Plasmids and the virulence of Proteus mirabilis

The effects of large plasmids on different virulence characteristics of Proteus mirabilis strains mostly from clinical origin were studied. Moreover the inhibitory effect of urea and its derivatives on the swarming property of the strains was investigated. A. All strains were screened for plasmid detection, antibiotic resistance and swarming ability. B. Three multiresistant plasmid-carrying strains (PM5, P49 and P991) were cured and two transconjugants (G9pPM5 and G9pP49) were obtained by conjugation between two p+ donors (PM5 and P49) and one p- recipient (G9). C. By comparing the virulence properties of cured and transconjugant strains with their parental isolates it was found that; 1. Plasmids confer resistance to P. mirabilis strains against one or more antibiotics. 2. The presence of most plasmids reduces the swarming ability of the strains. 3. Plasmids affect the motility and flagellation of P. mirabilis strains. 4. Plasmids enhance the adherence property of their host strains to inert surfaces and uroepithelial cells as well as autoagglutination. 5. Plasmids increase the hydrophobicity of P. mirabilis strains. 6. The presence of plasmids reduced the growth rate of the strains. This effect was more apparent in iron-restricted medium. 7. Plasmids reduced the growth rate of their host strains in the presence of detergent (SDS). 8. The presence of plasmids reduced the survival of P. mirabilis strains in human and rabbit serum. 9. Plasmids decreased the survival of the strains in aquatic systems. 10. Plasmids reduced the production of urease and increased some others such as haemolysin and protease. D. Urea and some of its relatives inhibited the swarming property of P. mirabilis strains and this effect was concentration dependent

Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions

The focus of this paper is on solving multi-robot planning problems in continuous spaces with partial observability. Decentralized partially observable Markov decision processes (Dec-POMDPs) are general models for multi-robot coordination problems, but representing and solving Dec-POMDPs is often intractable for large problems. To allow for a high-level representation that is natural for multi-robot problems and scalable to large discrete and continuous problems, this paper extends the Dec-POMDP model to the decentralized partially observable semi-Markov decision process (Dec-POSMDP). The Dec-POSMDP formulation allows asynchronous decision-making by the robots, which is crucial in multi-robot domains. We also present an algorithm for solving this Dec-POSMDP which is much more scalable than previous methods since it can incorporate closed-loop belief space macro-actions in planning. These macro-actions are automatically constructed to produce robust solutions. The proposed method's performance is evaluated on a complex multi-robot package delivery problem under uncertainty, showing that our approach can naturally represent multi-robot problems and provide high-quality solutions for large-scale problems

A New Modified Boundary Element Method (MBEM) for Boundary Domain Integral Method (BDIM)

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Hemi-ESES associated with agenesis of the corpus callosum and normal cognition.

Corpus callosum plays the important role in bilateral synchronous expression of focal discharges of ESES. Sparing dominant hemisphere form continuous spike and slow waves during sleep accounts for normal cognitive scores. Early detection and treatment of ESES have a great impact on cognitive and language scores and final prognosis

FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration

This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots' poses. The novel proposed solution utilizes state-of-the-art place recognition learned descriptors, that through the framework's main pipeline, offer a fast and robust region overlap estimation, hence eliminating the need for the time-consuming global feature extraction and feature matching process that is typically used in 3D map integration. The region overlap estimation provides a homogeneous rigid transform that is applied as an initial condition in the point cloud registration algorithm Fast-GICP, which provides the final and refined alignment. The efficacy of the proposed framework is experimentally evaluated based on multiple field multi-robot exploration missions in underground environments, where both ground and aerial robots are deployed, with different sensor configurations.Comment: to be publishe