Surface antigens and potential virulence factors from parasites detected by comparative genomics of perfect amino acid repeats

ABSTRACT: BACKGROUND: Many parasitic organisms, eukaryotes as well as bacteria, possess surface antigens with amino acid repeats. Making up the interface between host and pathogen such repetitive proteins may be virulence factors involved in immune evasion or cytoadherence. They find immunological applications in serodiagnostics and vaccine development. Here we use proteins which contain perfect repeats as a basis for comparative genomics between parasitic and free-living organisms. RESULTS: We have developed Reptile http://reptile.unibe.ch, a program for proteome-wide probabilistic description of perfect repeats in proteins. Parasite proteomes exhibited a large variance regarding the proportion of repeat-containing proteins. Interestingly, there was a good correlation between the percentage of highly repetitive proteins and mean protein length in parasite proteomes, but not at all in the proteomes of free-living eukaryotes. Reptile combined with programs for the prediction of transmembrane domains and GPI-anchoring resulted in an effective tool for in silico identification of potential surface antigens and virulence factors from parasites. CONCLUSION: Systemic surveys for perfect amino acid repeats allowed basic comparisons between free-living and parasitic organisms that were directly applicable to predict proteins of serological and parasitological importance. An on-line tool is available at http://genomics.unibe.ch/dora.

A connection between two nonparametric tests for perfect ranking in balanced ranked set sampling

In this note, we show that an extension of a test for perfect ranking in a balanced ranked set sample given by Li and Balakrishnan (2008) to the multi-cycle case turns out to be equivalent to the test statistic proposed by Frey et al. (2007). This provides an alternative interpretation and motivation for their test statistic.

A Parametric Test of Perfect Ranking in Balanced Ranked Set Sampling

Many techniques based on data which are drawn by Ranked Set Sampling (RSS) scheme assume that the ranking of observations is perfect. Therefore it is essential to develop some methods for testing this assumption. In this article, we propose a parametric location-scale free test for assessing the assumption of perfect ranking. The results of a simulation study in two special cases of normal and exponential distributions indicate that the proposed test performs well in comparison with its leading competitors.

Reconstructing the subsurface ocean decadal variability using surface nudging in a perfect model framework

Initialising the ocean internal variability for decadal predictability studies is a new area of research and a variety of ad hoc methods are currently proposed. In this study, we explore how nudging with sea surface temperature (SST) and salinity (SSS) can reconstruct the threedimensional variability of the ocean in a perfect model framework. This approach builds on the hypothesis that oceanic processes themselves will transport the surface information into the ocean interior as seen in ocean-only simulations. Five nudged simulations are designed to reconstruct a 150 years ‘‘target’’ simulation, defined as a portion of a long control simulation. The nudged simulations differ by the variables restored to, SST or SST + SSS, and by the area where the nudging is applied. The strength of the heat flux feedback is diagnosed from observations and the restoring coefficients for SSS use the same time-scale. We observed that this choice prevents spurious convection at high latitudes and near sea-ice border when nudging both SST and SSS. In the tropics, nudging the SST is enough to reconstruct the tropical atmosphere circulation and the associated dynamical and thermodynamical impacts on the underlying ocean. In the tropical Pacific Ocean, the profiles for temperature show a significant correlation from the surface down to 2,000 m, due to dynamical adjustment of the isopycnals. At mid-tohigh latitudes, SSS nudging is required to reconstruct both the temperature and the salinity below the seasonal thermocline. This is particularly true in the North Atlantic where adding SSS nudging enables to reconstruct the deep convection regions of the target. By initiating a previously documented 20-year cycle of the model, the SST + SSS nudging is also able to reproduce most of the AMOC variations, a key source of decadal predictability. Reconstruction at depth does not significantly improve with amount of time spent nudging and the efficiency of the surface nudging rather depends on the period/events considered. The joint SST + SSS nudging applied verywhere is the most efficient approach. It ensures that the right water masses are formed at the right surface density, the subsequent circulation, subduction and deep convection further transporting them at depth. The results of this study underline the potential key role of SSS for decadal predictability and further make the case for sustained largescale observations of this field.