The periplasm of Gram-negative bacteria is a complex, highly crowded molecular environment. Little is known about how antibiotics move across the periplasm and the interactions they experience. Here, ...atomistic molecular dynamics simulations are used to study the antibiotic polymyxin B1 within models of the periplasm, which are crowded to different extents. We show that PMB1 is likely to be able to “hitchhike” within the periplasm by binding to lipoprotein carriers—a previously unreported passive transport route. The simulations reveal that PMB1 forms both transient and long-lived interactions with proteins, osmolytes, lipids of the outer membrane, and the cell wall, and is rarely uncomplexed when in the periplasm. Furthermore, it can interfere in the conformational dynamics of native proteins. These are important considerations for interpreting its mechanism of action and are likely to also hold for other antibiotics that rely on diffusion to cross the periplasm.
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•Simulations of polymyxin B1 in the crowded environment of the E. coli periplasm•LolA may be able to carry PMB1 through the periplasm
Atomistic molecular simulations are used to study the antibiotic polymyxin B1 within models of the periplasm crowded to different extents. Pedebos et al. show that PMB1 may be able to “hitchhike” within the periplasm by binding to lipoprotein carriers, a transport route that has not previously been considered.
This paper focuses on the joint design of transmit waveforms and receive filters for airborne multiple-input-multiple-output (MIMO) radar systems in spectrally crowded environments. The purpose is to ...maximize the output signal-to-interference-plus-noise-ratio (SINR) in the presence of signal-dependent clutter. To improve the practicability of the radar waveforms, both a multi-spectral constraint and a peak-to-average-power ratio (PAPR) constraint are imposed. A cyclic method is derived to iteratively optimize the transmit waveforms and receive filters. In particu-lar, to tackle the encountered non-convex constrained fractional programming in designing the waveforms (for fixed filters), we resort to the Dinkelbach's transform, minorization-maximization (MM), and leverage the alternating direction method of multipliers (ADMM). We highlight that the proposed algorithm can iterate from an infeasible initial point and the waveforms at convergence not only satisfy the stringent constraints, but also attain superior performance.
We derive exact results for the dynamics of Rouse model in crowded environment modeled by stochastically varying diffusivity, the concept of “diffusing diffusivity”. We specifically generalize the ...initial analytical formulation of this concept of diffusivity in single particle diffusion to many particle systems with localized interactions, particularly to chains of connected beads, the Rouse model. Independent Rouse modes are allowed to diffuse with stochastically varying time dependent diffusivities to model diffusion in the crowded rearranging environment, where zeroth mode represents the chain center of mass coordinate. We develop an analytical formalism to calculate the probability distribution of these individual mode displacements and subsequently average bead displacement distribution of the Rouse chain and its moments in terms of these modes. The resulting diffusion behavior of thus modified Rouse model is a function of mode resolved parameters which model local interactions and crowded environment collectively. Our analytically tractable implementation of the “diffusing diffusivity” concept to Rouse model is one of its kind that yields non-Gaussian diffusion with the scope of being anomalous and plausibly applied to dynamics in polymeric liquids.
•Diffusing diffusivity applied to many particle systems with localized interactions.•Implementation of diffusing diffusivity to Rouse model yields non-Gaussian diffusion.•Rouse model with diffusing diffusivity can explain anomalous diffusion in polymer melts.
Mobile robot autonomous navigation in large-scale environments with crowded dynamic objects and static obstacles is still an essential yet challenging task. Recent works have demonstrated the ...potential of using deep reinforcement learning to enable autonomous navigation in crowds. However, only considering the human-robot interactions results in short-sighted and unsafe behaviors, and they typically use hand-crafted features and assume the global observation range, leading to large performance declines in large-scale crowded environments. Recent advances have shown the power of graph neural networks to learn local interactions among surrounding objects. In this paper, we consider autonomous navigation task in large-scale environments with crowded static and dynamic objects (such as humans). Particularly, local interactions among dynamic objects are learned for better-understanding their moving tendency and relational graph learning is introduced for aggregating both the object-object interactions and object-robot interactions. In addition, local observations are transformed into graphical inputs to achieve the scalability to various number of surrounding dynamic objects and various static obstacle patterns, and the globally guided reinforcement learning strategy is introduced to achieve the fixed-sized learning model even in large-scale complex environments. Simulation results validate our generalizability to various environments and advanced performance compared with existing works in large-scale crowded environments. In particular, our method with only local observations performs better than the benchmarks with global complete observability. Finally, physical robotic experiments demonstrate our effectiveness and practical applicability in real scenarios.
This article focuses on the joint design of transmit waveforms and receive filters for airborne multiple-input–multiple-output radar systems in spectrally crowded environments. The purpose is to ...maximize the output signal-to-interference-plus-noise-ratio in the presence of signal-dependent clutter. To improve the practicability of the radar waveforms, both a multispectral constraint and a peak-to-average-power ratio constraint are imposed. A cyclic method is derived to iteratively optimize the transmit waveforms and receive filters. In particular, to tackle the encountered nonconvex constrained fractional programming in designing the waveforms (for fixed filters), we resort to the Dinkelbach's transform, minorization–maximization, and leverage the alternating direction method of multipliers. We highlight that the proposed algorithm can iterate from an infeasible initial point and the waveforms at convergence not only satisfy the stringent constraints, but also attain superior performance.
Swarming is a macroscopic phenomenon in which surface bacteria organize into a motile population. The flagellar motor that drives swarming in
is powered by stators MotAB and MotCD. Deletion of the ...MotCD stator eliminates swarming, whereas deletion of the MotAB stator enhances swarming. Interestingly, we measured a strongly asymmetric stator availability in the wild-type (WT) strain, with MotAB stators produced at an approximately 40-fold higher level than MotCD stators. However, utilization of MotCD stators in free swimming cells requires higher liquid viscosities, while MotAB stators are readily utilized at low viscosities. Importantly, we find that cells with MotCD stators are ~10× more likely to have an active motor compared to cells uses the MotAB stators. The spectrum of motility intermittency can either cooperatively shut down or promote flagellum motility in WT populations. In
, transition from a static solid-like biofilm to a dynamic liquid-like swarm is not achieved at a single critical value of flagellum torque or stator fraction but is collectively controlled by diverse combinations of flagellum activities and motor intermittencies via dynamic stator utilization. Experimental and computational results indicate that the initiation or arrest of flagellum-driven swarming motility does not occur from individual fitness or motility performance but rather related to concepts from the "jamming transition" in active granular matter.IMPORTANCEIt is now known that there exist multifactorial influences on swarming motility for
, but it is not clear precisely why stator selection in the flagellum motor is so important. We show differential production and utilization of the stators. Moreover, we find the unanticipated result that the two motor configurations have significantly different motor intermittencies: the fraction of flagellum-active cells in a population on average with MotCD is active ~10× more often than with MotAB. What emerges from this complex landscape of stator utilization and resultant motor output is an intrinsically heterogeneous population of motile cells. We show how consequences of stator recruitment led to swarming motility and how the stators potentially relate to surface sensing circuitry.
This study considers designing constant‐modulus waveforms for multiple‐input‐multiple‐output radar in spectrally crowded environment. The mutual information is used as the waveform design metric. A ...constant‐modulus constraint is imposed on the waveforms to improve the hardware efficiency. To enhance the spectral compatibility of the transmit waveforms, a precise control on the interference energy produced on each reserved bandwidth is considered. To tackle the multi‐spectrally constrained waveform design problem, a minorisation‐maximisation based method is developed, and a quadratic function to minorise the objective function is derived. Then an alternating direction method of multipliers is used to tackle the quadratically constrained quadratic programming problem at each iteration. The effectiveness of the presented algorithm is demonstrated via simulation results.
This paper considers designing constant‐modulus waveforms for multiple‐input‐multiple‐output radar in spectrally crowded environment. To improve the spectral compatibility of the transmit waveforms, we consider a precise control on the interference energy produced on each reserved bandwidth. To tackle the multi‐spectrally constrained waveform design problem, we use a minorisation‐maximisation based method and an alternating direction method of multipliers to tackle the quadratically constrained quadratic programming problem at each iteration.
To achieve robot navigation in crowded environments having high densities of moving people, it is insufficient to simply consider humans as moving obstacles and avoid collisions with them. That is, ...the impact of an approaching robot on human movements must be considered as well. Moreover, various navigation methods have been tested in their own environments in the literature, which made them difficult to compare with one another. Thus, we propose an autonomous robot navigation method in densely crowded environments for data-based predictions of robot-human interactions, together with a reproducible experimental test under controlled conditions. Based on localized positional relationships with humans, this method extracts multiple alternative paths, which can implement either following or avoidance, and selects an optimal path based on time efficiency. Each path is selected using neural networks, and the various paths are evaluated by predicting the position after a given amount of time has elapsed. These positions are then used to calculate the time required to reach a certain target position to ensure that the optimal path can be determined. We trained the predictor using simulated data and conducted experiments using an actual mobile robot in an environment where humans were walking around. Using our proposed method, collisions were avoided more effectively than when conventional navigation methods were used, and navigation was achieved with good time efficiency, resulting in an overall reduction in interference with humans. Thus, the proposed method enables an effective navigation in a densely crowded environment, while collecting human-interaction experience for further improvement of its performance in the future.
A system formed by a crowded environment of catalytic obstacles and complex oscillatory chemical reactions is studied. The obstacles are static spheres of equal radius, which are placed in a random ...way. The chemical reactions are carried out in a fluid following a multiparticle collision scheme where the mass, energy and local momentum are conserved. Firstly, it is explored how the presence of catalytic obstacles changes the oscillatory dynamics from a limit cycle to a fixed point reached after a damping. The damping is characterized by the decay constant, which grows linearly with volume fraction for low values of the mesoscale collision time and the catalytic reaction constant. Additionally, it is shown that, although the distribution of obstacles is random, there are regions in the system where the catalytic chemical reactions are favored. This entails that in average the radius of gyrations of catalytic chemical reaction does not match with the radius of gyration of obstacles, that is, clusters of reactions emerge on the catalytic obstacles, even when the diffusion is significant.
•New measurements based on the concept of activity per agent are proposed.•The variance of the system activity can be used to indicate the critical points of the transition.•The frequency distribution of system activity is able to show the order of the phase transition.•A power law dependence between cluster activity and cluster size is verified.
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