Spatial Structure Research Articles

Architecture of new generation research institutions under reconstruction conditions

In the article, the authors focus on key methods and approaches that ensure optimal organization of space for scientific and research work. This contributes to achieving versatility, flexibility and the ability to transform the internal environment. Modern compositional techniques for the architectural transformation of objects of the type under consideration and methods of their application at various levels of architectural formation and development have been studied. The main criteria for the architectural and spatial formation of new generation research institutions have been identified, thanks to which an architectural ensemble is formed, the concept of spatial organization, the creation of a unified compositional structure, the organization of recognizable architecture. The basic principles and features that form the nature of sustainability, visual perception, and create a spatial structure of perception have been identified.

Semantic-spatial feature-fused cortical surface parcellation: a scale-unified spatial learning network with boundary contrastive loss.

The cortical surface parcellation provides prior guidance for studying mental disorders and human cognition. Graph neural networks (GNNs) have gained popularity in this task to preserve its spatial structure. However, previous GNNs struggled to effectively exploit the information contained in the complex spatial structure of the cortical surface and generally encountered an uneven node distribution issue. Meanwhile, labeling boundary nodes was also identified as a widespread problem in this task. Accordingly, this paper develops a scale-unified spatial learning network with a boundary contrastive loss (SSLNet) for cortical surface parcellation. Its core is the scale-unified spatial learning module. It devises neighbor feature extraction and aggregation strategies by fully integrating spatial coordinates and semantic structure to learn effective spatial features of local neighborhoods. More importantly, spatial scale unification is incorporated into this module to mitigate the negative effect on spatial learning caused by node distribution differences among local areas. Additionally, a universal boundary contrastive loss is constructed, enhancing the feature discriminability of boundary nodes by constraining them to be close to the same class nodes and apart from different class nodes in the feature space. It considerably improves boundary performance without increasing parameters or changing the network structure. Experiments regarding public Mindboggle demonstrate that the dice score and accuracy of SSLNet achieve and , respectively, surpassing existing methods.

A novel role of peroxiredoxin 2 in diabetic kidney disease progression by activating the classically activated macrophages.

Diabetic kidney disease (DKD) is the main cause of deaths due to diabetes mellitus (DM). Due to the complexity of its onset, it is difficult to achieve accurate prevention and treatment. The classically activated macrophage (M1) polarization is a crucial proinflammatory mechanism of DKD, while the interaction and cascade effects of oxidative stress and inflammatory response remain to be elucidated. A urine proteomic analysis of patients with DM indicated that peroxiredoxin 2 (PRDX2) had the higher abundance in DKD. We recently found that PRDX of parasitic protozoa Entamoeba histolytica, which was similar to human PRDX2 in amino acid sequence and spatial structure, could activate the inflammatory response of macrophages through toll-like receptor 4 (TLR4). Hence, our study was designed to explore the role of PRDX2 in chronic inflammation during DKD. Combined with in vivo and in vitro experiments, results showed that the PRDX2 was positively correlated with DKD progression and upregulated by high glucose or recombinant tumor necrosis factor-α in renal tubular epithelial cells; Besides, recombinant PRDX2 could promote M1 polarization of macrophages, and enhance the migration as well as phagocytic ability of macrophages through TLR4. In summary, our study has explored the novel role of PRDX2 in DKD to provide a basis for further research on the diagnosis and treatment of DKD.

Single‐Mode Lasing by Selective Mode Structure Breaking

AbstractSingle‐mode lasers are highly desirable for applications in optical communication, quantum information, and photonic computing, but their realization is challenging due to the competition of multiple closely spaced resonant modes in microcavities. This paper proposes a novel approach, termed mode structure breaking, to achieve high‐performance single‐mode lasing in an individual laser cavity. By introducing selective spatial structure defects, the mode structure of competing modes can be broken, enabling single‐mode selection without huge losses. As proof of concept, femtosecond (fs)‐laser ablation is experimentally employed to introduce external angle defects on a microdisk cavity, effectively eliminating mismatched competing modes and thus achieving single‐mode lasing output. The lasing characteristics are further optimized by incorporating a punched hole to suppress remaining high‐order lasing modes. Notably, the fabricated perovskite laser sources, which are called mode‐structure‐breaking lasers, demonstrate excellent single‐mode lasing properties, including stability, an ultralow threshold (≈2.31 µJ cm−2), and a narrow linewidth (≈0.15 nm). Conclusively, this comprehensive study of manipulating lasing mode by breaking mode structure may provide a promising approach to realizing single‐mode lasing in a single structure, which is significant for producing on‐chip laser source arrays for use in photonic integrated circuits.

A signal-diffusion-based unsupervised contrastive representation learning for spatial transcriptomics analysis.

Spatial transcriptomics allows for the measurement of high-throughput gene expression data while preserving the spatial structure of tissues and histological images. Integrating gene expression, spatial information, and image data to learn discriminative low-dimensional representations is critical for dissecting tissue heterogeneity and analyzing biological functions. However, most existing methods have limitations in effectively utilizing spatial information and high-resolution histological images. We propose a signal-diffusion-based unsupervised contrast learning method (SDUCL) for learning low-dimensional latent embeddings of cells/spots. SDUCL integrates image features, spatial relationships and gene expression information. We designed a signal diffusion microenvironment discovery algorithm, which effectively captures and integrates interaction information within the cellular microenvironment by simulating the biological signal diffusion process. By maximizing the mutual information between the local representation and the microenvironment representation of cells/spots, SDUCL learns more discriminative representations. SDUCL was employed to analyze spatial transcriptomics datasets from multiple species, encompassing both normal and tumor tissues. SDUCL performed well in downstream tasks such as clustering, visualization, trajectory inference, and differential gene analysis, thereby enhancing our understanding of tissue structure and tumor microenvironments. https://github.com/WeiMin-Li-visual/SDUCL. Supplementary data are available at Bioinformatics online.

Single-Nucleus and Spatial Transcriptomics Revealing Host Response Differences Triggered by Mutated Virus in Severe Dengue

Dengue virus (DENV) infection causes various disease manifestations ranging from an asymptomatic state to severe, life-threatening dengue. Despite intensive research, the molecular mechanisms underlying the abnormal host responses and severe disease symptoms caused by evolved DENV strains is not fully understood. First, the spatial structure of mutant DENV was compared via in silico molecular modeling analysis. Second, employing single-nucleus and spatial RNA sequencing, we analyzed and verified transcriptome samples in uninfected, mild (NGC group), and severe (N10 group) liver tissues from murine models. In this study, we obtained a cumulatively mutated DENV-2 N10 with enhanced capability of replication and pathogenicity post 10 serial passages in Ifnra−/− mice. This variant caused severe damage in the liver, as compared with other organs. Furthermore, mutated DENV infection elicited stronger responses in hepatocytes. The critical host factor Nrg4 was identified. It dominated mainly via the activation of the NRG/ErbB pathway in mice with severe symptoms. We report on evolved N10 viruses with changes observed in different organisms and tissue. This evolutionary variant results in high replicability, severe pathogenicity, and strong responses in murine. Moreover, the host responses may play a role by activating the NRG/ErbB signaling pathway. Our findings provide a realistic framework for defining disturbed host responses at the animal model level that might be one of the main causes of severe dengue and the potential application value.

Structural and hydrodynamic controls on fluid travel time distributions across fracture networks

Fracture networks are preferential flow paths playing a critical role in a wide range of environmental and industrial problems. Their complex multiscale structure leads to broad distributions of fluid travel times, affecting many biogeochemical processes. Yet, the relationship between the fracture network structures, their hydrodynamic properties, and the resulting anomalous transport dynamics remains unclear. We use a large database of fracture network models to investigate the factors controlling fluid velocity and travel-time distributions across a wide range of networks, from synthetic to field-calibrated models, with aperture variability at both fracture and network scales. Analysis reveals that transport statistics have generic properties across investigated networks, including notably heavy-tailed travel time distributions. Networks of increasing complexity and heterogeneity lead to broader velocity distributions and more channeled velocity fields, where flow concentrates in a narrow channel web in the three-dimensional (3D) fracture structure. While heterogeneity in point-velocity statistics increases travel-time variability, channeling tends to reduce it. This counterintuitive phenomenon challenges current theories, which assume that long travel time power law exponents are determined solely by point-velocity statistics. By analyzing velocity and travel time statistics for different flow structures, we develop a coupled Continuous Time Random Walk framework capturing the unexpected control of the velocity field's spatial structure on anomalous transport in fracture networks. This leads to a unique class of random walk models capturing the respective roles of velocity heterogeneity and spatial structure on transport in networks. These findings open a prospective for characterizing, modeling, and predicting transport dynamics in complex networks, with potential applications to geological, biological, and engineered networks.

Dengue virus genomic surveillance in the applying Wolbachia to eliminate dengue trial reveals genotypic efficacy and disruption of focal transmission.

Release of Aedes aegypti mosquitoes infected with Wolbachia pipientis (wMel strain) is a biocontrol approach against Ae. aegypti-transmitted arboviruses. The Applying Wolbachia to Eliminate Dengue (AWED) cluster-randomised trial was conducted in Yogyakarta, Indonesia in 2018-2020 and provided pivotal evidence for the efficacy of wMel-Ae. aegypti mosquito population replacement in significantly reducing the incidence of virologically-confirmed dengue (VCD) across all four dengue virus (DENV) serotypes. Here, we sequenced the DENV genomes from 318 dengue cases detected in the AWED trial, with the aim of characterising DENV genetic diversity, measuring genotype-specific intervention effects, and inferring DENV transmission dynamics in wMel-treated and untreated areas of Yogyakarta. Phylogenomic analysis of all DENV sequences revealed the co-circulation of five endemic DENV genotypes: DENV-1 genotype I (12.5%) and IV (4.7%), DENV-2 Cosmopolitan (47%), DENV-3 genotype I (8.5%), and DENV-4 genotype II (25.7%), and one recently imported genotype, DENV-4 genotype I (1.6%). The diversity of genotypes detected among AWED trial participants enabled estimation of the genotype-specific protective efficacies of wMel, which were similar (± 10%) to the point estimates of the respective serotype-specific efficacies reported previously. This indicates that wMel afforded protection to all of the six genotypes detected in Yogyakarta. We show that within this substantial overall viral diversity, there was a strong spatial and temporal structure to the DENV genomic relationships, consistent with highly focal DENV transmission around the home in wMel-untreated areas and a near-total disruption of transmission by wMel. These findings can inform long-term monitoring of DENV transmission dynamics in Wolbachia-treated areas including Yogyakarta.

Components and anisotropy of 3D QFP waves during the early solar eruption

Abstract The propagation of disturbances in the solar atmosphere is inherently three-dimensional (3D), yet comprehensive studies on the spatial structure and dynamics of 3D wavefronts are scarce. Here we conduct high-resolution 3D numerical simulations to investigate filament eruptions, focusing particularly on the 3D structure and genesis of EUV waves. Our results demonstrate that the EUV wavefront forms a dome-like configuration subdivided into three distinct zones. The foremost zone, preceding the flux rope, consists of fast-mode shock waves that heat the adjacent plasma. Adjacent to either side of the flux rope, the second zone contains expansion waves that cool the nearby plasma. The third zone, at the juncture of the first two, exhibits minimal disturbances. This anisotropic structure of the wavefront stems from the configuration and dynamics of the flux rope, which acts as a 3D piston during eruptions—compressing the plasma ahead to generate fast-mode shocks and evacuating the plasma behind to induce expansion waves. This dynamic results in the observed anisotropic wavefront. Additionally, with synthetic EUV images from simulation data, the EUV waves are observable in Atmospheric Imaging Assembly 193 and 211 $\AA$, which are identified as the fast-mode shocks. The detection of EUV waves varies with the observational perspective: the face-on view reveals EUV waves from the lower to the higher corona, whereas an edge-on view uncovers these waves only in the higher corona.

Image enhancement with art design: a visual feature approach with a CNN-transformer fusion model

Graphic design, as a product of the burgeoning new media era, has seen its users’ requirements for images continuously evolve. However, external factors such as light and noise often cause graphic design images to become distorted during acquisition. To enhance the definition of these images, this paper introduces a novel image enhancement model based on visual features. Initially, a histogram equalization (HE) algorithm is applied to enhance the graphic design images. Subsequently, image feature extraction is performed using a dual-flow network comprising convolutional neural network (CNN) and Transformer architectures. The CNN employs a residual dense block (RDB) to embed spatial local structure information with varying receptive fields. An improved attention mechanism module, attention feature fusion (AFF), is then introduced to integrate the image features extracted from the dual-flow network. Finally, through image perception quality guided adversarial learning, the model adjusts the initial enhanced image’s color and recovers more details. Experimental results demonstrate that the proposed algorithm model achieves enhancement effects exceeding 90% on two large image datasets, which represents a 5%–10% improvement over other models. Furthermore, the algorithm exhibits superior performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) image quality evaluation metrics. Our findings indicate that the fusion model significantly enhances image quality, thereby advancing the field of graphic design and showcasing its potential in cultural and creative product design.

A Bayesian spatio-temporal model for cluster detection: identifying HPV suboptimal vaccine coverage

AbstractHuman papillomavirus (HPV) is a common sexually transmitted virus responsible for several types of cancer. HPV vaccines have been included in the systematic vaccination of the Valencia Region since 2008. Despite clinical agreement on the safety and effectiveness of the vaccines, vaccination coverage remains suboptimal in many areas. In order to facilitate the implementation of targeted strategies to enhance vaccination coverage, we develop here an easy-to-implement spatio-temporal clustering model that identifies groups of health districts that share similar behaviour. Namely, health districts are clustered twice. First, they are grouped into spatial clusters based on their underlying vaccination coverage that remains constant over time. Second, they are clustered according to their evolution of annual vaccination rates. A feature of our model is that it does not impose that geographically neighboring areas are assigned to the same spatial cluster or the same temporal trend. This flexibility allows us to explore different spatial and temporal structures. Suboptimal HPV vaccination coverage was found in some health districts. The results reveal the presence of three spatial clusters, with estimated coverage ranging between 82 and 97%, for 14-year-old girls and five clusters for 12-year-old girls, with coverage ranging between 61 and 96%. However, most health districts in this age group show an increasing vaccination trend. The need for future efforts to identify factors causing suboptimal vaccination coverage so that they can be acted upon specifically is highlighted.

Effects of Diffusing Squalene on the Plastic Deformation of Ultrahigh-Molecular-Weight Polyethylene─Insights from Molecular Dynamics Simulations.

Ultrahigh-molecular-weight polyethylene (UHMWPE) stands out as a popular artificial joint material. However, wear limits its service life, which is mainly caused by accumulation of plastic deformation. The plastic deformation on the frictional interface reflects the early wear of UHMWPE. To investigate the effect of squalene, a typical component in the body fluid, on the tribological properties of UHMWPE at microscopic scale, the diffusion behavior of squalene into polyethylene and its influence on the plastic deformation of polyethylene are discussed using the molecular dynamics (MD) simulation. The lubrication model shows that polyethylene reconstructed from the interface to lower substrate, with refactor gaps between polyethylene chains. This promotes squalene molecules to gradually diffuse into polyethylene from these gaps and causes the polyethylene structure to become loose. On the other hand, in the diffused model, squalene in polyethylene substrates increases the plastic deformation of polyethylene. The separation of squalene reduces the interaction strength between adjacent polyethylene chains and accelerates the disentanglement of polyethylene. The flexibility of "C═C" bonds in squalene allows the continuous adjustment of its spatial structures to adapt the space between polyethylene chains. The squalene fragments will not hinder the plastic flow of polyethylene.

Assessing spatial structure in marine populations using network theory: A case study of Atlantic sea scallop (Placopecten magellanicus) connectivity.

Knowledge of the geographic distribution and connectivity of marine populations is essential for ecological understanding and informing management. Previous works have assessed spatial structure by quantifying exchange using Lagrangian particle-tracking simulations, but their scope of analysis is limited by their use of predefined subpopulations. To instead delineate subpopulations emerging naturally from marine population connectivity, we interpret this connectivity as a network, enabling the use of powerful analytic tools from the field of network theory. The modelling approach presented here uses particle-tracking to construct a transport network, and then applies the community detection algorithm Infomap to identify subpopulations that exhibit high internal connectivity and sparse connectivity with other subpopulations. An established quality metric, the coherence ratio, and a new metric we introduce indicating self-recruitment to subpopulations, dubbed the fortress ratio, are used to interpret community-level exchange. We use the Atlantic sea scallop (Placopecten magellanicus) in the northwest Atlantic as a case study. Results suggest that genetic lineages of P. magellanicus demonstrate spatial substructure that depends on horizontal transport, vertical motility, and suitable habitat. Our results support connectivity previously characterized on Georges Bank and Mid-Atlantic Bight. The Gulf of St. Lawrence genetic lineage is found to consist of five subpopulations that are classified as being a sink, source, permeable, or impermeable using quality metrics. This approach may be applied to other planktonic dispersers and prove useful to management.

Synergistic Regulation of DNA Morphology by Metal Cations and Low pH.

As a flexible biomolecule, the spatial structure of DNA is variable. The effects of concentration, metal cations, and low pH on DNA morphology were studied. For the high concentration of DNA, the cross-linked branch-like or network structures were formed. For the low concentration of DNA, isolated, random and freely loose linear DNA chains were presented. These phenomena were related to the intermolecular interactions. Branch-like DNA structures were reformed with the addition of metal cations to the low concentration of DNA at pH 7-4, suggesting the negative charges of DNA were neutralized, thus transforming the spatial structure of DNA into a low charge density morphology and presenting the hypochromic effect. Compared to the monovalent alkaline metal cations, more negative charges of DNA were screened by the alkaline-earth metal cations. Distinct DNA morphologies were observed for pH 3. The linear and condensed DNA structures were simultaneously observed, which was met regardless of the solution with or without the addition of metal cations. This was further confirmed by the absorbance of DNA. Compared to the pure DNA, bulky and aggregated DNA collapsed structures were formed when the sodium and magnesium cations were added to the reaction solution. In addition, it was verified that the condensed DNA structures failed to revert back to the chain structure by neutralizing acidic solutions with alkali, but the compacted DNA spheres became loose. The conductivities of various DNA morphologies were measured. They were morphology-dependent. This study provides guidance forthe behavior of DNA in the acidic solutions and further promotes the application of DNA in DNA-based nano-optoelectronic devices.

Doping Sulfur in Layered Double Hydroxides with High Hydrophilicity to Accelerate the Charge Transfer and Reduce the Energy Barrier for Efficient Electrocatalytic Splitting Water.

Nowadays, water splitting has been recognized as one of the most attractive ways to produce clean hydrogen energy. Herein, a novel sulfur-doped layered double-hydroxide (namely, S-NiCo-LDH) electrocatalyst with nanocage structure is prepared. After the etching treatment with Ni ions, the spatial structure of the catalysts is opened, and the hydrophilicity is improved, which will enhance the adsorption capacity for H2O to provide convenience for hydrogen evolution reaction (HER). Interestingly, the S doping can boost the capture capability toward OH- to create conditions for the occurrence of oxygen evolution reaction (OER). More importantly, the introduction of S element can improve the density of states located near the Fermi level of NiCo-LDH, thereby accelerating the electron transfer and increase the carrier density. Meanwhile, the existence of S element can remarkably reduce the energy barriers of *O and *H formation, boosting HER and OER in an alkaline solution. As a result, the S-NiCo-LDH electrocatalyst shows excellent performance in overall water splitting, affording low overpotentials of 168 and 235 mV at 10 mA/cm2 for HER and OER, respectively. Furthermore, the S-NiCo-LDH electrocatalyst exhibits good long-term stability in both HER and the OER. In short, the current work can give a meaningful reference for future research.

Randomising spatial patterns supports the integration of intraspecific variation in ecological niche models

Ecological niche models (ENMs) are an essential modelling technique in biodiversity prediction and conservation and are frequently used to forecast species responses to global changes. Classic species‐level models may show limitations as they assume species homogeneity, neglecting intraspecific variation. Composite ENMs allow the integration of intraspecific variation by combining intraspecific‐level ENMs, capturing individual environmental responses over the species' geographic range. While recent studies suggest that accounting for intraspecific variation improves model predictions, we currently lack methods to test the significance of the improvement. Here, we propose a null model approach that randomises observed intraspecific structures as an appropriate baseline for comparison. We illustrate this approach by comparing predictive performance of a species‐level ENM to composite ENMs for European beech Fagus sylvatica. To investigate the influence of spatial lineage structure, we tested all models against the same withheld data to allow comparison across models based on five common performance metrics. We found that the species‐level ENM expressed higher overall performance (i.e. AUC, TSS, and Boyce index) and specificity (ability to predict absences), while the composite ENMs achieved higher sensitivity (ability to predict presences). In line with this, the composite ENMs also showed increased sensitivity and decreased specificity compared to the null models that randomised lineage structure. We showed that the assessment of model performance strongly varies based on the used measures, emphasising a careful investigation of multiple measures for evaluation. The application of null models allowed us to disentangle the effect of observed patterns of intraspecific variation in ENMs. Further, we highlight the validation and use of well‐founded subgroups for modelling. Although intraspecific variation improves the prediction of occurrences of European beech, it did not fully outcompete the classic species‐level model and should be used with care and rather to improve understanding and to supplement, not replace, species‐level models.

Quantification and validation of uncertainties in subsoil models

AbstractIn infrastructure planning and construction, modeling the subsoil and its associated uncertainty is a fundamental task of geotechnical engineers. However, probabilistic methods and tools for quantifying and displaying the uncertainty of the subsoil models are rarely used in practice where deterministic interpolation dominates. In digital planning using Building Information Modeling (BIM), the probabilistic approach supports creating a discipline model in which the uncertainties of the spatial layer structure are statistically quantified to evaluate the georisks in the design and execution of civil constructions. This article presents a case study using a combination of Sequential Gaussian Simulation (SGSIM) and Sequential Indicator Simulation (SISIM) to account for uncertainties in soil layer geometry. In a case study at the Munich Town Hall, a geostatistical approach is applied and validated based on 70 bore logs, whereby the probabilities for the occurrence of a particular layer are spatially quantified. The case study illustrates the methodology‘s great potential and benefits compared to the conventional deterministic approach based on interpolation procedures.

Temperature and Abundance Effects on Spatial Structures of Northern Shrimp (Pandalus borealis) at Different Life Stages in the Oceanographically Variable Gulf of Maine

ABSTRACTThe Gulf of Maine (GOM) northern shrimp, Pandalus borealis, once supported a significant winter fishery, but a moratorium has been placed on the fishery since 2014 because of a population collapse and recruitment failures that have been attributed to unfavorably warm water temperatures. The GOM is at the southernmost end of the northern shrimp's range, suggesting its population dynamics and distribution may be vulnerable to warming water temperatures. In this study, we used survey data to estimate spatial indicators for GOM northern shrimp at four life history stages to identify possible temporal trends and examine relationships between the indicators and northern shrimp abundance and bottom temperature. We observed patchier distributions over time, which were related to declining population abundance, and a distributional shift northward that was associated with warming bottom water temperatures. Northern shrimp habitat distribution was strongly associated with bottom temperature. Shrimp of all life stages were found in bottom waters cooler than the station's average bottom temperature.

Construction methods and latest applications of kidney cancer organoids

Renal cell carcinoma (RCC) is one of the deadliest malignant tumors. Despite significant advances in RCC treatment over the past decade, complete remission is rarely achieved. Consequently, there is an urgent need to explore and develop new therapies to improve the survival rates and quality of life for patients. In recent years, the development of tumor organoid technology has attracted widespread attention as it can more accurately simulate the spatial structure and physiological characteristics of tumors within the human body. In this review, we summarize the main methods currently used to construct kidney cancer organoids, as well as their various biological and clinical applications. Furthermore, combining organoids with other technologies, such as co-culture techniques and microfluidic technologies, can further develop organoids and address their limitations, creating more practical models. This approach summarizes the interactions between different tissues or organs during tumor progression. Finally, we also provide an outlook on the construction and application of kidney cancer organoids. These rapidly evolving kidney cancer organoids may soon become a focal point in the development of in vitro clinical models and therapeutic research for kidney cancer.

Three-dimensional spatial structure and heterogeneity characterization of #5 coal of Shanxi Formation in eastern Ordos basin

Based on macroscopic qualitative observation, microscopic quantitative analysis, low-temperature adsorption, high-pressure mercury intrusion and three-dimensional CT scanning, the spatial structure and heterogeneity of the #5 coal from the eastern Ordos Basin, Shanxi Formation, are quantitatively characterized. The following observation were studied: 1) The main body of the #5 coal was intact structural coal seam, with the density of cleats gradually increasing from north to south. 2) The microscopic composition of the #5 coal is dominated by desmocollinite, which contains small amounts of inertinite and inorganic minerals, with the inorganic component mainly composed of carbonate rocks and a small amount of clay. The carbon content and the content of its inorganic minerals in the #5 coal gradually decrease toward the inner basin, caused by transport distance and depositional environment. 3) At the micrometer scale, no significant pores were observed, and the #5 coal bed is primarily composed of micro-mesopores smaller than 40 nm, with a small number of 50∼100 nm macropores. Simultaneously, micro-fractures in the coal bed were well developed; by using mercury intrusion data of the bimodal feature of the pore throat, indicates the coexistence of micrometer-scale micro-fractures and nanometer-scale mesopores within the coal, with pronounced differences and interconnected complexity. 4) The porosity and permeability of microfractures, as well as the number of microfracture bars and permeability, indicate that the connectivity of coal bed is primarily caused by the microfracture system. By reconstructing the three-dimensional spatial structure of the #5 coal through multiple tests, the heterogeneous characteristics are quantitatively characterized.