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.

Acceptance Threshold Hypothesis is Supported by Chemical Similarity of Cuticular Hydrocarbons in a Stingless Bee, Melipona asilvai

The ability to discriminate nestmates from non-nestmates in insect societies is essential to protect colonies from conspecific invaders. The acceptance threshold hypothesis predicts that organisms whose recognition systems classify recipients without errors should optimize the balance between acceptance and rejection. In this process, cuticular hydrocarbons play an important role as cues of recognition in social insects. The aims of this study were to determine whether guards exhibit a restrictive level of rejection towards chemically distinct individuals, becoming more permissive during the encounters with either nestmate or non-nestmate individuals bearing chemically similar profiles. The study demonstrates that Melipona asilvai (Hymenoptera: Apidae: Meliponini) guards exhibit a flexible system of nestmate recognition according to the degree of chemical similarity between the incoming forager and its own cuticular hydrocarbons profile. Guards became less restrictive in their acceptance rates when they encounter non-nestmates with highly similar chemical profiles, which they probably mistake for nestmates, hence broadening their acceptance level.

Phylogenetic and Similarity Analysis of HTLV-1 Isolates from HIV-Coinfected Patients from the South and Southeast Regions of Brazil

HTLV-1 is endemic in Brazil and HIV/ HTLV-1 coinfection has been detected, mostly in the northeast region. Cosmopolitan HTLV-1a is the main subtype that circulates in Brazil. This study characterized 17 HTLV-1 isolates from HIV coinfected patients of southern (n = 7) and southeastern (n = 10) Brazil. HTLV-1 provirus DNA was amplified by nested PCR (env and LTR) and sequenced. Env sequences (705 bp) from 15 isolates and LTR sequences (731 bp) from 17 isolates showed 99.5% and 98.8% similarity among sequences, respectively. Comparing these sequences with ATK (HTLV-1a) and Mel5 (HTLV-1c) prototypes, similarities of 99% and 97.4%, respectively, for env and LTR with ATK, and 91.6% and 90.3% with Mel5, were detected. Phylogenetic analysis showed that all sequences belonged to the transcontinental subgroup A of the Cosmopolitan subtype, clustering in two Latin American clusters.

PRoSPer: perceptual similarity queries in medical CBIR systems through user profiles

In this paper, we present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. Content-based image retrieval systems relying on relevance feedback techniques usually request the users to label relevant/irrelevant images. Thus, we present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The profiles maintain the settings desired for each user, allowing tuning of the similarity assessment, which encompasses the dynamic change of the distance function employed through an interactive process. Experiments on medical images show that the method is effective and can improve the decision making process during analysis.