6 resultados para principal components analysis (PCA) algorithm

em AMS Tesi di Dottorato - Alm@DL - Università di Bologna


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Introduzione: La sindrome delle gambe senza riposo (RLS) è un disturbo caratterizzato da sensazione spiacevole disestesica generalmente agli arti inferiori, che si presenta o peggiora nelle ore serali-notturne e che migliora con il movimento. Studi clinici hanno mostrato una maggiore prevalenza di RLS negli emicranici, mentre mancano studi condotti su popolazione generale non selezionata. Lo scopo di questo studio era quello di valutare la associazione tra emicrania e RLS in una popolazione italiana adulta. Inoltre è stata valutata l’associazione tra RLS e cefalea fenotipizzata attraverso metodica di principal components analysis (PCA). Materiali e metodi: la presenza di RLS e di emicrania è stata determinata attraverso questionari basati sui criteri diagnostici correnti in un campione di 1567 partecipanti di un fase preliminare di uno studio in corso sulla popolazione adulta della Val Venosta (BZ). Risultati: gli emicranici hanno presentato un significativo maggior rischio di soffrire di RLS rispetto ai non emicranici, anche dopo aggiustamento per fattori confondenti come età, sesso, depressione, ansia e qualità del sonno (p = 0.049). Questa associazione non era modificata dalla presenza di aura emicranica, di cause possibili di RLS secondaria e dalla frequenza di attacchi emicranici. Inoltre la RLS non era risultata significativamente associata alla cefalea di tipo tensivo (TTH). Dall’analisi di associazione tra RLS e cefalea fenotipizzata attraverso PCA era emerso che la componente 1, caratterizzata da sintomi di sensitivizzazione del sistema nervoso centrale (SNC), correlava significativamente con la presenza di RLS (p = 0.021). Conclusioni: RLS ed emicrania sono risultate associate nel nostro campione di popolazione adulta; inoltre la RLS ha mostrato una correlazione significativa con i sintomi di sensitivizzazione del SNC legati agli attacchi di cefalea. Questa associazione potrebbe risiedere in una possibile base patogenetica comune.

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This study focuses on the use of metabonomics applications in measuring fish freshness in various biological species and in evaluating how they are stored. This metabonomic approach is innovative and is based upon molecular profiling through nuclear magnetic resonance (NMR). On one hand, the aim is to ascertain if a type of fish has maintained, within certain limits, its sensory and nutritional characteristics after being caught; and on the second, the research observes the alterations in the product’s composition. The spectroscopic data obtained through experimental nuclear magnetic resonance, 1H-NMR, of the molecular profiles of the fish extracts are compared with those obtained on the same samples through analytical and conventional methods now in practice. These second methods are used to obtain chemical indices of freshness through biochemical and microbial degradation of the proteic nitrogen compounds and not (trimethylamine, N-(CH3)3, nucleotides, amino acids, etc.). At a later time, a principal components analysis (PCA) and a linear discriminant analysis (PLS-DA) are performed through a metabonomic approach to condense the temporal evolution of freshness into a single parameter. In particular, the first principal component (PC1) under both storage conditions (4 °C and 0 °C) represents the component together with the molecular composition of the samples (through 1H-NMR spectrum) evolving during storage with a very high variance. The results of this study give scientific evidence supporting the objective elements evaluating the freshness of fish products showing those which can be labeled “fresh fish.”

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This thesis is focused on the metabolomic study of human cancer tissues by ex vivo High Resolution-Magic Angle Spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy. This new technique allows for the acquisition of spectra directly on intact tissues (biopsy or surgery), and it has become very important for integrated metabonomics studies. The objective is to identify metabolites that can be used as markers for the discrimination of the different types of cancer, for the grading, and for the assessment of the evolution of the tumour. Furthermore, an attempt to recognize metabolites, that although involved in the metabolism of tumoral tissues in low concentration, can be important modulators of neoplastic proliferation, was performed. In addition, NMR data was integrated with statistical techniques in order to obtain semi-quantitative information about the metabolite markers. In the case of gliomas, the NMR study was correlated with gene expression of neoplastic tissues. Chapter 1 begins with a general description of a new “omics” study, the metabolomics. The study of metabolism can contribute significantly to biomedical research and, ultimately, to clinical medical practice. This rapidly developing discipline involves the study of the metabolome: the total repertoire of small molecules present in cells, tissues, organs, and biological fluids. Metabolomic approaches are becoming increasingly popular in disease diagnosis and will play an important role on improving our understanding of cancer mechanism. Chapter 2 addresses in more detail the basis of NMR Spectroscopy, presenting the new HR-MAS NMR tool, that is gaining importance in the examination of tumour tissues, and in the assessment of tumour grade. Some advanced chemometric methods were used in an attempt to enhance the interpretation and quantitative information of the HR-MAS NMR data are and presented in chapter 3. Chemometric methods seem to have a high potential in the study of human diseases, as it permits the extraction of new and relevant information from spectroscopic data, allowing a better interpretation of the results. Chapter 4 reports results obtained from HR-MAS NMR analyses performed on different brain tumours: medulloblastoma, meningioms and gliomas. The medulloblastoma study is a case report of primitive neuroectodermal tumor (PNET) localised in the cerebellar region by Magnetic Resonance Imaging (MRI) in a 3-year-old child. In vivo single voxel 1H MRS shows high specificity in detecting the main metabolic alterations in the primitive cerebellar lesion; which consist of very high amounts of the choline-containing compounds and of very low levels of creatine derivatives and N-acetylaspartate. Ex vivo HR-MAS NMR, performed at 9.4 Tesla on the neoplastic specimen collected during surgery, allows the unambiguous identification of several metabolites giving a more in-depth evaluation of the metabolic pattern of the lesion. The ex vivo HR-MAS NMR spectra show higher detail than that obtained in vivo. In addition, the spectroscopic data appear to correlate with some morphological features of the medulloblastoma. The present study shows that ex vivo HR-MAS 1H NMR is able to strongly improve the clinical possibility of in vivo MRS and can be used in conjunction with in vivo spectroscopy for clinical purposes. Three histological subtypes of meningiomas (meningothelial, fibrous and oncocytic) were analysed both by in vivo and ex vivo MRS experiments. The ex vivo HR-MAS investigations are very helpful for the assignment of the in vivo resonances of human meningiomas and for the validation of the quantification procedure of in vivo MR spectra. By using one- and two dimensional experiments, several metabolites in different histological subtypes of meningiomas, were identified. The spectroscopic data confirmed the presence of the typical metabolites of these benign neoplasms and, at the same time, that meningomas with different morphological characteristics have different metabolic profiles, particularly regarding macromolecules and lipids. The profile of total choline metabolites (tCho) and the expression of the Kennedy pathway genes in biopsies of human gliomas were also investigated using HR-MAS NMR, and microfluidic genomic cards. 1H HR-MAS spectra, allowed the resolution and relative quantification by LCModel of the resonances from choline (Cho), phosphorylcholine (PC) and glycerolphorylcholine (GPC), the three main components of the combined tCho peak observed in gliomas by in vivo 1H MRS spectroscopy. All glioma biopsies depicted an increase in tCho as calculated from the addition of Cho, PC and GPC HR-MAS resonances. However, the increase was constantly derived from augmented GPC in low grade NMR gliomas or increased PC content in the high grade gliomas, respectively. This circumstance allowed the unambiguous discrimination of high and low grade gliomas by 1H HR-MAS, which could not be achieved by calculating the tCho/Cr ratio commonly used by in vivo 1H MR spectroscopy. The expression of the genes involved in choline metabolism was investigated in the same biopsies. The present findings offer a convenient procedure to classify accurately glioma grade using 1H HR-MAS, providing in addition the genetic background for the alterations of choline metabolism observed in high and low gliomas grade. Chapter 5 reports the study on human gastrointestinal tract (stomach and colon) neoplasms. The human healthy gastric mucosa, and the characteristics of the biochemical profile of human gastric adenocarcinoma in comparison with that of healthy gastric mucosa were analyzed using ex vivo HR-MAS NMR. Healthy human mucosa is mainly characterized by the presence of small metabolites (more than 50 identified) and macromolecules. The adenocarcinoma spectra were dominated by the presence of signals due to triglycerides, that are usually very low in healthy gastric mucosa. The use of spin-echo experiments enable us to detect some metabolites in the unhealthy tissues and to determine their variation with respect to the healthy ones. Then, the ex vivo HR-MAS NMR analysis was applied to human gastric tissue, to obtain information on the molecular steps involved in the gastric carcinogenesis. A microscopic investigation was also carried out in order to identify and locate the lipids in the cellular and extra-cellular environments. Correlation of the morphological changes detected by transmission (TEM) and scanning (SEM) electron microscopy, with the metabolic profile of gastric mucosa in healthy, gastric atrophy autoimmune diseases (AAG), Helicobacter pylori-related gastritis and adenocarcinoma subjects, were obtained. These ultrastructural studies of AAG and gastric adenocarcinoma revealed lipid intra- and extra-cellularly accumulation associated with a severe prenecrotic hypoxia and mitochondrial degeneration. A deep insight into the metabolic profile of human healthy and neoplastic colon tissues was gained using ex vivo HR-MAS NMR spectroscopy in combination with multivariate methods: Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). The NMR spectra of healthy tissues highlight different metabolic profiles with respect to those of neoplastic and microscopically normal colon specimens (these last obtained at least 15 cm far from the adenocarcinoma). Furthermore, metabolic variations are detected not only for neoplastic tissues with different histological diagnosis, but also for those classified identical by histological analysis. These findings suggest that the same subclass of colon carcinoma is characterized, at a certain degree, by metabolic heterogeneity. The statistical multivariate approach applied to the NMR data is crucial in order to find metabolic markers of the neoplastic state of colon tissues, and to correctly classify the samples. Significant different levels of choline containing compounds, taurine and myoinositol, were observed. Chapter 6 deals with the metabolic profile of normal and tumoral renal human tissues obtained by ex vivo HR-MAS NMR. The spectra of human normal cortex and medulla show the presence of differently distributed osmolytes as markers of physiological renal condition. The marked decrease or disappearance of these metabolites and the high lipid content (triglycerides and cholesteryl esters) is typical of clear cell renal carcinoma (RCC), while papillary RCC is characterized by the absence of lipids and very high amounts of taurine. This research is a contribution to the biochemical classification of renal neoplastic pathologies, especially for RCCs, which can be evaluated by in vivo MRS for clinical purposes. Moreover, these data help to gain a better knowledge of the molecular processes envolved in the onset of renal carcinogenesis.

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Coastal sand dunes represent a richness first of all in terms of defense from the sea storms waves and the saltwater ingression; moreover these morphological elements constitute an unique ecosystem of transition between the sea and the land environment. The research about dune system is a strong part of the coastal sciences, since the last century. Nowadays this branch have assumed even more importance for two reasons: on one side the born of brand new technologies, especially related to the Remote Sensing, have increased the researcher possibilities; on the other side the intense urbanization of these days have strongly limited the dune possibilities of development and fragmented what was remaining from the last century. This is particularly true in the Ravenna area, where the industrialization united to the touristic economy and an intense subsidence, have left only few dune ridges residual still active. In this work three different foredune ridges, along the Ravenna coast, have been studied with Laser Scanner technology. This research didn’t limit to analyze volume or spatial difference, but try also to find new ways and new features to monitor this environment. Moreover the author planned a series of test to validate data from Terrestrial Laser Scanner (TLS), with the additional aim of finalize a methodology to test 3D survey accuracy. Data acquired by TLS were then applied on one hand to test some brand new applications, such as Digital Shore Line Analysis System (DSAS) and Computational Fluid Dynamics (CFD), to prove their efficacy in this field; on the other hand the author used TLS data to find any correlation with meteorological indexes (Forcing Factors), linked to sea and wind (Fryberger's method) applying statistical tools, such as the Principal Component Analysis (PCA).

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A critical point in the analysis of ground displacements time series is the development of data driven methods that allow the different sources that generate the observed displacements to be discerned and characterised. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows reducing the dimensionality of the data space maintaining most of the variance of the dataset explained. Anyway, PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem, i.e. in recovering and separating the original sources that generated the observed data. This is mainly due to the assumptions on which PCA relies: it looks for a new Euclidean space where the projected data are uncorrelated. The Independent Component Analysis (ICA) is a popular technique adopted to approach this problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, I use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources, giving a more reliable estimate of them. Here I present the application of the vbICA technique to GPS position time series. First, I use vbICA on synthetic data that simulate a seismic cycle (interseismic + coseismic + postseismic + seasonal + noise) and a volcanic source, and I study the ability of the algorithm to recover the original (known) sources of deformation. Secondly, I apply vbICA to different tectonically active scenarios, such as the 2009 L'Aquila (central Italy) earthquake, the 2012 Emilia (northern Italy) seismic sequence, and the 2006 Guerrero (Mexico) Slow Slip Event (SSE).

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Il presente lavoro si compone di tre capitoli, tra loro autonomi e allo stesso tempo intrinsecamente collegati. Nel primo capitolo si è voluto offrire una panoramica dello scenario agroalimentare italiano e della sua rilevanza nel sistema economico nazionale. Per fare ciò si è partiti da una disamina del contesto economico mondiale per poi centrare il discorso sull’andamento congiunturale dell’agroalimentare nazionale, analizzato secondo i principali indicatori macroeconomici. Successivamente vengono presentati gli attori del sistema agroalimentare, rilevando per ciascuno di essi le proprie specificità e tendenze. L’ultima parte del primo capitolo è un focus specifico sul ruolo giocato dall’agroalimentare italiano nel commercio e nei mercati internazionali. Nel secondo capitolo si è approntata una mappatura territoriale e per comparti delle principali specializzazioni commerciali del settore agroalimentare delle regioni italiane. Tramite l'utilizzo di appositi indici di specializzazione si è analizzata la realtà agroalimentare delle regioni italiane, mettendone in evidenza la struttura competitiva e approssimandola tramite l’analisi dei vantaggi comparati di cui gode. Infine, nel terzo capitolo, si è ampliato il campo d'analisi tentando di misurare il livello di internazionalizzazione delle regioni italiane, non solo in ambito agroalimentare, ma considerando l'intero sistema territoriale regionale. Si è tentato di fare ciò tramite tre strumenti: l’analisi delle componenti principali (PCA o ACP), il Mazziotta-Pareto Index e il Wroclaw taxonomic method. I risultati ottenuti tramite le tre modalità di elaborazione hanno permesso di approfondire la conoscenza del livello di internazionalizzazione registrato dalle regioni italiane, mettendo in luce ulteriori filoni di ricerca della tematica osservata.