Protein Kinase PKR Mediates the Apoptosis Induction and Growth Restriction Phenotypes of C Protein-Deficient Measles Virus

The measles virus (MV) accessory proteins V and C play important roles in MV replication and pathogenesis. Infection with recombinant MV lacking either V or C causes more cell death than infection with the parental vaccine-equivalent virus (MVvac), and C-deficient virus grows poorly relative to the parental virus. Here, we show that a major effector of the C phenotype is the RNA-dependent protein kinase PKR. Using human HeLa cells stably deficient in PKR as a result of RNA interference-mediated knockdown (PKR^kd cells), we demonstrated that a reduction in PKR partially rescued the growth defect of C knockout (C^ko) virus but had no effect on the growth of either wild-type (WT) or V knockout (V^ko) virus. Increased growth of the C^ko virus in PKR^kd cells correlated with increased viral protein expression, while defective growth and decreased protein expression in PKR-sufficient cells correlated with increased phosphorylation of PKR and the α subunit of eukaryotic initiation factor 2. Furthermore, infection with WT, V^ko, or especially C^ko virus caused significantly less apoptosis in PKR^kd cells than in PKR-sufficient cells. Although apoptosis induced by C^ko virus infection in PKR-sufficient cells was blocked by a caspase antagonist, the growth of C^ko virus was not restored to the WT level by treatment with this pharmacologic inhibitor. Taken together, these results indicate that PKR plays an important antiviral role during MV infection but that the virus growth restriction by PKR is not dependent upon the induction of apoptosis. Furthermore, the results establish that a principal function of the MV C protein is to antagonize the proapoptotic and antiviral activities of PKR.     

Analysis and visualization of H7 influenza using genomic,evolutionary and geographic information in a modular web service

We have reported previously on use of a web‐based application, Supramap ( http://supramap.org ) for the study of biogeographic, genotypic, and phenotypic evolution. Using Supramap we have developed maps of the spread of drug‐resistant influenza and host shifts in H1N1 and H5N1 influenza and coronaviruses such as SARS. Here we report on another zoonotic pathogen, H7 influenza, and provide an update on the implementation of Supramap as a web service. We find that the emergence of pathogenic strains of H7 is labile with many transitions from high to low pathogenicity, and from low to high pathogenicity. We use Supramap to put these events in a temporal and geospatial context. We identify several lineages of H7 influenza with biomarkers of high pathogenicity in regions that have not been reported in the scientific literature. The original implementation of Supramap was built with tightly coupled client and server software. Now we have decoupled the components to provide a modular web service for POY ( http://poyws.org ) that can be consumed by a data provider to create a novel application. To demonstrate the web service, we have produced an application, Geogenes ( http://geogenes.org ). Unlike in Supramap, in which the user is required to create and upload data files, in Geogenes the user works from a graphical interface to query an underlying dataset. Geogenes demonstrates how the web service can provide underlying processing for any sequence and metadata database. © The Willi Hennig Society 2012.     

A Temporally Explicit Production Efficiency Model for Fuel Load Allocation in Southern Africa

Abstract We present a regional fuel load model (1 km² spatial resolution) applied in the southern African savanna region. The model is based on a patch-scale production efficiency model (PEM) scaled up to the regional level using empirical relationships between patch-scale behavior and multi-source remote sensing data (spatio-temporal variability of vegetation and climatic variables). The model requires the spatial distribution of woody vegetation cover, which is used to determine separate respiration rates for tree and grass. Net primary production, grass and tree leaf death, and herbivory are also taken into account in this mechanistic modeling approach. The fuel load model has been calibrated and validated from independent measurements taken from savanna vegetation in Africa southward from the equator. A sensitivity analysis on the effect of climate variables (incoming radiation, air temperature, and precipitation) has been conducted to demonstrate the strong role that water availability has in determining productivity and subsequent fuel load over the southern African region. The model performance has been tested in four different areas representative of a regional increasing rainfall gradient—Etosha National Park, Namibia, Mongu and Kasama, Zambia, as well as in Kruger National Park, South Africa. Within each area, we analyze model output from three different magnitudes of canopy coverage (<5, 30, and 50%). We find that fuel load ranges predicted by the model are globally in agreement with field measurements for the same year. High rainfall sustains green herbaceous production late in the dry season and delays tree leaf litter production. Effect of water on production varies across the rainfall gradient with delayed start of green material production in more arid regions.     

Multiple origins of Southern Hemisphere Anemone (Ranunculaceae) based on plastid and nuclear sequence data

 Using two molecular data sets, the plastid atpB-rbcL intergenic spacer region and the nuclear ribosomal internal transcribed spacer regions (ITS), the taxonomic affinities of two newly available Anemone species from the Southern Hemisphere were tested. From previous work based on morphology and geographic distribution, it was assumed that A. tenuicaulis from New Zealand was most closely related to the Tasmanian A. crassifolia, whereas the affinity of A. antucensis from Chile and Argentina was regarded as uncertain. Analyses of molecular sequence data from these and 18 other species of Anemone s.lat. (with Clematis as outgroup) result in trees largely congruent with past analyses based on morphology and plastid restriction site data. They strongly support A. richardsonii and A. canadensis (with boreal distributions in the Northern Hemisphere) as paraphyletic to a well supported Southern Hemisphere clade consisting of A. antucensis and A. tenuicaulis. This group of four species is part of an otherwise predominantly Northern Hemisphere assemblage (subgenus Anemonidium s.lat., chromosome base number x=7), including A. narcissiflora, A. obtusiloba, A. keiskeana and A. (=Hepatica) americana. All other austral species included in the present sampling, A. crassifolia (Tasmania), A. knowltonia (=Knowltonia capensis), and A. caffra (both South African), form a separate clade, sister to A. (=Pulsatilla) occidentalis and other Northern Hemisphere anemones (subgenus Anemone s.lat., x=8). Possible phytogeographical links of the Southern Hemisphere species are discussed. Received April 23, 2001 Accepted October 4, 2001     

A Mathematical Framework for Combining Decisions of Multiple Experts toward Accurate and Remote Diagnosis of Malaria Using Tele-Microscopy

We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.e., infected vs. uninfected). To tackle this general medical diagnosis problem, we propose a probabilistic algorithm to fuse the decisions made by trained medical experts to robustly achieve higher levels of accuracy when compared to individual experts making such decisions. By modelling the decisions of experts as a three component mixture model and solving for the underlying parameters using the Expectation Maximisation algorithm, we demonstrate the efficacy of our approach which significantly improves the overall diagnostic accuracy of malaria infected cells. Additionally, we present a mathematical framework for performing ‘slide-level’ diagnosis by using individual ‘cell-level’ diagnosis data, shedding more light on the statistical rules that should govern the routine practice in examination of e.g., thin blood smear samples. This framework could be generalized for various other tele-pathology needs, and can be used by trained experts within an efficient tele-medicine platform.     

Bayesian Nonparametric Modeling for Comparison of Single-Neuron Firing Intensities

Summary .  We propose a fully inferential model-based approach to the problem of comparing the firing patterns of a neuron recorded under two distinct experimental conditions. The methodology is based on nonhomogeneous Poisson process models for the firing times of each condition with flexible nonparametric mixture prior models for the corresponding intensity functions. We demonstrate posterior inferences from a global analysis, which may be used to compare the two conditions over the entire experimental time window, as well as from a pointwise analysis at selected time points to detect local deviations of firing patterns from one condition to another. We apply our method on two neurons recorded from the primary motor cortex area of a monkey's brain while performing a sequence of reaching tasks.