The feasibility of radiolabeling for human serum albumin (HSA) adsorption studies

Human serum albumin (HSA) was labeled in various ways and with different radioactive labels (Technetium-99m and Iodine-125). Characterization with electrophoresis on polyacryl gel and immunoelectrophoresis did not reveal differences between labeled and nonlabeled HSA. The release of the label from labeled proteins in phosphate buffer (pH 7.4) was studied as a function of time. 125I-labeled proteins were stable and 99mTc-labeled proteins showed different stabilities depending on the labeling method which was used. The adsorption behavior of labeled HSA and HSA onto polystyrene (PS) and silicon rubber (SR) was studied by using two methods. It appeared that all labeled HSA compounds showed a preferential adsorption onto PS (and SR) substrates. The 99mTc-labeled HSA showed a high value of the preferential adsorption factor (φ 1). The φ value for 125I-labeled HSA was about 1.4. It was also shown that φ was dependent on the kind of substrate used. The methods developed to determine preferential adsorption of labeled proteins compared to their nonlabeled analogs are also generally applicable for different types of compounds

Human Trafficking in Europe: An Economic Perspective

Based on a document originally prepared for the Eleventh Economic Forum of the Organization for Economic Security and Cooperation in Europe, held in Prague between 20-23 May 2003. Attempts to comprehend and document human trafficking’s underlying economic dimensions, and places the concerns of trafficking within broader migration analysis (including the role of irregular migration). It also comments on the financial flows involved in trafficking, and on the different patterns of financing trafficking services. Further, it contains a brief review of the evidence, as to the extent to which organized crime is involved in human trafficking

Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound

Segmentation of the left atrium and deriving its size can help to predict and detect various cardiovascular conditions. Automation of this process in 3D Ultrasound image data is desirable, since manual delineations are time-consuming, challenging and observer-dependent. Convolutional neural networks have made improvements in computer vision and in medical image analysis. They have successfully been applied to segmentation tasks and were extended to work on volumetric data. In this paper we introduce a combined deep-learning based approach on volumetric segmentation in Ultrasound acquisitions with incorporation of prior knowledge about left atrial shape and imaging device. The results show, that including a shape prior helps the domain adaptation and the accuracy of segmentation is further increased with adversarial learning

Bayesian moment-based inference in a regression model with misclassification error

We present a Bayesian analysis of a regression model with a binary covariate that may have classification (measurement) error. Prior research demonstrates that the regression coefficient is only partially identified. We take a Bayesian approach which adds assumptions in the form of priors on the unknown misclassification probabilities. The approach is intermediate between the frequentist bounds of previous literature and strong assumptions which achieve point identification, and thus preferable in many settings. We present two simple algorithms to sample from the posterior distribution when the likelihood function is not fully parametric but only satisfies a set of moment restrictions. We focus on how varying amounts of information contained in a prior distribution on the misclassification probabilities change the posterior of the parameters of interest. While the priors add information to the model, they do not necessarily tighten the identified set. However, the information is sufficient to tighten Bayesian inferences. We also consider the case where the mismeasured binary regressor is endogenous. We illustrate the use of our Bayesian approach in a simulated data set and an empirical application investigating the association between narcotic pain reliever use and earnings

Bayesian Inference in a Sample Selection Model

This paper develops methods of Bayesian inference in a sample selection model. The main feature of this model is that the outcome variable is only partially observed. We first present a Gibbs sampling algorithm for a model in which the selection and outcome errors are normally distributed. The algorithm is then extended to analyze models that are characterized by nonnormality. Specifically, we use a Dirichlet process prior and model the distribution of the unobservables as a mixture of normal distributions with a random number of components. The posterior distribution in this model can simultaneously detect the presence of selection effects and departures from normality. Our methods are illustrated using some simulated data and an abstract from the RAND health insurance experiment

RATIONALE, MOTIVATION AND EFFECT OF FOREIGN INVESTMENT IN VIETNAMESE COMPANIES

Existing literatures have documented that foreign investors in developed markets hold disproportionately more shares of firms with high turnover rates. In Vietnam, I find evidence that foreign investor ownership percentage is higher in firms with lower turnover rates, firms located in the south of Vietnam, firms listed on the Hochiminh stock exchange, firms that are listed longer on the exchange, large firms, and firms with low past returns. The effect of turnover rate on foreign ownership percentage is weak, while the coefficients of firm size and firm age since IPO are consistently robust. I find that foreign investors hold more in firms with lower government ownership, and firms audited by a prestigious international audit company only holds for firms listed on the Hanoi exchange. I find that the thesis that foreign investor percentage ownership stakes are higher in firms with higher current ratios holds only for the firms listed on the Hochiminh stock exchange. Although average firm size is higher for State-Owned Enterprises (SOEs) and foreign investors strongly prefer investing in large size firms, they strongly show their preference in firms other than SOEs. By excluding the effect of size, I find that foreign ownership percentage stakes in firms listed on the Hanoi exchange are lower than foreign ownership percentage stakes in firms listed on the Hochiminh exchange. Interestingly, since dividend began being taxed, foreigner ownership percentage has been lower in firms with high dividend yields

Community Health Center Efficiency: The Role of Grant Revenues in Health Center Efficiency

Objective: To test the relationship between external environments, organizational characteristics, and technical efficiency in federally qualified health centers (FQHCs). We tested the relationship between grant revenue and technical efficiency in FQHCs. Data Sources/Study Design: Secondary data were collected in each year from the Uniform Data System (UDS) on 644 eligible U.S.-based FQHCs between 2005 and 2007. The study employs a retrospective longitudinal cohort design with instrumental variables. Principal Findings: Increased grant revenues did not increase the probability that a health center would be on the efficiency frontier. However, increased grant revenues had a negative association with technical efficiency for health centers that were not fully efficient. Conclusion: If all health centers were operating efficiently, anywhere from 39 to 45 million patient encounters could have been delivered instead of the actual total of 29 million in 2007. Policy makers should consider tying grant revenues to performance indicators, and future work is needed to understand the mechanisms through which diseconomies of scale are present in FQHCs