The Usefulness of Caries Frequency, Depth, and Location in Determining Cariogenicity and Past Subsistence: A Test on Early and Later Agriculturalists From the Peruvian Coast

Dental markers have been used to unravel particularities of paleodiet, subsistence, social structure, and health. This article aims to compare oral pathology among four pre-Columbian groups with different degrees of agricultural and socio-cultural development but comparable ecological conditions who lived on the coastal desert of Peru. Three of these groups are assigned to distinct phases of the Formative Period (2500-1 BC), a time critical for our understanding of the development of agriculture and social complexity. The fourth group corresponds to the Late Intermediate Period (1000-1470 AD), when agriculture had its apogee and society was highly stratified. In this study we test whether there is an increase (1) in the frequency of carious lesions and (2) in caries depth, and (3) if there is a shift from occlusal to extra-occlusal caries locations with the development of agriculture. Therefore, we analyze the frequencies of carious lesions and antemortem tooth loss (AMTL), the caries distributions by age, sex, and type of tooth, as well as the tissues affected by, and the location of the carious lesions. Since there are no significant differences in the frequencies of carious lesions and AMTL between the groups, we reject hypothesis 1. In contrast, caries depth does increase, and caries location changes from occlusal to extra-occlusal sites with agricultural development. However, we can only corroborate hypothesis 2 and 3 when taking into consideration dental wear. Thus, we recommend that caries depth and locations should be used with evaluations of dental wear to reconstruct subsistence in ancient populations. Am J Phys Anthropol 143:75-91, 2010. (C) 2010 Wiley-Liss, Inc.

Phylogenetic position of Dendropsophus gaucheri (Lescure and Marty 2000) highlights the need for an in-depth investigation of the phylogenetic relationships of Dendropsophus (Anura: Hylidae)

Dendropsophus gaucheri is a recently described species which inhabits open areas of the eastern part of the Guiana Shield and is currently assigned to the D. parviceps species group based on the presence of a subocular cream spot. Herein we investigate its phylogenetic position including material from the type locality and newly documented populations from Suriname and Brazil based on mtDNA sequences. The species, as well as D. riveroi which is assigned to the D. minimus species group, were recovered nested within the D. microcephalus species group which implies the paraphyly of the three Dendropsophus species groups. Such result, along with other evidences, highlights the need for a thorough revision of the genus. The genetic distances among D. gaucheri samples studied are low confirming their conspecificity and suggesting recent connections among populations from open areas currently isolated by rainforest in the lowlands of the Guiana Shield.

High-power diode laser in the disinfection in depth of the root canal dentin

Objective. The objective of this study was to evaluate the disinfection degree of dentine caused by the use of diode laser after biomechanical procedures. Study design. Thirty teeth were sectioned and roots were autoclaved and incubated for 4 weeks with a suspension of Enterococcus faecalis. The specimens were randomly divided into 3 groups (n = 10): G1, instrumented with rotary files, irrigated with 0.5% sodium hypochlorite and 17% EDTA-T, and then irradiated by 830-nm diode laser at 3 W; G2, the same procedures as G1 but without laser irradiation; and G3, irrigation with saline solution (control). Dentin samples of each third were collected with carbide burs and aliquots were sowed to count viable cells. Results. The disinfection degree achieved was 100% in G1 and 98.39% in G2, when compared to the control group (G3). Conclusion. Diode laser irradiation provided increased disinfection of the deep radicular dentin in the parameters and samples tested.

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.

EPR in the Characterization of the Shade Effect on Translucence, Remaining Free Radicals, and Polymerization Depth of Commercially Available Resin Composites

The aim of this study was to assess the relation between the number of free radicals generated and the polymerization depth in two different commercial brands of resin composites with different colors and translucence. Electron paramagnetic resonance quantified the radical populations through relative intensity (I (r)) of free radicals generated, and radical decay was monitored. Sample translucence and the classical polymerization depth were measured. The analysis indicated that resin with more color pigments (MA4, I (r) = 0.73 a.u) or more opacity components (ODA2, I (r) = 0.84 a.u) generated smaller populations of free radicals and have the lower polymerization depth than clearer (M, I (r) = 1.20 a.u and MA2, I (r) = 1.02) or more translucent (OEA2, I (r) = 1.00 a.u) composites for the same light-curing time. It seems that irradiation doses have to be adequate to more colored and less translucent resins.

MODIS aerosol optical depth Retrievals with high spatial resolution over an urban area using the critical reflectance

[1] The retrieval of aerosol optical depth (Ta) over land by satellite remote sensing is still a challenge when a high spatial resolution is required. This study presents a tool that uses satellite measurements to dynamically identify the aerosol optical model that best represents the optical properties of the aerosol present in the atmosphere. We use aerosol critical reflectance to identify the single scattering albedo of the aerosol layer. Two case studies show that the Sao Paulo region can have different aerosol properties and demonstrates how the dynamic methodology works to identify those differences to obtain a better T a retrieval. The methodology assigned the high single scattering albedo aerosol model (pi o( lambda = 0.55) = 0.90) to the case where the aerosol source was dominated by biomass burning and the lower pi(o) model (pi(o) (lambda = 0.55) = 0.85) to the case where the local urban aerosol had the dominant influence on the region, as expected. The dynamic methodology was applied using cloud-free data from 2002 to 2005 in order to retrieve Ta with Moderate Resolution Imaging Spectroradiometer ( MODIS). These results were compared with collocated data measured by AERONET in Sao Paulo. The comparison shows better results when the dynamic methodology using two aerosol optical models is applied (slope 1.06 +/- 0.08 offset 0.01 +/- 0.02 r(2) 0.6) than when a single and fixed aerosol model is used (slope 1.48 +/- 0.11 and offset - 0.03 +/- 0.03 r(2) 0.6). In conclusion the dynamical methodology is shown to work well with two aerosol models. Further studies are necessary to evaluate the methodology in other regions and under different conditions.

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.

Noninvasive Evaluation of Oral Lesions Using Depth-sensitive Optical Spectroscopy

BACKGROUND: Optical spectroscopy is a noninvasive technique with potential applications for diagnosis of oral dysplasia and early cancer. In this study, we evaluated the diagnostic performance of a depth-sensitive optical spectroscopy (DSOS) system for distinguishing dysplasia and carcinoma from non-neoplastic oral mucosa. METHODS: Patients with oral lesions and volunteers without any oral abnormalities were recruited to participate. Autofluorescence and diffuse reflectance spectra of selected oral sites were measured using the DSOS system. A total of 424 oral sites in 124 subjects were measured and analyzed, including 154 sites in 60 patients with oral lesions and 270 sites in 64 normal volunteers. Measured optical spectra were used to develop computer-based algorithms to identify the presence of dysplasia or cancer. Sensitivity and specificity were calculated using a gold standard of histopathology for patient sites and clinical impression for normal volunteer sites. RESULTS: Differences in oral spectra were observed in: (1) neoplastic versus nonneoplastic sites, (2) keratinized versus nonkeratinized tissue, and (3) shallow versus deep depths within oral tissue. Algorithms based on spectra from 310 nonkeratinized anatomic sites (buccal, tongue, floor of mouth, and lip) yielded an area under the receiver operating characteristic curve of 0.96 in the training set and 0.93 in the validation set. CONCLUSIONS: The ability to selectively target epithelial and shallow stromal depth regions appeared to be diagnostically useful. For nonkeratinized oral sites, the sensitivity and specificity of this objective diagnostic technique were comparable to that of clinical diagnosis by expert observers. Thus, DSOS has potential to augment oral cancer screening efforts in community settings. Cancer 2009;115:1669-79. (C) 2009 American Cancer Society.

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.

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.

PARALLEL ALGORITHMS FOR MAXIMAL CLIQUES IN CIRCLE GRAPHS AND UNRESTRICTED DEPTH SEARCH

We present parallel algorithms on the BSP/CGM model, with p processors, to count and generate all the maximal cliques of a circle graph with n vertices and m edges. To count the number of all the maximal cliques, without actually generating them, our algorithm requires O(log p) communication rounds with O(nm/p) local computation time. We also present an algorithm to generate the first maximal clique in O(log p) communication rounds with O(nm/p) local computation, and to generate each one of the subsequent maximal cliques this algorithm requires O(log p) communication rounds with O(m/p) local computation. The maximal cliques generation algorithm is based on generating all maximal paths in a directed acyclic graph, and we present an algorithm for this problem that uses O(log p) communication rounds with O(m/p) local computation for each maximal path. We also show that the presented algorithms can be extended to the CREW PRAM model.