Factor Analysis of Speech Signal for Parkinson’s Disease Prediction using Support Vector Machine

Abstract—Speech signal can be used as marker for identification of Parkinson’s disease. It is neurological disorder which is progressive in nature mainly effect the people in old age. Identification of relevant discriminant features from speech signal has been a challenge in this area. In this paper, factor analysis method is used to select distinguishing features from a set of features. These selected features are more effective for detection of the PD. From an empirical study on existing dataset and a generated dataset, it was found that the jitter, shimmer variants and noise to harmonic ratio are dominant features in detecting PD. Further, these features are employed in support vector machine for classifying PD from healthy subjects. This method provides an average accuracy of 85 % with sensitivity and specificity of about 86% and 84%. Important outcome of this study is that sustained vowels phonation captures distinguishing information for analysis and detection of PD

Dual Polarized Aperture Coupled Stacked Element for Base Station Antenna

The demand towards broad band efficient antennas for base station and mobile wireless applications have increased dramatically over the last few years. Today there is a huge increase in the number of subscribers and demand for equipments that is capable of handling cost-effective network capacity solutions in Spectrum limited markets. Our Paper describes the design of dual polarized antenna element which can be implemented in a base station antenna array using IE3D Zeland Software. The Element is based on aperture coupled architecture with stacked patch, maintaining the symmetry needed for dual polarization operation. Most of common antenna elements are linearly polarized with narrow band resonators. Our design has Broad-band and dual-polarized characteristics of traditional aperture coupled architecture. The Bandwidth for Return Loss \u3e 10 dB of the element covers 1710-2170 MHz frequency spectrum. The Isolation between the ports corresponding to the two different polarizations is greater than 32 dB over the bandwidt

A CONTROLLED RELEASE MICROSPHERE FORMULATION OF AN ANTI-DIABETIC DRUG AND CHARACTERIZATION OF THE MICROSPHERE

Objective: Here the objective of this study was to prepare and characterize sustained release metformin loaded microsphere formulation which was prepared by W1/O/W2 emulsion solvent evaporation technique.Methods: Guar gum and sodium alginate were used as a matrix building material, whereas ethyl cellulose was applied as a coating polymer. Here various formulations were prepared by changing the drug and guar gum ratio, and the subsequent drug entrapment efficiency (DEE) and drug release were compared and evaluated.Results: Scanning Electron Microscopy (SEM) studies revealed spherical particles with a smooth appearance. Fourier-transform infrared spectroscopy (FTIR) showed there was no interaction between the ingredients in the final formulation. X-ray Diffraction (XRD) studies showed the emergence of polymorphic forms in the final formulation. The drug entrapment in the final drug loaded microsphere formulations was varied from 30-66.78%. The drug release studies showed the continuous release of the drug through twelve hours. The optimized formulation (f2) found to release 71.5% of drugs at the end of the 12th hour following zero order release kinetics.Conclusion: The increase in gum concentration in the W1 phase, which enhances viscosity in the W1 phase, resulting in an increase in the drug entrapment up to an optimum level and a decrease in the release rate. So, it can prolong the action. So by using this tool, we can say that metformin loaded microsphere formulation would be a suitable pharmaceutical formulation for the treatment of diabetic patients in modern drug therapy for its prolonged action. Â

Table_1_Computational models for prediction of protein–protein interaction in rice and Magnaporthe grisea.docx

IntroductionPlant–microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein–protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease.MethodsIn this paper, four genomic information-based models such as (i) the interolog, (ii) the domain, (iii) the gene ontology, and (iv) the phylogenetic-based model are developed for predicting the interaction between O. sativa and M. grisea in a whole-genome scale.Results and DiscussionA total of 59,430 interacting pairs between 1,801 rice proteins and 135 blast fungus proteins are obtained from the four models. Furthermore, a machine learning model is developed to assess the predicted interactions. Using composition-based amino acid composition (AAC) and conjoint triad (CT) features, an accuracy of 88% and 89% is achieved, respectively. When tested on the experimental dataset, the CT feature provides the highest accuracy of 95%. Furthermore, the specificity of the model is verified with other pathogen–host datasets where less accuracy is obtained, which confirmed that the model is specific to O. sativa and M. grisea. Understanding the molecular processes behind rice resistance to blast fungus begins with the identification of PPIs, and these predicted PPIs will be useful for drug design in the plant science community.</p

DataSheet_1_Computational models for prediction of protein–protein interaction in rice and Magnaporthe grisea.csv

IntroductionPlant–microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein–protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease.MethodsIn this paper, four genomic information-based models such as (i) the interolog, (ii) the domain, (iii) the gene ontology, and (iv) the phylogenetic-based model are developed for predicting the interaction between O. sativa and M. grisea in a whole-genome scale.Results and DiscussionA total of 59,430 interacting pairs between 1,801 rice proteins and 135 blast fungus proteins are obtained from the four models. Furthermore, a machine learning model is developed to assess the predicted interactions. Using composition-based amino acid composition (AAC) and conjoint triad (CT) features, an accuracy of 88% and 89% is achieved, respectively. When tested on the experimental dataset, the CT feature provides the highest accuracy of 95%. Furthermore, the specificity of the model is verified with other pathogen–host datasets where less accuracy is obtained, which confirmed that the model is specific to O. sativa and M. grisea. Understanding the molecular processes behind rice resistance to blast fungus begins with the identification of PPIs, and these predicted PPIs will be useful for drug design in the plant science community.</p

Blood counts and homocysteine in acute stroke: What is the relation?

Objectives: Stroke is the second-leading cause of death and the third-leading cause of death and disability combined in the world. We need to find easy, available, and reliable parameters on which we can depend to prevent stroke, predict outcome, and determine prognosis. This will help in stroke management in the future. Materials and Methods: We noted blood counts and serum homocysteine levels in acute stroke patients after the exclusion of confounding factors. Our team recorded the detailed demographic and blood parameters (blood counts and homocysteine) of acute stroke patients who got admitted to different departments of our hospital in 5 months. We later correlated or tried to find the association between these parameters and stroke severity (NIHSS), other clinical parameters such as dysarthria and stroke outcome (Modified Rankin Score (mRS) at discharge and at 3 months). The team also tried to find relationships, if any, with different stroke subtypes as per the Trial of ORG 10172 in Acute Stroke Treatment classification. Results: Out of 100 patients (34 females and 66 males), we had 28 patients with hyperhomocysteinemia (HHcy). The high neutrophil count and total leukocyte count correlated with stroke severity, dysarthria, and outcome at 3 months (poor mRS). While, it was the opposite in the case of lymphocyte count. Homocysteine level did not correlate with stroke severity or outcome but with dyslipidemia. HHcy was more prevalent among patients with large artery atherosclerotic disease. Conclusion: Findings of our study were agreeable to earlier studies on the relationship between blood counts and stroke prognosis. Although high homocysteine level was associated with stroke, its level was not found to be related to stroke severity or outcome. However, further study with a larger sample size is required to draw a confident conclusion