Pesticides are extensively used in modern agriculture and are an effective and economical way to enhance the yield quality and quantity, thus ensuring food security for the ever-growing population around the globe. Approximately, 2 million tonnes of pesticides are utilized annually worldwide, where China is the major contributing country, followed by the USA and Argentina, which is increasing rapidly. However, by the year 2020, the global pesticide usage has been estimated to increase up to 3.5 million tonnes. Although pesticides are beneficial for crop production point of view, extensive use of pesticides can possess serious consequences because of their bio-magnification and persistent nature. Diverse pesticides directly or indirectly polluted air, water, soil and overall ecosystem which cause serious health hazard for living being. In the present manuscript, an attempt has been made to critically review the global usage of different pesticides and their major adverse impacts on ecosystem, which will provide guidance for a wide range of researchers in this area.

Plants are a rich source of amino acids and their individual abundance in plants is of great significance especially in terms of food. Therefore, it is of utmost necessity to create a database of the relative amino acid contents in plants as reported in literature. Since in most of the cases complete analysis of profiles of amino acids in plants was not reported, the units used and the methods applied and the plant parts used were different, amino acid contents were converted into relative units with respect to lysine for statistical analysis. The most abundant amino acids in plants are glutamic acid and aspartic acid. Pearson's correlation analysis among different amino acids showed that there were no negative correlations between the amino acids. Cluster analysis (CA) applied to relative amino acid contents of different families. Alismataceae, Cyperaceae, Capparaceae and Cactaceae families had close proximity with each other on the basis of their relative amino acid contents. First three components of principal component analysis (PCA) explained 79.5% of the total variance. Factor analysis (FA) explained four main underlying factors for amino acid analysis. Factor-1 accounted for 29.4% of the total variance and had maximum loadings on glycine, isoleucine, leucine, threonine and valine. Factor-2 explained 25.8% of the total variance and had maximum loadings on alanine, aspartic acid, serine and tyrosine. 14.2% of the total variance was explained by factor-3 and had maximum loadings on arginine and histidine. Factor-4 accounted 8.3% of the total variance and had maximum loading on the proline amino acid. The relative content of different amino acids presented in this paper is alanine (1.4), arginine (1.8), asparagine (0.7), aspartic acid (2.4), cysteine (0.5), glutamic acid (2.8), glutamine (0.6), glycine (1.0), histidine (0.5), isoleucine (0.9), leucine (1.7), lysine (1.0), methionine (0.4), phenylalanine (0.9), proline (1.1), serine (1.0), threonine (1.0), tryptophan (0.3), tyrosine (0.7) and valine (1.2).

Dominance and diversity are important characteristics for the description of communities. The most commonly used indices are Simpson's dominance indexand Shannon's and Simpson's indices of diversity. This paper uses the basic concepts of statistics as applied to community analysis to develop new dominance and diversity indices that will enable scientists to establish correlations among various indices. The present study proves that the variance of the number of individuals of different species in a sample can be used to calculateSimpson's dominance and diversity indices. New indices have been developed from the ratios ofthe variance to number of species, and the mean number of individuals per species in a quadrat. A wide range of data, varying from high dominance to high evenness, was simulated for 25 quadrats, with each quadrat having ten species and 100 individuals in different combinations. Variance and standard deviation-based indices were computed using the simulated data and were found to be highly correlated with Simpson's and Shannon's indices. The proposed indices will give both the dominance and diversity of a community on the same scale based on the same statistic. Another important contribution of the present study relates to the variance of a sample consisting of a single value. It has been proved that the variance of a sample having only one value is equal to the square of that value. The paper establishes a new link between diversity studies and statistics.

The amelioration of antibacterial efficacy along with the reduced minimum inhibitory concentration (MIC) of sanguinarine (SGR) drug have been demonstrated through the uptake of SGR by p-sulfonatocalix[6]arene functionalized silver nanoparticles.

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River Beas originates in the Himalayas and merges into river Sutlej at Harike, a Ramsar wetland. This river is a habitat of the endangered freshwater dolphin, Platanista gangetica minor R. Twenty-five water quality parameters, including eight heavy metals, were studied at four sampling sites over a stretch of 63 km between Beas and Harike towns for pre-monsoon, post-monsoon and winter seasons. Principal component analysis of the data proved to be an effective tool for data reduction as the first three principal components of all the water quality parameters explained 100% variance. Factor analysis delineated three factors underlying the water quality. Factor 1 comprised pollution-related parameters like BOD, COD, DO, PO4(-3) and hardness. Factor 2 was a natural water quality determinant and explained maximum variance in turbidity, alkalinity and TDS. Factor 3 comprised NO3(-1), a fertilizer-related parameter. Reflectance values from bands 2 (green), 3 (red) and 4 (near infra-red) of Landsat (TM) digital data were regressed on PO4(-3), turbidity and TDS using multiple linear regression analysis. PO4(-3) contributed positively to the spectral radiance, whereas TDS contributed negatively. Beta regression analysis revealed that PO4(-3) had a positive relation with BOD, whereas turbidity and TDS were negatively regressed with BOD. Artificial neural network models were fitted to the data. Correlations between the target values from ANN for turbidity, BOD and bands 2 (green), 3 (red) and 4 (near infra-red) were highly significant.

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