Browsing by Author "Liu, Li"
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Item Gene expression responses in male fathead minnows exposed to binary mixtures of an estrogen and antiestrogen(Springer Nature, 2009-07-13) Garcia-Reyero, Natàlia; Kroll, Kevin J; Liu, Li; Orlando, Edward F; Watanabe, Karen H; Sepúlveda, María S; Villeneuve, Daniel L; Perkins, Edward J; Ankley, Gerald T; Denslow, Nancy DAquatic organisms are continuously exposed to complex mixtures of chemicals, many of which can interfere with their endocrine system, resulting in impaired reproduction, development or survival, among others. In order to analyze the effects and mechanisms of action of estrogen/anti-estrogen mixtures, we exposed male fathead minnows (Pimephales promelas) for 48 hours via the water to 2, 5, 10, and 50 ng 17α-ethinylestradiol (EE2)/L, 100 ng ZM 189,154/L (a potent antiestrogen known to block activity of estrogen receptors) or mixtures of 5 or 50 ng EE2/L with 100 ng ZM 189,154/L. We analyzed gene expression changes in the gonad, as well as hormone and vitellogenin plasma levels. Steroidogenesis was down-regulated by EE2 as reflected by the reduced plasma levels of testosterone in the exposed fish and down-regulation of genes in the steroidogenic pathway. Microarray analysis of testis of fathead minnows treated with 5 ng EE2/L or with the mixture of 5 ng EE2/L and 100 ng ZM 189,154/L indicated that some of the genes whose expression was changed by EE2 were blocked by ZM 189,154, while others were either not blocked or enhanced by the mixture, generating two distinct expression patterns. Gene ontology and pathway analysis programs were used to determine categories of genes for each expression pattern. Our results suggest that response to estrogens occurs via multiple mechanisms, including canonical binding to soluble estrogen receptors, membrane estrogen receptors, and other mechanisms that are not blocked by pure antiestrogens.Item Ground Vehicle Acoustic Signal Processing Based on Biological Hearing Models(1999) Liu, Li; Baras, John S.; ISRThis thesis presents a prototype vehicle acoustic signal classification system with low classification error and short processing delay.To analyze the spectrum of the vehicle acoustic signal, we adopt biologically motivated feature extraction models - cochlear filter and A1-cortical wavelet transform. The multi-resolution representation obtained from these two models is used in the later classification system.
Different VQ based clustering algorithms are implemented and tested for real world vehicle acoustic signals. Among them, Learning VQ achieves the optimal Bayes classification performance, but its long search and training time make it not suitable for real time implementation. TSVQ needs a logarithmic search time and its tree structure naturally imitates the aggressive hearing in biological hearing systems, but it has a higher classification error. Finally, a high performance parallel TSVQ (PTSVQ)is introduced, which has classification performance close to the optimal LVQ, while maintains logarithmic search time.
Experiments on ACIDS database show that both PTSVQ and LVQ achieve high classification rate. PTSVQ has additional advantages such as easy online training and insensitivity to initial conditions. All these features make PTSVQ the most promising candidate for practical system implementation.
Another problem investigated in this thesis is combined DOA and classification, which is motivated by the biological sound localization model developed by Professor S. Shamma: the Stereausis neural network.
This model is used to perform DOA estimation for multiple vehicle recordings. The angle estimation is further used to construct a spectral separation template.
Experiments with the separated spectrum show significant improvement in classification performance. The biologically inspired separation scheme is quite different from traditional beamforming. However, it integrates all three biological hearing models into a unified framework, and it shows great potential for multiple target DOA and ID systems in the future.