Methods for Biodata Analysis in different Omic Contexts

The world of Computational Biology and Bioinformatics presently integrates many different expertise, including computer science and electronic engineering. A major aim in Data Science is the development and tuning of specific computational approaches to interpret the complexity of Biology. Molecular biologists and medical doctors heavily rely on an interdisciplinary expert capable of understanding the biological background to apply algorithms for finding optimal solutions to their problems. With this problem-solving orientation, I was involved in two basic research fields: Cancer Genomics and Enzyme Proteomics. For this reason, what I developed and implemented can be considered a general effort to help data analysis both in Cancer Genomics and in Enzyme Proteomics, focusing on enzymes which catalyse all the biochemical reactions in cells. Specifically, as to Cancer Genomics I contributed to the characterization of intratumoral immune microenvironment in gastrointestinal stromal tumours (GISTs) correlating immune cell population levels with tumour subtypes. I was involved in the setup of strategies for the evaluation and standardization of different approaches for fusion transcript detection in sarcomas that can be applied in routine diagnostic. This was part of a coordinated effort of the Sarcoma working group of "Alleanza Contro il Cancro". As to Enzyme Proteomics, I generated a derived database collecting all the human proteins and enzymes which are known to be associated to genetic disease. I curated the data search in freely available databases such as PDB, UniProt, Humsavar, Clinvar and I was responsible of searching, updating, and handling the information content, and computing statistics. I also developed a web server, BENZ, which allows researchers to annotate an enzyme sequence with the corresponding Enzyme Commission number, the important feature fully describing the catalysed reaction. More to this, I greatly contributed to the characterization of the enzyme-genetic disease association, for a better classification of the metabolic genetic diseases.

Assessing the fit of unidimensional IRT models for binary data under model misspecification

Model misspecification affects the classical test statistics used to assess the fit of the Item Response Theory (IRT) models. Robust tests have been derived under model misspecification, as the Generalized Lagrange Multiplier and Hausman tests, but their use has not been largely explored in the IRT framework. In the first part of the thesis, we introduce the Generalized Lagrange Multiplier test to detect differential item response functioning in IRT models for binary data under model misspecification. By means of a simulation study and a real data analysis, we compare its performance with the classical Lagrange Multiplier test, computed using the Hessian and the cross-product matrix, and the Generalized Jackknife Score test. The power of these tests is computed empirically and asymptotically. The misspecifications considered are local dependence among items and non-normal distribution of the latent variable. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the performance of the tests deteriorates. None of the tests considered show an overall superior performance than the others. In the second part of the thesis, we extend the Generalized Hausman test to detect non-normality of the latent variable distribution. To build the test, we consider a seminonparametric-IRT model, that assumes a more flexible latent variable distribution. By means of a simulation study and two real applications, we compare the performance of the Generalized Hausman test with the M2 limited information goodness-of-fit test and the Likelihood-Ratio test. Additionally, the information criteria are computed. The Generalized Hausman test has a better performance than the Likelihood-Ratio test in terms of Type I error rates and the M2 test in terms of power. The performance of the Generalized Hausman test and the information criteria deteriorates when the sample size is small and with a few items.

Data surveillance for infectious diseases: models, complex networks and machine learning

In this thesis, we investigate the role of applied physics in epidemiological surveillance through the application of mathematical models, network science and machine learning. The spread of a communicable disease depends on many biological, social, and health factors. The large masses of data available make it possible, on the one hand, to monitor the evolution and spread of pathogenic organisms; on the other hand, to study the behavior of people, their opinions and habits. Presented here are three lines of research in which an attempt was made to solve real epidemiological problems through data analysis and the use of statistical and mathematical models. In Chapter 1, we applied language-inspired Deep Learning models to transform influenza protein sequences into vectors encoding their information content. We then attempted to reconstruct the antigenic properties of different viral strains using regression models and to identify the mutations responsible for vaccine escape. In Chapter 2, we constructed a compartmental model to describe the spread of a bacterium within a hospital ward. The model was informed and validated on time series of clinical measurements, and a sensitivity analysis was used to assess the impact of different control measures. Finally (Chapter 3) we reconstructed the network of retweets among COVID-19 themed Twitter users in the early months of the SARS-CoV-2 pandemic. By means of community detection algorithms and centrality measures, we characterized users’ attention shifts in the network, showing that scientific communities, initially the most retweeted, lost influence over time to national political communities. In the Conclusion, we highlighted the importance of the work done in light of the main contemporary challenges for epidemiological surveillance. In particular, we present reflections on the importance of nowcasting and forecasting, the relationship between data and scientific research, and the need to unite the different scales of epidemiological surveillance.

Meaningful insights: explainability techniques for black-box models on tabular data

Artificial Intelligence (AI) and Machine Learning (ML) are novel data analysis techniques providing very accurate prediction results. They are widely adopted in a variety of industries to improve efficiency and decision-making, but they are also being used to develop intelligent systems. Their success grounds upon complex mathematical models, whose decisions and rationale are usually difficult to comprehend for human users to the point of being dubbed as black-boxes. This is particularly relevant in sensitive and highly regulated domains. To mitigate and possibly solve this issue, the Explainable AI (XAI) field became prominent in recent years. XAI consists of models and techniques to enable understanding of the intricated patterns discovered by black-box models. In this thesis, we consider model-agnostic XAI techniques, which can be applied to Tabular data, with a particular focus on the Credit Scoring domain. Special attention is dedicated to the LIME framework, for which we propose several modifications to the vanilla algorithm, in particular: a pair of complementary Stability Indices that accurately measure LIME stability, and the OptiLIME policy which helps the practitioner finding the proper balance among explanations' stability and reliability. We subsequently put forward GLEAMS a model-agnostic surrogate interpretable model which requires to be trained only once, while providing both Local and Global explanations of the black-box model. GLEAMS produces feature attributions and what-if scenarios, from both dataset and model perspective. Eventually, we argue that synthetic data are an emerging trend in AI, being more and more used to train complex models instead of original data. To be able to explain the outcomes of such models, we must guarantee that synthetic data are reliable enough to be able to translate their explanations to real-world individuals. To this end we propose DAISYnt, a suite of tests to measure synthetic tabular data quality and privacy.

The predictive role of the human gut microbiota in covid-19: implications for disease severity and post-covid syndrome

The COVID-19 pandemic, sparked by the SARS-CoV-2 virus, stirred global comparisons to historical pandemics. Initially presenting a high mortality rate, it later stabilized globally at around 0.5-3%. Patients manifest a spectrum of symptoms, necessitating efficient triaging for appropriate treatment strategies, ranging from symptomatic relief to antivirals or monoclonal antibodies. Beyond traditional approaches, emerging research suggests a potential link between COVID-19 severity and alterations in gut microbiota composition, impacting inflammatory responses. However, most studies focus on severe hospitalized cases without standardized criteria for severity. Addressing this gap, the first study in this thesis spans diverse COVID-19 severity levels, utilizing 16S rRNA amplicon sequencing on fecal samples from 315 subjects. The findings highlight significant microbiota differences correlated with severity. Machine learning classifiers, including a multi-layer convoluted neural network, demonstrated the potential of microbiota compositional data to predict patient severity, achieving an 84.2% mean balanced accuracy starting one week post-symptom onset. These preliminary results underscore the gut microbiota's potential as a biomarker in clinical decision-making for COVID-19. The second study delves into mild COVID-19 cases, exploring their implications for ‘long COVID’ or Post-Acute COVID-19 Syndrome (PACS). Employing longitudinal analysis, the study unveils dynamic shifts in microbial composition during the acute phase, akin to severe cases. Innovative techniques, including network approaches and spline-based longitudinal analysis, were deployed to assess microbiota dynamics and potential associations with PACS. The research suggests that even in mild cases, similar mechanisms to hospitalized patients are established regarding changes in intestinal microbiota during the acute phase of the infection. These findings lay the foundation for potential microbiota-targeted therapies to mitigate inflammation, potentially preventing long COVID symptoms in the broader population. In essence, these studies offer valuable insights into the intricate relationships between COVID-19 severity, gut microbiota, and the potential for innovative clinical applications.

PIATTAFORMA DI DATA VISUALIZATION GUIDATA DALLA MODELLAZIONE STATISTICA DEI DATI Applicazione a dati sanitari di pazienti oncologici della regione Emila-Romagna

Una gestione, un’analisi e un’interpretazione efficienti dei big data possono cambiare il modello lavorativo, modificare i risultati, aumentare le produzioni, e possono aprire nuove strade per l’assistenza sanitaria moderna. L'obiettivo di questo studio è incentrato sulla costruzione di una dashboard interattiva di un nuovo modello e nuove prestazioni nell’ambito della Sanità territoriale. Lo scopo è quello di fornire al cliente una piattaforma di Data Visualization che mostra risultati utili relativi ai dati sanitari in modo da fornire agli utilizzatori sia informazioni descrittive che statistiche sulla attuale gestione delle cure e delle terapie somministrate. Si propone uno strumento che consente la navigazione dei dati analizzando l’andamento di un set di indicatori di fine vita calcolati a partire da pazienti oncologici della Regione Emilia Romagna in un arco temporale che va dal 2010 ad oggi.

Screening The Life Cycle Of Schistosoma Mansoni Using High-resolution Mass Spectrometry.

Schistosomiasis is a common tropical disease caused by Schistosoma species Schistosomiasis' pathogenesis is known to vary according to the worms' strain. Moreover, high parasitical virulence is directly related to eggs release and granulomatous inflammation in the host's organs. This virulence might be influenced by different classes of molecules, such as lipids. Therefore, better understanding of the metabolic profile of these organisms is necessary, especially for an increased potential of unraveling strain virulence mechanisms and resistance to existing treatments. In this report, direct-infusion electrospray high-resolution mass spectrometry (ESI(+)-HRMS) along with the lipidomic platform were employed to rapidly characterize and differentiate two Brazilian S. mansoni strains (BH and SE) in three stages of their life cycle: eggs, miracidia and cercariae, with samples from experimental animals (Swiss/SPF mice). Furthermore, urine samples of the infected and uninfected mice were analyzed to assess the possibility of direct diagnosis. All samples were differentiated using multivariate data analysis, PCA, which helped electing markers from distinct lipid classes; phospholipids, diacylglycerols and triacylglycerols, for example, clearly presented different intensities in some stages and strains, as well as in urine samples. This indicates that biochemical characterization of S. mansoni may help narrowing-down the investigation of new therapeutic targets according to strain composition and aggressiveness of disease. Interestingly, lipid profile of infected mice urine varies when compared to control samples, indicating that direct diagnosis of schistosomiasis from urine may be feasible.

Spectrophotometric Evaluation Of Dental Bleaching Under Orthodontic Bracket In Enamel And Dentin.

Aware of the diffusion capacity of bleaching in the dental tissues, many orthodontists are subjecting their patients to dental bleaching during orthodontic treatment for esthetic purposes or to anticipate the exchange of esthetic restorations after the orthodontic treatment. For this purpose specific products have been developed in pre-loaded whitening trays designed to fit over and around brackets and wires, with clinical efficacy proven. The objective of this study was to evaluate, through spectrophotometric reflectance, the effectiveness of dental bleaching under orthodontic bracket. Thirty-two bovine incisors crown blocks of 8 mm x 8 mm height lengths were used. Staining of tooth blocks with black tea was performed for six days. They were distributed randomly into 4 groups (1-home bleaching with bracket, 2- home bleaching without bracket, 3- office bleaching with bracket, 4 office bleaching without bracket). The color evaluation was performed (CIE L * a * b *) using color reflectance spectrophotometer. Metal brackets were bonded in groups 1 and 3. The groups 1 and 2 samples were subjected to the carbamide peroxide at 15%, 4 hours daily for 21 days. Groups 3 and 4 were subjected to 3 in-office bleaching treatment sessions, hydrogen peroxide 38%. After removal of the brackets, the second color evaluation was performed in tooth block, difference between the area under the bracket and around it, and after 7 days to verified color stability. Data analysis was performed using the paired t-test and two-way variance analysis and Tukey's. The home bleaching technique proved to be more effective compared to the office bleaching. There was a significant difference between the margin and center color values of the specimens that were subjected to bracket bonding. The bracket bond presence affected the effectiveness of both the home and office bleaching treatments. Key words:Tooth bleaching, spectrophotometry, orthodontics.

A Metabolomic Protocol For Plant Systematics By Matrix-assisted Laser-desorption/ionization Time-of Flight Mass Spectrometry.

Matrix-assisted laser desorption/ionization time-of flight mass spectrometry (MALDI-TOF MS) has been widely used for the identification and classification of microorganisms based on their proteomic fingerprints. However, the use of MALDI-TOF MS in plant research has been very limited. In the present study, a first protocol is proposed for metabolic fingerprinting by MALDI-TOF MS using three different MALDI matrices with subsequent multivariate data analysis by in-house algorithms implemented in the R environment for the taxonomic classification of plants from different genera, families and orders. By merging the data acquired with different matrices, different ionization modes and using careful algorithms and parameter selection, we demonstrate that a close taxonomic classification can be achieved based on plant metabolic fingerprints, with 92% similarity to the taxonomic classifications found in literature. The present work therefore highlights the great potential of applying MALDI-TOF MS for the taxonomic classification of plants and, furthermore, provides a preliminary foundation for future research.

Derivados citotóxicos de vitanolidos isolados das folhas de Acnistus arborescens

In view of anticancer activity of 7 β-acetoxywithanolide D (2) and 7β-16α-diacetoxywithonide D (3), isolated from the leaves of Acnistus arborescens (Solanaceae), five withanolide derivatives were obtained and their structures were determined by NMR, MS and IV data analysis. The in vitro anticancer activity of these derivatives was evaluated in a panel of cancer cell lines: human breast (BC-1), human lung (Lu1), human colon (Col2) and human oral epidermoid carcinoma (KB). Compounds 2a (acetylation of 2), 3b (oxidation of 3) and 2c (hydrogenation of 2) exhibited the highest anticancer activity against human lung cancer cells, with ED50 values of 0.19, 0.25 and 0.63 μg/mL, respectively.

Mães ouvintes com filhos surdos: concepção de surdez e escolha da modalidade de linguagem

The aim of this study was to analyze the conceptions that hearing mothers of deaf children have about deafness and relate it to the language mode used by the mother and the child. We interviewed 10 mothers of deaf children, five of whom were prescholars and five of school age. The content was analyzed as to thematic and category types, with emphasis on the categories conception of deafness and choice of language mode . Data analysis showed that one mother seems to see deafness as a disease, another as a difference and the other mothers were found to be somewhere between these two views. In relation to the preferred language mode, half the mothers reported that their children predominantly use signs, the other half uses speech and signs, with the exception of one child who uses only speech. The child whose mother acts as if deafness is a disease uses speech while another one whose mother acts as if deafness is a difference uses speech as well as signs.

Avaliação da aceitação de vinhos brancos varietais brasileiros através de testes sensoriais afetivos e técnica multivariada de mapa de preferência interno

The acceptability of nine commercial brazilian varietal white table wines (Riesling, Chardonnay and Gewürztraminer) was evaluated using sensory affective tests. The samples were assessed by 43 consumers of brazilian white wines using he nine-point structured hedonic scale. Judges were recruited based on their responses to a questionnary about consumer?s behavior towards white wines consumption. Subsequently, Analysis of Variance (ANOVA) with means comparision (Tukey test) and Internal Analysis of Preference Mapping (MDPREF) were performed on data. Analysis of Variance showed that two samples (a Riesling and a Gewürztraminer, both sweet table wines) had significantly (p < 0.05) higher acceptance means, around 7 in the hedonic scale. The least acceptance means (4,3) was obtained by a demi-sec Chardonnay wine and the other six samples achieved means around 5 in the hedonic scale, all of them either demi-sec or dry table wines. MDPREF confirmed the results showed by ANOVA showing that samples were segmented into two groups of preference. The first group was composed by 86% of consumers who prefered the sweet table wines (higher acceptance), converging to the region on the map where these samples were represented. Only 14% showed preference for the demi-sec and dry table wines, being represented on the region of the MDPREF where these samples were located. This study suggests that sweet table wines are prefered by Brazilian consumers, instead of dry or demi-sec table wines.

O esporte paraolímpico no Brasil : abordagem da sociologia do esporte de Pierre Bourdieu

Universidade Estadual de Campinas . Faculdade de Educação Física

Qualidade de vida na empresa : conceitos e práticas em uso (no segmento do comércio de bens e serviços na cidade de São Paulo)

Universidade Estadual de Campinas . Faculdade de Educação Física

Determinação da velolcidade critica como referencia do limiar anaerobio para o nado costas

Universidade Estadual de Campinas . Faculdade de Educação Física