A spectroscopic and photometric study of stellar populations in a sample of clusters in the Magellanic Clouds

This Ph.D. Thesis has been carried out in the framework of a long-term and large project devoted to describe the main photometric, chemical, evolutionary and integrated properties of a representative sample of Large and Small Magellanic Cloud (LMC and SMC respectively) clusters. The globular clusters system of these two Irregular galaxies provides a rich resource for investigating stellar and chemical evolution and to obtain a detailed view of the star formation history and chemical enrichment of the Clouds. The results discussed here are based on the analysis of high-resolution photometric and spectroscopic datasets obtained by using the last generation of imagers and spectrographs. The principal aims of this project are summarized as follows: • The study of the AGB and RGB sequences in a sample of MC clusters, through the analysis of a wide near-infrared photometric database, including 33 Magellanic globulars obtained in three observing runs with the near-infrared camera SOFI@NTT (ESO, La Silla). • The study of the chemical properties of a sample of MCs clusters, by using optical and near-infrared high-resolution spectra. 3 observing runs have been secured to our group to observe 9 LMC clusters (with ages between 100 Myr and 13 Gyr) with the optical high-resolution spectrograph FLAMES@VLT (ESO, Paranal) and 4 very young (<30 Myr) clusters (3 in the LMC and 1 in the SMC) with the near-infrared high-resolution spectrograph CRIRES@VLT. • The study of the photometric properties of the main evolutive sequences in optical Color- Magnitude Diagrams (CMD) obtained by using HST archive data, with the final aim of dating several clusters via the comparison between the observed CMDs and theoretical isochrones. The determination of the age of a stellar population requires an accurate measure of the Main Sequence (MS) Turn-Off (TO) luminosity and the knowledge of the distance modulus, reddening and overall metallicity. For this purpose, we limited the study of the age just to the clusters already observed with high-resolution spectroscopy, in order to date only clusters with accurate estimates of the overall metallicity.

Law and Science through the Lens of Patent Law and Software Agents

In this investigation I look at patents and software agents as a way to study broader relation between law and science (the latter term broadly understood as inclusive of science and technology). The overall premise framing the entire discussion, my basic thesis, is that this relation, between law and science, cannot be understood without taking into account a number of intervening factors identifying which makes it necessary to approach the question from the standpoint of fields and disciplines other than law and science themselves.

Ex-post Liability Rules in Modern Patent Law

This doctoral thesis examines the use of liability rules to protect patent entitlements, focusing on a specific type of rule named ex-post since it is applied and designed ex-post by a court or an agency. The research starts from the premise that patents are defined by the legal and economic scholarship as exclusive rights but nevertheless, under certain circumstances there are economic as well as other compelling reasons to transform the exclusiveness of patent rights into a right to receive compensation.

Automated Segmentation and Non-Rigid Registration for the Quantitative Evaluation of Myocardial Perfusion from Magnetic Resonance Images

Myocardial perfusion quantification by means of Contrast-Enhanced Cardiac Magnetic Resonance images relies on time consuming frame-by-frame manual tracing of regions of interest. In this Thesis, a novel automated technique for myocardial segmentation and non-rigid registration as a basis for perfusion quantification is presented. The proposed technique is based on three steps: reference frame selection, myocardial segmentation and non-rigid registration. In the first step, the reference frame in which both endo- and epicardial segmentation will be performed is chosen. Endocardial segmentation is achieved by means of a statistical region-based level-set technique followed by a curvature-based regularization motion. Epicardial segmentation is achieved by means of an edge-based level-set technique followed again by a regularization motion. To take into account the changes in position, size and shape of myocardium throughout the sequence due to out of plane respiratory motion, a non-rigid registration algorithm is required. The proposed non-rigid registration scheme consists in a novel multiscale extension of the normalized cross-correlation algorithm in combination with level-set methods. The myocardium is then divided into standard segments. Contrast enhancement curves are computed measuring the mean pixel intensity of each segment over time, and perfusion indices are extracted from each curve. The overall approach has been tested on synthetic and real datasets. For validation purposes, the sequences have been manually traced by an experienced interpreter, and contrast enhancement curves as well as perfusion indices have been computed. Comparisons between automatically extracted and manually obtained contours and enhancement curves showed high inter-technique agreement. Comparisons of perfusion indices computed using both approaches against quantitative coronary angiography and visual interpretation demonstrated that the two technique have similar diagnostic accuracy. In conclusion, the proposed technique allows fast, automated and accurate measurement of intra-myocardial contrast dynamics, and may thus address the strong clinical need for quantitative evaluation of myocardial perfusion.

Patent Strategies and R&D in Complex Product Industries

The study aims at providing a framework conceptualizing patenting activities under the condition of intellectual property rights fragmentation. Such a framework has to deal with the interrelated problems of technological complexity in the modern patent landscape. In that respect, ex-post licensing agreements have been incorporated into the analysis. More precisely, by consolidating the right to use patents required for commercialization of a product, private market solutions, such as cross-licensing agreements and patent pools help firms to overcome problems triggered by the intellectual property rights fragmentation. Thereby, private bargaining between parties as such cannot be isolated from the legal framework. A result of this analysis is that policies ignoring market solutions and only focusing on static gains can mitigate the dynamic efficiency gains as induced by the patent system. The evidence found in this thesis supports the opinion that legal reforms that aim to decrease the degree of patent protection or to lift it all together can hamper the functioning of the current system.

Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.

A clustering method for robust and reliable large scale functional and structural protein sequence annotation

Bioinformatics, in the last few decades, has played a fundamental role to give sense to the huge amount of data produced. Obtained the complete sequence of a genome, the major problem of knowing as much as possible of its coding regions, is crucial. Protein sequence annotation is challenging and, due to the size of the problem, only computational approaches can provide a feasible solution. As it has been recently pointed out by the Critical Assessment of Function Annotations (CAFA), most accurate methods are those based on the transfer-by-homology approach and the most incisive contribution is given by cross-genome comparisons. In the present thesis it is described a non-hierarchical sequence clustering method for protein automatic large-scale annotation, called “The Bologna Annotation Resource Plus” (BAR+). The method is based on an all-against-all alignment of more than 13 millions protein sequences characterized by a very stringent metric. BAR+ can safely transfer functional features (Gene Ontology and Pfam terms) inside clusters by means of a statistical validation, even in the case of multi-domain proteins. Within BAR+ clusters it is also possible to transfer the three dimensional structure (when a template is available). This is possible by the way of cluster-specific HMM profiles that can be used to calculate reliable template-to-target alignments even in the case of distantly related proteins (sequence identity < 30%). Other BAR+ based applications have been developed during my doctorate including the prediction of Magnesium binding sites in human proteins, the ABC transporters superfamily classification and the functional prediction (GO terms) of the CAFA targets. Remarkably, in the CAFA assessment, BAR+ placed among the ten most accurate methods. At present, as a web server for the functional and structural protein sequence annotation, BAR+ is freely available at http://bar.biocomp.unibo.it/bar2.0.

Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction.

In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.

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.

Patent nonnuse: are patent pools as possible solution?

Studies have depicted that the rate of unused patents comprises a high portion of patents in North America, Europe and Japan. Particularly, studies have identified a considerable share of strategic patents which are left unused due to pure strategic reasons. While such patents might generate strategic rents to their owner, they may have harmful consequences for the society if by blocking alternative solutions that other inventions provide they hamper the possibility of better solutions. Accordingly, the importance of the issue of nonuse is highlighted within the literature on strategic patenting, IPR policy and innovation economics. Moreover, the current literature has emphasized on the role of patent pools in dealing with potential issues such as excessive transaction cost caused by patent thickets and blocking patents. In fact, patent pools have emerged as policy tools facilitating technology commercialization and alleviating patent litigation among rivals holding overlapping IPRs. In this dissertation I provide a critical literature review on strategic patenting, identify present gaps and discuss some future research paths. Moreover, I investigate the drivers of strategic non-use of patents with particular focus on unused strategic play patents. Finally, I examine if participation intensity in patent pools by pool members explains their willingness to use their non-pooled patents. I also investigate which characteristics of the patent pools are associated to the willingness to use non-pooled patents through pool participation. I show that technological uncertainty and technological complexity are two technology environment factors that drive unused play patents. I also show that pool members participating more intensively in patent pools are more likely to be willing to use their non-pooled patents through pool participation. I further depict that pool licensors are more likely to be willing to use their non-pooled patents by participating in pools with higher level of technological complementarity to their own technology.