Endogenous growth with capital in R&D production functions

[eng] In this paper we claim that capital is as important in the production of ideas as in the production of final goods. Hence, we introduce capital in the production of knowledge and discuss the associated problems arising from the public good nature of knowledge. We show that although population growth can affect economic growth, it is not necessary for growth to arise. We derive both the social planner and the decentralized economy growth rates and show the optimal subsidy that decentralizes it. We also show numerically that the effects of population growth on the market growth rate, the optimal growth rate and the optimal subsidy are small. Besides, we find that physical capital is more important for the production of knowledge than for the production of goods.

Asymmetric stochastic switching driven by intrinsic molecular noise

Low-copy-number molecules are involved in many functions in cells. The intrinsic fluctuations of these numbers can enable stochastic switching between multiple steady states, inducing phenotypic variability. Herein we present a theoretical and computational study based on Master Equations and Fokker-Planck and Langevin descriptions of stochastic switching for a genetic circuit of autoactivation. We show that in this circuit the intrinsic fluctuations arising from low-copy numbers, which are inherently state-dependent, drive asymmetric switching. These theoretical results are consistent with experimental data that have been reported for the bistable system of the gallactose signaling network in yeast. Our study unravels that intrinsic fluctuations, while not required to describe bistability, are fundamental to understand stochastic switching and the dynamical relative stability of multiple states.

Knowledge Licensing in a Model of R&D-driven Endogenous Growth

In this paper I present an endogenous growth model where the engine of growth is in-house R&D performed by high-tech firms. I model knowledge (patent) licensing among high-tech firms. I show that if there is knowledge licensing, high-tech firms innovate more and economic growth is higher than in cases when there are knowledge spillovers or there is no exchange of knowledge among high-tech firms. However, in case when there is knowledge licensing the number of high-tech firms is lower than in cases when there are knowledge spillovers or there is no exchange of knowledge.

Endogenous growth with capital in R&D production functions

[eng] In this paper we claim that capital is as important in the production of ideas as in the production of final goods. Hence, we introduce capital in the production of knowledge and discuss the associated problems arising from the public good nature of knowledge. We show that although population growth can affect economic growth, it is not necessary for growth to arise. We derive both the social planner and the decentralized economy growth rates and show the optimal subsidy that decentralizes it. We also show numerically that the effects of population growth on the market growth rate, the optimal growth rate and the optimal subsidy are small. Besides, we find that physical capital is more important for the production of knowledge than for the production of goods.

Endogenous ocular nocardiosis: an interventional case report with a review of the literature.

We present an illustrative case of endogenous ocular Nocardia (EON) infection in a man with Hodgkin disease treated by chemotherapy who underwent aggressive vitreoretinal surgery for diagnosis and treatment of a subretinal abscess. Visual acuity recovered from hand movements to 20/25. We review the 38 reported cases of EON published between 1967 and 2007, describe the clinical presentation from a systemic and ocular point of view, examine which ocular procedures were successful in identifying the bacterium, and analyze ocular morbidity and the factors affecting successful treatment.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Vitamin C and endogenous cortisol in foreign-body inflammatory response in pacus

The objective of this work was to evaluate the effect of food supplementation with vitamin C on macrophage and multinucleated giant cell (MGC) activities of pacus at two stocking densities. The experiment was carried out in a 2x2x3 split-plot factorial arrangement with: 0 and 500 mg kg-1 vitamin C; 5 and 20 kg m-3 stocking densities; and evaluation times at 3, 6, and 12 days after the subcutaneous implantation of glass coverslips (DPI). The number of macrophages and MGC, as well as cortisol and glucose plasma levels were determined. The number of macrophages and MGC with two to five nuclei was significantly greater in fish supplemented with vitamin C at 5 kg m-3 stocking density at 3 DPI in comparison to nonsupplemented ones. The macrophage and MGC counts were lower in fish with high-plasma cortisol concentration. Supplementation with 500 mg vitamin C benefits macrophage activity on foreign-body inflammation, and high-cortisol concentration has suppressive effects on this response.

Stability and adaptability of runner peanut genotypes based on nonlinear regression and AMMI analysis

The objective of this work was to estimate the stability and adaptability of pod and seed yield in runner peanut genotypes based on the nonlinear regression and AMMI analysis. Yield data from 11 trials, distributed in six environments and three harvests, carried out in the Northeast region of Brazil during the rainy season were used. Significant effects of genotypes (G), environments (E), and GE interactions were detected in the analysis, indicating different behaviors among genotypes in favorable and unfavorable environmental conditions. The genotypes BRS Pérola Branca and LViPE‑06 are more stable and adapted to the semiarid environment, whereas LGoPE‑06 is a promising material for pod production, despite being highly dependent on favorable environments.

Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks

This paper presents the general regression neural networks (GRNN) as a nonlinear regression method for the interpolation of monthly wind speeds in complex Alpine orography. GRNN is trained using data coming from Swiss meteorological networks to learn the statistical relationship between topographic features and wind speed. The terrain convexity, slope and exposure are considered by extracting features from the digital elevation model at different spatial scales using specialised convolution filters. A database of gridded monthly wind speeds is then constructed by applying GRNN in prediction mode during the period 1968-2008. This study demonstrates that using topographic features as inputs in GRNN significantly reduces cross-validation errors with respect to low-dimensional models integrating only geographical coordinates and terrain height for the interpolation of wind speed. The spatial predictability of wind speed is found to be lower in summer than in winter due to more complex and weaker wind-topography relationships. The relevance of these relationships is studied using an adaptive version of the GRNN algorithm which allows to select the useful terrain features by eliminating the noisy ones. This research provides a framework for extending the low-dimensional interpolation models to high-dimensional spaces by integrating additional features accounting for the topographic conditions at multiple spatial scales. Copyright (c) 2012 Royal Meteorological Society.

High capacity robust audio watermarking scheme based on FFT and linear regression

Peer-reviewed

The effect of 4-chlororesorcinol on the endogenous levels of IAA, ABA and oxidative enzymes in cuttings

Treatment of bean cuttings with 4-chlororesorcinol (4-CR), known to increase the number of roots and extend their distribution, prevented the accumulation of free indol-3-yl-acetic acid (IAA) in the hypocotyls within 24 h after cutting preparation. In mung bean there was no change in the distribution (upper half vs. 1 ower half of the hypocotyl) of IAA within the hypocotyl as a result of the treatment. In bean cuttings the treatment with 4-CR prevented the accumulation of IAA in the bottom of the cutting. Oxidation of IAA as a measure of IAA oxidase activity in bean was enhanced appreciably by 4-chlororesorcinol. The level of abscisic acid in mung bean, on the other hand, remained 3-4 fold higher than in the control, yet still about 50% lower than the zero time level. In untreated mung bean cuttings the activity of peroxidase increased after cutting preparation. In contrast, the activity of peroxidase in 4-Cr-treated cuttings was consistently lower. In order to relate to the effect of exogenously applied auxin the level of peroxidase was measured also in indol-3-yl-butyric acid-treated cuttings. The overall peroxidase activity in IBA-treated cuttings was not affected. However, when assaying for the different isozymes the drop in peroxidase activity was most evident in the inducible basic isoperoxidases both in 4-CR and IBA treatments. It appears that the exposure to 4-CR exerts an effect that is similar to that of exogenously applied auxin, affecting the activity of basic peroxidases and enhancing the oxidation of endogenous IAA, thus allowing the organization of the primordia.

Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

Multivariable regression analysis and hot-spot stress approach into fatigue life estimations of welded joints

Kolmen eri hitsausliitoksen väsymisikä arvio on analysoitu monimuuttuja regressio analyysin avulla. Regression perustana on laaja S-N tietokanta joka on kerätty kirjallisuudesta. Tarkastellut liitokset ovat tasalevy liitos, krusiformi liitos ja pitkittäisripa levyssä. Muuttujina ovat jännitysvaihtelu, kuormitetun levyn paksuus ja kuormitus tapa. Paksuus effekti on käsitelty uudelleen kaikkia kolmea liitosta ajatellen. Uudelleen käsittelyn avulla on varmistettu paksuus effektin olemassa olo ennen monimuuttuja regressioon siirtymistä. Lineaariset väsymisikä yhtalöt on ajettu kolmelle hitsausliitokselle ottaen huomioon kuormitetun levyn paksuus sekä kuormitus tapa. Väsymisikä yhtalöitä on verrattu ja keskusteltu testitulosten valossa, jotka on kerätty kirjallisuudesta. Neljä tutkimustaon tehty kerättyjen väsymistestien joukosta ja erilaisia väsymisikä arvio metodeja on käytetty väsymisiän arviointiin. Tuloksia on tarkasteltu ja niistä keskusteltu oikeiden testien valossa. Tutkimuksissa on katsottu 2mm ja 6mm symmetristäpitkittäisripaa levyssä, 12.7mm epäsymmetristä pitkittäisripaa, 38mm symmetristä pitkittäisripaa vääntökuormituksessa ja 25mm/38mm kuorman kantavaa krusiformi liitosta vääntökuormituksessa. Mallinnus on tehty niin lähelle testi liitosta kuin mahdollista. Väsymisikä arviointi metodit sisältävät hot-spot metodin jossa hot-spot jännitys on laskettu kahta lineaarista ja epälineaarista ekstrapolointiakäyttäen sekä paksuuden läpi integrointia käyttäen. Lovijännitys ja murtumismekaniikka metodeja on käytetty krusiformi liitosta laskiessa.

Dramatic response of vemurafenib-induced cutaneous lesions upon switch to dual BRAF/MEK inhibition in a metastatic melanoma patient.

BRAF inhibitory therapy is the mainstream treatment for BRAF mutant advanced melanoma. However vemurafenib, a type I mutant BRAF V600 inhibitor, induces an array of proliferative skin disorders from keratosis pilaris-like and keratoacanthoma-like lesions to locally aggressive cutaneous squamous cell carcinoma (cuSCC). Dual BRAF/MEK inhibition is known to lower the incidence of such manifestations, but it is not known whether it can counteract established lesions. Here we show, for the first time, a dramatic response and a restitution ad integro upon dual inhibition of a widespread proliferative affection induced by BRAF monotherapy. A 75-year-old woman was diagnosed with a BRAF V600E mutated metastatic melanoma. Following dacarbazine (DTIC) and ipilimumab, the patient was started on 960 mg twice daily vemurafenib (Zelboraf), which resulted in complete response, but the patient also developed grade IV skin toxicity. Despite dose-reduction to 720 mg twice daily the side effects persisted. We hypothesized that a switch to double inhibition of the mitogen-activated protein kinase pathway with dabrafenib and trametinib could lead to improvement of the skin lesions, while preserving tumor control. The patient was closely followed for changes in skin lesions. We witnessed a rapid regression followed by complete disappearance of all side effects of vemurafenib except for grade I fatigue. The biopsied skin lesions show regression of established keratoacanthoma-like lesions with signs of apoptosis. Switching from the current standard of care vemurafenib therapy to the double BRAF/MEK inhibition in BRAF mutant melanoma patients results in rapid disappearance of established proliferative skin disorders.