CCSD(T), W1, and other model chemistry predictions for gas-phase deprotonation reactions

A series of CCSD(T) single-point calculations on MP4(SDQ) geometries and the W1 model chemistry method have been used to calculate ΔH° and ΔG° values for the deprotonation of 17 gas-phase reactions where the experimental values have reported accuracies within 1 kcal/mol. These values have been compared with previous calculations using the G3 and CBS model chemistries and two DFT methods. The most accurate CCSD(T) method uses the aug-cc-pVQZ basis set. Extrapolation of the aug-cc-pVTZ and aug-cc-pVQZ results yields the most accurate agreement with experiment, with a standard deviation of 0.58 kcal/mol for ΔG° and 0.70 kcal/mol for ΔH°. Standard deviations from experiment for ΔG° and ΔH° for the W1 method are 0.95 and 0.83 kcal/mol, respectively. The G3 and CBS-APNO results are competitive with W1 and are much less expensive. Any of the model chemistry methods or the CCSD(T)/aug-cc-pVQZ method can serve as a valuable check on the accuracy of experimental data reported in the National Institutes of Standards and Technology (NIST) database.

Species-level selection reduces selfishness through competitive exclusion

Adaptation does not necessarily lead to traits which are optimal for the population. This is because selection is often the strongest at the individual or gene level. The evolution of selfishness can lead to a 'tragedy of the commons', where traits such as aggression or social cheating reduce population size and may lead to extinction. This suggests that species-level selection will result whenever species differ in the incentive to be selfish. We explore this idea in a simple model that combines individual-level selection with ecology in two interacting species. Our model is not influenced by kin or trait-group selection. We find that individual selection in combination with competitive exclusion greatly increases the likelihood that selfish species go extinct. A simple example of this would be a vertebrate species that invests heavily into squabbles over breeding sites, which is then excluded by a species that invests more into direct reproduction. A multispecies simulation shows that these extinctions result in communities containing species that are much less selfish. Our results suggest that species-level selection and community dynamics play an important role in regulating the intensity of conflicts in natural populations.

Inhibition of L-type amino acid transport with non-physiological amino acids in the Pahenu2 mouse model of phenylketonuria

Phenylketonuria, an autosomal recessive Mendelian disorder, is one of the most common inborn errors of metabolism. Although currently treated by diet, many suboptimal outcomes occur for patients. Neuropathological outcomes include cognitive loss, white matter abnormalities, and hypo- or demyelination, resulting from high concentrations and/or fluctuating levels of phenylalanine. High phenylalanine can also result in competitive exclusion of other large neutral amino acids from the brain, including tyrosine and tryptophan (essential precursors of dopamine and serotonin). This competition occurs at the blood brain barrier, where the L-type amino acid transporter, LAT1, selectively facilitates entry of large neutral amino acids. The hypothesis of these studies is that certain non-physiological amino acids (NPAA; DL-norleucine (NL), 2-aminonorbornane (NB; 2-aminobicyclo-(2,1,1)-heptane-2-carboxylic acid), α-aminoisobutyrate (AIB), and α-methyl-aminoisobutyrate (MAIB)) would competitively inhibit LAT1 transport of phenylalanine (Phe) at the blood-brain barrier interface. To test this hypothesis, Pah-/- mice (n=5, mixed gender; Pah+/-(n=5) as controls) were fed either 5% NL, 0.5% NB, 5% AIB or 3% MAIB (w/w 18% protein mouse chow) for 3 weeks. Outcome measurements included food intake, body weight, brain LNAAs, and brain monoamines measured via LCMS/MS or HPLC. Brain Phe values at sacrifice were significantly reduced for NL, NB, and MAIB, verifying the hypothesis that these NPAAs could inhibit Phe trafficking into the brain. However, concomitant reductions in tyrosine and methionine occurred at the concentrations employed. Blood Phe levels were not altered indicating no effect of NPAA competitors in the gut. Brain NL and NB levels, measured with HPLC, verified both uptake and transport of NPAAs. Although believed predominantly unmetabolized, NL feeding significantly increased blood urea nitrogen. Pah-/-disturbances of monoamine metabolism were exacerbated by NPAA intervention, primarily with NB (the prototypical LAT inhibitor). To achieve the overarching goal of using NPAAs to stabilize Phe transport levels into the brain, a specific Phe-reducing combination and concentration of NPAAs must be found. Our studies represent the first in vivo use of NL, NB and MAIB in Pah-/- mice, and provide proof-of-principle for further characterization of these LAT inhibitors. Our data is the first to document an effect of MAIB, a specific system A transport inhibitor, on large neutral amino acid transport.

The novel ATP-competitive inhibitor of the MET hepatocyte growth factor receptor EMD1214063 displays inhibitory activity against selected MET-mutated variants

The receptor tyrosine kinase MET is a prime target in clinical oncology due to its aberrant activation and involvement in the pathogenesis of a broad spectrum of malignancies. Similar to other targeted kinases, primary and secondary mutations seem to represent an important resistance mechanism to MET inhibitors. Here, we report the biologic activity of a novel MET inhibitor, EMD1214063, on cells that ectopically express the mutated MET variants M1268T, Y1248H, H1112Y, L1213V, H1112L, V1110I, V1206L, and V1238I. Our results demonstrate a dose-dependent decrease in MET autophosphorylation in response to EMD1214063 in five out of the eight cell lines (IC50 2-43nM). Blockade of MET by EMD1214063 was accompanied by a reduced activation of downstream effectors in cells expressing EMD1214063-sensitive mutants. In all sensitive mutant-expressing lines, EMD1214063 altered cell cycle distribution, primarily with an increase in G1 phase. EMD1214063 strongly influenced MET-driven biological functions, such as cellular morphology, MET-dependent cell motility and anchorage-independent growth. To assess the in vivo efficacy of EMD1214063, we used a xenograft tumor model in immunocompromised mice bearing NIH3T3 cells expressing sensitive and resistant MET mutated variants. Animals were randomized for the treatment with EMD1214063 (50mg/kg/day) or vehicle only. Remarkably, five days of EMD1214063 treatment resulted in a complete regression of the sensitive H1112L-derived tumors, while tumor growth remained unaffected in mice with L1213V tumors and in vehicle-treated animals. Collectively, the current data identifies EMD1214063 as a potent MET small molecule inhibitor with selective activity towards mutated MET variants.

Matrix models for quantifying competitive intransitivity from species abundance data

In a network of competing species, a competitive intransitivity occurs when the ranking of competitive abilities does not follow a linear hierarchy (A > B > C but C > A). A variety of mathematical models suggests that intransitive networks can prevent or slow down competitive exclusion and maintain biodiversity by enhancing species coexistence. However, it has been difficult to assess empirically the relative importance of intransitive competition because a large number of pairwise species competition experiments are needed to construct a competition matrix that is used to parameterize existing models. Here we introduce a statistical framework for evaluating the contribution of intransitivity to community structure using species abundance matrices that are commonly generated from replicated sampling of species assemblages. We provide metrics and analytical methods for using abundance matrices to estimate species competition and patch transition matrices by using reverse-engineering and a colonization-competition model. These matrices provide complementary metrics to estimate the degree of intransitivity in the competition network of the sampled communities. Benchmark tests reveal that the proposed methods could successfully detect intransitive competition networks, even in the absence of direct measures of pairwise competitive strength. To illustrate the approach, we analyzed patterns of abundance and biomass of five species of necrophagous Diptera and eight species of their hymenopteran parasitoids that co-occur in beech forests in Germany. We found evidence for a strong competitive hierarchy within communities of flies and parasitoids. However, for parasitoids, there was a tendency towards increasing intransitivity in higher weight classes, which represented larger resource patches. These tests provide novel methods for empirically estimating the degree of intransitivity in competitive networks from observational datasets. They can be applied to experimental measures of pairwise species interactions, as well as to spatio-temporal samples of assemblages in homogenous environments or environmental gradients.

The Effects of Density, Spatial Pattern, and Competitive Symmetry on Size Variation in Simulated Plant Populations

Patterns of size inequality in crowded plant populations are often taken to be indicative of the degree of size asymmetry of competition, but recent research suggests that some of the patterns attributed to size‐asymmetric competition could be due to spatial structure. To investigate the theoretical relationships between plant density, spatial pattern, and competitive size asymmetry in determining size variation in crowded plant populations, we developed a spatially explicit, individual‐based plant competition model based on overlapping zones of influence. The zone of influence of each plant is modeled as a circle, growing in two dimensions, and is allometrically related to plant biomass. The area of the circle represents resources potentially available to the plant, and plants compete for resources in areas in which they overlap. The size asymmetry of competition is reflected in the rules for dividing up the overlapping areas. Theoretical plant populations were grown in random and in perfectly uniform spatial patterns at four densities under size‐asymmetric and size‐symmetric competition. Both spatial pattern and size asymmetry contributed to size variation, but their relative importance varied greatly over density and over time. Early in stand development, spatial pattern was more important than the symmetry of competition in determining the degree of size variation within the population, but after plants grew and competition intensified, the size asymmetry of competition became a much more important source of size variation. Size variability was slightly higher at higher densities when competition was symmetric and plants were distributed nonuniformly in space. In a uniform spatial pattern, size variation increased with density only when competition was size asymmetric. Our results suggest that when competition is size asymmetric and intense, it will be more important in generating size variation than is local variation in density. Our results and the available data are consistent with the hypothesis that high levels of size inequality commonly observed within crowded plant populations are largely due to size‐asymmetric competition, not to variation in local density.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Microevolutionary responses in experimental populations of plants to CO2-enriched environments: parallel results from two model systems.

Despite the critical role that terrestrial vegetation plays in the Earth's carbon cycle, very little is known about the potential evolutionary responses of plants to anthropogenically induced increases in concentrations of atmospheric CO2. We present experimental evidence that rising CO2 concentration may have a direct impact on the genetic composition and diversity of plant populations but is unlikely to result in selection favoring genotypes that exhibit increased productivity in a CO2-enriched atmosphere. Experimental populations of an annual plant (Abutilon theophrasti, velvetleaf) and a temperate forest tree (Betula alleghaniensis, yellow birch) displayed responses to increased CO2 that were both strongly density-dependent and genotype-specific. In competitive stands, a higher concentration of CO2 resulted in pronounced shifts in genetic composition, even though overall CO2-induced productivity enhancements were small. For the annual species, quantitative estimates of response to selection under competition were 3 times higher at the elevated CO2 level. However, genotypes that displayed the highest growth responses to CO2 when grown in the absence of competition did not have the highest fitness in competitive stands. We suggest that increased CO2 intensified interplant competition and that selection favored genotypes with a greater ability to compete for resources other than CO2. Thus, while increased CO2 may enhance rates of selection in populations of competing plants, it is unlikely to result in the evolution of increased CO2 responsiveness or to operate as an important feedback in the global carbon cycle. However, the increased intensity of selection and drift driven by rising CO2 levels may have an impact on the genetic diversity in plant populations.

Who performs a stronger response to whom? Detecting individual competitive actions and reactions

This study analyzes the degree of competition through individual actions and reactions. Empirical support for this analysis has derived mainly from structural econometric models describing the nature of competition. This analysis extends the existing literature by empirically considering a direct measurement of competition through the analysis of the competitive actions and responses, and describing how firms compete within and between strategic groups. We estimate the firms’ conduct in the Spanish deposits market with 146 firms and 18,888 observations. This is a specially compelling context for the banking industry, in which a deregulation process gives rise to the adoption of aggressive strategies seeking to increase the market shares of deposit accounts; thus, producing a turbulent situation of increasing rivalry. Our results offer a deeper understanding of the firms’ competitive behavior, since we identify different patterns of actions and reactions depending upon the strategic group the firm belongs to.

3D model reconstruction using neural gas accelerated on GPU

In this work, we propose the use of the neural gas (NG), a neural network that uses an unsupervised Competitive Hebbian Learning (CHL) rule, to develop a reverse engineering process. This is a simple and accurate method to reconstruct objects from point clouds obtained from multiple overlapping views using low-cost sensors. In contrast to other methods that may need several stages that include downsampling, noise filtering and many other tasks, the NG automatically obtains the 3D model of the scanned objects. To demonstrate the validity of our proposal we tested our method with several models and performed a study of the neural network parameterization computing the quality of representation and also comparing results with other neural methods like growing neural gas and Kohonen maps or classical methods like Voxel Grid. We also reconstructed models acquired by low cost sensors that can be used in virtual and augmented reality environments for redesign or manipulation purposes. Since the NG algorithm has a strong computational cost we propose its acceleration. We have redesigned and implemented the NG learning algorithm to fit it onto Graphics Processing Units using CUDA. A speed-up of 180× faster is obtained compared to the sequential CPU version.

An Evaluation of Personal and Contextual Factors in Competitive Youth Sport

Because millions of youth are involved in sport, the sport context is important to consider in advancing the growth experiences of young people (Cˆot´e et al., 2007; Fraser-Thomas et al., 2005). Furthermore, research in developmental psychology has highlighted the value of structured programs, including sport, in helping to promote positive youth development (Fredricks & Eccles, 2006). Youth sport involvement has been linked to high levels of enjoyment (Scanlan et al., 1989), however, negative outcomes, such as burnout, have also been reported (Gould et al., 1996). In the present study, the Developmental Assets Profile (Search Institute, 2004) was used to explore personal (internal assets) and contextual (external assets) outcomes associated with youth sport. Results suggest that three particular assets (positive identity, empowerment, and support) are important to focus on in youth sport programs to decrease burnout symptoms and enhance enjoyment. Path analyses were also conducted to test a proposed model and exploratory results confirmed links of particular assets to sport outcomes. The results are discussed in terms of integration with Bronfenbrenner’s ecological theory (1999) and recommendations are suggested for sport programmers to consider to develop these assets within youth sport.

Understanding Dropout and Prolonged Engagement in Adolescent Competitive Sport

Objectives: The purpose of this study is to gain understanding of training patterns and roles of significant others (i.e. coaches, parents, peers, and siblings) in adolescent swimmers’ sport participation patterns. Design: The developmental model of sport participation [Côté, J., Baker, J., & Abernethy, B. (2003). From play to practice: A developmental framework for the acquisition of expertise in team sport. In J. Starkes, & K. A. Ericsson (Eds.), Recent advances in research on sport expertise (pp. 89–114). Champaign, IL: Human Kinetics; Côté, J., & Fraser-Thomas, J. (2007). Youth involvement in sport. In P. R. E. Crocker (Ed.), Introduction to sport psychology: A Canadian perspective (pp. 266–294). Toronto: Pearson Prentice Hall] was used as a framework. Method: Ten dropout and 10 engaged swimmers, matched on key demographic variables participated in a semi-structured qualitative interview. Results: Groups had many similar experiences (e.g. early training, supportive and unsupportive coaches, involved parents). However, only dropouts spoke of early peak performances, limited one-on-one coaching, pressuring parents during adolescence, lack of swimming peers during adolescence, and sibling rivalries. In contrast, only engaged athletes spoke of clubs’ developmental philosophies, coaches’ and parents’ open communication, school friends’ support, and siblings’ general positive influences. Conclusions: Findings highlight the importance of appropriately structured programs and the fragility of athletes’ relationships with significant others during the adolescent years. Implications for sport programmers, coaches, and parents are discussed.

Is the "Dubai model" a new paradigm for growth and investment strategies for oil-based economies? Case study: Eurasia. ACES Working Papers No. 8, 2008

The first part of the paper addresses the theoretical background of economic growth and competitive advantage models. Although there is a whole set of research on a relationship between foreign direct investments and economic growth, little has been said on foreign direct investments and national competitive advantage with respect to economic growth of oil and gas abundant countries of Middle East and Central Asia. The second part of our paper introduces the framework of the so-called "Dubai Model" in detail and outlines the key components necessary to develop sustainable comparative advantage for the oil-rich economies. The third part proceeds with the methodology employed to measure the success of the "Dubai Model" in the UAE and in application to other regions. The last part brings the results and investigates the degree to which other oil and gas countries in the region (i.e. Saudi Arabia, Kuwait, Qatar, Iran) have adopted the so-called "Dubai Model". It also examines if the Dubai Model is being employed in the Eurasian (Central Asian) oil and gas regions of Kazakhstan, Azerbaijan, Turkmenistan and Uzbekistan. The objective is to gauge if the Eurasian economies are employing the traditional growth strategies of oil-rich non-OECD countries in managing their natural resources or are they adopting the newer non-traditional model of economic growth, such as the "Dubai Model."

Kinetic model for selective cultivation of microfungi in a microscreen process for food processing wastewater treatment and biomass production

The research was aimed at developing a technology to combine the production of useful microfungi with the treatment of wastewater from food processing. A recycle bioreactor equipped with a micro-screen was developed as a wastewater treatment system on a laboratory scale to contain a Rhizopus culture and maintain its dominance under non-aseptic conditions. Competitive growth of bacteria was observed, but this was minimised by manipulation of the solids retention time and the hydraulic retention time. Removal of about 90% of the waste organic material (as BOD) from the wastewater was achieved simultaneously. Since essentially all fungi are retained behind the 100 mum aperture screen, the solids retention time could be controlled by the rate of harvesting. The hydraulic retention time was employed to control the bacterial growth as the bacteria were washed through the screen at a short HRT. A steady state model was developed to determine these two parameters. This model predicts the effluent quality. Experimental work is still needed to determine the growth characteristics of the selected fungal species under optimum conditions (pH and temperature).

Competitive advantage in public-sector organizations: explaining the public good/sustainable competitive advantage paradox

Resource-based views of the firm and in particular Kay's (Why Firms Succeed. Oxford: Oxford Univ. Press, 1995) model of sustainable competitive advantage have been used to advance an understanding of differences in the competitive advantage of private-sector firms. We extend the analysis to a public-sector firm where its major purpose includes engaging in public good by giving away its knowledge base and services. The case highlights the paradox that many public-sector organizations face in simultaneously pursuing public good and sustainable competitive advantage. While Kay's model is applicable for understanding intergovernmental agency competition, we find it necessary to incorporate resource dependency theory to address the paradox. Implications for theory and practice are provided. (C) 2002 Elsevier Inc. All rights reseved.