Abstract-We present a method for estimating conditionally Gaussian random vectors with random covariance matrices, which uses techniques from the field of machine learning. Such models are typical in communication systems, where the covariance matrix of the channel vector depends on random parameters, e.g., angles of propagation paths. If the covariance matrices exhibit certain Toeplitz and shift-invariance structures, the complexity of the MMSE channel estimator can be reduced to O(M log M ) floating point operations, where M is the channel dimension. While in the absence of structure the complexity is much higher, we obtain a similarly efficient (but suboptimal) estimator by using the MMSE estimator of the structured model as a blueprint for the architecture of a neural network. This network learns the MMSE estimator for the unstructured model, but only within the given class of estimators that contains the MMSE estimator for the structured model. Numerical simulations with typical spatial channel models demonstrate the generalization properties of the chosen class of estimators to realistic channel models.
Seven legume extracts containing phytoestrogens were analyzed for estrogenic activity. Methanol extracts were prepared from soybean (Glycine max L.), green bean (Phaseolus vulgaris L.), alfalfa sprout (Medicago sativa L.), mung bean sprout (Vigna radiata L.), kudzu root (Pueraria lobata L.), and red clover blossom and red clover sprout (Trifolium pratense L.). Extracts of kudzu root and red clover blossom showed significant competitive binding to estrogen receptor beta (ERbeta). Estrogenic activity was determined using an estrogen-dependent MCF-7 breast cancer cell proliferation assay. Kudzu root, red clover blossom and sprout, mung bean sprout, and alfalfa sprout extracts displayed increased cell proliferation above levels observed with estradiol. The pure estrogen antagonist, ICI 182,780, suppressed cell proliferation induced by the extracts, suggesting an ER-related signaling pathway was involved. The ER subtype-selective activities of legume extracts were examined using transiently transfected human embryonic kidney (HEK 293) cells. All seven of the extracts exhibited preferential agonist activity toward ERbeta. Using HPLC to collect fractions and MCF-7 cell proliferation, the active components in kudzu root extract were determined to be the isoflavones puerarin, daidzin, genistin, daidzein, and genistein. These results show that several legumes are a source of phytoestrogens with high levels of estrogenic activity.
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