Dynamic Heterogeneity Research Articles

I am, therefore I preserve: Exploring middle-class imagery, biased status perceptions, and pro-environmental behaviour

Extensive research on pro-environmental behaviour (PEB) reveals a significant knowledge gap in understanding the influence of social class, perceived status and the middling tendency on pro-environmental behaviour. Using the International Social Survey Programme Environment dataset, and conducting multilevel mixed-effects linear regressions, we find that the middling tendency and biased status perceptions significantly influences pro-environmental behaviour. Those who deflate their social position have higher pro-environmental behavior and this reinforces the idea that pro-environmental behaviour is driven by a post-materialist effect rather than a status enhancement effect. Moreover, the objective middle class is still a stronger contributor to higher PEB levels compared to subjective middle class. We also find the relation between class, status and PEB vary by country. These findings provide vital insights into the intricate and heterogenous dynamics between class, status and pro-environmental behaviour among different countries and shed light on class and status as driving forces behind pro-environmental behaviour.

Selecting relevant structural features for glassy dynamics by information imbalance

We numerically investigate the identification of relevant structural features that contribute to the dynamical heterogeneity in a model glass-forming liquid. By employing the recently proposed information imbalance technique, we select these features from a range of physically motivated descriptors. This selection process is performed in a supervised manner (using both dynamical and structural data) and an unsupervised manner (using only structural data). We then apply the selected features to predict future dynamics using a machine learning technique. One of the advantages of the information imbalance technique is that it does not assume any model a priori, i.e., it is a non-parametric method. Finally, we discuss the potential applications of this approach in identifying the dominant mechanisms governing the glassy slow dynamics.

Leveraging uncertainty in collective opinion dynamics with heterogeneity

Natural and artificial collectives exhibit heterogeneities across different dimensions, contributing to the complexity of their behavior. We investigate the effect of two such heterogeneities on collective opinion dynamics: heterogeneity of the quality of agents’ prior information and of degree centrality in the network. To study these heterogeneities, we introduce uncertainty as an additional dimension to the consensus opinion dynamics model, and consider a spectrum of heterogeneous networks with varying centrality. By quantifying and updating the uncertainty using Bayesian inference, we provide a mechanism for each agent to adaptively weigh their individual against social information. We observe that uncertainties develop throughout the interaction between agents, and capture information on heterogeneities. Therefore, we use uncertainty as an additional observable and show the bidirectional relation between centrality and information quality. In extensive simulations on heterogeneous opinion dynamics with Gaussian uncertainties, we demonstrate that uncertainty-driven adaptive weighting leads to increased accuracy and speed of consensus, especially with increasing heterogeneity. We also show the detrimental effect of overconfident central agents on consensus accuracy which can pose challenges in designing such systems. The opportunities for improved performance and observablility suggest the importance of considering uncertainty both for the study of natural and the design of artificial heterogeneous systems.

Exploring heterogeneous cell population dynamics in different microenvironments by novel analytical strategy based on images.

Understanding the dynamic states and transitions of heterogeneous cell populations is crucial for addressing fundamental biological questions. High-content imaging provides rich datasets, but it remains increasingly difficult to integrate and annotate high-dimensional and time-resolved datasets to profile heterogeneous cell population dynamics in different microenvironments. Using hepatic stellate cells (HSCs) LX-2 as model, we proposed a novel analytical strategy for image-based integration and annotation to profile dynamics of heterogeneous cell populations in 2D/3D microenvironments. High-dimensional features were extracted from extensive image datasets, and cellular states were identified based on feature profiles. Time-series clustering revealed distinct temporal patterns of cell shape and actin cytoskeleton reorganization. We found LX-2 showed more complex membrane dynamics and contractile systems with an M-shaped actin compactness trend in 3D culture, while they displayed rapid spreading in early 2D culture. This image-based integration and annotation strategy enhances our understanding of HSCs heterogeneity and dynamics in complex extracellular microenvironments.

Characterising Cancer Cell Responses to Cyclic Hypoxia Using Mathematical Modelling

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

Dynamics of cell-free tumor DNA correlate with early MRI response during chemoradiotherapy in rectal cancer

BackgroundIn locally advanced rectal cancer, the prediction of tumor response during and after neoadjuvant treatment remains challenging. In terms of organ preservation, adaptive radiotherapy, and intensified (total) neoadjuvant therapies, biomarkers are desirable for patient stratification.MethodsIn 16 patients, weekly blood samples (n = 86) to detect cell-free tumor DNA (ctDNA) during long-course neoadjuvant chemoradiotherapy were analyzed. Data were correlated with initial tumor volumes, MRI response in week 2 and 5 of radiotherapy as well as with pathologic tumor response after resection and outcome parameters.ResultsMost patients showed decreasing ctDNA during the course of radiochemotherapy. However, we found heterogenous dynamics of ctDNA and could identify three groups: (1) decline (2) no clear decline and/or late shedding (3) persistence of ctDNA. In seven patients we could detect significant amounts of ctDNA in week 5 or week 6 of treatment. In our pilot cohort, we did not find significant correlations of ctDNA dynamics with pathologic response or outcome parameters. However, patients with distinct decline of ctDNA had larger tumor volumes prior to treatment, and MRI imaging in week 2 and 5 revealed bigger absolute decrease of tumor volumes. If significant levels of ctDNA were found in week 5 and / or 6, patients showed less absolute tumor volume decrease in week 2 and 5.ConclusionsWeekly measurement of ctDNA during radiochemotherapy is feasible and might represent a promising biomarker. Bigger initial primary tumors showed different ctDNA shedding profiles compared with smaller primary tumors and correlations of ctDNA dynamics with early imaging response were found.

Dynamic impact of green finance on renewable energy development: Based on scale, structure, and efficiency perspectives

Utilizing China's provincial panel data from 2003 to 2021, this paper employs a dynamic panel quantile regression model and mediating test model to investigate the dynamic evolution characteristics, type heterogeneity, and mechanism of green finance (GF) driving renewable energy development (RED) in China, considering the scale, structure, and efficiency of GF. The findings of this study are as follows. While the scale, structure, and efficiency of GF all contribute to the promotion of RED, the impact of green financial structure is particularly significant. As renewable energy continues to develop, the impact of green financial scale and green financial structure gradually diminishes, while the effect of green financial efficiency increases. In comparison to hydropower and solar power, GF has the strongest promotion effect on wind power, and the marginal effect of green financial scale and green financial structure on wind power follows a U-shaped trajectory. The driving of GF on RED is mainly through direct effect, supplemented by indirect effect. In the mechanism by which GF impacts RED, the channels associated with green attributes are relatively smooth. Based on these research conclusions, this paper offers policy recommendations aimed at enhancing RED through GF from perspectives encompassing scale, structure, and efficiency.

Pectin alignment induced changes in ion solvation structure in ethylene carbonate-based liquid electrolytes

Classical molecular dynamics simulations are used to explore the impact of the alignment of pectin over the randomized configuration on ion solvation structure in pectin-loaded ethylene carbonate - lithium bis (trifluoromethanesulfonyl) imide electrolytes. Our study focuses on how biological macromolecules, specifically pectin, influence the behavior of liquid electrolytes, considering their applications in rechargeable batteries due to their ion solvation capabilities and ion transport characteristics. Aligned pectin causes a tightly packed first coordination shell of anions around lithium ions by weakening the long-ranged interactions beyond the first coordination shell compared to a random configuration. Consequently, the number of pectin oxygens around lithium decreases dramatically from 3 to 2, resulting in an overall diluted solvation shell containing fewer numbers of anions and pectin oxygens around lithium ions. With polymer alignment, the non-Gaussianity increases from 3.387 to 6.550 for lithium ions and from 0.475 to 0.621 for TFSI ions, reflecting a 90 % increase in dynamic heterogeneity for lithium ions and a 30 % increase for TFSI ions. Cation-cation correlations enhance ionic conductivity in randomized pectin, whereas isolated anion motion dominates in aligned pectin due to cation-pectin interactions. Our work not only highlights potential strategies for improving electrolyte performance in rechargeable batteries but also emphasizes the crucial role of molecular orientation in optimizing electrolyte properties, paving the way for more optimized and efficient battery technologies.

The Impact of Supersaturated Electrode on Heterogeneous Lithium Nucleation and Growth Dynamics.

The concept of a lithiophilic electrode proves inadequate in describing carbon-based electrode materials due to their substantial mismatch in surface energy with lithium metal. However, their notable capacity for lithium chemisorption can increase active lithium concentration required for nucleation and growth, thereby enhancing the electrochemical performance of lithium metal anodes (LMAs). In this study, we elucidate the effects of the supersaturated electrode which has high active lithium capacity around equilibrium lithium potential on LMAs through an in-depth electrochemical comparison using two distinct carbon electrode platforms with differing carbon structures but similar two-dimensional morphologies. In the supersaturated electrode, both the dynamics and thermodynamic states involved in lithium nucleation and growth mechanisms are significantly improved, particularly under continuous current supply conditions. Furthermore, the chemical structures of the solid-electrolyte-interface layers (SEIs) are greatly influenced by the elevated surface lithium concentration environment, resulting in the formation of more conductive lithium-rich SEI layers. The improved dynamics and thermodynamics of surface lithium, coupled with the formation of enhanced SEI layers, contribute to higher power capabilities, enhanced Coulombic efficiencies, and improved cycling performances of LMAs. These results provide new insight into understanding the enhancements in heterogeneous lithium nucleation and growth kinetics on the supersaturated electrode.

The Impact of Supersaturated Electrode on Heterogeneous Lithium Nucleation and Growth Dynamics

AbstractThe concept of a lithiophilic electrode proves inadequate in describing carbon‐based electrode materials due to their substantial mismatch in surface energy with lithium metal. However, their notable capacity for lithium chemisorption can increase active lithium concentration required for nucleation and growth, thereby enhancing the electrochemical performance of lithium metal anodes (LMAs). In this study, we elucidate the effects of the supersaturated electrode which has high active lithium capacity around equilibrium lithium potential on LMAs through an in‐depth electrochemical comparison using two distinct carbon electrode platforms with differing carbon structures but similar two‐dimensional morphologies. In the supersaturated electrode, both the dynamics and thermodynamic states involved in lithium nucleation and growth mechanisms are significantly improved, particularly under continuous current supply conditions. Furthermore, the chemical structures of the solid‐electrolyte‐interface layers (SEIs) are greatly influenced by the elevated surface lithium concentration environment, resulting in the formation of more conductive lithium‐rich SEI layers. The improved dynamics and thermodynamics of surface lithium, coupled with the formation of enhanced SEI layers, contribute to higher power capabilities, enhanced Coulombic efficiencies, and improved cycling performances of LMAs. These results provide new insight into understanding the enhancements in heterogeneous lithium nucleation and growth kinetics on the supersaturated electrode.

An ecological and evolutionary approach to arboreal communities under different environmental stress conditions

Biodiversity distribution patterns and the ecological processes underlying them reflect how organisms respond to abiotic and biotic factors that interact across both space and time. In this study, we aimed to test the hypothesis that small-scale environmental variations shape ecological and phylogenetic patterns in arboreal communities through environmental filters created by the specific conditions of each environment examined. We investigated a mosaic of Atlantic forest environments, analyzing species composition, community dynamics, and phylogenetic relationships among the various environments. This approach provided valuable insight into how environmental factors influence the ecological differentiation of these communities. Variations in conditions and resources produced environmental filters that resulted in significant small-scale heterogeneity in species composition, community dynamics, and evolutionary relationships among the environments. By considering the dynamic interations between biotic and abiotic factors, we conclude that the prevailing environmental conditions at these sites play a crucial role in shaping the behavior and distribution of the species that inhabit them.

Classification of solid and liquid structures using a deep neural network unveils origin of dynamical heterogeneities in supercooled liquids

As the temperature decreases, the dynamics of supercooled liquids significantly slow down and become increasingly heterogeneous in space. Many previous studies have found that static structures also become heterogeneous and are spatially correlated with the dynamical heterogeneity. However, there are still debates on whether the dynamical heterogeneity is controlled by the structures, and which structural order parameters should be used to describe the structural heterogeneities (if exist) in amorphous systems. The appropriate order parameter depends on the specific details of the system and needs to be determined for each system. To address this difficulty, here, we use a machine-learning-based method that was trained solely by the static structures. This method combines convolutional neural networks and gradient-weighted class activation mapping, providing interpretable characteristic structures, which can quantify the degrees of liquid-like and solid-like structures in every local part of the system. We apply this method to a canonical glass-forming system and demonstrate that particles in the liquid-like structures are mobile, while those in the solid-like structures are immobile. The present work develops a novel approach to accurately characterize amorphous structures, which will be particularly useful for systems where appropriate structural order parameters have not yet been identified.

Chlorophyll-Induced Lamellar to Nonlamellar Phase Transitions and Dynamical Heterogeneity in Plant Thylakoid Membranes.

Chlorophyll a (CLA) pigments and thylakoid membranes are crucial components of plants for photosynthesis. To understand the effect of CLA on the structure and dynamics of thylakoid membranes, coarse-grained molecular dynamics (CG MD) simulations of thylakoid membranes are performed by varying the numbers of CLA at 293 K using MARTINI-2 force fields. The membrane undergoes a lamellar to nonlamellar phase transition above a critical concentration of CLA. The CLAs dynamically form aggregates of different orders and preferentially fetch the least unsaturated nonbilayer-forming lipids around them, resulting in a nonlamellar phase with fused regions. These fused regions cause a structural arrest of CLA and lipids, inducing dynamic heterogeneity manifested by non-Gaussian parameters and van Hove correlation functions. The lamellar to nonlamellar phase transition of the membrane is associated with a drastic reduction in correlation length of the immobile CLA and lipids governed by the fused topology. Such insights into CLA-induced structural transitions in thylakoid membranes are pertinent for understanding nonphotochemical quenching mechanisms and hold promise for designing future artificial photosynthetic materials and applications in photodynamic therapy.

Quantitative proteomic mass spectrometry of protein kinases to determine dynamic heterogeneity of the human kinome.

The kinome is a dynamic system of kinases regulating signaling networks in cells and dysfunction of protein kinases contributes to many diseases. Regulation of the protein expression of kinases alters cellular responses to environmental changes and perturbations. We configured a library of 672 proteotypic peptides to quantify >300 kinases in a single LC-MS experiment using ten micrograms protein from human tissues including biopsies. This enables absolute quantitation of kinase protein abundance at attomole-femtomole expression levels, requiring no kinase enrichment and less than ten micrograms of starting protein from flash-frozen and formalin fixed paraffin embedded tissues. Breast cancer biopsies, organoids, and cell lines were analyzed using the SureQuant method, demonstrating the heterogeneity of kinase protein expression across and within breast cancer clinical subtypes. Kinome quantitation was coupled with nanoscale phosphoproteomics, providing a feasible method for novel clinical diagnosis and understanding of patient kinome responses to treatment.

Temporal generative models for learning heterogeneous group dynamics of ecological momentary assessment data.

One of the goals of precision psychiatry is to characterize mental disorders in an individualized manner, taking into account the underlying dynamic processes. Recent advances in mobile technologies have enabled the collection of ecological momentary assessments that capture multiple responses in real-time at high frequency. However, ecological momentary assessment data are often multi-dimensional, correlated, and hierarchical. Mixed-effect models are commonly used but may require restrictive assumptions about the fixed and random effects and the correlation structure. The recurrent temporal restricted Boltzmann machine (RTRBM) is a generative neural network that can be used to model temporal data, but most existing RTRBM approaches do not account for the potential heterogeneity of group dynamics within a population based on available covariates. In this paper, we propose a new temporal generative model, the HDRBM, to learn the heterogeneous group dynamics and demonstrate the effectiveness of this approach on simulated and real-world ecological momentary assessment datasets. We show that by incorporating covariates, HDRBM can improve accuracy and interpretability, explore the underlying drivers of the group dynamics of participants, and serve as a generative model for ecological momentary assessment studies.

An agent-based model of post-disaster recovery in multilayer socio-physical networks

Post-disaster recovery involves returning infrastructure to pre-disaster states or close, and humans back to communities or close, after disasters like hurricanes and earthquakes. It enables us to comprehend complex relationships between humans and infrastructure after natural hazards. Existing studies have identified individual recovery, which focuses on personal or household-level restoration, and systematic recovery, which focuses on broader community recovery processes. However, they have not described how individual recoveries contribute to the overall recovery. To close the gap, we propose an agent-based model (ABM), called Disaster Infrastructure Recovery Simulator (DIRSim). We apply DIRSim to analyze the recovery of five counties in Texas following Hurricane Harvey in 2017. Specifically, we construct a three-layer network, composed of a human layer, a social infrastructure layer, and a physical infrastructure layer, using mobile phone location data and point-of-interest foot traffic data. Based on this three-layer network, we develop the ABM to simulate how evacuated users return to their homes, and social and physical infrastructure recover using a household survey. By implementing the ABM, we unveil the heterogeneity in recovery dynamics regarding geographical locations, housing types, and income levels. Moreover, simulation results across nine scenarios quantify the positive recovery effects of social and physical infrastructure policies.

Robust Cooperative Control for Heterogeneous Uncertain Nonlinear High-Order Fully Actuated Multiagent Systems.

This research is intended to address a robust cooperative control problem of heterogeneous uncertain nonlinear high-order fully actuated multiagent systems (HUN-HOFAMASs). A nonlinear HOFA system model is used to describe the multiagent systems (MASs) with heterogeneous uncertain nonlinear dynamics, which is called the HUN-HOFAMASs. A predictive terminal sliding-mode control-based robust cooperative control scheme is presented to address this problem. In this scheme, heterogeneous nonlinear dynamics of original system are offset to establish a linear constant HOFA system with the help of full actuation feature. Then, a terminal sliding-mode variable for enhancing the system robustness is introduced to handle the uncertainties. Furthermore, a linear incremental prediction model is developed in a HOFA form by means of a Diophantine equation. According to this model, the multistep terminal sliding-mode predictions are yielded to optimize the robust cooperative control performance and compensate for the network-induced communication constraints in the feedback and forward channels. Based on a linear matrix inequality (LMI) method, a necessary and sufficient criterion is derived to discuss the simultaneous consensus and stability of closed-loop HUN-HOFAMASs. The simulation and comparison results of cooperative flying around of multiple spacecraft system are shown to illustrate the capability and advantage of the presented predictive terminal sliding-mode control for robust cooperative control.

Forest structure explains spatial heterogeneity of decadal carbon dynamics in a cool-temperate forest

Abstract Accurate evaluation of forest biomass distribution and its long-term change over wide areas is required for effective forest carbon management and prediction of landscape-scale forest dynamics. We evaluated a landscape-scale (225 km2) decadal forest carbon budget at a 1 ha spatial resolution in a cool-temperate forest, by repeating airborne laser observations 10 years apart and partitioning net forest biomass change (FBC) into growth and mortality. Using >10 000 samples, we revealed that naturally regenerated forests have large spatial heterogeneity in net biomass change, and 3/4 of the photosynthetically acquired carbon stock moved to necromass even without anthropogenic disturbances. Actual carbon residence time as living tree biomass was estimated by dividing biomass by growth or mortality rates. The residence time was 107 and 106 years, respectively with large spatial variation among stands (48 and 42 years, respectively, as the difference between 25 and 75 percentile), although studied forest stands have small variation in the forest functional type in a landscape-scale. The best predictors of subsequent decadal biomass changes were two forest structural factors, mean canopy height and canopy height variation in addition to one environmental factor, elevation. Considering the long lifetime of trees, these structural factors may be an indicator of forest soundness rather than a cause of forest growth or mortality. However, in any cases, these structural factors can be powerful predictors of subsequent FBC.

EP.08B.01 Heterogeneity of Peripheral Blood Cell Dynamics and Oncologic Outcomes Between LUAD and LUSC Receiving Neoadjuvant Immunochemotherapy

EP.08B.01 Heterogeneity of Peripheral Blood Cell Dynamics and Oncologic Outcomes Between LUAD and LUSC Receiving Neoadjuvant Immunochemotherapy

A novel method for identifying key nodes in multi-layer networks based on dynamic influence range and community importance

Identifying key nodes in multi-layer networks is a hot research topic in complex network science and has broad application prospects, such as in mining enterprises that significantly affecting multi-layer industrial chains. Unlike single-layer networks, nodes in multi-layer networks exhibit heterogeneity due to varying connections and locations. There are also correlations between different network layers, which is particularly evident in industrial chains where companies operate across multiple layers of production, supply and distribution. It is necessary to consider the impact of these layers on the global performance of key node identification. In addition, due to changes in connections, the community structure of each network layer should be different, reflecting the dynamic nature of industrial collaborations and partnerships. However, existing research lacks the model that addresses the above problems. Therefore, this paper proposes a key node identification method based on Dynamic Influence Range and Community Importance (DIRCI), using both local and global information of the multi-layer network simultaneously. DIRCI determines the importance of nodes through three centrality measures: dynamic influence range-based centrality, network layer centrality and community-based centrality. Dynamic influence range-based centrality models node heterogeneity by combining the influence range of nodes and their neighbors with lower computational costs. Network layer centrality captures the corresponding importance for different network layers. Community-based centrality comprehensively considers the importance of community, the importance of each node within the community and between different communities. Experimental results for nineteen multi-layer networks show that DIRCI achieves better performance of key node identification than the latest algorithms.