Classifier-Based Text Simplification for Improved Machine Translation

Machine Translation is one of the research fields of Computational Linguistics. The objective of many MT Researchers is to develop an MT System that produce good quality and high accuracy output translations and which also covers maximum language pairs. As internet and Globalization is increasing day by day, we need a way that improves the quality of translation. For this reason, we have developed a Classifier based Text Simplification Model for English-Hindi Machine Translation Systems. We have used support vector machines and Na\"ive Bayes Classifier to develop this model. We have also evaluated the performance of these classifiers.Comment: In Proceedings of International Conference on Advances in Computer Engineering and Applications 201

M. tuberculosis Sliding β-Clamp Does Not Interact Directly with the NAD+ -Dependent DNA Ligase

The sliding β-clamp, an important component of the DNA replication and repair machinery, is drawing increasing attention as a therapeutic target. We report the crystal structure of the M. tuberculosis β-clamp (Mtbβ-clamp) to 3.0 Å resolution. The protein crystallized in the space group C2221 with cell-dimensions a = 72.7, b = 234.9 & c = 125.1 Å respectively. Mtbβ-clamp is a dimer, and exhibits head-to-tail association similar to other bacterial clamps. Each monomer folds into three domains with similar structures respectively and associates with its dimeric partner through 6 salt-bridges and about 21 polar interactions. Affinity experiments involving a blunt DNA duplex, primed-DNA and nicked DNA respectively show that Mtbβ-clamp binds specifically to primed DNA about 1.8 times stronger compared to the other two substrates and with an apparent Kd of 300 nM. In bacteria like E. coli, the β-clamp is known to interact with subunits of the clamp loader, NAD+ -dependent DNA ligase (LigA) and other partners. We tested the interactions of the Mtbβ-clamp with MtbLigA and the γ-clamp loader subunit through radioactive gel shift assays, size exclusion chromatography, yeast-two hybrid experiments and also functionally. Intriguingly while Mtbβ-clamp interacts in vitro with the γ-clamp loader, it does not interact with MtbLigA unlike in bacteria like E. coli where it does. Modeling studies involving earlier peptide complexes reveal that the peptide-binding site is largely conserved despite lower sequence identity between bacterial clamps. Overall the results suggest that other as-yet-unidentified factors may mediate interactions between the clamp, LigA and DNA in mycobacteria

Analysis of deflection in visco-thermoelastic beam resonators subjected to harmonic loading

This paper analyses the transverse deflection in a homogeneous, isotropic, visco-thermoelastic beam when subjected to harmonic load. The ends of the beam are considered at different boundary conditions (both axial ends clamped, both axial ends simply supported and left end clamped and right end free). The deflection has been studied by using the Laplace transform. Numerical computation of analytical expression of deflection obtained after Inverse Laplace transform has been done using MATLAB software. The graphical observations have been discussed under various boundary conditions for different values of time and length. The above work has applications in design of resonators

Revisiting Terminalia arjuna – An Ancient Cardiovascular Drug

Terminalia arjuna, commonly known as arjuna, belongs to the family of Combretaceae. Its bark decoction is being used in the Indian subcontinent for anginal pain, hypertension, congestive heart failure, and dyslipidemia, based on the observations of ancient physicians for centuries. The utility of arjuna in various cardiovascular diseases needs to be studied further. Therefore, the present review is an effort to give a detailed survey of the literature summarizing the experimental and clinical studies pertinent to arjuna in cardiovascular disorders, which were particularly performed during the last decade. Systematic reviews, meta-analyses, and clinical studies of arjuna were retrieved through the use of PubMed, Google Scholar, and Cochrane databases. Most of the studies, both experimental and clinical, have suggested that the crude drug possesses anti-ischemic, antioxidant, hypolipidemic, and antiatherogenic activities. Its useful phytoconstituents are: Triterpenoids, β-sitosterol, flavonoids, and glycosides. Triterpenoids and flavonoids are considered to be responsible for its beneficial antioxidant cardiovascular properties. The drug has shown promising effect on ischemic cardiomyopathy. So far, no serious side effects have been reported with arjuna therapy. However, its long-term safety still remains to be elucidated. Though it has been found quite useful in angina pectoris, mild hypertension, and dyslipidemia, its exact role in primary/secondary coronary prevention is yet to be explored

Entropy Churn Metrics for Fault Prediction in Software Systems

Fault prediction is an important research area that aids software development and the maintenance process. It is a field that has been continuously improving its approaches in order to reduce the fault resolution time and effort. With an aim to contribute towards building new approaches for fault prediction, this paper proposes Entropy Churn Metrics (ECM) based on History Complexity Metrics (HCM) and Churn of Source Code Metrics (CHU). The study also compares performance of ECM with that of HCM. The performance of both these metrics is compared for 14 subsystems of 5different software projects: Android, Eclipse, Apache Http Server, Eclipse C/C++ Development Tooling (CDT), and Mozilla Firefox. The study also analyses the software subsystems on three parameters: (i) distribution of faults, (ii) subsystem size, and (iii) programming language, to determine which characteristics of software systems make HCM or ECM more preferred over others

Comorbid Illness, Bowel Preparation, and Logistical Constraints Are Key Reasons for Outpatient Colonoscopy Nonattendance

Background. Colonoscopy nonattendance is a challenge for outpatient clinics globally. Absenteeism results in a potential delay in disease diagnosis and loss of hospital resources. This study aims to determine reasons for colonoscopy nonattendance from a Canadian perspective. Design. Demographic data, reasons for nonattendance, and patient suggestions for improving compliance were elicited from 49 out of 144 eligible study participants via telephone questionnaire. The 49 nonattenders were compared to age and sex matched controls for several potential contributing factors. Results. Nonattendance rates were significantly higher in winter months; the OR of nonattendance was 5.2 (95% CI, 1.6 to 17.0, p<0.001) in winter versus other months. Being married was positively associated with attendance. There was no significant association between nonattendance and any of the other variables examined. The top 3 reasons for nonattendance were being too unwell to attend the procedure, being unable to complete bowel preparation, or experiencing logistical challenges. Conclusions. Colonoscopy attendance rates appear to vary significantly by season and it may be beneficial to book more colonoscopies in the summer or overbook in the winter. Targets for intervention include more tailored teaching sessions, reminders, taxi chits, and developing a hospital specific colonoscopy video regarding procedure and bowel preparation requirements

Mastering natural language processing with Python

Birmingham, UKviii, 222 p.: index; 25 c

Named Entity Recognition using Hidden Markov Model (HMM

Abstract Named Entity Recognition (NER) is the subtask of Natural Language Processing (NLP) which is the branch of artificial intelligence. It has many applications mainly in machine translation, text to speech synthesis, natural language understanding, Information Extraction, Information retrieval, question answering etc. The aim of NER is to classify words into some predefined categories like location name, person name, organization name, date, time etc. In this paper we describe the Hidden Markov Model (HMM) based approach of machine learning in detail to identify the named entities. The main idea behind the use of HMM model for building NER system is that it is language independent and we can apply this system for any language domain. In our NER system the states are not fixed means it is of dynamic in nature one can use it according to their interest. The corpus used by our NER system is also not domain specific