Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.

Digital 3D reconstruction as a research environment in art and architecture history: uncertainty classification and visualisation

The dissertation addresses the still not solved challenges concerned with the source-based digital 3D reconstruction, visualisation and documentation in the domain of archaeology, art and architecture history. The emerging BIM methodology and the exchange data format IFC are changing the way of collaboration, visualisation and documentation in the planning, construction and facility management process. The introduction and development of the Semantic Web (Web 3.0), spreading the idea of structured, formalised and linked data, offers semantically enriched human- and machine-readable data. In contrast to civil engineering and cultural heritage, academic object-oriented disciplines, like archaeology, art and architecture history, are acting as outside spectators. Since the 1990s, it has been argued that a 3D model is not likely to be considered a scientific reconstruction unless it is grounded on accurate documentation and visualisation. However, these standards are still missing and the validation of the outcomes is not fulfilled. Meanwhile, the digital research data remain ephemeral and continue to fill the growing digital cemeteries. This study focuses, therefore, on the evaluation of the source-based digital 3D reconstructions and, especially, on uncertainty assessment in the case of hypothetical reconstructions of destroyed or never built artefacts according to scientific principles, making the models shareable and reusable by a potentially wide audience. The work initially focuses on terminology and on the definition of a workflow especially related to the classification and visualisation of uncertainty. The workflow is then applied to specific cases of 3D models uploaded to the DFG repository of the AI Mainz. In this way, the available methods of documenting, visualising and communicating uncertainty are analysed. In the end, this process will lead to a validation or a correction of the workflow and the initial assumptions, but also (dealing with different hypotheses) to a better definition of the levels of uncertainty.

Efficient and low-computational predictive models for spectral sensors

Spectral sensors are a wide class of devices that are extremely useful for detecting essential information of the environment and materials with high degree of selectivity. Recently, they have achieved high degrees of integration and low implementation cost to be suited for fast, small, and non-invasive monitoring systems. However, the useful information is hidden in spectra and it is difficult to decode. So, mathematical algorithms are needed to infer the value of the variables of interest from the acquired data. Between the different families of predictive modeling, Principal Component Analysis and the techniques stemmed from it can provide very good performances, as well as small computational and memory requirements. For these reasons, they allow the implementation of the prediction even in embedded and autonomous devices. In this thesis, I will present 4 practical applications of these algorithms to the prediction of different variables: moisture of soil, moisture of concrete, freshness of anchovies/sardines, and concentration of gasses. In all of these cases, the workflow will be the same. Initially, an acquisition campaign was performed to acquire both spectra and the variables of interest from samples. Then these data are used as input for the creation of the prediction models, to solve both classification and regression problems. From these models, an array of calibration coefficients is derived and used for the implementation of the prediction in an embedded system. The presented results will show that this workflow was successfully applied to very different scientific fields, obtaining autonomous and non-invasive devices able to predict the value of physical parameters of choice from new spectral acquisitions.

Identification of potential miRNAs related to classification and treatment response of actinic keratosis

The Workflow activity was the following: Preliminary phase: Identification of 18 Formalin-fixed paraffin embedded (FFPE) samples (9 patients) («matched» 9 AK lesions and 9 SCC lesions). Working on biopsies samples we perform an extraction and RNA analysis with droplet Digital PCR (ddPCR) and we perform the data analysis. Second and final step phase: Evaluation of additional 39 subjects (36 men and 3 women). Results: We perform an evaluation and comparison of the following miRNA: miR-320 (a miRNA involved in apoptosis and cell proliferation control; miR-204, a miRNA involved in cell proliferation in and miRNA-16-5p, a miRNA involved in apoptosis).Conclusion: Our data suggest that there is no significant variation in the expression of the three tested microRNAs between adjacent AK lesions and squamous-cell carcinoma. However, a relevant trend has been observed Furthermore, by evaluating the miRNA expression trend between keratosis and carcinoma of the same patient, it is observed that there is no "uniform trend": for some samples the expression rises for the transition from AK to SCC and viceversa.

NMR, Metabonomics and Molecular Profiles: Applications to the Quality Assessment of Foodstuffs

Nuclear Magnetic Resonance (NMR) is a branch of spectroscopy that is based on the fact that many atomic nuclei may be oriented by a strong magnetic field and will absorb radiofrequency radiation at characteristic frequencies. The parameters that can be measured on the resulting spectral lines (line positions, intensities, line widths, multiplicities and transients in time-dependent experi-ments) can be interpreted in terms of molecular structure, conformation, molecular motion and other rate processes. In this way, high resolution (HR) NMR allows performing qualitative and quantitative analysis of samples in solution, in order to determine the structure of molecules in solution and not only. In the past, high-field NMR spectroscopy has mainly concerned with the elucidation of chemical structure in solution, but today is emerging as a powerful exploratory tool for probing biochemical and physical processes. It represents a versatile tool for the analysis of foods. In literature many NMR studies have been reported on different type of food such as wine, olive oil, coffee, fruit juices, milk, meat, egg, starch granules, flour, etc using different NMR techniques. Traditionally, univariate analytical methods have been used to ex-plore spectroscopic data. This method is useful to measure or to se-lect a single descriptive variable from the whole spectrum and , at the end, only this variable is analyzed. This univariate methods ap-proach, applied to HR-NMR data, lead to different problems due especially to the complexity of an NMR spectrum. In fact, the lat-ter is composed of different signals belonging to different mole-cules, but it is also true that the same molecules can be represented by different signals, generally strongly correlated. The univariate methods, in this case, takes in account only one or a few variables, causing a loss of information. Thus, when dealing with complex samples like foodstuff, univariate analysis of spectra data results not enough powerful. Spectra need to be considered in their wholeness and, for analysing them, it must be taken in consideration the whole data matrix: chemometric methods are designed to treat such multivariate data. Multivariate data analysis is used for a number of distinct, differ-ent purposes and the aims can be divided into three main groups: • data description (explorative data structure modelling of any ge-neric n-dimensional data matrix, PCA for example); • regression and prediction (PLS); • classification and prediction of class belongings for new samples (LDA and PLS-DA and ECVA). The aim of this PhD thesis was to verify the possibility of identify-ing and classifying plants or foodstuffs, in different classes, based on the concerted variation in metabolite levels, detected by NMR spectra and using the multivariate data analysis as a tool to inter-pret NMR information. It is important to underline that the results obtained are useful to point out the metabolic consequences of a specific modification on foodstuffs, avoiding the use of a targeted analysis for the different metabolites. The data analysis is performed by applying chemomet-ric multivariate techniques to the NMR dataset of spectra acquired. The research work presented in this thesis is the result of a three years PhD study. This thesis reports the main results obtained from these two main activities: A1) Evaluation of a data pre-processing system in order to mini-mize unwanted sources of variations, due to different instrumental set up, manual spectra processing and to sample preparations arte-facts; A2) Application of multivariate chemiometric models in data analy-sis.

AAGL 2021 classification system and risk of surgical complications in women submitted to surgery for endometriosis

Introduction: Recently, the American Association of Gynecologic Laparoscopists proposed a new classification and scoring system with the specific aim to assess surgical complexity. This study sought to assess if a higher AAGL score correlates with an increased risk of peri-operative complications in women submitted to surgery for endometriosis. Methods: This is a retrospective cohort study conducted in a third level referral center. We collected data from women with endometriosis submitted to complete surgical removal of endometriosis from January 2019 to December 2021. ENZIAN, r-ASRM classifications and AAGL total score was calculated for each patient. Population was divided in two groups according to the occurrence or not of at least one peri-operative complication. Our primary outcome was to evaluate the correlation between AAGL score and occurrence of complications. Results: During the study period we analyzed data from 282 eligible patients. Among them, 80 (28.4%) experienced peri-operative complications. No statistically significant difference was found between the two groups in terms of baseline characteristics, except for pre-operative hemoglobin (Hb), which was lower in patients with complications (p=0.001). Surgical variables associated with the occurrence of complications were recto-sigmoid surgery (p=0.003), ileocecal resection (0.034), and longer operative time (p=0.007). Furthermore, a higher ENZIAN B score (p=0.006), AAGL score (p=0.045) and stage (p=0.022) were found in the group of patients with complications. The multivariate analysis only confirmed the significant association between the occurrence of peri-operative complications and lower pre-operative Hb level (OR 0.74; 95% CI, 0.59 - 0.94; p=0.014), longer operative time (OR 1.00; 95% CI, 1.00 – 1.01; p=0.013), recto-sigmoid surgery - especially discoid resection (OR 8.73; 95% CI, 2.18 – 35; p=0.016) and ENZIAN B3 (OR 3.62; 95% CI, 1.46 – 8.99; p= 0.006). Conclusion: According to our findings, high AAGL scores or stages do not seem to increase the risk of peri-operative complications.

Angiogenesis and angioregression gene expression analyses in swine corpus luteum

The corpus luteum (CL) lifespan is characterized by a rapid growth, differentiation and controlled regression of the luteal tissue, accompanied by an intense angiogenesis and angioregression. Indeed, the CL is one of the most highly vascularised tissue in the body with a proliferation rate of the endothelial cells 4- to 20-fold more intense than in some of the most malignant human tumours. This angiogenic process should be rigorously controlled to allow the repeated opportunities of fertilization. After a first period of rapid growth, the tissue becomes stably organized and prepares itself to switch to the phenotype required for its next apoptotic regression. In pregnant swine, the lifespan of the CLs must be extended to support embryonic and foetal development and vascularisation is necessary for the maintenance of luteal function. Among the molecules involved in the angiogenesis, Vascular Endothelial Growth Factor (VEGF) is the main regulator, promoting endothelial cells proliferation, differentiation and survival as well as vascular permeability and vessel lumen formation. During vascular invasion and apoptosis process, the remodelling of the extracellular matrix is essential for the correct evolution of the CL, particularly by the action of specific class of proteolytic enzymes known as matrix metalloproteinases (MMPs). Another important factor that plays a role in the processes of angiogenesis and angioregression during the CL formation and luteolysis is the isopeptide Endothelin-1 (ET-1), which is well-known to be a potent vasoconstrictor and mitogen for endothelial cells. The goal of the present thesis was to study the role and regulation of vascularisation in an adult vascular bed. For this purpose, using a precisely controlled in vivo model of swine CL development and regression, we determined the levels of expression of the members of VEGF system (VEGF total and specific isoforms; VEGF receptor-1, VEGFR-1; VEGF receptor-2, VEGFR-2) and ET- 1 system (ET-1; endothelin converting enzyme-1, ECE-1; endothelin receptor type A, ET-A) as well as the activity of the Ca++/Mg++-dependent endonucleases and gelatinases (MMP-2 and MMP-9). Three experiments were conducted to reach such objectives in CLs isolated from ovaries of cyclic, pregnant or fasted gilts. In the Experiment I, we evaluated the influence of acute fasting on VEGF production and VEGF, VEGFR-2, ET-1, ECE-1 and ET-A mRNA expressions in CLs collected on day 6 after ovulation (midluteal phase). The results indicated a down-regulation of VEGF, VEGFR-2, ET-1 and ECE-1 mRNA expression, although no change was observed for VEGF protein. Furthermore, we observed that fasting stimulated steroidogenesis by luteal cells. On the basis of the main effects of VEGF (stimulation of vessel growth and endothelial permeability) and ET-1 (stimulation of endothelial cell proliferation and vasoconstriction, as well as VEGF stimulation), we concluded that feed restriction possibly inhibited luteal vessel development. This could be, at least in part, compensated by a decrease of vasal tone due to a diminution of ET-1, thus ensuring an adequate blood flow and the production of steroids by the luteal cells. In the Experiment II, we investigated the relationship between VEGF, gelatinases and Ca++/Mg++-dependent endonucleases activities with the functional CL stage throughout the oestrous cycle and at pregnancy. The results demonstrated differential patterns of expression of those molecules in correspondence to the different phases of the oestrous cycle. Immediately after ovulation, VEGF mRNA/protein levels and MMP-9 activity are maximal. On days 5–14 after ovulation, VEGF expression and MMP-2 and -9 activities are at basal levels, while Ca++/Mg++-dependent endonuclease levels increased significantly in relation to day 1. Only at luteolysis (day 17), Ca++/Mg++-dependent endonuclease and MMP-2 spontaneous activity increased significantly. At pregnancy, high levels of MMP-9 and VEGF were observed. These results suggested that during the very early luteal phase, high MMPs activities coupled with high VEGF levels drive the tissue to an angiogenic phenotype, allowing CL growth under LH (Luteinising Hormone) stimulus, while during the late luteal phase, low VEGF and elevate MMPs levels may play a role in the apoptotic tissue and extracellular matrix remodelling during structural luteolysis. In the Experiment III, we described the expression patterns of all distinct VEGF isoforms throughout the oestrous cycle. Furthermore, the mRNA expression and protein levels of both VEGF receptors were also evaluated. Four novel VEGF isoforms (VEGF144, VEGF147, VEGF182, and VEGF164b) were found for the first time in swine and the seven identified isoforms presented four different patterns of expression. All isoforms showed their highest mRNA levels in newly formed CLs (day 1), followed by a decrease during mid-late luteal phase (days 10–17), except for VEGF182, VEGF188 and VEGF144 that showed a differential regulation during late luteal phase (day 14) or at luteolysis (day 17). VEGF protein levels paralleled the most expressed and secreted VEGF120 and VEGF164 isoforms. The VEGF receptors mRNAs showed a different pattern of expression in relation to their ligands, increasing between day 1 and 3 and gradually decreasing during the mid-late luteal phase. The differential regulation of some VEGF isoforms principally during the late luteal phase and luteolysis suggested a specific role of VEGF during tissue remodelling process that occurs either for CL maintenance in case of pregnancy or for noncapillary vessel development essential for tissue removal during structural luteolysis. In summary, our findings allow us to determine relationships among factors involved in the angiogenesis and angioregression mechanisms that take place during the formation and regression of the CL. Thus, CL provides a very interesting model for studying such factors in different fields of the basic research.

Mass spectrometry-based protein profiling strategies for biomarker discovery in liver and inflammatory bowel diseases

The study of protein expression profiles for biomarker discovery in serum and in mammalian cell populations needs the continuous improvement and combination of proteins/peptides separation techniques, mass spectrometry, statistical and bioinformatic approaches. In this thesis work two different mass spectrometry-based protein profiling strategies have been developed and applied to liver and inflammatory bowel diseases (IBDs) for the discovery of new biomarkers. The first of them, based on bulk solid-phase extraction combined with matrix-assisted laser desorption/ionization - Time of Flight mass spectrometry (MALDI-TOF MS) and chemometric analysis of serum samples, was applied to the study of serum protein expression profiles both in IBDs (Crohn’s disease and ulcerative colitis) and in liver diseases (cirrhosis, hepatocellular carcinoma, viral hepatitis). The approach allowed the enrichment of serum proteins/peptides due to the high interaction surface between analytes and solid phase and the high recovery due to the elution step performed directly on the MALDI-target plate. Furthermore the use of chemometric algorithm for the selection of the variables with higher discriminant power permitted to evaluate patterns of 20-30 proteins involved in the differentiation and classification of serum samples from healthy donors and diseased patients. These proteins profiles permit to discriminate among the pathologies with an optimum classification and prediction abilities. In particular in the study of inflammatory bowel diseases, after the analysis using C18 of 129 serum samples from healthy donors and Crohn’s disease, ulcerative colitis and inflammatory controls patients, a 90.7% of classification ability and a 72.9% prediction ability were obtained. In the study of liver diseases (hepatocellular carcinoma, viral hepatitis and cirrhosis) a 80.6% of prediction ability was achieved using IDA-Cu(II) as extraction procedure. The identification of the selected proteins by MALDITOF/ TOF MS analysis or by their selective enrichment followed by enzymatic digestion and MS/MS analysis may give useful information in order to identify new biomarkers involved in the diseases. The second mass spectrometry-based protein profiling strategy developed was based on a label-free liquid chromatography electrospray ionization quadrupole - time of flight differential analysis approach (LC ESI-QTOF MS), combined with targeted MS/MS analysis of only identified differences. The strategy was used for biomarker discovery in IBDs, and in particular of Crohn’s disease. The enriched serum peptidome and the subcellular fractions of intestinal epithelial cells (IECs) from healthy donors and Crohn’s disease patients were analysed. The combining of the low molecular weight serum proteins enrichment step and the LCMS approach allowed to evaluate a pattern of peptides derived from specific exoprotease activity in the coagulation and complement activation pathways. Among these peptides, particularly interesting was the discovery of clusters of peptides from fibrinopeptide A, Apolipoprotein E and A4, and complement C3 and C4. Further studies need to be performed to evaluate the specificity of these clusters and validate the results, in order to develop a rapid serum diagnostic test. The analysis by label-free LC ESI-QTOF MS differential analysis of the subcellular fractions of IECs from Crohn’s disease patients and healthy donors permitted to find many proteins that could be involved in the inflammation process. Among them heat shock protein 70, tryptase alpha-1 precursor and proteins whose upregulation can be explained by the increased activity of IECs in Crohn’s disease were identified. Follow-up studies for the validation of the results and the in-depth investigation of the inflammation pathways involved in the disease will be performed. Both the developed mass spectrometry-based protein profiling strategies have been proved to be useful tools for the discovery of disease biomarkers that need to be validated in further studies.

Salute orale del figlio disabile in età evolutiva e sintomatologia depressiva della madre

Relationships between oral health status in children with disability and their mothers’ depressive symptoms Aim. The purpose of the present study was to evaluate the relationships between oral health status in children with chronic medical conditions and their mothers’ depressive symptoms. Methods. Fifty-one children (25 male and 26 female, ranging from 2 to 18 years) affected by chronic systemic diseases followed at the Sant’Orsola-Malpighi Hospital in Bologna, ,and, were referred with their mothers at the Dental Department of Bologna. Children were subclassified in 3 groups according to the ASA classification and orally examined for hygiene status, gingival condition and dental caries. The indexes used were O’Leary plaque Index (PI), bleeding on probing index (BOP), dmft/DMFT. Mothers were interviewed on knowledge about oral diseases prevention for their children and daily management (hygiene habits, sugared aliments consumption). Statistical analysis was performed through the use of linear regression. Results. The relationships between ASA and IP as well as between ASA and BOP are statistically significant (α = 0,01). Seventy percent of patients and their relatives in ASA groups 3 and 4 never received information on oral health and prevention of oral diseases by paediatricians and/or dentists. The 53% of mothers present depressive symptoms. The relationships between degree of depressive symptoms and dmft/DMFt as well as between degree of depressive symptoms and sugared aliments daily consumption are statistically significant (α = 0,05). Conclusion. Our results give support to the hypothesis of an association between degree of systemic disease and oral hygiene status. The psychological mothers condition seams to play a role on the oral conditions of their sons. Our analysis shows the needs for an interdisciplinar approach in order to promote the oral health of children with disability.

Bayesian Analysis of Linear Inverse Problems with Applications in Economics and Finance

In my PhD thesis I propose a Bayesian nonparametric estimation method for structural econometric models where the functional parameter of interest describes the economic agent's behavior. The structural parameter is characterized as the solution of a functional equation, or by using more technical words, as the solution of an inverse problem that can be either ill-posed or well-posed. From a Bayesian point of view, the parameter of interest is a random function and the solution to the inference problem is the posterior distribution of this parameter. A regular version of the posterior distribution in functional spaces is characterized. However, the infinite dimension of the considered spaces causes a problem of non continuity of the solution and then a problem of inconsistency, from a frequentist point of view, of the posterior distribution (i.e. problem of ill-posedness). The contribution of this essay is to propose new methods to deal with this problem of ill-posedness. The first one consists in adopting a Tikhonov regularization scheme in the construction of the posterior distribution so that I end up with a new object that I call regularized posterior distribution and that I guess it is solution of the inverse problem. The second approach consists in specifying a prior distribution on the parameter of interest of the g-prior type. Then, I detect a class of models for which the prior distribution is able to correct for the ill-posedness also in infinite dimensional problems. I study asymptotic properties of these proposed solutions and I prove that, under some regularity condition satisfied by the true value of the parameter of interest, they are consistent in a "frequentist" sense. Once I have set the general theory, I apply my bayesian nonparametric methodology to different estimation problems. First, I apply this estimator to deconvolution and to hazard rate, density and regression estimation. Then, I consider the estimation of an Instrumental Regression that is useful in micro-econometrics when we have to deal with problems of endogeneity. Finally, I develop an application in finance: I get the bayesian estimator for the equilibrium asset pricing functional by using the Euler equation defined in the Lucas'(1978) tree-type models.

International cooperation in endourology: percutaneous and flexible ureteroscopic treatment of lower pole kidney stones

Introduction Lower pole kidney stones represent at time a challenge for the urologist. The gold standard treatment for intrarenal stones <2 cm is Extracorporeal Shock Wave Lithotripsy (ESWL) while for those >2 cm is Percutaneous Nephrolithotomy (PCNL). The success rate of ESWL, however, decreases when it is employed for lower pole stones, and this is particularly true in the presence of narrow calices or acute infundibular angles. Studies have proved that ureteroscopy (URS) is an efficacious alternative to ESWL for lower pole stones <2 cm, but this is not reflected by either the European or the American guidelines. The aim of this study is to present the results of a large series of flexible ureteroscopies and PCNLs for lower pole kidney stones from high-volume centers, in order to provide more evidences on the potential indications of the flexible ureteroscopy for the treatment of kidney stones. Materials and Methods A database was created and the participating centres retrospectively entered their data relating to the percutaneous and flexible ureteroscopic management of lower pole kidney stones. Patients included were treated between January 2005 and January 2010. Variables analyzed included case load number, preoperative and postoperative imaging, stone burden, anaesthesia (general vs. spinal), type of lithotripter, access location and size, access dilation type, ureteral access sheath use, visual clarity, operative time, stone-free rate, complication rate, hospital stay, analgesic requirement and follow-up time. Stone-free rate was defined as absence of residual fragments or presence of a single fragment <2 mm in size at follow-up imaging. Primary end-point was to test the efficacy and safety of flexible URS for the treatment of lower pole stones; the same descriptive analysis was conducted for the PCNL approach, as considered the gold standard for the treatment of lower pole kidney stones. In this setting, no statistical analysis was conducted owing to the different selection criteria of the patients. Secondary end-point consisted in matching the results of stone-free rates, operative time and complications rate of flexible URS and PCNL in the subgroup of patients harbouring lower pole kidney stones between 1 and 2 cm in the higher diameter. Results A total 246 patients met the criteria for inclusion. There were 117 PCNLs (group 1) and 129 flexible URS (group 2). Ninety-six percent of cases were diagnosed by CT KUB scan. Mean stone burden was 175±160 and 50±62 mm2 for groups 1 and 2, respectively. General anaesthesia was induced in 100 % and 80% of groups 1 and 2, respectively. Pneumo-ultrasonic energy was used in 84% of cases in the PCNL group, and holmium laser in 95% of the cases in the flexible URS group. The mean operative time was 76.9±44 and 63±37 minutes for groups 1 and 2 respectively. There were 12 major complications (11%) in group 1 (mainly Grade II complications according to Clavidien classification) and no major complications in group 2. Mean hospital stay was 5.7 and 2.6 days for groups 1 and 2, respectively. Ninety-five percent of group 1 and 52% of group 2 required analgesia for a period longer than 24 hours. Intraoperative stone-free rate after a single treatment was 88.9% for group 1 and 79.1% for group 2. Overall, 6% of group 1 and 14.7% of group 2 required a second look procedure. At 3 months, stone-free rates were 90.6% and 92.2% for groups 1 and 2, respectively, as documented by follow-up CT KUB (22%) or combination of intra-venous pyelogram, regular KUB and/or kidney ultrasound (78%). In the subanalysis conducted comparing 82 vs 65 patients who underwent PCNL and flexible URS for lower pole stones between 1 and 2 cm, intreoperative stone-free rates were 88% vs 68% (p= 0.03), respectively; anyway, after an auxiliary procedure which was necessary in 6% of the cases in group 1 and 23% in group 2 (p=0.03), stone-free rates at 3 months were not statistically significant (91.5% vs 89.2%; p=0.6). Conversely, the patients undergoing PCNL maintained a higher risk of complications during the procedure, with 9 cases observed in this group versus 0 in the group of patients treated with URS (p=0.01) Conclusions These data highlight the value of flexible URS as a very effective and safe option for the treatment of kidney stones; thanks to the latest generation of flexible devices, this new technical approach seems to be a valid alternative in particular for the treatment of lower pole kidney stones less than 2 cm. In high-volume centres and in the hands of skilled surgeons, this technique can approach the stone-free rates achievable through PCNL in lower pole stones between 1 and 2 cm, with a very low risk of complications. Furthermore, the results confirm the high success rate and relatively low morbidity of modern PCNL for lower pole stones, with no difference detectable between the prone and supine position.

Use of behavioural patterns to analyze transport stress in piglets and young bulls. The influence of individual coping characteristics in stress response.

In modern farm systems the economic interests make reducing the risks related to transport practice an important goal. An increasing attention is directed to the welfare of animals in transit, also considering the new existing facilities. In recent years the results coming from the study of animal farm behaviour were used as tool to assess the welfare. In this thesis were analyzed behavioural patterns, jointly with blood variables, to evaluate the stress response of piglets and young bulls during transport. Since the animal behaviour could be different between individuals and these differences can affect animal responses to aversive situations, the individual behavioural characteristics were taken in account. Regarding young bulls, selected to genetic evaluation, the individual behaviour was investigated before, during and after transport, while for piglets was adopted a tested methodology classification and behavioural tests to observe their coping characteristics. The aim of this thesis was to analyse the behavioural and physiological response of young bulls and piglets to transport practice and to investigate if coping characteristics may affect how piglets cope with aversive situations. The thesis is composed by four experimental studies. The first one aims to identify the best existent methodology classification of piglets coping style between those that were credited in literature. The second one investigated the differences in response to novel situations of piglets with different coping styles. The last studies evaluated the stress response of piglets and young bulls to road transportation. The results obtained show that transport did not affect the behaviour and homeostasis of young animals which respond in a different way from adults. However the understanding of individual behavioural characteristic and the use of behavioural patterns, in addition to blood analyses, need to be more investigated in order to be useful tools to assess the animal response in aversive situation.

Data Mining in Clinical Practice for the Quantification of Motor Impairment in Parkinson's Disease

Advances in biomedical signal acquisition systems for motion analysis have led to lowcost and ubiquitous wearable sensors which can be used to record movement data in different settings. This implies the potential availability of large amounts of quantitative data. It is then crucial to identify and to extract the information of clinical relevance from the large amount of available data. This quantitative and objective information can be an important aid for clinical decision making. Data mining is the process of discovering such information in databases through data processing, selection of informative data, and identification of relevant patterns. The databases considered in this thesis store motion data from wearable sensors (specifically accelerometers) and clinical information (clinical data, scores, tests). The main goal of this thesis is to develop data mining tools which can provide quantitative information to the clinician in the field of movement disorders. This thesis will focus on motor impairment in Parkinson's disease (PD). Different databases related to Parkinson subjects in different stages of the disease were considered for this thesis. Each database is characterized by the data recorded during a specific motor task performed by different groups of subjects. The data mining techniques that were used in this thesis are feature selection (a technique which was used to find relevant information and to discard useless or redundant data), classification, clustering, and regression. The aims were to identify high risk subjects for PD, characterize the differences between early PD subjects and healthy ones, characterize PD subtypes and automatically assess the severity of symptoms in the home setting.

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.

A clustering method for robust and reliable large scale functional and structural protein sequence annotation

Bioinformatics, in the last few decades, has played a fundamental role to give sense to the huge amount of data produced. Obtained the complete sequence of a genome, the major problem of knowing as much as possible of its coding regions, is crucial. Protein sequence annotation is challenging and, due to the size of the problem, only computational approaches can provide a feasible solution. As it has been recently pointed out by the Critical Assessment of Function Annotations (CAFA), most accurate methods are those based on the transfer-by-homology approach and the most incisive contribution is given by cross-genome comparisons. In the present thesis it is described a non-hierarchical sequence clustering method for protein automatic large-scale annotation, called “The Bologna Annotation Resource Plus” (BAR+). The method is based on an all-against-all alignment of more than 13 millions protein sequences characterized by a very stringent metric. BAR+ can safely transfer functional features (Gene Ontology and Pfam terms) inside clusters by means of a statistical validation, even in the case of multi-domain proteins. Within BAR+ clusters it is also possible to transfer the three dimensional structure (when a template is available). This is possible by the way of cluster-specific HMM profiles that can be used to calculate reliable template-to-target alignments even in the case of distantly related proteins (sequence identity < 30%). Other BAR+ based applications have been developed during my doctorate including the prediction of Magnesium binding sites in human proteins, the ABC transporters superfamily classification and the functional prediction (GO terms) of the CAFA targets. Remarkably, in the CAFA assessment, BAR+ placed among the ten most accurate methods. At present, as a web server for the functional and structural protein sequence annotation, BAR+ is freely available at http://bar.biocomp.unibo.it/bar2.0.