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The release of endocrine disrupting chemicals into the environment is of increasing concern due to the formation of an intersex state in freshwater organisms and potential risks to human health. The aim of this study was to investigate the persistence of the naturally occurring hormones, estrone and 17 β-estradiol in three agricultural grassland soils in the presence and absence of cattle and sheep wastes (urine and manure). Biodegradation was investigated using 14C-labelled hormones which were applied to soil in three different solvents (water, artificial urine and natural sheep urine). When applied directly to soil the two hormones degraded at a similar rate, however, the speed of mineralization was soil type and solvent dependant. The half-life (t1/2) of the hormones in soils ranged from 5 to 25 d. The hormones were also applied to the soils in sheep and cattle manure of different ages (7 d to 2 yr). Generally, the rate of degradation in the animal manure amended soils was more rapid than in the unamended soils (t1/2=1-9 d), with mineralization being largely independent of manure age and type. We conclude that in comparison to many xenobiotics, estrogens are not persistent in agricultural soils. However, our calculations suggest that if they are lost to freshwater via runoff or leaching then they may have an appreciable effect on freshwater organisms. Assuming normal landspreading rates our results suggest that the risk of estrogen contamination of freshwater associated with manure spreading is very low.  相似文献   
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To understand the processes of growth and biomass production in plants, we ultimately need to elucidate the structure of the underlying regulatory networks at the molecular level. The advent of high-throughput postgenomic technologies has spurred substantial interest in reverse engineering these networks from data, and several techniques from machine learning and multivariate statistics have recently been proposed. The present article discusses the problem of inferring gene regulatory networks from gene expression time series, and we focus our exposition on the methodology of Bayesian networks. We describe dynamic Bayesian networks and explain their advantages over other statistical methods. We introduce a novel information sharing scheme, which allows us to infer gene regulatory networks from multiple sources of gene expression data more accurately. We illustrate and test this method on a set of synthetic data, using three different measures to quantify the network reconstruction accuracy. The main application of our method is related to the problem of circadian regulation in plants, where we aim to reconstruct the regulatory networks of nine circadian genes in Arabidopsis thaliana from four gene expression time series obtained under different experimental conditions.  相似文献   
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A 5-day-old Huacaya alpaca cria (Vicugna pacos) was euthanized due to deteriorating health. At birth, the cria had ophthalmologic abnormalities, but had appropriate mentation. At 2 days of age, the cria gradually stopped suckling and began to circle. At 5 days old, the owner elected euthanasia due to declining clinical condition. Grossly, the right iris had a scalloped pupillary margin, and the right olfactory bulb was malformed. Histopathology revealed persistent hyperplastic primary vitreous bilaterally and iridal abnormalities, as well as aplasia of the olfactory ventricle, olfactory tract, and olfactory foramen on the right side.  相似文献   
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