Dynamic Heterogeneity Research Articles

Solid-that-Flows Picture of Glass-Forming Liquids.

This perspective article reviews arguments that glass-forming liquids are different from those of standard liquid-state theory, which typically have a viscosity in the mPa·s range and relaxation times on the order of picoseconds. These numbers grow dramatically and become 1012 - 1015 times larger for liquids cooled toward the glass transition. This translates into a qualitative difference, and below the "solidity length" which is roughly one micron at the glass transition, a glass-forming liquid behaves much like a solid. Recent numerical evidence for the solidity of ultraviscous liquids is reviewed, and experimental consequences are discussed in relation to dynamic heterogeneity, frequency-dependent linear-response functions, and the temperature dependence of the average relaxation time.

Two-Dimensional Fluctuation Correlation Spectroscopy (2D-FlucCS): A Method to Determine the Origin of Relaxation Rate Dispersion.

Relaxation rate dispersion, i.e., nonexponential or multicomponent kinetics, is observed in complex systems when measuring relaxation kinetics. Often, the origin of rate dispersion is associated with the heterogeneity in the system. However, both homogeneous (where all molecules experience the same rate but inherently nonexponential) and heterogeneous (where all molecules experience different rates) systems can exhibit rate dispersion. A multidimensional correlation analysis method has been demonstrated to detect and quantify rate dispersion observed in molecular rotation, diffusion, solvation, and reaction kinetics. One-dimensional (1D) autocorrelation function detects rate dispersion and measures its extent. Two-dimensional (2D) autocorrelation function measures the origin of rate dispersion and distinguishes homogeneous from heterogeneous. In a heterogeneous system, implicitly there exist subensembles of molecules experiencing different rates. A three-dimensional (3D) autocorrelation function measures subensemble exchange if present and reveals if the system possesses static or dynamic heterogeneity. This perspective discusses the principles, applications, and potential and also presents a future outlook of two-dimensional fluctuation correlation spectroscopy (2D-FlucCS). The method is applicable to any experiment or simulation where a time series of fluctuation in an observable (emission, scattering, current, etc.) around a mean value can be obtained in steady state (equilibrium or nonequilibrium), provided the system is ergodic.

Functional subtypes of synaptic dynamics in mouse and human

Synapses preferentially respond to particular temporal patterns of activity with a large degree of heterogeneity that is informally or tacitly separated into classes. Yet, the precise number and properties of such classes are unclear. Do they exist on a continuum and, if so, when is it appropriate to divide that continuum into functional regions? In a large dataset of glutamatergic cortical connections, we perform model-based characterization to infer the number and characteristics of functionally distinct subtypes of synaptic dynamics. In rodent data, we find five clusters that partially converge with transgenic-associated subtypes. Strikingly, the application of the same clustering method in human data infers a highly similar number of clusters, supportive of stable clustering. This nuanced dictionary of functional subtypes shapes the heterogeneity of cortical synaptic dynamics and provides a lens into the basic motifs of information transmission in the brain.

Charge-driven dynamical heterogeneity in the intrinsically disordered C-terminal of phosphatase Fcp1

Charge-driven dynamical heterogeneity in the intrinsically disordered C-terminal of phosphatase Fcp1

Structure and dynamics heterogeneity in poly(vinyl acetal)s: The effect of side group length

Understanding on the structure and dynamics of poly(vinyl acetal)s is insufficient due to the complicated chemical structure and amorphous nature of this type of polymers. In this work, we investigate the effect of side group length on structure and dynamics heterogeneity at different length scales spanning from side group, segment, and polymer network by combing dynamic mechanical analysis (DMA), dielectric relaxation spectroscopy (DRS), solid-state NMR, and differential scanning calorimetry analysis (DSC). By increasing the length of acetal side group, its intrinsic dynamics slows down. Taking into account the scale of segment and networks, with the increase of acetal group length, the formation of cross-linking sites formed by the hydroxyl-rich domain is suppressed, the relaxation distribution of the segment widens, and the volume fraction of rigid component decreases. The effect of side group length on the structure and dynamics heterogeneity of poly(vinyl acetal)s at different scales results in significant changes in material properties such as the non-monotonic change in loss factor value (tan δ) and a monotonous reduction in glass transition temperature (Tg).

Fully distributed adaptive cooperative output regulation of heterogeneous multi-agent systems with hybrid event-triggering mechanism

This study focuses on the event-triggered cooperative output regulation problem (CORP) of multi-agent systems with heterogeneous dynamics. Two design criteria are required be achieved: event-triggered communication between agents has strictly positive minimum inter-event time (MIET), and global information with respect to communication topology is not needed to implement the event-triggered control strategy. These two criteria are significant, but cannot be simultaneously satisfied using the relevant existing results. In this work, two event-triggered observers with hybrid triggering conditions are developed to estimate the matrix and state of the exosystem, the MIET is enforced to be positive by incorporating an auxiliary function with decreasing dynamics into the event-triggering mechanism (ETM). This is more stringent than simply removing Zeno behavior and is more useful in engineering applications. Additionally, distributed controllers are developed based on the estimated state and regulator equation. The proposed method, which is better and more useful than the up-to-date results in literature, and can satisfy the two design criteria as mentioned before. Finally, numerical examples are provided to demonstrate the effectiveness.

Data-driven analysis of dynamical heterogeneity in polymer melts near surfaces

Polymer melts near solid substrates exhibit heterogeneous dynamics, which has gained significant attention in recent decades. In this study, we propose a data-driven method, referred to as unsupervised learning and environment similarity (ULES), to identify the particles that significantly contribute to the dynamic heterogeneity (DH). This method involves clustering the dynamic features of particles obtained from molecular dynamics simulations and categorizing their dynamic patterns. By employing ULES, we can effectively differentiate and characterize the dominant particles (“slow” particles) responsible for DH. Additionally, we investigate the dynamic disparities between the confined polymer system and the bulk system by quantifying the environmental similarity, thereby discerning the adsorption, transition, and bulk regions. Furthermore, we employ the ULES method to explore the influence of interface interactions and molecular weight on DH. Through our study, we unveil that those “slow” particles play a key role in driving DH in the confined system. Moreover, we establish a correlation between DH and interface interactions, particularly within the adsorption region. Conversely, in the transition region and the bulk region, the molecular weight gradually emerges as the dominant factor influencing the dynamics of the system. Our proposed ULES method introduces a novel approach for analyzing the dynamic environment of complex systems and comprehending the dynamic properties of individual particles. By combining unsupervised learning techniques and environment similarity analysis, we gain valuable insights into the intricate dynamics of polymer systems near solid substrates. This research not only advances our understanding of DH but also provides valuable guidance for designing and optimizing polymer-based materials and processes in various industrial applications.

Confinement effect of inter-arm interactions on glass formation in star polymer melts.

We utilized molecular dynamic simulation to investigate the glass formation of star polymer melts in which the topological complexity is varied by altering the number of star arms (f). Emphasis was placed on how the "confinement effect" of repulsive inter-arm interactions within star polymers influences the thermodynamics and dynamics of star polymer melts. All the characteristic temperatures of glass formation were found to progressively increase with increasing f, but unexpectedly the fragility parameter KVFT was found to decrease with increasing f. As previously observed, stars having more than 5 or 6 arms adopt an average particle-like structure that is more contracted relative to the linear polymer size having the same mass and exhibit a strong tendency for intermolecular and intramolecular segregation. We systematically analyzed how varying f alters collective particle motion, dynamic heterogeneity, the decoupling exponent ζ phenomenologically linking the slow β- and α-relaxation times, and the thermodynamic scaling index γt. Consistent with our hypothesis that the segmental dynamics of many-arm star melts and thin supported polymer films should exhibit similar trends arising from the common feature of high local segmental confinement, we found that ζ increases considerably with increasing f, as found in supported polymer films with decreasing thickness. Furthermore, increasing f led to greatly enhanced elastic heterogeneity, and this phenomenon correlates strongly with changes in ζ and γt. Our observations should be helpful in building a more rational theoretical framework for understanding how molecular topology and geometrical confinement influence the dynamics of glass-forming materials more broadly.

Exploratory analysis of biomarkers of response to durvalumab in advanced HER2-negative oesophago-gastric adenocarcinoma within a phase 2 clinical trial.

384 Background: Advanced oesophago-gastric cancers have showcased varied responses to immunotherapy based on biomarkers such as tumour mutational burden, microsatellite instability, and PD-L1 expression. However, these markers may not fully capture tumour heterogeneity and evolutionary dynamics. Hence, understanding the intricate relationship between immune selection, specifically immune dN/dS,1 and tumour evolution is vital for devising personalized therapeutic strategies. Methods: In our phase 2 open label, multicentre, randomised PLATFORM trial (NCT02678182), patients received maintenance durvalumab post first-line chemotherapy. A survival advantage was not seen with maintenance durvalumab compared to surveillance and exploratory analysis of PD-L1 expression was not associated with improved survival outcomes.2 However, a subset of patients treated with durvalumab experienced durable responses and some experienced incremental radiological responses. A pilot substudy on a selected cohort (n=24) of these patients was designed to explore the potential of immunogenomics evolutionary-based metrics, notably dN/dS, as predictive biomarkers. DNA and RNA from FFPE baseline tumour samples underwent analysis, examining genomic factors including tumour mutational burden, indel signatures, PDL1 status, and immune evasion mechanisms. Furthermore, the evolutionary metric, immune dN/dS, which has implications in tumour immunoediting and its response to immunotherapies, was scrutinized. Results: A dominant indel mutational signature emerged from the analysis. The majority of patients displayed a significantly higher number of indel variants, compared to non-synonymous and synonymous single-nucleotide variants. A recurring observation was dN/dS values consistently above 1, indicating positive selection for non-synonymous somatic variants. Additionally, hypermutation in driver genes, notably TP53 and XIRP2, was detected. One patient, with a unique dN/dS < 1, showcased survival beyond four years without liver metastases. Immune dN/dS > 1 and no evidence of liver metastases displayed the best prognosis after treatment in contrast with those that have immune dN/dS < 1 with or without liver metastases. Conclusions: Immune dN/dS emerges as a promising tool to decode tumour evolutionary mechanisms, providing profound insights into the dynamics of tumour evolution in advanced HER2 negative oesophago-gastric cancer patients receiving durvalumab immunotherapy. This pilot study underscores the potential of this novel immunogenomic approach in improving patient stratification and tailoring therapeutic strategies in cancer immunotherapy. 1. Zapata et al, Nature Genetics, 2023. 2. Fong et al, J Clin Oncol, 2021. Clinical trial information: NCT02678182 .

A novel sea surface pCO2-product for the global coastal ocean resolving trends over 1982–2020

Abstract. In recent years, advancements in machine learning based interpolation methods have enabled the production of high-resolution maps of sea surface partial pressure of CO2 (pCO2) derived from observations extracted from databases such as the Surface Ocean CO2 Atlas (SOCAT). These pCO2-products now allow quantifying the oceanic air–sea CO2 exchange based on observations. However, most of them do not yet explicitly include the coastal ocean. Instead, they simply extend the open ocean values onto the nearshore shallow waters, or their spatial resolution is simply so coarse that they do not accurately capture the highly heterogeneous spatiotemporal pCO2 dynamics of coastal zones. Until today, only one global pCO2-product has been specifically designed for the coastal ocean (Laruelle et al., 2017). This product, however, has shortcomings because it only provides a climatology covering a relatively short period (1998–2015), thus hindering its application to the evaluation of the interannual variability, decadal changes and the long-term trends of the coastal air–sea CO2 exchange, a temporal evolution that is still poorly understood and highly debated. Here we aim at closing this knowledge gap and update the coastal product of Laruelle et al. (2017) to investigate the longest global monthly time series available for the coastal ocean from 1982 to 2020. The method remains based on a two-step Self-Organizing Maps and Feed-Forward Network method adapted for coastal regions, but we include additional environmental predictors and use a larger pool of training and validation data with ∼18 million direct observations extracted from the latest release of the SOCAT database. Our study reveals that the coastal ocean has been acting as an atmospheric CO2 sink of −0.40 Pg C yr−1 (−0.18 Pg C yr−1 with a narrower coastal domain) on average since 1982, and the intensity of this sink has increased at a rate of 0.06 Pg C yr−1 decade−1 (0.02 Pg C yr−1 decade−1 with a narrower coastal domain) over time. Our results also show that the temporal changes in the air–sea pCO2 gradient plays a significant role in the long-term evolution of the coastal CO2 sink, along with wind speed and sea-ice coverage changes that can also play an important role in some regions, particularly at high latitudes. This new reconstructed coastal pCO2-product (https://doi.org/10.25921/4sde-p068; Roobaert et al., 2023) allows us to establish regional carbon budgets requiring high-resolution coastal flux estimates and provides new constraints for closing the global carbon cycle.

Spatial-temporal heterogeneity of spring phenology in boreal forests as estimated by satellite solar-induced chlorophyll fluorescence and vegetation index

Changes in spring phenology in boreal forests around the subarctic region have a profound impact on the global carbon and water cycles. To date, photosynthesis and greenness dynamics in evergreen coniferous forests have been found to be decoupled on a large scale, but whether the spatial heterogeneity of seasonal dynamics in boreal forests is coupled in various datasets remains unclear. Therefore, based on the solar-induced chlorophyll fluorescence (SIF) retrieved by satellite and the modelled SIF (GOME-2 SIF and GOSIF) during 2007–2018, we calculated the start of the growing season (SOS) of boreal forests (45–70°N) and compared the results with 2-band Enhanced Vegetation Index (EVI2) phenological products (MCD12Q2 C6) and flux data (Gross primary productivity, GPP) phenology. The spatial-temporal variations in the SOS were analyzed by linear regression and piecewise linear regression. The results exhibited that GOME-2 SIF showed an SOS of 11 days earlier than GOSIF, 8 days earlier than EVI2 in North America, and 5 days earlier than GOSIF, 3 days earlier than EVI2 in Eurasia. However, all relationships between SOSs of the three remote sensing datasets were significant in North America and Eurasia, and the significance was higher in Eurasia. With increasing latitude and longitude, almost all SOSs estimated from the three datasets significantly increased. Also, both in North America and Eurasia, the SOSs exhibited a trend of being advanced to being delayed from the western to the eastern parts of the continents over 2007–2018. This indicated the spatial consistency of photosynthesis and greenness dynamics in the spring phenology of boreal forests, especially in Eurasia. In this study, the validity of the spatial-temporal variation in spring phenology of boreal forests based on GOME-2 SIF retrieved from satellites and MCD12Q2 C6 was confirmed, and further exploration is needed to determine their potential advantages.

Train Once, Locate Anytime for Anyone: Adversarial Learning-based Wireless Localization

Among numerous indoor localization systems, WiFi fingerprint-based localization has been one of the most attractive solutions, which is known to be free of extra infrastructure and specialized hardware. To push forward this approach for wide deployment, three crucial goals on high deployment ubiquity, high localization accuracy, and low maintenance cost are desirable. However, due to severe challenges about signal variation, device heterogeneity, and database degradation root in environmental dynamics, pioneer works usually make a trade-off among them. In this article, we propose iToLoc, a deep learning-based localization system that achieves all three goals simultaneously. Once trained, iToLoc will provide accurate localization service for everyone using different devices and under diverse network conditions, and automatically update itself to maintain reliable performance anytime. iToLoc is purely based on WiFi fingerprints without relying on specific infrastructures. The core components of iToLoc are a domain adversarial neural network and a co-training-based semi-supervised learning framework. Extensive experiments across 7 months with eight different devices demonstrate that iToLoc achieves remarkable performance with an accuracy of 1.92 m and >95% localization success rate. Even 7 months after the original fingerprint database was established, the rate still maintains >90%, which significantly outperforms previous works.

Quantifying Dynamic Phenotypic Heterogeneity in Resistant Escherichia coli under Translation-Inhibiting Antibiotics.

Understanding the phenotypic heterogeneity of antibiotic-resistant bacteria following treatment and the transitions between different phenotypes is crucial for developing effective infection control strategies. The study expands upon previous work by explicating chloramphenicol-induced phenotypic heterogeneities in growth rate, gene expression, and morphology of resistant Escherichia coli using time-lapse microscopy. Correlating the bacterial growth rate and cspC expression, four interchangeable phenotypic subpopulations across varying antibiotic concentrations are identified, surpassing the previously described growth rate bistability. Notably, bacterial cells exhibiting either fast or slow growth rates can concurrently harbor subpopulations characterized by high and low gene expression levels, respectively. To elucidate the mechanisms behind this enhanced heterogeneity, a concise gene expression network model is proposed and the biological significance of the four phenotypes is further explored. Additionally, by employing Hidden Markov Model fitting and integrating the non-equilibrium landscape and flux theory, the real-time data encompassing diverse bacterial traits are analyzed. This approach reveals dynamic changes and switching kinetics in different cell fates, facilitating the quantification of observable behaviors and the non-equilibrium dynamics and thermodynamics at play. The results highlight the multi-dimensional heterogeneous behaviors of antibiotic-resistant bacteria under antibiotic stress, providing new insights into the compromised antibiotic efficacy, microbial response, and associated evolution processes.

Active prokaryotic and eukaryotic viral ecology across spatial scale in a deep-sea brine pool.

Deep-sea brine pools represent rare, extreme environments, providing unique insight into the limits of life on Earth, and by analogy, the plausibility of life beyond it. A distinguishing feature of many brine pools is presence of thick microbial mats that develop at the brine-seawater interface. While these bacterial and archaeal communities have received moderate attention, viruses and their host interactions in these environments remain underexplored. To bridge this knowledge gap, we leveraged metagenomic and metatranscriptomic data from three distinct zones within the NEOM brine pool system (Gulf of Aqaba) to reveal the active viral ecology around the pools. We report a remarkable diversity and activity of viruses infecting microbial hosts in this environment, including giant viruses, RNA viruses, jumbo phages, and Polinton-like viruses. Many of these form distinct clades-suggesting presence of untapped viral diversity in this ecosystem. Brine pool viral communities exhibit zone-specific differences in infection strategy-with lysogeny dominating the bacterial mat further away from the pool's center. We linked viruses to metabolically important prokaryotes-including association between a jumbo phage and a key manganese-oxidizing and arsenic-metabolizing bacterium. These foundational results illuminate the role of viruses in modulating brine pool microbial communities and biogeochemistry through revealing novel viral diversity, host associations, and spatial heterogeneity in viral dynamics.

No effect of snow on shrub xylem traits: Insights from a snow-manipulation experiment on Disko Island, Greenland

Widespread shrubification across the Arctic has been generally attributed to increasing air temperatures, but responses vary across species and sites. Wood structures related to the plant hydraulic architecture may respond to local environmental conditions and potentially impact shrub growth, but these relationships remain understudied. Using methods of dendroanatomy, we analysed shrub ring width (RW) and xylem anatomical traits of 80 individuals of Salix glauca L. and Betula nana L. at a snow manipulation experiment in Western Greenland. We assessed how their responses differed between treatments (increased versus ambient snow depth) and soil moisture regimes (wet and dry). Despite an increase in snow depth due to snow fences (28–39 %), neither RW nor anatomical traits in either species showed significant responses to this increase. In contrast, irrespective of the snow treatment, the xylem specific hydraulic conductivity (Ks) and earlywood vessel size (LA95) for the study period were larger in S. glauca (p < 0.1, p < 0.01) and B. nana (p < 0.01, p < 0.001) at the wet than the dry site, while both species had larger vessel groups at the dry than the wet site (p < 0.01). RW of B. nana was higher at the wet site (p < 0.01), but no differences were observed for S. glauca. Additionally, B. nana Ks and LA95 showed different trends over the study period, with decreases observed at the dry site (p < 0.001), while for other responses no difference was observed. Our results indicate that, taking into account ontogenetic and allometric trends, hydraulic related xylem traits of both species, along with B. nana growth, were influenced by soil moisture. These findings suggest that soil moisture regime, but not snow cover, may determine xylem responses to future climate change and thus add to the heterogeneity of Arctic shrub dynamics, though more long-term species- and site- specific studies are needed.

Asynchronous sampled-data control for connected vehicles with heterogeneous delays

This study investigates an asynchronous sampled-data control problem of vehicular platoons, with heterogeneous sampling, subjected to actuator delays. Without a synchronized clock, a completely asynchronous sampled-data controller is designed for each follower, where the state of the ith follower itself and its neighboring vehicles are sampled at their own sampling time instants. The caused closed-loop tracking error dynamics for the entire platoon considering the effect of the nonuniform sampling time intervals, heterogeneous vehicle dynamics, inter-vehicle topology and heterogeneous time delays. To simplify the stability analysis and controller design, the tracking-error dynamics of the entire platoon are decomposed into individual subsystems with reduced-order dynamics. Based on Lyapunov stability theory, the optimal conditions are explored to design an asynchronous sampled-data controller to guarantee the desired stability performance. Moreover, the exact values for the maximum allowable sampling interval and time delay are calculated for each follower using the designed feedback controller gain. The proposed asynchronous sampled-data control method is extended to a vehicular platoon using an event-based sampling scheme. Numerical examples are used to verify the effectiveness of the proposed sampled-data control method.

Phase coexistence in the fully heterogeneous Hegselmann-Krause opinion dynamics model.

We present an extensive study of the joint effects of heterogeneous social agents and their heterogeneous social links in a bounded confidence opinion dynamics model. The full phase diagram of the model is explored for two different network's topologies and compared to two opposed extreme cases: on one hand, the heterogeneous agents constitute a mixed population and on the other, their interactions are modeled by a lattice. The results show that when agents prone to compromise coexist with close-minded ones, the steady state of the dynamics shows coexistent phases. In particular, unlike the case of homogeneous agents in networks, or heterogeneous agents in a fully mixed population, it is possible that the society ends up in consensus around one extreme opinion. Moreover, during the dynamics, the consensus may be overturned from one extreme to the other of the opinion space. We also show that the standard order parameter, the normalized average size of the largest opinion cluster, may be misleading in this case, as it hides the existence of these phases. The phase where the opinion of the society is overturned does not require the presence of agents with special characteristics, (stubborn, extremists, etc.); it results from the interplay of agents which have agreed on an extreme opinion with the remaining group that holds the opposite one. Among the former, some may be prone to compromise with other agents which are out of the majority group, these agents, according to their location in the network, may act like bridges between the two groups and slowly attract the whole society to the other extreme.

Based Fault-Tolerance Consensus of Second-Order Heterogeneous System under Input Saturation with Dynamics and Static Leader

We focus on fault-tolerant consensus for heterogeneous dynamics systems with static and dynamic leaders under input saturation in this article. We apply theory of finite-time stability to multiagent system cooperative control. Also, we use integral sliding mode to overcome disturbance. The primary goal is a set of second-order linear and second-order nonlinear agents moving along the leader’s trajectory. We use topology graph to describe communication between multiple agents. By using integral sliding mode control way, corresponding controller is introduced to make system stable. Finally, correctness of experiment was confirmed by MATLAB numerical simulation.

Structure-property relationships in renewable composites of poly(lactic acid) reinforced by low amounts of micro- and nano-kraft-lignin.

We investigate the direct and indirect effects of micro- and nano-kraft lignin, kL and NkL, respectively, at a quite low amount of 0.5 wt%, in poly(lactic acid) (PLA)-based composites. These renewable composites were prepared via two routes, either simple melt compounding or in situ reactive extrusion. The materials are selected and prepared using targeted methods in order to vary two variables, i.e., the size of kL and the synthetic method, while maintaining constant polymer chain lengths, L-/D-lactide isomer ratio and filler amounts. The direct/indirect effects were respectively investigated in the amorphous/semicrystalline state, as crystallinity plays in general a dominant role in polymers. The investigation involves structural, thermal and molecular mobility aspects. Non-extensive polymer-lignin interactions were recorded here, whereas the presence of the fillers led to both enhancements and suppressions of properties, e.g., glass transition, crystallization, melting temperatures, etc. The local and segmental molecular dynamics map of the said systems was constructed and is shown here for the first time, demonstrating both expected and unexpected trends. An interesting discrepancy between the trends in the calorimetric measurement against the dielectric Tg is revealed, providing indications for 'dynamical heterogeneities' in the composites as compared to neat PLA. The reactive extrusion as compared to compounding-based systems was found to exhibit stronger effects on crystallizability and mobility, most, probably due to the severe enhancement of the chains' diffusion. In general, the effects are more pronounced when employing nano-lignin compared to micro-lignin, which is the expected beneficial behaviour of nanocomposites vs. conventional composites. Interestingly, the variety of these effects can be easily manipulated by the proper selection of the preparation method and/or the thermal treatment under relatively mild conditions. The latter capability is actually desirable for processing and targeted applications and is proved here, once again, as an advantage of biobased polyesters such as PLA.

Energy renormalization for temperature transferable coarse-graining of silicone polymer.

The bottom-up prediction of thermodynamic and mechanical behaviors of polymeric materials based on molecular dynamics (MD) simulation is of critical importance in polymer physics. Although the atomistically informed coarse-grained (CG) model can access greater spatiotemporal scales and retain essential chemical specificity, the temperature-transferable CG model is still a big challenge and hinders widespread application of this technique. Herein, we use a silicone polymer, i.e., polydimethylsiloxane (PDMS), having an incredibly low chain rigidity as a model system, combined with an energy-renormalization (ER) approach, to systematically develop a temperature-transferable CG model. Specifically, by introducing temperature-dependent ER factors to renormalize the effective distance and cohesive energy parameters, the developed CG model faithfully preserved the dynamics, mechanical and conformational behaviors compared with the target all-atomistic (AA) model from glassy to melt regimes, which was further validated by experimental data. With the developed CG model featuring tremendously improved computational efficiency, we systematically explored the influences of cohesive interaction strength and temperature on the dynamical heterogeneity and mechanical response of polymers, where we observed consistent trends with other linear polymers with varying chain rigidity and monomeric structures. This study serves as an extension of our proposed ER approach of developing temperature transferable CG models with diverse segmental structures, highlighting the critical role of cohesive interaction strength on CG modeling of polymer dynamics and thermomechanical behaviors.