Should we take measurements at an intermediate design point?

It is well known that, for estimating a linear treatment effect with constant variance, the optimal design divides the units equally between the two extremes of the design space. If the dose-response relation may be nonlinear, however, intermediate measurements may be useful in order to estimate the effects of partial treatments. We consider the decision of whether to gather data at an intermediate design point: do the gains from learning about nonlinearity outweigh the loss in efficiency in estimating the linear effect? Under reasonable assumptions about nonlinearity, we find that, unless sample size is very large, the design with no interior measurements is best, because with moderate total sample sizes, any nonlinearity in the dose-response will be difficult to detect. We discuss in the context of a simplified version of the problem that motivated this work-a study of pest-control treatments intended to reduce asthma symptoms in children.     

A nonparametric procedure associated with a clinically meaningful efficacy measure

Parametric methods such as analysis of (co)variance are commonly used for the analysis of data from clinical trials. They have the advantage of providing an easily interpretable measure of treatment efficacy such as a confidence interval for treatment difference. If there are doubts about the underlying distribution of the response variable, however, a nonparametric approach may be called for. The nonparametric approaches in such settings concentrate on hypothesis testing and are not typically used for providing easily interpretable measures of treatment efficacy. For comparing two treatments, we propose using a nonparametric measure based on the likelihood of observing a better response on one treatment than the other. The bootstrap method is used to construct a confidence interval for the treatment difference.     

Metabolomics technology and bioinformatics

Metabolomics is the global analysis of all or a large number of cellular metabolites. Like other functional genomics research, metabolomics generates large amounts of data. Handling, processing and analysis of this data is a clear challenge and requires specialized mathematical, statistical and bioinformatics tools. Metabolomics needs for bioinformatics span through data and information management, raw analytical data processing, metabolomics standards and ontology, statistical analysis and data mining, data integration and mathematical modelling of metabolic networks within a framework of systems biology. The major approaches in metabolomics, along with the modern analytical tools used for data generation, are reviewed in the context of these specific bioinformatics needs.     

The theory and application of plant competition models: an agronomic perspective

Many studies of plant competition have been directed towards understanding how plants respond to density in monocultures and how the presence of weeds affects yield in crops. In this Botanical Briefing, the development and current understanding of plant competition is reviewed, with particular emphasis being placed on the theory of plant competition and the development and application of mathematical models to crop-weed competition and the dynamics of weeds in crops. By consolidating the results of past research in this manner, it is hoped to offer a context in which researchers can consider the potential directions for future research in competition studies and its application to integrated weed management.     

Inference for an epidemic when susceptibility varies

A stochastic epidemic model featuring fixed-length latent periods, gamma-distributed infectious periods and randomly varying heterogeneity among susceptibles is considered. A Markov chain Monte Carlo algorithm is developed for performing Bayesian inference for the parameters governing the infectious-period length and the hyper-parameters governing the heterogeneity of susceptibility. This method of analysis applies to a wider class of diseases than methods proposed previously. An application to smallpox data confirms results about heterogeneity suggested by an earlier analysis that relied on less realistic assumptions.     

Characterization of a Whole Set of tRNA Molecules in an Aerobic Hyper-thermophilic Crenarchaeon, Aeropyrum pernix K1

The tRNA molecule has an important role in translation, thefunction of which is to carry amino acids to the ribosomes.It is known that tRNA is transcribed from tRNA genes, some ofwhich, in Eukarya and Archaea, contain introns. A computationalanalysis of the complete genome of Aeropyrum pernix K1 predictedthe presence of 14 intron-containing tRNA genes. To elucidatewhether these introns are actually processed in living cellsand what mechanism detects the intron regions, cDNAs for prematureand mature forms of the tRNA molecules transcribed from theintron-containing tRNA genes in the model aerobic acidothermophiliccrenarchaeon, A. pernix K1 were identified and analyzed. A comparisonbetween the nucleotide sequences of these two types of cDNAsindicated that the intron regions of the tRNA molecules wereindeed processed in A. pernix K1 living cells. Some cDNA clonesshowed that the actual splicing positions were different fromthose predicted by computational analysis. However, the bulge–helix–bulgestructure, which has been previously identified in exon–intronboundaries of archaeal tRNA genes, was evident in all boundaryregions confirmed in this work. These results indicate thatthe generally described mechanism for tRNA processing in Archaeais utilized for processing the intron region of the tRNA moleculesin A. pernix K1.     

Projections of lung cancer mortality in West Germany: a case study in Bayesian prediction

We apply a generalized Bayesian age-period-cohort (APC) model to a data-set on lung cancer mortality in West Germany, in the period 1952-1996. Our goal is to predict future death rates until the year 2010, separately for males and females. Since age and period are not measured on the same grid, we propose a generalized APC model where consecutive cohort parameters represent strongly overlapping birth cohorts. This approach results in a rather large number of parameters, where standard algorithms for statistical inference by Markov chain Monte Carlo methods turn out to be computationally intensive. We propose a more efficient implementation based on ideas of block sampling from the time series literature. We entertain two different formulations, penalizing either first or second differences of age, period and cohort parameters. To assess the predictive quality of both formulations, we first forecast the rates for the period 1987-1996 based on data until 1986. A comparison with the actual observed rates is made based on a predictive deviance criterion. Predictions of lung cancer mortality until 2010 are then reported and a modification of the formulation in order to include information on cigarette consumption is finally described.To whom correspondence should be addressed. Currently at Imperial College School of Medicine, Department of Epidemiology and Public Health, Norfolk Place, London W2 1PG, UK.     

Parameter optimization and field validation of the functional-structural model GREENLAB for maize

BACKGROUND AND AIMS: There are three reasons for the increasing demand for crop models that build the plant on the basis of architectural principles and organogenetic processes: (1) realistic concepts for developing new crops need to be guided by such models; (2) there is an increasing interest in crop phenotypic plasticity, based on variable architecture and morphology; and (3) engineering of mechanized cropping systems requires information on crop architecture. The functional-structural model GREENLAB was recently presented that simulates resource-dependent plasticity of plant architecture. This study introduces a new methodology for crop parameter optimization against measured data called multi-fitting, validates the calibrated model for maize with independent field data, and describes a technique for 3D visualization of outputs. METHODS: Maize was grown near Beijing during the 2000, 2001 and 2003 (two sowing dates) summer seasons in a block design with four to five replications. Detailed morphological and topological observations were made on the plant architecture throughout the development of the four crops. Data obtained in 2000 was used to establish target files for parameter optimization using the generalized least square method, and parameter accuracy was evaluated by coefficient of variance. In situ plant digitization was used to establish 3D symbol files for organs that were then used to translate model outputs directly into 3D representations for each time step of model execution. KEY RESULTS AND CONCLUSIONS: Multi-fitting against several target files obtained at different growth stages gave better parameter accuracy than single fitting at maturity only, and permitted extracting generic organ expansion kinetics from the static observations. The 2000 model gave excellent predictions of plant architecture and vegetative growth for the other three seasons having different temperature regimes, but predictions of inter-seasonal variability of biomass partitioning during grain filling were less accurate. This was probably due to insufficient consideration of processes governing cob sink size and terminal leaf senescence. Further perspectives for model improvement are discussed.     

The capacity of humans to identify components in complex odor-taste mixtures

Despite the fact that humans experience mixtures of odors and tastes each time they eat, little is known of their capacity to detect the individual components of foods. To investigate this capacity, 43 subjects were trained to identify three odors and three tastes and were required to indicate which of these could be identified in stimuli consisting of one to six components. Although the odor and taste components of most binary mixtures were identified, subjects encountered substantial difficulties with more complex mixtures with only two components being identified in the four- to six-component mixtures. In general, tastes were more easily identified than smells and were the only stimuli identified in the five- to six-component mixtures. Several mechanisms are proposed to account for the poor identification of components.     

Discrimination between enantiomers of linalool by olfactory receptor neurons in the cabbage moth Mamestra brassicae (L.)

Plants emit complex blends of volatiles, including chiral compounds that might be detected by vertebrates and invertebrates. Insects are ideal model organisms for studying the underlying receptor neuron mechanisms involved in olfactory discrimination of enantiomers. In the present study, we have employed two-column gas chromatography linked to recordings from single olfactory receptor neurons of Mamestra brassicae, in which separation of volatiles in a polar and a chiral column was performed. We here present the response properties of olfactory receptor neurons tuned to linalool. The narrow tuning of these receptor neurons was demonstrated by their strong responses to (R)-(-)-linalool, the weaker responses to the (+)-enantiomer as well as a few structurally related compounds, and no responses to the other numerous plant released volatiles. The enantioselectivity was verified by parallel dose-response curves, that of (R)-(-)-linalool shifted 1 log unit to the left of the (S)-(+)-linalool curve. A complete overlap of the temporal response pattern was found when comparing the responses of the same strength. Analysis of the spike amplitude and waveform indicated that the responses to the two enantiomers originated from the same neuron.