Identification of AFLP markers linked to Fusarium wilt disease in pigeonpea [Cajanus cajan (L.) Millsp.]

An experiment was conducted to identify markers linked to Fusarium wilt disease resistance, Parents namely TTB 7 and ICP 8863 were screened using 151 SSRs markers and 16 AFLP primer combinations. Parental screening revealed five SSR primers and 12 AFLP primer combinations polymorphic between parents. Bulk segregant analysis identified five AFLP primer combinations generating seven markers polymorphic between resistant and susceptible bulks while, none of the SSR markers were polymorphic. This indicates that, these markers are putatively linked to wilt disease. Screening of F2 segregating population of cross TTB 7 x ICP 8863 with these putatively linked markers revealed four markers (E-AAT/M-CTG850, ETCG/M-CTT650, E-TCG/M-CTA730 and E-TCG/M-CTT230) which segregated in 3:1 mendelian pattern. Simple linear regression performed on these four markers had identified two markers namely E-TCG/M-CTT650 and E-TCG/M-CTA730 linked to disease

Early Detection of Diabetic Retinopathy From Big Data In Hadoop Framework

In this article, we have designed a fast and reliable Diabetic Retinopathy (DR) detection technique in Hadoop framework, which can identify the early signs of diabetes from eye retinal images. In the proposed scheme the retinal images are classified into five categories: No Diabetic Retinopathy (DR), Mild DR, Moderate DR, Severe DR and Proliferative DR. The proposed scheme follows three distinct steps for classification of the diabetic retinopathy images: feature extraction, feature reduction and image classification. In the initial stage of the algorithm, the Histogram of Oriented Gradients (HOG) is used as a feature descriptor to represent each of the Diabetic Retinopathy images. Principal Component Analysis (PCA) is used for dimensional reduction of HOG features. In the final stage of the algorithm, K-Nearest Neighbors (KNN) classifier is used, in a distributed environment, to classify the retinal images to different classes. Experiments have been carried out on a substantial number of high-resolution retinal images taken under an assortment of imaging conditions. Both left and right eye images are provided for every subject. To handle such large datasets, Hadoop platform is used with MapReduce and Mahout framework for programming. The results obtained by the proposed scheme are compared with some of the close competitive state-of-the-art techniques. The proposed technique is found to provide better results than the existing approaches in terms of some standard performance evaluation measures

Catastrophic corrosion failures and corrosion management

Natural calamities like cyclones and earthquakes have a sudden and devastating impact. On the other hand, the losses caused by corrosion often have a tantamount but inconspicuous impact. Several catastrophic failures are discussed, and sound corrosion management practices are outline

Target tracking using a mean-shift occlusion aware particle filter

Most of the sequential importance resampling tracking algorithms use arbitrarily high number of particles to achieve better performance, with consequently huge computational costs. This article aims to address the problem of occlusion which arises in visual tracking, using fewer number of particles. To this extent, the mean-shift algorithm is incorporated in the probabilistic filtering framework which allows the smaller particle set to maintain multiple modes of the state probability density function. Occlusion is detected based on correlation coefficient between the reference target and the candidate at filtered location. If occlusion is detected, the transition model for particles is switched to a random walk model which enables gradual outward spread of particles in a larger area. This enhances the probability of recapturing the target post-occlusion, even when it has changed its normal course of motion while being occluded. The likelihood model of the target is built using the combination of both color distribution model and edge orientation histogram features, which represent the target appearance and the target structure, respectively. The algorithm is evaluated on three benchmark computer vision datasets: OTB100, V OT18 and TrackingNet. The performance is compared with fourteen state-of-the-art tracking algorithms. From the quantitative and qualitative results, it is observed that the proposed scheme works in real-time and also performs significantly better than state-of-the-arts for sequences involving challenges of occlusion and fast motions