC model built showing modest predictive power. In addition, preliminary external validation of the urine-based classification model afforded 100% sensitivity and 90% specificity, which are exciting results in terms of potential for future clinical application. Chapter 6 describes the analysis of urine from a subset of patients by a different profiling technique, namely, Ultra-Performance Liquid Chromatography coupled to Mass Spectrometry (UPLC-MS). Although the identification of discriminant metabolites was very limited, multivariate models showed high classification rate and predictive power, thus reinforcing the value of urine in the context of lung cancer diagnosis. Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the potential of integrated metabolomics of tissues and biofluids to improve current understanding of lung cancer altered metabolism and to reveal new marker profiles with diagnostic value.

Concentration of tumor biomarkers using aqueous biphasic systems

According to the World Health Organization, around 8.2 million people die each year with cancer. Most patients do not perform routine diagnoses and the symptoms, in most situations, occur when the patient is already at an advanced stage of the disease, consequently resulting in a high cancer mortality. Currently, prostate cancer is the second leading cause of death among males worldwide. In Portugal, this is the most diagnosed type of cancer and the third that causes more deaths. Taking into account that there is no cure for advanced stages of prostate cancer, the main strategy comprises an early diagnosis to increase the successful rate of the treatment. The prostate specific antigen (PSA) is an important biomarker of prostate cancer that can be detected in biological fluids, including blood, urine and semen. However, the commercial kits available are addressed for blood samples and the commonly used analytical methods for their detection and quantification requires specialized staff, specific equipment and extensive sample processing, resulting in an expensive process. Thus, the aim of this MSc thesis consisted on the development of a simple, efficient and less expensive method for the extraction and concentration of PSA from urine samples using aqueous biphasic systems (ABS) composed of ionic liquids. Initially, the phase diagrams of a set of aqueous biphasic systems composed of an organic salt and ionic liquids were determined. Then, their ability to extract PSA was ascertained. The obtained results reveal that in the tested systems the prostate specific antigen is completely extracted to the ionic-liquid-rich phase in a single step. Subsequently, the applicability of the investigated ABS for the concentration of PSA was addressed, either from aqueous solutions or urine samples. The low concentration of this biomarker in urine (clinically significant below 150 ng/mL) usually hinders its detection by conventional analytical techniques. The obtained results showed that it is possible to extract and concentrate PSA, up to 250 times in a single-step, so that it can be identified and quantified using less expensive techniques.

Concentration of tumor biomarkers using aqueous biphasic systems

According to the World Health Organization, around 8.2 million people die each year with cancer. Most patients do not perform routine diagnoses and the symptoms, in most situations, occur when the patient is already at an advanced stage of the disease, consequently resulting in a high cancer mortality. Currently, prostate cancer is the second leading cause of death among males worldwide. In Portugal, this is the most diagnosed type of cancer and the third that causes more deaths. Taking into account that there is no cure for advanced stages of prostate cancer, the main strategy comprises an early diagnosis to increase the successful rate of the treatment. The prostate specific antigen (PSA) is an important biomarker of prostate cancer that can be detected in biological fluids, including blood, urine and semen. However, the commercial kits available are addressed for blood samples and the commonly used analytical methods for their detection and quantification requires specialized staff, specific equipment and extensive sample processing, resulting in an expensive process. Thus, the aim of this MSc thesis consisted on the development of a simple, efficient and less expensive method for the extraction and concentration of PSA from urine samples using aqueous biphasic systems (ABS) composed of ionic liquids. Initially, the phase diagrams of a set of aqueous biphasic systems composed of an organic salt and ionic liquids were determined. Then, their ability to extract PSA was ascertained. The obtained results reveal that in the tested systems the prostate specific antigen is completely extracted to the ionic-liquid-rich phase in a single step. Subsequently, the applicability of the investigated ABS for the concentration of PSA was addressed, either from aqueous solutions or urine samples. The low concentration of this biomarker in urine (clinically significant below 150 ng/mL) usually hinders its detection by conventional analytical techniques. The obtained results showed that it is possible to extract and concentrate PSA, up to 250 times in a single-step, so that it can be identified and quantified using less expensive techniques.

Classification of incomplete feature vectors by radial basis function networks

The paper describes the use of radial basis function neural networks with Gaussian basis functions to classify incomplete feature vectors. The method uses the fact that any marginal distribution of a Gaussian distribution can be determined from the mean vector and covariance matrix of the joint distribution.

Neural network classification of cerebral embolic signals

The presence of circulating cerebral emboli represents an increased risk of stroke. The detection of such emboli is possible with the use of a transcranial Doppler ultrasound (TCD) system.

Searching biocompounds in marine natural extracts with potential use in tumor prevention and treatment

Cancer is a multistage process characterized by three stages: initiation, promotion and progression; and is one of the major killers worldwide. Oxidative stress acts as initiator in tumorigenesis; chronic inflammation promotes cancer; and apoptosis inactivation is an issue in cancer progression. In this study, it was investigated the antioxidant, antiinflammatory and antitumor properties of hexane, ether, chloroform, methanol and water extracts of five species of halophytes: A. macrostachyum, P. coronopus, J. acutus, C. edulis and A. halimus. Antioxidant activity was assessed by DPPH• and ABTS•+ methods, and the total phenolics content (TPC) was evaluated by the Folin-Ciocalteau method. The anti-inflammatory activity of the extracts was determined by the Griess method, and by evaluating the inhibition of NO production in LPS-stimulated RAW- 264.7 macrophages. The cytotoxic activity of the extracts against HepG2 and THP1 cell lines was estimated by the MTT assay, and the results obtained were further compared with the S17 non-tumor cell line. The induction of apoptosis of J. acutus ether extract was assessed by DAPI staining. The highest antioxidant activities was observed in C. edulis methanol and the J. acutus ether extracts against the DPPH• radical; and J. acutus ether and A. halimus ether extracts against the ABTS•+ radical. The methanol extracts of C. edulis and P. coronopus, and the ether extract of J. acutus revealed a high TPC. Generally the antioxidant activity had no correlation with the TPC. The A. halimus chloroform and P. coronopus hexane extracts demonstrated ability to reduce NO production in macrophages (> 50%), revealing their anti-inflammatory capacity. The ether extract of J. acutus showed high cytotoxicity against HepG2 cancer cells, with reduced cellular viability even at the lowest concentrations. This outcome was significantly lower than the obtained with the non-tumor cells (S17). This result was complemented by the induction of apoptosis.

Automatic classification of scientific records using the German Subject Heading Authority File (SWD)

The following paper deals with an automatic text classification method which does not require training documents. For this method the German Subject Heading Authority File (SWD), provided by the linked data service of the German National Library is used. Recently the SWD was enriched with notations of the Dewey Decimal Classification (DDC). In consequence it became possible to utilize the subject headings as textual representations for the notations of the DDC. Basically, we we derive the classification of a text from the classification of the words in the text given by the thesaurus. The method was tested by classifying 3826 OAI-Records from 7 different repositories. Mean reciprocal rank and recall were chosen as evaluation measure. Direct comparison to a machine learning method has shown that this method is definitely competitive. Thus we can conclude that the enriched version of the SWD provides high quality information with a broad coverage for classification of German scientific articles.

Feature Engineering in Fine-Grained Image Classification

Thesis (Ph.D.)--University of Washington, 2013

Distinct H3F3A and H3F3B driver mutations define chondroblastoma and giant cell tumor of bone

It is recognized that some mutated cancer genes contribute to the development of many cancer types, whereas others are cancer type specific. For genes that are mutated in multiple cancer classes, mutations are usually similar in the different affected cancer types. Here, however, we report exquisite tumor type specificity for different histone H3.3 driver alterations. In 73 of 77 cases of chondroblastoma (95%), we found p.Lys36Met alterations predominantly encoded in H3F3B, which is one of two genes for histone H3.3. In contrast, in 92% (49/53) of giant cell tumors of bone, we found histone H3.3 alterations exclusively in H3F3A, leading to p.Gly34Trp or, in one case, p.Gly34Leu alterations. The mutations were restricted to the stromal cell population and were not detected in osteoclasts or their precursors. In the context of previously reported H3F3A mutations encoding p.Lys27Met and p.Gly34Arg or p.Gly34Val alterations in childhood brain tumors, a remarkable picture of tumor type specificity for histone H3.3 driver alterations emerges, indicating that histone H3.3 residues, mutations and genes have distinct functions.

Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.