Statistical modeling and projections of lung cancer mortality in 4 industrialized countries

The purpose of this work was to model lung cancer mortality as a function of past exposure to tobacco and to forecast age-sex-specific lung cancer mortality rates. A 3-factor age-period-cohort (APC) model, in which the period variable is replaced by the product of average tar content and adult tobacco consumption per capita, was estimated for the US, UK, Canada and Australia by the maximum likelihood method. Age- and sex-specific tobacco consumption was estimated from historical data on smoking prevalence and total tobacco consumption. Lung cancer mortality was derived from vital registration records. Future tobacco consumption, tar content and the cohort parameter were projected by autoregressive moving average (ARIMA) estimation. The optimal exposure variable was found to be the product of average tar content and adult cigarette consumption per capita, lagged for 2530 years for both males and females in all 4 countries. The coefficient of the product of average tar content and tobacco consumption per capita differs by age and sex. In all models, there was a statistically significant difference in the coefficient of the period variable by sex. In all countries, male age-standardized lung cancer mortality rates peaked in the 1980s and declined thereafter. Female mortality rates are projected to peak in the first decade of this century. The multiplicative models of age, tobacco exposure and cohort fit the observed data between 1950 and 1999 reasonably well, and time-series models yield plausible past trends of relevant variables. Despite a significant reduction in tobacco consumption and average tar content of cigarettes sold over the past few decades, the effect on lung cancer mortality is affected by the time lag between exposure and established disease. As a result, the burden of lung cancer among females is only just reaching, or soon will reach, its peak but has been declining for I to 2 decades in men. Future sex differences in lung cancer mortality are likely to be greater in North America than Australia and the UK due to differences in exposure patterns between the sexes. (c) 2005 Wiley-Liss, Inc.

Analysis of line x environment interactions for yield in navy beans. 2. Pattern analysis of lines and environment within years

Seven years of multi-environment yield trials of navy bean (Phaseolus vulgaris L.) grown in Queensland were examined. As is common with plant breeding evaluation trials, test entries and locations varied between years. Grain yield data were analysed for each year using cluster and ordination analyses (pattern analyses). These methods facilitate descriptions of genotype performance across environments and the discrimination among genotypes provided by the environments. The observed trends for genotypic yield performance across environments were partly consistent with agronomic and disease reactions at specific environments and also partly explainable by breeding and selection history. In some cases, similarities in discrimination among environments were related to geographic proximity, in others management practices, and in others similarities occurred between geographically widely separated environments which differed in management practices. One location was identified as having atypical line discrimination. The analysis indicated that the number of test locations was below requirements for adequate representation of line x environment interaction. The pattern analyses methods used were an effective aid in describing the patterns in data for each year and illustrated the variations in adaptive patterns from year to year. The study has implications for assessing the number and location of test sites for plant breeding multi-environment trials, and for the understanding of genetic traits contributing to line x environment interactions.

Reflections on the statistical analysis of personality and norms in war, peace, and prejudice: Are deviant minorities the problem?

The compelling quality of the Global Change simulation study (Altemeyer, 2003), in which high RWA (right-wing authoritarianism)/high SDO (social dominance orientation) individuals produced poor outcomes for the planet, rests on the inference that the link between high RWA/SDO scores and disaster in the simulation can be generalized to real environmental and social situations. However, we argue that studies of the Person × Situation interaction are biased to overestimate the role of the individual variability. When variables are operationalized, strongly normative items are excluded because they are skewed and kurtotic. This occurs both in the measurement of predictor constructs, such as RWA, and in the outcome constructs, such as prejudice and war. Analyses of normal linear statistics highlight personality variables such as RWA, which produce variance, and overlook the role of norms, which produce invariance. Where both normative and personality forces are operating, as in intergroup contexts, the linear analysis generates statistics for the sample that disproportionately reflect the behavior of the deviant, antinormative minority and direct attention away from the baseline, normative position. The implications of these findings for the link between high RWA and disaster are discussed.

Pattern analysis on grain yield performance of Chinese and CIMMYT spring wheat cultivars sown in China and CIMMYT

Understanding the relationships among testing environments is essential for better targeting cultivars to production environments. To identify patterns of cultivar, environment, cultivar-by-environment interactions, and opportunities for indirect selection for grain yield, a set of 25 spring wheat cultivars from China and the International Maize and Wheat Improvement Center (CIMMYT) was evaluated in nine environments in China and four management environments at CIMMYT in Cd. Obregon, Mexico, during two wheat seasons. Genetic background and original environment were the main factors influencing grain yield performance of the cultivars. Baviacora M 92, Xinchun 2 and Xinchun 6 showed relatively more stable and higher grain yields, whereas highly photoperiod sensitive cultivars Xinkehan 9, Kefeng 6 and Longmai 19 proved consistently inferior across environments, except in Harbin and Keshan, the two high latitude environments. Longmai 26, also from high latitude environments in the northeastern Heilongjiang province, was however probably not as photoperiodicly sensitive as other cultivars; from that region, and produced much higher grain yield and expressed a broader adaptation. None of the environments reported major diseases. Pattern analyses revealed that photoperiod response and planting option on beds were the two main factors underlying the observed interactions for grain yield. The production environment of planting on the flat in Mexico grouped together with Huhhot and Urumqi in both wheat seasons, indicating an indirect response to selection for grain yield in this CIMMYT managed environment could benefit the two Chinese environments. Both the environment of planting on the flat with Chinese Hejin and Yongning, and the three CIMMYT enviromnents planting on raised beds with Chinese Yongning grouped together only in one season, showing that repeatability may not be stable in this case.

Pattern analysis on protein properties of Chinese and CIMMYT spring wheat cultivars sown in China and CIMMYT

Improvement of processing quality is a very important objective for Chinese wheat breeding programs. Twenty-five CIMMYT and Chinese spring wheat cultivars were grown at four managed conditions by CIMMYT in Cd. Obregon, Sonora, Mexico and in nine environments in China, over two successive wheat seasons from 2000 to 2002. These trials were used to identify patterns of cultivar, environment and cultivar x environment interactions, and to determine opportunities for indirect selection for protein content and the protein-quality related parameter, SDS sedimentation (SDSS) value. The cultivar Inqalab 91 showed low levels of interaction with environments in the 2000-01 crop cycle for protein content, and expressed intermediate levels for both protein content and SDSS value, across most of the environments in both years. Longmai 26 had consistently high protein content and SDSS value across environments in both years, indicating that it is possible to breed cultivars expressing high yields with good protein properties. Cluster analyses revealed that cultivars grouped differently for protein content and SDSS value. Besides photoperiod, water availability appeared to influence the ranking of cultivars for protein content and SDSS value. Temperature and soil type may underlie the observed interactions for protein content, while temperature may also be a factor associated with interactions for SDSS value. The full irrigation managed environment in Mexico, with the cultivars sown on raised beds two months later than optimum and exposing them to late heat, clustered together with the Chinese environments Huhhot, Yongning, and Hejin in the 2000-01 season for SDSS value. This indicates that there is an opportunity to exploit indirect responses to selection in the CIMMYT management environments for SDSS value with relevance for China's spring wheat regions. However, there seemed little chance for positive indirect selection in CIMMYT's managed environments for China in regard to protein content, as environments clustered distinctly. Pattern analyses permitted a sensible and useful summary for this multi environment experiment, helping in understanding natural relationships and variations in cultivar performance among the various environment groups, and assisting in the structuring of environments.

Modeling and exploiting behavior patterns in dynamic environments

This paper presents a new approach to improving the effectiveness of autonomous systems that deal with dynamic environments. The basis of the approach is to find repeating patterns of behavior in the dynamic elements of the system, and then to use predictions of the repeating elements to better plan goal directed behavior. It is a layered approach involving classifying, modeling, predicting and exploiting. Classifying involves using observations to place the moving elements into previously defined classes. Modeling involves recording features of the behavior on a coarse grained grid. Exploitation is achieved by integrating predictions from the model into the behavior selection module to improve the utility of the robot's actions. This is in contrast to typical approaches that use the model to select between different strategies or plays. Three methods of adaptation to the dynamic features of the environment are explored. The effectiveness of each method is determined using statistical tests over a number of repeated experiments. The work is presented in the context of predicting opponent behavior in the highly dynamic and multi-agent robot soccer domain (RoboCup)

Some vocabulary and grammar for the analysis of multi-environment trials, as applied to the analysis of FPB and PPB trials

For the improvement of genetic material suitable for on farm use under low-input conditions, participatory and formal plant breeding strategies are frequently presented as competing options. A common frame of reference to phrase mechanisms and purposes related to breeding strategies will facilitate clearer descriptions of similarities and differences between participatory plant breeding and formal plant breeding. In this paper an attempt is made to develop such a common framework by means of a statistically inspired language that acknowledges the importance of both on farm trials and research centre trials as sources of information for on farm genetic improvement. Key concepts are the genetic correlation between environments, and the heterogeneity of phenotypic and genetic variance over environments. Classic selection response theory is taken as the starting point for the comparison of selection trials (on farm and research centre) with respect to the expected genetic improvement in a target environment (low-input farms). The variance-covariance parameters that form the input for selection response comparisons traditionally come from a mixed model fit to multi-environment trial data. In this paper we propose a recently developed class of mixed models, namely multiplicative mixed models, also called factor-analytic models, for modelling genetic variances and covariances (correlations). Mixed multiplicative models allow genetic variances and covariances to be dependent on quantitative descriptors of the environment, and confer a high flexibility in the choice of variance-covariance structure, without requiring the estimation of a prohibitively high number of parameters. As a result detailed considerations regarding selection response comparisons are facilitated. ne statistical machinery involved is illustrated on an example data set consisting of barley trials from the International Center for Agricultural Research in the Dry Areas (ICARDA). Analysis of the example data showed that participatory plant breeding and formal plant breeding are better interpreted as providing complementary rather than competing information.

Vibration instability in rolling mills: Modeling and experimental results

Models for the occurrence of the vibrational instability during rolling known as third octave chatter are presented and discussed. An analysis of rolling mill chatter was performed for the purpose of identifying characteristics of the vibrations and to determine any dependency on the rolling schedule. In particular, a stability criterion for the critical rolling speed is used to predict the maximum rolling speed without chatter instability on schedules from a 5 stand tandem mill rolling thin steel product. The results correlate well with measurements of critical speed occurring on the mill using a vibration monitor: This research provides significant insights into the chatter phenomena and has been used to investigate control methods for suppression of the instability.

Does industry matter? Examining the role of industry structure and organizational learning in innovation and brand performance

A manager's perception of industry structure (dynamism) has the potential to impact various organizational strategies and behaviors. This may be particularly so with regard to perceptions driving organizational learning orientations and innovation based marketing strategy. The position taken here suggests that firms operating within a competitive industry tend to pursue innovative ways of performing value-creating activities, which requires the development of learning capabilities. The results of a study of SMEs suggest that market focused learning, relative to other learning capabilities plays a key role in the relationships between industry structure, innovation and brand performance. The findings also show that market focused learning and internally focused learning influence innovation and that innovation influences a brand's performance. (c) 2005 Elsevier Inc. All rights reserved.

Enhancement of drug affinity for cell membranes by conjugation with lipoamino acids II. Experimental and computational evidence using biomembrane models

Lipoamino acids (LAAs) are promoieties able to enhance the amphiphilicity of drugs, facilitating their interaction with cell membranes. Experimental and computational studies were carried out on two series of lipophilic amide conjugates between a model drug (tranylcypromine, TCP) and LAA or alkanoic acids containing a short, medium or long alkyl side chain (C-4 to C-16). The effects of these compounds were evaluated by monolayer surface tension analysis and differential scanning calorimetry using dimyristoylphosphatidylcholine nnonolayers and liposomes as biomembrane models. The experimental results were related to independent calculations to determine partition coefficient and blood-brain partitioning. The comparison of TCP-LAA conjugates with the related series of TCP alkanoyl amides confirmed that the ability to interact with the biomembrane models is not due to the mere increase of lipophilicity, but mainly to the amphipatic nature and the kind of LAA residue. (C) 2005 Elsevier B.V. All rights reserved.