Evidence for interplay between genes and maternal stress in utero: monoamine oxidase A polymorphism moderates effects of life events during pregnancy on infant negative emotionality at 5 weeks

The low activity variant of the monoamine oxidase A (MAOA) functional promoter polymorphism, MAOA-LPR, in interaction with adverse environments (G × E) is associated with child and adult antisocial behaviour disorders. MAOA is expressed during foetal development so in utero G × E may influence early neurodevelopment. We tested the hypothesis that MAOA G × E during pregnancy predicts infant negative emotionality soon after birth. In an epidemiological longitudinal study starting in pregnancy, using a two stage stratified design, we ascertained MAOA-LPR status (low vs. high activity variants) from the saliva of 209 infants (104 boys and 105 girls), and examined predictions to observed infant negative emotionality at 5 weeks post-partum from life events during pregnancy. In analyses weighted to provide estimates for the general population, and including possible confounders for life events, there was an MAOA status by life events interaction (P = 0.017). There was also an interaction between MAOA status and neighbourhood deprivation (P = 0.028). Both interactions arose from a greater effect of increasing life events on negative emotionality in the MAOA-LPR low activity, compared with MAOA-LPR high activity infants. The study provides the first evidence of moderation by MAOA-LPR of the effect of the social environment in pregnancy on negative emotionality in infancy, an early risk for the development of child and adult antisocial behaviour disorders.

Age and firm growth: evidence from three European countries

This article provides new insights into the dependence of firm growth on age along the entire distribution of growth rates, and conditional on survival. Using data from the European firms in a global economy survey, and adopting a quantile regression approach, we uncover evidence for a sample of French, Italian and Spanish manufacturing firms with more than ten employees in the period from 2001 to 2008. We find that: (1) young firms grow faster than old firms, especially in the highest growth quantiles; (2) young firms face the same probability of declining as their older counterparts; (3) results are robust to the inclusion of other firms’ characteristics such as labor productivity, capital intensity and the financial structure; (4) high growth is associated with younger chief executive officers and other attributes that capture the attitude of the firm toward growth and change. The effect of age on firm growth is rather similar across countries.

A robust sclera segmentation algorithm

Sclera segmentation is shown to be of significant importance for eye and iris biometrics. However, sclera segmentation has not been extensively researched as a separate topic, but mainly summarized as a component of a broader task. This paper proposes a novel sclera segmentation algorithm for colour images which operates at pixel-level. Exploring various colour spaces, the proposed approach is robust to image noise and different gaze directions. The algorithm’s robustness is enhanced by a two-stage classifier. At the first stage, a set of simple classifiers is employed, while at the second stage, a neural network classifier operates on the probabilities’ space generated by the classifiers at stage 1. The proposed method was ranked the 1st in Sclera Segmentation Benchmarking Competition 2015, part of BTAS 2015, with a precision of 95.05% corresponding to a recall of 94.56%.

e-Leadership through strategic alignment: an empirical study of small and medium sized enterprises in the digital age

Small and medium sized enterprises (SMEs) play an important role in the European economy. A critical challenge faced by SME leaders, as a consequence of the continuing digital technology revolution, is how to optimally align business strategy with digital technology to fully leverage the potential offered by these technologies in pursuit of longevity and growth. There is a paucity of empirical research examining how e-leadership in SMEs drives successful alignment between business strategy and digital technology fostering longevity and growth. To address this gap, in this paper we develop an empirically derived e-leadership model. Initially we develop a theoretical model of e-leadership drawing on strategic alignment theory. This provides a theoretical foundation on how SMEs can harness digital technology in support of their business strategy enabling sustainable growth. An in-depth empirical study was undertaken interviewing 42 successful European SME leaders to validate, advance and substantiate our theoretically driven model. The outcome of the two stage process – inductive development of a theoretically driven e-leadership model and deductive advancement to develop a complete model through in-depth interviews with successful European SME leaders – is an e-leadership model with specific constructs fostering effective strategic alignment. The resulting diagnostic model enables SME decision makers to exercise effective e-leadership by creating productive alignment between business strategy and digital technology improving longevity and growth prospects.

Osteoconductivity of modified fluorcanasite glass-ceramics for bone tissue augmentation and repair

Modified fluorcanasite glasses were fabricated by either altering the molar ratios of Na(2)O and CaO or by adding P(2)O(5) to the parent stoichiometric glass compositions. Glasses were converted to glass-ceramics by a controlled two-stage heat treatment process. Rods (2 mm x 4 mm) were produced using the conventional lost-wax casting technique. Osteoconductive 45S5 bioglass was used as a reference material. Biocompatibility and osteoconductivity were investigated by implantation into healing defects (2 mm) in the midshaft of rabbit femora. Tissue response was investigated using conventional histology and scanning electron microscopy. Histological and histomorphometric evaluation of specimens after 12 weeks implantation showed significantly more bone contact with the surface of 45S5 bioglass implants when compared with other test materials. When the bone contact for each material was compared between experimental time points, the Glass-Ceramic 2 (CaO rich) group showed significant difference (p = 0.027) at 4 weeks, but no direct contact at 12 weeks. Histology and backscattered electron photomicrographs showed that modified fluorcanasite glass-ceramic implants had greater osteoconductivity than the parent stoichiometric composition. Of the new materials, fluorcanasite glass-ceramic implants modified by the addition of P(2)O(5) showed the greatest stimulation of new mineralized bone tissue formation adjacent to the implants after 4 and 12 weeks implantation. (C) 2010 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 94A: 760-768, 2010

Histological Outcomes on the Development of New Space-making Devices for Maxillary Sinus Floor Augmentation

Background: Previous studies have pointed out that the mere elevation of the maxillary sinus membrane promotes bone formation without the use of augmentation materials. Purpose: This experimental study aimed at evaluating if the two-stage procedure for sinus floor augmentation could benefit from the use of a space-making device in order to increase the bone volume to enable later implant installation with good primary stability. Materials and Methods: Six male tufted capuchin primates (Cebus apella) were subjected to extraction of the three premolars and the first molar on both sides of the maxilla to create an edentulous area. The sinuses were opened using the lateral bone-wall window technique, and the membrane was elevated. One resorbable space-making device was inserted in each maxillary sinus, and the bone window was returned in place. The animals were euthanatized after 6 months, and biopsy blocks containing the whole maxillary sinus and surrounding soft tissues were prepared for ground sections. Results: The histological examination of the specimens showed bone formation in contact with both the schneiderian membrane and the device in most cases even when the device was displaced. The process of bone formation indicates that this technique is potentially useful for two-stage sinus floor augmentation. The lack of stabilization of the device within the sinus demands further improvement of space-makers for predictable bone augmentation. Conclusions: It is concluded that (1) the device used in this study did not trigger any important inflammatory reaction; (2) when the sinus membrane was elevated, bone formation was a constant finding; and (3) an ideal space-making device should be stable and elevate the membrane to ensure a maintained connection between the membrane and the secluded space.

Impact of chronic disease on quality of life among the elderly in the state of Sao Paulo, Brazil: a population-based study

Objectives. To assess the impact of chronic disease and the number of diseases on the various aspects of health-related quality of life (HRQOL) among the elderly in Sao Paulo, Brazil. Methods. The SF-36 (R) Health Survey was used to assess the impact of the most prevalent chronic diseases on HRQOL. A cross-sectional and population-based study was carried out with two-stage stratified cluster sampling. Data were obtained from a multicenter health survey administered through household interviews in several municipalities in the state of Sao Paulo. The study evaluated seven diseases-arthritis, back-pain, depression/anxiety, diabetes, hypertension, osteoporosis, and stroke-and their effects on quality of life. Results. Among the 1958 elderly individuals (60 years of age or older), 13.6% reported not having any of the illnesses, whereas 45.7% presented three or more chronic conditions. The presence of any of the seven chronic illnesses studied had a significant effect on the scores Of nearly all the SF-36 (R) scales. HRQOL achieved lower scores when related to depression/anxiety, osteoporosis, and stroke. The higher the number of diseases, the greater the negative effect on the SF-36 (R) dimensions. The presence of three or more diseases significantly affected HRQOL in all areas. The bodily pain, general health, and vitality scales were the most affected by diseases. Conclusions. The study detected a high prevalence of chronic diseases among the elderly population and found that the degree of impact on HRQOL depends on the type of disease. The results highlight the importance of preventing and controlling chronic diseases in order to reduce the number of comorbidities and lessen their impact on HRQOL among the elderly.

Production of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) P(3HB-co-3HV) with a broad range of 3HV content at high yields by Burkholderia sacchari IPT 189

The biosynthesis of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) from sucrose and propionic acid by Burkholderia sacchari IPT 189 was studied using a two-stage bioreactor process. In the first stage, this bacterium was cultivated in a balanced culture medium until sucrose exhaustion. In the second stage, a solution containing sucrose and propionic acid as carbon source was fed to the bioreactor at various sucrose/propionic acid (s/p) ratios at a constant specific flow rate. Copolymers with 3HV content ranging from 40 down to 6.5 (mol%) were obtained with 3HV yield from propionic acid (Y-3HV/prop) increasing from 1.10 to 1.34 g g(-1). Copolymer productivity of 1 g l(-1) h(-1) was obtained with polymer biomass content rising up to 60% by increasing a specific flow rate at a constant s/p ratio. Increasing values of 3HV content were obtained by varying the s/p ratios. A simulation of production costs considering Y-3HV/prop obtained in the present work indicated that a reduction of up to 73% can be reached, approximating US$ 1.00 per kg which is closer to the value to produce P3HB from sucrose (US$ 0.75 per kg).

On the cutting stock problem under stochastic demand

This paper addresses the one-dimensional cutting stock problem when demand is a random variable. The problem is formulated as a two-stage stochastic nonlinear program with recourse. The first stage decision variables are the number of objects to be cut according to a cutting pattern. The second stage decision variables are the number of holding or backordering items due to the decisions made in the first stage. The problem`s objective is to minimize the total expected cost incurred in both stages, due to waste and holding or backordering penalties. A Simplex-based method with column generation is proposed for solving a linear relaxation of the resulting optimization problem. The proposed method is evaluated by using two well-known measures of uncertainty effects in stochastic programming: the value of stochastic solution-VSS-and the expected value of perfect information-EVPI. The optimal two-stage solution is shown to be more effective than the alternative wait-and-see and expected value approaches, even under small variations in the parameters of the problem.

Sampling, WLS, and Mixed Models

Mixed models may be defined with or without reference to sampling, and can be used to predict realized random effects, as when estimating the latent values of study subjects measured with response error. When the model is specified without reference to sampling, a simple mixed model includes two random variables, one stemming from an exchangeable distribution of latent values of study subjects and the other, from the study subjects` response error distributions. Positive probabilities are assigned to both potentially realizable responses and artificial responses that are not potentially realizable, resulting in artificial latent values. In contrast, finite population mixed models represent the two-stage process of sampling subjects and measuring their responses, where positive probabilities are only assigned to potentially realizable responses. A comparison of the estimators over the same potentially realizable responses indicates that the optimal linear mixed model estimator (the usual best linear unbiased predictor, BLUP) is often (but not always) more accurate than the comparable finite population mixed model estimator (the FPMM BLUP). We examine a simple example and provide the basis for a broader discussion of the role of conditioning, sampling, and model assumptions in developing inference.

Predicting random effects with an expanded finite population mixed model

Prediction of random effects is an important problem with expanding applications. In the simplest context, the problem corresponds to prediction of the latent value (the mean) of a realized cluster selected via two-stage sampling. Recently, Stanek and Singer [Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 119-130] developed best linear unbiased predictors (BLUP) under a finite population mixed model that outperform BLUPs from mixed models and superpopulation models. Their setup, however, does not allow for unequally sized clusters. To overcome this drawback, we consider an expanded finite population mixed model based on a larger set of random variables that span a higher dimensional space than those typically applied to such problems. We show that BLUPs for linear combinations of the realized cluster means derived under such a model have considerably smaller mean squared error (MSE) than those obtained from mixed models, superpopulation models, and finite population mixed models. We motivate our general approach by an example developed for two-stage cluster sampling and show that it faithfully captures the stochastic aspects of sampling in the problem. We also consider simulation studies to illustrate the increased accuracy of the BLUP obtained under the expanded finite population mixed model. (C) 2007 Elsevier B.V. All rights reserved.

Open Issues in Control ofAutomotive R744 Air-ConditioningSystems

In this thesis, one of the current control algorithms for the R744 cycle, which tries tooptimize the performance of the system by two SISO control loops, is compared to acost-effective system with just one actuator. The operation of a key component of thissystem, a two stage orifice expansion valve is examined in a range of typical climateconditions. One alternative control loop for this system, which has been proposed byBehr group, is also scrutinized.The simulation results affirm the preference of using two control-loops instead of oneloop, but refute advantages of the Behr alternate control approach against one-loopcontrol. As far as the economic considerations of the A/C unit are concerned, usinga two-stage orifice expansion valve is desired by the automotive industry, thus basedon the experiment results, an improved logic for control of this system is proposed.In the second part, it is investigated whether the one-actuator control approach isapplicable to a system consisting of two parallel evaporators to allow passengers tocontrol different climate zones. The simulation results show that in the case of usinga two-stage orifice valve for the front evaporator and a fixed expansion valve forthe rear one, a proper distribution of the cooling power between the front and rearcompartment is possible for a broad range of climate conditions.

Applicatiion of Almost ideal demand system for a pharmaceutical

This thesis is an application of the Almost Ideal Demand System approach of Deaton and Muellbauer,1980, for a particular pharmaceutical, Citalopram, in which GORMAN´s (1971) multi-stage budgeting approach is applied basically since it is one of the most useful approach in estimating demand for differentiated products. Citalopram is an antidepressant drug that is used in the treatment of major depression. As for most other pharmaceuticals whose the patent has expired, there exist branded and generic versions of Citalopram. This paper is aimed to define its demand system with two stage models for the branded version and five generic versions, and to show whether generic versions are able to compete with the branded version. I calculated the own price elasticities, and it made me possible to compare and make a conclusion about the consumers’ choices over the brand and generic drugs. Even though the models need for being developed with some additional variables, estimation results of models and uncompensated price elasticities indicated that the branded version has still power in the market, and generics are able to compete with lower prices. One important point that has to be taken into consideration is that the Swedish pharmaceutical market faced a reform on October 1, 2002, that aims to make consumer better informed about the price and decrease the overall expenditures for pharmaceuticals. Since there were not significantly enough generic sales to take into calculation before the reform, my paper covers sales after the reform.

Triggering Solar-Powered Vehicle Activated Signs using Self Organising Maps with K-means

Solar-powered vehicle activated signs (VAS) are speed warning signs powered by batteries that are recharged by solar panels. These signs are more desirable than other active warning signs due to the low cost of installation and the minimal maintenance requirements. However, one problem that can affect a solar-powered VAS is the limited power capacity available to keep the sign operational. In order to be able to operate the sign more efficiently, it is proposed that the sign be appropriately triggered by taking into account the prevalent conditions. Triggering the sign depends on many factors such as the prevailing speed limit, road geometry, traffic behaviour, the weather and the number of hours of daylight. The main goal of this paper is therefore to develop an intelligent algorithm that would help optimize the trigger point to achieve the best compromise between speed reduction and power consumption. Data have been systematically collected whereby vehicle speed data were gathered whilst varying the value of the trigger speed threshold. A two stage algorithm is then utilized to extract the trigger speed value. Initially the algorithm employs a Self-Organising Map (SOM), to effectively visualize and explore the properties of the data that is then clustered in the second stage using K-means clustering method. Preliminary results achieved in the study indicate that using a SOM in conjunction with K-means method is found to perform well as opposed to direct clustering of the data by K-means alone. Using a SOM in the current case helped the algorithm determine the number of clusters in the data set, which is a frequent problem in data clustering.

Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability

A number of recent works have introduced statistical methods for detecting genetic loci that affect phenotypic variability, which we refer to as variability-controlling quantitative trait loci (vQTL). These are genetic variants whose allelic state predicts how much phenotype values will vary about their expected means. Such loci are of great potential interest in both human and non-human genetic studies, one reason being that a detected vQTL could represent a previously undetected interaction with other genes or environmental factors. The simultaneous publication of these new methods in different journals has in many cases precluded opportunity for comparison. We survey some of these methods, the respective trade-offs they imply, and the connections between them. The methods fall into three main groups: classical non-parametric, fully parametric, and semi-parametric two-stage approximations. Choosing between alternatives involves balancing the need for robustness, flexibility, and speed. For each method, we identify important assumptions and limitations, including those of practical importance, such as their scope for including covariates and random effects. We show in simulations that both parametric methods and their semi-parametric approximations can give elevated false positive rates when they ignore mean-variance relationships intrinsic to the data generation process. We conclude that choice of method depends on the trait distribution, the need to include non-genetic covariates, and the population size and structure, coupled with a critical evaluation of how these fit with the assumptions of the statistical model.