No abstract

Redox degenerative reactions of the biological system inevitably produce reactive oxygen species (ROS) and their derivatives. Oxidative stress is the result of an imbalance in pro-oxidant/antioxidant homeostasis that leads to the generation of toxic reactive oxygen species (ROS), such as hydrogen peroxide, organic hydro peroxides, nitric oxide, superoxide and hydroxyl radicals etc. Information are accumulating steadily, supporting the general importance of oxidative damage of tissue and cellular components as a primary or secondary causative factor in many different human diseases and aging processes. Many of the recent landmarks in scientific research have shown that in human beings, oxidative stress has been implicated in the progression of major health problems by inactivating the metabolic enzymes and damaging important cellular components, oxidizing the nucleic acids, leading to cardiovascular diseases, eye disorders, joint disorders, neurological diseases (Alzheimer’s disease, Parkinson’s disease and amyotrophic lateral sclerosis), atherosclerosis, lung and kidney disorders, liver and pancreatic diseases, cancer, ageing, disease of the reproductive system including the male and female infertility etc. The advent of a growing number of in vitro and in vivo models for evaluating the human disease pathology is aiding scientists in deciphering the detailed mechanisms of the point of intersection of the oxidative stress with other cellular components or events in the growing roadmap leading to different human disorders. The toxic effect of reactive oxygen and nitrogen species in human is balanced by the antioxidant action of non-enzymatic antioxidants, as well as by antioxidant enzymes. Such antioxidant defences are extremely important as they represent the direct removal of free radicals (prooxidants), thus providing maximal protection for biological sites. These systems not only assert with the problem of oxidative damage, but also play a crucial role in wellness, health maintenance, and prevention of chronic and degenerative diseases. In this review we have tried to generate a gross picture on the critical role of ROS in deteriorating human health and the importance of antioxidative defense system in ameliorating the toxicity of ROS

This study was conducted to know cropping cycles required to improve OM status in soil and to investigate the effects of medium-term tillage practices on soil properties and crop yields in Grey Terrace soil of Bangladesh under wheat-mungbean-T. aman cropping system. Four different tillage practices, namely, zero tillage (ZT), minimum tillage (MT), conventional tillage (CT), and deep tillage (DT), were studied in a randomized complete block (RCB) design with four replications. Tillage practices showed positive effects on soil properties and crop yields. After four cropping cycles, the highest OM accumulation, the maximum root mass density (0–15 cm soil depth), and the improved physical and chemical properties were recorded in the conservational tillage practices. Bulk and particle densities were decreased due to tillage practices, having the highest reduction of these properties and the highest increase of porosity and field capacity in zero tillage. The highest total N, P, K, and S in their available forms were recorded in zero tillage. All tillage practices showed similar yield after four years of cropping cycles. Therefore, we conclude that zero tillage with 20% residue retention was found to be suitable for soil health and achieving optimum yield under the cropping system in Grey Terrace soil (Aeric Albaquept).

Abstract-This paper presents a constructive algorithm for training cooperative neural-network ensembles (CNNEs). CNNE combines ensemble architecture design with cooperative training for individual neural networks (NNs) in ensembles. Unlike most previous studies on training ensembles, CNNE puts emphasis on both accuracy and diversity among individual NNs in an ensemble. In order to maintain accuracy among individual NNs, the number of hidden nodes in individual NNs are also determined by a constructive approach. Incremental training based on negative correlation is used in CNNE to train individual NNs for different numbers of training epochs. The use of negative correlation learning and different training epochs for training individual NNs reflect CNNEs emphasis on diversity among individual NNs in an ensemble. CNNE has been tested extensively on a number of benchmark problems in machine learning and neural networks, including Australian credit card assessment, breast cancer, diabetes, glass, heart disease, letter recognition, soybean, and Mackey-Glass time series prediction problems. The experimental results show that CNNE can produce NN ensembles with good generalization ability.

Feature extraction is a key issue in content-based image retrieval (CBIR). In the past, a number of texture features have been proposed in literature, including statistic methods and spectral methods. However, most of them are not able to accurately capture the edge information which is the most important texture feature in an image. Recent researches on multi-scale analysis, especially the curvelet research, provide good opportunity to extract more accurate texture feature for image retrieval. Curvelet was originally proposed for image denoising and has shown promising performance. In this paper, a new image feature based on curvelet transform has been proposed. We apply discrete curvelet transform on texture images and compute the low order statistics from the transformed images. Images are then represented using the extracted texture features. Retrieval results show, it significantly outperforms the widely used Gabor texture feature.