The chaotic milling behaviors of interacting swarms after collision.

We consider the problem of characterizing the dynamics of interacting swarms after they collide and form a stationary center of mass. Modeling efforts have shown that the collision of near head-on interacting swarms can produce a variety of post-collision dynamics including coherent milling, coherent flocking, and scattering behaviors. In particular, recent analysis of the transient dynamics of two colliding swarms has revealed the existence of a critical transition whereby the collision results in a combined milling state about a stationary center of mass. In the present work, we show that the collision dynamics of two swarms that form a milling state transitions from periodic to chaotic motion as a function of the repulsive force strength and its length scale. We used two existing methods as well as one new technique: Karhunen-Loeve decomposition to show the effective modal dimension chaos lives in, the 0-1 test to identify chaos, and then constrained correlation embedding to show how each swarm is embedded in the other when both swarms combine to form a single milling state after collision. We expect our analysis to impact new swarm experiments which examine the interaction of multiple swarms.

Stability of Kuramoto networks subject to large and small fluctuations from heterogeneous and spatially correlated noise.

Oscillatory networks subjected to noise are broadly used to model physical and technological systems. Due to their nonlinear coupling, such networks typically have multiple stable and unstable states that a network might visit due to noise. In this article, we focus on the assessment of fluctuations resulting from heterogeneous and spatially correlated noise inputs on Kuramoto model networks. We evaluate the typical, small fluctuations near synchronized states and connect the network variance to the overlap between stable modes of synchronization and the input noise covariance. Going beyond small to large fluctuations, we introduce the indicator mode approximation that projects the dynamics onto a single amplitude dimension. Such an approximation allows for estimating rates of fluctuations to saddle instabilities, resulting in phase slips between connected oscillators. Statistics for both regimes are quantified in terms of effective noise amplitudes that are compared and contrasted for several noise models. Bridging the gap between small and large fluctuations, we show that a larger network variance does not necessarily lead to higher rates of large fluctuations.

A chemosensory-like histidine kinase is dispensable for chemotaxis in vitro but regulates the virulence of Borrelia burgdorferi through modulating the stability of RpoS.

As an enzootic pathogen, the Lyme disease bacterium Borrelia burgdorferi possesses multiple copies of chemotaxis proteins, including two chemotaxis histidine kinases (CHK), CheA1 and CheA2. Our previous study showed that CheA2 is a genuine CHK that is required for chemotaxis; however, the role of CheA1 remains mysterious. This report first compares the structural features that differentiate CheA1 and CheA2 and then provides evidence to show that CheA1 is an atypical CHK that controls the virulence of B. burgdorferi through modulating the stability of RpoS, a key transcriptional regulator of the spirochete. First, microscopic analyses using green-fluorescence-protein (GFP) tags reveal that CheA1 has a unique and dynamic cellular localization. Second, loss-of-function studies indicate that CheA1 is not required for chemotaxis in vitro despite sharing a high sequence and structural similarity to its counterparts from other bacteria. Third, mouse infection studies using needle inoculations show that a deletion mutant of CheA1 (cheA1mut) is able to establish systemic infection in immune-deficient mice but fails to do so in immune-competent mice albeit the mutant can survive at the inoculation site for up to 28 days. Tick and mouse infection studies further demonstrate that CheA1 is dispensable for tick colonization and acquisition but essential for tick transmission. Lastly, mechanistic studies combining immunoblotting, protein turnover, mutagenesis, and RNA-seq analyses reveal that depletion of CheA1 affects RpoS stability, leading to reduced expression of several RpoS-regulated virulence factors (i.e., OspC, BBK32, and DbpA), likely due to dysregulated clpX and lon protease expression. Bulk RNA-seq analysis of infected mouse skin tissues further show that cheA1mut fails to elicit mouse tnf-α, il-10, il-1ß, and ccl2 expression, four important cytokines for Lyme disease development and B. burgdorferi transmigration. Collectively, these results reveal a unique role and regulatory mechanism of CheA1 in modulating virulence factor expression and add new insights into understanding the regulatory network of B. burgdorferi.

Whole genome sequencing of human Borrelia burgdorferi isolates reveals linked blocks of accessory genome elements located on plasmids and associated with human dissemination.

Lyme disease is the most common vector-borne disease in North America and Europe. The clinical manifestations of Lyme disease vary based on the genospecies of the infecting Borrelia burgdorferi spirochete, but the microbial genetic elements underlying these associations are not known. Here, we report the whole genome sequence (WGS) and analysis of 299 B. burgdorferi (Bb) isolates derived from patients in the Eastern and Midwestern US and Central Europe. We develop a WGS-based classification of Bb isolates, confirm and extend the findings of previous single- and multi-locus typing systems, define the plasmid profiles of human-infectious Bb isolates, annotate the core and strain-variable surface lipoproteome, and identify loci associated with disseminated infection. A core genome consisting of ~900 open reading frames and a core set of plasmids consisting of lp17, lp25, lp36, lp28-3, lp28-4, lp54, and cp26 are found in nearly all isolates. Strain-variable (accessory) plasmids and genes correlate strongly with phylogeny. Using genetic association study methods, we identify an accessory genome signature associated with dissemination in humans and define the individual plasmids and genes that make up this signature. Strains within the RST1/WGS A subgroup, particularly a subset marked by the OspC type A genotype, have increased rates of dissemination in humans. OspC type A strains possess a unique set of strongly linked genetic elements including the presence of lp56 and lp28-1 plasmids and a cluster of genes that may contribute to their enhanced virulence compared to other genotypes. These features of OspC type A strains reflect a broader paradigm across Bb isolates, in which near-clonal genotypes are defined by strain-specific clusters of linked genetic elements, particularly those encoding surface-exposed lipoproteins. These clusters of genes are maintained by strain-specific patterns of plasmid occupancy and are associated with the probability of invasive infection.

Borrelia burgdorferi Outer Surface Protein C Is Not the Sole Determinant of Dissemination in Mammals.

Lyme disease in the United States is most often caused by Borrelia burgdorferi sensu stricto. After a tick bite, the patient may develop erythema migrans at that site. If hematogenous dissemination occurs, the patient may then develop neurologic manifestations, carditis, or arthritis. Host-pathogen interactions include factors that contribute to hematogenous dissemination to other body sites. Outer surface protein C (OspC), a surface-exposed lipoprotein of B. burgdorferi, is essential during the early stages of mammalian infection. There is a high degree of genetic variation at the ospC locus, and certain ospC types are more frequently associated with hematogenous dissemination in patients, suggesting that OspC may be a major contributing factor to the clinical outcome of B. burgdorferi infection. In order to evaluate the role of OspC in B. burgdorferi dissemination, ospC was exchanged between B. burgdorferi isolates with different capacities to disseminate in laboratory mice, and these strains were then tested for their ability to disseminate in mice. The results indicated that the ability of B. burgdorferi to disseminate in mammalian hosts does not depend on OspC alone. The complete genome sequences of two closely related strains of B. burgdorferi with differing dissemination phenotypes were determined, but a specific genetic locus that could explain the differences in the phenotypes could not be definitively identified. The animal studies performed clearly demonstrated that OspC is not the sole determinant of dissemination. Future studies of the type described here with additional borrelial strains will hopefully clarify the genetic elements associated with hematogenous dissemination.

Whole genome sequencing of Borrelia burgdorferi isolates reveals linked clusters of plasmid-borne accessory genome elements associated with virulence.

Lyme disease is the most common vector-borne disease in North America and Europe. The clinical manifestations of Lyme disease vary based on the genospecies of the infecting Borrelia burgdorferi spirochete, but the microbial genetic elements underlying these associations are not known. Here, we report the whole genome sequence (WGS) and analysis of 299 patient-derived B. burgdorferi sensu stricto ( Bbss ) isolates from patients in the Eastern and Midwestern US and Central Europe. We develop a WGS-based classification of Bbss isolates, confirm and extend the findings of previous single- and multi-locus typing systems, define the plasmid profiles of human-infectious Bbss isolates, annotate the core and strain-variable surface lipoproteome, and identify loci associated with disseminated infection. A core genome consisting of ∻800 open reading frames and a core set of plasmids consisting of lp17, lp25, lp36, lp28-3, lp28-4, lp54, and cp26 are found in nearly all isolates. Strain-variable (accessory) plasmids and genes correlate strongly with phylogeny. Using genetic association study methods, we identify an accessory genome signature associated with dissemination and define the individual plasmids and genes that make up this signature. Strains within the RST1/WGS A subgroup, particularly a subset marked by the OspC type A genotype, are associated with increased rates of dissemination. OspC type A strains possess a unique constellation of strongly linked genetic changes including the presence of lp56 and lp28-1 plasmids and a cluster of genes that may contribute to their enhanced virulence compared to other genotypes. The patterns of OspC type A strains typify a broader paradigm across Bbss isolates, in which genetic structure is defined by correlated groups of strain-variable genes located predominantly on plasmids, particularly for expression of surface-exposed lipoproteins. These clusters of genes are inherited in blocks through strain-specific patterns of plasmid occupancy and are associated with the probability of invasive infection.

Evidence of taxonomic bias in public databases: The example of the genus Borrelia.

The taxon names used in public databases are of critical importance in all areas of biology because they are needed for linking organisms to sequence data and other information. Since most users of taxonomic classifications may be unprepared for dealing with synonyms, the names that are preferred in such databases are of high impact. Using the genus Borrelia as an example, we here show how simplistic approaches for determining the preferred synonym may lead to biases regarding the preferences for taxonomic opinions. We highlight that in this and other cases where genera were split, for reverting to the previous "merged" genus it is neither possible nor necessary to generate validly published and legitimate names that are newer than those that were proposed as new combinations when the genus was split. The policy to always prefer the latest validly published name in a public database may thus render this database oblivious to reversals in taxonomic opinion. We emphasize that users of public databases should be aware of such potential shortcomings, and that curators of databases which provide nomenclatural information should be open-minded about taxonomic views expressed in the literature.

Outbreak Size Distribution in Stochastic Epidemic Models.

Motivated by recent epidemic outbreaks, including those of COVID-19, we solve the canonical problem of calculating the dynamics and likelihood of extensive outbreaks in a population within a large class of stochastic epidemic models with demographic noise, including the susceptible-infected-recovered (SIR) model and its general extensions. In the limit of large populations, we compute the probability distribution for all extensive outbreaks, including those that entail unusually large or small (extreme) proportions of the population infected. Our approach reveals that, unlike other well-known examples of rare events occurring in discrete-state stochastic systems, the statistics of extreme outbreaks emanate from a full continuum of Hamiltonian paths, each satisfying unique boundary conditions with a conserved probability flux.

Lack of Convincing Evidence That Borrelia burgdorferi Infection Causes Either Alzheimer Disease or Lewy Body Dementia.

The role that microorganisms might have in the development of Alzheimer disease is a topic of considerable interest. In this article, we discuss whether there is credible evidence that Lyme disease is a cause of Alzheimer disease and critically review a recent publication that claimed that Borrelia burgdorferi sensu stricto infection, the primary cause of Lyme disease in the United States, may cause Lewy body dementia. We conclude that no convincing evidence exists that Lyme disease is a cause of either Alzheimer disease or Lewy body dementia.

Critical transition for colliding swarms.

Swarming patterns that emerge from the interaction of many mobile agents are a subject of great interest in fields ranging from biology to physics and robotics. In some application areas, multiple swarms effectively interact and collide, producing complex spatiotemporal patterns. Recent studies have begun to address swarm-on-swarm dynamics, and in particular the scattering of two large, colliding swarms with nonlinear interactions. To build on early numerical insights, we develop a self-propelled, rigid-body approximation that can be used to predict the parameters under which colliding swarms are expected to form a milling state. Our analytical method relies on the assumption that, upon collision, two swarms oscillate near a limit cycle, where each swarm rotates around the other while maintaining an approximately constant and uniform density. Using this approach we are able to predict the critical swarm-on-swarm interaction coupling, below which two colliding swarms merely scatter, as a function of physical swarm parameters. We show that the critical coupling gives a lower bound for all impact parameters, including head-on collision, and corresponds to a saddle-node bifurcation of a stable limit cycle in the uniform, constant density approximation. Our results are tested and found to agree with both small and large multiagent simulations.

Swarm shedding in networks of self-propelled agents.

Understanding swarm pattern formation is of great interest because it occurs naturally in many physical and biological systems, and has artificial applications in robotics. In both natural and engineered swarms, agent communication is typically local and sparse. This is because, over a limited sensing or communication range, the number of interactions an agent has is much smaller than the total possible number. A central question for self-organizing swarms interacting through sparse networks is whether or not collective motion states can emerge where all agents have coherent and stable dynamics. In this work we introduce the phenomenon of swarm shedding in which weakly-connected agents are ejected from stable milling patterns in self-propelled swarming networks with finite-range interactions. We show that swarm shedding can be localized around a few agents, or delocalized, and entail a simultaneous ejection of all agents in a network. Despite the complexity of milling motion in complex networks, we successfully build mean-field theory that accurately predicts both milling state dynamics and shedding transitions. The latter are described in terms of saddle-node bifurcations that depend on the range of communication, the inter-agent interaction strength, and the network topology.

A new genetic approach to distinguish strains of Anaplasma phagocytophilum that appear not to cause human disease.

Genetic diversity of Anaplasma phagocytophilum was assessed in specimens from 16 infected patients and 16 infected Ixodes scapularis ticks. A region immediately downstream of the 16S rRNA gene, which included the gene encoding SdhC, was sequenced. For the A. phagocytophilum strains from patients no sequence differences were detected in this region. In contrast, significantly fewer ticks had a sequence encoding SdhC that was identical to that of the human strains (11/16 vs. 16/16, p = 0.04). This variation is consistent with the premise that not all A. phagocytophilum strains present in nature are able to cause clinical illness in humans. A strain referred to as A. phagocytophilumVariant-1 that is regarded as non-pathogenic for humans was previously described using a different typing method. Data from the current study suggest that both typing methods are identifying the same non-pathogenic strains.

Optimal periodic closure for minimizing risk in emerging disease outbreaks.

Without vaccines and treatments, societies must rely on non-pharmaceutical intervention strategies to control the spread of emerging diseases such as COVID-19. Though complete lockdown is epidemiologically effective, because it eliminates infectious contacts, it comes with significant costs. Several recent studies have suggested that a plausible compromise strategy for minimizing epidemic risk is periodic closure, in which populations oscillate between wide-spread social restrictions and relaxation. However, no underlying theory has been proposed to predict and explain optimal closure periods as a function of epidemiological and social parameters. In this work we develop such an analytical theory for SEIR-like model diseases, showing how characteristic closure periods emerge that minimize the total outbreak, and increase predictably with the reproductive number and incubation periods of a disease- as long as both are within predictable limits. Using our approach we demonstrate a sweet-spot effect in which optimal periodic closure is maximally effective for diseases with similar incubation and recovery periods. Our results compare well to numerical simulations, including in COVID-19 models where infectivity and recovery show significant variation.

Multipartite Genome of Lyme Disease Borrelia: Structure, Variation and Prophages.

All members of the Borrelia genus that have been examined harbour a linear chromosome that is about 900 kbp in length, as well as a plethora of both linear and circular plasmids in the 5-220 kbp size range. Genome sequences for 27 Lyme disease Borrelia isolates have been determined since the elucidation of the B. burgdorferi B31 genome sequence in 1997. The chromosomes, which carry the vast majority of the housekeeping genes, appear to be very constant in gene content and organization across all Lyme disease Borrelia species. The content of the plasmids, which carry most of the genes that encode the differentially expressed surface proteins that interact with the spirochete's arthropod and vertebrate hosts, is much more variable. Lyme disease Borrelia isolates carry between 7-21 different plasmids, ranging in size from 5-84 kbp. All strains analyzed to date harbor three plasmids, cp26, lp54 and lp17. The plasmids are unusual, as compared to most bacterial plasmids, in that they contain many paralogous sequences, a large number of pseudogenes, and, in some cases, essential genes. In addition, a number of the plasmids have features indicating that they are prophages. Numerous methods have been developed for Lyme disease Borrelia strain typing. These have proven valuable for clinical and epidemiological studies, as well as phylogenomic and population genetic analyses. Increasingly, these approaches have been displaced by whole genome sequencing techniques. Some correlations between genome content and pathogenicity have been deduced, and comparative whole genome analyses promise future progress in this arena.

Stability of milling patterns in self-propelled swarms on surfaces.

In some physical and biological swarms, agents effectively move and interact along curved surfaces. The associated constraints and symmetries can affect collective-motion patterns, but little is known about pattern stability in the presence of surface curvature. To make progress, we construct a general model for self-propelled swarms moving on surfaces using Lagrangian mechanics. We find that the combination of self-propulsion, friction, mutual attraction, and surface curvature produce milling patterns where each agent in a swarm oscillates on a limit cycle with different agents splayed along the cycle such that the swarm's center-of-mass remains stationary. In general, such patterns loose stability when mutual attraction is insufficient to overcome the constraint of curvature, and we uncover two broad classes of stationary milling-state bifurcations. In the first, a spatially periodic mode undergoes a Hopf bifurcation as curvature is increased, which results in unstable spatiotemporal oscillations. This generic bifurcation is analyzed for the sphere and demonstrated numerically for several surfaces. In the second, a saddle-node-of-periodic orbits occurs in which stable and unstable milling states collide and annihilate. The latter is analyzed for milling states on cylindrical surfaces. Our results contribute to the general understanding of swarm pattern formation and stability in the presence of surface curvature and may aid in designing robotic swarms that can be controlled to move over complex surfaces and terrains.

Delay induced swarm pattern bifurcations in mixed reality experiments.

Swarms of coupled mobile agents subject to inter-agent wireless communication delays are known to exhibit multiple dynamic patterns in space that depend on the strength of the interactions and the magnitude of the communication delays. We experimentally demonstrate communication delay-induced bifurcations in the spatiotemporal patterns of robot swarms using two distinct hardware platforms in a mixed reality framework. Additionally, we make steps toward experimentally validating theoretically predicted parameter regions where transitions between swarm patterns occur. We show that multiple rotation patterns persist even when collision avoidance strategies are incorporated, and we show the existence of multi-stable, co-existing rotational patterns not predicted by usual mean field dynamics. Our experiments are the first significant steps toward validating existing theory and the existence and robustness of the delay-induced patterns in real robotic swarms.

Torus bifurcations of large-scale swarms having range dependent communication delay.

Dynamical emergent patterns of swarms are now fairly well established in nature and include flocking and rotational states. Recently, there has been great interest in engineering and physics to create artificial self-propelled agents that communicate over a network and operate with simple rules, with the goal of creating emergent self-organizing swarm patterns. In this paper, we show that when communicating networks have range dependent delays, rotational states, which are typically periodic, undergo a bifurcation and create swarm dynamics on a torus. The observed bifurcation yields additional frequencies into the dynamics, which may lead to quasi-periodic behavior of the swarm.

Unstable modes and bistability in delay-coupled swarms.

It is known that introducing time delays into the communication network of mobile-agent swarms produces coherent rotational patterns, from both theory and experiments. Often such spatiotemporal rotations can be bistable with other swarming patterns, such as milling and flocking. Yet, most known bifurcation results related to delay-coupled swarms rely on inaccurate mean-field techniques. As a consequence, the utility of applying macroscopic theory as a guide for predicting and controlling swarms of mobile robots has been limited. To overcome this limitation, we perform an exact stability analysis of two primary swarming patterns in a general model with time-delayed interactions. By correctly identifying the relevant spatiotemporal modes, we are able to accurately predict unstable oscillations beyond the mean-field dynamics and bistability in large swarms-laying the groundwork for comparisons to robotics experiments.

Rejection of the name Borreliella and all proposed species comb. nov. placed therein.

Rejection (nomen rejiciendum) of the name Borreliella and all new combinations therein is being requested on grounds of risk to human health and patient safety (Principle 1, subprinciple 2 and Rule 56a) and violation to aim for stability of names, to avoid useless creation of names (Principle 1, subprinciple 1 and 3) and that names should not be changed without sufficient reason (Principle 9 of the International Code of Nomenclature of Prokaryotes).