The dilemma of negative analysis of variance estimators of intraclass correlation

Summary At least two common practices exist when a negative variance component estimate is obtained, either setting it to zero or not reporting the estimate. The consequences of these practices are investigated in the context of the intraclass correlation estimation in terms of bias, variance and mean squared error (MSE). For the one-way analysis of variance random effects model and its extension to the common correlation model, we compare five estimators: analysis of variance (ANOVA), concentrated ANOVA, truncated ANOVA and two maximum likelihood-like (ML) estimators. For the balanced case, the exact bias and MSE are calculated via numerical integration of the exact sample distributions, while a Monte Carlo simulation study is conducted for the unbalanced case. The results indicate that the ANOVA estimator performs well except for designs with family size n = 2. The two ML estimators are generally poor, and the concentrated and truncated ANOVA estimators have some advantages over the ANOVA in terms of MSE. However, the large biases may make the concentrated and truncated ANOVA estimators objectionable when intraclass correlation () is small. Bias should be a concern when a pooled estimate is obtained from the literature since <0.05 in many genetic studies.     

Measurement of the exchange rates of rapidly exchanging amide protons: Application to the study of calmodulin and its complex with a myosin light chain kinase fragment

Summary A technique is described for measuring the approximate exchange rates of the more labile amide protons in a protein. The technique relies on a comparison of the intensities in¹H–¹⁵N correlation spectra recorded with and without presaturation of the water resonance. To distinguish resonance attenuation caused by hydrogen exchange from attenuation caused by cross relation, the experiment is repeated at several different pH values and the difference in attenuation of any particular amide resonance upon presaturation is used for calculating its exchange rate. The technique is demonstrated for calmodulin and for calmodulin complexed with its binding domain of skeletal muscle myosin light chain kinase. Upon complexation, increased amide exchange rates are observed for residues Lys⁷⁵ through Thr⁷⁹ located in the central helix of calmodulin, and for the C-terminal residues Ser¹⁴⁷ and Lys¹⁴⁸. In contrast, a decrease in amide exchange rate is observed at the C-terminal end of the F helix, from residues Thr¹¹⁰ through Glu¹¹⁴.Istituto Guido Donegani, Novara, Italy     

Structure des caboxines: Alcaloïdes oxindoliques du Cabucala fasciculata

The structures of three new 11-monomethoxy pentacyclic oxindole alkaloids have been elucidated by chemical correlations with reserpinine: caboxine-A was assigned to the allo C₁₉-méthyl α series: 3S, 4R, 7S, 19S; isocaboxine-A and B to the epi-allo C₁₉-methyl α series and have, respectively, the following configurations 3R, 4S, 7S, 19S and 3R, 4S, 7R, 19S.     

Artificial neural network and response surface methodology: a comparative analysis for optimizing rice straw pretreatment and saccharification

Abstract

The present study demonstrates a comparative analysis between the artificial neural network (ANN) and response surface methodology (RSM) as optimization tools for pretreatment and enzymatic hydrolysis of lignocellulosic rice straw. The efficacy for both the processes, that is, pretreatment and enzymatic hydrolysis was evaluated using correlation coefficient (R²) & mean squared error (MSE). The values of R² obtained by ANN after training, validation, and testing were 1, 0.9005, and 0.997 for pretreatment and 0.962, 0.923, and 0.9941 for enzymatic saccharification, respectively. On the other hand, the R² values obtained with RSM were 0.9965 for cellulose recovery and 0.9994 for saccharification efficiency. Thus, ANN and RSM together successfully identify the substantial process conditions for rice straw pretreatment and enzymatic saccharification. The percentage of error for ANN and RSM were 0.009 and 0.01 for cellulose recovery and for 0.004 and 0.005 for saccharification efficiency, respectively, which showed the authority of ANN in exemplifying the non-linear behavior of the system.