Sviluppo di un tomografo multi-energy per lo studio pre-clinico di nuove metodiche diagnostiche finalizzate al riconoscimento precoce della patologia tumorale

A new multi-energy CT for small animals is being developed at the Physics Department of the University of Bologna, Italy. The system makes use of a set of quasi-monochromatic X-ray beams, with energy tunable in a range from 26 KeV to 72 KeV. These beams are produced by Bragg diffraction on a Highly Oriented Pyrolytic Graphite crystal. With quasi-monochromatic sources it is possible to perform multi-energy investigation in a more effective way, as compared with conventional X-ray tubes. Multi-energy techniques allow extracting physical information from the materials, such as effective atomic number, mass-thickness, density, that can be used to distinguish and quantitatively characterize the irradiated tissues. The aim of the system is the investigation and the development of new pre-clinic methods for the early detection of the tumors in small animals. An innovative technique, the Triple-Energy Radiography with Contrast Medium (TER), has been successfully implemented on our system. TER consist in combining a set of three quasi-monochromatic images of an object, in order to obtain a corresponding set of three single-tissue images, which are the mass-thickness map of three reference materials. TER can be applied to the quantitative mass-thickness-map reconstruction of a contrast medium, because it is able to remove completely the signal due to other tissues (i.e. the structural background noise). The technique is very sensitive to the contrast medium and is insensitive to the superposition of different materials. The method is a good candidate to the early detection of the tumor angiogenesis in mice. In this work we describe the tomographic system, with a particular focus on the quasi-monochromatic source. Moreover the TER method is presented with some preliminary results about small animal imaging.

New approaches to open problems in gene expression microarray data

In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.

Computational methods for genome screening

Motivation An actual issue of great interest, both under a theoretical and an applicative perspective, is the analysis of biological sequences for disclosing the information that they encode. The development of new technologies for genome sequencing in the last years, opened new fundamental problems since huge amounts of biological data still deserve an interpretation. Indeed, the sequencing is only the first step of the genome annotation process that consists in the assignment of biological information to each sequence. Hence given the large amount of available data, in silico methods became useful and necessary in order to extract relevant information from sequences. The availability of data from Genome Projects gave rise to new strategies for tackling the basic problems of computational biology such as the determination of the tridimensional structures of proteins, their biological function and their reciprocal interactions. Results The aim of this work has been the implementation of predictive methods that allow the extraction of information on the properties of genomes and proteins starting from the nucleotide and aminoacidic sequences, by taking advantage of the information provided by the comparison of the genome sequences from different species. In the first part of the work a comprehensive large scale genome comparison of 599 organisms is described. 2,6 million of sequences coming from 551 prokaryotic and 48 eukaryotic genomes were aligned and clustered on the basis of their sequence identity. This procedure led to the identification of classes of proteins that are peculiar to the different groups of organisms. Moreover the adopted similarity threshold produced clusters that are homogeneous on the structural point of view and that can be used for structural annotation of uncharacterized sequences. The second part of the work focuses on the characterization of thermostable proteins and on the development of tools able to predict the thermostability of a protein starting from its sequence. By means of Principal Component Analysis the codon composition of a non redundant database comprising 116 prokaryotic genomes has been analyzed and it has been showed that a cross genomic approach can allow the extraction of common determinants of thermostability at the genome level, leading to an overall accuracy in discriminating thermophilic coding sequences equal to 95%. This result outperform those obtained in previous studies. Moreover, we investigated the effect of multiple mutations on protein thermostability. This issue is of great importance in the field of protein engineering, since thermostable proteins are generally more suitable than their mesostable counterparts in technological applications. A Support Vector Machine based method has been trained to predict if a set of mutations can enhance the thermostability of a given protein sequence. The developed predictor achieves 88% accuracy.

An experimental and theoretical approach to correct for the scattered radiation in an X-ray computer tomography system for industrial applications

The main problem connected to cone beam computed tomography (CT) systems for industrial applications employing 450 kV X-ray tubes is the high amount of scattered radiation which is added to the primary radiation (signal). This stray radiation leads to a significant degradation of the image quality. A better understanding of the scattering and methods to reduce its effects are therefore necessary to improve the image quality. Several studies have been carried out in the medical field at lower energies, whereas studies in industrial CT, especially for energies up to 450 kV, are lacking. Moreover, the studies reported in literature do not consider the scattered radiation generated by the CT system structure and the walls of the X-ray room (environmental scatter). In order to investigate the scattering on CT projections a GEANT4-based Monte Carlo (MC) model was developed. The model, which has been validated against experimental data, has enabled the calculation of the scattering including the environmental scatter, the optimization of an anti-scatter grid suitable for the CT system, and the optimization of the hardware components of the CT system. The investigation of multiple scattering in the CT projections showed that its contribution is 2.3 times the one of primary radiation for certain objects. The results of the environmental scatter showed that it is the major component of the scattering for aluminum box objects of front size 70 x 70 mm2 and that it strongly depends on the thickness of the object and therefore on the projection. For that reason, its correction is one of the key factors for achieving high quality images. The anti-scatter grid optimized by means of the developed MC model was found to reduce the scatter-toprimary ratio in the reconstructed images by 20 %. The object and environmental scatter calculated by means of the simulation were used to improve the scatter correction algorithm which could be patented by Empa. The results showed that the cupping effect in the corrected image is strongly reduced. The developed CT simulation is a powerful tool to optimize the design of the CT system and to evaluate the contribution of the scattered radiation to the image. Besides, it has offered a basis for a new scatter correction approach by which it has been possible to achieve images with the same spatial resolution as state-of-the-art well collimated fan-beam CT with a gain in the reconstruction time of a factor 10. This result has a high economic impact in non-destructive testing and evaluation, and reverse engineering.

Metodi per la valutazione strategica dei piani energetici. Applicazione al caso della Provincia di Ravenna.

Lo scopo di questo studio è descrivere nel dettaglio la procedura di Valutazione Ambientale Strategica (VAS) relativa ai Piani Energetici Provinciali (PEP) al fine di delinearne un metodo efficace di valutazione, a partire dallo studio del caso della Provincia di Ravenna. In seguito alla mancanza di Linee Guida sulla VAS, si è ritenuta utile un´analisi comparativa tra metodologie e strumenti, e gli obiettivi specifici e generali che andrebbero rispettati in ogni VAS di un PEP. Lo studio si basa su confronti paralleli tra quattro casi di VAS di Piani Energetici Provinciali al fine di elaborare un modello di valutazione delle VAS, semplice e diretto, basato su contenuti teorici e metodologici provenienti da una selezione di studi e documenti nazionali e internazionali, di cui si è tenuto conto e da cui si sono estrapolate le migliori "Buone Pratiche" per la VAS. L´analisi seguente è stata effettuata attraverso matrici qualitative in cui, per ciascuna connessione tra metodologia e "obiettivo VAS" si è espresso un giudizio che cerca di tenere conto, quando possibile, dei criteri e dei principi generali di sostenibilità dettati dalle maggiori autorità e associazioni internazionali e nazionali di valutazione ambientale. Il confronto tra i quattro casi, ha evidenziato dei punti di debolezza nell´applicazione della Direttiva VAS. Questo studio inoltre, ha tra i suoi obiettivi, quello ambizioso di delineare un metodo efficace di valutazione strategica dei piani energetici provinciali, a partire dallo studio del caso della Provincia di Ravenna. Per questi obiettivi, si è deciso di impostare un programma di lavoro basato sui sistemi informativi geografici, che ha permesso di individuare le aree con potenziale di sviluppo energetico della risorsa solare. Nello specifico è stato possibile calcolare quanta “superficie utile”, presente nelle aree industriali e commerciali della Provincia, potrebbe essere sfruttata installandovi pannelli fotovoltaici. Si è riusciti con questa metodologia a fornire una stima più dettagliata delle reali potenzialità della risorsa solare in Provincia di Ravenna, individuando nel dettaglio territoriale le rispettive quote percentuali che potrebbero essere installate, per raggiungere gli obiettivi di sostenibilità del piano. Il percorso iniziato con questa tesi consente di riflettere sulla necessità di approfondire il tema del rapporto tra valutazione ambientale qualitativa di uno strumento di pianificazione come la VAS, e la stima quantitativa sia della sostenibilità che del danno ambientale legato agli impatti negativi che questo strumento dovrebbe rilevare. Gli sviluppi futuri cui la tesi pone le basi sono l'implementazione di strumenti quantitativi di analisi delle potenzialità energetiche e di valutazione degli scenari. Questi strumenti sono necessari a definire i modelli ambientali per il supporto alle decisioni in campo energetico.