Regressor and random-effects dependencies in multilevel models

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72623/1/j.0039-0402.2003.00254.x.pd

Genomics and metagenomics of trimethylamine-utilizing Archaea in the human gut microbiome

International audienceThe biological significance of Archaea in the human gut microbiota is largely unclear. We recently reported genomic and biochemical analyses of the Methanomassiliicoccales, a novel order of methanogenic Archaea dwelling in soil and the animal digestive tract. We now show that these Methanomassiliicoccales are present in published microbiome data sets from eight countries. They are represented by five Operational Taxonomic Units present in at least four cohorts and phylogenetically distributed into two clades. Genes for utilizing trimethylamine (TMA), a bacterial precursor to an atherosclerogenic human metabolite, were present in four of the six novel Methanomassiliicoccales genomes assembled from ELDERMET metagenomes. In addition to increased microbiota TMA production capacity in long-term residential care subjects, abundance of TMA-utilizing Methanomassiliicoccales correlated positively with bacterial gene count for TMA production and negatively with fecal TMA concentrations. The two large Methanomassiliicoccales clades have opposite correlations with host health status in the ELDERMET cohort and putative distinct genomic signatures for gut adaptation

The winner of the 2011 IJRM Best Paper Award: Enhancing Marketing with Engineering: Optimal Product Line Design for Heterogeneous Markets

Successful product line design and development often require a balance of technical and market tradeoffs. Quantitative methods for optimizing product attribute levels using preference elicitation (e.g., conjoint) data are useful for many product types. However, products with substantial engineering content involve critical tradeoffs in the ability to achieve those desired attribute levels. Technical tradeoffs in product design must be made with an eye toward market consequences, particularly when heterogeneous market preferences make differentiation and strategic positioning critical to capturing a range of market segments and avoiding cannibalization. We present a unified methodology for product line optimization that coordinates positioning and design models to achieve realizable firm-level optima. The approach overcomes several shortcomings of prior product line optimization models by incorporating a general Bayesian account of consumer preference heterogeneity, managing attributes over a continuous domain to alleviate issues of combinatorial complexity, and avoiding solutions that are impossible to realize. The method is demonstrated for a line of dial-readout scales, using physical models and conjoint-based consumer choice data. The results show that the optimal number of products in the line is not necessarily equal to the number of market segments, that an optimal single product for a heterogeneous market differs from that for a homogeneous one, and that the representational form for consumer heterogeneity has a substantial impact on the design and profitability of the resulting optimal product line ¿ even for the design of a single product. The method is managerially valuable because it yields product line solutions efficiently, accounting for marketing-based preference heterogeneity as well as engineering-based constraints with which product attributes can be realized