Quantitative structure-property relationships for colour reagents and their colour reactions with ytterbium using regression analysis and computational neural networks

In this paper, the new topological indices A(x1)-A(x3) suggested in our laboratory and molecular connectivity indices have been applied to multivariate analysis in structure-property studies. The topological indices of twenty asymmetrical phosphono bisazo derivatives of chromotropic acid have been calculated. The structure-property relationships between colour reagents and their colour reactions with ytterbium have been studied by A(x1)-A(x3) indices and molecular connectivity indices with satisfactory results. Multiple regression analysis and neural networks were employed simultaneously in this study.

QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS FOR TOXICITY OF PHENOLS USING REGRESSION-ANALYSIS AND COMPUTATIONAL NEURAL NETWORKS

Quantitative structure-toxicity models were developed that directly link the molecular structures of a et of 50 alkYlated and/or halogenated phenols with their polar narcosis toxicity, expressed as the negative logarithm of the IGC50 (50% growth inhibitor

Phenoloxidase, a marker enzyme for differentiation of the neural ectoderm and the epidermal ectoderm during embryonic development of amphioxus Branchiostoma belcheri tsingtaunese

The development of phenoloxidase during amphioxus embryogenesis was spectrophotometrically and histochemically studied for the first time in the present study. It was found that (1) PO activity initially appeared in the general ectoderm including the neural ectoderm and the epidermal ectoderm at the early neurala stage but not in the mesoderm or the endoderm, and (2) PO activity disappeared in the neural plate cells but remained unchanged in the epidermal cells when the neural plate was morphologically quite distinct from the rest of the ectoderm. It is apparent that PO could serve as a marker enzyme for differentiation of the neural ectoderm from the epidermal ectoderm during embryonic development of amphioxus. (C) 2000 Elsevier Science ireland Ltd. All rights reserved.

Neural network retrieval of ocean surface parameters from SSM/I data

A new algorithm based on the multiparameter neural network is proposed to retrieve wind speed (WS), sea surface temperature (SST), sea surface air temperature, and relative humidity ( RH) simultaneously over the global oceans from Special Sensor Microwave Imager (SSM/I) observations. The retrieved geophysical parameters are used to estimate the surface latent heat flux and sensible heat flux using a bulk method over the global oceans. The neural network is trained and validated with the matchups of SSM/I overpasses and National Data Buoy Center buoys under both clear and cloudy weather conditions. In addition, the data acquired by the 85.5-GHz channels of SSM/I are used as the input variables of the neural network to improve its performance. The root-mean-square (rms) errors between the estimated WS, SST, sea surface air temperature, and RH from SSM/I observations and the buoy measurements are 1.48 m s(-1), 1.54 degrees C, 1.47 degrees C, and 7.85, respectively. The rms errors between the estimated latent and sensible heat fluxes from SSM/I observations and the Xisha Island ( in the South China Sea) measurements are 3.21 and 30.54 W m(-2), whereas those between the SSM/ I estimates and the buoy data are 4.9 and 37.85 W m(-2), respectively. Both of these errors ( those for WS, SST, and sea surface air temperature, in particular) are smaller than those by previous retrieval algorithms of SSM/ I observations over the global oceans. Unlike previous methods, the present algorithm is capable of producing near-real-time estimates of surface latent and sensible heat fluxes for the global oceans from SSM/I data.

Postnatal development of the retina in root vole Microtus oeconomus

Vision plays an important role in the living habits of animals, especially in feeding. We investigated the postnatal development of retina in root vole Microtus oeconornus. The result shows that the retina of the M. oeconornus is very primitive before postnatal day （PD） 3. The neuroblastic layer does not differentiate and makes up more than half of the retina layer. The outer plexiform layer （OPL） first comes into existence at PDS. At PD6, as the presence of the OPL becomes obvious, the outer nuclear layer （ONL） and inner nuclear layer （INL） are much clearer. At PD18, the retina is similar to an adult retina and each layer becomes distinct. The thickness and cell density of the ganglion cell layer （GCL） and ONL during different postnatal days were also examined. These results show that the thickness and density of ONL increase during ontogeny, while the thickness and density of GCL decrease. Compared with Rattus norvegicus, Apodemus agrarius , Cricetulus triton, Microtus mandarinus , Myospalax cansus , Spermophilus dauricus and Sciurotamias davidianus, the histological structure of the retina of M. oeconornus is between that of nocturnal and diurnal rodents.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.