Building insightful, memory-enriched models to capture long-time biochemical processes from short-time simulations.

The ability to predict and understand complex molecular motions occurring over diverse timescales ranging from picoseconds to seconds and even hours in biological systems remains one of the largest challenges to chemical theory. Markov state models (MSMs), which provide a memoryless description of the transitions between different states of a biochemical system, have provided numerous important physically transparent insights into biological function. However, constructing these models often necessitates performing extremely long molecular simulations to converge the rates. Here, we show that by incorporating memory via the time-convolutionless generalized master equation (TCL-GME) one can build a theoretically transparent and physically intuitive memory-enriched model of biochemical processes with up to a three order of magnitude reduction in the simulation data required while also providing a higher temporal resolution. We derive the conditions under which the TCL-GME provides a more efficient means to capture slow dynamics than MSMs and rigorously prove when the two provide equally valid and efficient descriptions of the slow configurational dynamics. We further introduce a simple averaging procedure that enables our TCL-GME approach to quickly converge and accurately predict long-time dynamics even when parameterized with noisy reference data arising from short trajectories. We illustrate the advantages of the TCL-GME using alanine dipeptide, the human argonaute complex, and FiP35 WW domain.

Memory can induce oscillations of microparticles in nonlinear viscoelastic media and cause a giant enhancement of driven diffusion.

We investigate analytically and numerically a basic model of driven Brownian motion with a velocity-dependent friction coefficient in nonlinear viscoelastic media featured by a stress plateau at intermediate shear velocities and profound memory effects. For constant force driving, we show that nonlinear oscillations of a microparticle velocity and position emerge by a Hopf bifurcation at a small critical force (first dynamical phase transition), where the friction's nonlinearity seems to be wholly negligible. They also disappear by a second Hopf bifurcation at a much larger force value (second dynamical phase transition). The bifurcation diagram is found in an analytical form confirmed by numerics. Surprisingly, the particles' inertial and the medium's nonlinear properties remain crucial even in a parameter regime where they were earlier considered entirely negligible. Depending on the force and other parameters, the amplitude of oscillations can significantly exceed the size of the particles, and their period can span several time decades, primarily determined by the memory time of the medium. Such oscillations can also be thermally excited near the edges of dynamical phase transitions. The second dynamical phase transition combined with thermally induced stochastic limit cycle oscillations leads to a giant enhancement of diffusion over the limit of vast driving forces, where an effective linearization of stochastic dynamics occurs.

Non-Markovian modeling of protein folding.

We extract the folding free energy landscape and the time-dependent friction function, the two ingredients of the generalized Langevin equation (GLE), from explicit-water molecular dynamics (MD) simulations of the α-helix forming polypeptide [Formula: see text] for a one-dimensional reaction coordinate based on the sum of the native H-bond distances. Folding and unfolding times from numerical integration of the GLE agree accurately with MD results, which demonstrate the robustness of our GLE-based non-Markovian model. In contrast, Markovian models do not accurately describe the peptide kinetics and in particular, cannot reproduce the folding and unfolding kinetics simultaneously, even if a spatially dependent friction profile is used. Analysis of the GLE demonstrates that memory effects in the friction significantly speed up peptide folding and unfolding kinetics, as predicted by the Grote-Hynes theory, and are the cause of anomalous diffusion in configuration space. Our methods are applicable to any reaction coordinate and in principle, also to experimental trajectories from single-molecule experiments. Our results demonstrate that a consistent description of protein-folding dynamics must account for memory friction effects.

Power Amplifier Predistortion Using Reduced Sampling Rates in the Forward and Feedback Paths.

The feasibility of implementing digital predistortion for next-generation wireless communication is faced with a dilemma due to the ever-increasing demand for faster data rates. This causes the utilized bandwidth to increase significantly, as seen in the 5G NR standard in which bandwidths as high as 400 MHz are utilized. Hence, the development of new predistortion techniques in which the forward and feedback paths operate at lower sampling rates is of utmost importance to realize efficient and practical predistortion solutions. In this work, a novel predistortion technique is presented by which the predistortion is divided between the digital and analog domains. The predistorter is composed of a memoryless AM/AM gain function that is implementable in the analog domain, and a nonlinear model with memory effects in the digital domain to relax the sampling rate requirements on both the forward and feedback paths. Experimental validation was carried out with a 20 MHz and a 40 MHz 5G signal, and the results indicate minimal linearization degradation with a sampling rate reduction of 50% and 30%, respectively. This sampling rate reduction is concurrently applied in the digital-to-analog converter of the forward path and the analog-to-digital converter of the feedback path.

Stochastic Compartment Model with Mortality and Its Application to Epidemic Spreading in Complex Networks.

We study epidemic spreading in complex networks by a multiple random walker approach. Each walker performs an independent simple Markovian random walk on a complex undirected (ergodic) random graph where we focus on the Barabási-Albert (BA), Erdös-Rényi (ER), and Watts-Strogatz (WS) types. Both walkers and nodes can be either susceptible (S) or infected and infectious (I), representing their state of health. Susceptible nodes may be infected by visits of infected walkers, and susceptible walkers may be infected by visiting infected nodes. No direct transmission of the disease among walkers (or among nodes) is possible. This model mimics a large class of diseases such as Dengue and Malaria with the transmission of the disease via vectors (mosquitoes). Infected walkers may die during the time span of their infection, introducing an additional compartment D of dead walkers. Contrary to the walkers, there is no mortality of infected nodes. Infected nodes always recover from their infection after a random finite time span. This assumption is based on the observation that infectious vectors (mosquitoes) are not ill and do not die from the infection. The infectious time spans of nodes and walkers, and the survival times of infected walkers, are represented by independent random variables. We derive stochastic evolution equations for the mean-field compartmental populations with the mortality of walkers and delayed transitions among the compartments. From linear stability analysis, we derive the basic reproduction numbers RM,R0 with and without mortality, respectively, and prove that RM1, the healthy state is unstable, whereas for zero mortality, a stable endemic equilibrium exists (independent of the initial conditions), which we obtained explicitly. We observed that the solutions of the random walk simulations in the considered networks agree well with the mean-field solutions for strongly connected graph topologies, whereas less well for weakly connected structures and for diseases with high mortality. Our model has applications beyond epidemic dynamics, for instance in the kinetics of chemical reactions, the propagation of contaminants, wood fires, and others.

Skin-Inspired Tough Elastomer with Moisture-Triggered Switchable Mechanical Properties.

Biological tissue usually exhibits good water adaptive mechanical properties, which can maintain high strength and toughness in both wet and dry states. However, synthetic tissue like hydrogel usually becomes hard and brittle in its dry state. Herein, this challenge is met by exploring iron-catechol complex (TA-Fe3+ ) as a great platform combining extremely different polymers (elastomer and hydrogel) to construct new tissue-like soft composite materials with two different continuous phases, which have not yet been reported. In its dry state, the xerogel phase becomes a reinforced segment to increase the strength of PB without losing its toughness. In its wet state, this soft material becomes high performance hydrogel, where hydrogel phase absorbs high water and elastomer phase can sustain high loading. Such heterogeneous phase structures provide a good idea for designing the soft materials, exhibiting a trade-off between its high strength and toughness in both wet and dry states. Furthermore, its shape memory behaviors in both its wet and dry state, which shows a great potential application for complex adaptive shape transformation and engineering application like lifting of heavy objects under remote control due to high photo-thermal transition of TA-Fe3+ is explored.

Informative and uninformative prestimulus cues at encoding benefit familiarity and source memory.

Previous research has shown that neural activity elicited by informative prestimulus cues during encoding differ with respect to subsequent memory outcomes. These findings indicate prestimulus cues create a "brain state" associated with subsequent memory that, potentially, also has downstream effects benefitting processes associated with successful encoding and subsequent memory performance. However, previous studies have not included the conditions necessary to appropriately test this latter assumption. The present study examines how informative and uninformative prestimulus encoding cues affect memory accuracy for upcoming stimuli compared to a no cue condition. At encoding, participants made one of two semantic judgments on words preceded by an informative prestimulus cue that identified the upcoming semantic judgment, an uninformative prestimulus cue that signalled an upcoming trial but no information about the semantic judgment, or no cue. Dual process estimates of familiarity, but not recollection, demonstrated a graded pattern with the informativeness of the prestimulus cues (i.e., informative > uninformative > no cues). Moreover, both informative and uninformative prestimulus cues enhanced subsequent source memory accuracy for the encoding task compared to the no cue condition. These findings suggest that prestimulus cues can strengthen the processes that support successful memory encoding and benefit subsequent familiarity and source memory.

Multiple epidemic waves as the outcome of stochastic SIR epidemics with behavioral responses: a hybrid modeling approach.

In the behavioral epidemiology (BE) of infectious diseases, little theoretical effort seems to have been devoted to understand the possible effects of individuals' behavioral responses during an epidemic outbreak in small populations. To fill this gap, here we first build general, behavior implicit, SIR epidemic models including behavioral responses and set them within the framework of nonlinear feedback control theory. Second, we provide a thorough investigation of the effects of different types of agents' behavioral responses for the dynamics of hybrid stochastic SIR outbreak models. In the proposed model, the stochastic discrete dynamics of infection spread is combined with a continuous model describing the agents' delayed behavioral response. The delay reflects the memory mechanisms with which individuals enact protective behavior based on past data on the epidemic course. This results in a stochastic hybrid system with time-varying transition probabilities. To simulate such system, we extend Gillespie's classic stochastic simulation algorithm by developing analytical formulas valid for our classes of models. The algorithm is used to simulate a number of stochastic behavioral models and to classify the effects of different types of agents' behavioral responses. In particular this work focuses on the effects of the structure of the response function and of the form of the temporal distribution of such response. Among the various results, we stress the appearance of multiple, stochastic epidemic waves triggered by the delayed behavioral response of individuals.

Memory Effects, Multiple Time Scales and Local Stability in Langevin Models of the S&P500 Market Correlation.

The analysis of market correlations is crucial for optimal portfolio selection of correlated assets, but their memory effects have often been neglected. In this work, we analyse the mean market correlation of the S&P500, which corresponds to the main market mode in principle component analysis. We fit a generalised Langevin equation (GLE) to the data whose memory kernel implies that there is a significant memory effect in the market correlation ranging back at least three trading weeks. The memory kernel improves the forecasting accuracy of the GLE compared to models without memory and hence, such a memory effect has to be taken into account for optimal portfolio selection to minimise risk or for predicting future correlations. Moreover, a Bayesian resilience estimation provides further evidence for non-Markovianity in the data and suggests the existence of a hidden slow time scale that operates on much slower times than the observed daily market data. Assuming that such a slow time scale exists, our work supports previous research on the existence of locally stable market states.

Smart Diffraction Gratings Based on the Shape Memory Effect.

The shape memory effect is the capability of a structure or a material that can be deformed into a certain temporary shape under external stimulus, and the shape will be fixed without the stimulus. The recovery process can be triggered by the same stimulus. The intelligent tunable device based on the shape memory effect has a wide range of applications in many fields. In the optical field, smart diffraction gratings can accomplish in situ optical diffractions according to requirements, meeting the high demand in the next generation of smart optical systems. However, it is essential to construct high-precision grating structures based on shape memory materials. Here, a smart diffraction grating based on UV-curable shape memory polymers (SMPs) via two-beam interference is reported, with nano-scale precision, excellent deformability and recovery ability, and adjustable spectroscopic performance. More importantly, based on the shape memory effect, grating structures that surpass the precision of the fabrication system can be obtained. The smart grating exhibits rapid deformation and recovery upon heating and long-term storage capability, which facilitates them to be applied in optics, electronics, and integrated sensing.

The Mpemba effect in spin glasses is a persistent memory effect.

The Mpemba effect occurs when a hot system cools faster than an initially colder one, when both are refrigerated in the same thermal reservoir. Using the custom-built supercomputer Janus II, we study the Mpemba effect in spin glasses and show that it is a nonequilibrium process, governed by the coherence length ξ of the system. The effect occurs when the bath temperature lies in the glassy phase, but it is not necessary for the thermal protocol to cross the critical temperature. In fact, the Mpemba effect follows from a strong relationship between the internal energy and ξ that turns out to be a sure-tell sign of being in the glassy phase. Thus, the Mpemba effect presents itself as an intriguing avenue for the experimental study of the coherence length in supercooled liquids and other glass formers.

Popularity of Video Games and Collective Memory.

Describing the permanence of cultural objects is an important step in understanding societal trends. A relatively novel cultural object is the video game, which is an interactive media, that is, the player is an active contributor to the overall experience. This article aims to investigate video game permanence in collective memory using their popularity as a proxy, employing data based on the Steam platform from July 2012 to December 2020. The objectives include characterizing the database; studying the growth of players, games, and game categories; providing a model for the relative popularity distribution; and applying this model in three strata, global, major categories, and among categories. We detected linear growth trends in the number of players and the number of categories, and an exponential trend in the number of games released. Furthermore, we verified that lognormal distributions, emerging from multiplicative processes, provide a first approximation for the popularity in all strata. In addition, we proposed an improvement via Box-Cox transformations with similar parameters (from -0.12 (95% CI: -0.18, -0.07) to -0.04 (95% CI: -0.08, 0)). We were able to justify this improved model by interpreting the magnitude of each Box-Cox parameter as a measure of memory effects.

Memory Effects in High-Dimensional Systems Faithfully Identified by Hilbert-Schmidt Speed-Based Witness.

A witness of non-Markovianity based on the Hilbert-Schmidt speed (HSS), a special type of quantum statistical speed, has been recently introduced for low-dimensional quantum systems. Such a non-Markovianity witness is particularly useful, being easily computable since no diagonalization of the system density matrix is required. We investigate the sensitivity of this HSS-based witness to detect non-Markovianity in various high-dimensional and multipartite open quantum systems with finite Hilbert spaces. We find that the time behaviors of the HSS-based witness are always in agreement with those of quantum negativity or quantum correlation measure. These results show that the HSS-based witness is a faithful identifier of the memory effects appearing in the quantum evolution of a high-dimensional system with a finite Hilbert space.

pH Responsive Strong Polyion Complex Shape Memory Hydrogel with Spontaneous Shape Changing and Information Encryption.

Polyion complex (PIC) hydrogels attract lots of studies because of the relatively definite network and excellent mechanical strength. However, the stability of the PIC hydrogel is poor in salt solutions due to the counter-ion screening effect, which restricts their applications. Besides, novel functions of the PIC hydrogels also need to be explored. In this work, a multifunctional PIC hydrogel is prepared by polymerizing a hydrophobic monomer 2-(diethylamino)ethyl methacrylate in poly(styrene sulfonic acid) aqueous solution with the presence of counter-ion NaCl. Fourier transform infrared (FTIR) spectra, water content, and mechanical properties of the hydrogel are investigated. The introduction of hydrophobic weak electrolyte into the hydrogel brings stable excellent mechanical strength even in NaCl solutions with high concentration and pH modulated softening and strengthening. Surprisingly, the hydrogel swells but is strengthened in HCl, while it shrinks but is softened in NaOH. pH induced shape memory and unique spontaneous shape changing is thus presented benefiting from this synergistic effect. Moreover, information encryption is realized on the PIC hydrogel owing to the transmittance change and the different water absorption capability of the hydrogel at different states. This new kind of PIC hydrogel proposes a new smart material in continuously actuating soft machines and secretive information transformation.

Slow domain reconfiguration causes power-law kinetics in a two-state enzyme.

Protein dynamics are typically captured well by rate equations that predict exponential decays for two-state reactions. Here, we describe a remarkable exception. The electron-transfer enzyme quiescin sulfhydryl oxidase (QSOX), a natural fusion of two functionally distinct domains, switches between open- and closed-domain arrangements with apparent power-law kinetics. Using single-molecule FRET experiments on time scales from nanoseconds to milliseconds, we show that the unusual open-close kinetics results from slow sampling of an ensemble of disordered domain orientations. While substrate accelerates the kinetics, thus suggesting a substrate-induced switch to an alternative free energy landscape of the enzyme, the power-law behavior is also preserved upon electron load. Our results show that the slow sampling of open conformers is caused by a variety of interdomain interactions that imply a rugged free energy landscape, thus providing a generic mechanism for dynamic disorder in multidomain enzymes.

Butane dihedral angle dynamics in water is dominated by internal friction.

The dihedral dynamics of butane in water is known to be rather insensitive to the water viscosity; possible explanations for this involve inertial effects or Kramers' turnover, the finite memory time of friction, and the presence of so-called internal friction. To disentangle these factors, we introduce a method to directly extract the friction memory function from unconstrained simulations in the presence of an arbitrary free-energy landscape. By analysis of the dihedral friction in butane for varying water viscosity, we demonstrate the existence of an internal friction contribution that does not scale linearly with water viscosity. At normal water viscosity, the internal friction turns out to be eight times larger than the solvent friction and thus completely dominates the effective friction. By comparison with simulations of a constrained butane molecule that has the dihedral as the only degree of freedom, we show that internal friction comes from the six additional degrees of freedom in unconstrained butane that are orthogonal to the dihedral angle reaction coordinate. While the insensitivity of butane's dihedral dynamics to water viscosity is solely due to the presence of internal friction, inertial effects nevertheless crucially influence the resultant transition rates. In contrast, non-Markovian effects due to the finite memory time are present but do not significantly influence the dihedral barrier-crossing rate of butane. These results not only settle the character of dihedral dynamics in small solvated molecular systems such as butane, they also have important implications for the folding of polymers and proteins.

Memory Effects in Quantum Dynamics Modelled by Quantum Renewal Processes.

Simple, controllable models play an important role in learning how to manipulate and control quantum resources. We focus here on quantum non-Markovianity and model the evolution of open quantum systems by quantum renewal processes. This class of quantum dynamics provides us with a phenomenological approach to characterise dynamics with a variety of non-Markovian behaviours, here described in terms of the trace distance between two reduced states. By adopting a trajectory picture for the open quantum system evolution, we analyse how non-Markovianity is influenced by the constituents defining the quantum renewal process, namely the time-continuous part of the dynamics, the type of jumps and the waiting time distributions. We focus not only on the mere value of the non-Markovianity measure, but also on how different features of the trace distance evolution are altered, including times and number of revivals.

Survival processing modulates the neurocognitive mechanisms of episodic encoding.

Memories formed in the context of an imagined survival scenario are more easily remembered, but the mechanisms underlying this effect are still under debate. We investigated the neurocognitive processes underlying the survival processing effect by examining event-related potentials (ERPs) during memory encoding. Participants imagined being either stranded in a foreign land and needing to survive, or in an overseas moving (control) scenario, while incidentally encoding a list of words. Words encountered in the survival context were associated with improved recall and reduced false-memory intrusions during a later memory test. Survival processing was associated with an increased frontal slow wave, while there was no effect on the overall P300 amplitude, relative to the control scenario. Furthermore, a subsequent memory effect in the P300 time window was found only in the control scenario. These findings suggest that survival processing leads to a shift away from lower level encoding processes, which are sensitive to motivation and stimulus salience and which were evident in the control scenario, to more active and elaborative forms of encoding. The results are consistent with a richness of encoding account of the survival processing effect and offer novel insights into the encoding processes that lead to enhanced memory for fitness-relevant information.

Reprocessable and Multiple Shape Memory Thermosets with Reconfigurability.

Multiple shape memory thermosets for various engineering applications have been quickly developed in recent years, mainly due to their stable thermomechanical performance, environmental stability, excellent chemical, solvent resistance, etc. However, these thermosets are inherently non-recyclable due to their chemically crosslinked properties, and it is difficult to make the permanent shapes of these thermosets sophisticated and geometrically complex, which significantly restricts a great variety of engineering applications in the industry and technology fields, mainly due to the economic inefficiency and environmental impact. Here, a thermoset with recyclability, multiple shape memory effect (multi-SME), and permanent shape reconfiguration is reported. After recycling, it also exhibits excellent mechanical properties without sacrificing the excellent multi-SME, combined elasticity (shape memory), and solid state plasticity, which can be easily scaled up and generalized to a variety of dynamic covalent networks.

A Facile and General Approach to Recoverable High-Strain Multishape Shape Memory Polymers.

Fabricating a single polymer network with no need to design complex structures to achieve an ideal combination of tunable high-strain multiple-shape memory effects and highly recoverable shape memory property is a great challenge for the real applications of advanced shape memory devices. Here, a facile and general approach to recoverable high-strain multishape shape memory polymers is presented via a random copolymerization of acrylate monomers and a chain-extended multiblock copolymer crosslinker. As-prepared shape memory networks show a large width at the half-peak height of the glass transition, far wider than current classical multishape shape memory polymers. A combination of tunable high-strain multishape memory effect and as high as 1000% recoverable strain in a single chemical-crosslinking network can be obtained. To the best of our knowledge, this is the first thermosetting material with a combination of highly recoverable strain and tunable high-strain multiple-shape memory effects.