Home About us Contact
|
Home About us Contact | ||
|
|
||
Robust Models (robust + models)
Selected AbstractsOn the variability of respiration in terrestrial ecosystems: moving beyond Q10GLOBAL CHANGE BIOLOGY, Issue 2 2006ERIC A. DAVIDSON Abstract Respiration, which is the second most important carbon flux in ecosystems following gross primary productivity, is typically represented in biogeochemical models by simple temperature dependence equations. These equations were established in the 19th century and have been modified very little since then. Recent applications of these equations to data on soil respiration have produced highly variable apparent temperature sensitivities. This paper searches for reasons for this variability, ranging from biochemical reactions to ecosystem-scale substrate supply. For a simple membrane-bound enzymatic system that follows Michaelis,Menten kinetics, the temperature sensitivities of maximum enzyme activity (Vmax) and the half-saturation constant that reflects the affinity of the enzyme for the substrate (Km) can cancel each other to produce no net temperature dependence of the enzyme. Alternatively, when diffusion of substrates covaries with temperature, then the combined temperature sensitivity can be higher than that of each individual process. We also present examples to show that soluble carbon substrate supply is likely to be important at scales ranging from transport across membranes, diffusion through soil water films, allocation to aboveground and belowground plant tissues, phenological patterns of carbon allocation and growth, and intersite differences in productivity. Robust models of soil respiration will require that the direct effects of substrate supply, temperature, and desiccation stress be separated from the indirect effects of temperature and soil water content on substrate diffusion and availability. We speculate that apparent Q10 values of respiration that are significantly above about 2.5 probably indicate that some unidentified process of substrate supply is confounded with observed temperature variation. [source] Equilibrium and growth shapes of crystals: how do they differ and why should we care?CRYSTAL RESEARCH AND TECHNOLOGY, Issue 4-5 2005Robert F. SekerkaArticle first published online: 15 MAR 200 Abstract Since the death of Prof. Dr. Jan Czochralski nearly 50 years ago, crystals grown by the Czochralski method have increased remarkably in size and perfection, resulting today in the industrial production of silicon crystals about 30 cm in diameter and two meters in length. The Czochralski method is of great technological and economic importance for semiconductors and optical crystals. Over this same time period, there have been equally dramatic improvements in our theoretical understanding of crystal growth morphology. Today we can compute complex crystal growth shapes from robust models that reproduce most of the features and phenomena observed experimentally. We should care about this because it is likely to result in the development of powerful and economical design tools to enable future progress. Crystal growth morphology results from an interplay of crystallographic anisotropy and growth kinetics by means of interfacial processes and long-range transport. The equilibrium shape of a crystal results from minimizing its anisotropic surface free energy under the constraint of constant volume; it is given by the classical Wulff construction but can also be represented by an analytical formula based on the ,-vector formalism of Hoffman and Cahn. We now have analytic criteria for missing orientations (sharp corners or edges) on the equilibrium shape, both in two (classical) and three (new) dimensions. Crystals that grow under the control of interfacial kinetic processes tend asymptotically toward a "kinetic Wulff shape", the analogue of the Wulff shape, except it is based on the anisotropic interfacial kinetic coefficient. If it were not for long range transport, crystals would presumably nucleate with their equilibrium shape and then evolve toward their "kinetic Wulff shape". Allowing for long range transport leads to morphological instabilities on the scale of the geometric mean of a transport length (typically a diffusivity divided by the growth speed) and a capillary length (of the order of atomic dimensions). Resulting crystal growth shapes can be cellular or dendritic, but can also exhibit corners and facets related to the underlying crystallographic anisotropy. Within the last decade, powerful phase field models, based on a diffuse interface, have been used to treat simultaneously all of the above phenomena. Computed morphologies can exhibit cells, dendrites and facets, and the geometry of isotherms and isoconcentrates can also be determined. Results of such computations are illustrated in both two and three dimensions. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] A comparison between multivariate Slash, Student's t and probit threshold models for analysis of clinical mastitis in first lactation cowsJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 5 2006Y-M. Chang Summary Robust threshold models with multivariate Student's t or multivariate Slash link functions were employed to infer genetic parameters of clinical mastitis at different stages of lactation, with each cow defining a cluster of records. The robust fits were compared with that from a multivariate probit model via a pseudo-Bayes factor and an analysis of residuals. Clinical mastitis records on 36 178 first-lactation Norwegian Red cows from 5286 herds, daughters of 245 sires, were analysed. The opportunity for infection interval, going from 30 days pre-calving to 300 days postpartum, was divided into four periods: (i) ,30 to 0 days pre-calving; (ii) 1,30 days; (iii) 31,120 days; and (iv) 121,300 days of lactation. Within each period, absence or presence of clinical mastitis was scored as 0 or 1 respectively. Markov chain Monte Carlo methods were used to draw samples from posterior distributions of interest. Pseudo-Bayes factors strongly favoured the multivariate Slash and Student's t models over the probit model. The posterior mean of the degrees of freedom parameter for the Slash model was 2.2, indicating heavy tails of the liability distribution. The posterior mean of the degrees of freedom for the Student's t model was 8.5, also pointing away from a normal liability for clinical mastitis. A residual was the observed phenotype (0 or 1) minus the posterior mean of the probability of mastitis. The Slash and Student's t models tended to have smaller residuals than the probit model in cows that contracted mastitis. Heritability of liability to clinical mastitis was 0.13,0.14 before calving, and ranged from 0.05 to 0.08 after calving in the robust models. Genetic correlations were between 0.50 and 0.73, suggesting that clinical mastitis resistance is not the same trait across periods, corroborating earlier findings with probit models. [source] Hyperspectral NIR image regression part II: dataset preprocessing diagnosticsJOURNAL OF CHEMOMETRICS, Issue 3-4 2006James Burger Abstract When known reference values such as concentrations are available, the spectra from near infrared (NIR) hyperspectral images can be used for building regression models. The sets of spectra must be corrected for errors, transformed to reflectance or absorbance values, and trimmed of bad pixel outliers in order to build robust models and minimize prediction errors. Calibration models can be computed from small (<100) sets of spectra, where each spectrum summarizes an individual image or spatial region of interest (ROI), and used to predict large (>20,000) test sets of spectra. When the distributions of these large populations of predicted values are viewed as histograms they provide mean sample concentrations (peak centers) as well as uniformity (peak widths) and purity (peak shape) information. The same predicted values can also be viewed as concentration maps or images adding spatial information to the uniformity or purity presentations. Estimates of large population statistics enable a new metric for determining the optimal number of model components, based on a combination of global bias and pooled standard deviation values computed from multiple test images or ROIs. Two example datasets are presented: an artificial mixture design of three chemicals with distinct NIR spectra and samples of different cheeses. In some cases it was found that baseline correction by taking first derivatives gave more useful prediction results by reducing optical problems. Other data pretreatments resulted in negligible changes in prediction errors, overshadowed by the variance associated with sample preparation or presentation and other physical phenomena. Copyright © 2007 John Wiley & Sons, Ltd. [source] Mitogenic effects of oestrogen mediated by a non-genomic receptor in human colonBRITISH JOURNAL OF SURGERY (NOW INCLUDES EUROPEAN JOURNAL OF SURGERY), Issue 12 2000Mr D. C. Winter Background Oestrogens are important mitogens in epithelial cancers, particularly where tumours express complementary receptors. While the traditional model of oestrogen action involves gene-directed (genomic) protein synthesis, it has been established that more rapid, non-genomic steroid hormone actions exist. This study investigated the hypothesis that oestrogen rapidly alters cell membrane activity, intracellular pH and nuclear kinetics in a mitogenic fashion. Methods Crypts isolated from human distal colon and colorectal cancer cell lines were used as robust models. DNA replication and intracellular pH were measured by radiolabelled thymidine incorporation (12 h) and spectrofluorescence imaging respectively. Genomic protein synthesis, sodium,hydrogen exchanger (NHE) and protein kinase C (PKC) activity were inhibited with cycloheximide, ethylisopropylamiloride and chelerythrine chloride respectively. Results Oestrogen induced a rapid (less than 5 min) cellular alkalinization of crypts and cancer cells that was sensitive to NHE blockade (P < 0·01) or PKC inhibition (P < 0·01). Oestrogen increased thymidine incorporation by 44 per cent in crypts and by up to 38 per cent in cancer cells (P < 0·01), and this was similarly reduced by inhibiting the NHE (P < 0·01) or PKC (P < 0·05). Conclusion Oestrogen rapidly activates cell membrane and nuclear kinetics by a non-genomic mechanism mediated by PKC but not gene-directed protein synthesis. © 2000 British Journal of Surgery Society Ltd [source] | ||||||||||