Fast Time Scale Research Articles

Plasma in Liquids induced modification of Cu surfaces

Plasma in Liquids induced modification of Cu surfaces

The impact of GLP-1 signalling on the energy metabolism of pancreatic islet β-cells and extrapancreatic tissues

Glucagon-like peptide-1 signalling impacts glucose homeostasis and appetite thereby indirectly affecting substrate availability at the whole-body level. The incretin canonically produces an insulinotropic effect, thereby lowering blood glucose levels by promoting the uptake and inhibiting the production of the sugar by peripheral tissues. Likewise, GLP-1 signalling within the central nervous system reduces the appetite and food intake, whereas its gastric effect delays the absorption of nutrients, thus improving glycaemic control and reducing the risk of postprandial hyperglycaemia.We review the molecular aspects of the GLP-1 signalling, focusing on its impact on intracellular energy metabolism. Whilst the incretin exerts its effects predominantly via a Gs receptor, which decodes the incretin signal into the elevation of intracellular cAMP levels, the downstream signalling cascades within the cell, acting on fast and slow timescales, resulting in an enhancement or an attenuation of glucose catabolism, respectively.

Analysis of long transients and detection of early warning signals of extinction in a class of predator-prey models exhibiting bistable behavior.

In this paper, we develop a method of analyzing long transient dynamics in a class of predator-prey models with two species of predators competing explicitly for their common prey, where the prey evolves on a faster timescale than the predators. In a parameter regime near a singular zero-Hopf bifurcation of the coexistence equilibrium state, we assume that the system under study exhibits bistability between a periodic attractor that bifurcates from the singular Hopf point and another attractor, which could be a periodic attractor or a point attractor, such that the invariant manifolds of the coexistence equilibrium point play central roles in organizing the dynamics. To find whether a solution that starts in a vicinity of the coexistence equilibrium approaches the periodic attractor or the other attractor, we reduce the equations to a suitable normal form, and examine the basin boundary near the singular Hopf point. A key component of our study includes an analysis of the long transient dynamics, characterized by their rapid oscillations with a slow variation in amplitude, by applying a moving average technique. We obtain a set of necessary and sufficient conditions on the initial values of a solution near the coexistence equilibrium to determine whether it lies in the basin of attraction of the periodic attractor. As a result of our analysis, we devise a method of identifying early warning signals, significantly in advance, of a future crisis that could lead to extinction of one of the predators. The analysis is applied to the predator-prey model considered in Sadhu (Discrete Contin Dyn Syst B 26:5251-5279, 2021) and we find that our theory is in good agreement with the numerical simulations carried out for this model.

Pandemic Equation and COVID-19 Evolution

The Pandemic Equation describes multiple pandemic waves and has been applied to describe the COVID-19 pandemic. Using the generalized approaches of solid-state physics, we derive the Pandemic Equation, which accounts for the effects of pandemic mitigation measures and multiple pandemic waves. The Pandemic Equation uses slow and fast time scales for “curve flattening” and describing vaccination and mitigation measures and the Scaled Fermi–Dirac distribution functions for describing transitions between pandemic waves. The Pandemic Equation parameters extracted from the pandemic curves can be used for comparing different scenarios of the pandemic evolution and for extrapolating the pandemic evolution curves for the periods of time on the order of the instantaneous Pandemic Equation characteristic time constant. The parameter extraction for multiple locations could also allow for uncertainty quantification for such pandemic evolution predictions.

Fast learning without synaptic plasticity in spiking neural networks

Spiking neural networks are of high current interest, both from the perspective of modelling neural networks of the brain and for porting their fast learning capability and energy efficiency into neuromorphic hardware. But so far we have not been able to reproduce fast learning capabilities of the brain in spiking neural networks. Biological data suggest that a synergy of synaptic plasticity on a slow time scale with network dynamics on a faster time scale is responsible for fast learning capabilities of the brain. We show here that a suitable orchestration of this synergy between synaptic plasticity and network dynamics does in fact reproduce fast learning capabilities of generic recurrent networks of spiking neurons. This points to the important role of recurrent connections in spiking networks, since these are necessary for enabling salient network dynamics. We show more specifically that the proposed synergy enables synaptic weights to encode more general information such as priors and task structures, since moment-to-moment processing of new information can be delegated to the network dynamics.

A tonically active master neuron modulates mutually exclusive motor states at two timescales.

Continuity of behaviors requires animals to make smooth transitions between mutually exclusive behavioral states. Neural principles that govern these transitions are not well understood. Caenorhabditis elegans spontaneously switch between two opposite motor states, forward and backward movement, a phenomenon thought to reflect the reciprocal inhibition between interneurons AVB and AVA. Here, we report that spontaneous locomotion and their corresponding motor circuits are not separately controlled. AVA and AVB are neither functionally equivalent nor strictly reciprocally inhibitory. AVA, but not AVB, maintains a depolarized membrane potential. While AVA phasically inhibits the forward promoting interneuron AVB at a fast timescale, it maintains a tonic, extrasynaptic excitation on AVB over the longer timescale. We propose that AVA, with tonic and phasic activity of opposite polarities on different timescales, acts as a master neuron to break the symmetry between the underlying forward and backward motor circuits. This master neuron model offers a parsimonious solution for sustained locomotion consisted of mutually exclusive motor states.

Motor learning- and consolidation-related resting state fast and slow brain dynamics across wake and sleep

Motor skills dynamically evolve during practice and after training. Using magnetoencephalography, we investigated the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods after the end of learning (boost window, within 30 min) and at delayed time scales (silent 4 h and next day 24 h windows) with intermediate daytime sleep or wakefulness. Resting-state neural dynamics were investigated at fast (sub-second) and slower (supra-second) timescales using Hidden Markov modelling (HMM) and functional connectivity (FC), respectively, and their relationship to motor performance. HMM results show that fast dynamic activities in a Temporal/Sensorimotor state network predict individual motor performance, suggesting a trait-like association between rapidly recurrent neural patterns and motor behaviour. Short, post-training task re-exposure modulated neural network characteristics during the boost but not the silent window. Re-exposure-related induction effects were observed on the next day, to a lesser extent than during the boost window. Daytime naps did not modulate memory consolidation at the behavioural and neural levels. These results emphasise the critical role of the transient boost window in motor learning and memory consolidation and provide further insights into the relationship between the multiscale neural dynamics of brain networks, motor learning, and consolidation.

Photosensitizers Based on Bichromophoric Dyads Combining Ru(II)-Polypyridyl Complexes and Dissymmetric Perylene Monoimide Derivatives: The Nontrivial Role of Ligand Substitution.

The covalent modification of Ru(II) polypyridyl complexes (RPCs) with organic chromophores is a powerful strategy to obtain metal-based photosensitizer agents (PSs) with improved performance for application in photodynamic therapy (PDT). In this respect, perylene-imides are of particular interest due to their rich chemical-physical repertoire, and it is therefore quite surprising that their combination with RPCs has been poorly considered so far. Herein, we report on the photophysical behavior of two newly synthesized RPCs bearing a perylene monoimide appendant (PMI-Ad). Differently from the majority of RPCs-perylene-imides dyads, these chromophores are dissymmetric and are tethered to the metal centers through a single C-C bond in the 3- or 5-position of 1,10-phenanthroline (Ru-3PMI-Ad and Ru-5PMI-Ad). Both compounds show excellent singlet oxygen photosensitizing activity, with quantum yields reaching >90% in the case of Ru-3PMI-Ad. A combined spectroscopic and theoretical analysis, also involving transient absorption and luminescence lifetime measurements, demonstrates that both compounds undergo intersystem crossing on a very fast time scale (tens of picoseconds) and with high efficiency. Our results further demonstrate that the increased electron delocalization between the metal center and the PMI-Ad chromophore observed for Ru-3PMI-Ad additionally contributes to increase the singlet oxygen quantum yields by prolonging the lifetime of the triplet state.

Biexponential I = 3/2 Spin-Lattice Relaxation in the Solid State: Multiple-Quantum 7Li NMR as a Probe of Fast Ion Dynamics.

Spin-lattice relaxation measurements are used in 7Li NMR studies of materials of potential use in solid-state Li-ion batteries as a probe of ion mobility on a fast (nanosecond to picosecond) time scale. The relaxation behavior is often analyzed by assuming exponential behavior or, equivalently, a single T1 time constant. However, the spin-lattice relaxation of spin I = 3/2 nuclei, such as 7Li, is in general biexponential; this is a fundamental property of I = 3/2 nuclei and unrelated to any compartmentalization within the solid. Although the possibility of biexponential 7Li (and other I = 3/2 nuclei) spin-lattice relaxation in the solid state has been noted by a number of authors, it can be difficult to observe unambiguously using conventional experimental NMR techniques, such as inversion or saturation recovery. In this work, we show that triple-quantum-filtered NMR experiments, as previously exploited in I = 3/2 NMR of liquids, can be used in favorable circumstances to observe and readily quantify biexponential 7Li spin-lattice relaxation in solids with high ion mobility. We demonstrate a triple-quantum-filtered inversion-recovery experiment on the candidate solid electrolyte material Li2OHCl at 325 K, which has previously been shown to exhibit fast ion mobility, and we also introduce a novel triple-quantum-filtered saturation-recovery experiment. The results of these solid-state NMR experiments are less straightforward than those in liquids as a consequence of the unwanted direct excitation of triple-quantum coherences by the weak (compared with the unaveraged 7Li quadrupolar interaction) pulses used, but we show that this unwanted excitation can be accounted for and, in the example shown here, does not impede the extraction of the two 7Li spin-lattice relaxation times.

The Spiraling Cognitive-Emotional Brain: Combinatorial, Reciprocal, and Reentrant Macro-organization.

This article proposes a framework for understanding the macro-scale organization of anatomical pathways in the mammalian brain. The architecture supports flexible behavioral decisions across a spectrum of spatio-temporal scales. The proposal emphasizes the combinatorial, reciprocal, and reentrant connectivity-called CRR neuroarchitecture-between cortical, BG, thalamic, amygdala, hypothalamic, and brainstem circuits. Thalamic nuclei, especially midline/intralaminar nuclei, are proposed to act as hubs routing the flow of signals between noncortical areas and pFC. The hypothalamus also participates in multiregion circuits via its connections with cortex and thalamus. At slower timescales, long-range behaviors integrate signals across levels of the neuroaxis. At fast timescales, parallel engagement of pathways allows urgent behaviors while retaining flexibility. Overall, the proposed architecture enables context-dependent, adaptive behaviors spanning proximate to distant spatio-temporal scales. The framework promotes an integrative perspective and a distributed, heterarchical view of brain function.

Fast and Slow Time-Scale Effects of Photoinduced Surface Oxygen Vacancies on the Charge Carrier Dynamics of TiO2

Fast and Slow Time-Scale Effects of Photoinduced Surface Oxygen Vacancies on the Charge Carrier Dynamics of TiO₂

Interaction of acetylcholine and oxytocin neuromodulation in the hippocampus.

A postulated role of subcortical neuromodulators is to control brain states. Mechanisms by which different neuromodulators compete or cooperate at various temporal scales remain an open question. We investigated the interaction of acetylcholine (ACh) and oxytocin (OXT) at slow and fast timescales during various brain states. Although these neuromodulators fluctuated in parallel during NREM packets, transitions from NREM to REM were characterized by a surge of ACh but a continued decrease of OXT. OXT signaling lagged behind ACh. High ACh was correlated with population synchrony and gamma oscillations during active waking, whereas minimum ACh predicts sharp-wave ripples (SPW-Rs). Optogenetic control of ACh and OXT neurons confirmed the active role of these neuromodulators in the observed correlations. Synchronous hippocampal activity consistently reduced OXT activity, whereas inactivation of the lateral septum-hypothalamus path attenuated this effect. Our findings demonstrate how cooperative actions of these neuromodulators allow target circuits to perform specific functions.

The collapse of a sonoluminescent cavitation bubble imaged with X-ray free-electron laser pulses

Single bubble sonoluminescence (SBSL) is the phenomenon of synchronous light emission due to the violent collapse of a single spherical bubble in a liquid, driven by an ultrasonic field. During the bubble collapse, matter inside the bubble reaches extreme conditions of several gigapascals and temperatures on the order of 10000 K, leading to picosecond flashes of visible light. To this day, details regarding the energy focusing mechanism rely on simulations due to the fast dynamics of the bubble collapse and spatial scales below the optical resolution limit. In this work we present phase-contrast holographic imaging with single x-ray free-electron laser (XFEL) pulses of a SBSL cavitation bubble in water. X-rays probe the electron density structure and by that provide a uniquely new view on the bubble interior and its collapse dynamics. The involved fast time-scales are accessed by sub-100 fs XFEL pulses and a custom synchronization scheme for the bubble oscillator. We find that during the whole oscillation cycle the bubble’s density profile can be well described by a simple step-like structure, with the radius R following the dynamics of the Gilmore model. The quantitatively measured internal density and width of the boundary layer exhibit a large variance. Smallest reconstructed bubble sizes reach down to R≃0.8μm , and are consistent with spherical symmetry. While we here achieved a spatial resolution of a few 100 nm, the visibility of the bubble and its internal structure is limited by the total x-ray phase shift which can be scaled with experimental parameters.

Electromechanical enhancement of live jellyfish for ocean exploration.

The vast majority of the ocean's volume remains unexplored, in part because of limitations on the vertical range and measurement duration of existing robotic platforms. In light of the accelerating rate of climate change impacts on the physics and biogeochemistry of the ocean, the need for new tools that can measure more of the ocean on faster timescales is becoming pressing. Robotic platforms inspired or enabled by aquatic organisms have the potential to augment conventional technologies for ocean exploration. Recent work demonstrated the feasibility of directly stimulating the muscle tissue of live jellyfish via implanted microelectronics. We present a biohybrid robotic jellyfish that leverages this external electrical swimming control, while also using a 3D printed passive mechanical attachment to streamline the jellyfish shape, increase swimming performance, and significantly enhance payload capacity. A six-meter-tall, 13 600 l saltwater facility was constructed to enable testing of the vertical swimming capabilities of the biohybrid robotic jellyfish over distances exceeding 35 body diameters. We found that the combination of external swimming control and the addition of the mechanical forebody resulted in an increase in swimming speeds to 4.5 times natural jellyfish locomotion. Moreover, the biohybrid jellyfish were capable of carrying a payload volume up to 105% of the jellyfish body volume. The added payload decreased the intracycle acceleration of the biohybrid robots relative to natural jellyfish, which could also facilitate more precise measurements by onboard sensors that depend on consistent platform motion. While many robotic exploration tools are limited by cost, energy expenditure, and varying oceanic environmental conditions, this platform is inexpensive, highly efficient, and benefits from the widespread natural habitats of jellyfish. The demonstrated performance of these biohybrid robots suggests an opportunity to expand the set of robotic tools for comprehensive monitoring of the changing ocean.

Submovement interpersonal coupling is associated to audio-motor coordination performance

Acting in concert with others, a key aspect of our social life, requires behavioral coordination between persons on multiple timescales. When zooming in on the kinematic properties of movements, it appears that small speed fluctuations, called submovements, are embedded within otherwise smooth end-point trajectories. Submovements, by occurring at a faster timescale than that of movements, offer a novel window upon the functional relationship between distinct motor timescales. In this regard, it has previously been shown that when partners visually synchronize their movements, they also coordinate the timing of their submovement by following an alternated pattern. However, it remains unclear whether the mechanisms behind submovement coordination are domain-general or specific to the visual modality, and whether they have relevance for interpersonal coordination also at the scale of whole movements. In a series of solo and dyadic tasks, we show that submovements are also present and coordinated across partners when sensorimotor interactions are mediated by auditory feedback only. Importantly, the accuracy of task-instructed interpersonal coordination at the movement level correlates with the strength of submovement coordination. These results demonstrate that submovement coordination is a potentially fundamental mechanism that participates in interpersonal motor coordination regardless of the sensory domain mediating the interaction.

Probing the dynamic landscape of peptides in molecular assemblies by synergized NMR experiments and MD simulations

Peptides or proteins containing small biomolecular aggregates, such as micelles, bicelles, droplets and nanodiscs, are pivotal in many fields ranging from structural biology to pharmaceutics. Monitoring dynamics of such systems has been limited by the lack of experimental methods that could directly detect their fast (picosecond to nanosecond) timescale dynamics. Spin relaxation times from NMR experiments are sensitive to such motions, but their interpretation for biomolecular aggregates is not straightforward. Here we show that the dynamic landscape of peptide-containing molecular assemblies can be determined by a synergistic combination of solution state NMR experiments and molecular dynamics (MD) simulations. Solution state NMR experiments are straightforward to implement without an excessive amount of sample, while direct combination of spin relaxation data to MD simulations enables interpretation of dynamic landscapes of peptides and other aggregated molecules. To demonstrate this, we interpret NMR data from transmembrane, peripheral, and tail anchored peptides embedded in micelles. Our results indicate that peptides and detergent molecules do not rotate together as a rigid body, but peptides rotate in a viscous medium composed of detergent micelle. Spin relaxation times also provide indirect information on peptide conformational ensembles. This work gives new perspectives on peptide dynamics in complex biomolecular assemblies.

In Situ X-ray Scattering Reveals Coarsening Rates of Superlattices Self-Assembled from Electrostatically Stabilized Metal Nanocrystals Depend Nonmonotonically on Driving Force.

Self-assembly of colloidal nanocrystals (NCs) into superlattices (SLs) is an appealing strategy to design hierarchically organized materials with promising functionalities. Mechanistic studies are still needed to uncover the design principles for SL self-assembly, but such studies have been difficult to perform due to the fast time and short length scales of NC systems. To address this challenge, we developed an apparatus to directly measure the evolving phases in situ and in real time of an electrostatically stabilized Au NC solution before, during, and after it is quenched to form SLs using small-angle X-ray scattering. By developing a quantitative model, we fit the time-dependent scattering patterns to obtain the phase diagram of the system and the kinetics of the colloidal and SL phases as a function of varying quench conditions. The extracted phase diagram is consistent with particles whose interactions are short in range relative to their diameter. We find the degree of SL order is primarily determined by fast (subsecond) initial nucleation and growth kinetics, while coarsening at later times depends nonmonotonically on the driving force for self-assembly. We validate these results by direct comparison with simulations and use them to suggest dynamic design principles to optimize the crystallinity within a finite time window. The combination of this measurement methodology, quantitative analysis, and simulation should be generalizable to elucidate and better control the microscopic self-assembly pathways of a wide range of bottom-up assembled systems and architectures.

Dual Timescales Voltages Regulation in Distribution Systems Using Data-Driven and Physics-Based Optimization

A large number of electric vehicles (EVs), distributed solar and/or wind turbine generators (WTGs) connected to distribution systems lead to frequent and sharp voltages fluctuations. The action rates of conventional adjustable devices and smart inverters are very different. In this context, a novel dual-timescale voltage control scheme is proposed by organically combining data-driven with physics-based optimization. On fast timescale, a quadratic programming (QP) for balanced and unbalanced distribution systems is developed based on branch flow equations. The optimal reactive power of renewable distributed generators (DGs) and static VAR compensators (SVCs) is configured on several minutes or seconds. Whereas, on slow timescale, a data-driven Markovian decision process (MDP) is developed, in which the charge/discharge power of energy storage systems (ESSs), statuses/ratios of switchable capacitors reactors (SCRs), and voltage regulators (VRs) are configured hourly to minimize long-term discounted squared voltages magnitudes deviations using an adapted deep deterministic policy gradient (DDPG) deep reinforcement learning (DRL) algorithm. The capabilities of the proposed method are validated with IEEE 33-bus balanced and 123-bus unbalanced distribution systems.

The RAPTR furnace: a rapid heating and cooling sample furnace for in situ X-ray scattering studies of temperature-induced reactions

In situ X-ray scattering provides valuable insights into the mechanisms and kinetics of reactions and structural transformations. For reactions and structural transformations primarily driven by temperature, and not coupled to chemical/electrochemical triggers, our ability to initiate and quench processes thermally is a practical limit for probing fast reactive phenomena. Meaningful quantitative analysis requires the dynamic phenomena to be triggered on fast time scales relative to the reaction/transformation kinetics. This article describes a new sample furnace, the Rapid-Actuating Pneumatic Thermal Reactor or RAPTR, for time-resolved in situ X-ray scattering studies initiated by temperature. The RAPTR quickly heats and cools samples by translating them into and out of a pre-heated hot zone. Using diffraction thermometry, it is shown that the samples can be heated/cooled in 10 s or less, with temperatures up to ∼1000°C being accessible. The application of the RAPTR furnace is demonstrated by exploring a fast solid-state reaction: the synthesis of scheelite-type lead tungstate, PbWO4, from PbO and WO3 for which Pb3WO6 is identified as a previously unrecognized reaction intermediate.

Osl-dynamics, a toolbox for modeling fast dynamic brain activity.

Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes.