ABSTRACT
Volatile methyl siloxanes (VMSs) are of great concern in the past few years due to their high production volume, ubiquitousness in the environment and toxicities. The bioaccumulation of VMSs is reported to be sensitive to their metabolism rates, however, little is known about their metabolic rates in aquatic organisms. The present study measured intrinsic clearance rates of 16 common VMSs (D3-D6 and L3-L14) incubated in liver microsomes of weever and quail. The intrinsic clearance rates of VMSs in weever microsomes were 0-0.031 mL·(h·mg)-1. The fact that D5 exhibited no significant decline trend in the incubations with liver microsomes of weever corresponded with its trophic magnification behaviour in aquatic food webs. L4-L14, similar to D5, were also persistent in microsomes, suggesting the high bioaccumulation potentials of the chemicals. The intrinsic clearance rates of VMSs incubated in quail microsomes [0.25-1.7 mL·(h·mg)-1] were significantly higher than those incubated in weever microsomes. The relatively high biotransformation rates of D3-D6 and L3-L14 suggested that these compounds may not exhibit high biomagnification potentials in birds. B[a]P was used as a benchmark chemical to normalize variations in determining the metabolism rates among batches, and the structure-activity relationships were explored for the normalized intrinsic clearance rates of quail microsomes. Hydrophobicity and electric properties were found to be major factors influencing the bird microsomal intrinsic clearance rates of VMSs.
Subject(s)
Birds/metabolism , Food Chain , Siloxanes/metabolism , Volatile Organic Compounds/metabolism , Animals , Environmental Monitoring , Microsomes, Liver/metabolism , Quail/metabolism , Water Pollutants, ChemicalABSTRACT
OBJECTIVE: To analyze the speech characters with computation complexity. METHOD: Voices of 51 testees spoke the same paragraph were recorded and the same sentence of voice waveform was intercepted as source data. There were two kinds of sample voices: same testee speaking the same sentence at different time and different testee speaking the same sentence. RESULT: The computation complexity curves of the different testee were obviously distinguishing, while those of the same testee were almost the same. In a 2D embedded space the computation complexity features of individual testee differs with others even if testees speak the same sentences. CONCLUSION: This complexity features might be applied to speaker recognition system and using complexity method to analyze speech signals has wide application prospect.
