Genome sequence of the pattern forming Paenibacillus vortex bacterium reveals potential for thriving in complex environments

<p>Abstract</p> <p>Background</p> <p>The pattern-forming bacterium <it>Paenibacillus vortex </it>is notable for its advanced social behavior, which is reflected in development of colonies with highly intricate architectures. Prior to this study, only two other <it>Paenibacillus </it>species (<it>Paenibacillus </it>sp. JDR-2 and <it>Paenibacillus larvae</it>) have been sequenced. However, no genomic data is available on the <it>Paenibacillus </it>species with pattern-forming and complex social motility. Here we report the <it>de novo </it>genome sequence of this Gram-positive, soil-dwelling, sporulating bacterium.</p> <p>Results</p> <p>The complete <it>P. vortex </it>genome was sequenced by a hybrid approach using 454 Life Sciences and Illumina, achieving a total of 289× coverage, with 99.8% sequence identity between the two methods. The sequencing results were validated using a custom designed Agilent microarray expression chip which represented the coding and the non-coding regions. Analysis of the <it>P. vortex </it>genome revealed 6,437 open reading frames (ORFs) and 73 non-coding RNA genes. Comparative genomic analysis with 500 complete bacterial genomes revealed exceptionally high number of two-component system (TCS) genes, transcription factors (TFs), transport and defense related genes. Additionally, we have identified genes involved in the production of antimicrobial compounds and extracellular degrading enzymes.</p> <p>Conclusions</p> <p>These findings suggest that <it>P. vortex </it>has advanced faculties to perceive and react to a wide range of signaling molecules and environmental conditions, which could be associated with its ability to reconfigure and replicate complex colony architectures. Additionally, <it>P. vortex </it>is likely to serve as a rich source of genes important for agricultural, medical and industrial applications and it has the potential to advance the study of social microbiology within Gram-positive bacteria.</p

Vision-based hand-gesture applications

The article of record as published may be found at http://dx.doi.org/10.10.1145/1897816.1897838There is strong evidence that future human-computer interfaces will enable more natural, intuitive communication between people and all kinds of sensor-based devices, thus more closely resembling human-human communication. Progress in the field of human-computer interaction has introduced innovative technologies that empower users to interact with computer systems in increasingly natural and intuitive ways; systems adopting them show increased efficiency, speed, power, and realism. However, users comfortable with traditional interaction methods like mice and keyboards are often unwilling to embrace new, alternative interfaces. Ideally, new interface technologies should be more accessible without requiring long periods of learning and adaptation. They should also provide more natural human-machine communication. As described in Myron Krueger’s pioneering 1991 book Artificial Reality “natural interaction” means voice and gesture. Pursuing this vision requires tools and features that mimic the principles of human communication. Employing hand-gesture communication, such interfaces have been studied and developed by many researchers over the past 30 years in multiple application areas. It is thus worthwhile to review these efforts and identify the requirements needed to win general social acceptance.National Research Council Research Associate AwardPaul Ivanier Center for Robotics Research & Production at Ben-Gurion University of the Nege

Human-Robot Collaborative Learning of a Bag Shaking Trajectory

Abstract—This paper presents a collaborative reinforcement learning algorithm

Virtual Reality Telerobotic System

This paper describes a telerobotic system operated through a virtual reality (VR) interface. A least squares method is used to find the transformation mapping, from the virtual to real environments. Results revealed an average transformation error of 3mm. The system was tested for the task of planning minimum time shaking trajectories to discharge the contents of a suspicious package onto a workstation platform. Performance times to carry out the task directly through the VR interface showed rapid learning, reaching standard time (288 seconds) within 7 to 8 trials- exhibiting a learning rate of 0.79. 1

Bag Classification Using Support Vector Machines

Abstract. This paper describes the design of multi-category support vector machines (SVMs) for classification of bags. To train and test the SVMs a collection of 120 images of different types of bags were used (backpacks, small shoulder bags, plastic flexible bags, and small briefcases). Tests were conducted to establish the best polynomial and Gaussian RBF (radial basis function) kernels. As it is well known that SVMs are sensitive to the number of features in pattern classification applications, the performance of the SVMs as a function of the number and type of features was also studied. Our goal here, in feature selection is to obtain a smaller set of features that accurately represent the original set. A K-fold cross validation procedure with three subsets was applied to assure reliability. In a kernel optimization experiment using nine popular shape features (area, bounding box ratio, major axis length, minor axis length, eccentricity, equivalent diameter, extent, roundness and convex perimeter), a classification rate of 95 % was achieved using a polynomial kernel with degree six, and a classification rate of 90% was achieved using a RBF kernel with 27 sigma. To improve these results a feature selection procedure was performed. Using the optimal feature set, comprised of bounding box ratio, major axis length, extent and roundness, resulted in a classification rate of 96.25 % using a polynomial kernel with degree of nine. The collinearity between the features was confirmed using principle component analysis, where a reduction to four components accounted for 99.3 % of the variation for each of the bag types

From the Lab to the Field: Combined Application of Plant-Growth-Promoting Bacteria for Mitigation of Salinity Stress in Melon Plants

Soil salinization is a major and increasing problem adversely impacting plant growth and crop production. Accordingly, coping with this problem has become a central topic in agriculture. In this study, we address this issue by evaluating the potential effectiveness of two bacterial species, Azospirillum brasilense and Paenibacillus dendritiformis, in enhancing growth and yield of melon and tomato plants under salinity stress. In vitro laboratory experiments indicated that these bacteria can efficiently colonize plant roots, and increase root length (25–33%) and root biomass (46–210%) of three melon plant varieties under saline stress. Similarly, greenhouse experiments showed that these bacteria significantly induced root (78–102%) and shoot weights (37–57%) of the three melon varieties irrigated with saline water. Tomato plants grown under the same conditions did not exhibit growth deficiency upon exposure to the saline stress and their growth was not enhanced in response to bacterial inoculation. Interestingly, saline-stressed melon plants inoculated with P. dendritiformis and A. brasilense exhibited lower total antioxidant activity compared to un-inoculated plants (80% vs. 60% of DPPH radical scavenging activity, respectively), suggesting that the inoculated plants experienced lower stress levels. These positive effects were further manifested by an increase of 16% in the crop yield of melon plants grown in the field under standard agricultural fertilization practices, but irrigated with saline water. Overall, these results demonstrate the beneficial effects of two plant-growth-promoting rhizobacteria, which can significantly alleviate the negative outcome of salt stress