The uptake and transport of mercury (Hg) through vegetation play an important role in the biogeochemical cycling of Hg. However, quantitative information regarding Hg translocation in plants is poorly understood. In the present study, Hg uptake, accumulation, and translocation in 4 crops-rice (Oryza.sativa L.), wheat (Triticum L.), corn (Zea mays L.), and oilseed rape (Brassica campestris L.)-grown in Hoagland solution were investigated using a stable isotope ((198)Hg) tracing technique. The distribution of (198)Hg in root, stem, and leaf after uptake was quantified, and the release of (198)Hg into the air from crop leaf was investigated. It was found that the concentration of Hg accumulated in the root, stem, and leaf of rice increased linearly with the spiked (198)Hg concentration. The uptake equilibrium constant was estimated to be 2.35 mol Hg/g dry weight in rice root per mol/L Hg remaining in the Hoagland solution. More than 94% of (198)Hg uptake was accumulated in the roots for all 4 crops examined. The translocation to stem and leaf was not significant because of the absence of Hg(2+) complexes that facilitate Hg transport in plants. The accumulated (198)Hg in stem and leaf was not released from the plant at air Hg(0) concentration ranging from 0 ng/m(3) to 10 ng/m(3). Transfer factor data analysis showed that Hg translocation from stems to leaves was more efficient than that from roots to stems.

In this paper, we introduce the Chinese AI and Law challenge dataset (CAIL2018), the first large-scale Chinese legal dataset for judgment prediction. CAIL2018 contains more than 2.6 million criminal cases published by the Supreme People's Court of China, which are several times larger than other datasets in existing works on judgment prediction. Moreover, the annotations of judgment results are more detailed and rich. It consists of applicable law articles, charges, and prison terms, which are expected to be inferred according to the fact descriptions of cases. For comparison, we implement several conventional text classification baselines for judgment prediction and experimental results show that it is still a challenge for current models to predict the judgment results of legal cases, especially on prison terms. To help the researchers make improvements on legal judgment prediction, both CAIL2018 and baselines will be released after the CAIL competition 1 . * indicates equal contribution. 1 http://cail.cipsc.org.cn/

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