In the industrialized world, functional foods have become a part of an everyday diet and are demonstrated to offer potential health benefits beyond the widely accepted nutritional effects. Currently, the most important and frequently used functional food compounds are probiotics and prebiotics, or they are collectively known as 'synbiotics'. Moreover, with an already healthy image, dairy products appear to be an excellent mean for inventing nutritious foods. Such probiotic dairy foods beneficially affect the host by improving survival and implantation of live microbial dietary supplements in the gastrointestinal flora, by selectively stimulating the growth or activating the catabolism of one or a limited number of health-promoting bacteria in the intestinal tract, and by improving the gastrointestinal tract's microbial balance. Hence, the paper reviews the current scenario of probiotics and their prospective potential applications for functional foods for better health and nutrition of the society.

World is facing agrarian as well as nutritional challenges. Agricultural lands with irrigation facilities have been exploited to maximum, and hence we need to focus on dry lands to further increase grain production. Owing to low fertility, utilization of dry lands to produce sufficient quality grains is a big challenge. Millets as climate change compliant crops score highly over other grains like wheat and rice in terms of marginal growing conditions and high nutritional value. These nutri-cereals abode vitamins, minerals, essential fatty acids, phyto-chemicals and antioxidants that can help to eradicate the plethora of nutritional deficiency diseases. Millets cultivation can keep dry lands productive and ensure future food and nutritional security.

Proteases are hydrolytic enzymes capable of degrading proteins into small peptides and amino acids. They account for nearly 60% of the total industrial enzyme market. Proteases are extensively exploited commercially, in food, pharmaceutical, leather and detergent industry. Given their potential use, there has been renewed interest in the discovery of proteases with novel properties and a constant thrust to optimize the enzyme production. This review summarizes a fraction of the enormous reports available on various aspects of alkaline proteases. Diverse sources for isolation of alkaline protease producing microorganisms are reported. The various nutritional and environmental parameters affecting the production of alkaline proteases in submerged and solid state fermentation are described. The enzymatic and physicochemical properties of alkaline proteases from several microorganisms are discussed which can help to identify enzymes with high activity and stability over extreme pH and temperature, so that they can be developed for industrial applications.

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Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg-Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to 1846 S. Mohanty et al. that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well.

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Field experiments were conducted in Bhubaneswar, Orissa, India, during the dry season (JanuaryMay) in 2008 and 2009 to investigate whether practices of the System of Rice Intensification (SRI), including alternate wetting and drying (AWD) during the vegetative stage of plant growth, could improve rice plants' morphology and physiology and what would be their impact on resulting crop performance, compared with currently recommended scientific management practices (SMP), including continuous flooding (CF) of paddies. With SRI practices, grain yield was increased by 48% in these trials at the same time, there was an average water saving of 22% compared with inundated SMP rice. Water productivity with AWD-SRI management practices was almost doubled (0.68 g l -1 ) compared to CF-SMP (0.36 g l -1 ). Significant improvements were observed in the morphology of SRI plants in terms of root growth, plant/culm height, tiller number per hill, tiller perimeter, leaf size and number, leaf area index (LAI), specific leaf weight (SLW), and open canopy structure. These phenotypic improvements of the AWD-SRI crop were accompanied by physiological changes: greater xylem exudation rate, crop growth rate, mean leaf elongation rate (LER), and higher light interception by the canopy compared to rice plants grown under CF-SMP. SRI plants showed delayed leaf senescence and greater light utilization, and they maintained higher photosynthetic rates during reproductive and grain-filling stages. This was responsible for improvement in yieldcontributing characteristics and higher grain yield than from flooded rice with SMP. We conclude that SRI practices with AWD improve rice plants' morphology, and this benefits physiological processes that result in higher grain yield and water productivity.