Intrinsic Dynamics Research Articles

Experimental and theoretical investigation of an ionic liquid-based biphasic solvent for post-combustion CO2 Capture: Breaking through the “Trade-off” effect of viscosity and loading

To break through the “trade-off” effect of biphasic solvents between rich-phase viscosity and CO2 loading, we synthesized a novel ionic liquid (diethylenetriamine-3-hydroxypyridine, [DETA][3HPyr]) with multiple active sites and combined it with the organic solvent diethylene glycol monobutyl ether (DGME) and water to prepare a biphasic solvent for CO2 absorption, the best absorption condition was optimized. The results demonstrated that after absorption, the solvent transformed from homogeneous to biphasic with highly self-concentrated absorption products in the rich phase. The volume of the rich phase accounted for only 36.3 % of the total solvent, while its viscosity decreased significantly to 28.1 mPa·s, with a high blend absorption capacity of 2.67 mol·kg−1. The species of absorption product and absorption mechanism was investigated using 13C NMR. The stable presence of carbamic acid in the system was confirmed, contributing to enhanced absorption capacity. Density functional theory calculations revealed that DGME stabilized carbamic acid through intermolecular hydrogen bonding interactions, and highly polar ions generated during absorption and uneven charge distribution between ions dominated the phase-change process and increased the water content in the rich phase. Thermodynamic energy barrier calculation showed that the solvent effect of DGME reduced the Gibbs free energy barriers of proton transfer and facilitated reaction progress. 3IL4D3H exhibited excellent cyclic performance with low regeneration energy consumption at 1.77 GJ·t−1. This is the first work to revealing the intrinsic dynamics of carbamic acid formation, and provides a new perspective for the phase-change mechanism of ionic liquid-based biphasic solvent.

A mathematical model for the role of dopamine-D2 self-regulation in the production of ultradian rhythms.

Many self-motivated and goal-directed behaviours display highly flexible, approximately 4 hour ultradian (shorter than a day) oscillations. Despite lacking direct correspondence to physical cycles in the environment, these ultradian rhythms may be involved in optimizing functional interactions with the environment and reflect intrinsic neural dynamics. Current evidence supports a role of mesostriatal dopamine (DA) in the expression and propagation of ultradian rhythmicity, however, the biochemical processes underpinning these oscillations remain to be identified. Here, we use a mathematical model to investigate D2 autoreceptor-dependent DA self-regulation as the source of ultradian behavioural rhythms. DA concentration at the midbrain-striatal synapses is governed through a dual-negative feedback-loop structure, which naturally gives rise to rhythmicity. This model shows the propensity of striatal DA to produce an ultradian oscillation characterized by a flexible period that is highly sensitive to parameter variations. Circadian (approximately 24 hour) regulation consolidates the ultradian oscillations and alters their response to the phase-dependent, rapid-resetting effect of a transient excitatory stimulus. Within a circadian framework, the ultradian rhythm orchestrates behavioural activity and enhances responsiveness to an external stimulus. This suggests a role for the circadian-ultradian timekeeping hierarchy in governing organized behaviour and shaping daily experience through coordinating the motivation to engage in recurring, albeit not highly predictable events, such as social interactions.

Non-trivial relationship between behavioral avalanches and internal neuronal dynamics in a recurrent neural network.

Neuronal activity gives rise to behavior, and behavior influences neuronal dynamics, in a closed-loop control system. Is it possible then, to find a relationship between the statistical properties of behavior and neuronal dynamics? Measurements of neuronal activity and behavior have suggested a direct relationship between scale-free neuronal and behavioral dynamics. Yet, these studies captured only local dynamics in brain sub-networks. Here, we investigate the relationship between internal dynamics and output statistics in a mathematical model system where we have access to the dynamics of all network units. We train a recurrent neural network (RNN), initialized in a high-dimensional chaotic state, to sustain behavioral states for durations following a power-law distribution as observed experimentally. Changes in network connectivity due to training affect the internal dynamics of neuronal firings, leading to neuronal avalanche size distributions approximating power-laws over some ranges. Yet, randomizing the changes in network connectivity can leave these power-law features largely unaltered. Specifically, whereas neuronal avalanche duration distributions show some variations between RNNs with trained and randomized decoders, neuronal avalanche size distributions are invariant, in the total population and in output-correlated sub-populations. This is true independent of whether the randomized decoders preserve power-law distributed behavioral dynamics. This demonstrates that a one-to-one correspondence between the considered statistical features of behavior and neuronal dynamics cannot be established and their relationship is non-trivial. Our findings also indicate that statistical properties of the intrinsic dynamics may be preserved, even as the internal state responsible for generating the desired output dynamics is perturbed.

Pinning control of simplicial complexes

In this paper, pinning control of simplicial complexes is investigated. The D-dimensional simplicial complex can be successfully pinned to a desired state (i.e., an equilibrium of the intrinsic dynamics) under the proposed control scheme. The local stability of synchronization in the pinned simplicial complex is analyzed and discussed. The relationship between pinning synchronization and the individual dynamics, higher-order structures, coupling strengths, as well as the control strategy is established. Moreover, the effects of control gain and the number of controlled nodes on synchronizability are examined in detail. The obtained results can provide valuable references for designing optimal higher-order networks in practical applications.

Elucidating the Role of Wildtype and Variant FGFR2 Structural Dynamics in (Dys)Function and Disorder.

The fibroblast growth factor receptor 2 (FGFR2) gene is one of the most extensively studied genes with many known mutations implicated in several human disorders, including oncogenic ones. Most FGFR2 disease-associated gene mutations are missense mutations that result in constitutive activation of the FGFR2 protein and downstream molecular pathways. Many tertiary structures of the FGFR2 kinase domain are publicly available in the wildtype and mutated forms and in the inactive and activated state of the receptor. The current literature suggests a molecular brake inhibiting the ATP-binding A loop from adopting the activated state. Mutations relieve this brake, triggering allosteric changes between active and inactive states. However, the existing analysis relies on static structures and fails to account for the intrinsic structural dynamics. In this study, we utilize experimentally resolved structures of the FGFR2 tyrosine kinase domain and machine learning to capture the intrinsic structural dynamics, correlate it with functional regions and disease types, and enrich it with predicted structures of variants with currently no experimentally resolved structures. Our findings demonstrate the value of machine learning-enabled characterizations of structure dynamics in revealing the impact of mutations on (dys)function and disorder in FGFR2.

Popping and Locking: Balanced Rigidity and Porosity of Zeolitic Imidazolate Frameworks for High-Productivity Methane Purification.

Zeolitic imidazolate frameworks (ZIFs) hold great promise in carbon capture, owing to their structural designability and functional porosity. However, intrinsic linker dynamics limit their pressure-swing adsorption application to biogas upgrading and methane purification. Recently, a functionality-locking strategy has shown feasibility in suppressing such dynamics. Still, a trade-off between structural rigidity and uptake capacity remains a key challenge for optimizing their high-pressure CO2/CH4 separation performance. Here, we report a sequential structural locking (SSL) strategy for enhancing the CO2 capture capacity and CH4 purification productivity in dynamic ZIFs (dynaZIFs). Specifically, we isolated multiple functionality-locked phases, ZIF-78-lt, -ht1, and -ht2, by activation at 50, 160, and 210 °C, respectively. We observed multiple-level locking through gas adsorption and powder X-ray diffraction. We uncovered an SSL mechanism dominated by linker-linker π-π interactions that transit to C-H···O hydrogen bonds with binding energies increasing from -0.64 to -2.77 and -5.72 kcal mol-1, respectively, as evidenced by single-crystal X-ray diffraction and density functional theory calculations. Among them, ZIF-78-ht1 exhibits the highest CO2 capture capacity (up to 18.6 mmol g-1) and CH4 purification productivity (up to 7.6 mmol g-1) at 298 K and 30 bar. These findings provide molecular and energetic insights into leveraging framework flexibility through the SSL mechanism to optimize porous materials' separation performance.

Early altered directionality of resting brain network state transitions in the TgF344-AD rat model of Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disease resulting in memory loss and cognitive decline. Synaptic dysfunction is an early hallmark of the disease whose effects on whole-brain functional architecture can be identified using resting-state functional MRI (rsfMRI). Insights into mechanisms of early, whole-brain network alterations can help our understanding of the functional impact of AD's pathophysiology. Here, we obtained rsfMRI data in the TgF344-AD rat model at the pre- and early-plaque stages. This model recapitulates the major pathological and behavioral hallmarks of AD. We used co-activation pattern (CAP) analysis to investigate if and how the dynamic organization of intrinsic brain functional networks states, undetectable by earlier methods, is altered at these early stages. We identified and characterized six intrinsic brain states as CAPs, their spatial and temporal features, and the transitions between the different states. At the pre-plaque stage, the TgF344-AD rats showed reduced co-activation of hub regions in the CAPs corresponding to the default mode-like and lateral cortical network. Default mode-like network activity segregated into two distinct brain states, with one state characterized by high co-activation of the basal forebrain. This basal forebrain co-activation was reduced in TgF344-AD animals mainly at the pre-plaque stage. Brain state transition probabilities were altered at the pre-plaque stage between states involving the default mode-like network, lateral cortical network, and basal forebrain regions. Additionally, while the directionality preference in the network-state transitions observed in the wild-type animals at the pre-plaque stage had diminished at the early-plaque stage, TgF344-AD animals continued to show directionality preference at both stages. Our study enhances the understanding of intrinsic brain state dynamics and how they are impacted at the early stages of AD, providing a nuanced characterization of the early, functional impact of the disease's neurodegenerative process.

Pinning intra-layer synchronization in multiplex networks of nonidentical layers

In this paper, we investigate intra-layer synchronization in multiplex networks via pinning control strategies. All layers have different topologies and different intrinsic node dynamics. Novel sufficient criteria are derived for pinning intra-layer synchronization of multiplex networks, which indicates that the pinned node set can stabilize the multiplex network if, for each layer, the pinned node(s) can access all unpinned nodes. A distributed adaptive algorithm and finite-time pinning algorithm for intra-layer synchronization of multiplex networks are also proposed. Finally, we give some numerical examples to validate the effectiveness of these theoretical results.

Holographic complexity: braneworld gravity versus the Lloyd bound

We explore the complexity equals volume proposal for planar black holes in anti-de Sitter (AdS) spacetime in 2+1 dimensions, with an end of the world (ETW) brane behind the horizon. We allow for the possibility of intrinsic gravitational dynamics in the form of Jackiw-Teitelboim (JT) gravity to be localized on the brane. We compute the asymptotic rate of change of volume complexity analytically and obtain the full time dependence using numerical techniques. We find that the inclusion of JT gravity on the brane leads to interesting effects on time dependence of holographic complexity. We identify the region in parameter space (the brane location and the JT coupling) for which the rate of change of complexity violates the Lloyd bound. In an equivalent description of the model in terms of an asymptotically AdS wormhole, we connect the violation of the Lloyd bound to the violation of a suitable energy condition in the bulk that we introduce. We also compare the Lloyd bound constraints to previously derived constraints on the bulk parameters in this model that are based on bounds on entanglement growth in the dual CFT state.

A Photoswitchable Metallocycle Based on Azobenzene: Synthesis, Characterization, and Ultrafast Dynamics.

The novel photoswitchable ligand 3,3'-Azobenz(metPA)2 (1) is used to prepare a [Cu2(1)2](BF4)2 metallocycle (2), whose photoisomerization was characterized using static and time-resolved spectroscopic methods. Optical studies demonstrate the highly quantitative and reproducible photoinduced cyclic E/Z switching without decay of the complex. Accordingly and best to our knowledge, [Cu2(1)2](BF4)2 constitutes the first reversibly photoswitchable (3d)‑metallocycle based on azobenzene. The photoinduced multiexponential dynamics in the sub-picosecond to few picosecond time domain of 1 and 2 have been assessed. These ultrafast dynamics as well as the yield of the respective photostationary state (PSSZ = 65%) resemble the behavior of archetypical azobenzene. Also, the innovative pump-probe laser technique of gas phase transient photodissociation (t-PD) in a mass spectrometric ion trap was used to determine the intrinsic relaxation dynamics for the isolated complex. These results are consistent with the results from femtosecond UV/Vis transient absorption (fs-TA) in solution, emphasizing the azobenzene-like dynamics of 2. This unique combination of fs-TA and t-PD enables valuable insights into the prevailing interplay of dynamics and solvation. Both analyses (in solution and gas phase) and quantum chemical calculations reveal a negligible effect of the metal coordination on the switching mechanism and electronic pathway, which suggests a non-cooperative isomerization process.

A machine-learning tool to identify bistable states from calcium imaging data.

Mapping neuronal activation using calcium imaging in vivo during behavioural tasks has advanced our understanding of nervous system function. In almost all of these studies, calcium imaging is used to infer spike probabilities because action potentials activate voltage-gated calcium channels and increase intracellular calcium levels. However, neurons not only fire action potentials, but also convey information via intrinsic dynamics such as by generating bistable membrane potential states. Although a number of tools for spike inference have been developed and are currently being used, no tool exists for converting calcium imaging signals to maps of cellular state in bistable neurons. Purkinje neurons in the larval zebrafish cerebellum exhibit membrane potential bistability, firing either tonically or in bursts. Several studies have implicated the role of a population code in cerebellar function, with bistability adding an extra layer of complexity to this code. In the present study, we develop a tool, CaMLSort, which uses convolutional recurrent neural networks to classify calcium imaging traces as arising from either tonic or bursting cells. We validate this classifier using a number of different methods and find that it performs well on simulated event rasters as well as real biological data that it had not previously seen. Moreover, we find that CaMLsort generalizes to other bistable neurons, such as dopaminergic neurons in the ventral tegmental area of mice. Thus, this tool offers a new way of analysing calcium imaging data from bistable neurons to understand how they participate in network computation and natural behaviours. KEY POINTS: Calcium imaging, compriising the gold standard of inferring neuronal activity, does not report cellular state in neurons that are bistable, such as Purkinje neurons in the cerebellum of larval zebrafish. We model the relationship between Purkinje neuron electrical activity and its corresponding calcium signal to compile a dataset of state-labelled simulated calcium signals. We apply machine-learning methods to this dataset to develop a tool that can classify the state of a Purkinje neuron using only its calcium signal, which works well on real data even though it was trained only on simulated data. CaMLsort (Calcium imaging and Machine Learning based tool to sort intracellular state) also generalizes well to bistable neurons in a different brain region (ventral tegmental area) in a different model organism (mouse). This tool can facilitate our understanding of how these neurons carry out their functions in a circuit.

Stochastic brain dynamics exhibits differential regional distribution and maturation-related changes

Functional magnetic resonance imaging (fMRI) is a powerful non-invasive method for studying brain function by analyzing blood oxygenation level-dependent (BOLD) signals. These signals arise from intricate interplays of deterministic and stochastic biological elements. Quantifying the stochastic part is challenging due to its reliance on assumptions about the deterministic segment. We present a methodological framework to estimate intrinsic stochastic brain dynamics in fMRI data without assuming deterministic dynamics. Our approach utilizes Approximate Entropy and its behavior in noisy series to identify and characterize dynamical noise in unobservable fMRI dynamics. Applied to extensive fMRI datasets (645 Cam-CAN, 1086 Human Connectome Project subjects), we explore lifelong maturation of intrinsic brain noise. Findings indicate 10% to 60% of fMRI signal power is due to intrinsic stochastic brain elements, varying by age. These components demonstrate a physiological role of neural noise which shows a distinct distributions across brain regions and increase linearly during maturation.

Biophysical modulation and robustness of itinerant complexity in neuronal networks.

Transient synchronization of bursting activity in neuronal networks, which occurs in patterns of metastable itinerant phase relationships between neurons, is a notable feature of network dynamics observed in vivo. However, the mechanisms that contribute to this dynamical complexity in neuronal circuits are not well understood. Local circuits in cortical regions consist of populations of neurons with diverse intrinsic oscillatory features. In this study, we numerically show that the phenomenon of transient synchronization, also referred to as metastability, can emerge in an inhibitory neuronal population when the neurons' intrinsic fast-spiking dynamics are appropriately modulated by slower inputs from an excitatory neuronal population. Using a compact model of a mesoscopic-scale network consisting of excitatory pyramidal and inhibitory fast-spiking neurons, our work demonstrates a relationship between the frequency of pyramidal population oscillations and the features of emergent metastability in the inhibitory population. In addition, we introduce a method to characterize collective transitions in metastable networks. Finally, we discuss potential applications of this study in mechanistically understanding cortical network dynamics.

Ultrafast universal fabrication of configurable porous silicone-based elastomers by Joule heating chemistry

Silicone-based elastomers (SEs) have been extensively applied in numerous cutting-edge areas, including flexible electronics, biomedicine, 5G smart devices, mechanics, optics, soft robotics, etc. However, traditional strategies for the synthesis of polymer elastomers, such as bulk polymerization, suspension polymerization, solution polymerization, and emulsion polymerization, are inevitably restricted by long-time usage, organic solvent additives, high energy consumption, and environmental pollution. Here, we propose a Joule heating chemistry method for ultrafast universal fabrication of SEs with configurable porous structures and tunable components (e.g., graphene, Ag, graphene oxide, TiO2, ZnO, Fe3O4, V2O5, MoS2, BN, g-C3N4, BaCO3, CuI, BaTiO3, polyvinylidene fluoride, cellulose, styrene-butadiene rubber, montmorillonite, and EuDySrAlSiOx) within seconds by only employing H2O as the solvent. The intrinsic dynamics of the in situ polymerization and porosity creation of these SEs have been widely investigated. Notably, a flexible capacitive sensor made from as-fabricated silicone-based elastomers exhibits a wide pressure range, fast responses, long-term durability, extreme operating temperatures, and outstanding applicability in various media, and a wireless human-machine interaction system used for rescue activities in extreme conditions is established, which paves the way for more polymer-based material synthesis and wider applications.

An interpretable shapelets-based method for myocardial infarction detection using dynamic learning and deep learning

Objective. Myocardial infarction (MI) is a prevalent cardiovascular disease that contributes to global mortality rates. Timely diagnosis and treatment of MI are crucial in reducing its fatality rate. Currently, electrocardiography (ECG) serves as the primary tool for clinical diagnosis. However, detecting MI accurately through ECG remains challenging due to the complex and subtle pathological ECG changes it causes. To enhance the accuracy of ECG in detecting MI, a more thorough exploration of ECG signals is necessary to extract significant features. Approach. In this paper, we propose an interpretable shapelet-based approach for MI detection using dynamic learning and deep learning. Firstly, the intrinsic dynamics of ECG signals are learned through dynamic learning. Then, a deep neural network is utilized to extract and select shapelets from ECG dynamics, which can capture locally specific ECG changes, and serve as discriminative features for identifying MI patients. Finally, the ensemble model for MI detection is built by integrating shapelets of multi-dimensional ECG dynamic signals. Main results. The performance of the proposed method is evaluated on the public PTB dataset with accuracy, sensitivity, and specificity of 94.11%, 94.97%, and 90.98%. Significance. The shapelets obtained in this study exhibit significant morphological differences between MI and healthy subjects.

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equations, thus demanding extensive computational resources. In contrast, data-driven approaches leverage deep learning algorithms to describe system evolution in low-dimensional spaces. We introduce an architecture, termed Latent Dynamics Network, capable of uncovering low-dimensional intrinsic dynamics in potentially non-Markovian systems. Latent Dynamics Networks automatically discover a low-dimensional manifold while learning the system dynamics, eliminating the need for training an auto-encoder and avoiding operations in the high-dimensional space. They predict the evolution, even in time-extrapolation scenarios, of space-dependent fields without relying on predetermined grids, thus enabling weight-sharing across query-points. Lightweight and easy-to-train, Latent Dynamics Networks demonstrate superior accuracy (normalized error 5 times smaller) in highly-nonlinear problems with significantly fewer trainable parameters (more than 10 times fewer) compared to state-of-the-art methods.

Individual-Specific Animated Profiles of Mental Health.

How important is the timing of the pretreatment evaluation? If we consider mental health to be a relatively fixed condition, the specific timing (e.g., day, hour) of the evaluation is immaterial and often determined on the basis of technical considerations. Indeed, the fundamental assumption underlying the vast majority of psychotherapy research and practice is that mental health is a state that can be captured in a one-dimensional snapshot. If this fundamental assumption, underlying 80 years of empirical research and practice, is incorrect, it may help explain why for decades psychotherapy failed to rise above the 50% efficacy rate in the treatment of mental-health disorders, especially depression, a heterogeneous disorder and the leading cause of disability worldwide. Based on recent studies suggesting within-individual dynamics, this article proposes that mental health and its underlying therapeutic mechanisms have underlying intrinsic dynamics that manifest across dimensions. Computational psychotherapy is needed to develop individual-specific pretreatment animated profiles of mental health. Such individual-specific animated profiles are expected to improve the ability to select the optimal treatment for each patient, devise adequate treatment plans, and adjust them on the basis of ongoing evaluations of mental-health dynamics, creating a new understanding of therapeutic change as a transition toward a more adaptive animated profile.

Echo State Property upon Noisy Driving Input

The echo state property (ESP) is a key concept for understanding the working principle of the most widely used reservoir computing model, the echo state network (ESN). The ESP is achieved most of the operation time under general conditions, yet the property is lost when a combination of driving input signals and intrinsic reservoir dynamics causes unfavorable conditions for forgetting the initial transient state. A widely used treatment, setting the spectral radius of the weight matrix below the unity, is not sufficient as it may not properly account for the nature of driving inputs. Here, we characterize how noisy driving inputs affect the dynamical properties of an ESN and the empirical evaluation of the ESP. The standard ESN with a hyperbolic tangent activation function is tested using the MNIST handwritten digit datasets at different additive white Gaussian noise levels. The correlations among the neurons, input mapping, and memory capacity of the reservoir nonlinearly decrease with the noise level. These trends agree with the deterioration of the MNIST classification accuracy against noise. In addition, the ESP index for noisy driving input is developed as a tool to help easily assess ESPs in practical applications. Bifurcation analysis explicates how the noise destroys an asymptotical convergence in an ESN and confirms that the proposed index successfully captures the ESP against noise. These results pave the way for developing noise-robust reservoir computing systems, which may promote the validity and utility of reservoir computing for real-world machine learning applications.

Dynamic fading memory and expectancy effects in the monkey primary visual cortex

In order to investigate the involvement of the primary visual cortex (V1) in working memory (WM), parallel, multisite recordings of multi-unit activity were obtained from monkey V1 while the animals performed a delayed match-to-sample (DMS) task. During the delay period, V1 population firing rate vectors maintained a lingering trace of the sample stimulus that could be reactivated by intervening impulse stimuli that enhanced neuronal firing. This fading trace of the sample did not require active engagement of the monkeys in the DMS task and likely reflects the intrinsic dynamics of recurrent cortical networks in lower visual areas. This renders an active, attention-dependent involvement of V1 in the maintenance of WM contents unlikely. By contrast, population responses to the test stimulus depended on the probabilistic contingencies between sample and test stimuli. Responses to tests that matched expectations were reduced which agrees with concepts of predictive coding.

Modeling and dissociation of intrinsic and input-driven neural population dynamics underlying behavior

Neural dynamics can reflect intrinsic dynamics or dynamic inputs, such as sensory inputs or inputs from other brain regions. To avoid misinterpreting temporally structured inputs as intrinsic dynamics, dynamical models of neural activity should account for measured inputs. However, incorporating measured inputs remains elusive in joint dynamical modeling of neural-behavioral data, which is important for studying neural computations of behavior. We first show how training dynamical models of neural activity while considering behavior but not input or input but not behavior may lead to misinterpretations. We then develop an analytical learning method for linear dynamical models that simultaneously accounts for neural activity, behavior, and measured inputs. The method provides the capability to prioritize the learning of intrinsic behaviorally relevant neural dynamics and dissociate them from both other intrinsic dynamics and measured input dynamics. In data from a simulated brain with fixed intrinsic dynamics that performs different tasks, the method correctly finds the same intrinsic dynamics regardless of the task while other methods can be influenced by the task. In neural datasets from three subjects performing two different motor tasks with task instruction sensory inputs, the method reveals low-dimensional intrinsic neural dynamics that are missed by other methods and are more predictive of behavior and/or neural activity. The method also uniquely finds that the intrinsic behaviorally relevant neural dynamics are largely similar across the different subjects and tasks, whereas the overall neural dynamics are not. These input-driven dynamical models of neural-behavioral data can uncover intrinsic dynamics that may otherwise be missed.