Bond Strength and Etching Pattern of Adhesive Systems to Enamel: Effects of Conditioning Time and Enamel Preparation

Statement of the problem: The performance of self-etch systems on enamel is controversial and seems to be dependent on the application technique and the enamel preparation. Purpose of the Study: To examine the effects of conditioning time and enamel surface preparation on bond strength and etching pattern of adhesive systems to enamel. Materials and Methods: Ninety-six teeth were divided into 16 conditions (N = 6) in function of enamel preparation and conditioning time for bond strength test. The adhesive systems OptiBond FL (Kerr, Orange, CA, USA), OptiBond SOLO Plus (Kerr), Clearfil SE Bond (Kuraray, Osaka, Japan), and Adper Prompt L-Pop (3M ESPE, St. Paul, MN, USA) were applied on unground or ground enamel following the manufacturers` directions or doubling the conditioning time. Cylinders of Filtek Flow (0.5-mm height) were applied to each bonded enamel surface using a Tygon tube (0.7 mm in diameter; Saint-Gobain Corp., Aurora, OH, USA). After storage (24 h/37 degrees C), the specimens were subjected to shear force (0.5 mm/min). The data were treated by a three-way analysis of variance and Tukey`s test (alpha = 0.05). The failure modes of the debonded interfaces and the etching pattern of adhesives were observed using scanning electron microscopy. Results: Only the main factor ""adhesive"" was statistically significant (p < 0.001). The lowest bond strength value was observed for OptiBond FL. The most defined etching pattern was observed for 35% phosphoric acid and for Adper Prompt L-Pop. Mixed failures were observed for all adhesives, but OptiBond FL showed cohesive failures in resin predominantly. Conclusions: The increase in the conditioning time as well as the enamel pretreatment did not provide an increase in the resin-enamel bond strength values for the studied adhesives. CLINICAL SIGNIFICANCE The surface enamel preparation and the conditioning time do not affect the performance of self-etch systems to enamel. (J Esthet Restor Dent 20:322-336, 2008)

Dynamic Changes in the Mental Rotation Network Revealed by Pattern Recognition Analysis of fMRI Data

We investigated the temporal dynamics and changes in connectivity in the mental rotation network through the application of spatio-temporal support vector machines (SVMs). The spatio-temporal SVM [Mourao-Miranda, J., Friston, K. J., et al. (2007). Dynamic discrimination analysis: A spatial-temporal SVM. Neuroimage, 36, 88-99] is a pattern recognition approach that is suitable for investigating dynamic changes in the brain network during a complex mental task. It does not require a model describing each component of the task and the precise shape of the BOLD impulse response. By defining a time window including a cognitive event, one can use spatio-temporal fMRI observations from two cognitive states to train the SVM. During the training, the SVM finds the discriminating pattern between the two states and produces a discriminating weight vector encompassing both voxels and time (i.e., spatio-temporal maps). We showed that by applying spatio-temporal SVM to an event-related mental rotation experiment, it is possible to discriminate between different degrees of angular disparity (0 degrees vs. 20 degrees, 0 degrees vs. 60 degrees, and 0 degrees vs. 100 degrees), and the discrimination accuracy is correlated with the difference in angular disparity between the conditions. For the comparison with highest accuracy (08 vs. 1008), we evaluated how the most discriminating areas (visual regions, parietal regions, supplementary, and premotor areas) change their behavior over time. The frontal premotor regions became highly discriminating earlier than the superior parietal cortex. There seems to be a parcellation of the parietal regions with an earlier discrimination of the inferior parietal lobe in the mental rotation in relation to the superior parietal. The SVM also identified a network of regions that had a decrease in BOLD responses during the 100 degrees condition in relation to the 0 degrees condition (posterior cingulate, frontal, and superior temporal gyrus). This network was also highly discriminating between the two conditions. In addition, we investigated changes in functional connectivity between the most discriminating areas identified by the spatio-temporal SVM. We observed an increase in functional connectivity between almost all areas activated during the 100 degrees condition (bilateral inferior and superior parietal lobe, bilateral premotor area, and SMA) but not between the areas that showed a decrease in BOLD response during the 100 degrees condition.

Network genealogy of 195-bp satellite DNA supports the superimposed hybridization hypothesis of Trypanosoma cruzi evolutionary pattern

Trypanosoma cruzi is highly diverse genetically and has been partitioned into six discrete typing units (DTUs), recently re-named T. cruzi I-VI. Although T. cruzi reproduces predominantly by binary division, accumulating evidence indicates that particular DTUs are the result of hybridization events. Two major scenarios for the origin of the hybrid lineages have been proposed. It is accepted widely that the most heterozygous TcV and TcVI DTUs are the result of genetic exchange between TcII and TcIII strains. On the other hand, the participation of a TcI parental in the current genome structure of these hybrid strains is a matter of debate. Here, sequences of the T. cruzi-specific 195-bp satellite DNA of TcI, TcII, Tat, TcV, and TcVI strains have been used for inferring network genealogies. The resulting genealogy showed a high degree of reticulation, which is consistent with more than one event of hybridization between the Tc DTUs. The data also strongly suggest that Tat is a hybrid with two distinct sets of satellite sequences, and that genetic exchange between TcI and TcII parentals occurred within the pedigree of the TcV and TcVI DTUs. Although satellite DNAs belong to the fast-evolving portion of eukaryotic genomes, in >100 satellite units of nine T. cruzi strains we found regions that display 100% identity. No DTU-specific consensus motifs were identified, inferring species-wide conservation. (C) 2010 Elsevier B.V. All rights reserved.

Spatiotemporal Pattern Formation in the Oscillatory Electro-Oxidation of Sulfide on a Platinum Disk

Spatiotemporal pattern formation in the electrocatalytic oxidation of sulfide on a platinum disk is investigated using electrochemical methods and a charge-coupled device (CCD) camera simultaneously. The system is characterized by different oscillatory regions spread over a wide potential range. An additional series resistor and a large electrode area facilitate observation of multiple regions of kinetic instabilities along the current/potential curve. Spatiotemporal patterns on the working electrode, such as fronts, pulses, spirals, twinkling eyes, labyrinthine stripes, and alternating synchronized deposition and dissolution, are observed at different operating conditions of series resistance and sweep rate.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.