5 research outputs found
KAHRP dynamically relocalizes to remodeled actin junctions and associates with knob spirals in Plasmodium falciparum-infected erythrocytes
The knob-associated histidine-rich protein (KAHRP) plays a pivotal role in the pathophysiology of Plasmodium falciparum malaria by forming membrane protrusions in infected erythrocytes, which anchor parasite-encoded adhesins to the membrane skeleton. The resulting sequestration of parasitized erythrocytes in the microvasculature leads to severe disease. Despite KAHRP being an important virulence factor, its physical location within the membrane skeleton is still debated, as is its function in knob formation. Here, we show by super-resolution microscopy that KAHRP initially associates with various skeletal components, including ankyrin bridges, but eventually colocalizes with remnant actin junctions. We further present a 35 Å map of the spiral scaffold underlying knobs and show that a KAHRP-targeting nanoprobe binds close to the spiral scaffold. Single-molecule localization microscopy detected ~60 KAHRP molecules/knob. We propose a dynamic model of KAHRP organization and a function of KAHRP in attaching other factors to the spiral scaffold
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Comparative Study of Message Passing and Shared Memory Parallel Programming Models in Neural Network Training
It is presented a comparative performance study of a coarse grained parallel neural network training code, implemented in both OpenMP and MPI, standards for shared memory and message passing parallel programming environments, respectively. In addition, these versions of the parallel training code are compared to an implementation utilizing SHMEM the native SGI/CRAY environment for shared memory programming. The multiprocessor platform used is a SGI/Cray Origin 2000 with up to 32 processors. It is shown that in this study, the native CRAY environment outperforms MPI for the entire range of processors used, while OpenMP shows better performance than the other two environments when using more than 19 processors. In this study, the efficiency is always greater than 60% regardless of the parallel programming environment used as well as of the number of processors
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A parallel neural network training algorithm for control of discrete dynamical systems.
In this work we present a parallel neural network controller training code, that uses MPI, a portable message passing environment. A comprehensive performance analysis is reported which compares results of a performance model with actual measurements. The analysis is made for three different load assignment schemes: block distribution, strip mining and a sliding average bin packing (best-fit) algorithm. Such analysis is crucial since optimal load balance can not be achieved because the work load information is not available a priori. The speedup results obtained with the above schemes are compared with those corresponding to the bin packing load balance scheme with perfect load prediction based on a priori knowledge of the computing effort. Two multiprocessor platforms: a SGI/Cray Origin 2000 and a IBM SP have been utilized for this study. It is shown that for the best load balance scheme a parallel efficiency of over 50% for the entire computation is achieved by 17 processors of either parallel computers