STRUCTURAL COMPARISON OF PROKARYOTIC AND EUKARYOTIC FOLATE TRANSPORTERS BY COMPUTATIONAL APPROACH

Objective: In silico approach has particularly drawn attention in providing a realistic representation needed to understand the fundamental molecular structure of a transporter. The importance of folate metabolism and role in the internalization of antifolates in eukaryotes have been studied extensively, but the structural study of folate transporters in Homo sapiens (HFT), Plasmodium falciparum (PFT), and Streptococcus sp. (SFT) is still lacking. This study was conducted to study and compare the structures of prokaryotic and eukaryotic folate transporters. Methods: HFT, PFT, and SFT were queried using blast and sequences were retrieved using National Center for Biotechnology and Information (NCBI) databases. This was superseded by structural and functional prediction of transporters. The structure has been generated using Swiss model which was visualized using PyMol and validated by Procheck and ERRAT analysis along with the values of different secondary structures mapping to diverse sections of the Ramachandran plot. The structural and functional comparison was performed by PROSO, ProFunc, TM Score, Porewalker, TMHMM, and Protscale. Result: All the parameters for structural comparison suggest that H. sapiens folate transporter is 16.67% and 17.72% identical to Plasmodium and Streptococcus whereas Plasmodium is 21.59% identical to Streptococcus. The evaluation of transmembrane helices and hydrophobicity resulted in the presence of 1, 4, and 12 membrane-spanning segments with predicted US, UDUD, and UDS as pore shape in Plasmodium, Streptococcus, and humans. Conclusion: Such folate receptors are the main targets for the specific conveyance of antifolates. The differences found between these species may offer possibilities for the development of new drugs in future.Â

HSRA: Hindi stopword removal algorithm

In the last few years, electronic documents have been the main source of data in many research areas like Web Mining, Information Retrieval, Artificial Intelligence, Natural Language Processing etc. Text Processing plays a vital role for processing structured or unstructured data from the web. Preprocessing is the main step in any text processing systems. One significant preprocessing technique is the elimination of functional words, also known as stopwords, which affects the performance of text processing tasks. An efficient stopword removal technique is required in all text processing tasks. In this paper, we are proposing a stopword removal algorithm for Hindi Language which is using the concept of a Deterministic Finite Automata (DFA). A large number of available works on stopword removal techniques are based on dictionary containing stopword lists. Then pattern matching technique is applied and the matched patterns, which is a stopword, is removed from the document. It is a time consuming task as searching process takes a long time. This makes the method inefficient and very expensive. In comparison of that, our algorithm has been tested on 200 documents and achieved 99% accuracy and also time efficient

HSAS: Hindi Subjectivity Analysis System

With the development of Web 2.0, we are abundant with the documents expressing user's opinions, attitudes and sentiments in the textual form. This user generated textual content is an important source of information to make sound decisions by the organizations and the government. The textual information can be categorized into two types: facts and opinions. Subjectivity analysis is the automatic extraction of subjective information from the opinions posted by users and divides the content into subjective and objective sentences. Most of the works in subjectivity analysis exists for English language data but with the introduction of unicode standards UTF-8, Hindi language content on the web is growing very rapidly. In this paper, Hindi Subjectivity Analysis System (HSAS) is proposed. It explores two different methods of generating subjectivity lexicon using the available resources in English language and their comparative evaluation in performing the task of subjectivity analysis at the sentence level. The first method uses English language OpinionFinder subjectivity lexicon. The second method uses a small seed word list of Hindi language and expands it to generate subjectivity lexicon. Different evaluation strategies are used to validate the lexicon. We achieved 71.4% agreement with human annotators and ~80% accuracy in classification on a parallel data set in English and Hindi. Extensive simulations conducted on the test dataset confirm the validity of the suggested method

Breast Cancer: From Transcriptional Control to Clinical Outcome

Breast cancer is the most common malignancy in women worldwide. The risk of breast cancer in women increases with age, and this is partly attributable to the accumulation of genetic lesions. Growing evidence demonstrates the role played by epigenetic modifiers and the tumor microenvironment in contributing to the increased risk of breast cancer. This chapter provides a comprehensive overview of the epigenetic regulatory signatures that impact the well-studied signaling pathways in breast tissues. Additionally, we will also delve into the therapeutic and diagnostic potential of noncoding RNAs in breast cancer

Diabetes-social and economic perspectives in the new Millenium

Prevalence of diabetes is increasing in pandemic proportions, particularly in developing countries like India. The recent reports from the World Health Organization rates India as the country with the largest number of diabetic subjects in the world. The Chennai Urban Population Study (CUPS), The Chennai Urban Rural Epidemiology Study (CURES) and the National Urban Diabetes Survey (NUDS) revealed rising prevalence of diabetes in India. Some of the significant risk factors associated with diabetes are similar worldwide, but their intensities vary between races, regions and countries. The reason for escalation of diabetes prevalence in India could be attributed to a combination of genetic factors and environmental factors due to urbanization and industrialization, which has led to sedentary lifestyle, physical inactivity, stress and obesity arising from energy and fat rich diets. The long-term complications of diabetes occurring during the most productive years of their lives create a devastating burden of morbidity and mortality, which poses an economic and social burden both at the individual and at the national level. Compared to non-diabetic individuals, diabetic individuals are more than twice as costly to treat, mainly due to the high costs related with management of associated complications. Prevention seems to be the need of the hour to tackle this epidemic. This article highlights the social and economic implications of diabetes in India and emphasis the measures required to prevent diabetes

HMDSAD: Hindi multi-domain sentiment aware dictionary

Sentiment Analysis is a fast growing sub area of Natural Language Processing which extracts user's opinion and classify it according to its polarity into positive, negative or neutral classes. This task of classification is required for many purposes like opinion mining, opinion summarization, contextual advertising and market analysis but it is domain dependent. The words used to convey sentiments in one domain is different from the words used to express sentiments in other domain and it is a costly task to annotate the corpora in every possible domain of interest before training the classifier for the classification. We are making an attempt to solve this problem by creating a sentiment aware dictionary using multiple domain data. The source domain data is labeled into positive and negative classes at the document level and the target domain data is unlabeled. The dictionary is created using both source and target domain data. The words used to express positive or negative sentiments in labeled data has relatedness weights assigned to it which signifies its co-occurrence frequency with the words expressing the similar sentiments in target domain. This work is carried out in Hindi, the official language of India. The web pages in Hindi language is booming very quickly after the introduction of UTF-8 encoding style. The dictionary can be used to classify the unlabeled data in the target domain by training a classifier

Development of Facial Expression Classifier using Neural Networks

A person's emotional and mental well being, together with the age, sex, race, can be easily depicted by one's face. A crucial role is played by facial expressions in day-today social interactions. An individual's emotional level as well as behavioral manners can be interpreted by these expressions. Facial expression classifier is a evolving, demanding and curious problem in computer vision. It has its potential applications in the field of robotics, behavioral science, human computer interaction, video games etc.. It assists in building more intelligent systems which have better ability to interpret human emotions. In this paper, a facial expression classifier is proposed based on Convolution Neural Networks (CNN). CNNs are biologically-inspired variants of multi-layer preceptor (MLP) networks. They use an architecture which is particularly well suitable to classify images. Detection of facial expression can be enhanced by

Sentiment analysis in a resource scarce language: Hindi

A common human behavior is to take other’s opinion before taking any decision. With the tremendous availability of documents which express opinions on different issues, the challenge arises to analyze it and produce useful knowledge from it. Many works in the area of Sentiment Analysis is available for English language. From last few years, opinion-rich resources are booming in other languages and hence there is a need to perform Sentiment Analysis in those languages. In this paper, a Sentiment Analysis in Hindi Languag

Generating multilingual subjectivity resources using english language

The text data can be of two types: facts and opinions. With the introduction of UTF-8 standards and development of Web 2.0, we are in abundance of opinionated text data available in many languages on the web. Subjectivity analysis aims at dividing those opinionated data into subjective and objective sentences and automatic extraction of subjective information from it. Many subjectivity resources as well as subjectivity analysis works are available in English language. In this paper, we examine different methods of generating subjectivity resources in Hindi language and other Indian languages using resources and tools available in English language. Two methods are proposed using wordlevel subjectivity annotations. These methods use English language OpinionFinder subjectivity lexicon and a small seed word list of Hindi language which can be expanded to generate subjectivity lexicon, respectively. Four methods are proposed using sentencelevel subjectivity annotations. These methods use subjectivity annotated corpora and tools available in English language. Different evaluation strategies are used to validate the generated lexicon and corpora in Hindi language. The simulations conducted confirm that these methods are effective in rapidly creating subjectivity resources in Hindi language and other Indian languages