Explore the vast range of titles available.

Background The use of HbA1c ≥6.5% for diagnosis of diabetes has been challenged for post-transplantation diabetes mellitus (PTDM) also known as new onset diabetes after transplantation (NODAT) due to a low sensitivity early after renal transplantation. PTDM diagnosed with an oral glucose tolerance test (OGTT) is highly predictable for long-term patient mortality. HbA1c was introduced for diagnosis based on the risk of developing diabetic retinopathy. The utility of HbA1c measures versus glucose criteria has not been widely assessed in stable transplant patients but still HbA1c is widely used in this population. The aim of the present analyses was to validate the utility of fasting plasma glucose (FPG) together with HbA1c in diagnosing PTDM in stable renal transplant recipients (RTRs). Methods OGTT’s were performed one year after transplantation in 494 consecutive RTRs without diabetes. FPG and HbA1c were obtained the same day, before starting the OGTT. Validation was performed using C-statistics and logistic regression analyses. Results PTDM was diagnosed in 51 patients (10.3%) by glucose criteria, 38 (74%) patients were diagnosed by FPG ≥7.0 mmol/L [126.1 mg/dl], and 13 (26%) only by 2-h plasma glucose. Six of the latter had HbA1c ≥6.5%. Only seven patients out of the 51 (13.7%) PTDM patients remained undiagnosed when HbA1c ≥6.5% was used together with FPG, and five of these regressed to normal after a median follow-up of 14 months. ROC curves including FPG and HbA1c versus OGTT derived criteria revealed an AUC of 0.858. Conclusions Combining standard diagnostic FPG and HbA1c criteria captured almost all patients with persistent PTDM in stable RTRs. The combined use of the criteria appears to be an applicable diagnostic strategy for PTDM without the need of an OGTT one year post-transplant. Trial registration Retrospectively registered.

Background Cardiovascular complications are common in kidney transplant patients and calcification propensity of blood, measured as T 50 , is associated with cardiovascular outcomes. Paricalcitol supplementation affects calcium/phosphate homeostasis and may affect calcification propensity. To assess this hypothesis we measured T 50 in kidney transplant recipients participating in a randomized study comparing paricalcitol versus no treatment during the first year after kidney transplantation. Methods Stored serum samples from 76 kidney transplant recipients (paricalcitol n  = 37, no treatment n  = 39) were analyzed. Analyses were performed at inclusion (8 weeks after transplantation) and repeated one year after transplantation. Results There were no statistically significant differences in T 50 between the paricalcitol and placebo groups, neither at baseline ( p  = 0.56) nor at 1 year ( p  = 0.61). Also, there were no significant changes in T 50 over time in either group or when pooling all data ( p  <  0.20). In multivariate regression analysis, out of 16 potentially relevant covariates, comprising clinical and biochemical parameters, only plasma PTH and T 50 at baseline were significantly correlated to T 50 after one year. ( p  <  0.03 and p  < 0.01, respectively). Conclusions Calcium propensity measured as T 50 score remained unchanged with paricalcitol treatment in kidney transplant recipients, and was not changed over time during the study period of one year. Trial registration ClinicalTrials.gov, NCT01694160 , registered 23 September 2012.

This paper combines the elegant technique of Data Assimilation and a Monte Carlo procedure to analyze time series data for the North East Arctic Cod stock (NEACs). A simple nonlinear dynamic resource model is calibrated to time series data using the variational adjoint parameter estimation method and the Monte Carlo technique. By exploring the efficient features of the variational adjoint technique coupled with the Monte Carlo method, optimal or best parameter estimates with their error statistics are obtained. Thereafter, the weak constraint formulation resulting in a stochastic ordinary differential equation (SODE) is used to find an improved estimate of the dynamical variable, i.e. the stock. Empirical results show that the average fishing mortality imposed on the NEACs is about 16% more than the intrinsic growth rate of the biological species. Copyright Springer 2005

\"Analysis of workplace injuries in the United States and four other countries indicates that economic expansion can result in increases in workplace injury rates, however, safety measures have a counter effect and may lead to a net reduction in claim counts...This article consists of a twofold focus. First, it investigates the impact of employment on injury counts in the United States, Canada, France, Finland, and Sweden, using both qualitative and analytical tools. These countries are chosen because of their data availability. And second, it introduces a measure of the aggregate effect of all factors that tend to mitigate workplace injuries and illnesses.\" (Monthly Labor Review)

A new approach of model parameter estimation is used with simulated measurements to recover both biological and economic input parameters of a natural resource model. The data assimilation technique is the variational adjoint method (VAM) for parameter estimation. It efficiently combines time series of artificial data with a simple bioeconomic fisheries model to optimally estimate the model parameters. Using identical twin experiments, it is shown that the parameters of the model can be retrieved. The procedure provides an efficient way of calculating poorly known model parameters by fitting model results to simulated data. In separate experiments with exact and noisy data, we have demonstrated that the VAM can be an efficient method of analyzing bioeconomic data. [PUBLICATION ABSTRACT]

This paper has two main objectives. The first is to develop a dynamic model of commercial fisheries different from most existing models that assume optimizing behavior. The industry is assumed to have a well-defined index of performance. Based upon this index, the decision to invest or not is made. We do not, however, assume that the industry or firm is efficient or optimal in its operations. The second is that a new approach of fitting model dynamics to time series data is employed to simultaneously estimate the poorly known initial conditions and parameters of nonlinear fisheries dynamics. The approach is a data assimilation technique known as the variational adjoint method. Estimation of the poorly known initial conditions is one of the attractive features of the variational adjoint method.

A biological model belonging to the Beverton-Holt age-structured family for the Norwegian spring spawning herring (Clupea harengus) (NSSH) is simulated, the outcome of which compares well with actual data on the fishery. This model is then combined with an economic model to help investigate how optimal a management policy of constant fishing mortality will be for a fishery such as the NSSH, which has a highly fluctuating stock biomass. For the range of constant values of fishing mortality explored, and a simulation time horizon of 20 years, a constant fishing mortality of 0.15 turns out to be economically optimal. It should be noted that this result is sensitive to variations in the assumptions underlying key variables of the fishery. For example, when a constant rather than variable recruitment was assumed, a different optimal fishing mortality rate was obtained.

Market structure is tested in 47 subsectors of the US food industry between 1958-1994. All industries exert market power in at least some of the sample years. Deadweight loss estimates resulting from noncompetitive behavior ranging from over$5 billion in soft drinks to under $ 1 million in the malt industry.