The principal neuronal gD-type 3-O-sulfotransferases and their products in central and peripheral nervous system tissues☆

Within the nervous system, heparan sulfate (HS) of the cell surface and extracellular matrix influences developmental, physiologic and pathologic processes. HS is a functionally diverse polysaccharide that employs motifs of sulfate groups to selectively bind and modulate various effector proteins. Specific HS activities are modulated by 3-O-sulfated glucosamine residues, which are generated by a family of seven 3-O-sulfotransferases (3-OSTs). Most isoforms we herein designate as gD-type 3-OSTs because they generate HSgD+, 3-O-sulfated motifs that bind the gD envelope protein of herpes simplex virus 1 (HSV-1) and thereby mediate viral cellular entry. Certain gD-type isoforms are anticipated to modulate neurobiologic events, because a Drosophila gD-type 3-OST is essential for a conserved neurogenic signaling pathway regulated by Notch. Information about 3-OST isoforms expressed in the nervous system of mammals is incomplete. Here, we identify the 3-OST isoforms having properties compatible with their participation in neurobiologic events. We show that 3-OST-2 and 3-OST-4 are principal isoforms of brain. We find these are gD-type enzymes, as they produce products similar to a prototypical gD-type isoform, and they can modify HS to generate receptors for HSV-1 entry into cells. Therefore, 3-OST-2 and 3-OST-4 catalyze modifications similar or identical to those made by the Drosophila gD-type 3-OST that has a role in regulating Notch signaling. We also find that 3-OST-2 and 3-OST-4 are the predominant isoforms expressed in neurons of the trigeminal ganglion, and 3-OST-2/4-type 3-O-sulfated residues occur in this ganglion and in select brain regions. Thus, 3-OST-2 and 3-OST-4 are the major neural gD-type 3-OSTs, and so are prime candidates for participating in HS-dependent neurobiologic events

A svm-based method for sentiment analysis in Persian language

Persian language is the official language of Iran, Tajikistan and Afghanistan. Local online users often represent their opinions and experiences on the web with written Persian. Although the information in those reviews is valuable to potential consumers and sellers, the huge amount of web reviews make it difficult to give an unbiased evaluation to a product. In this paper, standard machine learning techniques SVM and naive Bayes are incorporated into the domain of online Persian Movie reviews to automatically classify user reviews as positive or negative and performance of these two classifiers is compared with each other in this language. The effects of feature presentations on classification performance are discussed. We find that accuracy is influenced by interaction between the classification models and the feature options. The SVM classifier achieves as well as or better accuracy than naive Bayes in Persian movie. Unigrams are proved better features than bigrams and trigrams in capturing Persian sentiment orientation