In vivo aging of gutta‐percha dental cone

Gutta‐percha cone is the most widely used material for root canal filling. The in vivo aging of this cone focus on the degradation of its main organic component, trans‐1,4‐polyisoprene, was studied. Aged cones (25 samples) from 2 to 30 years of root canal filling were extracted from different patients in the occasion of retreatment by mechanical way. The information about the aging time was given by the patients. Gel‐permeation chromatography (GPC) and infrared spectroscopy (FTIR) were the analytical techniques used. Polyisoprene degrades with time of aging, but in a slow process. Decrease in polymer molar mass from 5.7 × 10⁵ to 1.7 × 10⁵ g/mol was observed in polyisoprene from cone after 30 years of root canal filling and inside a noninfected tooth. In tooth with caries and periodontal infection, the decrease in molar mass is higher (4.6 × 10⁴ g/mol in cone with 10 years of aging). The production of carbonyl and hydroxyl groups in the aged material indicates that the process is oxidative, even in closed teeth. In these cases, the oxygen could be provided from tissue fluid. The degradation mechanism is complex and depends on many factors, besides time of root canal filling. The dental problem caused by the aging could be the production and migration of cytotoxic substances to periodontal ligament and the reduction on the canal sealing property due to the polymer weight loss. Both of them could contribute to the root canal treatment failure. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100:4082–4088, 2006     

Analytical ray tracing system: Introducing art, a C-library designed for seismic applications

Ray tracing technique is an important tool not only to forward but also for inverse problems in Geophysics, which most of the seismic processing steps depend on. However, implementing ray tracing codes can be very time consuming. This article presents a computer library to trace rays in 2.5D media composed by a stack of layers. The velocity profile inside each layer is such that the eikonal equation can be analytically solved. Therefore, the ray tracing within such profile is made fast and accurate. The great advantage of an analytical ray tracing library is the numerical precision of the quantities computed and the fast execution of the implemented codes. Even though ray tracing programs exist for a long time, for example the seis88 package by ?ervený, most of those programs use a numerical approach to compute the ray. Regardless of the fact that numerical methods can solve more general problems, the analytical ones could be part of a more sophisticated simulation process, where the ray tracing time is completely relevant. We demonstrate the feasibility of our codes using several examples (Miqueles et al., 2013)  [1]. The library can also be used for other applications besides seismic, e.g., optics and tomography.     

Dynamic thread mapping of shared memory applications by exploiting cache coherence protocols

In current computer architectures, the communication performance between threads varies depending on the memory hierarchy. This performance difference must be considered when mapping parallel applications to processor cores. In parallel applications based on the shared memory paradigm, the communication is difficult to detect because it is implicit. Furthermore, dynamic mapping introduces several challenges, since it needs to find a suitable mapping and migrate the threads with a low overhead during the execution of the application. We propose a mechanism to detect the communication pattern of shared memory applications by monitoring cache coherence protocols. We also propose heuristics that, combined with our communication detection mechanism, allow the mapping to be performed dynamically by the operating system. Experiments with the NAS Parallel Benchmarks showed a reduction of up to 13.9% of the execution time, 30.5% of the cache misses and 39.4% of the number of invalidation messages.     

Comparative analysis of methods for determining the metabolic rate in order to provide a balance between man and the environment

The incorrect determination of metabolic rate can be linked to discrepancies between the model of the PMV (Predicted Mean Vote) and real thermal sensation collected in field studies. Aiming to improve the correlation of the PMV model and the real thermal sensation, this work established new values for the metabolic rate: one way being called “calculated” using Newton's Method and the other called "measured" using a metabolic analyzer. Welder's activities were evaluated, through the measurements of environmental and personal variables. New values of metabolic rate were determined for this activity. The values found for the calculated form and the measured one were, respectively, 178.63 and 145.46 W/m², different from the range provided by the table of ISO 8996 (2004) for this activity (75–125 W/m²). In order to verify which of the values of the metabolic rate was closer to the real thermal sensation of PMV, a linear regression was made between the PMV and the real thermal sensation in three ways: S × PMV_tabulated (R² = 0.1749), S × PMV_calculated (R ² = 0.7481) and S × PMV_measured (R² = 0.7854). It was found that the values measured by the instrument gave a higher coefficient of determination which was chosen for the correction of the table. The correction of the table provides a value of M_predicted, that is a value of metabolic rate that corrects the values provided by the tables of ISO 8996 (2004), by means of a correction coefficient. For the welder's activities in a metal-mechanics industry, tabulated values can be multiplied by the correction coefficient 1.4648 in order to minimize inaccuracies. The PMV_predicted, obtained through the M_predicted, when related to the actual thermal sensation, provides a coefficient of determination of 0.7511, thereby improving the model of the PMV.     

Development and implementation of clinical guidelines: An artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.     

Low-cost sensor to detect overtaking based on optical flow

The automotive industry invests substantial amounts of money in driver-security and driver-assistance systems. We propose an overtaking detection system based on visual motion cues that combines feature extraction, optical flow, solid-objects segmentation and geometry filtering, working with a low-cost compact architecture based on one focal plane and an on-chip embedded processor. The processing is divided into two stages: firstly analog processing on the focal plane processor dedicated to image conditioning and relevant image-structure selection, and secondly, vehicle tracking and warning-signal generation by optical flow, using a simple digital microcontroller. Our model can detect an approaching vehicle (multiple-lane overtaking scenarios) and warn the driver about the risk of changing lanes. Thanks to the use of tightly coupled analog and digital processors, the system is able to perform this complex task in real time with very constrained computing resources. The proposed method has been validated with a sequence of more than 15,000 frames (90 overtaking maneuvers) and is effective under different traffic situations, as well as weather and illumination conditions.     

Background subtraction model based on color and depth cues

Background subtraction consists of segmenting objects in movement in a video captured by a static camera. This is typically performed using color information, but it leads to wrong estimations due to perspective and illumination issues. We show that multimodal approaches based on the integrated use of color and depth cues produce more accurate and robust results than using either data source independently. Depth is less affected by issues such as shadows or foreground objects similar to background. However, objects close to the background may not be detected when using only range information, being color information complementary in those cases. We propose an extension of a well-known background subtraction technique which fuses range and color information, as well as a post-processing mask fusion stage to get the best of each feature. We have evaluated the method proposed using a well-defined dataset and different disparity estimation algorithms, showing the benefits of our method for fusion color and depth cues.     

Cholesterol synthesis and esterification in isolated enterocytes: Regulation by cholesterol and cholestyramine feeding

The purpose of the present study was to investigate the physiological control of the main regulatory enzymes of cholesterol metabolism in isolated enterocytes obtained from chick duodenum, jejunum and ileum. Cholesterol feeding resulted in an inhibition of 3-hydroxy-3-methylglutaryl-CoA reductase and mevalonate 5-pyrophosphate decarboxylase, while cholestyramine feeding increased reductase activity in all the regions studied and decarboxylase activity only in duodenum. Cholesterol feeding markedly increased acyl-CoA:cholesterol acyltransferase, but the effects of cholestyramine were less clear. The effects on transferase activity cannot be due to differences in the availability of acyl-CoA as exogenous substrate as no significant differences were found in acyl-CoA hydrolase activity after any of the dietary treatments. The effects of cholesterol feeding were related to changes in the cholesterol content of epithelial cells, whereas in the case of cholestyramine this relationship was less apparent.     

Inductive Model Generation for Text Classification Using a Bipartite Heterogeneous Network

Algorithms for numeric data classification have been applied for text classification. Usually the vector space model is used to represent text collections. The characteristics of this representation such as sparsity and high dimensionality sometimes impair the quality of general-purpose classifiers. Networks can be used to represent text collections, avoiding the high sparsity and allowing to model relationships among different objects that compose a text collection. Such network- based representations can improve the quality of the classification results. One of the simplest ways to represent textual collections by a network is through a bipartite heterogeneous network, which is composed of objects that represent the documents connected to objects that represent the terms. Heterogeneous bipartite networks do not require computation of similarities or relations among the objects and can be used to model any type of text collection. Due to the advantages of representing text collections through bipartite heterogeneous networks, in this article we present a text classifier which builds a classification model using the structure of a bipartite heterogeneous network. Such an algorithm, referred to as IMBHN （Inductive Model Based on Bipartite Heterogeneous Network）, induces a classification model assigning weights to objects that represent the terms for each class of the text collection. An empirical evaluation using a large amount of text collections from different domains shows that the proposed IMBHN algorithm produces significantly better results than k-NN, C4.5, SVM, and Naive Bayes algorithms.     

Molecular dynamics studies of a model polymer-catalyst-carbon interface

Classical molecular dynamics simulations are performed to obtain insights into structural and dynamical behavior of water and O₂ transport through a model polymer membrane, and at the interface of such hydrated polymer with graphite-supported nanocatalyst platinum particles. Water clustering is observed near the membrane hydrophilic sites constituted by sulfonic groups, which due to their affinity with platinum, are located near the metallic surface. It is found that the diffusion of water through the model hydrated polymer membrane depends strongly on the level of membrane hydration due to contribution from various mechanisms whose relative weights change with the degree of hydration. Possible scenarios for O₂ diffusion are also analyzed.