Influence of birth weight on gene regulators of lipid metabolism and utilization in subcutaneous adipose tissue and skeletal muscle of neonatal pigs.

Epidemiological studies suggest that low-birth weight infants show poor neonatal growth and increased susceptibility to metabolic syndrome, in particular, obesity and diabetes. Adipose tissue development is regulated by many genes, including members of the peroxisome proliferator-activated receptor (PPAR) and the fatty acid-binding protein (FABP) families. The aim of this study was to determine the influence of birth weight on key adipose and skeletal muscle tissue regulating genes. Piglets from 11 litters were ranked according to birth weight and 3 from each litter assigned to small, normal, or large-birth weight groups. Tissue samples were collected on day 7 or 14. Plasma metabolite concentrations and the expression of PPARG2, PPARA, FABP3, and FABP4 genes were determined in subcutaneous adipose tissue and skeletal muscle. Adipocyte number and area were determined histologically. Expression of FABP3 and 4 was significantly reduced in small and large, compared with normal, piglets in adipose tissue on day 7 and in skeletal muscle on day 14. On day 7, PPARA and PPARG2 were significantly reduced in adipose tissue from small and large piglets. Adipose tissue from small piglets contained more adipocytes than normal or large piglets. Birth weight had no effect on adipose tissue and skeletal muscle lipid content. Low-birth weight is associated with tissue-specific and time-dependent effects on lipid-regulating genes as well as morphological changes in adipose tissue. It remains to be seen whether these developmental changes alter an individual's susceptibility to metabolic syndrome.

Probability density estimation with tunable kernels using orthogonal forward regression

A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and diagonal covariance matrix, by minimizing a leave-one-out test criterion. The kernel mixing weights of the constructed sparse density estimate are finally updated using the multiplicative nonnegative quadratic programming algorithm to ensure the nonnegative and unity constraints, and this weight-updating process additionally has the desired ability to further reduce the model size. The proposed tunable-kernel model has advantages, in terms of model generalization capability and model sparsity, over the standard fixed-kernel model that restricts kernel centers to the training data points and employs a single common kernel variance for every kernel. On the other hand, it does not optimize all the model parameters together and thus avoids the problems of high-dimensional ill-conditioned nonlinear optimization associated with the conventional finite mixture model. Several examples are included to demonstrate the ability of the proposed novel tunable-kernel model to effectively construct a very compact density estimate accurately.

Summer drought in northern midlatitudes in a time-dependent CO2 climate experiment

A time-dependent climate-change experiment with a coupled ocean–atmosphere general circulation model has been used to study changes in the occurrence of drought in summer in southern Europe and central North America. In both regions, precipitation and soil moisture are reduced in a climate of greater atmospheric carbon dioxide. A detailed investigation of the hydrology of the model shows that the drying of the soil comes about through an increase in evaporation in winter and spring, caused by higher temperatures and reduced snow cover, and a decrease in the net input of water in summer. Evaporation is reduced in summer because of the drier soil, but the reduction in precipitation is larger. Three extreme statistics are used to define drought, namely the frequency of low summer precipitation, the occurrence of long dry spells, and the probability of dry soil. The last of these is arguably of the greatest practical importance, but since it is based on soil moisture, of which there are very few observations, the authors’ simulation of it has the least confidence. Furthermore, long time series for daily observed precipitation are not readily available from a sufficient number of stations to enable a thorough evaluation of the model simulation, especially for the frequency of long dry spells, and this increases the systematic uncertainty of the model predictions. All three drought statistics show marked increases owing to the sensitivity of extreme statistics to changes in their distributions. However, the greater likelihood of long dry spells is caused by a tendency in the character of daily rainfall toward fewer events, rather than by the reduction in mean precipitation. The results should not be taken as firm predictions because extreme statistics for small regions cannot be calculated reliably from the output of the current generation of GCMs, but they point to the possibility of large increases in the severity of drought conditions as a consequence of climate change caused by increased CO2.

A method for merging flow-dependent forecast error statistics from an ensemble with static statistics for use in high resolution variational data assimilation

The background error covariance matrix, B, is often used in variational data assimilation for numerical weather prediction as a static and hence poor approximation to the fully dynamic forecast error covariance matrix, Pf. In this paper the concept of an Ensemble Reduced Rank Kalman Filter (EnRRKF) is outlined. In the EnRRKF the forecast error statistics in a subspace defined by an ensemble of states forecast by the dynamic model are found. These statistics are merged in a formal way with the static statistics, which apply in the remainder of the space. The combined statistics may then be used in a variational data assimilation setting. It is hoped that the nonlinear error growth of small-scale weather systems will be accurately captured by the EnRRKF, to produce accurate analyses and ultimately improved forecasts of extreme events.

Evaluating raw ensembles with the continuous ranked probability score

The continuous ranked probability score (CRPS) is a frequently used scoring rule. In contrast with many other scoring rules, the CRPS evaluates cumulative distribution functions. An ensemble of forecasts can easily be converted into a piecewise constant cumulative distribution function with steps at the ensemble members. This renders the CRPS a convenient scoring rule for the evaluation of ‘raw’ ensembles, obviating the need for sophisticated ensemble model output statistics or dressing methods prior to evaluation. In this article, a relation between the CRPS score and the quantile score is established. The evaluation of ‘raw’ ensembles using the CRPS is discussed in this light. It is shown that latent in this evaluation is an interpretation of the ensemble as quantiles but with non-uniform levels. This needs to be taken into account if the ensemble is evaluated further, for example with rank histograms.

Rank histograms of stratified Monte-Carlo ensembles

The application of forecast ensembles to probabilistic weather prediction has spurred considerable interest in their evaluation. Such ensembles are commonly interpreted as Monte Carlo ensembles meaning that the ensemble members are perceived as random draws from a distribution. Under this interpretation, a reasonable property to ask for is statistical consistency, which demands that the ensemble members and the verification behave like draws from the same distribution. A widely used technique to assess statistical consistency of a historical dataset is the rank histogram, which uses as a criterion the number of times that the verification falls between pairs of members of the ordered ensemble. Ensemble evaluation is rendered more specific by stratification, which means that ensembles that satisfy a certain condition (e.g., a certain meteorological regime) are evaluated separately. Fundamental relationships between Monte Carlo ensembles, their rank histograms, and random sampling from the probability simplex according to the Dirichlet distribution are pointed out. Furthermore, the possible benefits and complications of ensemble stratification are discussed. The main conclusion is that a stratified Monte Carlo ensemble might appear inconsistent with the verification even though the original (unstratified) ensemble is consistent. The apparent inconsistency is merely a result of stratification. Stratified rank histograms are thus not necessarily flat. This result is demonstrated by perfect ensemble simulations and supplemented by mathematical arguments. Possible methods to avoid or remove artifacts that stratification induces in the rank histogram are suggested.

Occurrence probability, width and number of steps of cusp precipitation for fully pulsed reconnection at the dayside magnetopause

We discuss the characteristics of magnetosheath plasma precipitation in the “cusp” ionosphere for when the reconnection at the dayside magnetopause takes place only in a series of pulses. It is shown that even in this special case, the low-altitude cusp precipitation is continuous, unless the intervals between the pulses are longer than observed intervals between magnetopause flux transfer event (FTE) signatures. We use FTE observation statistics to predict, for this case of entirely pulsed reconnection, the occurrence frequency, the distribution of latitudinal widths, and the number of ion dispersion steps of the cusp precipitation for a variety of locations of the reconnection site and a range of values of the local de-Hoffman Teller velocity. It is found that the cusp occurrence frequency is comparable with observed values for virtually all possible locations of the reconnection site. The distribution of cusp width is also comparable with observations and is shown to be largely dependent on the distribution of the mean reconnection rate, but pulsing the reconnection does very slightly increase the width of that distribution compared with the steady state case. We conclude that neither cusp occurrence probability nor width can be used to evaluate the relative occurrence of reconnection behaviors that are entirely pulsed, pulsed but continuous and quasi-steady. We show that the best test of the relative frequency of these three types of reconnection is to survey the distribution of steps in the cusp ion dispersion characteristics.

Incentive effects on risk attitude in small probability prospects

We report between-subject results on the effect of monetary stakes on risk attitudes. While we find the typical risk seeking for small probabilities, risk seeking is reduced under high stakes. This suggests that utility is not consistently concave.

A global empirical system for probabilistic seasonal climate prediction

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961–2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño–Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

A Variance-Minimizing Filter for Large-Scale Applications

A truly variance-minimizing filter is introduced and its per for mance is demonstrated with the Korteweg– DeV ries (KdV) equation and with a multilayer quasigeostrophic model of the ocean area around South Africa. It is recalled that Kalman-like filters are not variance minimizing for nonlinear model dynamics and that four - dimensional variational data assimilation (4DV AR)-like methods relying on per fect model dynamics have dif- ficulty with providing error estimates. The new method does not have these drawbacks. In fact, it combines advantages from both methods in that it does provide error estimates while automatically having balanced states after analysis, without extra computations. It is based on ensemble or Monte Carlo integrations to simulate the probability density of the model evolution. When obser vations are available, the so-called importance resampling algorithm is applied. From Bayes’ s theorem it follows that each ensemble member receives a new weight dependent on its ‘ ‘distance’ ’ t o the obser vations. Because the weights are strongly var ying, a resampling of the ensemble is necessar y. This resampling is done such that members with high weights are duplicated according to their weights, while low-weight members are largely ignored. In passing, it is noted that data assimilation is not an inverse problem by nature, although it can be for mulated that way . Also, it is shown that the posterior variance can be larger than the prior if the usual Gaussian framework is set aside. However , i n the examples presented here, the entropy of the probability densities is decreasing. The application to the ocean area around South Africa, gover ned by strongly nonlinear dynamics, shows that the method is working satisfactorily . The strong and weak points of the method are discussed and possible improvements are proposed.

On competitive bidding: scoring and position probability graphs

Iso-score curves graph (iSCG) and mathematical relationships between Scoring Parameters (SP) and Forecasting Parameters (FP) can be used in Economic Scoring Formulas (ESF) used in tendering to distribute the score among bidders in the economic part of a proposal. Each contracting authority must set an ESF when publishing tender specifications and the strategy of each bidder will differ depending on the ESF selected and the weight of the overall proposal scoring. The various mathematical relationships and density distributions that describe the main SPs and FPs, and the representation of tendering data by means of iSCGs, enable the generation of two new types of graphs that can be very useful for bidders who want to be more competitive: the scoring and position probability graphs.

On ""Schwinger mechanism for gluon pair production in the presence of arbitrary time dependent chromo-electric field""

Recently the paper ""Schwinger mechanism for gluon pair production in the presence of arbitrary time dependent chromo-electric field"" by G. C. Nayak was published [Eur. Phys. J. C 59: 715, 2009; arXiv:0708.2430]. Its aim is to obtain an exact expression for the probability of non-perturbative gluon pair production per unit time per unit volume and per unit transverse momentum in an arbitrary time-dependent chromo-electric background field. We believe that the obtained expression is open to question. We demonstrate its inconsistency on some well-known examples. We think that this is a consequence of using the socalled ""shift theorem""[arXiv:hep-th/0609192] in deriving the expression for the probability. We make some critical comments on the theorem and its applicability to the problem in question.

XRCC1 haploinsufficiency in mice has little effect on aging, but adversely modifies exposure-dependent susceptibility

Oxidative DNA damage plays a role in disease development and the aging process. A prominent participant in orchestrating the repair of oxidative DNA damage, particularly single-strand breaks, is the scaffold protein XRCC1. A series of chronological and biological aging parameters in XRCC1 heterozygous (HZ) mice were examined. HZ and wild-type (WT) C57BL/6 mice exhibit a similar median lifespan of similar to 26 months and a nearly identical maximal life expectancy of similar to 37 months. However, a number of HZ animals (7 of 92) showed a propensity for abdominal organ rupture, which may stem from developmental abnormalities given the prominent role of XRCC1 in endoderm and mesoderm formation. For other end-points evaluated-weight, fat composition, blood chemistries, condition of major organs, tissues and relevant cell types, behavior, brain volume and function, and chromosome and telomere integrity-HZ mice exhibited by-and-large a normal phenotype. Treatment of animals with the alkylating agent azoxymethane resulted in both liver toxicity and an increased incidence of precancerous lesions in the colon of HZ mice. Our study indicates that XRCC1 haploinsufficiency in mammals has little effect on chronological longevity and many key biological markers of aging in the absence of environmental challenges, but may adversely affect normal animal development or increase disease susceptibility to a relevant genotoxic exposure.

Electrochemical Control of the Time-Dependent Intensity Fluctuations in Surface-Enhanced Raman Scattering (SERS)

Time-dependent fluctuations in surface-enhanced Raman scattering (SERS) intensities were recorded from a roughened silver electrode immersed in diluted solutions of rhodamine 6G (R6G) and congo red (CR). These fluctuations were attributed to a small number of SERS-active molecules probing regions of extremely high electromagnetic field (hot spots) at the nanostructured surface. The time-dependent distribution of SERS intensities followed a tailed statistics at certain applied potentials, which has been linked to single-molecule dynamics. The shape of the distribution was reversibly tuned by the applied voltage. Mixtures of both dyes, R6G and CR, at low concentrations were also investigated. Since R6G is a cationic dye and CR is an anionic dye, the statistics of the SERS intensity distribution of either dye in a mixture were independently controlled by adjusting the applied potential. The potential-controlled distribution of SERS intensities was interpreted by considering the modulation of the surface coverage of the adsorbed dye by the interfacial electric field. This interpretation was supported by a two-dimensional Monte Carlo simulation that took into account the time evolution of the surface configuration of the adsorbed species and their probability to populate a hypothetical hot spot. The potential-controlled SERS dynamics reported here is a first step toward the spectroelectrochemical investigation of redox processes at the single-molecule level by SERS.

A estrutura de decisão do operador de transporte de cargas pelo usuário varejista de pequeno porte

Este trabalho tem por objetivo investigar e identificar a influência dos atributos que estruturam a escolha do transportador de carga geral fracionada pelos usuários, em uma determinada rota nacional, baseado na modelagem da demanda. xii A modelagem da demanda é efetuada com base em Modelos Comportamentais Desagregados, utilizando-se as técnicas de Preferência Declarada (Stated Preference), na obtenção dos dados. A determinação das preferências dos decisores são analisadas, buscandose assim quantificar o valor das variáveis que compõem o nível de serviço desejado pelos varejistas usuários. O estudo enfoca o comportamento do varejista usuário de serviços de transporte de cargas com relação a tomada de decisão sobre a transportadora que executará o serviço de transporte de carga. Esta tomada de decisão do varejista usuário leva em consideração que cada operador valoriza os atributos em diferentes graus e que estes fazem parte do nível de serviço de cada transportadora. As técnicas de Preferência Declarada forneceram dados para estimar as funções de Utilidade levando em consideração os diferentes níveis de atributos de cada transportadora. A partir da função de Utilidade de cada transportadora, é estimada a probabilidade de escolha de cada transportadora. A modelagem permite a realização de simulações, a partir de alterações no grau dos atributos das variáveis do modelo, na qual se determinará a parcela de mercado de cada transportadora e a sua respectiva participação no mercado em estudo. Dentre os principais resultados, pode se observar que a modelagem da demanda em transporte de cargas, apesar de pouco utilizada, é coerente com a realidade analisada, validando a metodologia utilizada neste estudo.