Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts

The classification of texts has become a major endeavor with so much electronic material available, for it is an essential task in several applications, including search engines and information retrieval. There are different ways to define similarity for grouping similar texts into clusters, as the concept of similarity may depend on the purpose of the task. For instance, in topic extraction similar texts mean those within the same semantic field, whereas in author recognition stylistic features should be considered. In this study, we introduce ways to classify texts employing concepts of complex networks, which may be able to capture syntactic, semantic and even pragmatic features. The interplay between various metrics of the complex networks is analyzed with three applications, namely identification of machine translation (MT) systems, evaluation of quality of machine translated texts and authorship recognition. We shall show that topological features of the networks representing texts can enhance the ability to identify MT systems in particular cases. For evaluating the quality of MT texts, on the other hand, high correlation was obtained with methods capable of capturing the semantics. This was expected because the golden standards used are themselves based on word co-occurrence. Notwithstanding, the Katz similarity, which involves semantic and structure in the comparison of texts, achieved the highest correlation with the NIST measurement, indicating that in some cases the combination of both approaches can improve the ability to quantify quality in MT. In authorship recognition, again the topological features were relevant in some contexts, though for the books and authors analyzed good results were obtained with semantic features as well. Because hybrid approaches encompassing semantic and topological features have not been extensively used, we believe that the methodology proposed here may be useful to enhance text classification considerably, as it combines well-established strategies. (c) 2012 Elsevier B.V. All rights reserved.

Antimicrobial activity of two techniques for arm skin disinfection of blood donors in Brazil

Objective: Evaluation of the antimicrobial effect of skin disinfection techniques is essential to avoid the transmission of infectious agents during blood transfusion. The aim of this study was to examine the effectiveness of two methods of arm skin disinfection used in blood donors at a Hemotherapy Center in Brazil that represents an important centre for distributing haemocomponents to many cities in the country. Methods: Two skin disinfection techniques in 50 blood donors were evaluated. For the first arm, 10% povidone-iodine/two-stage technique was used. On the opposite arm, 0.5% chlorhexidine digluconate alcohol solution/one-stage technique was used. The swabs were seeded on three culture media: blood agar, mannitol salt agar and Mac Conkey agar. Automated bacterial classification based on biochemical tests/specific substrates was performed. Donor characteristics were collected using the computerised system of the Hemotherapy Center. Results: We found that microbial reduction was significantly higher for 10% povidone-iodine technique (98.57-98.87%) when compared with 0.5% chlorhexidine technique (94.38-95.06%). The species Leuconostoc mesenteroides and Staphylococcus hominis showed resistance to both disinfection techniques. We did not find statistically significant relationships between donor characteristics and microbial reduction. Conclusions: Arm skin disinfection with 10% povidone-iodine produced better antimicrobial activity. We must acknowledge that 10% povidone-iodine technique has the limitation of being a two-stage method. However, prevention of adverse events due to bacterial contamination and transfusion reactions should be prioritised. Production of hypoallergenic and stronger antiseptics that allowed a safe one-stage disinfection technique should be encouraged in health systems, not only in Brazil but also around the world.

EEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer's disease

Recent experimental evidence has suggested a neuromodulatory deficit in Alzheimer's disease (AD). In this paper, we present a new electroencephalogram (EEG) based metric to quantitatively characterize neuromodulatory activity. More specifically, the short-term EEG amplitude modulation rate-of-change (i.e., modulation frequency) is computed for five EEG subband signals. To test the performance of the proposed metric, a classification task was performed on a database of 32 participants partitioned into three groups of approximately equal size: healthy controls, patients diagnosed with mild AD, and those with moderate-to-severe AD. To gauge the benefits of the proposed metric, performance results were compared with those obtained using EEG spectral peak parameters which were recently shown to outperform other conventional EEG measures. Using a simple feature selection algorithm based on area-under-the-curve maximization and a support vector machine classifier, the proposed parameters resulted in accuracy gains, relative to spectral peak parameters, of 21.3% when discriminating between the three groups and by 50% when mild and moderate-to-severe groups were merged into one. The preliminary findings reported herein provide promising insights that automated tools may be developed to assist physicians in very early diagnosis of AD as well as provide researchers with a tool to automatically characterize cross-frequency interactions and their changes with disease.