RESUMEN
Infectious bronchitis virus (IBV) and H9N2 avian influenza virus (AIV) are frequently identified in chickens with respiratory disease. However, the role and mechanism of IBV and H9N2 AIV co-infection remain largely unknown. Specific-pathogen-free (SPF) chickens were inoculated with IBV 2 days before H9N2 virus inoculation (IBV/H9N2); with IBV and H9N2 virus simultaneously (IBV+H9N2); with H9N2 virus 2 days before IBV inoculation (H9N2/IBV); or with either IBV or H9N2 virus alone. Severe respiratory signs, pathological damage, and higher morbidity and mortality were observed in the co-infection groups compared with the IBV and H9N2 groups. In general, a higher virus load and a more intense inflammatory response were observed in the three co-infection groups, especially in the IBV/H9N2 group. The same results were observed in the transcriptome analysis of the trachea of the SPF chickens. Therefore, IBV might play a major role in the development of respiratory disease in chickens, and secondary infection with H9N2 virus further enhances the pathogenicity by inducing a severe inflammatory response. These findings may provide a reference for the prevention and control of IBV and H9N2 AIV in the poultry industry and provide insight into the molecular mechanisms of IBV and H9N2 AIV co-infection in chickens.
RESUMEN
In this paper, an optimization algorithm is presented based on a distance and angle probability model for indoor non-line-of-sight (NLOS) environments. By utilizing the sampling information, a distance and angle probability model is proposed so as to identify the NLOS propagation. Based on the established model, the maximum likelihood estimation (MLE) method is employed to reduce the error of distance in the NLOS propagation. In order to reduce the computational complexity, a modified Monte Carlo method is applied to search the optimal position of the target. Moreover, the extended Kalman filtering (EKF) algorithm is introduced to achieve localization. The simulation and experimental results show the effectiveness of the proposed algorithm in the improvement of localization accuracy.
RESUMEN
New biomarkers continue to be developed for the purpose of diagnosis, and their diagnostic performances are typically compared with an existing reference biomarker used for the same purpose. Considerable amounts of research have focused on receiver operating characteristic curves analysis when the reference biomarker is dichotomous. In the situation where the reference biomarker is measured on a continuous scale and dichotomization is not practically appealing, an index was proposed in the literature to measure the accuracy of a continuous biomarker, which is essentially a linear function of the popular Kendall's tau. We consider the issue of estimating such an accuracy index when the continuous reference biomarker is measured with errors. We first investigate the impact of measurement errors on the accuracy index, and then propose methods to correct for the bias due to measurement errors. Simulation results show the effectiveness of the proposed estimator in reducing biases. The methods are exemplified with hemoglobin A1c measurements obtained from both the central lab and a local lab to evaluate the accuracy of the mean data obtained from the metered blood glucose monitoring against the centrally measured hemoglobin A1c from a behavioral intervention study for families of youth with type 1 diabetes.
