Fast Time Scale Research Articles

Analysis Strategies for MHz XPCS at the European XFEL

The nanometer length-scale holds precious information on several dynamical processes that develop from picoseconds to seconds. In the past decades, X-ray scattering techniques have been developed to probe the dynamics at such length-scales on either ultrafast (sub-nanosecond) or slow ((milli-)second) time scales. With the start of operation of the European XFEL, thanks to the MHz repetition rate of its X-ray pulses, even the intermediate μs range have become accessible. Measuring dynamics on such fast timescales requires the development of new technologies such as the Adaptive Gain Integrating Pixel Detector (AGIPD). μs-XPCS is a promising technique to answer many scientific questions regarding microscopic structural dynamics, especially for soft condensed matter systems. However, obtaining reliable results with complex detectors at free-electron laser facilities is challenging and requires more sophisticated analysis methods compared to experiments at storage rings. Here, we discuss challenges and possible solutions to perform XPCS experiments with the AGIPD at European XFEL; in particular, at the Materials Imaging and Dynamics (MID) instrument. We present our data analysis pipeline and benchmark the results obtained at the MID instrument with a well-known sample composed by silica nanoparticles dispersed in water.

Extreme intermodal energy transfers through vibro-impacts for highly effective and rapid blast mitigation

This work investigates intermodal targeted energy transfers (IMTET) for passive mitigation of a large-scale nine-story structure subjected to blast excitation. This is achieved by inducing extreme, fast time scale energy transfers from lower-frequency structural modes which are mainly excited by the blast to higher-frequency ones. These targeted (directed) energy transfers are governed by a non-resonant nonlinear dynamical mechanism induced by inelastic Hertzian vibro-impacts between the nine-story structure (referred to “primary structure”) and an internal secondary “core structure” assumed to be rigid. The clearance distribution between the primary structure and the core structure is optimized using a multi-objective genetic algorithm by minimizing both the characteristic damping time of the transient response of the primary structure, and the energy redistributed from the lowest-frequency (fundamental) structural mode to higher modes. The results show that the IMTET mechanism enables extremely rapid and nearly irreversible low-to-high frequency scattering of the blast energy in the primary structure. In turn, this nonlinear energy scattering rapidly reduces the overall amplitude of the transient structural response, even in the case of purely elastic Hertzian contacts. The mitigation performance is substantially enhanced when more realistic inelastic vibro-impact nonlinearities are considered. In the studied example with the realistic model of a nine-story structure, the synergy between extremely rapid low-to-high frequency energy redistribution and dissipation due to inelastic vibro-impacts yields a reduction of the characteristic damping time by a factor of 20 compared to the linear case. We envision that the proposed concept of rapid nonlinear IMTET is of broad applicability to general classes of dynamical and acoustical systems.

Mechanisms of Flexible Information Sharing through Noisy Oscillations.

Simple SummaryTo properly interact with our environment, the brain must be able to identify external stimuli, process them, and make the right decisions all in a short time. This may involve several brain regions interacting together by sharing information birectionally via rhythmic activity. Such flexibility requires the functional connectivity between the areas to be dynamic, and a key question is the relevant parameter and operating regimes that make this possible in spite of fixed structural connectivity. Working towards this goal, we consider two coupled brain regions, each of which exhibits a noisy rhythm, a commonly observed type of neural activity. Such rhythms can be induced by the stochasticity in the neural circuitry, or be autonomously generated through nonlinearities and not necessitating noise. For these two types of rhythms, we computed the amount of information shared between the brain areas and the preferred direction(s) of sharing. We found that without the coupling delay, the flexibility needed by the brain to perform cognitive tasks requires the rhythms to be autogenerated rather than noise-induced. This is the case even with asymmetry or heterogeneity. This suggests that the importance of the dynamical regime has to be taken into account when modeling interacting neural rhythms from an information theoretical point of view.Brain areas must be able to interact and share information in a time-varying, dynamic manner on a fast timescale. Such flexibility in information sharing has been linked to the synchronization of rhythm phases between areas. One definition of flexibility is the number of local maxima in the delayed mutual information curve between two connected areas. However, the precise relationship between phase synchronization and information sharing is not clear, nor is the flexibility in the face of the fixed structural connectivity and noise. Here, we consider two coupled oscillatory excitatory-inhibitory networks connected through zero-delay excitatory connections, each of which mimics a rhythmic brain area. We numerically compute phase-locking and delayed mutual information between the phases of excitatory local field potential (LFPs) of the two networks, which measures the shared information and its direction. The flexibility in information sharing is shown to depend on the dynamical origin of oscillations, and its properties in different regimes are found to persist in the presence of asymmetry in the connectivity as well as system heterogeneity. For coupled noise-induced rhythms (quasi-cycles), phase synchronization is robust even in the presence of asymmetry and heterogeneity. However, they do not show flexibility, in contrast to noise-perturbed rhythms (noisy limit cycles), which are shown here to exhibit two local information maxima, i.e., flexibility. For quasi-cycles, phase difference and information measures for the envelope-phase dynamics obtained from previous analytical work using the Stochastic Averaging Method (SAM) are found to be in good qualitative agreement with those obtained from the original dynamics. The relation between phase synchronization and communication patterns is not trivial, particularly in the noisy limit cycle regime. There, complex patterns of information sharing can be observed for a single value of the phase difference. The mechanisms reported here can be extended to I-I networks since their phase synchronizations are similar. Our results set the stage for investigating information sharing between several connected noisy rhythms in neural and other complex biological networks.

Modeling of atmospheric gas-stream processing using a microwave excited all-dielectric resonant plasma discharge

Computational modeling of an all-dielectric resonant plasma discharge for the processing of atmospheric air streams is presented. A single dielectric resonator (DR) pair separated by a small gap is considered. Governing equations for plasma, flow and electromagnetics are coupled to resolve the entire process characterized by fast timescale plasma breakdown followed by slow timescale transport of radicals catalyzed in the discharge. The resonant frequencies of the DR system are determined by solving for the electromagnetic wave across a range of microwave frequencies in the absence of the plasma. The DR operating at the resonant frequency (on-resonance mode) results in 12 times larger electric field amplification in the dielectric gap as compared to its operation in an off-resonance mode. Plasma breakdown occurs in the DR gap where the electric field amplification is the highest, due to constructive interference of electromagnetic wave modes from the adjoining cylindrical dielectrics. The plasma frequency in on-resonance mode is smaller than the resonant frequency of the DR. This inhibits the formation of surface wave modes and results in a uniform electromagnetic power deposition and volumetric plasma generation in the dielectric gap. It is seen that oxygen radicals (O) and oxygen metastable states O2 a1 and O2 b1 are dominant products of the plasma catalysis process. The active radical species are transported with the flow where they can be used in downstream plasma processing applications.

Electrophysiological and Molecular Mechanisms of Sinoatrial Node Mechanosensitivity.

The understanding of the electrophysiological mechanisms that underlie mechanosensitivity of the sinoatrial node (SAN), the primary pacemaker of the heart, has been evolving over the past century. The heart is constantly exposed to a dynamic mechanical environment; as such, the SAN has numerous canonical and emerging mechanosensitive ion channels and signaling pathways that govern its ability to respond to both fast (within second or on beat-to-beat manner) and slow (minutes) timescales. This review summarizes the effects of mechanical loading on the SAN activity and reviews putative candidates, including fast mechanoactivated channels (Piezo, TREK, and BK) and slow mechanoresponsive ion channels [including volume-regulated chloride channels and transient receptor potential (TRP)], as well as the components of mechanochemical signal transduction, which may contribute to SAN mechanosensitivity. Furthermore, we examine the structural foundation for both mechano-electrical and mechanochemical signal transduction and discuss the role of specialized membrane nanodomains, namely, caveolae, in mechanical regulation of both membrane and calcium clock components of the so-called coupled-clock pacemaker system responsible for SAN automaticity. Finally, we emphasize how these mechanically activated changes contribute to the pathophysiology of SAN dysfunction and discuss controversial areas necessitating future investigations. Though the exact mechanisms of SAN mechanosensitivity are currently unknown, identification of such components, their impact into SAN pacemaking, and pathological remodeling may provide new therapeutic targets for the treatment of SAN dysfunction and associated rhythm abnormalities.

Measuring neuronal activity with diffuse correlation spectroscopy: a theoretical investigation

.Significance: Diffuse correlation spectroscopy (DCS) measures cerebral blood flow non-invasively. Variations in blood flow can be used to detect neuronal activities, but its peak has a latency of a few seconds, which is slow for real-time monitoring. Neuronal cells also deform during activation, which, in principle, can be utilized to detect neuronal activity on fast timescales (within 100 ms) using DCS.Aims: We aim to characterize DCS signal variation quantified as the change of the decay time of the speckle intensity autocorrelation function during neuronal activation on both fast (within 100 ms) and slow (100 ms to seconds) timescales.Approach: We extensively modeled the variations in the DCS signal that are expected to arise from neuronal activation using Monte Carlo simulations, including the impacts of neuronal cell motion, vessel wall dilation, and blood flow changes.Results: We found that neuronal cell motion induces a DCS signal variation of . We also estimated the contrast and number of channels required to detect hemodynamic signals at different time delays.Conclusions: From this extensive analysis, we do not expect to detect neuronal cell motion using DCS in the near future based on current technology trends. However, multi-channel DCS will be able to detect hemodynamic response with sub-second latency, which is interesting for brain–computer interfaces.

49.5: True Glass‐based Micro‐display with 3µm pixel size using Deformed Helix Ferroelectric Liquid Crystal for VR/AR Displays

A glass‐based micro‐display with pixel size (<3µm) has been fabricated and verified in this work using deformed helix ferroelectric liquid crystal. High contrast (over 600:1), fast response time (< 400μs) and continuous gray scale within 5 V driving voltage are achieved in the prototype.

Neural Synchrony for Adaptive Control.

Cognitive control can be adaptive along several dimensions, including intensity (how intensely do control signals influence bottom-up processing) and selectivity (what information is selected for further processing). Furthermore, control can be exerted along slow or fast time scales. Whereas control on a slow time scale is used to proactively prepare for upcoming challenges, control can also be used on a faster time scale to react to unexpected events that require control. Importantly, a systematic comparison of these dimensions and time scales remains lacking. Moreover, most current models of adaptive control allow predictions only at a behavioral, not neurophysiological, level, thus seriously reducing the range of available empirical restrictions for informing model formulation. The current article addresses this issue by implementing a control loop in an earlier model of neural synchrony. The resulting model is tested on a Stroop task. We observe that only the model that exerts cognitive control on intensity and selectivity dimensions, as well as on two time scales, can account for relevant behavioral and neurophysiological data. Our findings hold important implications for both cognitive control and how computational models can be empirically constrained.

The influences of interfacial recombination loss on the perovskite solar cell performance studied by transient photovoltage spectroscopy

The charge-carrier transport and recombination loss in MAPbI3-based perovskite solar cells (PSCs) have been investigated by transient photovoltage (TPV) spectroscopy measurements. The TPV decays exhibit multi-exponential decay characteristics, where the fast decay seems to be correlated with both the cell performance (J-V characteristics) and the presence of low arc in the Nyquist plot obtained from the intensity-modulated photovoltage spectroscopy (IMVS) measurements. This fast decay exhibits the characteristic of a stretched exponential decay, which is a typical non-simple first-order trapping and recombination process, in the present case, by involving electron transport among defect states on the surface of grain boundaries or interlayer interface. Temperature dependence of TPV decays may also lead to a suggestion that the process can be also accompanied by local lattice distortion. Furthermore, the present experimental data also indicate the interplay between electronic processes in the fast time scale (microseconds in TPV data) and unpaired/mobile ion migration in the slow time scale (hundreds of milliseconds in IMVS data) on the overall cell performance (J-V characteristics). In addition, in practical viewpoints, the results may also demonstrate the potential application of this TPV spectroscopy, in conjunction with IMVS measurement, as a simple tool for identifying the fabricated perovskites solar cells that exhibit a good cell performance with low recombination loss due to surface defect states and ion migration.

Transparent window 2D IR spectroscopy of proteins.

Proteins are complex, heterogeneous macromolecules that exist as ensembles of interconverting states on a complex energy landscape. A complete, molecular-level understanding of their function requires experimental tools to characterize them with high spatial and temporal precision. Infrared (IR) spectroscopy has an inherently fast time scale that can capture all states and their dynamics with, in principle, bond-specific spatial resolution. Two-dimensional (2D) IR methods that provide richer information are becoming more routine but remain challenging to apply to proteins. Spectral congestion typically prevents selective investigation of native vibrations; however, the problem can be overcome by site-specific introduction of amino acid side chains that have vibrational groups with frequencies in the "transparent window" of protein spectra. This Perspective provides an overview of the history and recent progress in the development of transparent window 2D IR of proteins.

Spontaneous transient brain states in EEG source space in disorders of consciousness

Spontaneous transient states were recently identified by functional magnetic resonance imaging and magnetoencephalography in healthy subjects. They organize and coordinate neural activity in brain networks. How spontaneous transient states are altered in abnormal brain conditions is unknown. Here, we conducted a transient state analysis on resting-state electroencephalography (EEG) source space and developed a state transfer analysis to patients with disorders of consciousness (DOC). They uncovered different neural coordination patterns, including spatial power patterns, temporal dynamics, spectral shifts, and connectivity construction varies at potentially very fast (millisecond) time scales, in groups with different consciousness levels: healthy subjects, patients in minimally conscious state (MCS), and patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS). Machine learning based on transient state features reveal high classification accuracy between MCS and VS/UWS. This study developed methodology of transient states analysis on EEG source space and abnormal brain conditions. Findings correlate spontaneous transient states with human consciousness and suggest potential roles of transient states in brain disease assessment.

A Process‐Oriented Diagnostic to Assess Precipitation‐Thermodynamic Relations and Application to CMIP6 Models

AbstractA process‐oriented diagnostic (POD) is introduced to measure the thermodynamic sensitivity of convection in climate models. The physical basis for this POD is the observed tropical precipitation‐buoyancy relationship. Fast timescale precipitation and thermodynamic profiles over oceans are POD inputs; these are used to evaluate model precipitation sensitivities to lower‐tropospheric measures of subsaturation (SUBSATL) and undilute conditional instability. The POD is used to diagnose 24 coupled model inter‐comparison project phase six (CMIP6) models. Half the diagnosed models exhibit SUBSATL sensitivity close to observed, while six models are excessively sensitive. Parameter perturbation experiments with the Community Atmospheric Model (CAM5) support the physical basis for the POD. Increasing entrainment increases the CAM5 precipitation SUBSATL sensitivity. Switching off the convective scheme or modifying the convective trigger to be oversensitive to moisture reproduces the excessive SUBSATL sensitivity seen among CMIP6 models. Models with excessive SUBSATL sensitivities have precipitating mean states closer to grid‐scale saturation and likely support more grid‐scale convection.

Online Learning and Memory of Neural Trajectory Replays for Prefrontal Persistent and Dynamic Representations in the Irregular Asynchronous State.

In the prefrontal cortex (PFC), higher-order cognitive functions and adaptive flexible behaviors rely on continuous dynamical sequences of spiking activity that constitute neural trajectories in the state space of activity. Neural trajectories subserve diverse representations, from explicit mappings in physical spaces to generalized mappings in the task space, and up to complex abstract transformations such as working memory, decision-making and behavioral planning. Computational models have separately assessed learning and replay of neural trajectories, often using unrealistic learning rules or decoupling simulations for learning from replay. Hence, the question remains open of how neural trajectories are learned, memorized and replayed online, with permanently acting biological plasticity rules. The asynchronous irregular regime characterizing cortical dynamics in awake conditions exerts a major source of disorder that may jeopardize plasticity and replay of locally ordered activity. Here, we show that a recurrent model of local PFC circuitry endowed with realistic synaptic spike timing-dependent plasticity and scaling processes can learn, memorize and replay large-size neural trajectories online under asynchronous irregular dynamics, at regular or fast (sped-up) timescale. Presented trajectories are quickly learned (within seconds) as synaptic engrams in the network, and the model is able to chunk overlapping trajectories presented separately. These trajectory engrams last long-term (dozen hours) and trajectory replays can be triggered over an hour. In turn, we show the conditions under which trajectory engrams and replays preserve asynchronous irregular dynamics in the network. Functionally, spiking activity during trajectory replays at regular timescale accounts for both dynamical coding with temporal tuning in individual neurons, persistent activity at the population level, and large levels of variability consistent with observed cognitive-related PFC dynamics. Together, these results offer a consistent theoretical framework accounting for how neural trajectories can be learned, memorized and replayed in PFC networks circuits to subserve flexible dynamic representations and adaptive behaviors.

Lagrangian three-dimensional transport and dispersion by submesoscale currents at an upper-ocean front

The three-dimensional transport pathways, the time and space scales of vertical transport, and the dispersion characteristics (single-, pair- and multi-particle statistics) of submesoscale currents at an upper-ocean front are investigated using material points (tracer particles) that advect with the local fluid velocity. Coherent vortex filaments and eddies, generated and sustained during the evolution of baroclinic instability, dominate submesoscale (0.1 - 10 km) dynamics. These coherent structures play a crucial role in the particle transport and dispersion, which we quantify here. Particles in the central frontal region organize into vertically inclined lobes with anticyclonic circulation, each associated with a coherent eddy with cyclonic rotation. Furthermore, the coherent filaments associated with heavy/light edges of the front transfer particles from the edges to the lobes by downwelling/upwelling. This flux of new particles into the central region causes the particles circulating in the lobes to adjust, which leads to slumping of the front. The particle motion in the vertical shows multiple time scales – a fast time scale with O(10m) vertical displacement within an hour and a slower near-inertial time scale, comparable to the intrinsic time scale of the growing instability. The fast O(1h) time scale is also seen in the decorrelation of vertical velocity and the tapering of the initial ballistic growth of relative particle dispersion. The overall slumping process is slower than what one might anticipate from the large magnitude of vertical velocity in the filaments since it requires a sustained correlation over time between the lateral and the vertical motion. By tracking clouds of particles, we show that their centers of mass downwell/upwell over 1−2 inertial time periods, after which an adjustment follows with a sub-inertial time scale. The shape change in clusters of four particles reveals filamentogenesis, i.e. deformation into thin, needle-like structures by the coherent, anisotropic motions of the submesoscale, which occurs as a rapid process that is complete within approximately an hour.

Spectroscopic view of ultrafast charge carrier dynamics in single- and bilayer transition metal dichalcogenide semiconductors

The quasiparticle spectra of atomically thin semiconducting transition metal dichalcogenides (TMDCs) and their response to an ultrafast optical excitation critically depend on interactions with the underlying substrate. Here, we present a comparative time- and angle-resolved photoemission spectroscopy (TR-ARPES) study of the transient electronic structure and ultrafast carrier dynamics in the single- and bilayer TMDCs MoS2 and WS2 on three different substrates: Au(111), Ag(111) and graphene/SiC. The photoexcited quasiparticle bandgaps are observed to vary over the range of 1.9–2.5 eV between our systems. The transient conduction band signals decay on a sub-50 fs timescale on the metals, signifying an efficient removal of photoinduced carriers into the bulk metallic states. On graphene, we instead observe a fast timescale on the order of 170 fs, followed by a slow dynamics for the conduction band decay in MoS2. These timescales are explained by Auger recombination involving MoS2 and in-gap defect states. In bilayer TMDCs on metals we observe a complex redistribution of excited holes along the valence band that is substantially affected by interactions with the continuum of bulk metallic states.

Theta and alpha power across fast and slow timescales in cognitive control.

Theta and alpha frequency neural oscillations are important for learning and cognitive control, but their exact role has remained obscure. In particular, it is unknown whether they operate at similar timescales, and whether they support different cognitive processes. We recorded EEG in 30 healthy human participants while they performed a learning task containing both novel (block-unique) and repeating stimuli. We investigated behavior and electrophysiology at both fast (i.e., within blocks) and slow (i.e., between blocks) timescales. Behaviorally, both response time and accuracy improved (respectively decrease and increase) over both fast and slow timescales. However, on the spectral level, theta power significantly decreased along the slow timescale, whereas alpha power significantly increased along the fast timescale. We thus demonstrate that theta and alpha both play a role during learning, but operate at different timescales. This result poses important empirical constraints for theories on learning, cognitive control, and neural oscillations.

Synthesis of an active flutter suppression system in the transonic domain using a computational model

ABSTRACTControl laws for implementing active flutter suppression are generally derived from linear aeroelastic models. In this paper, families of control laws for implementing an active flutter suppression system were initially designed using linearised aeroelastic models based on the doublet lattice method after ignoring the aerodynamic loads associated with relatively faster time scales. Using these preliminary sets of control laws and the nonlinear transonic small disturbance theory, near-optimum control laws were chosen in the transonic domain to maximally increase the flutter speed of a typical aircraft wing by at least 16% or more. Thus it is shown that it is feasible to systematically design near-optimal control laws for active flutter suppression using computational models in transonic flow. The doublet lattice method coupled with the zeroth-order matrix Padé approximant provided the fastest method for synthesising a large number of preliminary control laws. The methodology was successfully demonstrated by applying it to two benchmarking examples.

Structure of the endocytic adaptor complex reveals the basis for efficient membrane anchoring during clathrin-mediated endocytosis

During clathrin-mediated endocytosis, a complex and dynamic network of protein-membrane interactions cooperate to achieve membrane invagination. Throughout this process in yeast, endocytic coat adaptors, Sla2 and Ent1, must remain attached to the plasma membrane to transmit force from the actin cytoskeleton required for successful membrane invagination. Here, we present a cryo-EM structure of a 16-mer complex of the ANTH and ENTH membrane-binding domains from Sla2 and Ent1 bound to PIP2 that constitutes the anchor to the plasma membrane. Detailed in vitro and in vivo mutagenesis of the complex interfaces delineate the key interactions for complex formation and deficient cell growth phenotypes demonstrate its biological relevance. A hetero-tetrameric unit binds PIP2 molecules at the ANTH-ENTH interfaces and can form larger assemblies to contribute to membrane remodeling. Finally, a time-resolved small-angle X-ray scattering study of the interaction of these adaptor domains in vitro suggests that ANTH and ENTH domains have evolved to achieve a fast subsecond timescale assembly in the presence of PIP2 and do not require further proteins to form a stable complex. Together, these findings provide a molecular understanding of an essential piece in the molecular puzzle of clathrin-coated endocytic sites.

Evidence for immunity to SARS-CoV-2 from epidemiological data series

The duration of immunity to SARS-CoV-2 is uncertain. Delineating immune memory typically requires longitudinal serological studies that track antibody prevalence in the same cohort for an extended time. However, this information is needed in faster timescales. Notably, the dynamics of an epidemic where recovered patients become immune for any period should differ significantly from those of one where the recovered promptly become susceptible. Here, we exploit this difference to provide a reliable protocol that can estimate immunity early in an epidemic. We verify this protocol with synthetic data, discuss its limitations, and then apply it to evaluate human immunity to SARS-CoV-2 in mortality data series from New York City. Our results indicate that New York’s mortality figures are incompatible with immunity lasting anything below 105 or above 211 days (90% CI.), and set an example on how to assess immune memory in emerging pandemics before serological studies can be deployed.

Dichotomy in temporal and thermal spin correlations observed in the breathing pyrochlore LiGa1\u2212xInxCr4O8

A breathing pyrochlore system is predicted to host a variety of quantum spin liquids. Despite tremendous experimental and theoretical efforts, such sought-after states remain elusive as perturbation terms and lattice distortions lead to magnetic order. Here, we utilize bond alternation and disorder to tune a magnetic ground state in the Cr-based breathing pyrochlore LiGa1−xInxCr4O8. By combining thermodynamic and magnetic resonance techniques, we provide experimental signatures of a spin-liquid-like state in x = 0.8, namely, a nearly T2-dependent magnetic specific heat and persistent spin dynamics by muon spin relaxation (μSR). Moreover, 7Li NMR, ZF-μSR, and ESR unveil the temporal and thermal dichotomy of spin correlations: a tetramer singlet on a slow time scale vs. a spin-liquid-like state on a fast time scale. Our results showcase that a bond disorder in the breathing pyrochlore offers a promising route to disclose exotic magnetic phases.