Spatiotemporal Systems Research Articles

Decoding the interplay between m6A modification and stress granule stability by live-cell imaging.

N6-methyladenosine (m6A)-modified mRNAs and their cytoplasmic reader YTHDFs are colocalized with stress granules (SGs) under stress conditions, but the interplay between m6A modification and SG stability remains unclear. Here, we presented a spatiotemporal m6A imaging system (SMIS) that can monitor the m6A modification and the translation of mRNAs with high specificity and sensitivity in a single live cell. SMIS showed that m6A-modified reporter mRNAs dynamically enriched into SGs under arsenite stress and gradually partitioned into the cytosol as SG disassembled. SMIS revealed that knockdown of YTHDF2 contributed to SG disassembly, resulting in the fast redistribution of mRNAs from SGs and rapid recovery of stalled translation. The mechanism is that YTHDF2 can regulate SG stability through the interaction with G3BP1 in m6A-modified RNA-dependent manner. Our results suggest a mechanism for the interplay between m6A modification and SG through YTHDF2 regulation.

Spatiotemporal Proximity-Enhanced Biocatalytic Cascades Within Metal-Organic Frameworks for Wearable and Theranostic Applications.

Enzymatic catalysis, particularly multi-enzyme cascade catalytic, is often limited by the spatial and temporal separation of enzymes and their signal substrates. Herein, a facile method for producing a spatiotemporal proximity-enhanced biocatalytic cascade system is introduced by encasing enzymes within metal-organic frameworks (MOFs) that are modulated with sulfonic acid-functionalized signal substrates. The modulated behavior relies on the sulfonic acid groups coordinated with Zn2+. As a proof of concept, by utilizing 2,2'-Azinobis (3-ethylbenzothiazoline-6-sulfonic acid ammonium salt) (ABTS), a widely-used signal substrate for horseradish peroxidase, two-enzyme/substrate, and three-enzyme/substrate MOFs, which demonstrated a 7.4- and 10.2-fold increase in biocatalytic efficiency over free systems are successfully synthesized. Incorporating the synthesized MOFs into homemade wearable patches and in vivo settings, noninvasive sweat glucose colorimetric detection and photoacoustic imaging-guided photothermal tumor therapy are enabled, respectively. This advancement stems from the newly established coordinative bonds between Zn2+ centers and substrates' sulfonic acid groups, which negates the need for additional signal substrates, thereby not only enhancing but also streamlining bioapplication processes.

Intracellular dynamics of ubiquitin-like 3 visualized using an inducible fluorescent timer expression system.

Exosomes are small extracellular vesicles (sEVs) secreted via multivesicular bodies (MVBs)/late endosomes and mediators of cell-cell communication. We previously reported a novel post-translational modification by ubiquitin-like 3 (UBL3). UBL3 is localized in MVBs and the plasma membrane and released outside as sEVs, including exosomes. Approximately 60% of proteins sorted in sEVs are affected by UBL3 and localized in various organelles, the plasma membrane, and the cytosol, suggesting that its dynamic movement in the cell before entering the MVBs. To examine the intracellular dynamics of UBL3, we constructed a sophisticated visualization system via fusing fluorescent timers that changed from blue to red form over time with UBL3 and by its expression under Tet-on regulation. Intriguingly, we found that after synthesis, UBL3 was initially distributed within the cytosol. Subsequently, UBL3 was localized to MVBs and the plasma membrane and finally showed predominant accumulation in MVBs. Furthermore, by super-resolution microscopy analysis, UBL3 was found to be associated with one of its substrates, α-tubulin, in the cytosol, and the complex was subsequently transported to MVBs. This spatiotemporal visualization system for UBL3 will form a basis for further studies to elucidate when and where UBL3 associates with its substrates/binding proteins before localization in MVBs.

Applying Spatiotemporal Modeling of Cell Dynamics to Accelerate Drug Development.

Cells act as physical computational programs that utilize input signals to orchestrate molecule-level protein-protein interactions (PPIs), generating and responding to forces, ultimately shaping all of the physiological and pathophysiological behaviors. Genome editing and molecule drugs targeting PPIs hold great promise for the treatments of diseases. Linking genes and molecular drugs with protein-performed cellular behaviors is a key yet challenging issue due to the wide range of spatial and temporal scales involved. Building predictive spatiotemporal modeling systems that can describe the dynamic behaviors of cells intervened by genome editing and molecular drugs at the intersection of biology, chemistry, physics, and computer science will greatly accelerate pharmaceutical advances. Here, we review the mechanical roles of cytoskeletal proteins in orchestrating cellular behaviors alongside significant advancements in biophysical modeling while also addressing the limitations in these models. Then, by integrating generative artificial intelligence (AI) with spatiotemporal multiscale biophysical modeling, we propose a computational pipeline for developing virtual cells, which can simulate and evaluate the therapeutic effects of drugs and genome editing technologies on various cell dynamic behaviors and could have broad biomedical applications. Such virtual cell modeling systems might revolutionize modern biomedical engineering by moving most of the painstaking wet-laboratory effort to computer simulations, substantially saving time and alleviating the financial burden for pharmaceutical industries.

Photoactivated Hydrogel Therapeutic System with MXene-Based Nanoarchitectonics Potentiates Endogenous Bone Repair Through Reshaping the Osteo-Vascularization Network.

The repair and reconstruction of large-scale bone defects face enormous challenges because of the failure to reconstruct the osteo-vascularization network. Herein, a near-infrared (NIR) light-responsive hydrogel system is reported to achieve programmed tissue repair and regeneration through the synergetic effects of on-demand drug delivery and mild heat stimulation. The spatiotemporal hydrogel system (HG/MPa) composed of polydopamine-coated Ti3C2Tx MXene (MP) nanosheets decorated with acidic fibroblast growth factor (aFGF, a potent angiogenic drug) and hydroxypropyl chitosan/gelatin (HG) hydrogel is developed to orchestrate the reconstruction of the osteo-vascularization network and boost bone regeneration. Upon exposure to NIR light irradiation, the engineered HG/MPa hydrogel can achieve the initial complete release of aFGF to induce rapid angiogenesis and provide sufficient blood supply, maximizing its biofunction in the defect area. This integrated hydrogel system demonstrated good therapeutic efficacy in promoting cell adhesion, proliferation, migration, angiogenesis, and osteogenic differentiation through periodic NIR irradiation. In vivo, animal experiments further revealed that the spatiotemporalized hydrogel platform synergized with mild photothermal treatment significantly accelerated critical-sized bone defect healing by increasing the osteo-vascularization network density, recruiting endogenous stem cells, and facilitating the production of osteogenesis/angiogenesis-related factors. Overall, smart-responsive hydrogel couldenhance the reconstruction of the osteo-vascularization network in bone regeneration.

Chaotic dynamics of a delayed fractional order MLNCML system

Abstract We present a delayed fractional order spatiotemporal dynamical system using the mixed linear-nonlinear coupling connections between lattices. In the simulation experiments, the dynamical characteristics of the proposed model are looked into and analyzed by using bifurcation diagrams and metric entropy density. The proposed model has fewer periodic windows in its bifurcation diagrams, large key space and strong ergodicity due to the delay. These excellent properties are of great significance in encryption. Moreover, the parameter μ breaks its fixed range in the traditional logistic map, which can contribute to enlarge the key space of cryptosystem. Through numerical simulations and security analysis, it is found that the proposed model is applied for encryption.

The study of non-constant steady states and pattern formation for an interacting population model in a spatial environment

This manuscript accounts for an investigation of the complex dynamics of a spatial model for interacting populations. We discuss the existence and boundedness of solutions for the proposed spatio-temporal system. The global stability of the co-existing steady state of the proposed system is analyzed with the help of a suitable Lyapunov function. We provide results on the existence and non-existence of positive non-constant solutions of the model. The priori estimate for the positive steady state is obtained for the nonexistence of the non-constant positive steady state by using the maximum principle. The existence of a non-constant positive steady state is studied with the help of Leray–Schauder degree theory. The stability and Hopf bifurcation are briefly revisited for the co-existing steady state in the corresponding temporal model, where a bubble-like structure is observed. The onset of Hopf bifurcation has been analyzed, and different conditions for the formation of the Turing pattern have been established through diffusion-driven instability analysis. Numerical simulations are performed in detail to figure out the effects of saturated harvesting on Turing patterns. The Turing as well as non-Turing patterns in their respective domains are also examined. Finally, the criteria of Turing–Hopf bifurcation is briefly demonstrated with relevant numerical examples and corresponding plots that give a better illustration of this work.

Reinforcement learning-based estimation for spatio-temporal systems

State estimators such as Kalman filters compute an estimate of the instantaneous state of a dynamical system from sparse sensor measurements. For spatio-temporal systems, whose dynamics are governed by partial differential equations (PDEs), state estimators are typically designed based on a reduced-order model (ROM) that projects the original high-dimensional PDE onto a computationally tractable low-dimensional space. However, ROMs are prone to large errors, which negatively affects the performance of the estimator. Here, we introduce the reinforcement learning reduced-order estimator (RL-ROE), a ROM-based estimator in which the correction term that takes in the measurements is given by a nonlinear policy trained through reinforcement learning. The nonlinearity of the policy enables the RL-ROE to compensate efficiently for errors of the ROM, while still taking advantage of the imperfect knowledge of the dynamics. Using examples involving the Burgers and Navier-Stokes equations with parametric uncertainties, we show that in the limit of very few sensors, the trained RL-ROE outperforms a Kalman filter designed using the same ROM and yields accurate instantaneous estimates of high-dimensional states corresponding to unknown initial conditions and physical parameter values. The RL-ROE opens the door to lightweight real-time sensing of systems governed by parametric PDEs.

Multi-image encryption based on 3D space scrambling and new spatiotemporal chaotic system

This paper introduces a groundbreaking spatiotemporal chaotic system, named DCMLMDF, and a novel encryption method that synergizes scrambling and diffusion synchronization for multi-image encryption. The DCMLMDF system, which incorporates a dynamic coupling approach and a random delay feedback mechanism, significantly enhances the randomness and complexity of the encryption process. By applying this system within the newly designed multi-image encryption framework, the method achieves three-dimensional space scrambling and diffusion synchronization, overcoming traditional encryption challenges such as extended encryption time and periodic vulnerabilities. The results demonstrate that this innovative approach not only effectively confuses image data but also substantially improves overall system security, marking a significant advancement in the application of chaotic systems to image encryption.

Hydrogel and Microgel Collaboration for Spatiotemporal Delivery of Biofactors to Awaken Nucleus Pulposus‐Derived Stem Cells for Endogenous Repair of Disc

AbstractDepletion of nucleus pulposus‐derived stem cells (NPSCs) is a major contributing factor to the attenuation of endogenous regenerative capacity in intervertebral disc degeneration (IVDD). Introducing a hydrogel drug delivery system is a potential strategy for counteracting endogenous cell depletion. The present study proposes a delivery platform for the spatiotemporal release of multiple drugs by combining sodium alginate hydrogels with gelatin microgels (SCGP hydrogels). The SCGP hydrogels facilitated the initial release of chondroitin sulfate (ChS) and the gradual release of an independently developed parathyroid hormone‐related peptide (P2). The combined action of these two small molecule drugs “awakened” the reserve NPSCs, mitigated cell damage induced by H2O2, significantly enhanced their biological activity, and promoted their differentiation toward nucleus pulposus cells. The mechanical and viscoelastic properties of the hydrogel are enhanced by physical and chemical dual cross‐linking to adapt to the loading environment of the degenerated disc. A rat IVDD model is used to validate that the SCGP hydrogel can significantly inhibit the progression of IVDD and stimulate the endogenous repair of IVDD. Therefore, the spatiotemporal differential drug delivery system of the SCGP hydrogel holds promise as a convenient and efficacious therapeutic strategy for minimally invasive IVDD treatment.

Chaotic Dynamics of a Two-Dimensional Spatiotemporal Discrete System with Delayed Perturbations

This paper is mainly concerned with the existence of chaos in a two-dimensional spatiotemporal discrete system with delayed perturbations. By applying the snap-back repeller theory, two criteria of chaos in the sense of both Li–Yorke and Devaney are established. Moreover, the lower bounds of the parameters that make the system chaotic are obtained. Finally, an example and its computer simulations of the dynamical behavior, spatiotemporal graphs, and the trends of the largest Lyapunov exponents with respect to the initial values and parameters are given to demonstrate the correctness of the theoretical results.

Information-Theoretic Modeling of Categorical Spatiotemporal GIS Data.

An information-theoretic data mining method is employed to analyze categorical spatiotemporal Geographic Information System land use data. Reconstructability Analysis (RA) is a maximum-entropy-based data modeling methodology that works exclusively with discrete data such as those in the National Land Cover Database (NLCD). The NLCD is organized into a spatial (raster) grid and data are available in a consistent format for every five years from 2001 to 2021. An NLCD tool reports how much change occurred for each category of land use; for the study area examined, the most dynamic class is Evergreen Forest (EFO), so the presence or absence of EFO in 2021 was chosen as the dependent variable that our data modeling attempts to predict. RA predicts the outcome with approximately 80% accuracy using a sparse set of cells from a spacetime data cube consisting of neighboring lagged-time cells. When the predicting cells are all Shrubs and Grasses, there is a high probability for a 2021 state of EFO, while when the predicting cells are all EFO, there is a high probability that the 2021 state will not be EFO. These findings are interpreted as detecting forest clear-cut cycles that show up in the data and explain why this class is so dynamic. This study introduces a new approach to analyzing GIS categorical data and expands the range of applications that this entropy-based methodology can successfully model.

Advancing neural network-based data assimilation for large-scale spatiotemporal systems with sparse observations

Data assimilation (DA) is a powerful technique for improving the forecast accuracy of dynamic systems by optimally integrating model forecasts with observations. Traditional DA approaches, however, encounter significant challenges when applied to complex, large-scale, highly nonlinear systems with sparse and noisy observations. To overcome these challenges, this study presents a new Neural Network-based Data Assimilation (DANet) model, specifically employing a Convolutional Long Short-Term Memory architecture. By leveraging the strengths of neural networks, DANet establishes the relationship among model forecasts, observations, and ground truth, facilitating efficient DA in large-scale spatiotemporal forecasting with sparse observations. The effectiveness of the DANet model is demonstrated through an initial case study of wind-driven oceanic flow forecasting, as described by a Quasi-Geostrophic (QG) model. Compared to the traditional Ensemble Kalman Filter (EnKF), DANet exhibits superior performance in cases involving both structured and unstructured sparse observations. This is evidenced by reduced Root Mean Square Errors (RMSEs) and improved correlation coefficients (R) and Structural Similarity Index. Moreover, DANet is seamlessly integrated with the QG model to operationally forecast vorticity and stream function in the long term, further confirming the accuracy and reliability of the DANet model. DANet achieves operational forecasting 60 times faster than EnKF, underscoring its efficiency and potential in DA advancement.

Spatio-Temporal Big Data Collaborative Storage Mechanism Based on Incremental Aggregation Subvector Commitment in On-Chain and Off-Chain Systems

As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an ideal choice for processing large-scale spatio-temporal big data due to its unique security features, but its storage scalability is limited because the data need to be replicated throughout the network. To solve this problem, a common approach is to combine blockchain with off-chain storage to form a hybrid storage blockchain. However, these solutions cannot guarantee the authenticity, integrity, and consistency of on-chain and off-chain data storage, and preprocessing is required in the setup phase to generate public parameters proportional to the data length, which increases the computational burden and reduces transmission efficiency. Therefore, this paper proposes a collaborative storage mechanism for spatio-temporal big data based on incremental aggregation sub-vector commitments, which uses vector commitment binding technology to ensure the secure storage of on-chain and off-chain data. By generating public parameters of fixed length, the computational complexity is reduced and the communication efficiency is improved while improving the security of the system. In addition, we design an aggregation proof protocol that integrates aggregation algorithms and smart contracts to improve the efficiency of data query and verification and ensure the consistency and integrity of spatio-temporal big data storage. Finally, simulation experiments verify the correctness and security of the proposed protocol, providing a solid foundation for the blockchain-based spatio-temporal big data storage system.

Developing a Research Network of Early Warning Systems for Infectious Diseases Transmission Between China and Australia.

This article offers a thorough review of current early warning systems (EWS) and advocates for establishing a unified research network for EWS in infectious diseases between China and Australia. We propose that future research should focus on improving infectious disease surveillance by integrating data from both countries to enhance predictive models and intervention strategies. The article highlights the need for standardized data formats and terminologies, improved surveillance capabilities, and the development of robust spatiotemporal predictive models. It concludes by examining the potential benefits and challenges of this collaborative approach and its implications for global infectious disease surveillance. This is particularly relevant to the ongoing project, early warning systems for Infectious Diseases between China and Australia (NetEWAC), which aims to use seasonal influenza as a case study to analyze influenza trends, peak activities, and potential inter-hemispheric transmission patterns. The project seeks to integrate data from both hemispheres to improve outbreak predictions and develop a spatiotemporal predictive modeling system for seasonal influenza transmission based on socio-environmental factors.

CUPID: An efficient spatio-temporal data engine

In the IoT era, abundant spatio-temporal data is generated from various devices thanks to the prevalence of positioning techniques. Due to a lack of effective systems to manipulate the data, advanced scalable and efficient data management systems are necessary to support more and more urban applications.We propose Cupid, which is powered by scholars from Chongqing University and Guangzhou Urban Planning and Design Survey Research Institute, an efficient spatio-temporal Data engine. It extends JUST by introducing many functionalities to make it more applicable to deployment, and can efficiently manage large-scale spatio-temporal data. In Cupid, Apache HBase is utilized as the storage, GeoMesa serves as the spatio-temporal data indexing tool, and Apache Spark acts as the execution engine. We introduce many optimizations to ensure usability and reduce computational overhead. To make Cupid easy to use, we design and implement a SQL-like query language. Furthermore, to deploy the system as a PaaS while speeding up the execution, we leverage Apache Livy, a service that enables easy interaction with a Spark cluster over REST interfaces, together with gRPC to effectively collect results. Extensive experiments illustrate that Cupid outperforms other state-of-the-art spatio-temporal big data management systems in terms of efficiency and scalability.

A spatiotemporal chaos based deep learning model watermarking scheme

With deep learning techniques achieving great results in modern industry, the intellectual property (IP) protection for deep learning models has attracted the attention of academics and engineers. However, training a commercially viable deep learning model usually needs professional resources and time. Once a malicious user clones, illegally distributes and uses the model, it can infringe on the model owner's IP and even steal its market share. Among the existing IP protection methods, scholars prefer the black-box watermarking approaches, of which the content of the trigger set and the label are the key part of the watermarking technique. However, most schemes do not consider the security and invisibility of the trigger set, which allows attackers to easily trigger the model by creating a fake trigger set, thereby committing a fraudulent ownership claim attack and claiming the ownership belongs to themselves. To overcome these drawbacks, we proposed a spatiotemporal chaotic data annotation method. Firstly, the unpredictability and acyclicity of chaos make the model resistant to fraudulent ownership claim attacks, statistical inference and other common machine learning attacks; Secondly, the trigger set and parameters are independent of each other, guaranteeing the security of the key; Thirdly, the spatiotemporal chaotic system provides a large key space, which meets the commercialization needs of deep learning models. Theoretical analysis and experimental results show that our scheme has security, practicality and robustness. To further validate the superiority of the proposed method, we also compare it with the Logistic chaotic annotation watermarking-based method, and the results show that our method performs better in terms of robustness, effectiveness, completeness, fidelity, security and practicality.

Exploring cooperative hunting dynamics and PRCC analysis: insights from a spatio-temporal mathematical model

The proposed mathematical model explores the intricate dynamics of a predator-prey system involving prey infection and cooperative hunting of predators. The model incorporates habitat complexity, emphasizing its influence on ecological interactions. The well-posedness of the system has rigorously been examined in a temporal setting and also conducted stability analysis. The bifurcation analysis reveals the existence of several local bifurcations on the system, namely transcritical bifurcation, saddle-node bifurcation, and Hopf bifurcation. Furthermore, these investigations delineate the two-dimensional bifurcations including Bogdanov–Takens and cusp bifurcations for different parametric combinations. With suitable choices of parameter values, the proposed model exhibits diverse dynamic phenomena, including bistable and tri-stable behavior. Latin hypercube sampling is utilized to conduct uncertainty analysis on input parameters, aiming to observe their effects on population dynamics. Subsequently, Kendall’s tau and Spearman’s rank correlation coefficients are also computed to investigate the impact of these uncertainties on the population. In the later part, a spatio-temporal system is proposed with two-dimensional diffusion terms to obtain the conditions for Turing instability. Numerical simulations have been conducted to observe the emergence of spatial patterns and the impact of predator cooperation in these patterns. The study provides valuable insights into the dynamics of complex ecological systems, emphasizing the interplay of spatial and temporal factors in shaping population dynamics and predator-prey interactions.

A new microscopic traffic-flow model based on the spatiotemporal continuous system concept considering nonlinear human response

A brand-new traffic model is introduced to precisely simulate the individual movement of vehicles, departing from the traditional macroscopic perspective (Eulerian scope or fluid dynamics concept) to adopt a microscopic approach (Lagrangian scope). This model is built on the first principle of Newton’s kinetic equation, explicitly incorporating nonlinear relations to represent human responses to a physical stimulus (observed information), such as the velocity gap between one’s own vehicle and a certain targeted one or the gap to a preceding vehicle, unlike the conventional car-following concept. The model posits that a driver’s recognition occurs intermittently rather than continuously, following specific probabilistic distributions of which the formulation is physically justified. Overall, the model can be described as a spatiotemporally continuous formulation unlike cellular automaton (CA) traffic models, crucial for capturing traffic dynamics at the micro and macro levels. The model is validated using a real traffic-flow dataset from a highway, and the results of the conventional CA traffic model are compared with those of the present one” along with a note for authorial verification. Animated simulations confirm that the present model can reproduce realistic flow dynamics characterized by smoother acceleration and deceleration compared to the conventional CA traffic model. The complete simulation source code for replicating the model is made publicly available.

Interpretable Graph Reservoir Computing With the Temporal Pattern Attention.

Graph reservoir computing (GraphRC) gains increasing attention by virtue of its high training efficiency. However, since GraphRC is developed without knowledge of its internal mechanism, it cannot be fully trusted to deploy in practice. Although there are some existing approaches that can be extended to interpret GraphRC, the specific role played by each neuron (i.e., reservoir node) of GraphRC is far less explored. To address this issue, the latent short-term memory property of each reservoir node of GraphRC is qualitatively characterized to unravel its role in predicting the graph signal, thereby enabling an interpretable GraphRC. Specifically, we first deduce the equivalence between the GraphRC and conventional reservoir computing (RC). Then, the underlying memory properties of the GraphRC and its reservoir nodes can be characterized in theory by the multisource reachability among the reservoir nodes in the transformed RC. Moreover, the distinct temporal patterns hidden in reservoir nodes are identified, and then, an attention mechanism based on the identified temporal patterns is deployed in the GraphRC to improve its performance. In addition, the effectiveness of the interpretability for GraphRC and improved GraphRC is verified on the Lorenz-96 spatiotemporal dynamical system. The experimental results of the Lorenz-96 spatiotemporal chaotic system and three real-world traffic datasets demonstrate that the improved GraphRC is superior to original GraphRC and can achieve prediction performance comparable to the state-of-the-art baseline models, but with much less training cost.