Transient Networks Research Articles

Elucidating Nonlinear Stress Relaxation in Transient Networks through Two-Dimensional Rheo-Optics.

This study aims to elucidate the origin of nonlinear stress relaxation behaviors in transient networks using a systematically controlled model system consisting of the tetra-armed polyethylene glycols (Tetra-PEG slime) in conjunction with two-dimensional rheo-optics observations. Transient networks, characterized by their temporary cross-links, are extensively utilized in self-healing and robust materials. However, the molecular mechanisms governing their viscoelastic responses to large deformations have remained elusive. This is primarily due to the heterogeneous structures inherent in conventional transient networks and a scarcity of detailed experimental evaluations. By employing Tetra-PEG slime, which is distinguished by its regular structure with uniform strand lengths and functionalities, and the polarization imaging method, we overcome these obstacles. Our results reveal that the damping phenomena observed under large step strains arise from spatially heterogeneous relaxation, predominantly driven by network strand pullout. These insights lay a solid foundation for understanding the intricate rheological properties of transient networks.

Predicting the effects of degradation on viscoelastic relaxation time using model transient networks

In this study, we utilized tetra-armed polyethylene glycol (Tetra-PEG) slimes, which are model transient networks with well-controlled structures, to predict the effects of long-term degradation on the viscoelastic properties of liquids. Viscoelastic liquids, such as sodium hyaluronate, are frequently used in biomedical applications within the human body. However, precisely controlling the viscoelastic properties of these liquids in the long-term is challenging, as the main chains of the liquids undergo stochastic degradation. To establish a predictable model for studying long-term degradation effects, we employed Tetra-PEG slime, and modifications were performed to introduce specific cleavage sites in areas with connections. The Tetra-PEG slimes were characterized by single relaxation modes, and these modes were independent from the degree of degradation, which was determined by hydrolyzing the cleavage sites. Overall, this work provides a universal design for viscoelastic liquids with precisely-controllable degradation.

Phototriggered Equilibrated and Transient Orthogonally Operating Constitutional Dynamic Networks Guiding Biocatalytic Cascades.

The photochemical deprotection of structurally engineered o-nitrobenzylphosphate-caged hairpin nucleic acids is introduced as a versatile method to evolve constitutional dynamic networks, CDNs. The photogenerated CDNs, in the presence of fuel strands, interact with auxiliary CDNs, resulting in their dynamically equilibrated reconfiguration. By modification of the constituents associated with the auxiliary CDNs with glucose oxidase (GOx)/horseradish peroxidase (HRP) or the lactate dehydrogenase (LDH)/nicotinamide adenine dinucleotide (NAD+) cofactor, the photogenerated CDN drives the orthogonal operation upregulated/downregulated operation of the GOx/HRP and LDH/NAD+ biocatalytic cascade in the conjugate mixture of auxiliary CDNs. Also, the photogenerated CDN was applied to control the reconfiguration of coupled CDNs, leading to upregulated/downregulated formation of the antithrombin aptamer units, resulting in the dictated inhibition of thrombin activity (fibrinogen coagulation). Moreover, a reaction module consisting of GOx/HRP-modified o-nitrobenzyl phosphate-caged DNA hairpins, photoresponsive caged auxiliary duplexes, and nickase leads upon irradiation to the emergence of a transient, dissipative CDN activating in the presence of two alternate auxiliary triggers, achieving transient operation of up- and downregulated GOx/HRP biocatalytic cascades.

Aberrant resting-state co-activation network dynamics in major depressive disorder

Major depressive disorder (MDD) is a globally prevalent and highly disabling disease characterized by dysfunction of large-scale brain networks. Previous studies have found that static functional connectivity is not sufficient to reflect the complicated and time-varying properties of the brain. The underlying dynamic interactions between brain functional networks of MDD remain largely unknown, and it is also unclear whether neuroimaging-based dynamic properties are sufficiently robust to discriminate individuals with MDD from healthy controls since the diagnosis of MDD mainly depends on symptom-based criteria evaluated by clinical observation. Resting-state functional magnetic resonance imaging (fMRI) data of 221 MDD patients and 215 healthy controls were shared by REST-meta-MDD consortium. We investigated the spatial-temporal dynamics of MDD using co-activation pattern analysis and made individual diagnoses using support vector machine (SVM). We found that MDD patients exhibited aberrant dynamic properties (such as dwell time, occurrence rate, transition probability, and entropy of Markov trajectories) in some transient networks including subcortical network (SCN), activated default mode network (DMN), de-activated SCN-cerebellum network, a joint network, activated attention network (ATN), and de-activated DMN-ATN, where some dynamic properties were indicative of depressive symptoms. The trajectories of other networks to deactivated DMN-ATN were more accessible in MDD patients. Subgroup analyses also showed subtle dynamic changes in first-episode drug-naïve (FEDN) MDD patients. Finally, SVM achieved preferable accuracies of 84.69%, 76.77%, and 88.10% in discriminating patients with MDD, FEDN MDD, and recurrent MDD from healthy controls with their dynamic metrics. Our findings reveal that MDD is characterized by aberrant dynamic fluctuations of brain network and the feasibility of discriminating MDD patients using dynamic properties, which provide novel insights into the neural mechanism of MDD.

Role of hierarchy structure on the mechanical adaptation of self-healing hydrogels under cyclic stretching.

Soft materials with mechanical adaptability have substantial potential for various applications in tissue engineering. Gaining a deep understanding of the structural evolution and adaptation dynamics of soft materials subjected to cyclic stretching gives insight into developing mechanically adaptive materials. Here, we investigate the effect of hierarchy structure on the mechanical adaptation of self-healing hydrogels under cyclic stretching training. A polyampholyte hydrogel, composed of hierarchical structures including ionic bonds, transient and permanent polymer networks, and bicontinuous hard/soft-phase networks, is adopted as a model. Conditions for effective training, mild overtraining, and fatal overtraining are demonstrated in soft materials. We further reveal that mesoscale hard/soft-phase networks dominate the long-term memory effect of training and play a crucial role in the asymmetric dynamics of compliance changes and the symmetric dynamics of hydrogel shape evolution. Our findings provide insights into the design of hierarchical structures for adaptive soft materials.

Probing the Molecular Mechanism of Viscoelastic Relaxation in Transient Networks.

Hydrogels, which have polymer networks through supramolecular and reversible interactions, exhibit various mechanical responsibilities to its surroundings. The influence of the reversible bonds on a hydrogel's macroscopic properties, such as viscoelasticity and dynamics, is not fully understood, preventing further innovative material development. To understand the relationships between the mechanical properties and molecular structures, it is required to clarify the molecular understanding of the networks solely crosslinked by reversible interactions, termed "transient networks". This review introduces our recent progress on the studies on the molecular mechanism of viscoelasticity in transient networks using multiple methods and model materials. Based on the combination of the viscoelasticity and diffusion measurements, the viscoelastic relaxation of transient networks does not undergo the diffusion of polymers, which is not explained by the framework of conventional molecular models for the viscoelasticity of polymers. Then, we show the results of the comparison between the viscoelastic relaxation and binding dynamics of reversible bonds. Viscoelastic relaxation is primarily affected by "dissociation dynamics of the bonds" and "network structures". These results are explained in the framework that the backbone, which is composed of essential chains supporting the stress, is broken by multiple dissociation events. This understanding of molecular dynamics in viscoelasticity will provide the foundation for designing transient networks.

A Review of Stock Price Prediction Based on LSTM and TCN Methods

In financial analysis, stock price prediction is a difficult and important problem that has received a lot of attention from researchers and practitioners in recent years. The application of machine learning and artificial intelligence algorithms to stock price forecasting has demonstrated significant potential for increasing forecasting accuracy. Long momentary memory (LSTM) and transient convolutional networks (TCN) are two famous profound learning calculations that have been generally utilized for stock cost expectation. The most recent approaches to stock price prediction using LSTM and TCN methods are reviewed in this paper. We highlight the most recent research trends in this field and talk about these methods' benefits and drawbacks. Additionally, we discuss potential future research directions in this field. The survey is expected to give a knowledge into the present status of exploration on stock value forecast and guide specialists and experts in working on the exactness of expectation.

A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes

The brain dynamics underlying working memory (WM) unroll via transient frequency-specific large-scale brain networks. This multidimensionality (time, space, and frequency) challenges traditional analyses. Through an unsupervised technique, the time delay embedded-hidden Markov model (TDE-HMM), we pursue a functional network analysis of magnetoencephalographic data from 38 healthy subjects acquired during an n-back task. Here we show that this model inferred task-specific networks with unique temporal (activation), spectral (phase-coupling connections), and spatial (power spectral density distribution) profiles. A theta frontoparietal network exerts attentional control and encodes the stimulus, an alpha temporo-occipital network rehearses the verbal information, and a broad-band frontoparietal network with a P300-like temporal profile leads the retrieval process and motor response. Therefore, this work provides a unified and integrated description of the multidimensional working memory dynamics that can be interpreted within the neuropsychological multi-component model of WM, improving the overall neurophysiological and neuropsychological comprehension of WM functioning.

Dynamic allosteric networks drive adenosine A1 receptor activation and G-protein coupling.

G-protein coupled receptors (GPCRs) present specific activation pathways and signaling among receptor subtypes. Hence, an extensive knowledge of the structural dynamics of the receptor is critical for the development of therapeutics. Here, we target the adenosine A1 receptor (A1R), for which a negligible number of drugs have been approved. We combine molecular dynamics simulations, enhanced sampling techniques, network theory and pocket detection to decipher the activation pathway of A1R, decode the allosteric networks and identify transient pockets. The A1R activation pathway reveal hidden intermediate and pre-active states together with the inactive and fully-active states observed experimentally. The protein energy networks computed throughout these conformational states successfully unravel the extra and intracellular allosteric centers and the communication pathways that couples them. We observe that the allosteric networks are dynamic, being increased along activation and fine-tuned in presence of the trimeric G-proteins. Overlap of transient pockets and energy networks uncover how the allosteric coupling between pockets and distinct functional regions of the receptor is altered along activation. By an in-depth analysis of the bridge between activation pathway, energy networks and transient pockets, we provide a further understanding of A1R. This information can be useful to ease the design of allosteric modulators for A1R.

Dynamic allosteric networks drive adenosine A1 receptor activation and G-protein coupling

G-protein coupled receptors (GPCRs) present specific activation pathways and signaling among receptor subtypes. Hence, an extensive knowledge of the structural dynamics of the receptor is critical for the development of therapeutics. Here, we target the adenosine A1 receptor (A1R), for which a negligible number of drugs have been approved. We combine molecular dynamics simulations, enhanced sampling techniques, network theory, and pocket detection to decipher the activation pathway of A1R, decode the allosteric networks, and identify transient pockets. The A1R activation pathway reveals hidden intermediate and pre-active states together with the inactive and fully-active states observed experimentally. The protein energy networks computed throughout these conformational states successfully unravel the extra and intracellular allosteric centers and the communication pathways that couple them. We observe that the allosteric networks are dynamic, being increased along activation and fine-tuned in the presence of the trimeric G-proteins. Overlap of transient pockets and energy networks uncovers how the allosteric coupling between pockets and distinct functional regions of the receptor is altered along activation. Through an in-depth analysis of the bridge between the activation pathway, energy networks, and transient pockets, we provide a further understanding of A1R. This information can be useful to ease the design of allosteric modulators for A1R.

Disorder enhanced dynamical heterogeneity in strain glass alloys.

An open question related to strain-glass (STG) alloys is whether they process similar dynamical behaviors to other glass systems. In the present work, we investigate the reorientation process of martensite domains in both STG and martensitic alloys. Our results show the presence of highly doped point defects can greatly intensify the dynamical heterogeneity and spatiotemporal correlation in ferroelastic or shape memory alloy systems, which are also two main hallmarks of structural glasses. What's more, we find that such dynamic heterogeneity exists in a different range spanning microscopic to mesoscopic scales, indicated by our molecular dynamic (MD) simulations and time-dependent Ginzburg-Landau (TDGL) modeling. Dopant atoms induced transient strain networks, i.e., spatial correlated local lattice distortion, is a response for such heterogeneous dynamics. The present study thus solidifies strain glass as a new state of matter and may provide guidelines for developing new strain glass alloys.&#xD.

Thermo-aqueous coupling behaviors for shape memory hydrogels: A statistical–mechanical model and simulations faced with experiments

The shape memory behavior of hydrogels endows themselves with the capacities of shape programming and shape recovering, which have great potential in many fields, such as drug release and soft robotics. In thermally responsive hydrogel, particularly, there are two critical factors, temperature and water, that can induce the shape memory effect(SME). Temperature-induced SME has been extensively studied, while water-induced SME has been rarely investigated. In our work, the combined effect of water and temperature on shape memory behavior of hydrogels is experimentally studied and deeply analyzed from a physical perspective. We found that the shape fixed ratio of shape memory hydrogels (SMHs) increases monotonically with temperature, but first increases and then decreases with water content. Moreover, it exits a nonlinear coupling between the water and temperature effects on the SME. On the basis of these findings, a constitutive model is developed to capture this thermo-aqueous coupling using the statistical theory of transient networks, from which we obtain an explicit evolution equation of deformation gradient. In the proposed theory, the temperature-induced SME is investigated through the temperature-water responsive association and dissociation of dynamic bonds while the water-induced SME is explained by the variation of the glass transition temperature according to the Fox–Flory model. Finally, and to provide a final test of the theoretical work, it is implemented into a user element (UEL) subroutine which provide simulations in agreement with our experiments. Our work is expected to provide guidance for future application of SMHs.

Landscape of Network Connectivity in Transient Polymer Networks with Competing Homo- and Hetero-complementary Bonds

Polymer networks expose a wide range of mechanical properties depending on their microstructure and connectivity. Specifically, transient networks based on end-linking of mixed telechelic polymer precursors capable of forming competing homo- and hetero-complementary bonds demonstrate a wide range of network structures and consequently mechanical properties depending on the composition and reactivity of employed precursors. To address this gap, the Miller–Macosko’s recursive model is extended to tetra-arm polymer precursors capable of forming homo- and hetero-complementary bonds. As such, the composition and time evolution of n-fold network connectivities are visualized as a function of precursor composition and bond selectivity. The model potential is highlighted by confronting its predictions with the rheological behavior of transient metallo-supramolecular polymer networks (MSPNs) obtained by mixing tetra-arm poly(ethylene glycol) functionalized with either sterically demanding mesitylene-substituted phenanthroline or unsubstituted slim phenanthroline or terpyridine ligands. The former precursor is incapable of homoleptic complexation; however, in combination with the latter two, they can form a mixture of homo- and heteroleptic complexes, depending on the coordination geometry preference of the utilized metal ion. These results not only visualize the connectivity landscape that can be explored in transient polymer networks, even beyond MSPNs, but can also provide a selectivity index for competing transient bonds based on rheological properties.

On the Effective Lifetime of Reversible Bonds in Transient Networks

AbstractThe renormalized bond lifetime model (RBLM) is a popular scaling theory for the effective lifetime of reversible bonds in transient networks. It recognizes that stickers connected by a reversible bond undergo many (J) cycles of dissociation and reassociation. After finally separating, one of these stickers finds a new open partner in time τopen via a subdiffusive process whose mean‐squared displacement is proportional to tα, where t is the time elapsed, and α is the subdiffusion exponent. The RBLM makes convenient mathematical approximations to obtain analytical expressions for J and τopen. The consequences of relaxing these approximations is investigated by performing fractional Brownian motion (FBM) simulations. It is found that the scaling relations developed in the RBLM hold surprisingly well. However, RBLM overestimates both τopen and J, especially at lower values of α. For α = 0.5, corresponding to the Rouse limit, it is found that τopen is overestimated by a factor of approximately 4x, while the approximation for J is nearly exact. The degree of overestimation worsens as α decreases, and increases to 1–2 orders of magnitude at α = 0.25, corresponding to the reptation limit. This has important ramifications for experimental studies that use RBLM to interpret rheology and dielectric spectroscopy observations.

Control of TurboID-dependent biotinylation intensity in proximity ligation screens

Proximity biotinylation screens are a widely used strategy for the unbiased identification of interacting or vicinal proteins. The latest generation biotin ligase TurboID has broadened the range of potential applications, as this ligase promotes an intense and faster biotinylation, even in subcellular compartments like the endoplasmic reticulum. On the other hand, the uncontrollable high basal biotinylation rates deny the system's inducibility and are often associated with cellular toxicity precluding its use in proteomics. We report here an improved method for TurboID-dependent biotinylation reactions based on the tight control of free biotin levels. Blockage of free biotin with a commercial biotin scavenger reversed the high basal biotinylation and toxicity of TurboID, as shown by pulse-chase experiments. Accordingly, the biotin-blockage protocol restored the biological activity of a bait protein fused to TurboID in the endoplasmic reticulum and rendered the biotinylation reaction inducible by exogenous biotin. Importantly, the biotin-blockage protocol was more effective than biotin removal with immobilized avidin and did not affect the cellular viability of human monocytes over several days. The method presented should be useful to researchers interested in exploiting the full potential of biotinylation screens with TurboID and other high-activity ligases for challenging proteomics questions. SignificanceProximity biotinylation screens using the last generation biotin ligase TurboID represent a powerful approach for the characterisation of transient protein-protein interaction and signaling networks. However, a constant and high basal biotinylation rate and the associated cytotoxicity often preclude the use of this method in proteomic studies. We report a protocol based on modulation of free biotin levels that prevents the deleterious effects of TurboID while allowing inducible biotinylation, even in subcellular compartments such as the endoplasmic reticulum. This optimised protocol greatly expands the applications of TurboID in proteomic screens.

Biodegradable and Flexible Polymer‐Based Memristor Possessing Optimized Synaptic Plasticity for Eco‐Friendly Wearable Neural Networks with High Energy Efficiency

Organic memristors are promising candidates for the flexible synaptic components of wearable intelligent systems. With heightened concerns for the environment, considerable effort has been made to develop organic transient memristors to realize eco‐friendly flexible neural networks. However, in the transient neural networks, achieving flexible memristors with biorealistic synaptic plasticity for energy efficient learning processes is still challenging. Herein, a biodegradable and flexible polymer‐based memristor, suitable for the spike‐dependent learning process, is demonstrated. An electrochemical metallization phenomenon for the conductive nanofilament growth in a polymer medium of poly (vinyl alcohol) (PVA) is analyzed and a PVA‐based transient and flexible artificial synapse is developed. The developed device exhibits superior biodegradability and stable mechanical flexibility due to the high water solubility and excellent tensile strength of the PVA film, respectively. In addition, the developed flexible memristor is operated as a reliable synaptic device with optimized synaptic plasticity, which is ideal for artificial neural networks with the spike‐dependent operations. The developed device is found to be effectively served as a reliable synaptic component with high energy efficiency in practical neural networks. This novel strategy for developing transient and flexible artificial synapses can be a fundamental platform for realizing eco‐friendly wearable intelligent systems. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.166603245.58711630/v1.

Self-Sorting of Transient Polymer Networks by the Selective Formation of Heteroleptic Metal–Ligand Complexes

Advances in self-sorting mechanisms have resulted in the development of a broad library of well-defined supramolecular coordination complexes (SCCs) with unprecedented geometries. These SCCs have promising applications like those in catalysis and solar cells. Moreover, the dynamic nature of coordination bonds allows them to undergo stimuli-responsive topology rearrangements, which are used in a plethora of molecular machines. These advances are, however, rarely employed in the design of metallo-supramolecular polymer networks (MSPNs). To address this gap, we use steric hindrance as a powerful self-sorting mechanism to construct MSPNs based on the selective formation of heteroleptic complexes. We graft tetra-arm poly(ethylene glycol) precursors with sterically demanding 2,9-bis(mesitylene)-1,10-phenanthroline or two slim unsubstituted phenanthroline and terpyridine ligands and study their complexation-induced gel formation. Rheological data disclose that the heteroleptic complexation, which is controlled by the coordination geometry preference of the utilized metal ion, can induce up to 5 orders of magnitude delay in the network relaxation and more than twofold increase in plateau modulus due to the removal of primary loops. The structural/electronic characterization of complexes by density functional theory nicely explains the distinct performance of different metal ions in gel formation. These results open a promising interface between polymer science and coordination chemistry that can ultimately lead to the development of new hybrid materials with exceptional properties and novel stimuli responsiveness.

FOXD1 is required for 3D patterning of the kidney interstitial matrix.

The interstitial extracellular matrix (ECM) is comprised of proteins and glycosaminoglycans and provides structural and biochemical information during development. Our previous work revealed the presence of transient ECM-based structures in the interstitial matrix of developing kidneys. Stromal cells are the main contributors to interstitial ECM synthesis, and the transcription factor Forkhead Box D1 (Foxd1) is critical for stromal cell function. To investigate the role of Foxd1 in interstitial ECM patterning, we combined 3D imaging and proteomics to explore how the matrix changes in the murine developing kidney when Foxd1 is knocked out. We found that COL26A1, FBN2, EMILIN1, and TNC, interstitial ECM proteins that are transiently upregulated during development, had a similar distribution perinatally but then diverged in patterning in the adult. Abnormally clustered cortical vertical fibers and fused glomeruli were observed when Foxd1 was knocked out. The changes in the interstitial ECM of Foxd1 knockout kidneys corresponded to disrupted Foxd1+ cell patterning but did not precede branching dysmorphogenesis. The transient ECM networks affected by Foxd1 knockout may provide support for later-stage nephrogenic structures.

10-Hz tACS up-regulates a visual attention network and down-regulates the rich club

Abstract Synchronization of oscillatory brain activity may play a critical role in linking distributed neuronal populations into transient functional networks, with up-regulation of engaged networks during task performance accompanied by down-regulation of less salient networks. Can tACS be used to elicit network-specific changes in brain function, given that the distributed nodes of a network may experience varying amplitudes of exogenous current flow? We utilized MEG to characterize brain dynamics following bilateral parietal 10-Hz tACS during a visual attention task (8 healthy adults; 1 mA, verum/sham 20 min/30 s). Regions of interest included a cortico-cerebellar sensory processing network (SP: frontal, parietal, temporal cortex, hippocampus, cerebellum) and the Rich Club (bilateral superior parietal and frontal cortex, precuneus, hippocampus). Persistent changes in local oscillatory dynamics were seen in the SP network across a broad spectrum of frequency bands. Changes in alpha-band activity extended before, during, and after presentation of global/local letter images, consistent with a tonic influence of the 10-Hz tACS on alpha-band dynamics. Up-regulation of alpha and down-regulation of theta throughout the SP network was consistent with results seen in previous working memory studies. Moreover, increases in alpha were accompanied by increased gamma-band activity, consistent with a cross-frequency relationship between these rhythms. In contrast, down-regulation of alpha was characteristic of regions in Rich Club, with the exception of up-regulation in superior parietal cortex. Interestingly, despite a lack of consistency of a reciprocal relationship between alpha and theta in the Rich Club, the stimulation also reduced both theta and gamma-band activity, suggesting a role for theta-gamma coupling in the Rich Club. Our results suggest that tACS may provide a method for identifying and up-/down- regulating individual brain networks. Importantly, identifying tACS parameters that maximize changes to specific networks may be critical to increase efficacy and reduce potential negative side-effects of tACS. Research Category and Technology and Methods Basic Research: 8. Transcranial Alternating Current Stimulation (tACS) Keywords: tACS, MEG, Network, Visual Attention

Dynamic Transcription Machineries Guide the Synthesis of Temporally Operating DNAzymes, Gated and Cascaded DNAzyme Catalysis.

Transient transcription machineries play important roles in the dynamic modulation of gene expression and the sequestered regulation of cellular networks. The present study emulates such processes by designing artificial reaction modules consisting of transcription machineries that guide the transient synthesis of catalytic DNAzymes, the transient operation of gated DNAzymes, and the temporal activation of an intercommunicated DNAzyme cascade. The reaction modules rely on functional constituents that lead to the triggered activation of transcription machineries in the presence of the nucleoside triphosphates oligonucleotide fuel, yielding the transient formation and dissipative depletion of the intermediate DNAzyme(s) products. The kinetics of the transient DNAzyme networks are computationally simulated, allowing to predict and experimentally validate the performance of the systems under different auxiliary conditions. The study advances the field of systems chemistry by introducing transcription machinery-based networks for the dynamic control over transient catalysis─a primary step toward life-like cellular assemblies.