A method for DNA extraction and molecular identification of Aphids.

Aphid species (Insecta, Hemiptera) are economically important invasive pest throughout the world, though their identification is intricate due to tiny size and inconspicuous nature of morphology. Mitochondrial cytochrome c oxidase I (mtCOI) region has been proven to be a standard barcode to identify the diverse array of insect groups. Isolation of good quality DNA is a fundamental first step in insect DNA barcoding which is obtained by standardizing the DNA isolation method. In this study, we demonstrate a modified CTAB method for the isolation of DNA to maximize the quality and yield from small aphids. This method will help the researchers to efficiently isolate DNA from small aphid and the method can be utilized for other small insects as well. We evaluated the quality of the isolated DNA and the mtCOI gene region were subjected to PCR amplification. Further, the gene segment was sequenced and gene annotation was done by NCBI BLAST program through which the insect was found to be Aphis gossypii. This study provides a set of molecular tools that can be used for identification of insect at species level through DNA barcoding and biodiversity analysis.•Detailed method to maximize quality and quantity of genomic DNA isolated from aphids.•Molecular identification of aphids using mtCOI gene amplification and sequence validation.•First report on Aphis gossypii infecting Solanum trilobatum provides insights of pest identification and management.

Comparative bioefficacy of Bacillus and Pseudomonas chitinase against Helopeltis theivora in tea (Camellia sinensis (L.) O.Kuntze.

Tea (Camellia sinensis (L.) O.Kuntze) is an industry-oriented economical crop in India. Among the sap sucking pests, tea mosquito bug (Helopeltis theivora) is one of the most serious pests causing heavy crop loss in tea plantation. Continuous use of chemical pesticides causes environmental pollution and health hazards besides developing pesticide residues in tea powder. The control of pests by bacterial metabolite is an alternative that may contribute to reduce or eliminate the chemical pesticide use. The use of chitinase as a biological control is an emerging field of research. In the present study, Chitinase (~ 25 kDa) was purified from Bacillus cereus C-13 strain using gel-filtration chromatography and further characterized for its optimum pH, temperature and substrate specificity. Bioefficacy of chitinase from B. cereus C-13 was compared with our previously reported Pseudomonas fluorescens MP-13 chitinase against H. theivora. Result concluded that, 100% and 78% mortality was observed by using P. fluorescens MP-13 chitinase and B. cereus C-13 chitinase, respectively. In future, bacterial chitinase can be utilized in eco-friendly pest management strategies.

Segmentation and Boundary Detection of Fetal Kidney Images in Second and Third Trimesters Using Kernel-Based Fuzzy Clustering.

Organ segmentation is an important step in Ultrasound fetal images for early prediction of congenital abnormalities and to estimate delivery date. In many applications of 2D medical imaging, they face problems with speckle noise and object contours. Frequent scanning of fetal leads to clinical disturbances to the fetal growth and the quantitative interpretation of Ultrasonic images also a difficult task compared to other image modalities. In the present work a three-stage hybrid algorithm has been developed to segment the US fetal kidney images for the detection of shape and contour. At the first stage the hybrid Mean Median (Hybrid MM) filter is applied to reduce the speckle noise. Then a kernel based Fuzzy C - means clustering is used to detect the shape and contour. Finally, the texture features are obtained from the segmented images. Based on the obtained texture features, the abnormalities are detected. The Gaussian Radial basis function provides an accuracy of 80% at the second and third trimesters with weighted constant ranging from 4 to 8, compared to other global kernel functions. Similarly the proposed method has an accuracy of 86% with compared to other FCM techniques.

A Clinical Support System for Brain Tumor Classification Using Soft Computing Techniques.

A brain tumor is an accumulation of abnormal cells in human brain. As tumor increases in size, it induces brain damage. Hence it is essential to diagnose the type of brain tumor. The effective modality used for brain tumor diagnose is MRI because of its remarkable image resolution, the speed of acquisition, and high safety profile for patients. The analysis of brain MRI is an important part of patient care and decision. Hence in the proposed Clinical Support System, the brain MRI image is preprocessed using Genetic Optimized Median Filter followed by brain tumor region segmentation using Hierarchical Fuzzy Clustering Algorithm. The features of the tumor region are extracted through GLCM feature extraction method. Lion Optimized Boosting Support Vector machine model is used for further classification of tumor by Brain Tumor Image Segmentation (BraTS) dataset. Hence the proposed clinical support system provides an integrated model for Detection and classification of brain tumor which assists the doctors in appropriate evaluation of tumor.

A Novel Enhanced Gray Scale Adaptive Method for Prediction of Breast Cancer.

Breast cancer is the important problem across the globe in which, most of the women are suffering without knowing the causes and effects of the cancer cells. Mammographic is the most powerful tool for the diagnosis of the Breast cancer. The analysis of this mammogram images proves to be more vital in terms of diagnosis but the accuracy level still needs improvisation. Several intelligent techniques are suggested for the detection of Microcalcification, Clusters, Masses, Spiculate lesions, Asymmetry and Architectural distortions in the mammograms. But the prediction of the cancer levels needs more research light. For the determination of the higher level of accuracy and prediction, the proposed algorithm called Enhanced Gray Scale Adaptive Method (EGAM) which works on the principle of combination of K-GLCM and Extreme Fuzzy Learning Machines (EFLM). The proposed algorithm has achieved 99% accuracy and less computation time in terms of classification, detection and prediction when compared with the existing intelligent algorithms.

Chitinase from Pseudomonas fluorescens and its insecticidal activity against Helopeltis theivora.

The tea mosquito bug (TMB), Helopeltis spp. (Hemiptera: Miridae) is an insidious pest that poses a significant economical threat to tea plantations. Pseudomonas cultures are being used extensively for pest management which, however, resulting in a low mortality rate of insects and which has prompted us to search for a new microbial metabolite for TMB control. A chitinase purified from P. fluorescens and partially characterized by our group showed insecticidal activity against TMB. The mode of action behind chitinase toxicity is the enzymatic hydrolysis of chitin, which is a common constituent of the insect exoskeleton and gut lining of the peritrophic membrane. A chitinase-secreting strain MP-13 was characterized based on 16S rRNA sequencing and validated as Pseudomonas fluorescens. In the present study, purified chitinase (0.048 units/ml) enzyme from P. fluorescens MP-13 revealed 100% TMB mortality under in-vitro conditions. The results of this study can be utilized for future crop improvement programs and integrated pest management strategies.

Molecular characterization of tea mosquito bug from tea growing regions of India.

The tea mosquito bug, Helopeltis (Hemiptera: Miridae), is an insidious pest that poses a significant economical threat to tea plantations. As a basic first step to control this pest is authentic identification, but the inability to determine morphological characters of Helopeltis species makes this process very difficult. DNA barcoding is a reliable alternative to traditional morphological identification of this pest. Since tea is cultivated in different parts of the country, an attempt was made to molecular characterization of Helopeltis. This is the first report on molecular identification and diversity characterization of Helopeltis collected from tea growing regions of southern and north India, using cytochrome c oxidase subunit I (COI) gene of mitochondrial (mt) DNA. Beginning with the molecular identification of this pest is essential to start an effective pest management strategy, and will provide basic information for diffusion pattern, population dynamics and chemical application.