Topography of four different endodontic rotary systems, before and after being used for the 12th time

Root canal preparation may damage NiTi instruments resulting in wear and deformation. The aim of this study was to make a comparative evaluation of the surface topography of the cervical third of four different rotary systems, before and after being used twelve times, in 1.440 resin blocks with simulated root canals with standardized 45 degrees curvatures, and analyzed by atomic force microscopy AFM. The blocks were divided into four groups and prepared according to the manufacturers recommendations: Group 1 - K3 (R); Group 2 - Protaper Universal (R); Group 3 - Twisted Files (R) and Group 4 - Biorace (R). After each preparation, the instruments were washed and autoclaved. A total of 240 instruments were selected, being 30 new instruments and 30 after having been used for the 12th time, from each group. These instruments were analyzed by AFM and for quantitative evaluation, the mean RMS (Root mean square) values of the cervical third of the specimens from the four groups were used. The result showed that all the rotary files used for the 12th time suffered wear with change in the topography of the cervical region of the active portion of the file (ANOVA p < 0.01). Classifying the specimens in increasing order, from the least to the greatest wear suffered, Group 3 (2.8993 nm) presented the least wear, followed by Group 4 (12.2520 nm), Group 1 (36.0043 nm) and lastly, Group 2 (59.8750 nm) with the largest amount of cervical surface wear. Microsc. Res. Tech. 75:97-102, 2012. (c) 2011 Wiley Periodicals, Inc.

An online data access prediction and optimization approach for distributed systems

Current scientific applications have been producing large amounts of data. The processing, handling and analysis of such data require large-scale computing infrastructures such as clusters and grids. In this area, studies aim at improving the performance of data-intensive applications by optimizing data accesses. In order to achieve this goal, distributed storage systems have been considering techniques of data replication, migration, distribution, and access parallelism. However, the main drawback of those studies is that they do not take into account application behavior to perform data access optimization. This limitation motivated this paper which applies strategies to support the online prediction of application behavior in order to optimize data access operations on distributed systems, without requiring any information on past executions. In order to accomplish such a goal, this approach organizes application behaviors as time series and, then, analyzes and classifies those series according to their properties. By knowing properties, the approach selects modeling techniques to represent series and perform predictions, which are, later on, used to optimize data access operations. This new approach was implemented and evaluated using the OptorSim simulator, sponsored by the LHC-CERN project and widely employed by the scientific community. Experiments confirm this new approach reduces application execution time in about 50 percent, specially when handling large amounts of data.

Semantic integration of gene expression analysis tools and data sources using software connectors

Abstract Background The study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heteregeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data. Results We have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data. Conclusions The proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools and data sources. The methodology can be used in the development of connectors supporting both simple and nontrivial processing requirements, thus assuring accurate data exchange and information interpretation from exchanged data.