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Contacts prediction of linear peptides from genomic data
Full text linkThe rise of metagenomics and the technological improvements in the fields of bioinformatics and computational biology led to an exponential increase in the amount of biological data available to be studied. However, the rate at which biological data are studied is much slower than the rate at which they are stored. This issue pushed the development of programs capable of extracting significant information from newly sourced data without the need of human intervention. More specifically, some of these programs have been developed to infer structural information from protein sequences. Since the structure of a protein is strictly bound to its function, it is easy to understand the importance of such task. Among the structural information which can be inferred looking at a protein sequence, there are contact maps. Contact maps define whether two residues are functionally linked within the same protein chain or two different ones. Despite much work has been carried out for intra-chain contact maps prediction using sequence information, less can be found about inter-chain contact
maps. Moreover, methods are usually presented and tested on benchmark dataset generated for such purpose. In this, a whole pipeline for both intra-chain and inter-chain contact predictions is presented. Instead of using a generic benchmark set of protein sequences as input, the pipeline starts from predictions of linear interacting peptides at residues level. Linear interacting peptides are regions in a protein sequence which are thought to not have a fixed folding, but to adapt their structure to the functional needs of the protein itself. Needles to say, fewer studies have been conducted about this specific issue in literature. Finally, an analysis of the results is carried out. The analysis focuses on the evaluation of methods implied for contact predictions over the given dataset. Particular attention is paid to the comparison of the performances on inter-chain alignments with respect to the ones achieved on intra-chain alignments. Furthermore, the effect of linear interacting peptides is taken into account
Numerical solution of the three dimensional Optimal Transport Problem
Full text linkIn this thesis we analyze a model introduced in [14, 17] and its extensions [14, 15]. These works conjectured a new formulation of Optimal Transport, an expanding area of mathematics whose aim is the identification of the most efficient strategy to reallocate resources from one place to another. The numerical approximation of the equations of the model represents a simple yet effective numerical approach to solve Optimal Transport problems. However, the numerical scheme was analyzed only in the two dimensional
case. The aim of this thesis is to exploit the model to solve three dimensional Optimal Transport problems, where few examples of numerical solution are known from the literature. We present all the non trivial challenges required by the three dimensional extension, together with an ample series of numerical experiments, that confirms the conjectured equivalence with the Optimal Transport problem. The results show that the numerical scheme is robust and efficient, with ample space for improvement from the computational point of view
Sustainability and digital technologies: a comparative analysis of the environmental impact between the Euro cash payment system and the Bitcoin payment system using an LCA-based approach
Full text linkSpatialization on grid of meteorological variables with machine learning methods
No full textIn this thesis we will develop some machine learning methods in order to interpolate me- teorological information and distribute the knowledge in a grid over Italy. The variables analyzed in this work are the minimum and the maximum temperature and the rain, all at the ground level. To perform the spatialization of these variables we use multi linear regressions with a machine learning approach, Boost Decision Trees and Neural Networks and we compare the results obtained. A common procedure of data preprocessing with this type of variables is also widely covered. After the comparison we show some application of these methods in order to produce operative grids over Italy
Probing primordial non-Gaussianity via cosmological gravitational wave anisotropies
Full text linkIn modern Cosmology, primordial non-Gaussianity (PNG) is regarded as a fundamental and
independent source of information about the physics of the early Universe. The ideal observable to
measure the non-Gaussian nature of primordial perturbations, and its eventual scale dependence, is
the cosmic microwave background (CMB) radiation. In this work another promising probe of primordial
non-Gaussianity is considered, namely the cosmological gravitational wave background (CGWB)
generated during inflation. An eventual detection of such a GW background in the near future may
possibly provide with new and exciting information, inaccessible to CMB measurements. Signatures of
primordial non-Gaussianity are in fact expected to be picked up via the GW propagation across large-
scale scalar perturbations. Particular attention is given to the CGWB energy density anisotropies 3-
point angular correlator, or bispectrum, as the lowest-order indicator of the presence of PNG. Explicit
expressions can be derived in the case non-Gaussianity is parametrized in terms of the local ansatz
for the primordial curvature perturbation. The inclusion of the scale dependence, or running, in the
discussion is then achieved by means of a kernel approach for the non-linear parameter fNL and of a
subsequent matching with bispectrum templates recurring in the literature. A specific scenario of
CGWB sourced at second order from enhanced small-scale scalar perturbations, arising with the
formation of primordial black holes (PBHs), is also considered. In this context the presence of
primordial non-Gaussianity would show up already at the emission, as an initial condition. The PBHs
mass is taken to be such that they may as well comprise all of the dark matter (DM). If this was
actually the case it would be remarkable, in presence of a sufficient running of non-Gaussianity, the
possibility to obtain an arbitrarily anisotropic CGWB which is otherwise, in the non-running scenario,
constrained to be highly isotropic due to cold dark matter isocurvature (CDI) bounds
Evaluating market timing occurrence with quantile regression. Model design and application to U.S. mutual funds
Full text linkNon-linear cosmic structure formation in alternative theories of gravity
Full text linkIl lavoro di tesi propone un metodo per vincolare le teorie di gravità alternativa attraverso la formazione di strutture cosmiche nell’ambito della Kinetic Field Theory. Oltre allo sviluppo del metodo a livello matematico (incentrato sullo spettro di potenza), vengono presentati alcuni esempi di applicazione a teorie specifiche di gravità alternativa.
The thesis work proposes a method for constraining alternative gravity theories through cosmic structure formation in the framework of Kinetic Field Theory. Besides the mathematical development (focused on the power spectrum), some examples of application to specific alternative gravity theories are presented
Un percorso dalle misure metriche alla dimensione frattale
No full textUna introduzione alla teoria dell'autosimilarità. A partire dalle misure
metriche, vengono costruite tutte le necessarie misure e dimensioni di base per osservare le prime proprietà degli insiemi autosimilari. Le dimensioni trattate sono quelle di Hausdorff e Minkowski
