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

Aqueous Al2O3 nanofluids: the important factors impacting convective heat transfer

A high accuracy, counter flow double pipe heat exchanger system is designed for the measurement of convective heat transfer coefficients with different nanofluids. Both positive and negative enhancement of convective heat transfer of alumina nanofluids are found in the experiments. A modified equation was proposed to explain above phenomena through the physic properties of nanofluids such as thermal conductivity, special heat capacity and viscosity

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

Bi-view semi-supervised active learning for cross-lingual sentiment classification

Recently, sentiment classification has received considerable attention within the natural language processing research community. However, since most recent works regarding sentiment classification have been done in the English language, there are accordingly not enough sentiment resources in other languages. Manual construction of reliable sentiment resources is a very difficult and time-consuming task. Cross-lingual sentiment classification aims to utilize annotated sentiment resources in one language (typically English) for sentiment classification of text documents in another language. Most existing research works rely on automatic machine translation services to directly project information from one language to another. However, different term distribution between original and translated text documents and translation errors are two main problems faced in the case of using only machine translation. To overcome these problems, we propose a novel learning model based on active learning and semi-supervised co-training to incorporate unlabelled data from the target language into the learning process in a bi-view framework. This model attempts to enrich training data by adding the most confident automatically-labelled examples, as well as a few of the most informative manually-labelled examples from unlabelled data in an iterative process. Further, in this model, we consider the density of unlabelled data so as to select more representative unlabelled examples in order to avoid outlier selection in active learning. The proposed model was applied to book review datasets in three different languages. Experiments showed that our model can effectively improve the cross-lingual sentiment classification performance and reduce labelling efforts in comparison with some baseline methods

New methods to cope with temperature elevations in heated segments of flat plates cooled by boundary layer flow

This paper documents two reliable methods to cope with the rising temperature in an array of heated segments with a known overall heat load and exposed to forced convective boundary layer flow. Minimization of the hot spots (peak temperatures) in the array of heated segments constitutes the primary goal that sets the platform to develop the methods. The two proposed methods consist of: 1) Designing an array of unequal heaters so that each heater has a different size and generates heat at different rates, and 2) Distancing the unequal heaters from each other using an insulated spacing. Multi-scale design based on constructal theory is applied to estimate the optimal insulated spacing, heaters size and heat generation rates, such that the minimum hot spots temperature is achieved when subject to space constraint and fixed overall heat load. It is demonstrated that the two methods can considerably reduce the hot spot temperatures and consequently, both can be utilized with confidence in industry to achieve optimized heat transfer