Management of Potato Nematodes:An overview

Root-knot nematodes and cyst nematodes are important constraints that reduce potato yields in India. Three species of Meloidogyne cause root-knots on the crop throughout the country, of which, M. incognita is more wide-spread. Infected tubers also result in marketable-yield-loss particularly in the seed potatoes. The cyst nematodes include two species of Globodera restricted to the hilly regions of Tamil Nadu and are of quarantine importance, inhibiting seedpotato production. Potato produce from these hills is used only for consumption. The endoparasitic nature of their life cycle, deposition of eggs into a gelatinous egg mass in root knot and the female turning in to a hard cyst encompassing the eggs within them in cyst nematode makes them difficult organisms to manage. Both these nematodes exhibit physiologic variation, hence, their management is not absolute with host-resistance. Therefore, an Integrated Nematode Management (INM) is adopted in both the cases. Root-knot nematode in North India is managed using nematode-free seed tubers, crop rotation with maize or wheat and application of 1-2 kg ai /ha Carbofuran 3% G at the time of potato planting. Cyst nematode in Tamil Nadu hills is managed by crop rotation with vegetables, particularly cabbage and carrot, intercropping potato with beans or wheat, alternating nematode resistant potato variety 'Kufri Swarna' and application of 2 kg ai /ha Carbofuran 3% G at planting. A two-year adoption of INM for root-knot and a three-year INM practice for cyst nematodes gives efficient and economical production system. Potato farmers in Himachal Pradesh and Tamil Nadu hills follow practices standardized at the Central Potato Research Institute, Shimla and it's substation in the Nilgiri hills

Facilitation of peptide fibre formation by arginine-phosphate/carboxylate interactions

This study describes peptide fibre formation in a hexapeptide, derived from the V3 loop of HIV-1, mediated by the interactions between arginine residues and phosphate/carboxylate anions. This charge neutralization approach was further confirmed when the deletion of arginine residue from the hexapeptide sequence resulted in fibre formation, which was studied by a combination of microscopic techniques

Two Dimensional Clipping Based Segmentation Algorithm for Grayscale Fingerprint Images

One of the huge methods in Automated Fingerprint Identification System (AFIS) is the segment or separation of the fingerprint. The process of decomposing an image into exclusive components is referred as segmentation. Fingerprint segmentation is the one of the predominant process involved in fingerprint pre-processing and it refers to the method of dividing or separating the image into disjoint areas as the foreground and the background region. The foreground also called as Region of Interest (ROI) due to the fact only the region which contains ridge and valley structure is used for processing, whilst the background carries noisy and irrelevant content material and so that it will be discarded in later enhancement or orientation or classification method. The challenge proper right here is to decide which a part of the image belongs to the foreground, retrieved as an input from the fingerprint sensor device or from benchmark datasets and which part belongs to the background. A 100% correct segmentation is continually very tough, specifically inside the very poor quality image or partial image together with the presence of latent. In this paper, we discuss a modified clipped based segmentation algorithm by adopting threshold value and canny edge detection techniques. We segment the background image is x and y dimensions or in other words left the edge, right edge, top edge and bottom edge of the image. For the purpose of analyzing the algorithm FVC ongoing 2002 benchmark dataset is considered. The entire algorithm is implemented using MATLAB 2015a. The algorithm is able to find affectively ROI of the fingerprint image or separates the foreground region from the background area of the fingerprint image very effectively. In high configuration system proposed algorithm achieves execution time of 1.75 seconds