Differential expression analysis for sequence count data via mixtures of negative binomials

The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.

Knowledge Flows and Networks: the Interplay between Local and Global Linkages within Bangalore IT Cluster

This doctoral work gains deeper insight into the dynamics of knowledge flows within and across clusters, unfolding their features, directions and strategic implications. Alliances, networks and personnel mobility are acknowledged as the three main channels of inter-firm knowledge flows, thus offering three heterogeneous measures to analyze the phenomenon. The interplay between the three channels and the richness of available research methods, has allowed for the elaboration of three different papers and perspectives. The common empirical setting is the IT cluster in Bangalore, for its distinguished features as a high-tech cluster and for its steady yearly two-digit growth around the service-based business model. The first paper deploys both a firm-level and a tie-level analysis, exploring the cases of 4 domestic companies and of 2 MNCs active the cluster, according to a cluster-based perspective. The distinction between business-domain knowledge and technical knowledge emerges from the qualitative evidence, further confirmed by quantitative analyses at tie-level. At firm-level, the specialization degree seems to be influencing the kind of knowledge shared, while at tie-level both the frequency of interaction and the governance mode prove to determine differences in the distribution of knowledge flows. The second paper zooms out and considers the inter-firm networks; particularly focusing on the role of cluster boundary, internal and external networks are analyzed, in their size, long-term orientation and exploration degree. The research method is purely qualitative and allows for the observation of the evolving strategic role of internal network: from exploitation-based to exploration-based. Moreover, a causal pattern is emphasized, linking the evolution and features of the external network to the evolution and features of internal network. The final paper addresses the softer and more micro-level side of knowledge flows: personnel mobility. A social capital perspective is here developed, which considers both employees’ acquisition and employees’ loss as building inter-firm ties, thus enhancing company’s overall social capital. Negative binomial regression analyses at dyad-level test the significant impact of cluster affiliation (cluster firms vs non-cluster firms), industry affiliation (IT firms vs non-IT fims) and foreign affiliation (MNCs vs domestic firms) in shaping the uneven distribution of personnel mobility, and thus of knowledge flows, among companies.

Outcome of HIV-infected patients with hepatocellular carcinoma: a comparison with HIV negative controls

Background and rationale for the study. This study investigated whether human immunodeficiency virus (HIV) infection adversely affects the prognosis of patients diagnosed with hepatocellular carcinoma (HCC).Thirty-four HIV-positive patients with chronic liver disease, consecutively diagnosed with HCC from 1998 to 2007 were one-to-one matched with 34 HIV negative controls for: sex, liver function (Child-Turcotte-Pugh class [CTP]), cancer stage (BCLC model) and, whenever possible, age, etiology of liver disease and modality of cancer diagnosis. Survival in the two groups and independent prognostic predictors were assessed. Results. Among HIV patients 88% were receiving HAART. HIV-RNA was undetectable in 65% of cases; median lymphocyte CD4+ count was 368.5/mmc. Etiology of liver disease was mostly related to HCV infection. CTP class was: A in 38%, B in 41%, C in 21% of cases. BCLC cancer stage was: early in 50%, intermediate in 23.5%, advanced in 5.9%, end-stage in 20.6% of cases. HCC treatments and death causes did not differ between the two groups. Median survival did not differ, being 16 months (95% CI: 6-26) in HIV positive and 23 months (95% CI: 5-41) in HIV negative patients (P=0.391). BCLC cancer stage and HCC treatment proved to be independent predictors of survival both in the whole population and in HIV patients. Conclusions. Survival of HIV infected patients receiving antiretroviral therapy and diagnosed with HCC is similar to that of HIV negative patients bearing this tumor. Prognosis is determined by the cancer bulk and its treatment.

Judicial Specialisation in Intellectual Property: the Unified Patent Court as a Model of Specialised Court

The aim of this thesis is to discuss and develop the Unified Patent Court project to account for the role it could play in implementing judicial specialisation in the Intellectual Property field. To provide an original contribution to the existing literature on the topic, this work addresses the issue of how the Unified Patent Court could relate to the other forms of judicial specialisation already operating in the European Union context. This study presents a systematic assessment of the not-yet-operational Unified Patent Court within the EU judicial system, which has recently shown a trend towards being developed outside the institutional framework of the European Union Court of Justice. The objective is to understand to what extent the planned implementation of the Unified Patent Court could succeed in responding to the need for specialisation and in being compliant with the EU legal and constitutional framework. Using the Unified Patent Court as a case study, it is argued that specialised courts in the field of Intellectual Property have a significant role to play in the European judicial system and offer an adequate response to the growing complexity of business operations and relations. The significance of this study is to analyse whether the UPC can still be considered as an appropriate solution to unify the European patent litigation system. The research considers the significant deficiencies, which risks having a negative effect on the European Union institutional procedures. In this perspective, this work aims to make a contribution in identifying the potential negative consequences of this reform. It also focuses on considering different alternatives for a European patent system, which could effectively promote innovation in Europe.

A Unified Model for XVA, including Interest Rates and Rating

We start in Chapter 2 to investigate linear matrix-valued SDEs and the Itô-stochastic Magnus expansion. The Itô-stochastic Magnus expansion provides an efficient numerical scheme to solve matrix-valued SDEs. We show convergence of the expansion up to a stopping time τ and provide an asymptotic estimate of the cumulative distribution function of τ. Moreover, we show how to apply it to solve SPDEs with one and two spatial dimensions by combining it with the method of lines with high accuracy. We will see that the Magnus expansion allows us to use GPU techniques leading to major performance improvements compared to a standard Euler-Maruyama scheme. In Chapter 3, we study a short-rate model in a Cox-Ingersoll-Ross (CIR) framework for negative interest rates. We define the short rate as the difference of two independent CIR processes and add a deterministic shift to guarantee a perfect fit to the market term structure. We show how to use the Gram-Charlier expansion to efficiently calibrate the model to the market swaption surface and price Bermudan swaptions with good accuracy. We are taking two different perspectives for rating transition modelling. In Section 4.4, we study inhomogeneous continuous-time Markov chains (ICTMC) as a candidate for a rating model with deterministic rating transitions. We extend this model by taking a Lie group perspective in Section 4.5, to allow for stochastic rating transitions. In both cases, we will compare the most popular choices for a change of measure technique and show how to efficiently calibrate both models to the available historical rating data and market default probabilities. At the very end, we apply the techniques shown in this thesis to minimize the collateral-inclusive Credit/ Debit Valuation Adjustments under the constraint of small collateral postings by using a collateral account dependent on rating trigger.

Microenvironment and therapeutic modulation of myelofibrosis in an experimental mouse model.

Primary myelofibrosis(PMF) is the most severe form of Philadelphia-negative myeloproliferative neoplasms(MPNs), characterized by splenomegaly, extramedullary hematopoiesis and bone marrow(BM) fibrosis, with disease progression to leukemia and low survival. The best therapy currently available includes treatment with a JAK inhibitor(Ruxolitinib), which only ameliorates symptoms. Unfortunately, the pathogenesis of the disease is still poorly understood. It has been hypothesized that its progression may be determined by the presence of inflammatory cytokines produced by the bone marrow microenvironment that promote fibrosis. The three aims of this PhD thesis, using the Gata1low mouse model of myelofibrosis, were: 1. Investigate the presence of different cytokines in the bone marrow microenvironment; 2. Test the efficacy of treatment with Reparixin, a CXCR1/2 receptor inhibitor; 3. Test the efficacy of treatment with RB40.34 (P-selectin inhibitor), alone and in combination with Ruxolitinib. In the first study, we demonstrated by immunohistochemistry(IHC) the presence in the BM of Gata1low mice of elevated levels of CXCL1, and its receptors CXCR1/2, and TGF-β1. Particularly, the cells with higher expression of these cytokines were the megakaryocytes. In the second study, we found that treatment with Reparixin in Gata1low mice showed dose-dependent efficacy in reducing bone marrow and splenic fibrosis. Furthermore, by IHC analysis we demonstrated that the treatment induced a decrease in the expression of TGF-β1. In the third study, we found that treatment with RB40.34 in combination with Ruxolitinib normalizes the phenotype of Gata1low mice, reducing fibrosis and the content of TGF-β and CXCL1 in the bone marrow, and restoring the architecture of hematopoiesis in the bone marrow and spleen. In summary, these data provide preclinical evidence that treatment with Reparixin and RB40.34 in combination with Ruxolitinib are effective on reversing the myelofibrotic trait in the Gata1low mouse model and encourage clinical trials to validate these compounds in human patients with PMF.