Microhardness of Radicular Dentin Treated with 980-nm Diode Laser and Different Irrigant Solutions

Objective: The aim of this study was to evaluate the microhardness of radicular dentin after treatment with 980-nm diode laser and different irrigant solutions. Background data: There are few reports of the consequences of diode laser irradiation emitted at 980 nm on the mechanical properties of dentin. Methods: Seventy-two single canal, human canines with complete root formation were randomly distributed among three groups (n = 24), according to the irrigant solution used in the biomechanical preparation: distilled water; 1% NaOCl; and, 1% NaOCl + 17% EDTA. These groups subsequently were divided into three subgroups (n = 8), according to the diode laser parameter: no irradiation (control); 1.5W/100 Hz; and 3.0 W/100 Hz. Laser was applied with helicoidal movements for 20 sec. Roots were sectioned in slices and the fragment corresponding to the middle third was submitted to the microhardness test (KHN) at depths of 30, 90, 150, and 300 mu m. Results: ANOVA and Tukey tests showed that the microhardness of the groups irradiated with 1.5 W/100 Hz (49.7 +/- 11.2) and 3.0W/100 Hz (50.6 +/- 11.9) were statistically similar to each other (p > 0.05) and different (p < 0.05) from the non-irradiated group (45.0 +/- 9.7). Higher microhardness values were obtained at 150 mu m (49.2 +/- 11.0) and 300 mu m (52.3 +/- 11.3) which were similar among themselves and different (p < 0.05) only at the depth of 30 mu m (44.4 +/- 10.5). No differences were found among the irrigant solutions (p > 0.05). Conclusions: The microhardness of the radicular dentin increased after irradiation with 980-nm diode laser.

Microhardness of a dental restorative composite resin containing nanoparticles polymerized with argon ion laser

The objective of this study was to compare the microhardness of two resin composites (microhybrid and nanoparticles). Light activation was performed with argon ion laser 1.56J (L) and halogen light 2.6J (H) was used as control. Measurements were taken on the irradiated surfaces and those opposite them, at thicknesses of 1, 2 and 3 mm. To evaluate the quality of polymerization, the percentages of maximum hardness were calculated (PMH). For statistical analysis the ANOVA and Tukey tests were used (p <= 0.05). To microhybrid was shown that the hardness with laser was inferior to the hardness achieved with halogen light, for both the 1 mm and 2 mm. The nanoparticles polymerized with laser, presented lower hardness even on the irradiated surface, than the same surface light activated with halogen light. The microhybrid attained a minimum PMH of 80% up to the thickness of 2 mm with halogen light, and with laser, only up to 1 mm. The nanoparticles attained a minimum PMH of 80% up to 3 mm thickness with halogen light and with laser this minimum was not obtained at any thickness. Based on these results, it could be concluded that light activation with argon ion laser is contra-indicated for the studied nanoparticles. Published by Elsevier GmbH.

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.

Effect of sodium hypochlorite and edta irrigation, individually and in alternation, on dentin microhardness at the furcation area of mandibular molars

The aim of this study was to evaluate the effect of irrigation regimens on dentin microhardness at the furcation area of mandibular molars, using sodium hypochlorite and ethylenediaminetetraacetic acid (EDTA), individually and in alternation. The occlusal surface and the roots of 20 non-carious extracted human permanent mandibular molars were cut transversally and discarded. The tooth blocks were embedded in acrylic resin and randomly assigned to 4 groups (n=5) according to the irrigating regimens: 1% NaOCl solution, 17% EDTA solution, 1% NaOCl and 17% EDTA and distilled water (control). Knoop microhardness of dentin at the furcation area was evaluated. Data were analyzed using one-way ANOVA and Tukey's multiple comparison tests (α=0.05). The results of this study indicated that all irrigation solutions, except for distilled water (control), decreased dentin microhardness. EDTA did not show a significant difference with NaOCl/EDTA (p>0.05), but showed a significant difference with NaOCl (p<0.01). EDTA and NaOCl/EDTA showed a maximum decrease in microhardness. The 17% EDTA solution, either alone or in combination with 1% NaOCl reduced significantly dentin microhardness at the furcation area of mandibular molars.

Analysis of surface hardness of artificially aged resin composites

This study evaluated the effect of artificially accelerated aging (AAA) on the surface hardness of eight composite resins: Filtek Z250, Filtek Supreme, 4 Seasons, Herculite, P60, Tetric Ceram, Charisma, and Filtek Z100. Sixteen specimens were made from the test piece of each material, using an 8.0 × 2.0 mm teflon matrix. After 24 hours, eight specimens from each material were submitted to three surface hardness readings using a Shimadzu Microhardness Tester for 5 seconds at a load of 50 gf. The other eight specimens remained in the artificially accelerated aging machine for 382 hours and were submitted to the same surface hardness analysis. The means of each test specimen were submitted to the Kolmogorov-Smirnov test (p > 0.05), ANOVA and Tukey test (p < 0.05). With regard to hardness (F = 86.74, p < 0.0001) the analysis showed significant differences among the resin composite brands. But aging did not influence the hardness of any of the resin composites (F = 0.39, p = 0.53). In this study, there was interaction between the resin composite brand and the aging factors (F = 4.51, p < 0.0002). It was concluded that notwithstanding the type of resin, AAA did not influence surface hardness. However, with regard to hardness there was a significant difference among the resin brands.

Influence of HSM cutting parameters on the surface integrity characteristics of hardened AISI H13 steel

The purpose of this study is to evaluate the influence of the cutting parameters of high-speed machining milling on the characteristics of the surface integrity of hardened AISI H13 steel. High-speed machining has been used intensively in the mold and dies industry. The cutting parameters used as input variables were cutting speed (v c), depth of cut (a p), working engagement (a e) and feed per tooth (f z ), while the output variables were three-dimensional (3D) workpiece roughness parameters, surface and cross section microhardness, residual stress and white layer thickness. The subsurface layers were examined by scanning electron and optical microscopy. Cross section hardness was measured with an instrumented microhardness tester. Residual stress was measured by the X-ray diffraction method. From a statistical standpoint (the main effects of the input parameters were evaluated by analysis of variance), working engagement (a e) was the cutting parameter that exerted the strongest effect on most of the 3D roughness parameters. Feed per tooth (f z ) was the most important cutting parameter in cavity formation. Cutting speed (v c) and depth of cut (a p) did not significantly affect the 3D roughness parameters. Cutting speed showed the strongest influence on residual stress, while depth of cut exerted the strongest effect on the formation of white layer and on the increase in surface hardness.

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.

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.