Digital image correlation analysis of the load transfer by implant-supported restorations

This study compared splinted and non-splinted implant-supported prosthesis with and without a distal proximal contact using a digital image correlation method. An epoxy resin model was made with acrylic resin replicas of a mandibular first premolar and second molar and with threaded implants replacing the second premolar and first molar. Splinted and non-splinted metal-ceramic screw-retained crowns were fabricated and loaded with and without the presence of the second molar. A single-camera measuring system was used to record the in-plane deformation on the model surface at a frequency of 1.0 Hz under a load from 0 to 250 N. The images were then analyzed with specialist software to determine the direct (horizontal) and shear strains along the model. Not splinting the crowns resulted in higher stress transfer to the supporting implants when the second molar replica was absent. The presence of a second molar and an effective interproximal contact contributed to lower stress transfer to the supporting structures even for non-splinted restorations. Shear strains were higher in the region between the molars when the second molar was absent, regardless of splinting. The opposite was found for the region between the implants, which had higher shear strain values when the second molar was present. When an effective distal contact is absent, non-splinted implant-supported restorations introduce higher direct strains to the supporting structures under loading. Shear strains appear to be dependent also on the region within the model, with different regions showing different trends in strain changes in the absence of an effective distal contact. (C) 2011 Elsevier Ltd. All rights reserved.

Body image dissatisfaction and eating symptoms in mothers of adolescents with eating disorders

The purpose of the present study was to assess body dissatisfaction and eating symptoms in mothers of eating disorder (ED) female patients and to compare results with those of a control group. The case group consisted of 35 mothers of female adolescents (aged between 10 and 17 yrs) diagnosed with ED who attended the Interdisciplinary Project for Care, Teaching and Research on Eating Disorders in Childhood and Adolescence (PROTAD) at Clinicas Hospital Institute of Psychiatry of the Universidade de Sao Paulo Medical School. Demographic and socioeconomic data were collected. Eating symptoms were assessed using the Eating Attitudes Test (EAT-26) and body image was assessed by the Body Image Questionnaire (BSQ) and Stunkard Figure Rating Scale (FRS). The case group was compared to a control group consisting of 35 mothers of female adolescents (between 10 and 17 years) who attended a private school in the city of Sao Paulo, southeastern Brazil. With regard to EAT, BSQ and FRS scores, we found no statistically significant differences between the two groups. However, we found a positive correlation between BMI and BSQ scores in the control group (but not in the case group) and a positive correlation between EAT and FRS scores in the case group (but not in the control group). It appears to be advantageous to assess body image by combining more than one scale to evaluate additional components of the construct. (Eating Weight Disord. 15: e219-e225, 2010). (C)2010, Editrice Kurtis

Visual analysis of image collections

Multidimensional Visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces PEx-Image-Projection Explorer for Images-a tool aimed at supporting analysis of image collections. The tool supports a methodology that employs interactive visualizations to aid user-driven feature detection and classification tasks, thus offering improved analysis and exploration capabilities. The visual mappings employ similarity-based multidimensional projections and point placement to layout the data on a plane for visual exploration. In addition to its application to image databases, we also illustrate how the proposed approach can be successfully employed in simultaneous analysis of different data types, such as text and images, offering a common visual representation for data expressed in different modalities.

Topological multi-contour decomposition for image analysis and image retrieval

Successful classification, information retrieval and image analysis tools are intimately related with the quality of the features employed in the process. Pixel intensities, color, texture and shape are, generally, the basis from which most of the features are Computed and used in such fields. This papers presents a novel shape-based feature extraction approach where an image is decomposed into multiple contours, and further characterized by Fourier descriptors. Unlike traditional approaches we make use of topological knowledge to generate well-defined closed contours, which are efficient signatures for image retrieval. The method has been evaluated in the CBIR context and image analysis. The results have shown that the multi-contour decomposition, as opposed to a single shape information, introduced a significant improvement in the discrimination power. (c) 2008 Elsevier B.V. All rights reserved,

Image indexing and retrieval using an ART-2A neural network architecture

Traditional content-based image retrieval (CBIR) systems use low-level features such as colors, shapes, and textures of images. Although, users make queries based on semantics, which are not easily related to such low-level characteristics. Recent works on CBIR confirm that researchers have been trying to map visual low-level characteristics and high-level semantics. The relation between low-level characteristics and image textual information has motivated this article which proposes a model for automatic classification and categorization of words associated to images. This proposal considers a self-organizing neural network architecture, which classifies textual information without previous learning. Experimental results compare the performance results of the text-based approach to an image retrieval system based on low-level features. (c) 2008 Wiley Periodicals, Inc.

Medical image retrieval based on complexity analysis

Texture is one of the most important visual attributes used in image analysis. It is used in many content-based image retrieval systems, where it allows the identification of a larger number of images from distinct origins. This paper presents a novel approach for image analysis and retrieval based on complexity analysis. The approach consists of a texture segmentation step, performed by complexity analysis through BoxCounting fractal dimension, followed by the estimation of complexity of each computed region by multiscale fractal dimension. Experiments have been performed with MRI database in both pattern recognition and image retrieval contexts. Results show the accuracy of the method and also indicate how the performance changes as the texture segmentation process is altered.

Performance Improvement of Tomographic Image Reconstruction Based on DSP Processors

This paper presents the use of a multiprocessor architecture for the performance improvement of tomographic image reconstruction. Image reconstruction in computed tomography (CT) is an intensive task for single-processor systems. We investigate the filtered image reconstruction suitability based on DSPs organized for parallel processing and its comparison with the Message Passing Interface (MPI) library. The experimental results show that the speedups observed for both platforms were increased in the same direction of the image resolution. In addition, the execution time to communication time ratios (Rt/Rc) as a function of the sample size have shown a narrow variation for the DSP platform in comparison with the MPI platform, which indicates its better performance for parallel image reconstruction.

Layer-by-Layer Assembly of Carbon Nanotubes Incorporated in Light-Addressable Potentiometric Sensors

The integration of carbon nanotubes in conjunction with a chemical or biological recognition element into a semiconductor field-effect device (FED) may lead to new (bio)chemical sensors. In this study, we present a new concept to develop field-effect-based sensors, using a light-addressable potentiometric sensor (LAPS) platform modified with layer-by-layer (LbL) films of single-walled carbon nanotubes (SWNTs) and polyamidoamine (PAMAM) dendrimers. Film growth was monitored for each layer adsorbed on the LAPS chip by Measuring current-voltage (IIV) curves. The morphology of the films was analyzed via atomic force microscopy (AFM) and field-emission scanning electron microscopy (FESEM), revealing the formation of a highly interconnected nanostructure of SWNTs-network into the dendrimer layers. Constant current (CC) Measurements showed that the incorporation of the PAMAM/SWNT LbL film containing LIP to 6 bilayers onto the LAPS Structure has a high pH sensitivity of ca. 58 mV/pH. The biosensing ability of the devices was tested for penicillin G via adsorptive immobilization of the enzyme penicillinase atop the LgL film. LAPS architectures modified with the LbL film exhibited higher sensitivity, ca. 100 mV/decade, in comparison to ca. 79 mV/decade for all unmodified LAPS, which demonstrates the potential application of the CNT-LbL Structure in field-effect-based (bio)chemical sensors.

Carbon nanotubes in nanostructured films: Potential application as amperometric and potentiometric field-effect (bio-)chemical sensors

The assembly of carbon nanotubes (CNTs) into nanostructured films is attractive for producing functionalized hybrid materials and (bio-)chemical sensors, but this requires experimental methods that allow for control of molecular architecturcs. In this study, we exploit the layer-by-layer (LbL) technique to obtain two types of sensors incorporating CNTs. In the first, LbL films of alternating layers of multi-walled carbon nanotubes (MWNTs) dispersed in polyarninoamide (PAMAM) dendrimers and nickel phthalocyanine (NiTsPc) were used in amperometric detection of the neurotransmitter dopamine (DA). The electrochemical properties evaluated with cyclic voltammetry indicated that the incorporation of MWNTs in the PAMAM-NT/NiTsPc LbL films led to a 3-fold increase in the peak current, in addition to a decrease of 50 mV in the oxidation potential of DA. The latter allowed detection of DA even in the presence of ascorbic acid (AA), a typical interferent for DA. Another LbL film was obtained with layers of PAMAM and single-walled carbon nanotubes (SWNTs) employed in field-effect-devices using a capacitive electrolyte-insulator-semiconductor structure (EIS). The adsorption of the film components was monitored by measuring the flat-band voltage shift in capacitance-voltage (C-P) curves, caused by the charges from the components. Constant capacitance (ConCap) measurements showed that the EISPAMAM/SWNT film displayed a high pH sensitivity (ca. 54.5 mV/pH), being capable of detecting penicillin G between 10(-4) mol L(-1) and 10(-2) mol L-1, when a layer of penicillinase was adsorbed atop the PAMAM/SWNT film. (C) 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Investigation of AISI 1040 steel corrosion H(2)S solution containing chloride ions by digital image processing coupled with in electrochemical techniques

This paper presents a study of AISI 1040 steel corrosion in aqueous electrolyte of acetic acid buffer containing 3.1 and 31 x 10(-3) mol dm(-3) of Na(2)S in both the presence and absence of 3.5 wt.% NaCl. This investigation of steel corrosion was carried out using potential polarization, and open-circuit and in situ optical microscopy. The morphological analysis and classification of types of surface corrosion damage by digital image processing reveals grain boundary corrosion and shows a non-uniform sulfide film growth, which occurs preferentially over pearlitic grains through successive formation and dissolution of the film. (C) 2011 Elsevier Ltd. All rights reserved.

An entropy-based approach to automatic image segmentation of satellite images

An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation. (C) 2010 Elsevier B.V. All rights reserved.

Immobilization of Poly(propylene imine) Dendrimer/Nickel Phtalocyanine as Nanostructured Multilayer Films To Be Used as Gate Membranes for SEGFET pH Sensors

We describe the assembly of layer-by-layer films based on the poly(propylene imine) dendrimer (PPID) generation 3 and nickel tetrasulfonated phthalocyanine (NiTsPc) for application as chemically sensitive membranes in sepal alive extended-gate field effect transistor (SEGFET) pH sensors PPID/NiTsPc films wet e adsorbed on quartz, glass. indium tin oxide. or gold (Au)-covered glass substrates Multilayer formation was monitored via UV-vis absorption upon following the increment in the Q-band intensity (615 nm) of NiTsPc The nanostructured membranes were very stable in a pH range of 4-10 and displayed a good sensitivity toward H(+), ca 30 mV/pH for PPID/N(1)TsPc films deposited on Au-covered substrates For films deposited on ITO, the sensitivity was ca 52 4 mV/pH. close to the expected theoretical value for ton-sensitive membranes. The use of chemically stable PPID/NiTsPc films as gate membranes in SEGFETs, as introduced here, may represent an alternative for the fabrication of nanostructured, porous platforms for enzyme immobilization to be used in enzymatic biosensors.

Silsesquioxane as a New Building Block Material for Modified Electrodes Fabrication and Application as Neurotransmitters Sensors

Nanostructured films comprising a 3-n-propylpyridiniunn silsesquioxane polymer (designated as SiPy(+)Cl(-)) and copper (II) tetrasulfophthalocyanine (CuTsPc) were produced using the Layer-by-Layer technique (LbL). To our knowledge this is the first report on the use of silsesquioxane derivative polymers as building blocks for nanostructured thin films fabrication. Deposition of the multilayers were monitored by UV-Vis spectroscopy revealing the linear increment in the absorbance of the Q-band from CuTsPc at 617 nm with the number of SiPy(+)Cl(-)/CuTsPc or CuTsPc/SiPy(+)Cl(-) bilayers. FTIR analyses showed that specific interactions between SiPy+Cl- and CuTsPc occurred between SO(3)(-) groups of tetrasulfophthalocyanine and the pyridinium groups of the polycation. Morphological studies were carried out using the AFM technique, which showed that the roughness and thickness of the films increase with the number of bilayers. The films displayed electroactivity and were employed to detection of dopamine (DA) and ascorbic acid (AA) using cyclic voltammetry, at concentrations ranging from 1.96 x 10(-4) to 1.31 x 10(-3) molL(-1). The number and the sequence of bilayers deposition influenced the electrochemical response in presence of DA and AA. Using differential pulse technique, films comprising SiPy(+)/CuTsPc were able to distinguish between DA and ascorbic acid (AA), with a potential difference of approximately with 500 mV, in the concentration range of 9.0 x 10(-5) to 2.0 x 10(-4) molL(-1), in pH 3.0.

A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms

In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen`s Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology. (C) 2010 Elsevier B.V. All rights reserved.

Immobilization of lutetium bisphthalocyanine in nanostructured biomimetic sensors using the LbL technique for phenol detection

This study describes the development of amperometric sensors based on poly(allylamine hydrochloride) (PAH) and lutetium bisphthalocyanine (LuPc(2)) films assembled using the Layer-by-Layer (LbL) technique. The films have been used as modified electrodes for catechol quantification. Electrochemical measurements have been employed to investigate the catalytic properties of the LuPc(2) immobilized in the LbL films. By chronoamperometry, the sensors present excellent sensitivity (20 nA mu M(-1)) in a wide linear range (R(2) = 0.994) up to 900 mu M and limit of detection (s/n = 3) of 37.5 x 10(-8) M for catechol. The sensors have good reproducibility and can be used at least for ten times. The work potential is +0.3 V vs. saturated calomel electrode (SCE). In voltammetry measurements, the calibration curve shows a good linearity (R(2) = 0.992) in the range of catechol up to 500 mu M with a sensitivity of 90 nA mu M(-1) and LD of 8 mu M. (C) 2011 Elsevier B.V. All rights reserved.