Occurrence of organochlorine compounds in Euphausia superba and unhatched eggs of Pygoscelis genus penguins from Admiralty Bay (King George Island, Antarctica) and estimation of biomagnification factors

Polychlorinated biphenyls (PCBs) and organochlorine pesticides are compounds that do not occur naturally in the environment and are not easily degraded by chemical or microbiological action. In the present work, those compounds were analysed in unhatched penguin eggs and whole krill collected in Admiralty Bay, King George Island, Antarctica in the austral summers of 2004-2005 and 2005-2006. The compounds found in higher levels (in a wet weight basis) were, in most of the egg samples, the PCBs (2.53-78.7 ng g(-1)), DDTs (2.07-38.0 ng g(-1)) and HCB (4.99-39.1 ng g(-1)) and after Kruskal-Wallis ANOVA, the occurrence seemed to be species-specific for the Pygoscelis genus. In all of the cases, the levels found were not higher than the ones in Arctic birds in a similar trophic level. The krill samples analysis allowed estimating the biomagnification factors (which resulted in up to 363 for HCB, one order of magnitude higher than DDTs and chlordanes and two orders of magnitude higher than the other groups) of the compounds found in eggs, whose only source of contamination is the female-offspring transfer. (C) 2009 Elsevier Ltd. All rights reserved.

TYPE-ZERO SADDLE-NODE BIFURCATIONS AND STABILITY REGION ESTIMATION OF NONLINEAR AUTONOMOUS DYNAMICAL SYSTEMS

A complete characterization of the stability boundary of a class of nonlinear dynamical systems that admit energy functions is developed in this paper. This characterization generalizes the existing results by allowing the type-zero saddle-node nonhyperbolic equilibrium points on the stability boundary. Conceptual algorithms to obtain optimal estimates of the stability region (basin of attraction) in the form of level sets of a given family of energy functions are derived. The behavior of the stability region and the corresponding estimates are investigated for parameter variation in the neighborhood of a type-zero saddle-node bifurcation value.

Estimation of lung vital capacity before and after coronary artery bypass grafting surgery: a comparison of incentive spirometer and ventilometry

Abstract Background Measurement of vital capacity (VC) by spirometry is the most widely used technique for lung function evaluation, however, this form of assessment is costly and further investigation of other reliable methods at lower cost is necessary. Objective: To analyze the correlation between direct vital capacity measured with ventilometer and with incentive inspirometer in patients in pre and post cardiac surgery. Methodology Cross-sectional comparative study with patients undergoing cardiac surgery. Respiratory parameters were evaluated through the measurement of VC performed by ventilometer and inspirometer. To analyze data normality the Kolmogorov-Smirnov test was applied, for correlation the Pearson correlation coefficient was used and for comparison of variables in pre and post operative period Student's t test was adopted. We established a level of ignificance of 5%. Data was presented as an average, standard deviation and relative frequency when needed. The significance level was set at 5%. Results We studied 52 patients undergoing cardiac surgery, 20 patients in preoperative with VC-ventilometer: 32.95 ± 11.4 ml/kg and VC-inspirometer: 28.9 ± 11 ml/Kg, r = 0.7 p < 0.001. In the post operatory, 32 patients were evaluated with VC-ventilometer: 28.27 ± 12.48 ml/kg and VC-inspirometer: 26.98 ± 11 ml/Kg, r = 0.95 p < 0.001. Presenting a very high correlation between the evaluation forms studied. Conclusion There was a high correlation between DVC measures with ventilometer and incentive spirometer in pre and post CABG surgery. Despite this, arises the necessity of further studies to evaluate the repercussion of this method in lowering costs at hospitals.

The Estimation of the Ecological Condition of the City Environment by Methods of Phytoindication (as Applied to the Vasileostrovsky District of St. Petersburg)

The project aimed to use results of contamination of city vegetation with heavy metals and sulphur compounds as the basis for analysing the integral response of trees and shrubs to contamination, through a complex method of phytoindication. The results were used to draw up recommendations on pollution reduction in the city and to develop the method of phytoindication as a means of monitoring environmental pollution in St. Petersburg and other large cities. Field investigations were carried out in August 1996, and 66 descriptions of green areas were made in order to estimate the functional state of plants in the Vasileostrovsky district. Investigations of the spectrum reflecting properties of plants showed considerable variation of albedo meanings of leaves under the influence of various internal and external factors. The results indicated that lime trees most closely reflect the condition of the environment. Practically all the green areas studied were in poor condition, the only exceptions being areas of ash trees, which are more resistant to environmental pollution, and one lime-tree alley in a comparatively unpolluted street. The study identified those types of trees which are more or less resistant to complex environmental pollution and Ms. Terekhina recommends that the species in the present green areas be changed to include a higher number of the more resistant species. The turbidimetric analysis of tree barks for sulphates gave an indication of the level and spatial distribution of each pollutant, and the results also confirmed other findings that electric conductivity is a significant feature in determining the extent of sulphate pollution. In testing for various metals, the lime tree showed the highest contents for all elements except magnesium, copper, zinc, cadmium and strontium, again confirming the species' vulnerability to pollution. Medium rates of concentration in the city and environs showed that city plants concentrate 3 times as many different elements and 10 times more chromium, copper and lead than do those in the suburbs. The second stage of the study was based on the concept of phytoindication, which presupposes that changes in the relation of chemical elements in regional biological circulation under the influence of technogenesis provide a criterion for predicting displacements in people's health. There are certain basic factors in this concept. The first is that all living beings are related ecologically as well as by their evolutionary origin, and that the lower an organism is on the evolutionary scale, the less adaptational reserve it has. The second is that smaller concentrations of chemical elements are needed for toxicological influence on plants than on people and so the former's reactions to geochemical factors are easier to characterise. Visual indicational features of urban plants are well defined and can form the basis of a complex "environment - public health" analysis. Specific plant reactions reflecting atmospheric pollution and other components of urbogeosystems make it possible to determine indication criteria for predicting possible disturbances in the general state of health of the population. Thirdly the results of phytoindication investigations must be taken together with information about public health in the area. It only proved possibly to analyse general indexes of public health based on statistical data from the late 1980s and early 1990s as the data of later years were greatly influenced by social factors. These data show that the rates of illness in St. Petersburg (especially for children) are higher than in Russia as a whole, for most classes of diseases, indicating that the population there is more sensitive to the ecological state of the urban environment. The Vasileostrovsky district had the second highest sick rate for adullts, while the rate of infant mortality in the first year of life was highest there. Ms. Terekhina recommends further studies to more precisely assess the effectiveness of the methods she tested, but has drawn up a proposed map of environmental hazard for the population, taking into account prevailing wind directions.

Non-invasive quantitative estimation of bone density in rats throughout the life cycle and in arthritic osteopenia: preliminary results

A longitudinal bone survey was conducted in 86 female Wistar rats in order to assess mineral density kinetics from young age (5 weeks: 115 g) till late adulthood (64 weeks: 586 g). In vivo quantitative radiographic scanning was performed on the caudal vertebrae, taking trabecular mass as the parameter. Measurements were expressed as Relative Optical Density (ROD) units by means of a high resolution densitometric device. Results showed a progressive increase in mineral density throughout the life cycle, with a tendency to level in the higher weight range, indicating that progressive mineral aposition occurs in rats in dependency of age. This phenomenon, however, should be always considered within the context of continuous skeletal growth and related changes typical of this species. Twelve different animals were also examined following induction of articular inflammation with Freund's adjuvant in six of them. Bone survey conducted 12 to 18 days after inoculation revealed a significant (P less than 0.01) reduction in trabecular bone mass of scanned vertebrae in comparison with the weight-matched untreated controls. It is concluded that the in vivo quantitative assessment of bone density illustrated in this report represents a sensitive and useful tool for the long-term survey of naturally occurring or experimentally induced bone changes. Scanning of the same part of the skeleton can be repeated, thereby avoiding sacrifice of the animal and time-consuming preparation of post-mortem material.

Assessment of Skill and Portability in Regional Marine Biogeochemical Models: Role of Multiple Planktonic Groups

Application of biogeochemical models to the study of marine ecosystems is pervasive, yet objective quantification of these models' performance is rare. Here, 12 lower trophic level models of varying complexity are objectively assessed in two distinct regions (equatorial Pacific and Arabian Sea). Each model was run within an identical one-dimensional physical framework. A consistent variational adjoint implementation assimilating chlorophyll-a, nitrate, export, and primary productivity was applied and the same metrics were used to assess model skill. Experiments were performed in which data were assimilated from each site individually and from both sites simultaneously. A cross-validation experiment was also conducted whereby data were assimilated from one site and the resulting optimal parameters were used to generate a simulation for the second site. When a single pelagic regime is considered, the simplest models fit the data as well as those with multiple phytoplankton functional groups. However, those with multiple phytoplankton functional groups produced lower misfits when the models are required to simulate both regimes using identical parameter values. The cross-validation experiments revealed that as long as only a few key biogeochemical parameters were optimized, the models with greater phytoplankton complexity were generally more portable. Furthermore, models with multiple zooplankton compartments did not necessarily outperform models with single zooplankton compartments, even when zooplankton biomass data are assimilated. Finally, even when different models produced similar least squares model-data misfits, they often did so via very different element flow pathways, highlighting the need for more comprehensive data sets that uniquely constrain these pathways.

The impact of common versus separate estimation of orbit parameters on GRACE gravity field solutions

Gravity field parameters are usually determined from observations of the GRACE satellite mission together with arc-specific parameters in a generalized orbit determination process. When separating the estimation of gravity field parameters from the determination of the satellites’ orbits, correlations between orbit parameters and gravity field coefficients are ignored and the latter parameters are biased towards the a priori force model. We are thus confronted with a kind of hidden regularization. To decipher the underlying mechanisms, the Celestial Mechanics Approach is complemented by tools to modify the impact of the pseudo-stochastic arc-specific parameters on the normal equations level and to efficiently generate ensembles of solutions. By introducing a time variable a priori model and solving for hourly pseudo-stochastic accelerations, a significant reduction of noisy striping in the monthly solutions can be achieved. Setting up more frequent pseudo-stochastic parameters results in a further reduction of the noise, but also in a notable damping of the observed geophysical signals. To quantify the effect of the a priori model on the monthly solutions, the process of fixing the orbit parameters is replaced by an equivalent introduction of special pseudo-observations, i.e., by explicit regularization. The contribution of the thereby introduced a priori information is determined by a contribution analysis. The presented mechanism is valid universally. It may be used to separate any subset of parameters by pseudo-observations of a special design and to quantify the damage imposed on the solution.

A categorical data analysis approach to estimation of demographic characteristics for Texas county populations

The need for timely population data for health planning and Indicators of need has Increased the demand for population estimates. The data required to produce estimates is difficult to obtain and the process is time consuming. Estimation methods that require less effort and fewer data are needed. The structure preserving estimator (SPREE) is a promising technique not previously used to estimate county population characteristics. This study first uses traditional regression estimation techniques to produce estimates of county population totals. Then the structure preserving estimator, using the results produced in the first phase as constraints, is evaluated.^ Regression methods are among the most frequently used demographic methods for estimating populations. These methods use symptomatic indicators to predict population change. This research evaluates three regression methods to determine which will produce the best estimates based on the 1970 to 1980 indicators of population change. Strategies for stratifying data to improve the ability of the methods to predict change were tested. Difference-correlation using PMSA strata produced the equation which fit the data the best. Regression diagnostics were used to evaluate the residuals.^ The second phase of this study is to evaluate use of the structure preserving estimator in making estimates of population characteristics. The SPREE estimation approach uses existing data (the association structure) to establish the relationship between the variable of interest and the associated variable(s) at the county level. Marginals at the state level (the allocation structure) supply the current relationship between the variables. The full allocation structure model uses current estimates of county population totals to limit the magnitude of county estimates. The limited full allocation structure model has no constraints on county size. The 1970 county census age - gender population provides the association structure, the allocation structure is the 1980 state age - gender distribution.^ The full allocation model produces good estimates of the 1980 county age - gender populations. An unanticipated finding of this research is that the limited full allocation model produces estimates of county population totals that are superior to those produced by the regression methods. The full allocation model is used to produce estimates of 1986 county population characteristics. ^

Literature review of estimation of prevalence rate of HIV/AIDS in India using population survey and sentinel surveillance based data

HIV/AIDS is a treatable although incurable disease that presents immense challenges to those infected including physical, social and psychological effects. As of 2009, an estimated 2.4 million people were living with HIV or AIDS in India, 0.3% of the country's population. In India, it is difficult to not only treat but also to track because it is associated with socio-economic factors such as illiteracy, social biases, poor sanitation, malnutrition and social class. Nevertheless, it is important to know the prevalence of HIV/AIDS for several reasons. At the individual level, the quality of life of people living with HIV/AIDS is markedly lower than their counterparts without the disease and is associated with challenges. At the community level, it is important to identify high risk groups, monitor prevention efforts, and allocate appropriate resources to target programs for the reduction of transmission of HIV. ^

Bayesian estimation of multilevel item response model with missing data

This study proposed a novel statistical method that modeled the multiple outcomes and missing data process jointly using item response theory. This method follows the "intent-to-treat" principle in clinical trials and accounts for the correlation between outcomes and missing data process. This method may provide a good solution to chronic mental disorder study. ^ The simulation study demonstrated that if the true model is the proposed model with moderate or strong correlation, ignoring the within correlation may lead to overestimate of the treatment effect and result in more type I error than specified level. Even if the within correlation is small, the performance of proposed model is as good as naïve response model. Thus, the proposed model is robust for different correlation settings if the data is generated by the proposed model.^

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

The Production Technology of Philippine Domestic Banks in the Pre-Asian Crisis Periods: Estimation of Cost Function in the 1990-96 Period

This is to analyzes the operational behavior and technical progress among Philippine domestic banks, using micro-level data on individual banks. First, we summarize their major business activities and gain insight on how the structure is changing. Then, we formally estimate the cost function of Philippine domestic banks using panel data covering a seven-year period (1990-96). The presence of economies of scale and economies of scope is investigated and technical progress in the banking industry is measured. In addition, the results of analysis for the Philippines are compared with those of similar studies on Thailand conducted by the author previously.

How does firm heterogeneity information impact the estimation of embodied carbon emissions in Chinese exports?

Using an augmented Chinese input–output table in which information about firm ownership and type of traded goods are explicitly reported, we show that ignoring firm heterogeneity causes embodied CO2 emissions in Chinese exports to be overestimated by 20% at the national level, with huge differences at the sector level, for 2007. This is because different types of firm that are allocated to the same sector of the conventional Chinese input–output table vary greatly in terms of market share, production technology and carbon intensity. This overestimation of export-related carbon emissions would be even higher if it were not for the fact that 80% of CO2 emissions embodied in exports of foreign-owned firms are, in fact, emitted by Chinese-owned firms upstream of the supply chain. The main reason is that the largest CO2 emitter, the electricity sector located upstream in Chinese domestic supply chains, is strongly dominated by Chinese-owned firms with very high carbon intensity.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50–100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.