Wild watermelon from the Botswana desert had an ability to survive under severe drought conditions by maintaining its water status (water content and water potential). In the analysis by two-dimensional electrophoresis of leaf proteins, seven spots were newly induced after watering stopped. One with the molecular mass of 40 kilodaltons of the spots was accumulated abundantly. The cDNA encoding for the protein was cloned based on its amino-terminal sequence and the amino acid sequence deduced from the determined nucleotide sequences of the cDNA exhibited homology to the enzymes belong to the ArgE/DapE/Acy1/Cpg2/YscS protein family (including acetylornithine deacetylase, carboxypeptidase and aminoacylase-1). This suggests that the protein is involved in the release of free amino acid by hydrolyzing a peptidic bond. As the drought stress progressed, citrulline became one of the major components in the total free amino acids. Eight days after withholding watering, although the lower leaves wilted significantly, the upper leaves still maintained their water status and the content of citrulline reached about 50% in the total free amino acids. The accumulation of citrulline during the drought stress in wild watermelon is an unique phenomenon in C3-plants. These results suggest that the drought tolerance of wild watermelon is related to (1) the maintenance of the water status and (2) a metabolic change to accumulate citrulline.

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The differential effects of the stereoisomers of ropivacaine and bupivacaine on cerebral pial vessels could, at least in part, depend on their chirality.

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SUMMARYWith the rapid increase of link speed in recent years, packet sampling has become a very attractive and scalable means in collecting flow statistics; however, it also makes inferring original flow characteristics much more difficult. In this paper, we develop techniques and schemes to identify flows with a very large number of packets (also known as heavy-hitter flows) from sampled flow statistics. Our approach follows a two-stage strategy: We first parametrically estimate the original flow length distribution from sampled flows. We then identify heavy-hitter flows with Bayes' theorem, where the flow length distribution estimated at the first stage is used as an a priori distribution. Our approach is validated and evaluated with publicly available packet traces. We show that our approach provides a very flexible framework in striking an appropriate balance between false positives and false negatives when sampling frequency is given.