A novel XML document structure comparison framework based-on sub-tree commonalities and label semantics

XML similarity evaluation has become a central issue in the database and information communities, its applications ranging over document clustering, version control, data integration and ranked retrieval. Various algorithms for comparing hierarchically structured data, XML documents in particular, have been proposed in the literature. Most of them make use of techniques for finding the edit distance between tree structures, XML documents being commonly modeled as Ordered Labeled Trees. Yet, a thorough investigation of current approaches led us to identify several similarity aspects, i.e., sub-tree related structural and semantic similarities, which are not sufficiently addressed while comparing XML documents. In this paper, we provide an integrated and fine-grained comparison framework to deal with both structural and semantic similarities in XML documents (detecting the occurrences and repetitions of structurally and semantically similar sub-trees), and to allow the end-user to adjust the comparison process according to her requirements. Our framework consists of four main modules for (i) discovering the structural commonalities between sub-trees, (ii) identifying sub-tree semantic resemblances, (iii) computing tree-based edit operations costs, and (iv) computing tree edit distance. Experimental results demonstrate higher comparison accuracy with respect to alternative methods, while timing experiments reflect the impact of semantic similarity on overall system performance.

Metronidazole Immediate Release Formulations: A Fasting Randomized Open-Label Crossover Bioequivalence Study in Healthy Volunteers

Metronidazole is a BCS (Biopharmaceutics Classification System) class 1 drug, traditionally considered the choice drug in the infections treatment caused by protozoa and anaerobic microorganisms. This study aimed to evaluate bioequivalence between 2 different marketed 250 mg metronidazole immediate release tablets. A randomized, open-label, 2 x 2 crossover study was performed in healthy Brazilian volunteers under fasting conditions with a 7-day washout period. The formulations were administered as single oral dose and blood was sampled over 48 h. Metronidazole plasma concentrations were determined by a liquid chromatography mass spectrometry (LC-MS/MS) method. The plasma concentration vs. time profile was generated for each volunteer and the pharmacokinetic parameters C-max, T-max, AUC(0-t), AUC(0-infinity), k(e), and t(1/2) were calculated using a noncompartmental model. Bioequivalence between pharmaceutical formulations was determined by calculating 90% CIs (Confidence Intervall) for the ratios of C-max, AUC(0-t), and AUC(0-infinity) values for test and reference using log-transformed data. 22 healthy volunteers (11 men, 11 women; mean (SD) age, 28 (6.5) years [range, 21-45 years]; mean (SD) weight, 66 (9.3) kg [range, 51-81 kg]; mean (SD) height, 169 (6.5) cm [range, 156-186 cm]) were enrolled in and completed the study. The 90% CIs for C-max (0.92-1.06), AUC(0-t) (0.97-1.02), and AUC(0-infinity) (0.97-1.03) values for the test and reference products fitted in the interval of 0.80-1.25 proposed by most regulatory agencies, including the Brazilian agency ANVISA. No clinically significant adverse effects were reported. After pharmacokinetics analysis, it concluded that test 250 mg metronidazole formulation is bioequivalent to the reference product according to the Brazilian agency requirements.

Incorporating label dependency into the binary relevance framework for multi-label classification

In multi-label classification, examples can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multi-label learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. Aiming to accurately predict label combinations, in this paper we propose a simple approach that enables the binary classifiers to discover existing label dependency by themselves. An experimental study using decision trees, a kernel method as well as Naive Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.

Hierarchical multi-label classification using local neural networks

Hierarchical multi-label classification is a complex classification task where the classes involved in the problem are hierarchically structured and each example may simultaneously belong to more than one class in each hierarchical level. In this paper, we extend our previous works, where we investigated a new local-based classification method that incrementally trains a multi-layer perceptron for each level of the classification hierarchy. Predictions made by a neural network in a given level are used as inputs to the neural network responsible for the prediction in the next level. We compare the proposed method with one state-of-the-art decision-tree induction method and two decision-tree induction methods, using several hierarchical multi-label classification datasets. We perform a thorough experimental analysis, showing that our method obtains competitive results to a robust global method regarding both precision and recall evaluation measures.