Multiple Treatment Comparisons in Linear Models When the Standard Error of a Difference is not Constant

Users of analysis of variance (ANOVA) procedures are accustomed to an ANOVA table, followed by a table of means. When the underlying linear model is variance‐balanced, i.e. the standard error of a difference is constant for all pairwise comparisons, non‐significant differences can be indicated by underlining. Unfortunately, when the design is unbalanced, it may turn out to be impossible to consistently represent all significant differences by standard underlining procedures. This paper proposes simple approaches, which allow a “connected lines” representation of treatment comparisons in the unbalanced case. The price for the improved display of results is a potential need to set‐aside some significances and report them separately. Experience shows that often all significances can be displayed by underlining, especially when variance‐imbalance is moderate. Alternatively, a “broken lines” representation can be used, which is guaranteed to allow a display of all significances. This type of display seems particularly suitable for implementation as a letters representation into statistical packages for linear models.     

An impurity characterization based approach for the rapid development of integrated downstream purification processes

In this study, we describe a new approach for the characterization of process‐related impurities along with an in silico tool to generate orthogonal, integrated downstream purification processes for biological products. A one‐time characterization of process‐related impurities from product expression in Pichia pastoris was first carried out using linear salt and pH gradients on a library of multimodal, salt‐tolerant, and hydrophobic charge induction chromatographic resins. The Reversed‐phase ultra‐performance liquid chromatography (UPLC) analysis of the fractions from these gradients was then used to generate large data sets of impurity profiles. A retention database of the biological product was also generated using the same linear salt and pH gradients on these resins, without fraction collection. The resulting two data sets were then analyzed using an in silico tool, which incorporated integrated manufacturing constraints to generate and rank potential three‐step purification sequences based on their predicted purification performance as well as whole‐process “orthogonality” for impurity removal. Highly ranked sequences were further examined to identify templates for process development. The efficacy of this approach was successfully demonstrated for the rapid development of robust integrated processes for human growth hormone and granulocyte‐colony stimulating factor.     

Consensual predictions of potential distributional areas for invasive species: a case study of Argentine ants in the Iberian Peninsula

Invasive species are known to influence the structure and function of invaded ecological communities, and preventive measures appear to be the most efficient means of controlling these effects. However, management of biological invasions requires use of adequate tools to understand and predict invasion patterns in recently introduced areas. The present study: (1) estimates the potential geographic distribution and ecological requirements of the Argentine ant (Linepithema humile Mayr), one of the most conspicuous invasive species throughout the world, in the Iberian Peninsula using ecological niche modeling, and (2) provides new insights into the process of selection of consensual areas among predictions from several modeling methodologies. Ecological niche models were developed using 5 modeling techniques: generalized linear models (GLM), generalized additive models (GAM), generalized boosted models (GBM), Genetic Algorithm for Rule-Set Prediction (GARP), and Maximum Entropy (Maxent). Models for the eastern and western portions of the Iberian Peninsula were built using subsets of occurrence and environmental data to investigate the potential for ecological niche differences between the invading populations. Our results indicate geographic differences between predictions of different approaches, and the utility of ensemble predictions in identifying areas of uncertainty regarding the species’ invasive potential. More generally, our models predict coastal areas and major river corridors as highly suitable for Argentine ants, and indicate that western and eastern Iberian Peninsula populations occupy similar environmental conditions.

A new meta-ANOVA approach for synthesizing information under signal-heterogeneity setting with application to nuclear magnetic resonance spectroscopic data

A new synthesizing statistical methodology is proposed to resolve issues of signal-heterogeneity in data sets collected through high-resolution ¹H nuclear magnetic resonance (NMR) spectroscopy. This signal-heterogeneity is typically caused by subjective operations for processing spectral profiles and measuring peak areas, non-homogeneous biological phases of experimental subjects, and variations of systems in multi-center. All these causes are likely to simultaneously impact signals of metabolic changes and their precision in a nonlinear fashion. As a combined effect, signal-heterogeneity chiefly manifests through non-homomorphic patterns of standardized treatment mean deviations spanning all experiments, and makes most remedial statistical models with linearity structure invalid. By avoiding a huge and very complex model, we develop a simple meta-ANOVA approach to synthesize many one-way-layout ANOVA analyses from individual experiments. A scale-invariant F-ratio statistic is taken as the summarizing sufficient statistic of a non-centrality parameter that supposedly captures the information about metabolic change from each experiment. Then a joint-likelihood function of a common non-centrality is constructed as the basis for maximum likelihood estimation and Chi-square likelihood ratio testing for statistical inference. We apply the meta-ANOVA to detect metabolic changes of three metabolites identified through pattern recognition on NMR spectral profiles obtained from muscle and liver tissues. We also detect effect differences among different treatments via meta-ANOVA multiple comparison.     

A tailored LNA clamping design principle: Efficient,economized, specific and ultrasensitive for the detection of point mutations

In the development of personalized medicine, the ultrasensitive detection of point mutations that correlate with diseases is important to improve the efficacy of treatment and guide clinical medication. In this study, locked nucleic acid (LNA) was introduced as an amplification suppressor of a massive number of wild-type alleles in an amplification refractory mutation system (ARMS) to achieve the detection of low-abundance mutations with high specificity and sensitivity of at least 0.1%. By integrating the length of clamp, base type, number and position of LNA modifications, we have established a “shortest length with the fewest LNA bases” principle from which each LNA base would play a key role in the affinity and the ability of single base discrimination could be improve. Finally, based on this LNA design guideline, a series of the most important single point mutation sites of epidermal growth factor receptor (EGFR) was verified to achieve the optimal amplification state which as low as 0.1% mutation gene amplification was not affected under the wild gene amplification was completely inhibited, demonstrating that the proposed design principle has good applicability and versatility and is of great significance for the detection of circulating tumor DNA.     

A stage‐wise approach for the analysis of multi‐environment trials

Plant breeders and variety testing agencies routinely test candidate genotypes (crop varieties, lines, test hybrids) in multiple environments. Such multi‐environment trials can be efficiently analysed by mixed models. A single‐stage analysis models the entire observed data at the level of individual plots. This kind of analysis is usually considered as the gold standard. In practice, however, it is more convenient to use a two‐stage approach, in which experiments are first analysed per environment, yielding adjusted means per genotype, which are then summarised across environments in the second stage. Stage‐wise approaches suggested so far are approximate in that they cannot fully reproduce a single‐stage analysis, except in very simple cases, because the variance–covariance matrix of adjusted means from individual environments needs to be approximated by a diagonal matrix. This paper proposes a fully efficient stage‐wise method, which carries forward the full variance–covariance matrix of adjusted means from the individual environments to the analysis across the series of trials. Provided the variance components are known, this method can fully reproduce the results of a single‐stage analysis. Computations are made efficient by a diagonalisation of the residual variance–covariance matrix, which necessitates a corresponding linear transformation of both the first‐stage estimates (e.g. adjusted means and regression slopes for plot covariates) and the corresponding design matrices for fixed and random effects. We also exemplify the extension of the general approach to a three‐stage analysis. The method is illustrated using two datasets, one real and the other simulated. The proposed approach has close connections with meta‐analysis, where environments correspond to centres and genotypes to medical treatments. We therefore compare our theoretical results with recently published results from a meta‐analysis.     

Recruitment and diversification of an ecdysozoan family of neuropeptide hormones for black widow spider venom expression

Venoms have attracted enormous attention because of their potent physiological effects and dynamic evolution, including the convergent recruitment of homologous genes for venom expression. Here we provide novel evidence for the recruitment of genes from the Crustacean Hyperglycemic Hormone (CHH) and arthropod Ion Transport Peptide (ITP) superfamily for venom expression in black widow spiders. We characterized latrodectin peptides from venom gland cDNAs from the Western black widow spider (Latrodectus hesperus), the brown widow (Latrodectus geometricus) and cupboard spider (Steatoda grossa). Phylogenetic analyses of these sequences with homologs from other spider, scorpion and wasp venom cDNAs, as well as CHH/ITP neuropeptides, show latrodectins as derived members of the CHH/ITP superfamily. These analyses suggest that CHH/ITP homologs are more widespread in spider venoms, and were recruited for venom expression in two additional arthropod lineages. We also found that the latrodectin 2 gene and nearly all CHH/ITP genes include a phase 2 intron in the same position, supporting latrodectin's placement within the CHH/ITP superfamily. Evolutionary analyses of latrodectins suggest episodes of positive selection along some sequence lineages, and positive and purifying selection on specific codons, supporting its functional importance in widow venom. We consider how this improved understanding of latrodectin evolution informs functional hypotheses regarding its role in black widow venom as well as its potential convergent recruitment for venom expression across arthropods.