In greenhouse experiments, Aychade, a fragrant rice variety, was grown under one level of salt solution (EC of 3800 ± 400 μS·cm(-1)) sufficient to induce salt stress in rice. Timing and duration of salt solution application varied according to the growth stages. 2-Acetyl-1-pyrroline (2AP), a characteristic flavor compound of fragrant rice as well as biogenetically related compounds, proline, and γ-aminobutyric acid (GABA) were quantified. Salt treatments induced 2AP synthesis in the leaves, but the increase was often higher in the vegetative phase. This increase was correlated with proline level but not with that of GABA. Interestingly the grains from all the salt treated plants contained significantly higher levels of 2AP (733-998 μg·kg(-1)) than those from the control (592 μg·kg(-1)). The highest 2AP synthesis occurred when the plants were subjected to salt treatment during whole vegetative or reproductive phases. However in the latter case crop yield decreased significantly.

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The use of sequence analysis in the social sciences has significantly increased during the last decade or two. Sequence analysis explores and describes trajectories and “fishes for patterns” (Abbott, 2000). Many dissimilarity metrics exist in various domains (bioinformatics, data mining, etc.); therefore a crucial and pervasive issue in papers using sequence analysis is robustness. To what extent do the various techniques lead to consistent and converging results? What kinds of patterns are more easily fished out by each of the metrics? Here we propose a systematic comparison of about ten metrics that have been used in the social science literature, based on the examination of dissimilarity matrices computed from a simulated sequence data set including various patterns that sociologists can try to identify. This should help scholars in picking the method best suited to their data design and inquiry objectives.