Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

Identification of sense and antisense transcripts regulated by drought in sugarcane

Sugarcane is an important sugar and energy crop that can be used efficiently for biofuels production. The development of sugarcane cultivars tolerant to drought could allow for the expansion of plantations to sub-prime regions. Knowledge on the mechanisms underlying drought responses and its relationship with carbon partition would greatly help to define routes to increase yield. In this work we studied sugarcane responses to drought using a custom designed oligonucleotide array with 21,901 different probes. The oligoarrays were designed to contain probes that detect transcription in both sense and antisense orientation. We validated the results obtained using quantitative real-time PCR (qPCR). A total of 987 genes were differentially expressed in at least one sample of sugarcane plants submitted to drought for 24, 72 and 120 h. Among them, 928 were sense transcripts and 59 were antisense transcripts. Genes related to Carbohydrate Metabolism, RNA Metabolism and Signal Transduction were selected for gene expression validation by qPCR that indicated a validation percentage of 90 %. From the probes presented on the array, 75 % of the sense probes and 11.9 % of the antisense probes have signal above background and can be classified as expressed sequences. Our custom sugarcane oligonucleotide array provides sensitivity and good coverage of sugarcane transcripts for the identification of a representative proportion of natural antisense transcripts (NATs) and sense-antisense transcript pairs (SATs). The antisense transcriptome showed, in most cases, co-expression with respective sense transcripts.