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.

Determining the effective dimensionality of the genetic variance-covariance matrix

Determining the dimensionality of G provides an important perspective on the genetic basis of a multivariate suite of traits. Since the introduction of Fisher's geometric model, the number of genetically independent traits underlying a set of functionally related phenotypic traits has been recognized as an important factor influencing the response to selection. Here, we show how the effective dimensionality of G can be established, using a method for the determination of the dimensionality of the effect space from a multivariate general linear model introduced by AMEMIYA (1985). We compare this approach with two other available methods, factor-analytic modeling and bootstrapping, using a half-sib experiment that estimated G for eight cuticular hydrocarbons of Drosophila serrata. In our example, eight pheromone traits were shown to be adequately represented by only two underlying genetic dimensions by Amemiya's approach and factor-analytic modeling of the covariance structure at the sire level. In, contrast, bootstrapping identified four dimensions with significant genetic variance. A simulation study indicated that while the performance of Amemiya's method was more sensitive to power constraints, it performed as well or better than factor-analytic modeling in correctly identifying the original genetic dimensions at moderate to high levels of heritability. The bootstrap approach consistently overestimated the number of dimensions in all cases and performed less well than Amemiya's method at subspace recovery.

Cardiotrophin-like cytokine/cytokine-like factor 1 is an essential trophic factor for lumbar and facial motoneurons in vivo

The ciliary neurotrophic factor alpha-receptor(CNTFRalpha) is required for motoneuron survival during development, but the relevant ligand(s) has not been determined. One candidate is the heterodimer formed by cardiotrophin-like cytokine (CLC) and cytokine-like factor 1 (CLF). CLC/CLF binds to CNTFRalpha and enhances the survival of developing motoneurons in vitro; whether this novel trophic factor plays a role in neural development in vivo has not been tested. We examined motor and sensory neurons in embryonic chicks treated with CLC and in mice with a targeted deletion of the clf gene. Treatment with CLC increased the number of lumbar spinal cord motoneurons that survived the cell death period in chicks. However, this effect was regionally specific, because brachial and thoracic motoneurons were unaffected. Similarly, newborn clf -/- mice exhibited a significant reduction in lumbar motoneurons, with no change in the brachial or thoracic cord. Clf deletion also affected brainstem motor nuclei in a regionally specific manner; the number of motoneurons in the facial but not hypoglossal nucleus was significantly reduced. Sensory neurons of the dorsal root ganglia were not affected by either CLC treatment or clf gene deletion. Finally, mRNA for both clc and clf was found in skeletal muscle fibers of embryonic mice during the motoneuron cell death period. These findings support the view that CLC/CLF is a target-derived factor required for the survival of specific pools of motoneurons. The in vivo actions of CLC and CLF can account for many of the effects of CNTFRalpha on developing motoneurons.

Effect of clarithromycin on nuclear factor-kappa B and transforming growth factor-beta in chronic rhinosinusitis

Objectives: Long-term, low-dose macrolide therapy is effective in the treatment of chronic rhinosinusitis. It is believed that macrolide antibiotics produce this benefit through an anti-inflammatory effect. In this study, the effect of clarithromycin treatment on the expression of transforming growth factor (TGF)-beta and the key pro-inflammatory nuclear transcription factor, NF-kappaB, was examined in vitro and in vivo. Study Design and Methods: In vitro: nasal mucosa was obtained from 10 patients with chronic sinusitis and was cultured for 24 hours in the presence of clarithromycin or control. Cellular expression of TGF-beta and NF-kappaB was determined by immunohistochemistry. In vivo: 10 patients with chronic rhinosinusitis were treated for 3 months with clarithromycin. Nasal mucosal biopsies were taken pre- and posttreatment. Cellular expression of TGF-beta and NF-kappaB was again determined by immunohistochemistry. Results: Clarithromycin, when applied to nasal biopsies in vitro, reduced cellular expression of TGF-beta and NF-kappaB. Nasal biopsies taken before and after clarithromycin treatment showed no differences in cellular expression of NF-kappaB or TGF-beta. Conclusion: Clarithromycin can reduce cellular expression of TGF-beta and NF-kappaB when applied in vitro, but its action during clinical therapy is less clear. Clarithromycin is capable of inhibiting pro-inflammatory cytokines in vitro, and reductions of TGF-beta and NF-kappaB may represent additional mechanisms by which macrolides reduce inflammation in chronic airway disease. Discrepancies between the actions of clarithromycin on nasal biopsies in vitro and after clinical therapy warrant further investigation.

Localization and movement of mineral oil in plants by fluorescence and confocal microscopy

Fluorescence and confocal laser scanning microscopy were explored to investigate the movement and localization of mineral oils in citrus. In a laboratory experiment, fluorescence microscopy observation indicated that when a 'narrow' distillation fraction of an nC23 horticultural mineral oil was applied to adaxial and opposing abaxial leaf surfaces of potted orange [Citrus x aurantium L. (Sapindales: Rutaceae)] trees, oil penetrated steadily into treated leaves and, subsequently, moved to untreated petioles of the leaves and adjacent untreated stems. In another experiment, confocal laser scanning microscopy was used to visualize the penetration into, and the subsequent cellular distribution of, an nC24 agricultural mineral oil in C. trifoliata L. seedlings. Oil droplets penetrated or diffused into plants via both stomata and the cuticle of leaves and stems, and then moved within intercellular spaces and into various cells including phloem and xylem. Oil accumulated in droplets in intercellular spaces and within cells near the cell membrane. Oil entered cells without visibly damaging membranes or causing cell death. In a field experiment with mature orange trees, droplets of an nC23 horticultural mineral oil were observed, by fluorescence microscopy, in phloem sieve elements in spring flush growth produced 4-5 months and 16-17 months after the trees were sprayed with oil. These results suggest that movement of mineral oil in plants is both apoplastic via intercellular spaces and symplastic via plasmodesmata. The putative pattern of the translocation of mineral oil in plants and its relevance to oil-induced chronic phytotoxicity are discussed.

Comparing the concentration of calcium hydroxide in the diffuse double layer by modeling and experiment

The leaching of elements from the surface of charged fly ash particles is known to be an unsteady process. The mass transfer resistance provided by the diffuse double layer has been quantified as one of the reasons for this delayed leaching. In this work, a model based on mass transfer principles for predicting the concentration of calcium hydroxide in the diffuse double layer is presented. The significant difference between predicted calcium hydroxide concentration and the experimentally measured is explained.

Constriction flows of monodisperse linear entangled polymers: Multiscale modeling and flow visualization

We explore both the rheology and complex flow behavior of monodisperse polymer melts. Adequate quantities of monodisperse polymer were synthesized in order that both the materials rheology and microprocessing behavior could be established. In parallel, we employ a molecular theory for the polymer rheology that is suitable for comparison with experimental rheometric data and numerical simulation for microprocessing flows. The model is capable of matching both shear and extensional data with minimal parameter fitting. Experimental data for the processing behavior of monodisperse polymers are presented for the first time as flow birefringence and pressure difference data obtained using a Multipass Rheometer with an 11:1 constriction entry and exit flow. Matching of experimental processing data was obtained using the constitutive equation with the Lagrangian numerical solver, FLOWSOLVE. The results show the direct coupling between molecular constitutive response and macroscopic processing behavior, and differentiate flow effects that arise separately from orientation and stretch. (c) 2005 The Society of Rheology.

Integrated actions of transforming growth factor-beta(1) and connective tissue growth factor in renal fibrosis

Matrix accumulation in the renal tubulointerstitium is predictive of a progressive decline in renal function. Transforming growth factor-beta(1) (TGF-beta(1)) and, more recently, connective tissue growth factor (CTGF) are recognized to play key roles in mediating the fibrogenic response, independently of the primary renal insult. Further definition of the independent and interrelated effects of CTGF and TGF-beta(1) is critical for the development of effective antifibrotic strategies. CTGF (20 ng/ml) induced fibronectin and collagen IV secretion in primary cultures of human proximal tubule cells (PTC) and cortical fibroblasts (CF) compared with control values (P < 0.005 in all cases). This effect was inhibited by neutralizing antibodies to either TGF-beta or to the TGF-beta type II receptor (TbetaRII). TGF-beta(1) induced a greater increase in fibronectin and collagen IV secretion in both PTC (P < 0.01) and CF (P < 0.01) compared with that observed with CTGF alone. The combination of TGF-beta(1) and CTGF was additive in their effects on both PTC and CF fibronectin and collagen IV secretion. TGF-beta(1) (2 ng/ml) stimulated CTGF mRNA expression within 30 min, which was sustained for up to 24 h, with a consequent increase in CTGF protein (P < 0.05), whereas CTGF had no effect on TGF-beta(1) mRNA or protein expression. TGF-beta(1) (2 ng/ml) induced phosphorylated (p)Smad-2 within 15 min, which was sustained for up to 24 h. CTGF had a delayed effect on increasing pSmad-2 expression, which was evident at 24 h. In conclusion, this study has demonstrated the key dependence of the fibrogenic actions of CTGF on TGF-beta. It has further uniquely demonstrated that CTGF requires TGF-beta, signaling through the TbetaRII in both PTCs and CFs, to exert its fibrogenic response in this in vitro model.

Review of tropospheric bomb C-14 data for carbon cycle modeling and age calibration purposes

Comprehensive published radiocarbon data from selected atmospheric records, tree rings, and recent organic matter were analyzed and grouped into 4 different zones (three for the Northern Hemisphere and one for the whole Southern Hemisphere). These C-14 data for the summer season of each hemisphere were employed to construct zonal, hemispheric, and global data sets for use in regional and global carbon model calculations including calibrating and comparing carbon cycle models. In addition, extended monthly atmospheric C-14 data sets for 4 different zones were compiled for age calibration purposes. This is the first time these data sets were constructed to facilitate the dating of recent organic material using the bomb C-14 curves. The distribution of bomb C-14 reflects the major zones of atmospheric circulation.

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)