Strongly correlated quantum gases in one dimension

In this work, we discuss some theoretical topics related to many-body physics in ultracold atomic and molecular gases. First, we present a comparison between experimental data and theoretical predictions in the context of quantum emulator of quantum field theories, finding good results which supports the efficiency of such simulators. In the second and third parts, we investigate several many-body properties of atomic and molecular gases confined in one dimension.

Mechanism of resistance to tyrosine kinase inhibitors in philadelphia-positive acute lymphblastic leukaemia (all): from genetic alterations to impaired RNA editing

The Ph chromosome is the most frequent cytogenetic aberration associated with adult ALL and it represents the single most significant adverse prognostic marker. Despite imatinib has led to significant improvements in the treatment of patients with Ph+ ALL, in the majority of cases resistance developed quickly and disease progressed. Some mechanisms of resistance have been widely described but the full knowledge of contributing factors, driving both the disease and resistance, remains to be defined. The observation of rapid development of lymphoblastic leukemia in mice expressing altered Ikaros (Ik) isoforms represented the background of this study. Ikaros is a zinc finger transcription factor required for normal hemopoietic differentiation and proliferation, particularly in the lymphoid lineages. By means of alternative splicing, Ikaros encodes several proteins that differ in their abilities to bind to a consensus DNA-binding site. Shorter, DNA nonbinding isoforms exert a dominant negative effect, inhibiting the ability of longer heterodimer partners to bind DNA. The differential expression pattern of Ik isoforms in Ph+ ALL patients was analyzed in order to determine if molecular abnormalities involving the Ik gene could associate with resistance to imatinib and dasatinib. Bone marrow and peripheral blood samples from 46 adult patients (median age 55 yrs, 18-76) with Ph+ ALL at diagnosis and during treatment with imatinib (16 pts) or dasatinib (30 pts) were collected. We set up a fast, high-throughput method based on capillary electrophoresis technology to detect and quantify splice variants. 41% Ph+ ALL patients expressed high levels of the non DNA-binding dominant negative Ik6 isoform lacking critical N-terminal zinc-fingers which display abnormal subcellular compartmentalization pattern. Nuclear extracts from patients expressed Ik6 failed to bind DNA in mobility shift assay using a DNA probe containing an Ikaros-specific DNA binding sequence. In 59% Ph+ ALL patients there was the coexistence in the same PCR sample and at the same time of many splice variants corresponded to Ik1, Ik2, Ik4, Ik4A, Ik5A, Ik6, Ik6 and Ik8 isoforms. In these patients aberrant full-length Ikaros isoforms in Ph+ ALL characterized by a 60-bp insertion immediately downstream of exon 3 and a recurring 30-bp in-frame deletion at the end of exon 7 involving most frequently the Ik2, Ik4 isoforms were also identified. Both the insertion and deletion were due to the selection of alternative splice donor and acceptor sites. The molecular monitoring of minimal residual disease showed for the first time in vivo that the Ik6 expression strongly correlated with the BCR-ABL transcript levels suggesting that this alteration could depend on the Bcr-Abl activity. Patient-derived leukaemia cells expressed dominant-negative Ik6 at diagnosis and at the time of relapse, but never during remission. In order to mechanistically demonstrated whether in vitro the overexpression of Ik6 impairs the response to tyrosine kinase inhibitors (TKIs) and contributes to resistance, an imatinib-sensitive Ik6-negative Ph+ ALL cell line (SUP-B15) was transfected with the complete Ik6 DNA coding sequence. The expression of Ik6 strongly increased proliferation and inhibited apoptosis in TKI sensitive cells establishing a previously unknown link between specific molecular defects that involve the Ikaros gene and the resistance to TKIs in Ph+ ALL patients. Amplification and genomic sequence analysis of the exon splice junction regions showed the presence of 2 single nucleotide polymorphisms (SNPs): rs10251980 [A/G] in the exon2/3 splice junction and of rs10262731 [A/G] in the exon 7/8 splice junction in 50% and 36% of patients, respectively. A variant of the rs11329346 [-/C], in 16% of patients was also found. Other two different single nucleotide substitutions not recognized as SNP were observed. Some mutations were predicted by computational analyses (RESCUE approach) to alter cis-splicing elements. In conclusion, these findings demonstrated that the post-transcriptional regulation of alternative splicing of Ikaros gene is defective in the majority of Ph+ ALL patients treated with TKIs. The overexpression of Ik6 blocking B-cell differentiation could contribute to resistance opening a time frame, during which leukaemia cells acquire secondary transforming events that confer definitive resistance to imatinib and dasatinib.

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.

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.