Row–column interaction models,with an R implementation

We propose a family of models called row–column interaction models (RCIMs) for two-way table responses. RCIMs apply some link function to a parameter (such as the cell mean) to equal a row effect plus a column effect plus an optional interaction modelled as a reduced-rank regression. What sets this work apart from others is that our framework incorporates a very wide range of statistical models, e.g., (1) log-link with Poisson counts is Goodman’s RC model, (2) identity-link with a double exponential distribution is median polish, (3) logit-link with Bernoulli responses is a Rasch model, (4) identity-link with normal errors is two-way ANOVA with one observation per cell but allowing semi-complex modelling of interactions of the form  \(\mathbf{A}\mathbf{C}^T\) , (5) exponential-link with normal responses are quasi-variances. Proposed here also is a least significant difference plot augmentation of quasi-variances. Being a special case of RCIMs, quasi-variances are naturally extended from the \(M=1\) linear/additive predictor  \(\eta \) case (within the exponential family) to the \(M>1\) case (vector generalized linear model families). A rank-1 Goodman’s RC model is also shown to estimate the site scores and optimums of an equal-tolerances Poisson unconstrained quadratic ordination. New functions within the VGAM R package are described with examples. Altogether, RCIMs facilitate the analysis of matrix responses of many data types, therefore are potentially useful to many areas of applied statistics.     

Metal oxide additives for the sintering of silicon carbide: Reactivity and densification

The effectiveness of sintering additives for β-SiC was examined based on thermodynamic calculations and experimental observations under hot pressing conditions of 1700–1800 °C. Various types of oxides, such as Al₂O₃, Fe₂O₃, MgO, TiO₂, WO₃ and Y₂O₃, were examined theoretically by considering the Gibbs formation free energy and vapor pressure. According to experimental observations expanded to their binary and ternary systems after hot pressing at 1750 °C and 20 MPa, Al₂O₃, MgO, Y₂O₃ and their mixed systems were found to be the only effective sintering additives that do not react with β-SiC at high temperatures. On the other hand, Fe₂O₃, TiO₂, WO₃ and their combinations with other oxides were not effective because of the reaction with β-SiC by forming the corresponding metal carbides and/or silicides, as predicted by thermodynamic simulations. Moreover, the experimental results for the additional possible components were also included.     

Computational Study of Asian Propolis Compounds as Potential Anti-Type 2 Diabetes Mellitus Agents by Using Inverse Virtual Screening with the DIA-DB Web Server,Tanimoto Similarity Analysis,and Molecular Dynamic Simulation

Propolis contains a wide range of pharmacological activities because of their various bioactive compounds. The beneficial effect of propolis is interesting for treating type-2 diabetes mellitus (T2DM) owing to dysregulation of multiple metabolic processes. In this study, 275 of 658 Asian propolis compounds were evaluated as potential anti-T2DM agents using the DIA-DB web server towards 18 known anti-diabetes protein targets. More than 20% of all compounds could bind to more than five diabetes targets with high binding affinity (<−9.0 kcal/mol). Filtering with physicochemical and pharmacokinetic properties, including ADMET parameters, 12 compounds were identified as potential anti-T2DM with favorable ADMET properties. Six of those compounds, (2R)-7,4′-dihydroxy-5-methoxy-8-methylflavone; (RR)-(+)-3′-senecioylkhellactone; 2′,4′,6′-trihydroxy chalcone; alpinetin; pinobanksin-3-O-butyrate; and pinocembrin-5-methyl ether were first reported as anti-T2DM agents. We identified the significant T2DM targets of Asian propolis, namely retinol-binding protein-4 (RBP4) and aldose reductase (AKR1B1) that have important roles in insulin sensitivity and diabetes complication, respectively. Molecular dynamic simulations showed stable interaction of selected propolis compounds in the active site of RBP4 and AKR1B1. These findings suggest that Asian propolis compound may be effective for treatment of T2DM by targeting RBP4 and AKR1B1.