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Introduction The purpose of this study was to measure the temperature rise on the external root surface of filled root canals using Touch'n Heat (Analytic Endodontics, Orange, CA), the TC System (TC; Tanaka de Castro & Minatel Ltda, Cascavel, PR, Brazil), and the Tagger technique. Methods Forty-five single-canal mandibular premolar human teeth were used in the 3 experimental groups. The root canals were enlarged to accommodate up to an R40 Reciproc file (VDW, Munich, Germany). Next, the specimens were filled according to the technique evaluated. The measurement of the temperature was performed by K chromium-aluminum thermocouples attached to the coronal, middle, and apical root levels. Results There was a significant difference among the 3 techniques ( P < .001) in relation to the temperature variation between the highest temperature and the initial temperature. The highest temperature change was found with the Tagger technique at the middle third root level (11.8°C), and the lowest variation was in TC at the cervical third (2.05°C). Conclusions There was a rise of temperature on the external root surface for all of the techniques evaluated. TC showed the lowest temperature rise. [ABSTRACT FROM AUTHOR]

Assessing water quality in streams is usually measured at the local scale and often it is spatially restricted. To scale-up water-condition assessment, there is a need to use new tools that enable prediction of large-scale changes in water quality by expanding the analysis to landscape-levels and bioclimatic predictors. In addition, the traditional use of linear models in biomonitoring can be inappropriate in detecting complex relationships, such as changing patterns of aquatic community structure and complex environmental gradients. In this context, Artificial Intelligence (AI) techniques such as Rough Sets Theory (RST) can be particularly useful for dealing with vague, imprecise, inconsistent and uncertain knowledge involving biotic and abiotic data to enable the classification and prediction of changes in stream water. Here, we applied RST to estimate the water quality in streams of the Brazilian Atlantic Forest by analyzing connections between landscape and climate data with the inclusion of up to 15 families of aquatic insect groups from the orders Ephemeroptera, Plecoptera and Trichoptera (usually known as EPT taxa). First, we developed different decision sets which were the arrangements of the response variable (EPT index) and the predictor classifications. Then, we applied the best decision sets to monitor the condition of stream water in the Atlantic Forest on a large-scale. Our results showed the best decision rules were 61% accurate. Depending on the initial stream condition, this approach on a large-scale led to variable accuracy. By combining the development of different decision sets, the application of the best one on a large-scale, and the use of open-access data (landscape and climate predictors), our study approach demonstrated the potential applicability to evaluate streams in an objective with low-cost manner. This method can complement the environmental assessment of streams based only on local variables. Our framework also creates new perspectives in the analysis of water quality to generate scenarios of changes in streams based on landscape measurements to optimize monitoring networks. • Rough Sets Theory could be applied in stream water quality biomonitoring on a large-scale. • Landscape and bioclimates variables can be used to infer about water quality and support biomonitoring. • Atlantic Forest streams condition were classified with decision sets built based on landscape and climate variables. • Multidimensional problem addressed on a large-scale prediction can be solved by Rough Sets Theory. • Most accurate decision set show the potential of use Rough Sets Theory with lack of data on a large-scale. [ABSTRACT FROM AUTHOR]