Evaluation of a generalized regression artificial neural network for extending cadmium’s working calibration range in graphite furnace atomic absorption spectrometry

Evaluation of a generalized regression artificial neural network for extending cadmium’s working calibration range in graphite furnace atomic absorption spectrometry. (Hernández C., Edwin A.; Rivas E., Francklin I., Ávila G., Rita M.) Abstract Abstract A generalized regression artificial neural network (GRANN) was developed and evaluated for modeling cadmium's nonlinear calibration curve in order to extend its upper concentration limit from 4.0 mg L ¯¹ up to 22.0 mg L ¯¹. This type of neural network presents important advantages over the more popular backpropagation counterpart which are worth exploiting in analytical applications, namely, (1) a smaller number of variables have to be optimized, with the subsequent reduction in ''development hassle''; and, (2) shorter development times, thanks to the fact that the adjustment of the weights (the artificial synapses) is a non-iterative, one-pass process. A backpropagation artificial neural network (BPANN), a second-order polynomial, and some less frequently employed polynomial and exponential functions (e.g., Gaussian, Lorentzian, and Boltzmann), were also evaluated for comparison purposes. The quality of the fit of the various models, assessed by calculating the root mean square of the percentage deviations, was as follows: GRANN > Boltzmann > second-order polynomial > BPANN > Gauss > Lorentz. The accuracy and precision of the models were further estimated through the determination of cadmium in the certified reference material ''Trace Metals in Drinking Water'' (High Purity Standards, Lot No. 490915), which has a cadmium certified concentration (12.00 ± 0.06 mg L¯¹) that lies in the nonlinear regime of the calibration curve. Only the models generated by the GRANN and BPANN accurately predicted the concentrations of a series of solutions, prepared by serial dilution of the CRM, with cadmium concentrations below and above the maximum linear calibration limit (4.0 mg L¯¹). Extension of the working range by using the proposed methodology represents an attractive alternative from the analytical point of view, since it results in less specimen manipulation and consequently reduced contamination risks without compromising either the accuracy or the precision of the analyses. The implementation of artificial neural networks also helps to reduce the trialand-error task of looking for the right mathematical model from among the many possibilities currently available in the various scientific and statistic software packages. Artículo publicado en: Anal Bioanal Chem (2005) 381: 788-794 DOI 10.1007/[email protected]@[email protected] monográfic

An extracellular matrix microarray for probing cellular differentiation

We present an extracellular matrix (ECM) microarray platform for the culture of patterned cells atop combinatorial matrix mixtures. This platform enables the study of differentiation in response to a multitude of microenvironments in parallel. The fabrication process required only access to a standard robotic DNA spotter, off-the-shelf materials and 1,000 times less protein than conventional means of investigating cell-ECM interactions. To demonstrate its utility, we applied this platform to study the effects of 32 different combinations of five extracellular matrix molecules (collagen I, collagen III, collagen IV, laminin and fibronectin) on cellular differentiation in two contexts: maintenance of primary rat hepatocyte phenotype indicated by intracellular albumin staining and differentiation of mouse embryonic stem (ES) cells toward an early hepatic fate, indicated by expression of a beta-galactosidase reporter fused to the fetal liver-specific gene, Ankrd17 (also known as gtar). Using this technique, we identified combinations of ECM that synergistically impacted both hepatocyte function and ES cell differentiation. This versatile technique can be easily adapted to other applications, as it is amenable to studying almost any insoluble microenvironmental cue in a combinatorial fashion and is compatible with several cell type