3 resultados para NETTRA-G3-FIFO
em AMS Tesi di Dottorato - Alm@DL - Università di Bologna
Resumo:
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.
Resumo:
ALICE, that is an experiment held at CERN using the LHC, is specialized in analyzing lead-ion collisions. ALICE will study the properties of quarkgluon plasma, a state of matter where quarks and gluons, under conditions of very high temperatures and densities, are no longer confined inside hadrons. Such a state of matter probably existed just after the Big Bang, before particles such as protons and neutrons were formed. The SDD detector, one of the ALICE subdetectors, is part of the ITS that is composed by 6 cylindrical layers with the innermost one attached to the beam pipe. The ITS tracks and identifies particles near the interaction point, it also aligns the tracks of the articles detected by more external detectors. The two ITS middle layers contain the whole 260 SDD detectors. A multichannel readout board, called CARLOSrx, receives at the same time the data coming from 12 SDD detectors. In total there are 24 CARLOSrx boards needed to read data coming from all the SDD modules (detector plus front end electronics). CARLOSrx packs data coming from the front end electronics through optical link connections, it stores them in a large data FIFO and then it sends them to the DAQ system. Each CARLOSrx is composed by two boards. One is called CARLOSrx data, that reads data coming from the SDD detectors and configures the FEE; the other one is called CARLOSrx clock, that sends the clock signal to all the FEE. This thesis contains a description of the hardware design and firmware features of both CARLOSrx data and CARLOSrx clock boards, which deal with all the SDD readout chain. A description of the software tools necessary to test and configure the front end electronics will be presented at the end of the thesis.
Resumo:
Scopo dello studio: valutare i cambiamenti indotti da diversi trattamenti di mordenzatura sulla morfologia superficiale e sulla microstruttura di due vetro-ceramiche a base disilicato di litio (IPS e.max® Press e IPS e.max® CAD) ed esaminarne gli effetti sia sull’adesione con un cemento resinoso che sulla resistenza alla flessione. Materiali e metodi: Settanta dischetti (12 mm di diametro, 2 mm di spessore) di ogni ceramica sono stati preparati e divisi in 5 gruppi: nessun trattamento (G1), HF 5% 20s (G2), HF 5% 60s (G3), HF 9.6% 20s (G4), HF 9.6% 60s (G5). Un campione per ogni gruppo è stato analizzato mediante profilometro ottico e osservato al SEM. Per gli altri campioni è stato determinato lo shear bond strength (SBS) con un cemento resinoso. Dopo l’SBS test, i campioni sono stati caricati fino a frattura utilizzando il piston-on-three-ball test per determinarne la resistenza biassiale alla flessione. Risultati: L’analisi morfologica e microstrutturale dei campioni ha rivelato come diversi trattamenti di mordenzatura producano delle modifiche nella rugosità superficiale che non sono direttamente collegate ad un aumento dei valori di adesione e dei cambiamenti microstrutturali che sono più rilevanti con l’aumento del tempo di mordenzatura e di concentrazione dell’acido. I valori medi di adesione (MPa) per IPS e.max® CAD sono significativamente più alti in G2 e G3 (21,28 +/- 4,9 e 19,55 +/- 5,41 rispettivamente); per IPS e.max® Press, i valori più elevati sono in G3 (16,80 +/- 3,96). La resistenza biassiale alla flessione media (MPa) è più alta in IPS e.max® CAD (695 +/- 161) che in IPS e.max® Press (588 +/- 117), ma non è non influenzata dalla mordenzatura con HF. Conclusioni: il disilicato di litio va mordenzato preferibilmente con HF al 5%. La mordenzatura produce alcuni cambiamenti superficiali e microstrutturali nel materiale, ma tali cambiamenti non ne influenzano la resistenza in flessione.