PURPOSE: To compare the effect of incomplete caries removal (ICR) and indirect pulp capping (IPC) with calcium hydroxide (CH) or an inert material (wax) on color, consistency and contamination of the remaining dentin of primary molars. METHODS: This double-blind, parallel-design, randomized controlled trial included 30 children presenting one primary molar with deep caries lesion. Children were randomly assigned after ICR to receive IPC with CH or wax. All teeth were then restored with resin composite. Baseline dentin color and consistency were evaluated after ICR, and dentin samples were collected for contamination analyses using scanning electron microscopy. After 3 months, restorations were removed and the three parameters were re-evaluated. In both groups, dentin became significantly darker after 3 months. RESULTS: No cases of yellow dentin were observed after 3 months with CH compared to 33.3% of the wax cases (P < 0.05). A statistically significant difference over time was observed only for CH regarding consistency. CH stimulated a dentin hardening process in a statistically higher number of cases than wax (86.7% vs. 33.3%; P = 0.008). Contamination changed significantly over time in CH and wax without significant difference between groups. It was concluded that CH and wax arrested the carious process of the remaining carious dentin after indirect pulp capping, but CH showed superior dentin color and consistency after 3 months.

Discrimination and classification time series becomes almost indispensable since the large amountof information available nowadays. The problem of time series discrimination and classification isdiscussed in [1]. In this work the authors propose a novel clustering algorithm based on a class ofquasi U-statistics and subgroup decomposition tests. The decomposition may be applied to any con-cave time-series distance. The resulting test statistics is proved to be asymptotically normal for eitheri.i.d. or non-identically distributed groups of time-series under mild conditions. In practice thereare many time series that are correlated among themselves. An example that can describe this fact isthe financial markets globalization. When one of these markets is affected by an exogenous factor, achain reaction can affect many others. So the independence condition fail.We are interested in analyzing how the correlation among the groups of time series can affectclassification and clustering methods especially the one proposed by [1]. Empirical results show thatthe proposed method is robust to the presence of correlation among time series. The convergence ofthe test statistic for dependent time series will be one of the goals in this work.

References[1]

Valk, M., Pinheiro, A. 2012: Time-series clustering via quasi U-statistics,J. Time Ser. Anal.,Vol. 33, 4, 608 -619.