Dynamic Heterogeneity Research Articles

Divergence of Critical Fluctuations on Approaching Catastrophic Phase Inversion in Turbulent Emulsions.

Catastrophic phase inversion, the breakdown of a concentrated emulsion characterized by the most puzzling sudden feature, is crucial in numerous industrial applications. Here we combine well-controlled experiments and fully resolved numerical simulations to study the critical dynamics of catastrophic phase inversion in oil-water emulsions under turbulent flow as the phase-inversion volume fraction is approached. We reveal that the phase inversion is characterized by the critical power-law divergence of fluctuations in the global drag force. We determine the enhanced dynamical heterogeneity in the local droplet structures at approaching the phase inversion, and tightly connect it to the diverging drag fluctuations. Moreover, we show that near to the critical point the phase inversion is triggered as a stochastic process by large fluctuations at both large and small scales. Our findings pave the way to modeling the phase inversion process as an out-of-equilibrium critical-like phenomena.

ScMMO-atlas: a single cell multimodal omics atlas and portal for exploring fine cell heterogeneity and cell dynamics.

Single-cell multimodal sequencing parallelly captures multiple modalities of the same cell, providing unparalleled insights into cell heterogeneity and cell dynamics. For example, joint profiling of chromatin accessibility and transcriptome from the same single cell (scATAC+RNA) identified new cell subsets within the well-defined clusters. However, lack of single-cell multimodal omics (scMMO) database has led to data fragmentation, seriously hindering access, utilization and mining of scMMO data. Here, we constructed a scMMO atlas by collecting and integrating various scMMO data, then constructed scMMO database and portal called scMMO-atlas (https://www.biosino.org/scMMO-atlas/). scMMO-atlas includes scATAC+RNA (ISSAAS-seq, SNARE-seq, paired-seq, sci-CAR, scCARE-seq, 10X Multiome and so on), scRNA+protein, scATAC+proteinand scTri-modal omics data, with 3168824 cells from 27 cell tissues/organs. scMMO-atlas offered an interactive portal for visualization and featured analysis for each modality and the integrated data. Integrated analysis of scATAC+RNA data of mouse cerebral cortex in scMMO-atlas identified more cell subsets compared with unimodal omics data. Among these new cell subsets, there is an early astrocyte subset highly expressed Grm3, called Astro-Grm3. Furthermore, we identified Ex-L6-Tle4-Nrf1, a progenitor of Ex-L6-Tle4, indicating the statistical power provided by the big data in scMMO-atlas. In summary, scMMO-atlas offers cell atlas, database and portal to facilitate data utilization and biological insight.

FACIES PARTITIONING AT REGIONAL AND FIELD SCALES IN THE BARREMIAN KHARAIB‐2 CARBONATES, UAE

Carbonates in the Lower Cretaceous (Barremian to early Aptian) Kharaib Formation are reservoir rocks at giant oil fields in the UAE and Qatar. The Barremian Kharaib‐2 member (K60), the focus of this study, is in general composed of a regionally continuous succession of high‐energy, shallow‐water limestones bounded above and below by “dense” low‐energy mud‐rich strata. Despite several decades of research, conventional carbonate facies classification schemes and resulting facies groupings for the Kharaib‐2 member have failed to show a statistically acceptable correlation with core‐ and log‐derived petrophysical data. Moreover, sedimentary bodies potentially responsible for dynamic reservoir heterogeneities have not clearly been identified. This paper proposes a standardized facies classification scheme for the Kharaib‐2 carbonates based on vertical facies proportion curves (VPCs) and variogram analyses of core data to construct stratigraphic correlations at both field and regional scales. Data came from 295 cored wells penetrating the Kharaib‐2 member at ten fields in the on‐ and offshore UAE. Thin, dense intervals separating reservoir units were adopted as fourth‐order transgressive units and were used for stratigraphic correlation. Field‐scale probability maps were used to identify sedimentary bodies such as shallow‐water rudistid shoals.Regional stratigraphic correlations of the Kharaib‐2 member carbonates based on the VPCs identified variations in depositional environments, especially for the lower part of the reservoir unit; depositional facies at fields in the SE of the UAE were interpreted to be more distal compared to those at offshore fields to the NW. At a field scale, the VPCs failed to identify significant lateral variations in the carbonates. However, variogram analyses of cored wells showed spatial concentrations of specific facies in the inner ramp domain which could be correlated with high‐energy depositional bodies such as shoals dominated by rudist debris. The bodies were sinusoidal in plan view with lengths of up to 8 km and widths of ca. 1 km. Although similar‐shaped bodies with these dimensions have been reported from other carbonate depositional systems, they have not previously been reported in the Kharaib Formation. At a regional (inter‐field) scale, the stratigraphic correlation of standardized sedimentary facies remains problematic; however, mapping of facies associations and their relative proportions relative to their environments of deposition demonstrated new patterns for the stratigraphic architecture of the Kharaib‐2 member in the UAE.

Abstract C169: Investigating the effects of ancestry in KRAS mediated lung adenocarcinoma: Exploring intratumoral heterogeneity and mitochondrial dynamics

Abstract Demonstrating a combination of high prevalence, late detection, and low survival, Lung cancer is the leading cause of cancer related deaths worldwide (Sainz de Aja J, 2021) and Lung Adenocarcinoma (LUAD) is its most prevalent pathologic subtype (Zhou, 2021). Emerging from within the distal alveolar epithelium, LUAD presents with vast heterogeneity in histology, mutational spectra, and epigenomic dysregulation (Sainz de Aja J, 2021) (Zhou, 2021). Additionally, LUAD’s considerable intratumoral heterogeneity manifests vast genomic and phenotypic diversity across tumor cells and cellular components of the tumor microenvironment (TME) within the same tumor tissue. LUAD also reveals significant racial disparities in incidence, survival and mortality rates between White and Black men in the United States. The occurrence of LUAD is approximately 68.3 per capita for Black men and 61.5 per capita for White men, reflecting an 11% higher rate in the former. Further, survival outcomes indicate that Black men generally exhibit lower 5-year relative survival rates compared to White men, highlighting greater mortality risk and conspicuously poorer prognosis (Siegel RL et al., 2023). While addressing the social determinants of health is vital for the mitigation of health disparities, understanding the genetic and molecular foundations that underly racial health inequities is essential for developing comprehensive interventions and advancing more precise therapeutic modalities for more diverse patient populations. Single Nucleotide Polymorphisms (SNPs) can influence oncogenic transformation and may be associated with distinct molecular landscapes in tumors and adjacent TME. Furthermore, ancestry may play a pivotal role in shaping individual susceptibility and risk for mitochondrial dysfunction and ensuing oncogenic transformation. To elucidate the biological underpinnings of racial health disparities in LUAD, 10 Black and 10 White male LUAD patients’ tumor and normal corresponding adjacent tissues underwent molecular profiling for KRAS mutation status, Whole Exome Sequencing (WES) to determine ancestry, and analysis of associated LUAD induced Mitochondrial Somatic Nucleotide Variants (mtSNVs). We also employed the 10X Visium HD spatial transcriptomic platform to evaluate spatially resolved molecular and cellular differences in KRAS mediated LUAD between Black and White men. Hypothesis: Thus, we propose that global ancestry is associated with variations in intratumoral heterogeneity, TME composition, and mitochondrial dynamics, indicating a potential role in the observed disparities in susceptibility, malignancy and mortality in LUAD across diverse patient populations. Citation Format: Eun Kyu Sung, Yuxin Jin, Yonatan Amzaleg, John Carpten, William Dean Wallace, Dan Raz, Aaron M. Neely. Investigating the effects of ancestry in KRAS mediated lung adenocarcinoma: Exploring intratumoral heterogeneity and mitochondrial dynamics [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C169.

Seasonal dynamics of environmental heterogeneity augment microbial interactions by regulating community structure in different trophic lakes

Understanding how environmental heterogeneity drives microbial communities in lakes is essential for developing effective strategies to manage and restore aquatic ecosystems. However, the mechanisms by which environmental heterogeneity influences microbial community structure, network patterns, and interactions remain largely unexplored. To bridge this gap, we collected 84 water samples from four typical lakes in China (Fuxian, Tianmu, Taihu, and Xingyun) representing a range of trophic levels, across wet and dry seasons. We assessed environmental heterogeneity using 14 water quality parameters, analyzed community structure with Jaccard and Bray-Curtis dissimilarity indices, and developed a comprehensive index to elucidate microbial network complexity. Our study reveals three key findings: (1) Environmental heterogeneity was significantly greater in dry season compared to wet season across all lakes (P < 0.05). (2) Increased environmental heterogeneity led to higher bacterioplankton community dissimilarity, with greater β-diversity observed in dry season (P < 0.05). (3) Shifts in community structure due to increased environmental heterogeneity further enhanced microbial interactions, as evidenced by more complex and interconnected co-occurrence networks in the dry season. In summary, our study demonstrates that environmental heterogeneity significantly impacts bacterioplankton community structure and subsequently enhances microbial interactions. These findings underscore the importance of considering environmental heterogeneity in lake ecosystem management, as it plays a crucial role in regulating microbial community dynamics and interactions.

Characterizing Rotational Dynamics and Heterogeneity via Single-Molecule Intensity Measurements.

Dynamic heterogeneity in glassy systems has typically been characterized at the single-molecule level by extracting rotational relaxation time scales from linear dichroism (LD) collected via two orthogonally polarized channels. However, in such measurements, localization precision is diminished due to photons lost relative to collection in a single detection channel. This poses challenges in characterizing rotational and translational dynamics simultaneously, as translational measurements require high localization precision. In this paper, we present a method for extracting rotational dynamics of glassy systems at the single-molecule level from intensity fluctuations of fluorescent probe molecules in a wide-field configuration without the use of a polarizing optical component. Through numerical analysis, we show that LD and intensity measurements probing rotational dynamics report similar, approximately second-order rotational correlation decays even at low signal to noise. Thus, within the assumptions of small, isotropic rotations, LD and intensity autocorrelation analysis should provide identical information on the time scale and heterogeneity of rotational dynamics. We then present experimental results that validate this numerical result, with direct comparison of LD and intensity-based approaches across probe molecules in both polymeric and small-molecule glass formers as well as across optical configurations. Our results demonstrate moderate correlation on a per-probe basis between rotational time scales obtained from both approaches, with deviations consistent with those expected in a dynamically heterogeneous system. We envision that this easily accessible strategy will be of use across disciplines for characterizing single-molecule rotational dynamics in limited signal situations and/or when high-precision localization is required.

Phase transitions in a heterogeneous lattice hydrodynamic model involving both communication distance and memory time duration differences

An in-depth analysis of vehicle heterogeneity characteristics in intelligent heterogeneous traffic flows, involving connected autonomous vehicles and human-driven vehicles, is essential to reveal the evolutionary mechanism of congestion and to understand its interaction among traffic factors. It has significant theoretical value in guiding the future field deployment and testing of large-scale connected autonomous vehicles through the systematic analysis of the complex dynamic characteristics of intelligent heterogeneous traffic flows from a traffic engineering perspective, as well as traffic management and control in intelligent heterogeneous traffic flow environments. In this study, the lattice hydrodynamics model is applied to construct a corresponding traffic flow model for the differences of communication capability and historical information memory capability for heterogeneous vehicles. The roles of these heterogeneous properties and their interactions are investigated. Through linear stability analysis and nonlinear analysis, we in this study explore the influence of key traffic factors involving the differences of communication capability and historical information memory capability contributing to ameliorating intelligent heterogeneous traffic dynamics, and numerical simulations are carried out to verify the accuracy and reliability of theoretical analysis. An interesting discovery is that longer memory duration gets better, but the optimizing impact of memory duration on the system being marginally decreasing.

Heterogeneity in Slow Synaptic Transmission Diversifies Purkinje Cell Timing.

The cerebellum plays an important role in diverse brain functions, ranging from motor learning to cognition. Recent studies have suggested that molecular and cellular heterogeneity within cerebellar lobules contributes to functional differences across the cerebellum. However, the specific relationship between molecular and cellular heterogeneity and diverse functional outputs of different regions of the cerebellum remains unclear. Here, we describe a previously unappreciated form of synaptic heterogeneity at parallel fiber synapses to Purkinje cells in the mouse cerebellum (both sexes). In contrast to uniform fast synaptic transmission, we found that the properties of slow synaptic transmission varied by up to threefold across different lobules of the mouse cerebellum, resulting in surprising heterogeneity. Depending on the location of a Purkinje cell, the time of peak of slow synaptic currents varied by hundreds of milliseconds. The duration and decay time of these currents also spanned hundreds of milliseconds, based on lobule. We found that, as a consequence of the heterogeneous synaptic dynamics, the same brief input stimulus was transformed into prolonged firing patterns over a range of timescales that depended on Purkinje cell location.

Predefined-time convergence strategies for multi-cluster games in hybrid heterogeneous systems

This paper explores the problem of (generalized) Nash equilibrium search in multi-cluster games with heterogeneous dynamics and multiple constraints. Within this research framework, each agent acquires information solely through local interactions with its neighbors and forms clusters based on similarity of interests. These clusters manifest dual relationships of cooperation and competition: agents within the same cluster enhance decision-making capabilities through cooperation, while different clusters compete to maximize their respective benefits. To delve into these complex interactions among clusters and the learning and evolution processes among agents, four distributed control algorithms suitable for various scenario requirements are designed and implemented. These algorithms ensure that each agent converges to a Nash equilibrium (NE) or generalized Nash equilibrium (GNE) of the multi-cluster system within predefined time points. Finally, we apply these algorithms to the connectivity control problem of unmanned aerial vehicle swarms with diverse dynamics, validating the theoretical results through comprehensive simulations.

Neuron configuration enhances the synchronization dynamics in ring networks with heterogeneous firing patterns

Neuron synchronization is fundamental for brain dynamics. While several efforts have been made to understand neuron coupling between tonic and bursting neurons, the position of neurons in a neural network and its relationship with synchronization is not fully understood. This work investigates the impact of neuronal heterogeneity on firing pattern transitions and synchronization in networks comprising tonic and bursting neurons. Using the Huber-Braun model, we explore two configurations: one with neurons grouped closely and another with maximal separation. Our findings reveal that while increased coupling strength generally promotes synchronization, the specific mix of neuron types and their spatial configuration crucially modulate both synchronization and firing pattern transitions, leading to phenomena such as cluster synchronization and global phase synchrony. The results indicate that the configuration with maximal separation and with mostly bursting neurons synchronizes more quickly. Firing pattern transitions are also configuration-dependent. For example, in the network with half tonic and half bursting, the configuration grouped closely undergoes diverse complex dynamics transition in the synchronized state, while the other configuration synchronize with chaotic bursting dynamics. Behaviors like cluster synchronization are observed in this network. The study underscores the significance of considering neuronal heterogeneity in understanding network dynamics.

Uncovering the hidden world of riverbed sediments: The role of sediment heterogeneity and cross-bar channel fills in the hydrogeochemical dynamics of the hyporheic zone

Groundwater-surface water interaction (hyporheic exchange) is critical in numerous hydrogeochemical processes; however, hyporheic exchange is difficult to characterize due to the various spatial (e.g., sedimentary architecture) and temporal (e.g., stage fluctuations) variables that influence it. This interdisciplinary study brings forth novel insights by integrating various methodologies including geophysical surveys, physical and chemical sediment characterization, and water chemistry analysis to explore the interplay of the numerous facets governing hyporheic zone processes within a compound bar deposit. The findings reveal distinct sedimentary facies and geochemical zones within the compound bar, driven by the sedimentary architecture. Cross-bar channel fills are identified as critical structures influencing hydrogeochemical dynamics, acting as baffles to groundwater flow and modulating nutrient transformations. Geophysical imaging and hydrogeochemical analyses highlight the complex interplay between sediment characteristics and subsurface hydraulic connectivity, emphasizing the role of sediment heterogeneity in controlling hyporheic exchange and solute mixing. The study concludes that sediment heterogeneity, particularly the presence of cross-bar channel fills, plays a pivotal role in the hydrogeochemical dynamics of the hyporheic zone. These structures significantly influence hyporheic flow paths, solute residence times, and nutrient cycling, underscoring the necessity to consider the fine-scale sedimentary architecture in models of hyporheic exchange. The findings contribute to a deeper understanding of riverine ecosystem processes, offering insights that can inform management strategies for water quality and ecological integrity.

Moment-Based Reinforcement Learning for Ensemble Control.

Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and control engineering. Owing to the severely under-actuated nature and the difficulty of placing large-scale sensor networks, ensemble systems are limited to being actuated and monitored at the population level. Moreover, mathematical models describing the dynamics of ensemble systems are often elusive. Therefore, it is essential to design broadcast controls that excite the entire population in such a way that the heterogeneity in system dynamics is robustly compensated. In this article, we propose a reinforcement learning (RL)-based data-driven control framework incorporating population-level aggregated measurement data to learn a global control signal for steering a dynamic population in the desired manner. In particular, we introduce the notion of ensemble moments induced by aggregated measurements and derive the associated moment system to the original ensemble system. Then, using the moment system, we learn an approximation of optimal value functions and the associated policies in terms of ensemble moments through RL. We illustrate the feasibility and scalability of the proposed moment-based approach via numerical experiments using a population of linear, bilinear, and nonlinear dynamic ensemble systems. We report that the proposed method achieves the desired control objectives of various ensemble control tasks and obtains significantly better averaged-reward when compared with three existing methods.

Defining and modeling dynamic spatial heterogeneity within tumor microenvironments

Many solid tumors exhibit significant genetic, cellular, and biophysical heterogeneity which dynamically evolves during disease progression and after treatment. This constant flux in cell composition, phenotype, spatial relationships, and tissue properties poses significant challenges in accurately diagnosing and treating patients. Much of the complexity lies in unraveling the molecular changes in different tumor compartments, how they influence one another in space and time and where vulnerabilities exist that might be appropriate to target therapeutically. Recent advances in spatial profiling tools and technologies are enabling new insight into the underlying biology of complex tumors, creating a greater understanding of the intricate relationship between cell types, states, and the microenvironment. Here we reflect on some recent discoveries in this area, where the key knowledge and technology gaps lie, and the advancements in spatial measurements and in vitro models for the study of spatial intratumoral heterogeneity.

Superdiffusion and heterogeneous dynamics in liquid crystals by microrheology

Microrheology (MR) has emerged as a powerful tool for unraveling the intricate local viscoelastic properties of various soft materials. By tracking the free (passive MR) or forced (active MR) diffusion of a tracer, valuable insights into the mechanical characteristics of the host system can be obtained. In this study, we investigate the forced diffusion of a spherical tracer within isotropic and smectic liquid crystal phases of hard rod-like particles. Our findings reveal superdiffusive behaviour induced by external forces, particularly pronounced when these are aligned parallel to the nematic director. Analysis of the dynamical susceptibility unveils heterogeneities strongly correlated with the magnitude and orientation of applied forces, highlighting the system's critical dependence on structural ordering. Intriguingly, we observe that tracer superdiffusion, driven by external forces and evident across all relevant system directions, does not demonstrate a strong correlation with resulting dynamical heterogeneities.

Prior exposure to pathogens augments host heterogeneity in susceptibility and has key epidemiological consequences.

Pathogen epidemics are key threats to human and wildlife health. Across systems, host protection from pathogens following initial exposure is often incomplete, resulting in recurrent epidemics through partially-immune hosts. Variation in population-level protection has important consequences for epidemic dynamics, but how acquired protection influences inter-individual heterogeneity in susceptibility and its epidemiological consequences remains understudied. We experimentally investigated whether prior exposure (none, low-dose, or high-dose) to a bacterial pathogen alters host heterogeneity in susceptibility among songbirds. Hosts with no prior pathogen exposure had little variation in protection, but heterogeneity in susceptibility was significantly augmented by prior pathogen exposure, with the highest variability detected in hosts given high-dose prior exposure. An epidemiological model parameterized with experimental data found that heterogeneity in susceptibility from prior exposure more than halved epidemic sizes compared with a homogeneous population with identical mean protection. However, because infection-induced mortality was also greatly reduced in hosts with prior pathogen exposure, reductions in epidemic size were smaller than expected in hosts with prior exposure. These results highlight the importance of variable protection from prior exposure and/or vaccination in driving population-level heterogeneity and epidemiological dynamics.

Study of oxygen transport in glassy polymers on a nanometer length scale utilizing the kinetic Monte Carlo simulations.

Diffusion-controlled deactivation of excited phenanthrene and oxidation of triplet aryl-nitrene by molecular oxygen were used to determine the energetics of oxygen jump rates in the set of glassy polymers: poly(methyl methacrylate), poly(n-butyl methacrylate), polycarbonate, polystyrene, and polysulfone. To interpret experimental results, a simple model based on the transition state theory of diffusion jump has been used. The kinetic Monte Carlo simulations of phenanthrene deactivation and nitrene oxidation were carried out in a cubic lattice that modeled a polymer matrix. The bonds of the lattice were assigned to be activation barriers for the diffusion jumps of oxygen molecules from one site of the lattice to another. The standard deviation, σ‡, and spatial correlation length, rc, of the free energy of diffusion jump have been determined. It is shown that the spatial correlation of oxygen jump rates on a nanometer scale and the entropic nature of the dynamic heterogeneity are common features of all the studied polymers.

Similarity Metrics for Subcellular Analysis of FRET Microscopy Videos.

Understanding the heterogeneity of molecular environments within cells is an outstanding challenge of great fundamental and technological interest. Cells are organized into specialized compartments, each with distinct functions. These compartments exhibit dynamic heterogeneity under high-resolution microscopy, which reflects fluctuations in molecular populations, concentrations, and spatial distributions. To enhance our comprehension of the spatial relationships among molecules within cells, it is crucial to analyze images of high-resolution microscopy by clustering individual pixels according to their visible spatial properties and their temporal evolution. Here, we evaluate the effectiveness of similarity metrics based on their ability to facilitate fast and accurate data analysis in time and space. We discuss the capability of these metrics to differentiate subcellular localization, kinetics, and structures of protein-RNA interactions in Forster resonance energy transfer (FRET) microscopy videos, illustrated by a practical example from recent literature. Our results suggest that using the correlation similarity metric to cluster pixels of high-resolution microscopy data should improve the analysis of high-dimensional microscopy data in a wide range of applications.

Enhancer-Insulator Pairing Reveals Heterogeneous Dynamics in Long-Distance 3D Gene Regulation

Enhancer-Insulator Pairing Reveals Heterogeneous Dynamics in Long-Distance 3D Gene Regulation

The impact of the scale and hierarchical structure of health human resources on the level of medical services-based on China’s four major economic regions

BackgroundEnsuring that the scale and hierarchical structure of health human resources are rational, and that medical services are efficient and fair, is an important task of practical significance. On this basis, examining the impact of health human resources on the level of medical services presents a new and formidable challenge. This study aims to delve into how the scale and hierarchical structure of health human resources in China’s four major economic regions affect the fairness and efficiency of medical services, and to identify optimization strategies.MethodsThis study utilizes provincial panel data from China’s four major economic regions spanning the years 2009 to 2021. Initially, it provides a statistical description of the current state of health human resources and the level of medical services. Subsequently, it employs a fixed-effects model to analyze the impact of the scale and hierarchical structure of health human resources, as well as their interactive effects, on the fairness and efficiency of medical services, and discusses the interactive mechanisms between medical service fairness and medical service efficiency. Furthermore, after conducting a comprehensive evaluation of the level of medical services using the entropy weight method, it explores the regional heterogeneity and temporal dynamics in the influence of the scale and hierarchical structure of health human resources on the level of medical services. Finally, the study examines the scientific validity and rationality of the research findings through various robustness checks, including the substitution of research variables and models.ResultsThe study found that the scale of health human resources has a promoting effect on the equity of medical services (β ≤ 0.643, p ≤ 0.01), but exhibits an inhibitory effect on the efficiency of medical services (β ≥ -0.079, p ≤ 0.1); the hierarchical structure of health human resources shows a positive impact on both the equity and efficiency of medical services (βequity ≤ 0.160, p ≤ 0.01; βefficiency ≤ 0.341, p ≤ 0.05); at the same time, the results indicate that the interactive effect of the scale and hierarchical structure of health human resources promotes equity in medical services (β = 0.067, p ≤ 0.01), but restricts the efficiency of medical services (β ≥ -0.039, p ≤ 0.01); the mechanism by which health human resources affect the level of medical services in China’s western and northeastern regions is more pronounced than in the central and eastern regions; after the implementation of the “Healthy China 2030” Planning Outline, the role of health human resources in the level of medical services has been strengthened; in the robustness tests, the model remains robust after replacing the core explanatory variables, with R2 maintained between 0.869 and 0.972, and the dynamic GMM model test shows a significant second-order lag in the level of medical services (βequity ≤ 0.149, p ≤ 0.01; βefficiency ≤ 0.461, p ≤ 0.01); the channel test results prove that managerial personnel and other technical personnel are key pathways in regulating the impact of medical staff on the level of medical services.ConclusionThis study provides an in-depth analysis of the impact of health human resources on the level of medical services, revealing that both the scale and hierarchical structure of health human resources significantly affect the equity and efficiency of medical services. Furthermore, the influence of health human resources on the level of medical services exhibits regional heterogeneity and temporal characteristics. Robustness tests ensure the scientific validity and robustness of the research conclusions. This provides effective references for optimizing the allocation of health human resources and improving the level of medical services.

The root microlandscape of arbuscular mycorrhizal fungi.

Understanding the drivers of assemblages of arbuscular mycorrhizal fungi (AMF) is essential to leverage the benefits of AMF for plant growth and health. Arbuscular mycorrhizal fungi are heterogeneously distributed in space even at small scale. We review the role of plant distribution in driving AMF assemblages (the passenger hypothesis), using a transposition of the conceptual framework of landscape ecology. Because rooting systems correspond to habitat patches with limited carrying capacity that differ in quality due to host-preference effects, we suggest considering plant communities as mosaics of AMF microhabitats. We review how predictions from landscape ecology apply to plant community effects on AMF, and the existing evidence that tests these predictions. Although many studies have been conducted on the effect of plantcompositional heterogeneity on AMF assemblages, they mostly focused on the effect ofplant richness, while only a few investigated the effect of configurational heterogeneity, plant connectivity or plant community temporal dynamics. We propose key predictions and future prospects to fill these gaps. Considering plant communities as landscapes extends the passenger hypothesis by including a spatially explicit dimension and its associated ecological processes and may help understand and manipulate AMF assemblages at small spatial scales.