Rabbit Anti-Human AR monoclonal antibody, clone TU1564

Anticoagulant rodenticides (ARs) are commonly used to control rodent pests. However, worldwide, their use is associated with secondary and tertiary poisoning of nontarget species, especially predatory and scavenging birds. No medical device can rapidly test for AR exposure of avian wildlife. Prothrombin time (PT) is a useful biomarker for AR exposure, and multiple commercially available point-of-care (POC) devices measure PT of humans, and domestic and companion mammals. We evaluated the potential of one commercially available POC device, the Coag-Sense PT/INR Monitoring System, to rapidly detect AR exposure of living birds of prey. The Coag-Sense device delivered repeatable PT measurements on avian blood samples collected from four species of raptors trapped during migration (Intraclass Correlation Coefficient > 0.9; overall intra-sample variation CV: 5.7%). However, PT measurements reported by the Coag-Sense system from 81 ferruginous hawk (Buteo regalis) nestlings were not correlated to those measured by a one-stage laboratory avian PT assay (r = - 0.017, p = 0.88). Although precise, the lack of agreement in PT estimates from the Coag-Sense device and the laboratory assay indicates that this device is not suitable for detecting potential AR exposure of birds of prey. The lack of suitability may be related to the use of a mammalian reagent in the clotting reaction, suggesting that the device may perform better in testing mammalian wildlife

Appraisal of spatio-temporal variability of precipitation is important for studying climate change and managing water resources. In this article, data on monsoon precipitation of Pakistan are analyzed for spatio-temporal interpolation using two hierarchical Bayesian spatio-temporal methods which are DLM and AR models. The precipitation records along with three meteorological covariates (temperature, wind speed and humidity) are collected from 53 stations for the years 2000-2008. Initially, we simulated four data sets using AR model with four levels of autoregressive parameter rho = (0.2, 0.5, 0.7, 0.9) and both models are fitted. It is observed that for the larger values of rho, DLM gives better results as compared to AR model while AR model performs better for the smaller values of rho. The estimated value of rho for precipitation data in the whole study domain is 0.02 which supports AR model for final spatio -temporal mapping. For real data application, data of 45 locations are used for modeling while data of 8 locations have been set aside for cross validation and we use hyper-parameters a and b of inverse gamma distribution as variance parameters for AR and DLM models. The prediction performances of both models have been examined using Bayesian and non-Bayesian model choice criteria. The predictive inferences are drawn using MCMC algorithm. Prediction plots show that the areas that lie between 32 degrees-36 degrees east latitude and 70 degrees -74 degrees north longitude are high precipitation areas in Pakistan and are helpful in the identification of homogeneous climate zones.