Profile hidden Markov models (HMMs) were used to predict the configuration of secondary alcohols and α-methyl branches of modular polyketides. Based on the configurations of two chiral centers in these polyketides, 78 ketoreductases were classified into four different types of polyketide producers. The identification of positions that discriminate between these protein families was followed by fitting six profile HMMs to the data set and the corresponding subsets, to model the conserved regions of the protein types. Ultimately, the profile HMMs described herein predict protein subtypes based on the complete information-rich region; consequently, slight changes in a multiple sequence alignment do not significantly alter the outcome of this classification method. Additionally, Viterbi scores can be used to assess the reliability of the classification.

Testing for or against a qualitative interaction is relevant in randomized clinical trials that use a common primary factor treatment and have a secondary factor, such as the centre, region, subgroup, gender or biomarker. Interaction contrasts are formulated for ratios of differences between the levels of the primary treatment factor. Simultaneous confidence intervals allow for interpreting the magnitude and the relevance of the qualitative interaction. The proposed method is demonstrated by means of a multi-centre clinical trial, using the R package mratios.

The isolation, structure elucidation, and synthesis of antalid (1), a novel secondary metabolite from Polyangium sp., is described herein. The structure elucidation of 1 was performed with the aid of mass spectrometry, high field NMR experiments, and crystal structure analysis. The absolute configuration of antalid was confirmed through the Mosher ester method and ultimately by total synthesis. In addition, the biosynthetic origin of this hybrid PKS-NRPS natural product was unraveled by the in silico analysis of its biosynthetic gene cluster.

Two or higher‐order factorial designs are very common in agricultural and horticultural experiments. The evaluation of such trials by analysis of variance (anova) and the corresponding F‐tests for the interaction effects covers only a global decision concerning the presence of interactions. This study presents a straightforward method, which provides a more detailed analysis of interactions via multiple contrast tests. The presented approach takes both the structure of each factor and the research question into account by building user‐defined product‐type contrasts. Simultaneous inference for these user‐specified interaction contrasts that controls the overall error rate is available. In addition to adjusted P‐values, it is recommended to use simultaneous confidence intervals to present the magnitude, direction and the biological relevance of the interaction effects. The proposed method is demonstrated using two horticultural trials. Furthermore, the authors provide a collection of worked examples using the R (A Language and Environment for Statistical Computing, 2013, R Foundation for Statistical Computing, Vienna, Austria) add‐on package statint stored on github (https://github.com/AKitsche/statint).

No abstract