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鱼类暴发性死亡的原因和对策(上)
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作者 姜金忠 《科学养鱼》 北大核心 2005年第10期77-77,共1页
关键词 鱼类 暴发性死亡 毒性病害 细菌性病害 寄生虫性病害 毒性物质作用 水质环境因素 营养代谢影响 防治方法
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Preliminary analysis of species sensitivity distribution based on gene expression effect 被引量:7
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作者 YAN ZhenGuang YANG NiYun WANG XiaoNan WANG WanHua MENG ShuangShuang LIU ZhengTao 《Science China Earth Sciences》 SCIE EI CAS 2012年第6期907-913,共7页
Species sensitivity analysis is one of the major techniques applied to derive water quality criteria. Presently, the toxicity data used for development of water quality criteria are mainly in the biological individual... Species sensitivity analysis is one of the major techniques applied to derive water quality criteria. Presently, the toxicity data used for development of water quality criteria are mainly in the biological individual level. With the increase of ecotoxicogenomics toxicity data, it is worth studying whether the gene expression effect data can be used to derive water quality criteria. Taking cadmium, copper and zinc as examples, we analyzed the toxic effects of the three heavy metals by constructing the species sensitivity distribution curves on the basis of extensive toxicity data. The results showed that the rank of species sensitivity for the acute, chronic and gene expression effect toxicity data of cadmium is "chronic>gene>acute". Although the gene expression effect data of copper and zinc are insufficient, the trend of data sensitivity of zinc is similar to cadmium. However, the trend of species sensitivity of copper is different from that of cadmium and zinc with higher sensitivity of gene expression data. It suggested that though the existing data of gene expression effects are not sufficient enough, they have the potential to be used in the development of chronic water quality criteria. For application in the derivation of water quality criteria, illogical test concentration design and insufficient target genes are two main weaknesses in the study of gene expression effects. 展开更多
关键词 water quality criteria heavy metal gene expression effect species sensitivity distribution data analysis
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