Characterization of Schistosome Tegumental Alkaline Phosphatase (SmAP)

Schistosomes are parasitic platyhelminths that currently infect over 200 million people globally. The parasites can live for years in a putatively hostile environment - the blood of vertebrates. We have hypothesized that the unusual schistosome tegument (outer-covering) plays a role in protecting parasites in the blood; by impeding host immunological signaling pathways we suggest that tegumental molecules help create an immunologically privileged environment for schistosomes. In this work, we clone and characterize a schistosome alkaline phosphatase (SmAP), a predicted ∼60 kDa glycoprotein that has high sequence conservation with members of the alkaline phosphatase protein family. The SmAP gene is most highly expressed in intravascular parasite life stages. Using immunofluorescence and immuno-electron microscopy, we confirm that SmAP is expressed at the host/parasite interface and in internal tissues. The ability of living parasites to cleave exogenous adenosine monophosphate (AMP) and generate adenosine is very largely abolished when SmAP gene expression is suppressed following RNAi treatment targeting the gene. These results lend support to the hypothesis that schistosome surface enzymes such as SmAP could dampen host immune responses against the parasites by generating immunosuppressants such as adenosine to promote their survival. This notion does not rule out other potential functions for the adenosine generated e.g. in parasite nutrition

DESIGN OF GEODETIC NETWORKS BASED ON OUTLIER IDENTIFICATION CRITERIA: AN EXAMPLE APPLIED TO THE LEVELING NETWORK

We present a numerical simulation method for designing geodetic networks. The quality criterion considered is based on the power of the test of data snooping testing procedure. This criterion expresses the probability of the data snooping to identify correctly an outlier. In general, the power of the test is defined theoretically. However, with the advent of the fast computers and large data storage systems, it can be estimated using numerical simulation. Here, the number of experiments in which the data snooping procedure identifies the outlier correctly is counted using Monte Carlos simulations. If the network configuration does not meet the reliability criterion at some part, then it can be improved by adding required observation to the surveying plan. The method does not use real observations. Thus, it depends on the geometrical configuration of the network; the uncertainty of the observations; and the size of outlier. The proposed method is demonstrated by practical application of one simulated leveling network. Results showed the needs of five additional observations between adjacent stations. The addition of these new observations improved the internal reliability of approximately 18%. Therefore, the final designed network must be able to identify and resist against the undetectable outliers – according to the probability levels