scholarly journals Frontispiece: Inhibitors of Bacterial Swarming Behavior

2020 ◽  
Vol 26 (5) ◽  
Author(s):  
Sina Rütschlin ◽  
Thomas Böttcher
Keyword(s):  
Bacterial Swarming ◽  
Swarming Behavior

scholarly journals Inhibitors of Bacterial Swarming Behavior

2019 ◽  
Vol 26 (5) ◽  
pp. 964-979 ◽  
Author(s):  
Sina Rütschlin ◽  
Thomas Böttcher
Keyword(s):  
Bacterial Swarming ◽  
Swarming Behavior

scholarly journals Swarming Behavior in Plant Roots

PLoS ONE ◽  
2012 ◽  
Vol 7 (1) ◽  
pp. e29759 ◽  
Author(s):  
Marzena Ciszak ◽  
Diego Comparini ◽  
Barbara Mazzolai ◽  
Frantisek Baluska ◽  
F. Tito Arecchi ◽  
...  
Keyword(s):  
Plant Roots ◽  
Swarming Behavior

scholarly journals Rhamnolipids and their 3-(3-hydroxyalkanoyloxy)alkanoic acid precursors activate Arabidopsis innate immunity through two independent mechanisms

2020 ◽  
Author(s):  
Romain Schellenberger ◽  
Jérôme Crouzet ◽  
Arvin Nickzad ◽  
Alexander Kutschera ◽  
Tim Gerster ◽  
...  
Keyword(s):  
Immune Response ◽  
Innate Immunity ◽  
Hydroxy Fatty Acid ◽  
Receptor Kinase ◽  
Local Resistance ◽  
Swarming Motility ◽  
Lectin Receptor ◽  
Invasion Pattern ◽  
Bacterial Swarming ◽  
Motility Behavior

AbstractPlant innate immunity is activated upon perception of invasion pattern molecules by plant cell-surface immune receptors. Several bacteria of the genera Pseudomonas and Burkholderia produce rhamnolipids (RLs) from L-rhamnose and (R)-3-hydroxyalkanoate precursors (HAAs). RL and HAA secretion is required to modulate bacterial swarming motility behavior. The bulb-type lectin receptor kinase LIPOOLIGOSACCHARIDE-SPECIFIC REDUCED ELICITATION/S-DOMAIN-1-29 (LORE/SD1-29) mediates medium-chain 3-hydroxy fatty acid (mc-3-OH-FA) sensing in the plant Arabidopsis thaliana. Here, we show that the lipidic secretome from Pseudomonas aeruginosa comprising RLs, HAAs and mc-3-OH-FAs stimulates Arabidopsis immunity. HAAs, like mc-3-O-FAs, are sensed by LORE and induce canonical immune signaling and local resistance to plant pathogenic Pseudomonas infection. By contrast, RLs trigger an atypical immune response and resistance to Pseudomonas infection independent of LORE. Thus, the glycosyl moieties of RLs, albeit abolishing sensing by LORE, do not impair their ability to trigger plant defense. In addition, our results show that RL-triggered immune response is affected by the sphingolipid composition of the plasma membrane. In conclusion, RLs and their precursors released by bacteria can both be perceived by plants but through distinct mechanisms.

scholarly journals Self-organization and information transfer in Antarctic krill swarms

2021 ◽  
Author(s):  
Alicia L. Burns ◽  
Timothy M. Schaerf ◽  
Joseph T. Lizier ◽  
So Kawaguchi ◽  
Martin Cox ◽  
...  
Keyword(s):  
Information Flow ◽  
Social Factors ◽  
Information Transfer ◽  
Antarctic Krill ◽  
Keystone Species ◽  
Self Organization ◽  
Detailed Understanding ◽  
Individual Level ◽  
Directional Alignment ◽  
Swarming Behavior

AbstractAntarctic krill swarms are one of the largest known animal aggregations. However, despite being the keystone species of the Southern Ocean, little is known about how swarms are formed and maintained, and we lack a detailed understanding of the local interactions between individuals that provide the basis for these swarms. Here we analyzed the trajectories of captive, wild-caught krill in 3D to determine individual level interaction rules and quantify patterns of information flow. Our results suggest krill operate a novel form of collective organization, with measures of information flow and individual movement adjustments expressed most strongly in the vertical dimension, a finding not seen in other swarming species. In addition, local directional alignment with near neighbors, and strong regulation of both direction and speed relative to the positions of groupmates suggest social factors are vital to the formation and maintenance of swarms. This research represents a first step in understanding the fundamentally important swarming behavior of krill.

Verification of Acoustic-Wave-Oriented Simple State Estimation and Application to Swarm Navigation

2021 ◽  
Vol 33 (1) ◽  
pp. 119-128
Author(s):  
Tomoha Kida ◽  
◽  
Yuichiro Sueoka ◽  
Hiro Shigeyoshi ◽  
Yusuke Tsunoda ◽  
...  
Keyword(s):  
State Estimation ◽  
Disaster Response ◽  
High Speed ◽  
Behavioral Control ◽  
Group Behavior ◽  
Sound Waves ◽  
Swarm Robots ◽  
Swarming Behavior ◽  
High Speed Image Processing ◽  
Share Information

Cooperative swarming behavior of multiple robots is advantageous for various disaster response activities, such as search and rescue. This study proposes an idea of communication of information between swarm robots, especially for estimating the orientation and direction of each robot, to realize decentralized group behavior. Unlike the conventional camera-based systems, we developed robots equipped with a speaker array system and a microphone system to utilize the time difference of arrival (TDoA). Sound waves outputted by each robot was used to estimate the relative direction and orientation. In addition, we attempt to utilize two characteristics of sound waves in our experiments, namely, diffraction and superposition. This paper also investigates the accuracy of state estimation in cases where the robots output sounds simultaneously and are not visible to each other. Finally, we applied our method to achieve behavioral control of a swarm of five robots, and demonstrated that the leader robot and follower robots exhibit good alignment behavior. Our methodology is useful in scenarios where steps or obstacles are present, in which cases camera-based systems are rendered unusable because they require each robot to be visible to each other in order to collect or share information. Furthermore, camera-based systems require expensive devices and necessitate high-speed image processing. Moreover, our method is applicable for behavioral control of swarm robots in water.

The Role of Photoreception in the Swarming Behavior of the Copepod Dioithona Oculata

2021 ◽  
pp. 301-313
Author(s):  
E. J. Buskey ◽  
J. O. Peterson ◽  
J. W. Ambler
Keyword(s):  
Swarming Behavior

Illustration of Centralized Command and Control for Flocking Behavior

2014 ◽  
Vol 5 (2) ◽  
pp. 1-22
Author(s):  
Sami Oweis ◽  
Subramaniam Ganesan ◽  
Ka C Cheok
Keyword(s):  
Obstacle Avoidance ◽  
Command And Control ◽  
Flow Chart ◽  
Controlled Systems ◽  
Swarm Robots ◽  
Group A ◽  
Swarming Behavior ◽  
And Control ◽  
Flocking Behavior ◽  
Ground Systems

Flocking is a term that describes the behavior of a group of birds (a “flock”) in flight, or the swarming behavior of insects. This paper presents detailed information about how to use the flocking techniques to control a group of embedded controlled systems - ‘'Boids''- such as ground systems (robotic vehicles/ swarm robots). Each one of these systems collectively moves inside/outside of a building to reach a target. The flocking behavior is implemented on a server-based control, which processes each of the boids' properties e.g. position, speed & target. Subsequently, the server will assign the appropriate move to a specific boid. The calculated information will be used locally to control and direct the movements/flocking for each boid in the group. A simulation technique and detailed flow chart is presented. In addition to Reynolds three original rules for flocking, two other rules- targeting obstacle avoidance - are presented-. Our result shows that the obstacles' avoiding rule was utilized to ensure that the flock didn't collide with obstacles in each of the boids' paths.

scholarly journals Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization

2020 ◽  
Vol 10 (24) ◽  
pp. 8961
Author(s):  
Peng-Yeng Yin ◽  
Po-Yen Chen ◽  
Ying-Chieh Wei ◽  
Rong-Fuh Day
Keyword(s):  
Global Optimization ◽  
Firefly Algorithm ◽  
State Of The Art ◽  
Pattern Search ◽  
Experimental Result ◽  
Adaptive Memory ◽  
Glowworm Swarm Optimization ◽  
Metaheuristic Methods ◽  
Swarming Behavior ◽  
Better Than

Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are convinced with much evidence that the EAs can be more effective if appropriate responsive strategies contained in the adaptive memory programming (AMP) domain are considered in the execution. This paper contemplates this line and proposes the Cyber Firefly Algorithm (CFA), which integrates key elements of the GSO and the FA and further proliferates the advantages by featuring the AMP-responsive strategies including multiple guiding solutions, pattern search, multi-start search, swarm rebuilding, and the objective landscape analysis. The robustness of the CFA has been compared against the GSO, FA, and several state-of-the-art metaheuristic methods. The experimental result based on intensive statistical analyses showed that the CFA performs better than the other algorithms for global optimization of benchmark functions.

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