Fast Time Scale Research Articles

Small-rotative fixed-target serial synchrotron crystallography (SR-FT-SSX) for molecular crystals.

The increasing availability of ultrabright Light Sources is facilitating the study of smaller crystals at faster timescales but with an increased risk of severe X-ray damage, leading to developments in multi-crystal methods such as serial crystallography (SX). SX studies on crystals with small unit cells are challenging as very few reflections are recorded in a single data image, making it difficult to determine the orientation matrix for each crystal and thus preventing the combination of the data from all crystals for structure solution. We herein present a Small-Rotative Fixed-Target Serial Synchrotron Crystallography (SR-FT-SSX) methodology, in which rotation of the serial target through a small diffraction angle at each crystal delivers high-quality data, facilitating ab initio unit cell determination and atomic-scale structure solution. The method is benchmarked using microcrystals of the small-molecule photoswitch sodium nitroprusside dihydrate, obtaining complete data to dmin = 0.6 Å by combining just 66 partial datasets selected against rigorous quality criteria.

An Efficient Analysis of Amplitude and Phase Dynamics in Networked MEMS-Colpitts Oscillators

Abstract This paper explores the interactions of both phase and amplitude in a network of N MEMS-Colpitts oscillators that are resistively coupled. The numerical simulations of the extensive networks of oscillators, required for emerging applications such as clock synchronization and neuromorphic computing, become computationally prohibitive as the number of oscillators increases. This complicates the design and evaluation of such systems, as understanding the effects of changes in coupling and design parameters can require many simulations. This study employs the method of multiple scales (MS) in combination with the harmonic balance method to convert the coupled differential equations governing the system of oscillators into a set of nonlinear evolution equations for the amplitude and phase of the oscillators. The amplitude and phase evolve on a timescale that is slow, commensurate with the damping in the system, compared with the fast timescale of the oscillation frequencies. The approach used in this study to describe the amplitude and phase dynamics offers significant computational efficiency (gains of 10× to 50× are shown) compared to direct numerical integration while maintaining an accurate representation of the response. The results of the presented simulations demonstrate the effect of coupling strength on the dynamics of the network, accounting for both phase and amplitude dynamics.

Characterising Cancer Cell Responses to Cyclic Hypoxia Using Mathematical Modelling

In vivo observations show that oxygen levels in tumours can fluctuate on fast and slow timescales. As a result, cancer cells can be periodically exposed to pathologically low oxygen levels; a phenomenon known as cyclic hypoxia. Yet, little is known about the response and adaptation of cancer cells to cyclic, rather than, constant hypoxia. Further, existing in vitro models of cyclic hypoxia fail to capture the complex and heterogeneous oxygen dynamics of tumours growing in vivo. Mathematical models can help to overcome current experimental limitations and, in so doing, offer new insights into the biology of tumour cyclic hypoxia by predicting cell responses to a wide range of cyclic dynamics. We develop an individual-based model to investigate how cell cycle progression and cell fate determination of cancer cells are altered following exposure to cyclic hypoxia. Our model can simulate standard in vitro experiments, such as clonogenic assays and cell cycle experiments, allowing for efficient screening of cell responses under a wide range of cyclic hypoxia conditions. Simulation results show that the same cell line can exhibit markedly different responses to cyclic hypoxia depending on the dynamics of the oxygen fluctuations. We also use our model to investigate the impact of changes to cell cycle checkpoint activation and damage repair on cell responses to cyclic hypoxia. Our simulations suggest that cyclic hypoxia can promote heterogeneity in cellular damage repair activity within vascular tumours.

Accuracy and Reproducibility of Lipari-Szabo Order Parameters From Molecular Dynamics.

The Lipari-Szabo generalized order parameter probes the picosecond to nanosecond time scale motions of a protein and is useful for rationalizing a multitude of biological processes such as protein recognition and ligand binding. Although these fast motions are an important and intrinsic property of proteins, it remains unclear what simulation conditions are most suitable to reproduce methyl symmetry axis side chain order parameter data (Saxis2) from molecular dynamics simulations. In this study, we show that, while Saxis2 tends to converge within tens of nanoseconds, it is essential to run 10 to 20 replicas starting from configurations close to the experimental structure to obtain the best agreement with experimental Saxis2 values. Additionally, in a comparison of force fields, AMBER ff14SB outperforms CHARMM36m in accurately capturing these fast time scale motions, and we suggest that the origin of this performance gap is likely attributed to differences in side chain torsional parametrization and not due to differences in the global protein conformations sampled by the force fields. This study provides insight into obtaining accurate and reproducible Saxis2 values from molecular simulations and underscores the necessity of using replica simulations to compute equilibrium properties.

Understanding Selectivity in Product Distributions from Laser Ablation of Organic Liquids.

Pulsed laser ablation in organic solvents is widely used to produce oxide-free metal and metal carbide nanoparticles, often with carbon coatings resulting from laser-induced reactions in the organic solvent. To gain insight into how the molecular structure of the solvent affects these reaction pathways, this work investigates ablation of the C6H14 isomers n-hexane, 2-methylpentane, and 3-methylpentane through characterization of the gas and liquid products with mass spectrometry. Ablation of each C6H14 isomer produces a distinct distribution of product molecular weights and isomers. 2-methylpentane preferentially produces C3 and C9, whereas 3-methylpentane produces C2, C4, C8, and C10 products. These preferential product distributions, along with the lack of such selectivity in n-hexane, arise from differences in the most favorable C-C bond scission pathways in each C6H14 isomer. Moreover, the particular isomers of C8H18, C9H20, C10H22, and C12H26 produced by ablation of each C6H14 isomer indicate that the vast majority of reaction pathways involve addition reactions between a fragment radical and parent C6H14 or between two C6H14 molecules, without molecular rearrangement. This propensity toward direct addition suggests that the chemical reactions induced by ultrashort pulsed laser ablation proceed on faster time scales than those of radical rearrangements.

A dissipative extension to ideal hydrodynamics

Abstract We present a formulation of special relativistic, dissipative hydrodynamics (SRDHD) derived from the well-established Müller-Israel-Stewart (MIS) formalism using an expansion in deviations from ideal behaviour. By re-summing the non-ideal terms, our approach extends the Euler equations of motion for an ideal fluid through a series of additional source terms that capture the effects of bulk viscosity, shear viscosity and heat flux. For efficiency these additional terms are built from purely spatial derivatives of the primitive fluid variables. The series expansion is parametrized by the dissipation strength and timescale coefficients, and is therefore rapidly convergent near the ideal limit. We show, using numerical simulations, that our model reproduces the dissipative fluid behaviour of other formulations. As our formulation is designed to avoid the numerical stiffness issues that arise in the traditional MIS formalism for fast relaxation timescales, it is roughly an order of magnitude faster than standard methods near the ideal limit.

Nonlinear parametric models of viscoelastic fluid flows.

Reduced-order models (ROMs) have been widely adopted in fluid mechanics, particularly in the context of Newtonian fluid flows. These models offer the ability to predict complex dynamics, such as instabilities and oscillations, at a considerably reduced computational cost. In contrast, the reduced-order modelling of non-Newtonian viscoelastic fluid flows remains relatively unexplored. This work leverages the sparse identification of nonlinear dynamics (SINDy) algorithm to develop interpretable ROMs for viscoelastic flows. In particular, we explore a benchmark oscillatory viscoelastic flow on the four-roll mill geometry using the classical Oldroyd-B fluid. This flow exemplifies many canonical challenges associated with non-Newtonian flows, including transitions, asymmetries, instabilities, and bifurcations arising from the interplay of viscous and elastic forces, all of which require expensive computations in order to resolve the fast timescales and long transients characteristic of such flows. First, we demonstrate the effectiveness of our data-driven surrogate model to predict the transient evolution and accurately reconstruct the spatial flow field for fixed flow parameters. We then develop a fully parametric, nonlinear model capable of capturing the dynamic variations as a function of the Weissenberg number. While the training data are predominantly concentrated on a limit cycle regime for moderate , we show that the parametrized model can be used to extrapolate, accurately predicting the dominant dynamics in the case of high Weissenberg numbers. The proposed methodology represents an initial step in applying machine learning and reduced-order modelling techniques to viscoelastic flows.

Energy-efficient picosecond spin-orbit torque magnetization switching in ferro- and ferrimagnetic films.

Electrical current pulses can be used to manipulate magnetization efficiently via spin-orbit torques. Pulse durations as short as a few picoseconds have been used to switch the magnetization of ferromagnetic films, reaching the terahertz regime. However, little is known about the reversal mechanisms and energy requirements in the ultrafast switching regime. In this work we quantify the energy cost for magnetization reversal over seven orders of magnitude in pulse duration, in both ferromagnetic and ferrimagnetic samples, bridging quasi-static spintronics and femtomagnetism. To this end, we develop a method to stretch picosecond pulses generated by a photoconductive switch by an order of magnitude. Thereby we can create current pulses from picoseconds to durations approaching the pulse width available with commercial instruments. We show that the energy cost for spin-orbit torque switching decreases by more than an order of magnitude in all samples when the pulse duration enters the picosecond range. We project an energy cost of 9 fJ for a 100 × 100 nm2 ferrimagnetic device. Micromagnetic and macrospin simulations unveil a transition from a non-coherent to a coherent magnetization reversal with a strong modification of the magnetization dynamical trajectories as pulse duration is reduced. Our results show the potential for high-speed magnetic spin-orbit torque memories and highlight alternative magnetization reversal pathways at fast timescales.

Convergence of reputations under indirect reciprocity

Previous research has shown how indirect reciprocity can promote cooperation through evolutionary game theoretic models. Most work in this field assumes a separation of time-scales: individuals’ reputations equilibrate at a fast time scale for given frequencies of strategies while the strategies change slowly according to the replicator dynamics. Much of the previous research has focused on the behaviour and stability of equilibria for the replicator dynamics. Here we focus on the underlying reputational dynamics that occur on a fast time scale. We describe reputational dynamics as systems of differential equations and conduct stability analyses on their equilibria. We prove that reputations converge to a unique equilibrium under a solitary observer model for each of the five standard norms and whether assessments are public or private. These results confirm a crucial but previously understudied assumption underlying the theory of indirect reciprocity for the most studied set of norms.

Spike Count Analysis for MultiPlexing Inference (SCAMPI).

Understanding how neurons encode multiple simultaneous stimuli is a fundamental question in neuroscience. We have previously introduced a novel theory of stochastic encoding patterns wherein a neuron's spiking activity dynamically switches among its constituent single-stimulus activity patterns when presented with multiple stimuli (Groh et al., 2024). Here, we present an enhanced, comprehensive statistical testing framework for such " multiplexing " or " code juggling ". Our new approach evaluates whether dual-stimulus responses can be accounted for as mixtures of Poissons either anchored to or bounded by single-stimulus benchmarks. Our enhanced framework improves upon previous methods in two key ways. First, it introduces a stronger set of foils for multiplexing, including an "overreaching" category that captures overdispersed activity patterns unrelated to the single-stimulus benchmarks, reducing false detection of multiplexing/code-juggling. Second, it detects faster fluctuations - i.e. at sub-trial timescales - that would have been overlooked before. We utilize a Bayesian inference framework, considering the hypothesis with the highest posterior probability as the winner, and employ predictive recursion marginal likelihood method for the involving nonparametric density estimation. Reanalysis of previous findings confirms the general observation of "code juggling" and indicates that such juggling may well occur on faster timescales than previously suggested. We further confirm that juggling is more prevalent in (a) the inferotemporal face patch system for combinations of face stimuli than for faces and non-face objects; and (b) the primary visual cortex for distinct vs fused objects.

Attentional Information Routing in The Human Brain.

Brain-wide communication supports behaviors that require coordination between sensory and associative regions. However, how large-scale brain networks route sensory information at fast timescales to guide upcoming actions remains unclear. Using spiking neural networks and human intracranial electrophysiology during spatial attention tasks, where participants detected a target at cued locations, we show that high-frequency activity bursts (HFAb) serve as information-carrying events, facilitating fast and long-range communications. HFAbs emerged as bouts of neural population spiking and were coordinated brain-wide through low-frequency rhythms. At the network-level, HFAb coordination identified distinct cue- and target-activated subnetworks. HFAbs following the cue onset in cue-subnetworks predicted successful target detection and preceded the information in target-subnetworks following target onset. Our findings suggest HFAbs as a neural mechanism for fast brain-wide information routing that supports attentional performance.

Insights into Ligand-Mediated Activation of an Oligomeric Ring-Shaped Gene-Regulatory Protein from Solution- and Solid-State NMR

The 91 kDa oligomeric ring-shaped ligand binding protein TRAP (trp RNA binding attenuation protein) regulates the expression of a series of genes involved in tryptophan (Trp) biosynthesis in bacilli. When cellular Trp levels rise, the free amino acid binds to sites buried in the interfaces between each of the 11 (or 12, depending on the species) protomers in the ring. Crystal structures of Trp-bound TRAP show the Trp ligands are sequestered from solvent by a pair of loops from adjacent protomers that bury the bound ligand via polar contacts to several threonine residues. Binding of the Trp ligands occurs cooperatively, such that successive binding events occur with higher apparent affinity but the structural basis for this cooperativity is poorly understood. We used solution methyl-TROSY NMR relaxation experiments focused on threonine and isoleucine sidechains, as well as magic angle spinning solid-state NMR 13C–13C and 15N-13C chemical shift correlation spectra on uniformly labeled samples recorded at 800 and 1200 MHz, to characterize the structure and dynamics of the protein. Methyl 13C relaxation dispersion experiments on ligand-free apo TRAP revealed concerted exchange dynamics on the µs-ms time scale, consistent with transient sampling of conformations that could allow ligand binding. Cross-correlated relaxation experiments revealed widespread disorder on fast timescales. Chemical shifts for methyl-bearing side chains in apo- and Trp-bound TRAP revealed subtle changes in the distribution of sampled sidechain rotameric states. These observations reveal a pathway and mechanism for induced conformational changes to generate homotropic Trp-Trp binding cooperativity.

Proton discrimination in CLYC for fast neutron spectroscopy

The Cs2LiYCl6:Ce (CLYC) elpasolite scintillator is known for its response to fast and thermal neutrons along with good γ-ray energy resolution. While the 35Cl(n,p) reaction has been identified as a potential means for CLYC-based fast neutron spectroscopy in the absence of time-of-flight (TOF), previous efforts to functionalize CLYC as a fast neutron spectrometer have been thwarted by the inability to isolate proton interactions from 6Li(n,α) and 35Cl(n,α) signals. This work introduces a new approach to particle discrimination in CLYC for fission spectrum neutrons using a multi-gate charge integration algorithm that provides excellent separation between protons and heavier charged particles. Neutron TOF data were collected using a 252Cf source, an array of EJ-309 organic liquid scintillators, and a 6Li-enriched CLYC scintillator outfitted with fast electronics. Modal waveforms were constructed corresponding to the different reaction channels, revealing significant differences in the pulse characteristics of protons and heavier charged particles at ultrafast, fast, and intermediate time scales. These findings informed the design of a pulse shape discrimination algorithm, which was validated using the TOF data. This study also proposes an iterative subtraction method to mitigate contributions from confounding reaction channels in proton and heavier charged particle pulse height spectra, opening the door for CLYC-based fast neutron and γ-ray spectroscopy while preserving sensitivity to thermal neutron capture signals.

Air‐Ice‐Ocean Coupling During a Strong Mid‐Winter Cyclone: Observing Coupled Dynamic Interactions Across Scales

AbstractArctic cyclones are key drivers of sea ice and ocean variability. During the 2019–2020 Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, joint observations of the coupled air‐ice‐ocean system were collected at multiple spatial scales. Here, we present observations of a strong mid‐winter cyclone that impacted the MOSAiC site as it drifted in the central Arctic pack ice. The sea ice dynamical response showed spatial structure at the scale of the evolving and translating cyclonic wind field. Internal ice stress and ocean stress play significant roles, resulting in timing offsets between the atmospheric forcing and the ice response and post‐cyclone inertial ringing in the ice and ocean. Ice motion in response to the wind field then forces the upper ocean currents through frictional drag. The strongest impacts to the sea ice and ocean from the passing cyclone occur as a result of the surface impacts of a strong atmospheric low‐level jet (LLJ) behind the trailing cold front and changing wind directions between the warm‐sector LLJ and post cold‐frontal LLJ. Impacts of the cyclone are prolonged through the coupled ice‐ocean inertial response. Local impacts of the approximately 120 km wide LLJ occur over a 12 hr period or less and at scales of a kilometer to a few tens of kilometers, meaning that these impacts occur at combined smaller spatial scales and faster time scales than most satellite observations and coupled Earth system models can resolve.

Diversity, Stability, and the Forecast Challenge in Forest Lepidopteran Predictive Ecology: Are Multi-Scale Plant–Insect Interactions the Key to Increased Forecast Precision?

I report on long-term patterns of outbreak cycling in four study systems across Canada and illustrate how forecasting in these systems is highly imprecise because of complexity in the cycling and a lack of spatial synchrony amongst sample locations. I describe how a range of bottom-up effects could be generating complexity in these otherwise periodic systems. (1) The spruce budworm in Québec exhibits aperiodic and asynchronous behavior at fast time-scales, and a slow modulation of cycle peak intensity that varies regionally. (2) The forest tent caterpillar across Canada exhibits eruptive spiking behavior that is aperiodic locally, and asynchronous amongst regions, yet aggregates to produce a pattern of periodic outbreaks. In Québec, forest tent caterpillar cycles differ in the aspen-dominated northwest versus the maple-dominated southeast, with opposing patterns of cycle intensity between the two regions. (3) In Alberta, forest tent caterpillar outbreak cycles resist synchronization across a forest landscape gradient, even at very fine spatial scales, resulting in a complex pattern of cycling that defies simple forecasting techniques. (4) In the Border Lakes region of Ontario and Minnesota, where the two insect species coexist in a mixedwood landscape of hardwood and conifers, outbreak cycle intensity in each species varies spatially and temporally in response to host forest landscape structure. Much attention has been given to the effect of top-down agents in driving synchronizable population cycles. However, foliage loss, tree death, and forest succession at stem, stand, and landscape scales affect larval and adult dispersal success, and may serve to override regulatory processes that cause otherwise top-down-driven periodic, synchronized, and predictable population oscillations to become aperiodic, asynchronous, and unpredictable. Incorporating bottom-up effects at multiple spatial and temporal scales may be the key to making significant improvements in forest insect outbreak forecasting.

Alpha suppression in the context of cross-frequency interactions between fast and intermediate timescales

Alpha suppression in the context of cross-frequency interactions between fast and intermediate timescales

N-terminal domain of polypyrimidine-tract binding protein is a dynamic folding platform for adaptive RNA recognition.

The N-terminal RNA recognition motif domain (RRM1) of polypyrimidine tract binding protein (PTB) forms an additional C-terminal helix α3, which docks to one edge of the β-sheet upon binding to a stem-loop RNA containing a UCUUU pentaloop. Importantly, α3 does not contact the RNA. The α3 helix therefore represents an allosteric means to regulate the conformation of adjacent domains in PTB upon binding structured RNAs. Here we investigate the process of dynamic adaptation by stem-loop RNA and RRM1 using NMR and MD in order to obtain mechanistic insights on how this allostery is achieved. Relaxation data and NMR structure determination of the free protein show that α3 is partially ordered and interacts with the domain transiently. Stem-loop RNA binding quenches fast time scale dynamics and α3 becomes ordered, however microsecond dynamics at the protein-RNA interface is observed. MD shows how RRM1 binding to the stem-loop RNA is coupled to the stabilization of the C-terminal helix and helps to transduce differences in RNA loop sequence into changes in α3 length and order. IRES assays of full length PTB and a mutant with altered dynamics in the α3 region show that this dynamic allostery influences PTB function in cultured HEK293T cells.

Tabular and Deep Learning for the Whittle Index

The Whittle index policy is a heuristic that has shown remarkably good performance (with guaranteed asymptotic optimality) when applied to the class of problems known as Restless Multi-Armed Bandit Problems (RMABPs). In this article, we present QWI and QWINN, two reinforcement learning algorithms, respectively tabular and deep, to learn the Whittle index for the total discounted criterion. The key feature is the use of two time-scales, a faster one to update the state-action Q -values, and a relatively slower one to update the Whittle indices. In our main theoretical result, we show that QWI, which is a tabular implementation, converges to the real Whittle indices. We then present QWINN, an adaptation of QWI algorithm using neural networks to compute the Q -values on the faster time-scale, which is able to extrapolate information from one state to another and scales naturally to large state-space environments. For QWINN, we show that all local minima of the Bellman error are locally stable equilibria, which is the first result of its kind for DQN-based schemes. Numerical computations show that QWI and QWINN converge faster than the standard Q -learning algorithm, neural-network based approximate Q-learning, and other state-of-the-art algorithms.

Vibrational heavy atom effect on relaxation and solvent shell dynamics in group VIII trimetallic carbonyls.

Infrared pump-probe and two-dimensional infrared (2D-IR) spectroscopies were used to study the vibrational dynamics of a homologous set of trimetallic dodecacarbonyls with increasingly heavy atomic masses in tetrahydrofuran solution. The vibrational lifetimes showed some evidence of the vibrational heavy atom effect (VHAE) but were not consistent across the sample set. Spectral diffusion was measured by 2D-IR spectroscopy to investigate whether the changes produced by the VHAE had influenced other aspects of vibrational dynamics. The triiron species was found to be more dynamic on very fast timescales and may exhibit evidence of a transient bridging CO structure. Centerline slope analysis of the high-frequency CO peak for each complex revealed that the vibrational dynamics were subtly but consistently slowed for the compounds with heavier metal atoms.

Thermo-mechanical investigation of a friction oscillator under high-frequency excitation

The frictional behavior, temperature evolution, and system dynamics in tribological systems are closely coupled. However, in many cases, at least one of the mentioned phenomena is neglected or not modeled consistently. This paper presents a model of a thermo-mechanically coupled oscillator, where the reduction of friction force due to high-frequency excitation is analyzed. The oscillator’s equation of motion, the impulse balance, and the heat equation for the thermo-mechanical continuum are evaluated so that a set of nonlinear equations describes the coupled system. Using specific mathematical procedures like Laplace-transformation and a particular transformation ansatz for integral equations, the system can be transformed into a set of ordinary differential equations, enabling efficient simulation. Based on this equation set, an averaging method is applied to eliminate the fast time scale of the mechanical oscillation due to high-frequency excitation so that only the averaged influence of the motion goes into the heat evolution. This procedure leads to concise expressions to determine the stationary state of the system, and the quantities of interest can be calculated conveniently. These equations yield parameter combinations where the friction force can be reduced due to the high-frequency excitation, even though thermal expansion is considered. However, there are also parameter regions where the excitation yields a significantly larger friction force.