Jawaharlal Nehru University

About: Jawaharlal Nehru University is a education organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Candida albicans. The organization has 6082 authors who have published 13455 publications receiving 245407 citations. The organization is also known as: JNU.

Bacterial insecticidal toxins

Abstract: Over the years it has been important for humans to control the populations of harmful insects and insecticides have been used for this purpose in agricultural and horticultural sectors. Synthetic insecticides, owing to their various side effects, have been widely replaced by biological insecticides. In this review we attempt to describe three bacterial species that are known to produce insecticidal toxins of tremendous biotechnological, agricultural, and economic importance. Bacillus thuringiensis (BT) accounts for 90% of the bioinsecticide market and it produces insecticidal toxins referred to as delta endotoxins. The other two bacteria belong to the genera Photorhabdus and Xenorhabdus, which are symbiotically associated with entomopathogenic nematodes of the families Heterorhabditidae and Steinernematidae respectively. Whereas, Xenorhabdus and Photorhabdus exist in a mutualistic association with the entomopathogenic nematodes, BT act alone. BT formulations are widely used in the field against insects; however, over the years there has been a gradual development of insect resistance against BT toxins. No resistance against Xenorhabdus or Photorhabdus has been reported to date. More recently BT transgenic crops have been prepared; however, there are growing concerns about the safety of these genetically modified crops. Nematodal formulations are also used in the field to curb harmful insect populations. Resistance development to entomopathogenic nematodes is unlikely due to the physical macroscopic nature of infection. Xenorhabdus and Photorhabdus transgenes have not yet been prepared; but are predicted to be available in the near future. In this review we start with an overview of the synthetic insecticides and then discuss Bacillus thuringiensis, Xenorhabdus nematophilus, and Photorhabdus luminescens in greater detail.

Transcriptome map for seedling stage specific salinity stress response indicates a specific set of genes as candidate for saline tolerance in Oryza sativa L.

Abstract: Oryza sativa L. cv IR64 is a widely cultivated, salt-sensitive indica rice, while Pokkali is a well-known, naturally salt-tolerant relative. To understand the molecular basis of differences in their salinity tolerance, three subtractive cDNA libraries were constructed. A total of 1,194 salinity-regulated cDNAs are reported here that may serve as repositories for future individual gene-based functional genomics studies. Gene expression data using macroarrays and Northern blots gives support to our hypothesis that salinity tolerance of Pokkali may be due to constitutive overexpression of many genes that function in salinity tolerance and are stress inducible in IR64. Analysis of genome architecture revealed the presence of these genes on all the chromosomes with several distinct clusters. Notably, a few mapped on one of the major quantitative trait loci - Saltol - on chromosome 1 and were found to be differentially regulated in the two contrasting genotypes. The present study also defines a set of known abiotic stress inducible genes, including CaMBP, GST, LEA, V-ATPase, OSAP1 zinc finger protein, and transcription factor HBP1B, that were expressed at high levels in Pokkali even in the absence of stress. These proposed genes may prove useful as "candidates" in improving salinity tolerance in crop plants using transgenic approach.

An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation

Abstract: Original and segmented simulated brain image by different algorithms: (a) axial view of original simulated T1-weighted brain image with INU=0 and 1% noise, (b) skull stripping simulated brain image, (c) manual segmented CSF, GM and WM images, (d) IIFCM algorithm, (e) IFCM algorithm, (f) FLICM algorithm, (g) EnFCM algorithm, (h) FGFCM algorithm, (i) FCM_S1 algorithm, (j) FCM_S2 algorithm, (k) ImFCM algorithm. The segmentation of brain magnetic resonance (MR) images plays an important role in the computer-aided diagnosis and clinical research. However, due to presence of noise and uncertainty on the boundary between different tissues in the brain image, the segmentation of brain image is a challenging task. Many variants of standard fuzzy c-means (FCM) algorithm have been proposed to handle the noise. Intuitionistic fuzzy c-means (IFCM) algorithm, one of the variants of FCM, is found suitable for image segmentation. It incorporates the advantage of intuitionistic fuzzy sets theory. The IFCM successfully handles the uncertainty but it is sensitive to noise as it does not incorporate any local spatial information. In this paper, we have presented a novel approach, named an improved intuitionistic fuzzy c-means (IIFCM), which considers the local spatial information in an intuitionistic fuzzy way. The IIFCM preserves the image details, is insensitive to noise, and is free of requirement of any parameter tuning. The obtained segmentation results on synthetic square image, real and simulated MRI brain image demonstrate the efficacy of the IIFCM algorithm and superior performance in comparison to existing segmentation methods. A nonparametric statistical analysis is also carried out to show the significant performance of the IIFCM algorithm in comparison to other existing segmentation algorithms.