Cost-Based Models of Economic Growth

In this paper we highlight the importance of the operational costs in explaining economic growth and analyze how the industrial structure affects the growth rate of the economy. If there is monopolistic competition only in an intermediate goods sector, then production growth coincides with consumption growth. Moreover, the pattern of growth depends on the particular form of the operational cost. If the monopolistically competitive sector is the final goods sector, then per capita production is constant but per capita effective consumption or welfare grows. Finally, we modify again the industrial structure of the economy and show an economy with two different growth speeds, one for production and another for effective consumption. Thus, both the operational cost and the particular structure of the sector that produces the final goods determines ultimately the pattern of growth.

Optimal fiscal policy in overlapping generations models

[eng] This paper provides, from a theoretical and quantitative point of view, an explanation of why taxes on capital returns are high (around 35%) by analyzing the optimal fiscal policy in an economy with intergenerational redistribution. For this purpose, the government is modeled explicitly and can choose (and commit to) an optimal tax policy in order to maximize society's welfare. In an infinitely lived economy with heterogeneous agents, the long run optimal capital tax is zero. If heterogeneity is due to the existence of overlapping generations, this result in general is no longer true. I provide sufficient conditions for zero capital and labor taxes, and show that a general class of preferences, commonly used on the macro and public finance literature, violate these conditions. For a version of the model, calibrated to the US economy, the main results are: first, if the government is restricted to a set of instruments, the observed fiscal policy cannot be disregarded as sub optimal and capital taxes are positive and quantitatively relevant. Second, if the government can use age specific taxes for each generation, then the age profile capital tax pattern implies subsidizing asset returns of the younger generations and taxing at higher rates the asset returns of the older ones.

Local and global error models to improve uncertainty quantification

In groundwater applications, Monte Carlo methods are employed to model the uncertainty on geological parameters. However, their brute-force application becomes computationally prohibitive for highly detailed geological descriptions, complex physical processes, and a large number of realizations. The Distance Kernel Method (DKM) overcomes this issue by clustering the realizations in a multidimensional space based on the flow responses obtained by means of an approximate (computationally cheaper) model; then, the uncertainty is estimated from the exact responses that are computed only for one representative realization per cluster (the medoid). Usually, DKM is employed to decrease the size of the sample of realizations that are considered to estimate the uncertainty. We propose to use the information from the approximate responses for uncertainty quantification. The subset of exact solutions provided by DKM is then employed to construct an error model and correct the potential bias of the approximate model. Two error models are devised that both employ the difference between approximate and exact medoid solutions, but differ in the way medoid errors are interpolated to correct the whole set of realizations. The Local Error Model rests upon the clustering defined by DKM and can be seen as a natural way to account for intra-cluster variability; the Global Error Model employs a linear interpolation of all medoid errors regardless of the cluster to which the single realization belongs. These error models are evaluated for an idealized pollution problem in which the uncertainty of the breakthrough curve needs to be estimated. For this numerical test case, we demonstrate that the error models improve the uncertainty quantification provided by the DKM algorithm and are effective in correcting the bias of the estimate computed solely from the MsFV results. The framework presented here is not specific to the methods considered and can be applied to other combinations of approximate models and techniques to select a subset of realizations

Impact of incorrect assumptions on the covariance structure of random effects and/or residuals in nonlinear mixed models for repeated measures data

In this paper we analyse, using Monte Carlo simulation, the possible consequences of incorrect assumptions on the true structure of the random effects covariance matrix and the true correlation pattern of residuals, over the performance of an estimation method for nonlinear mixed models. The procedure under study is the well known linearization method due to Lindstrom and Bates (1990), implemented in the nlme library of S-Plus and R. Its performance is studied in terms of bias, mean square error (MSE), and true coverage of the associated asymptotic confidence intervals. Ignoring other criteria like the convenience of avoiding over parameterised models, it seems worst to erroneously assume some structure than do not assume any structure when this would be adequate.

Generalizing sample tree information with semiparametric and parametric models

Summary

Role of the inflammasome in murine experimental models of arthritis

Summary : The purpose of this study was to investigate the role of the inflammasome in human and experimental murine models (such as ΑΙΑ and K/BxN) of rheumatoid arthritis (RA)RA, affecting 1% of the population is the most frequent inflammatory disease characterized by synovial hyperplasia and cartilage and bone erosion, leading to joint destruction. In general, women are 3 times more affected by RA suggesting a role of estrogen in this disease. The inflammasome is a multiproteic complex triggering the activation of caspase-1 leading to the activation of IL-1 β, an important pro-inflammatory cytokine implicated in arthritis. The inflammasome has been implicated in several inflammatory diseases and particularly in gout. To highlight a possible role of the inflammasome in murine arthritis, we obtained ASC, caspase-1 and NALP3 +/+ and -/- littermate mice to perform ΑΙΑ and K/BxN arthritis. NALP3 -/- and caspase-1 -/- mice were as arthritic as wild type littermate mice in both ΑΙΑ and K/BxN models implicating that the NALP3 inflammasome is not involved in experimental arthritis. By contrast, ΑΙΑ severity was significantly diminished in ASC- deficient male and female mice, and in the K/BxN model, in ASC-deficient female mice. These results were supported by histological scoring and acute phase protein serum amyloid A (SAA) levels that were equivalent between NALP+/+ and NALP3-/- mice and diminished in ASC -/- mice. In ΑΙΑ and K/BxN murine experimental models, we observed a sexdependent phenotype. We studied the role of estradiol in both the ALA and the K/BxN models. Castrated female or male ASC -/- mice that received estradiol had a decreased arthritis severity. This implies a protective role of estrogen in the absence of ASC. In the ΑΙΑ model, proliferation assay were performed using splenocytes from mBSA- immunized ASC +/+ and -/- mice. The mBSA-induced proliferation was significantly lower in ASC-/- splenocytes. Moreover the CD3-specific proliferation of purified splenic Τ cells was significantly lower in ASC-/- cells. Finally, Τ cells from ASC-/- mice produced significantly decreased levels of IFN-gamma associated with increased levels of IL-10. These results imply a possible role of ASC in the TCR-signaling pathway and Τ cell cytokine production. In parallel the expression of the different inflammasome components were analyzed in biopsies from rheumatoid arthritis (RA) and osteoarthritis (OA) patiens. The expression of the 14 different NALPs, their effector protein ASC, and caspase-1 and -5 was readily measurable by RT-PCR in a similar proportion in RA and OA synovial samples, with the exception of NALP-5 and NALP-13, which weren't found in samples from either disease. The corresponding NALP1, -3, -12 and ASC proteins were expressed at similar levels in both OA and RA biopsies, as determined by immunohistochemistry and Western-blot analysis. By contrast, caspase-1 levels were significantly enhanced in RA synovial tissues compared to those from OA patients. NALP-1, -2, -3, -10, -12 and -14, as well as ASC, caspase-1, and -5 were detected in RNA from unstimulated and stimulated RA synoviocytes. In FLS, only ASC and caspase-1 were expressed at the protein level. NALP1, 3 and 12 were not detected. However, upon stimulation, no secreted IL-Ιβ was detectable in either RA or in OA synoviocytes culture medium. Résumé : Le but de ce projet était d'étudier le rôle de l'inflammasome dans des modèles expérimentaux d'arthrite tels que les modèles ΑΙΑ et K/BxN ainsi que dans la polyarthrite humaine (RA). La polyarthrite est une maladie inflammatoire très fréquente avec 1 % de la population affectée et touche 3 fois plus les femmes que les hommes, suggérant un rôle des hormones sexuelles dans cette pathologie. L'inflammasome est un complexe multiprotéique qui permet l'activation de la caspase-1, une cystéine protéase qui va ensuite cliver et activer rinterleukine-ΐβ (IL-Ιβ). L'inflammasome a été impliqué ces dernières années dans de nombreuses maladies inflammatoires notamment dans la goutte. Pour mettre en évidence un éventuel rôle de l'inflammasome dans l'arthrite expérimentale nous avons obtenu des souris déficientes pour certains des composants de l'inflammasome tels que ASC, NALP3 et caspase-1. Les souris NALP3 déficientes et caspase-1 déficientes sont aussi arthritiques que les souris wild type correspondantes que ce soit dans le modèle ΑΙΑ ou K/BxN. Par contre les souris mâles et femelles ASC-déficientes sont moins arthritiques que les souris +/+ correspondantes dans le modèle ΑΙΑ. Dans le modèle KRN, le même phénotype (diminution de la sévérité de l'arthrite) est observé uniquement chez les femelles ASC-/- Ce phénotype est corrélé avec l'histologie ainsi qu'avec le dosage du serum amyloid A (SAA) qui reflète l'inflammation systémique et qui est diminué chez les souris ASC-déficientes. Nous avons ensuite étudié le rôle de Γ estradiol (une des formes active des estrogènes) dans les modèles K/BxN et ΑΙΑ. Les souris castrées maies ou femelles déficientes pour ASC ayant reçu de l'estradiol ont une arthrite moins sévère ce qui implique que les estradiol ont un effet protecteur en l'absence de ASC. Dans le modèle ΑΙΑ, nous nous sommes aussi intéressés à la réponse immune. Des tests de prolifération ont été effectués sur des splénocytes en présence de mBSA (qui est l'antigène utilisé dans le modèle ΑΙΑ). Les splénocytes ASC -/- ont une proliferation qui est diminuée en présence de l'antigène. De plus la proliferation de cellules Τ spléniques purifiées en présence d'anti-CD3 est diminuée chez les cellules Τ ASC-/-. Ces résultats nous indiquent une éventuelle implication de ASC dans la signalisation par le récépteur des cellules T. En parallèle l'expression des différents composants de l'inflammasome a été analysée dans des biopsies de patients atteints de polyarthrite rhumatoide (RA) et d'arthrose (OA). L'expression des 14 différents NALPs, de l'adaptateur ASC, ainsi que des caspase-1 et -5 était similaires dans les échantillons RA et OA, à l'exception de NALP5 et 13 qui n'étaient pas détéctables. L'expression protéique de NALP1, 3, 12 et ASC effectuée par Western blot et immunohistochimie était similaire dans les biopsies RA et OA. Par contre la quantité de la caspase-1 mesurée par ELISA était augmentée de façon significative dans les extraits protéiques de biopsies RA. NALP-1, -2. -3, -10, -12, and -14 ainsi que ASC, caspase-1 et -5 étaient exprimés de façon similaire par les synoviocytes RA non stimulés et stimulés. Dans les synoviocytes seuls ASC et caspase-1 étaient détéctable au niveau protéique. NALP-1, -3 et -12 n'était pas détéctables. Cependant après stimulation il n'y avait d'IL-Ιβ sécrété que ce soit dans les surnageants de cultures de synoviocytes RA ou OA.

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools.

BACKGROUND: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. RESULTS: We present the Systems Biology Markup Language (SBML) Qualitative Models Package ("qual"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. CONCLUSIONS: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.

Pattern recognition ecological niche models fit to presence-only and presence-absence data

1. Identifying the boundary of a species' niche from observational and environmental data is a common problem in ecology and conservation biology and a variety of techniques have been developed or applied to model niches and predict distributions. Here, we examine the performance of some pattern-recognition methods as ecological niche models (ENMs). Particularly, one-class pattern recognition is a flexible and seldom used methodology for modelling ecological niches and distributions from presence-only data. The development of one-class methods that perform comparably to two-class methods (for presence/absence data) would remove modelling decisions about sampling pseudo-absences or background data points when absence points are unavailable. 2. We studied nine methods for one-class classification and seven methods for two-class classification (five common to both), all primarily used in pattern recognition and therefore not common in species distribution and ecological niche modelling, across a set of 106 mountain plant species for which presence-absence data was available. We assessed accuracy using standard metrics and compared trade-offs in omission and commission errors between classification groups as well as effects of prevalence and spatial autocorrelation on accuracy. 3. One-class models fit to presence-only data were comparable to two-class models fit to presence-absence data when performance was evaluated with a measure weighting omission and commission errors equally. One-class models were superior for reducing omission errors (i.e. yielding higher sensitivity), and two-classes models were superior for reducing commission errors (i.e. yielding higher specificity). For these methods, spatial autocorrelation was only influential when prevalence was low. 4. These results differ from previous efforts to evaluate alternative modelling approaches to build ENM and are particularly noteworthy because data are from exhaustively sampled populations minimizing false absence records. Accurate, transferable models of species' ecological niches and distributions are needed to advance ecological research and are crucial for effective environmental planning and conservation; the pattern-recognition approaches studied here show good potential for future modelling studies. This study also provides an introduction to promising methods for ecological modelling inherited from the pattern-recognition discipline.

A Computerized Method for the Hydrologic Design of Culverts, February 1974

Nationwide, about five cents of each highway construction dollar is spent on culverts. In Iowa, average annual construction costs on the interstate, primary, and federal-aid secondary systems are about $120,000,000. Assuming the national figure applies to Iowa, about $6,000,000 are spent on culvert construction annually. For each one percent reduction in overall culvert costs, annual construction costs would be reduced by $60,000. One area of potential cost reduction lies in the sizing of the culvert. Determining the flow area and hydraulic capacity is accomplished in the initial design of the culvert. The normal design sequence is accomplished in two parts. The hydrologic portion consists of the determination of a design discharge in cubic feet per second using one of several available methods. This discharge is then used directly in the hydraulic portion of the design to determine the proper type, size, and shape of culvert to be used, based on various site and design restrictions. More refined hydrologic analyses, including rainfall-runoff analysis, flood hydrograph development, and streamflow routing techniques, are not pursued in the existing design procedure used by most county and state highway engineers.

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

Predicting the growth response to thinning for Scots pine stands using individual-tree growth models

Summary

Calibration of the century, apsim and ndicea models of decomposition and n mineralization of plant residues in the humid tropics

The aim of this study was to calibrate the CENTURY, APSIM and NDICEA simulation models for estimating decomposition and N mineralization rates of plant organic materials (Arachis pintoi, Calopogonium mucunoides, Stizolobium aterrimum, Stylosanthes guyanensis) for 360 days in the Atlantic rainforest bioma of Brazil. The models´ default settings overestimated the decomposition and N-mineralization of plant residues, underlining the fact that the models must be calibrated for use under tropical conditions. For example, the APSIM model simulated the decomposition of the Stizolobium aterrimum and Calopogonium mucunoides residues with an error rate of 37.62 and 48.23 %, respectively, by comparison with the observed data, and was the least accurate model in the absence of calibration. At the default settings, the NDICEA model produced an error rate of 10.46 and 14.46 % and the CENTURY model, 21.42 and 31.84 %, respectively, for Stizolobium aterrimum and Calopogonium mucunoides residue decomposition. After calibration, the models showed a high level of accuracy in estimating decomposition and N- mineralization, with an error rate of less than 20 %. The calibrated NDICEA model showed the highest level of accuracy, followed by the APSIM and CENTURY. All models performed poorly in the first few months of decomposition and N-mineralization, indicating the need of an additional parameter for initial microorganism growth on the residues that would take the effect of leaching due to rainfall into account.