Multicentre study for a robust protocol in single-voxel spectroscopy: quantification of MRS signals by time-domain fitting algorithms

Magnetic Resonance Spectroscopy (MRS) is an advanced clinical and research application which guarantees a specific biochemical and metabolic characterization of tissues by the detection and quantification of key metabolites for diagnosis and disease staging. The "Associazione Italiana di Fisica Medica (AIFM)" has promoted the activity of the "Interconfronto di spettroscopia in RM" working group. The purpose of the study is to compare and analyze results obtained by perfoming MRS on scanners of different manufacturing in order to compile a robust protocol for spectroscopic examinations in clinical routines. This thesis takes part into this project by using the GE Signa HDxt 1.5 T at the Pavillion no. 11 of the S.Orsola-Malpighi hospital in Bologna. The spectral analyses have been performed with the jMRUI package, which includes a wide range of preprocessing and quantification algorithms for signal analysis in the time domain. After the quality assurance on the scanner with standard and innovative methods, both spectra with and without suppression of the water peak have been acquired on the GE test phantom. The comparison of the ratios of the metabolite amplitudes over Creatine computed by the workstation software, which works on the frequencies, and jMRUI shows good agreement, suggesting that quantifications in both domains may lead to consistent results. The characterization of an in-house phantom provided by the working group has achieved its goal of assessing the solution content and the metabolite concentrations with good accuracy. The goodness of the experimental procedure and data analysis has been demonstrated by the correct estimation of the T2 of water, the observed biexponential relaxation curve of Creatine and the correct TE value at which the modulation by J coupling causes the Lactate doublet to be inverted in the spectrum. The work of this thesis has demonstrated that it is possible to perform measurements and establish protocols for data analysis, based on the physical principles of NMR, which are able to provide robust values for the spectral parameters of clinical use.

New markov chain based methods for single and cross-domain sentiment classification

Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.

Strumenti e framework per lo sviluppo di linguaggi di programmazione e linguaggi domain-specific in java

La tesi ha lo scopo di indagare le tecnologie disponibili per la realizzazione di linguaggi di programmazione e linguaggi domain specific in ambiente Java. In particolare, vengono proposti e analizzati tre strumenti presenti sul mercato: JavaCC, ANTLR e Xtext. Al termine dell’elaborato, il lettore dovrebbe avere un’idea generale dei principali meccanismi e sistemi utilizzati (come lexer, parser, AST, parse trees, etc.), oltre che del funzionamento dei tre tools presentati. Inoltre, si vogliono individuare vantaggi e svantaggi di ciascuno strumento attraverso un’analisi delle funzionalità offerte, così da fornire un giudizio critico per la scelta e la valutazione dei sistemi da utilizzare.