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 28,634 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The purpose of this case series was to report on the use of a technique of revascularization for necrotic immature permanent teeth, several problems encountered, and solutions to those problems. Eighteen pulp revascularizations were performed in 2009 using the original protocol of revascularization (adapted from the AAE/AAPD joint meeting in 2007 in Chicago). The protocol consisted of opening the canal and disinfecting it with sodium hypochlorite, sealing in a triple antibiotic paste for 2-6 weeks, re-opening, re-irrigating, creating a blood clot in the canal, and sealing with an MTA barrier over the clot. Three problems were encountered during the treatment: (1) bluish discoloration of the crown; (2) failure to produce bleeding; and (3) collapse of the mineral trioxide aggregate (MTA) material into the canal. Modifications to solve these problems included: changing one of the antibiotics, using a local anesthesia without epinephrine, and adding collagen matrix to the blood clot.
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PMID:Clinical complications in the revascularization of immature necrotic permanent teeth. 2321 19

As a powerful approach for exploratory data analysis, unsupervised clustering is a fundamental task in computer vision and pattern recognition. Many clustering algorithms have been developed, but most of them perform unsatisfactorily on the data with complex structures. Recently, adversarial autoencoder (AE) (AAE) shows effectiveness on tackling such data by combining AE and adversarial training, but it cannot effectively extract classification information from the unlabeled data. In this brief, we propose dual AAE (Dual-AAE) which simultaneously maximizes the likelihood function and mutual information between observed examples and a subset of latent variables. By performing variational inference on the objective function of Dual-AAE, we derive a new reconstruction loss which can be optimized by training a pair of AEs. Moreover, to avoid mode collapse, we introduce the clustering regularization term for the category variable. Experiments on four benchmarks show that Dual-AAE achieves superior performance over state-of-the-art clustering methods. In addition, by adding a reject option, the clustering accuracy of Dual-AAE can reach that of supervised CNN algorithms. Dual-AAE can also be used for disentangling style and content of images without using supervised information.
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PMID:Dual Adversarial Autoencoders for Clustering. 3124 79