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.

Abrasiveness of conventional and specific denture-cleansing dentifrices

This study analyzed the weight loss and surface roughness caused in Plexiglass specimens by conventional dentifrices (Sorriso, Colgate and Close Up) and specific dentifrices used for cleaning of dentures (Corega and Dentu Creme). Plexiglass specimens were divided into 6 groups (n=6) including: a control (distilled water - DW) and experimental groups. Brushing was performed in a toothbrushing machine with a soft brush and a dentifrice suspension and DW according to different brushing times (50, 100, 200 and 250 min -18,000, 36,000, 72,000 and 90,000 cycles, respectively, calculated to correspond to 1, 2, 4 and 5 years of regular brushing). The results of weight loss and surface roughness were analyzed by ANOVA and Tukey’s test at 5% significance level. In all tested times, the effect of DW was insignificant. Dentifrices differed significantly from DW in the initial period. Corega dentifrice caused greater mass loss in all studied times, followed by Close Up. Dentifrices resulted in a surface roughness similar to the DW at 50 min. In the other times, Sorriso, Colgate and Corega caused more surface roughness than DW. In conclusion, specific dentifrices caused larger mass loss and lower surface roughness as conventional dentifrice.

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.

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.

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.

Molecular dynamics simulations of sheared brush-like systems

Mit Hilfe von Molekulardynamik-Simulationen untersuchen wir bürstenartige Systeme unter guten Lösungsmittelbedingungen. Diese Systeme sind, dank ihren vielfältigen Beschaffenheiten, die von Molekularparametern und äußeren Bedingungen abhängig sind, wichtig für viele industrielle Anwendungen. Man vermutet, dass die Polymerbürsten eine entscheidende Rolle in der Natur wegen ihrer einzigartigen Gleiteigenschaften spielen. Ein vergröbertes Modell wird verwendet, um die strukturellen und dynamischen Eigenschaften zweier hochkomprimierter Polymerbürsten, die eine niedrige Reibung aufweisen, zu untersuchen. Allerdings sind die Lubrikationseigenschaften dieser Systeme, die in vielen biologischen Systemen vorhanden sind, beeinflußt. Wir untersuchen so-genannte "weiche Kolloide", die zwischen den beiden Polymerbürsten eingebettet sind, und wie diese Makroobjekte auf die Polymerbürsten wirken.rnrnNicht-Gleichgewichts-Molekulardynamik-Simulationen werden durchgeführt, in denen die hydrodynamischen Wechselwirkungen durch die Anwendung des DPD-Thermostaten mit expliziten Lösungsmittelmolekülen berücksichtigt werden. Wir zeigen, dass die Kenntnis der Gleichgewichtseigenschaften des Systems erlaubt, dynamische Nichtgleichgewichtsigenschaften der Doppelschicht vorherzusagen.rnrnWir untersuchen, wie die effektive Wechselwirkung zwischen kolloidalen Einschlüßen durch die Anwesenheit der Bürsten (in Abhängigkeit der Weichheit der Kolloide und der Pfropfdichte der Bürsten) beeinflußt wird. Als nächsten Schritt untersuchen wir die rheologische Antwort von solchen komplexen Doppelschichten auf Scherung. Wir entwickeln eine Skalen-Theorie, die die Abhängigkeit der makroskopischen Transporteigenschaften und der lateralen Ausdehnung der verankerten Ketten von der Weissenberg Zahl oberhalb des Bereichs, in dem die lineare Antwort-Theorie gilt, voraussagt. Die Vorhersagen der Theorie stimmen gut mit unseren und früheren numerischen Ergebnissen und neuen Experimenten überein. Unsere Theorie bietet die Möglichkeit, die Relaxationszeit der Doppelschicht zu berechnen. Wenn diese Zeit mit einer charakteristischen Längenskala kombiniert wird, kann auch das ''transiente'' (nicht-stationäre) Verhalten beschrieben werden.rnrnrnWir untersuchen die Antwort des Drucktensors und die Deformation der Bürsten während der Scherinvertierung für grosse Weissenberg Zahlen. Wir entwickeln eine Vorhersage für die charakteristische Zeit, nach der das System wieder den stationären Zustand erreicht.rnrnrnElektrostatik spielt eine bedeutende Rolle in vielen biologischen Prozessen. Die Lubrikationseigenschaften der Polymerbürsten werden durch die Anwesenheit langreichweitiger Wechselwirkungen stark beeinflusst. Für unterschiedliche Stärken der elektrostatischen Wechselwirkungen untersuchen wir rheologische Eigenschaften der Doppelschicht und vergleichen mit neutralen Systemen. Wir studieren den kontinuierlichen Übergang der Systemeigenschaften von neutralen zu stark geladenen Bürsten durch Variation der Bjerrumlänge und der Ladungsdichte.

Stress & structure of thin polymer brush films

Polymer brushes have unique properties with a large variety of possible applications ranging from responsive coatings and drug delivery to lubrication and sensing. For further development a detailed understanding of the properties is needed. Established characterization methods, however, only supply information of the surface. Experimental data about the inner “bulk” structure of polymer brushes is still missing.rnScattering methods under grazing incidence supply structural information of surfaces as well as structures beneath it. Nanomechanical cantilevers supply stress data, which is giving information about the forces acting inside the polymer brush film. In this thesis these two techniques are further developed and used to deepen the understanding of polymer brushes. rnThe experimental work is divided into four chapters. Chapter 2 deals with the preparation of polymer brushes on top of nanomechanical cantilever sensors as well as large area sample by using a “grafting-to” technique. The further development of nanomechanical cantilever readout is subject of chapter 3. In order to simplify cantilever sensing, a method is investigated which allows one to perform multiple bending experiments on top of a single cantilever. To do so, a way to correlate different curvatures is introduced as well as a way to conveniently locate differently coated segments. In chapter 4 the change in structure upon solvent treatment of mixed polymer brushes is investigated by using scattering methods and nanomechanical cantilevers amongst others. This allows one to explain the domain memory effect, which is typically found in such systems. Chapter 5 describes the implementation of a phase shifting interferometer - used for readout of nanomechanical cantilevers - into the µ-focused scattering beamline BW4, allowing simultaneous measurements of stress and structure information. The last experimental chapter 6 deals with the roughness correlation in polymer brushes and its dependence on the chain tethered density.rnIn summary, the thesis deals with utilization of new experimental techniques for the investigation of polymer brushes and further development of the techniques themselves.rn

Innovative Man Machine Interfaces In Aeronautics

The research activity focused on the study, design and evaluation of innovative human-machine interfaces based on virtual three-dimensional environments. It is based on the brain electrical activities recorded in real time through the electrical impulses emitted by the brain waves of the user. The achieved target is to identify and sort in real time the different brain states and adapt the interface and/or stimuli to the corresponding emotional state of the user. The setup of an experimental facility based on an innovative experimental methodology for “man in the loop" simulation was established. It allowed involving during pilot training in virtually simulated flights, both pilot and flight examiner, in order to compare the subjective evaluations of this latter to the objective measurements of the brain activity of the pilot. This was done recording all the relevant information versus a time-line. Different combinations of emotional intensities obtained, led to an evaluation of the current situational awareness of the user. These results have a great implication in the current training methodology of the pilots, and its use could be extended as a tool that can improve the evaluation of a pilot/crew performance in interacting with the aircraft when performing tasks and procedures, especially in critical situations. This research also resulted in the design of an interface that adapts the control of the machine to the situation awareness of the user. The new concept worked on, aimed at improving the efficiency between a user and the interface, and gaining capacity by reducing the user’s workload and hence improving the system overall safety. This innovative research combining emotions measured through electroencephalography resulted in a human-machine interface that would have three aeronautical related applications: • An evaluation tool during the pilot training; • An input for cockpit environment; • An adaptation tool of the cockpit automation.

Le tecniche di machine learning per il trattamento della sindrome dell'arto fantasma

Questo elaborato ha come scopo quello di analizzare ed esaminare una patologia oggetto di attiva ricerca scientifica, la sindrome dell’arto fantasma o phantom limb pain: tracciando la storia delle terapie più utilizzate per la sua attenuazione, si è giunti ad analizzarne lo stato dell’arte. Consapevoli che la sindrome dell’arto fantasma costituisce, oltre che un disturbo per chi la prova, uno strumento assai utile per l’analisi delle attività nervose del segmento corporeo superstite (moncone), si è svolta un’attività al centro Inail di Vigorso di Budrio finalizzata a rilevare segnali elettrici provenienti dai monconi superiori dei pazienti che hanno subito un’amputazione. Avendo preliminarmente trattato l’argomento “Machine learning” per raggiungere una maggiore consapevolezza delle potenzialità dell’apprendimento automatico, si sono analizzate la attività neuronali dei pazienti mentre questi muovevano il loro arto fantasma per riuscire a settare nuove tipologie di protesi mobili in base ai segnali ricevuti dal moncone.

Machine Learning applicato al Web Semantico: Statistical Relational Learning vs Tensor Factorization

Obiettivo della tesi è analizzare e testare i principali approcci di Machine Learning applicabili in contesti semantici, partendo da algoritmi di Statistical Relational Learning, quali Relational Probability Trees, Relational Bayesian Classifiers e Relational Dependency Networks, per poi passare ad approcci basati su fattorizzazione tensori, in particolare CANDECOMP/PARAFAC, Tucker e RESCAL.

Perceived difficulty and time effectiveness in translation: a comparison of machine translation post-editing and translation memory use

Con il presente studio si è inteso analizzare l’impatto dell’utilizzo di una memoria di traduzione (TM) e del post-editing (PE) di un output grezzo sul livello di difficoltà percepita e sul tempo necessario per ottenere un testo finale di alta qualità. L’esperimento ha coinvolto sei studenti, di madrelingua italiana, del corso di Laurea Magistrale in Traduzione Specializzata dell’Università di Bologna (Vicepresidenza di Forlì). I partecipanti sono stati divisi in tre coppie, a ognuna delle quali è stato assegnato un estratto di comunicato stampa in inglese. Per ogni coppia, ad un partecipante è stato chiesto di tradurre il testo in italiano usando la TM all’interno di SDL Trados Studio 2011. All’altro partecipante è stato chiesto di fare il PE completo in italiano dell’output grezzo ottenuto da Google Translate. Nei casi in cui la TM o l’output non contenevano traduzioni (corrette), i partecipanti avrebbero potuto consultare Internet. Ricorrendo ai Think-aloud Protocols (TAPs), è stato chiesto loro di riflettere a voce alta durante lo svolgimento dei compiti. È stato quindi possibile individuare i problemi traduttivi incontrati e i casi in cui la TM e l’output grezzo hanno fornito soluzioni corrette; inoltre, è stato possibile osservare le strategie traduttive impiegate, per poi chiedere ai partecipanti di indicarne la difficoltà attraverso interviste a posteriori. È stato anche misurato il tempo impiegato da ogni partecipante. I dati sulla difficoltà percepita e quelli sul tempo impiegato sono stati messi in relazione con il numero di soluzioni corrette rispettivamente fornito da TM e output grezzo. È stato osservato che usare la TM ha comportato un maggior risparmio di tempo e che, al contrario del PE, ha portato a una riduzione della difficoltà percepita. Il presente studio si propone di aiutare i futuri traduttori professionisti a scegliere strumenti tecnologici che gli permettano di risparmiare tempo e risorse.

Applicazione smartphone Social Time Machine

La prima parte del documento contiene una breve introduzione al mondo mobile, cloud computing e social network. La seconda parte si concentra sulla progettazione di un'applicazione per i dispositivi mobili usando le tecnologie Facebook e Parse. Infine, viene implementata un'applicazione Android usando le techiche descritte in precedenza.