Can neuroimaging be used as a support to diagnosis of borderline personality disorder? An approach based on computational neuroanatomy and machine learning

Several recent studies in literature have identified brain morphological alterations associated to Borderline Personality Disorder (BPD) patients. These findings are reported by studies based on voxel-based-morphometry analysis of structural MRI data, comparing mean gray-matter concentration between groups of BPD patients and healthy controls. On the other hand, mean differences between groups are not informative about the discriminative value of neuroimaging data to predict the group of individual subjects. In this paper, we go beyond mean differences analyses, and explore to what extent individual BPD patients can be differentiated from controls (25 subjects in each group), using a combination of automated-morphometric tools for regional cortical thickness/volumetric estimation and Support Vector Machine classifier. The approach included a feature selection step in order to identify the regions containing most discriminative information. The accuracy of this classifier was evaluated using the leave-one-subject-out procedure. The brain regions indicated as containing relevant information to discriminate groups were the orbitofrontal, rostral anterior cingulate, posterior cingulate, middle temporal cortices, among others. These areas, which are distinctively involved in emotional and affect regulation of BPD patients, were the most informative regions to achieve both sensitivity and specificity values of 80% in SVM classification. The findings suggest that this new methodology can add clinical and potential diagnostic value to neuroimaging of psychiatric disorders. (C) 2012 Elsevier Ltd. All rights reserved.

Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

Abstract Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

Estimación de la potencia eólica en una zona mediante el apoyo de estaciones meteorológicas de referencia y el empleo de técnicas estadísticas no paramétricas, funcionales y de machine learning: aplicación a las islas Canarias

Programa de doctorado: Tecnología industrial

Automatic (data-based) learning of expressive indicators for machine failure detection : a proposal

Máster Universitario en Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)

Predizione dell'effetto delle mutazioni sulla variazione della stabilita' proteica con metodi di "machine-learning".

La tesi consiste nell’implementare un software in grado a predire la variazione della stabilità di una proteina sottoposta ad una mutazione. Il predittore implementato fa utilizzo di tecniche di Machine-Learning ed, in particolare, di SVM. In particolare, riguarda l’analisi delle prestazioni di un predittore, precedentemente implementato, sotto opportune variazioni dei parametri di input e relativamente all’utilizzo di nuova informazione rispetto a quella utilizzata dal predittore basilare.

Il conflitto tra ragione e passioni nel pensiero morale, economico e politico di Bernard Mandeville

Effects of the conflict between reason and passion in Bernard Mandeville’s moral, economic and political thought My PhD dissertation focuses on Bernard Mandeville (1670-1732), a Dutch philosopher who moved to London in his late twenties. The aspect of Mandeville’s thought I take into account in my research is the conflicting relation between reason and passions, and the consequences that Mandeville’s view of this conflict has in the development of his theory of human nature which, I argue, is what grounds his moral, economic and, above all, political theory. According to Mandeville, reason is fundamentally weak. Passions influence with more strength human actions, and, eventually, are the ones which motivate them. The role of reason is merely instrumental, restricted to finding appropriate means in order to reach the desired ends, which are capricious and inconstant, since they all come from unstable passions. Reason cannot take decisions meant to act in the long term, pursuing an object which has not a selfish and temporary nature. There is no possibility, thus, that men’s actions aim just to achieve a good and just society, without their interests being directly involved. The basically selfish root of every desire leads Mandeville to claim that there is neither benevolence nor altruism which guides human behaviour. Hence he expresses a judgement on the moral character of human beings, always busy with their self-satisfaction, and hardly ever considering what would be good on a wider perspective, including other people’s sake. The anthropological features ascribed to men by Mandeville, are those which lead him to prefer a political system where governors are not supposed to have particular abilities, either from an intellectual or from a moral point of view, and peace and order are preserved by the bureaucratic machine, which is meant to work with the least effort on the part of the politicians, and no big harm can be done even by corrupted or wicked governors. This system is adopted with an eye at remedying human deficiencies: Mandeville takes into primary account, when he thinks of how to build a peaceful and functioning society, that everyone is concerned with his selfish interest, and that the rationality of a single politician, or of a group of them belonging to a same generation, cannot find a good “solution” to govern men able to last over the long period, and to work in different ages. This implies a refusal of the Hobbesian theory of the pactum subjectionis, which has the character of a rational and definitive choice, and leads Mandeville to consider the order which arises spontaneously, without any plan or rational intervention.

Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins

The goal of this thesis work is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of cysteine residues in proteins, which is a sub-problem of a bigger and yet unsolved problem of protein structure prediction. Improvement in the prediction of disulfide bonding states of cysteine residues will help in putting a constraint in the three dimensional (3D) space of the respective protein structure, and thus will eventually help in the prediction of 3D structure of proteins. Results of this work will have direct implications in site-directed mutational studies of proteins, proteins engineering and the problem of protein folding. We have used a combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), the so-called Hidden Neural Network (HNN) as a machine learning technique to develop our prediction method. By using different global and local features of proteins (specifically profiles, parity of cysteine residues, average cysteine conservation, correlated mutation, sub-cellular localization, and signal peptide) as inputs and considering Eukaryotes and Prokaryotes separately we have reached to a remarkable accuracy of 94% on cysteine basis for both Eukaryotic and Prokaryotic datasets, and an accuracy of 90% and 93% on protein basis for Eukaryotic dataset and Prokaryotic dataset respectively. These accuracies are best so far ever reached by any existing prediction methods, and thus our prediction method has outperformed all the previously developed approaches and therefore is more reliable. Most interesting part of this thesis work is the differences in the prediction performances of Eukaryotes and Prokaryotes at the basic level of input coding when ‘profile’ information was given as input to our prediction method. And one of the reasons for this we discover is the difference in the amino acid composition of the local environment of bonded and free cysteine residues in Eukaryotes and Prokaryotes. Eukaryotic bonded cysteine examples have a ‘symmetric-cysteine-rich’ environment, where as Prokaryotic bonded examples lack it.

Spin-Restricted Coupled-Cluster-Theorie fuer offenschalige Zustaende

Spin-Restricted Coupled-Cluster-Theorie fuer offenschaligeZustaende Die Berechnung von Energien und Eigenschaften offenschaligerAtome undMolekuele mit Hilfe der hochgenauenCoupled-Cluster-(CC)-Theoriewar bisher mit einem - im Vergleich zur BerechnunggeschlossenschaligerZustaende - erhoehten Rechenaufwand und der sogenannten'Spinkontamination' behaftet. Um diesen Problemenentgegenzuwirken,stellten P.G.Szalay und J.Gauss die 'Spin-RestrictedCoupled-Cluster-Theorie' vor. Im Rahmen dieser Arbeit wird die urspruenglich aufDublett-Zustaendebeschraenkte Theorie so verallgemeinert, dass jederbeliebige Spinzustandmit einem einheitlichen Satz von Gleichungen beschriebenwerden kann. Dadie Moller-Plesset-(MP)-Stoerungstheorie bei der BerechnungoffenschaligerZustaende mit aehnlichen Problemen behaftet ist, wirddarueberhinaus dieSpin-Restricted-(SR)-MP-Stoerungstheorie zweiter und dritterOrdnungeingefuehrt. Um Molekueleigenschaften berechnen zu koennen,werdenanalytische Ableitungen der Energie sowohl fuer den SR-CC-als auch denSR-MP-Ansatz hergeleitet. Bei den folgenden Testrechnungenstellt sichheraus, dass sowohl SR-CC- als auch SR-MP-Ansaetze diegleiche Genauigkeitbieten wie konventionelle CC- und MP-Ansaetze. Dabei sinddieSpinerwartungswerte der SR-CC-Wellenfunktionen identisch mitdem exaktenWert. Im Rahmen der Testrechnungen stellt sich heraus, dassder SR-CC-Ansatz nicht 'size-konsistent', der numerische Fehler abervernachlaessigbar klein ist. Abschliessend werden dieHintergruende derfehlenden 'Size-Konsistenz' diskutiert.

Analysis of the influence of epitope flanking regions on MHC class I restricted antigen presentation

Peptides presented by MHC class I molecules for CTL recognition are derived mainly from cytosolic proteins. For antigen presentation on the cell surface, epitopes require correct processing by cytosolic and ER proteases, efficient TAP transport and MHC class I binding affinity. The efficiency of epitope generation depends not only on the epitope itself, but also on its flanking regions. In this project, the influence of the C-terminal region of the model epitope SIINFEKL (S8L) from chicken ovalbumin (aa 257-264) on antigen processing has been investigated. S8L is a well characterized epitope presented on the murine MHC class I molecule, H-2Kb. The Flp-In 293Kb cell line was transfected with different constructs each enabling the expression of the S8L sequence with different defined C-terminal flanking regions. The constructs differed at the two first C-terminal positions after the S8L epitope, so called P1’ and P2’. At these sites, all 20 amino acids were exchanged consecutively and tested for their influence on H-2Kb/S8L presentation on the cell surface of the Flp-In 293Kb cells. The detection of this complex was performed by immunostaining and flow cytometry. The prevailing assumption is that proteasomal cleavages are exclusively responsible for the generation of the final C-termini of CTL epitopes. Nevertheless, recent publications showed that TPPII (tripeptidyl peptidase II) is required for the generation of the correct C-terminus of the HLA-A3-restricted HIV epitope Nef(73-82). With this background, the dependence of the S8L generation on proteasomal cleavage of the designed constructs was characterized using proteasomal inhibitors. The results obtained indicate that it is crucial for proteasomal cleavage, which amino acid is flanking the C-terminus of an epitope. Furthermore, partially proteasome independent S8L generation from specific S8L-precursor peptides was observed. Hence, the possibility of other existing endo- or carboxy-peptidases in the cytosol that could be involved in the correct trimming of the C-terminus of antigenic peptides for MHC class I presentation was investigated, performing specific knockdowns and using inhibitors against the target peptidases. In parallel, a purification strategy to identify the novel peptidase was established. The purified peaks showing an endopeptidase activity were further analyzed by mass spectrometry and some potential peptidases (like e.g. Lon) were identified, which have to be further characterized.

Spectrum reconstruction from a scattering measurement using the adjoint Boltzmann transport equation for photons

Quality control of medical radiological systems is of fundamental importance, and requires efficient methods for accurately determine the X-ray source spectrum. Straightforward measurements of X-ray spectra in standard operating require the limitation of the high photon flux, and therefore the measure has to be performed in a laboratory. However, the optimal quality control requires frequent in situ measurements which can be only performed using a portable system. To reduce the photon flux by 3 magnitude orders an indirect technique based on the scattering of the X-ray source beam by a solid target is used. The measured spectrum presents a lack of information because of transport and detection effects. The solution is then unfolded by solving the matrix equation that represents formally the scattering problem. However, the algebraic system is ill-conditioned and, therefore, it is not possible to obtain a satisfactory solution. Special strategies are necessary to circumvent the ill-conditioning. Numerous attempts have been done to solve this problem by using purely mathematical methods. In this thesis, a more physical point of view is adopted. The proposed method uses both the forward and the adjoint solutions of the Boltzmann transport equation to generate a better conditioned linear algebraic system. The procedure has been tested first on numerical experiments, giving excellent results. Then, the method has been verified with experimental measurements performed at the Operational Unit of Health Physics of the University of Bologna. The reconstructed spectra have been compared with the ones obtained with straightforward measurements, showing very good agreement.

Entanglement entropy in restricted integrable spin-chains

In questa tesi abbiamo presentato il calcolo dell’Entropia di Entanglement di un sistema quantistico unidimensionale integrabile la cui rappresentazione statistica é data dal modello RSOS, il cui punto critico é una realizzazione su reticolo di tutti i modelli conformi minimali. Sfruttando l’integrabilitá di questi modelli, abbiamo svolto il calcolo utilizzando la tecnica delle Corner Transfer Matrices (CTM). Il risultato ottenuto si discosta leggermente dalla previsione di J. Cardy e P. Calabrese ricavata utilizzando la teoria dei campi conformi descriventi il punto critico. Questa differenza é stata imputata alla non-unitarietá del modello studiato, in quanto la tecnica CTM studia il ground state, mentre la previsione di Cardy e Calabrese si focalizza sul vuoto conforme del modello: nel caso dei sistemi non-unitari questi due stati non coincidono, ma possono essere visti come eccitazioni l’uno dell’altro. Dato che l’Entanglement é un fenomeno genuinamente quantistico e il modello RSOS descrive un sistema statistico classico bidimensionale, abbiamo proposto una Hamiltoniana quantistica unidimensionale integrabile la cui rappresentazione statistica é data dal modello RSOS.

Thermal fluctuations and boundary conditions in the lattice Boltzmann method

The lattice Boltzmann method is a popular approach for simulating hydrodynamic interactions in soft matter and complex fluids. The solvent is represented on a discrete lattice whose nodes are populated by particle distributions that propagate on the discrete links between the nodes and undergo local collisions. On large length and time scales, the microdynamics leads to a hydrodynamic flow field that satisfies the Navier-Stokes equation. In this thesis, several extensions to the lattice Boltzmann method are developed. In complex fluids, for example suspensions, Brownian motion of the solutes is of paramount importance. However, it can not be simulated with the original lattice Boltzmann method because the dynamics is completely deterministic. It is possible, though, to introduce thermal fluctuations in order to reproduce the equations of fluctuating hydrodynamics. In this work, a generalized lattice gas model is used to systematically derive the fluctuating lattice Boltzmann equation from statistical mechanics principles. The stochastic part of the dynamics is interpreted as a Monte Carlo process, which is then required to satisfy the condition of detailed balance. This leads to an expression for the thermal fluctuations which implies that it is essential to thermalize all degrees of freedom of the system, including the kinetic modes. The new formalism guarantees that the fluctuating lattice Boltzmann equation is simultaneously consistent with both fluctuating hydrodynamics and statistical mechanics. This establishes a foundation for future extensions, such as the treatment of multi-phase and thermal flows. An important range of applications for the lattice Boltzmann method is formed by microfluidics. Fostered by the "lab-on-a-chip" paradigm, there is an increasing need for computer simulations which are able to complement the achievements of theory and experiment. Microfluidic systems are characterized by a large surface-to-volume ratio and, therefore, boundary conditions are of special relevance. On the microscale, the standard no-slip boundary condition used in hydrodynamics has to be replaced by a slip boundary condition. In this work, a boundary condition for lattice Boltzmann is constructed that allows the slip length to be tuned by a single model parameter. Furthermore, a conceptually new approach for constructing boundary conditions is explored, where the reduced symmetry at the boundary is explicitly incorporated into the lattice model. The lattice Boltzmann method is systematically extended to the reduced symmetry model. In the case of a Poiseuille flow in a plane channel, it is shown that a special choice of the collision operator is required to reproduce the correct flow profile. This systematic approach sheds light on the consequences of the reduced symmetry at the boundary and leads to a deeper understanding of boundary conditions in the lattice Boltzmann method. This can help to develop improved boundary conditions that lead to more accurate simulation results.

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.