The buzz within: the role of the gut microbiome in honeybee social behavior

Gut symbionts influence the physiology and behavior of their host, but the extent to which these effects scale to social behaviors is an emerging area of research. The use of the western honeybee (Apis mellifera) as a model enables researchers to investigate the gut microbiome and behavior at several levels of social organization. Insight into gut microbial effects at the societal level is critical for our understanding of how involved microbial symbionts are in host biology. In this Commentary, we discuss recent findings in honeybee gut microbiome research and synthesize these with knowledge of the physiology and behavior of other model organisms to hypothesize how host-microbe interactions at the individual level could shape societal dynamics and evolution.

Probing terahertz dynamics of multidomain protein in cell-like confinement

The development of meaningful descriptions of multidomain proteins exhibiting complex inter-domain dynamics modes is a key challenge for understanding their roles in molecular recognition and signalling processes. Here we developed a generally applicable approach for probing the low frequency collective hydration dynamics of multidomain proteins that uses terahertz spectroscopy of a protein molecule confined in a phospholipid reverse micelles environment (named Droplet THz). With the combination of normal mode analysis, we demonstrated the binding of calcium ions modulates the local inter-domain motion of the human coagulant factor VIII protein in a concentration-dependent manner. These findings highlight the Droplet THz as a valuable tool for dissecting the ultrafast dynamics of domain motion in the multidomain proteins and suggest a modulating mechanism of calcium ions on the structural flexibility and function of human coagulant proteins.

Thalamocortical Spectral Transmission Relies on Balanced Input Strengths

The thalamus is a key element of sensory transmission in the brain, as it gates and selects sensory streams through a modulation of its internal activity. A preponderant role in these functions is played by its internal activity in the alpha range ([8–14] Hz), but the mechanism underlying this process is not completely understood. In particular, how do thalamocortical connections convey stimulus driven information selectively over the back-ground of thalamic internally generated activity? Here we investigate this issue with a spiking network model of feedforward connectivity between thalamus and primary sensory cortex reproducing the local field potential of both areas. We found that in a feedforward network, thalamic oscillations in the alpha range do not entrain cortical activity for two reasons: (i) alpha range oscillations are weaker in neurons projecting to the cortex, (ii) the gamma resonance dynamics of cortical networks hampers oscillations over the 10–20 Hz range thus weakening alpha range oscillations. This latter mechanism depends on the balance of the strength of thalamocortical connections toward excitatory and inhibitory neurons in the cortex. Our results highlight the relevance of corticothalamic feedback to sustain alpha range oscillations and pave the way toward an integrated understanding of the sensory streams traveling between the periphery and the cortex.

State-dependent sequential allostery exhibited by chaperonin TRiC/CCT revealed by network analysis of Cryo-EM maps

The eukaryotic chaperonin TRiC/CCT plays a major role in assisting the folding of many proteins through an ATP-driven allosteric cycle. Recent structures elucidated by cryo-electron microscopy provide a broad view of the conformations visited at various stages of the chaperonin cycle, including a sequential activation of its subunits in response to nucleotide binding. But we lack a thorough mechanistic understanding of the structure-based dynamics and communication properties that underlie the TRiC/CCT machinery. In this study, we present a computational methodology based on elastic network models adapted to cryo-EM density maps to gain a deeper understanding of the structure-encoded allosteric dynamics of this hexadecameric machine. We have analysed several structures of the chaperonin resolved in different states toward mapping its conformational landscape. Our study indicates that the overall architecture intrinsically favours cooperative movements that comply with the structural variabilities observed in experiments. Furthermore, the individual subunits CCT1-CCT8 exhibit state-dependent sequential events at different states of the allosteric cycle. For example, in the ATP-bound state, subunits CCT5 and CCT4 selectively initiate the lid closure motions favoured by the overall architecture; whereas in the apo form of the heteromer, the subunit CCT7 exhibits the highest predisposition to structural change. The changes then propagate through parallel fluxes of allosteric signals to neighbours on both rings. The predicted state-dependent mechanisms of sequential activation provide new insights into TRiC/CCT intra- and inter-ring signal transduction events.

Data-driven discovery of emergent behaviors in collective dynamics

Particle- and agent-based systems are a ubiquitous modeling tool in many disciplines. We consider the fundamental problem of inferring the governing structure, i.e. interaction kernels, in a nonparametric fashion, from observations of agent-based dynamical systems. In particular, we are interested in collective dynamical systems exhibiting emergent behaviors with complicated interaction kernels, and for kernels which are parameterized by a single unknown parameter. This work extends the estimators introduced in Lu et al. (2019), which are based on suitably regularized least squares estimators, to these larger classes of systems. We provide extensive numerical evidence that the estimators provide faithful approximations to the interaction kernels, and provide accurate predictions for trajectories started at new initial conditions, both throughout the "training" time interval in which the observations were made, and often much beyond. We demonstrate these features on prototypical systems displaying collective behaviors, ranging from opinion dynamics, flocking dynamics, self-propelling particle dynamics, synchronized oscillator dynamics, to a gravitational system. Our experiments also suggest that our estimated systems can display the same emergent behaviors as the observed systems, including those that occur at larger timescales than those in the training data. Finally, in the case of families of systems governed by a parametric family of interaction kernels, we introduce novel estimators that estimate the parametric family of kernels, splitting it into a common interaction kernel and the action of parameters. We demonstrate this in the case of gravity, by learning both the "common component" 1/r ² and the dependency on mass, without any a priori knowledge of either one, from observations of planetary motions in our solar system.

A systematic review of collective tactical behaviour in futsal using positional data

Although many studies on collective tactical behaviour have been published in the last decade, no study has revised and summarized the findings provided for futsal. The main aim of this systematic review was to identify and discuss the geometrical centre (GC), distance and area tactical variables used to assess team behaviour in futsal. In addition, it summarizes the findings on the tactical response during futsal competition and training. A systematic review of the relevant articles provided on futsal was carried out using seven electronic databases (SPORTDiscus, ProQuest, Cochrane Plus, Scopus, Google Scholar, PubMed and Web of Science) until September 25, 2019. From a total of 1,209 studies initially found, 12 were included in the qualitative synthesis. There were some trends in the analysis of positional data in futsal with the most relevant situations analysed being 1 vs 1 and 5 vs 4+Goalkeeper. The distances and angles between two points were the most assessed tactical variables. Five types of distance variables were used to assess collective tactical behaviour in futsal: GC-GC, GC-player, player-player, player-ball and player-space. Pressure (GC-GC) was greater in shots on goal than in tackles during professional futsal matches. Area variables were reduced to occupied space, exploration space and dominant area. Occupied space was measured only during competition while the dominant area was measured only during training sessions. The surface area and dominant regions were greater when players were attacking in comparison to when they were defending. In addition, two non-linear techniques (i.e. relative phase and entropy) were applied to analyse synchronisation and complexity and regularity or predictability. Defenders were highly synchronous, while attackers tried to break this coordination to achieve possibilities for action. Task constraints are suitable to induce different regularity patterns. This review is an opportunity to develop studies aimed at bridging the gap in collective tactical behaviour in futsal.

Dynamics phenotyping across length and time scales in collective cell migration

Processes in collective migration span many length and time scales. In this review, we focus on length scales ranging from tens of microns (single cells) to a few millimeters (cell clusters) and the motion of these cells and cell groups on time scales of minutes to hours. We focus on epithelial cell sheets and metrics of motion developed to measure migration phenotypes in this system. Comparisons between cell motion and fluid flows, facilitated by the popular image analysis technique particle image velocimetry, yield metrics that can be used to study migration across a range of length and time scales. Measuring collective cell migration across these scales provides a complex, quantitative phenotype useful for migration models, in particular those that compare and contrast collective cell migration to movement of particles near a transition to jamming. Contrasting the motion of epithelial cells and the jamming transition illustrates aspects of collective motion that can be attributed to the jammed character of cell clusters, and highlights aspects of collective behavior that likely involve active motility and cell-cell guidance. The application of multiple migration metrics, which span multiple scales of the system, thus allows us to link cell-scale signals and mechanics to collective behavior.

A discrete particle model reproducing collective dynamics of a bee swarm

In this article, we present a microscopic discrete mathematical model describing collective dynamics of a bee swarm. More specifically, each bee is set to move according to individual strategies and social interactions, the former involving the desire to reach a target destination, the latter accounting for repulsive/attractive stimuli and for alignment processes. The insects tend in fact to remain sufficiently close to the rest of the population, while avoiding collisions, and they are able to track and synchronize their movement to the flight of a given set of neighbors within their visual field. The resulting collective behavior of the bee cloud therefore emerges from non-local short/long-range interactions. Differently from similar approaches present in the literature, we here test different alignment mechanisms (i.e., based either on an Euclidean or on a topological neighborhood metric), which have an impact also on the other social components characterizing insect behavior. A series of numerical realizations then shows the phenomenology of the swarm (in terms of pattern configuration, collective productive movement, and flight synchronization) in different regions of the space of free model parameters (i.e., strength of attractive/repulsive forces, extension of the interaction regions). In this respect, constraints in the possible variations of such coefficients are here given both by reasonable empirical observations and by analytical results on some stability characteristics of the defined pairwise interaction kernels, which have to assure a realistic crystalline configuration of the swarm. An analysis of the effect of unconscious random fluctuations of bee dynamics is also provided.

Towards plasma-like collisionless trajectories in the brain

Plasma studies depict collisionless, collective movements of charged particles. In touch with these concepts, originally developed by the far-flung branch of high energy physics, here we evaluate the role of collective behaviors and long-range functional couplingsof charged particlesin brain dynamics. We build a novel, empirically testable, brain model which takes into account collisionless movements of charged particles in a system, the brain, equipped with oscillations. The model is cast in a mathematical fashion with the potential of being operationalized, because it can be assessed in terms of McKean-Vlasov equations, derived from the classical Vlasov equations for plasma. A plasma-like brain also elucidates cortical phase transitions in the context of a brain at the edge of chaos, describing the required order parameters. In sum, showing how the brain might exhibit plasma-like features,we go through the concept of holistic behavior of nervous functions.

Microscale spatiotemporal dynamics during neocortical propagation of human focal seizures

Some of the most clinically consequential aspects of focal epilepsy, e.g. loss of consciousness, arise from the generalization or propagation of seizures through local and large-scale neocortical networks. Yet, the dynamics of such neocortical propagation remain poorly understood. Here, we studied the microdynamics of focal seizure propagation in neocortical patches (4×4 mm) recorded via high-density microelectrode arrays (MEAs) implanted in people with pharmacologically resistant epilepsy. Our main findings are threefold: (1) a newly developed stage segmentation method, applied to local field potentials (LFPs) and multiunit activity (MUA), revealed a succession of discrete seizure stages, each lasting several seconds. These different stages showed characteristic evolutions in overall activity and spatial patterns, which were relatively consistent across seizures within each of the 5 patients studied. Interestingly, segmented seizure stages based on LFPs or MUA showed a dissociation of their spatiotemporal dynamics, likely reflecting different contributions of non-local synaptic inputs and local network activity. (2) As previously reported, some of the seizures showed a peak in MUA that happened several seconds after local seizure onset and slowly propagated across the MEA. However, other seizures had a more complex structure characterized by, for example, several MUA peaks, more consistent with the succession of discrete stages than the slow propagation of a simple wavefront of increased MUA. In both cases, nevertheless, seizures characterized by spike-wave discharges (SWDs, ~2-3 Hz) eventually evolved into patterns of phase-locked MUA and LFPs. (3) Individual SWDs or gamma oscillation cycles (25-60 Hz), characteristic of two different types of recorded seizures, tended to propagate with varying degrees of directionality, directions of propagation and speeds, depending on the identified seizure stage. However, no clear relationship was observed between the MUA peak onset time (in seizures where such peak onset occurred) and changes in MUA or LFP propagation patterns. Overall, our findings indicate that the recruitment of neocortical territories into ictal activity undergoes complex spatiotemporal dynamics evolving in slow discrete states, which are consistent across seizures within each patient. Furthermore, ictal states at finer spatiotemporal scales (individual SWDs or gamma oscillations) are organized by slower time scale network dynamics evolving through these discrete stages.