K. V. Kale

Prediction of MHC Binding Peptides and Epitopes from Alfalfa mosaic virus

Peptide fragments from alfalfa mosaic virus involved multiple antigenic components directing and empowering the immune system to protect the host from infection. MHC molecules are cell surface proteins, which take active part in host immune reactions and involvement of MHC class-I & II in response to almost all antigens. Coat protein of alfalfa mosaic virus contains 221 aa residues. Analysis found five MHC ligands in coat protein as 64-LSSFNGLGV-72; 86- RILEEDLIY-94; 96-MVFSITPSY-104; 100- ITPSYAGTF-108; 110- LTDDVTTED-118; having rescaled binding affinity and c-terminal cleavage affinity more than 0.5. The predicted binding affinity is normalized by the 1% fractil. The MHC peptide binding is predicted using neural networks trained on c-terminals of known epitopes. In analysis predicted MHC/peptide binding is a log transformed value related to the IC50 values in nM units. Total numbers of peptides found are 213. Predicted MHC binding regions act like red flags for antigen specific and generate immune response against the parent antigen. So a small fragment of antigen can induce immune response against whole antigen. This theme is implemented in designing subunit and synthetic peptide vaccines. The sequence analysis method allows potential drug targets to identify active sites against plant diseases. The method integrates prediction of peptide MHC class I binding; proteosomal c-terminal cleavage and TAP transport efficiency.

Cotton Crop Discrimination Using Fuzzy Classification Approach

Crop growth information represented through temporal remote sensing data is of great importance for specific agriculture crop discrimination. In this paper, the effect of various indices was empirically investigated using temporal images for cotton crop discrimination. Five spectral indices SR (Simple Ratio), NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and TVI (Triangular Vegetation Index) were investigated to identify cotton crop using temporal multi-spectral images. Data used for this study was AWIFS (coarser resolution) for soft classification and LISS-III (medium coarser) data for soft testing from Resourcesat-1 (IRS-P6) satellite. The mixed pixel (i.e. multiple classes within a single pixel) problem had been handled using soft computing techniques. Possibilistic fuzzy classification approach is used to handle mixed pixels for extracting single class of interest. The classification results with respect to various indices were compared in terms of image to image fuzzy overall classification accuracy. It was observed that temporal SAVI indices database with data set-2 outperformed other temporal indices database for cotton crop discrimination. Temporal SAVI indices database gave highest fuzzy overall accuracy of 93.12% with data set-2 in comparison to others.

Computer Aided Multi Parameter Antigen Design: Impact of Synthetic Peptide Vaccines from Soybean Mosaic Virus

The potyvirus coat protein (CP) is involved in aphid transmission, cell-to-cell movement and virus assembly, not only by binding to viral RNA, but also by self-interaction or interactions with other factors. Peptide fragments of genome coatprotein can be used to select nonamers for use in rational vaccine design and to increase the understanding of roles of the immune system in infectious diseases. For development of MHC binder prediction method, an elegant machine learning technique support vector machine (SVM) has been used. SVM has been trained on the binary input of single amino acid sequence. The MHC peptide binding is predicted using neural networks trained on C terminals of known epitopes. SVM has been trained on the binary input of single amino acid sequence. The average accuracy of SVM based method for 42 alleles is ~80%. In this analysis, we found the MHCII-IAb peptide regions, 880-YKTAKDLLT, 2577-PILAPDGTI, 1438-KVTKVDGRT, 2647- TWLYDTLST, (optimal score is 1.506); MHCII-IAd peptide regions 2079-GSFIITNGH, 1911-FIHLYGVEP, 1306-GSSNIVVMT, 695-AAYMLTVFH, (optimal score is 0.893); MHCII-IAg7 peptide regions 2962-SDAAEAYIE, 2891-WYNAVKDEY, 1544-FIATEAAFL, 1123-KIVAFMALL (optimal score is 1.915); MHCII-RT1.B peptide regions 1114-KTATQLQLE, 413-STAENASLQ, 162-TKERRATSQ, 1112-QAKTATQLQ, (optimal score is 1.807); which are represent predicted binders from genome polyprotein. Computer aided multi parameter antigen design was used to developed synthetic peptide vaccines from soybean mosaic virus.

Proteomics: Technologies for Protein Analysis

Proteomics technologies have produced an abundance of drug targets, which is creating a bottleneck in drug development process. There is an increasing need for better target validation for new drug development and proteomic technologies are contributing to it. Identifying a potential protein drug target within a cell is a major challenge in modern drug discovery; techniques for screening the proteome are, therefore, an important tool. Major difficulties for target identification include the separation of proteins and their detection. These technologies are compared to enable the selection of the one by matching the needs of a particular project. There are prospects for further improvement, and proteomics technologies will form an important addition to the existing genomic and chemical technologies for new target validation. Proteomics is applicable for protein analysis and bioinformatics based analysis gives the comprehensive molecular description of the actual protein component. Bioinformatics is being increasingly used to support target validation by providing functionally predictive information mined from databases and experimental datasets using a variety of computational tools. This review is focused on key technologies for proteomics strategy and their application in protein analysis.

Analysis, Classification, and Estimation of Pattern for Land of Aurangabad Region Using High-Resolution Satellite Image

Land use land cover (LULC) information extraction is a crucial exercise for agricultural land. The present study highlights the advantages of remote sensing, GIS, and GPS techniques for LULC mapping from high-resolution remote sensing data. High spatial resolution (5.8 m) satellite imagery of IRS-P6 Resourcesat-II LISS-IV having three spectral bands was utilized for LULC classification and for data processing ENVI 4.4 tool and Arc GIS10 software were used. Eight training samples for LULC classes have been selected from the image. Supervised classification using maximum likelihood (ML), Mahalanobis distance (MD), and minimum distance to means (MDM) were applied. The performances of above classifiers were evaluated in terms of the classification accuracy with respect to the collected real-time ground truth information. The evaluation result shows that the overall accuracies of LULC classifications are approximately 84.40, 77.98, and 74.31 % with Kappa coefficients 0.82, 0.74, and 0.70 for the ML, MD, and MDM, respectively. It is noticed that ML has a better accuracy than the MD and MDM classifiers and it is a more effective method for complex and noisy remote sensing data because of its unified approach for estimation of parameters.

Human Face Recognition Using Superior Principal Component Analysis (SPCA)

Principal Component Analysis (PCA) is a statistical technique used for dimension reduction and recognition, & widely used for facial feature extraction and recognition. In this paper a cluster based SPCA face recognition method has been proposed. Experiments based on ORL face database have performed to compare the recognition rate between tradition PCA, Advanced principal component analysis (APCA), & SPCA. It is found that SPCA is giving the best classification result. has been considered, not taking the difference between the classes into account. Therefore the differences in the face images of the same person are also increasing when the differences of all images are increasing. It is disillusionary defect of PCA. This paper employed a new feature projection approach based on Advanced PCA method, doing the optimum transformation for the differences between the classes. In section 2 the Traditional PCA & Advanced PCA methodology is discussed. The Proposed methodology is discussed in section 3 and experimental results are listed in section 4. Finally, sections 5 conclude and suggest the future scope..

Assessment of Package Cohesion and Coupling Principles for Predicting the Quality of Object Oriented Design

In determining the quality of design two factors are important, namely coupling and cohesion. This paper highlights the principles of package architecture from cohesion and coupling point of view and discusses the method for extracting metric associated with them. The method is supported with the help of case study. The results arrived at from the case study are discussed further for utilizing them for predicting the quality of software.

Prediction of Brugia malayi Antigenic Peptides: Candidates for Synthetic Vaccine Design Against Lymphatic Filariasis

Brugia malayi is a threadlike nematode causes swelling of lymphatic organs, condition well known as lymphatic filariasis; till date no invention made to effectively address lymphatic filariasis. In this analysis we a have predicted suitable antigenic peptides from Brugia malayi antigen protein for peptide vaccine design against lymphatic filariasis based on cross protection phenomenon as, an ample immune response can be generated with a single protein subunit. We found MHC class II binding peptides of Brugia malayi antigen protein are important determinant against the diseased condition. The analysis shows Brugia malayi antigen protein having 505 amino acids, which shows 497 nonamers. In this assay, we have predicted MHC-I binding peptides for 8mer_H2_Db (optimal score- 15.966), 9mer_H2_Db (optimal score- 15.595), 10mer_H2_Db (optimal score- 19.405), 11mer_H2_Dballeles (optimal score- 23.801). We also predicted the SVM based MHCII-IAb nonamers, 51-FQQIDPLDA, 442-FAAIACLVH, 206-YLNPFGHQF, 167-WYVIMAACY, 367-YAMIVIRLL, 434- LVITTAANF, 176-LDSYCLWKP, 435-VITTAANFA, 364-WPGYAMIVI (optimal score- 13.963); MHCII-IAd nonamers, 52-QQIDPLDAE, 171-MAACYLDSY, 239-QWRSVILCN, 168-YVIMAACYL, 3-QYLSVHSLS, 322-EILLHAKVV, 417- LGIIASFVS, 396-KAIFLAHFG, 167-WYVIMAACY, 269-LALHCINVI, 93-FINKAAPKQ, 259-NCIIVLKAF, 79- QGVLLIIPR, 22-TILQRSQAI, 63-RGFVYGNVS, 109-NISSLAFET,(optimal score- 16.748); and MHCII-IAg7 nonamers 171-MAACYLDSY, 73-KIVNGAQGV, 259-NCIIVLKAF, 209-PFGHQFSFE, 102-SCDTLLKNI, 25-QRSQAIRIV, 444- AIACLVHLF, 88-SLVNGFINK, 252-FPRHQLLNC, 471-RFVLANDNE, 52-QQIDPLDAE, 469-HRRFVLAND, 457- SNRHYFLAD, 362-KSWPGYAMI, 476-NDNEGEDFE, 370-IVIRLLQAL (optimal score- 19.847) which represents potential binders from Brugia malayi antigen protein. The method integrates prediction of MHC class I binding proteasomal C-terminal cleavage peptides and Eighteen potential antigenic peptides at average propensity 1.063 having highest local hydrophilicity. Thus a small antigen fragment can induce immune response against whole antigen. This approach can be applied for designing subunit and synthetic peptide vaccines.

Microcontroller based RR-Interval measurement using PPG signals for Heart Rate Variability based biometric application

Heart Rate Variability (HRV) is a natural property of heart rate. Medical science since last two decades has been viewing at it as a diagnostic and prognostic tool. This study is intended towards harnessing the HRV property of heart for person identification. The highest peak in the ECG signal as well as PPG signal as seen in Figure 1, is known as the R-peak, while the time duration between two adjacent R-peak is known as RRInterval. RR-Intervals are the only requirement for HRV analysis. Traditionally it is measured from an Electrocardiography (ECG) signals, but we used photoplethysmography (PPG) based pulse sensor and in-house designed microcontroller based RR-Interval measurement system. PPG sensors come in two basic types, one uses transmission and the other one makes use of reflection. We have tested the hardware with both transmission and reflection type sensors. This article is intended to document the performance analysis of both types of PPG sensors. And also present results of biometric identification based on RR-Intervals collected at the fingertips. Classification is done using KNN classifier.

SWT based Composite Method for Fingerprint Image Enhancement

New technology for recognizing fingerprints for security purposes is proving reliable but efficient recognition depends on the quality of the input fingerprint image. Recognition of the fingerprint becomes a complex computer problem while dealing with noisy and low quality images. The proposed SWT based composite method is used for fingerprint image enhancement using SWT transform, morphological operation, quick mask and spatial filters. We have applied the proposed method on FVC2002 fingerprint database and experimental results show that this composite method is more effective and robust than the other existing method. Finally the result is compared using the texture descriptors