Spatial Properties Research Articles

Influence of peripheral axon geometry and local anatomy on magnetostimulation chronaxie

Objective. Rapid switching of magnetic resonance imaging (MRI) gradient fields induces electric fields that can cause peripheral nerve stimulation (PNS) and so accurate characterization of PNS is required to maintain patient safety and comfort while maximizing MRI performance. The minimum magnetic gradient amplitude that causes stimulation, the PNS threshold, depends on intrinsic axon properties and the spatial and temporal properties of the induced electric field. The PNS strength–duration curve is widely used to characterize simulation thresholds for periodic waveforms and is parameterized by the chronaxie and rheobase. Safety limits to avoid unwanted PNS in MRI rely on a single chronaxie value to characterize the response of all nerves. However, experimental magnetostimulation peripheral nerve chronaxie values vary by an order of magnitude. Given the diverse range of chronaxies observed and the importance of this number in MRI safety models, we seek a deeper understanding of the mechanisms contributing to chronaxie variability. Approach. We use a coupled electromagnetic-neurodynamic PNS model to assess geometric sources of chronaxie variability. We study the impact of the position of the stimulating magnetic field coil relative to the body, along with the effect of local anatomical features and nerve trajectories on the driving function and the resulting chronaxie. Main results. We find realistic variation of local axon and tissue geometry can modulate a given axon’s chronaxie by up to two-fold. Our results identify the temporal rate of charge redistribution as the underlying determinant of the chronaxie. Significance. This charge distribution is a function of both intrinsic axon properties and the spatial stimulus along the nerve; thus, examination of the local tissue topology, which shapes the electric fields, as well as the nerve trajectory, are critical for better understanding chronaxie variations and defining more biologically informed MRI safety guidelines.

Cygnus OB2 as a test case for particle acceleration in young massive star clusters

In this paper, we focus on the scientific case of Cygnus OB2, a young massive star cluster (YMSC) located in the northern sky towards the Cygnus X star-forming complex. We consider a model that assumes cosmic-ray acceleration occurring only at the termination shock of the collective wind of the YMSC and address the question of whether or not, and under what hypotheses, hadronic emission by the accelerated particles can account for the observations of Cygnus OB2 obtained by Fermi-LAT and HAWC. To this end, we carefully review the available information on this source, also confronting different estimates of the relevant parameters with ad hoc developed simulations. Once other model parameters are fixed, the spectral and spatial properties of the emission are found to be very sensitive to the unknown properties of the turbulent magnetic field. Comparison with the data shows that our suggested scenario is incompatible with Kolmogorov turbulence. Assuming Kraichnan- or Bohm-type turbulence spectra, the model accounts well for the very high energy (VHE) data, but fails to reproduce the centrally peaked morphology observed by Fermi-LAT, suggesting that additional effects might be important for lower energy γ-ray emission. We discuss how additional progress can be made with more detailed and extended knowledge of the spectral and morphological properties of the emission.

Network enrichment significance testing in brain-phenotype association studies.

Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about spatial properties of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from a method widely used in genetics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose network enrichment significance testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks or other sub-regions of the brain. We apply NEST to study enrichment of associations with structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.

3D printed anatomical models for preoperative planning of complex orthopaedic surgical operations of the lower limb

Complex multifragmentary fractures of the pelvis and lower limb are a major challenge for operative orthopaedic surgery. The successful metallic osteosynthesis of the fractures requires an extensive planning process, which can be dramatically improved with the 3D printed anatomical models – replicas of the bones with high fidelity generated from CT and MRI imaging studies. The models represent the spatial properties of the skeleton with a dimensional error of approximately 8 μm/mm. They can be manufactured easily and with high reproducibility with commercial or open-access software and FDM 3D printing. Orthopaedic surgeons use the preoperative models as a highly accurate physical model of complex fractures and allow them to choose and prepare the optimal operation window, surgical tools, metallic implants, and as a template for recontouring (pre-bending) of fixation plates, which will be used during the surgery. The approach provides a new level of personalisation in operative orthopaedic surgery and significantly reduces the duration of the operation, the amount of blood loss and the intraoperative X-rays. The proper anatomical repositioning of the fracture is achieved at a higher rate in the surgeries, which are planned with 3D-printed anatomical models. The planning of surgical operations with 3D-printed models increases the overall effectiveness of the surgery, reduces the rate of post-surgical complications, and allows for a patient-specific approach.The paper will describe the methods for manufacturing accurate 3D-printed anatomical models representing complex fractures and their application for preoperative planning of orthopaedic operation.The anatomical 3D models were generated from CT datasets with open-access medical informatics software (3D Slicer) and 3D printed on an FDM 3D printer with minimal thermal deformation (Polylactate, PLA). The finished models were used for preoperative planning of complex orthopaedic operations, including high-energy multifragmentary hip, knee and ankle fractures. The preoperative planning included selecting surgical access, preparing tools and implants, and contouring (pre-bending) metal plates for metallic osteosynthesis. Several parameters, such as operation time, blood loss, intraoperative X-rays, and the achievement of anatomical reduction of the fractures, were observed in order to measure the quality of the operations.Accurate anthropomorphic 3D models representing the spatial properties of the bones can be generated from tomographic imaging studies easily and accurately, even with open-source software. They can be utilised as a tool for preoperatively planning complex orthopaedical operations of the lower limb. Using 3D-printed models allows a patient-specific approach, which leads to good anatomical reduction and favourable functional results in complex surgeries regarding the pelvis, acetabulum, tibial plateau, and calcaneus.The methods described in the paper are routinely used for the preoperative planning of complex orthopaedical operations regarding the lower limb. In the future, they will be combined with the implementation of 3D-printed personalised titanium implants to achieve good anatomical reduction even for the most challenging multigragmental fractures.In the paper, we described the technical aspects and clinical considerations for the preoperative planning of complex orthopaedical operations, which can assist engineers and clinicians alike in implementing the useful method in clinical practice.

The Influence of Oxidized Imino-Allantoin in the Presence of OXOG on Double Helix Charge Transfer: A Theoretical Approach.

The genome is continuously exposed to a variety of harmful factors that result in a significant amount of DNA damage. This article examines the influence of a multi-damage site containing oxidized imino-allantoin (OXIa) and 7,8-dihydro-8-oxo-2'-deoxyguanosine (OXOdG) on the spatial geometry, electronic properties, and ds-DNA charge transfer. The ground stage of a d[A1OXIa2A3OXOG4A5]*d[T5C4T3C2T1] structure was obtained at the M06-2X/6-D95**//M06-2X/sto-3G level of theory in the condensed phase, with the energies obtained at the M06-2X/6-31++G** level. The non-equilibrated and equilibrated solvent-solute interactions were also considered. Theoretical studies reveal that the radical cation prefers to settle on the OXOG moiety, irrespective of the presence of OXIa in a ds-oligo. The lowest vertical and adiabatic ionization potential values were found for the OXOG:::C base pair (5.94 and 5.52 [eV], respectively). Conversely, the highest vertical and adiabatic electron affinity was assigned for OXIaC as follows: 3.15 and 3.49 [eV]. The charge transfers were analyzed according to Marcus' theory. The highest value of charge transfer rate constant for hole and excess electron migration was found for the process towards the OXOGC moiety. Surprisingly, the values obtained for the driving force and activation energy of electro-transfer towards OXIa2C4 located this process in the Marcus inverted region, which is thermodynamically unfavorable. Therefore, the presence of OXIa can slow down the recognition and removal processes of other DNA lesions. However, with regard to anticancer therapy (radio/chemo), the presence of OXIa in the structure of clustered DNA damage can result in improved cancer treatment outcomes.

Sustainable Learning Process: Assessing the Effectiveness of Teaching Methodology by Analyzing Spatial and Temporal Properties of a Student as a Subject

Sustainable Learning Process: Assessing the Effectiveness of Teaching Methodology by Analyzing Spatial and Temporal Properties of a Student as a Subject

Intracellular "In Silico Microscopes"-Comprehensive 3D Spatio-Temporal Virus Replication Model Simulations.

Despite their small and simple structure compared with their hosts, virus particles can cause severe harm and even mortality in highly evolved species such as humans. A comprehensive quantitative biophysical understanding of intracellular virus replication mechanisms could aid in preparing for future virus pandemics. By elucidating the relationship between the form and function of intracellular structures from the host cell and viral components, it is possible to identify possible targets for direct antiviral agents and potent vaccines. Biophysical investigations into the spatio-temporal dynamics of intracellular virus replication have thus far been limited. This study introduces a framework to enable simulations of these dynamics using partial differential equation (PDE) models, which are evaluated using advanced numerical mathematical methods on leading supercomputers. In particular, this study presents a model of the replication cycle of a specific RNA virus, the hepatitis C virus. The diffusion-reaction model mimics the interplay of the major components of the viral replication cycle, including non structural viral proteins, viral genomic RNA, and a generic host factor. Technically, surface partial differential equations (sufPDEs) are coupled on the 3D embedded 2D endoplasmic reticulum manifold with partial differential equations (PDEs) in the 3D membranous web and cytosol volume. The membranous web serves as a viral replication factory and is formed on the endoplasmic reticulum after infection and in the presence of nonstructural proteins. The coupled sufPDE/PDE model was evaluated using realistic cell geometries based on experimental data. The simulations incorporate the effects of non structural viral proteins, which are restricted to the endoplasmic reticulum surface, with effects appearing in the volume, such as host factor supply from the cytosol and membranous web dynamics. Because the spatial diffusion properties of genomic viral RNA are not yet fully understood, the model allows for viral RNA movement on the endoplasmic reticulum as well as within the cytosol. Visualizing the simulated intracellular viral replication dynamics provides insights similar to those obtained by microscopy, complementing data from in vitro/in vivo viral replication experiments. The output data demonstrate quantitative consistence with the experimental findings, prompting further advanced experimental studies to validate the model and refine our quantitative biophysical understanding.

1H and 13C NMR spectra of infinitene and the ring current effect of the aromatic molecule.

The spatial magnetic properties (through-space NMR shieldings-TSNMRSs-actually the ring current effect in 1H NMR spectroscopy) of the recently synthesized infinitene (the helically twisted [12]circulene) have been calculated using the GIAO perturbation method employing the nucleus-independent chemical shift (NICS) concept and visualized as iso-chemical-shielding surfaces (ICSS) of various size and direction. Both 1H and 13C chemical shifts of infinitene and the aromaticity of this esthetically very appealing molecule have been studied subject to the ring current effect thus obtained. This spatial magnetic response property of TSNMRSs dominates the different magnitude of 1H and 13C chemical shifts, especially in the cross-over section of infinitene, which is unequivocally classified as an aromatic molecule based on the deshielding belt of its ring current effect. Differences in aromaticity of infinitene compared with isolated benzene can also be qualified and quantified on the magnetic criterion.

SpaNCMG: improving spatial domains identification of spatial transcriptomics using neighborhood-complementary mixed-view graph convolutional network.

The advancement of spatial transcriptomics (ST) technology contributes to a more profound comprehension of the spatial properties of gene expression within tissues. However, due to challenges of high dimensionality, pronounced noise and dynamic limitations in ST data, the integration of gene expression and spatial information to accurately identify spatial domains remains challenging. This paper proposes a SpaNCMG algorithm for the purpose of achieving precise spatial domain description and localization based on a neighborhood-complementary mixed-view graph convolutional network. The algorithm enables better adaptation to ST data at different resolutions by integrating the local information from KNN and the global structure from r-radius into a complementary neighborhood graph. It also introduces an attention mechanism to achieve adaptive fusion of different reconstructed expressions, and utilizes KPCA method for dimensionality reduction. The application of SpaNCMG on five datasets from four sequencing platforms demonstrates superior performance to eight existing advanced methods. Specifically, the algorithm achieved highest ARI accuracies of 0.63 and 0.52 on the datasets of the human dorsolateral prefrontal cortex and mouse somatosensory cortex, respectively. It accurately identified the spatial locations of marker genes in the mouse olfactory bulb tissue and inferred the biological functions of different regions. When handling larger datasets such as mouse embryos, the SpaNCMG not only identified the main tissue structures but also explored unlabeled domains. Overall, the good generalization ability and scalability of SpaNCMG make it an outstanding tool for understanding tissue structure and disease mechanisms. Our codes are available at https://github.com/ZhihaoSi/SpaNCMG.

Uncovering mangrove range limits using very high resolution satellite imagery to detect fine‐scale mangrove and saltmarsh habitats in dynamic coastal ecotones

AbstractMangroves are important ecosystems for coastal biodiversity, resilience and carbon dynamics that are being threatened globally by human pressures and the impacts of climate change. Yet, at several geographic range limits in tropical–temperate transition zones, mangrove ecosystems are expanding poleward in response to changing macroclimatic drivers. Mangroves near range limits often grow to smaller statures and form dynamic, patchy distributions with other coastal habitats, which are difficult to map using moderate‐resolution (30‐m) satellite imagery. As a result, many of these mangrove areas are missing in global distribution maps. To better map small, scrub mangroves, we tested Landsat (30‐m) and Sentinel (10‐m) against very high resolution (VHR) Planet (3‐m) and WorldView (1.8‐m) imagery and assessed the accuracy of machine learning classification approaches in discerning current (2022) mangrove and saltmarsh from other coastal habitats in a rapidly changing ecotone along the east coast of Florida, USA. Our aim is to (1) quantify the mappable differences in landscape composition and complexity, class dominance and spatial properties of mangrove and saltmarsh patches due to image resolution; and (2) to resolve mapping uncertainties in the region. We found that the ability of Landsat to map mangrove distributions at the leading range edge was hampered by the size and extent of mangrove stands being too small for detection (50% accuracy). WorldView was the most successful in discerning mangroves from other wetland habitats (84% accuracy), closely followed by Planet (82%) and Sentinel (81%). With WorldView, we detected 800 ha of mangroves within the Florida range‐limit study area, 35% more mangroves than were detected with Planet, 114% more than Sentinel and 537% more than Landsat. Higher‐resolution imagery helped reveal additional variability in landscape metrics quantifying diversity, spatial configuration and connectedness among mangrove and saltmarsh habitats at the landscape, class and patch scales. Overall, VHR satellite imagery improved our ability to map mangroves at range limits and can help supplement moderate‐resolution global distributions and outdated regional maps.

Evaluation of microstructure heterogeneity in INCONEL 625 thin-wall fabricated by Laser Powder Bed Fusion additive manufacturing

This study provides a thorough investigation of microstructural heterogeneity and its relationship to the mechanical properties of Inconel 625 (IN625) alloy in as-built Laser Powder Bed Fusion (LPBF) parts. Specifically, detailed high-resolution electron microscopy coupled with thermodynamic simulation, finite element analysis, and nano-hardness testing were carried out. Through the combination of these methods, a new method is proposed to develop a process-structure–property map to illustrate the complex relationship between spatial thermal conditions, microstructure, and mechanical properties in various areas of an LPBF IN625 sample. Different precipitates, including γ′′, Laves, NbC, and Al2O3 oxide were observed along the build direction from the bottom to the top layers of the LPBF part as a function of build height. The hardness value was shown to be minimum in the middle layers while attaining maximum values in the bottom (near the substrate) and top (near the surface) layers of the build’s height. The mechanism for the formation of precipitates during multi-layer additive manufacturing and the dependency of the mechanical properties on the type and distribution of these precipitates are discussed.

Laboratory investigation of the spatial thermodynamic properties of the bridge approach in the permafrost region

Laboratory investigation of the spatial thermodynamic properties of the bridge approach in the permafrost region

DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability

Therapeutic antibody development faces challenges due to high viscosities and aggregation tendencies. The spatial charge map (SCM) and spatial aggregation propensity (SAP) are computational techniques that aid in predicting viscosity and aggregation, respectively. These methods rely on structural data derived from molecular dynamics (MD) simulations, which are computationally demanding. DeepSCM, a deep learning surrogate model based on sequence information to predict SCM, was recently developed to screen high-concentration antibody viscosity. This study further utilized a dataset of 20,530 antibody sequences to train a convolutional neural network deep learning surrogate model called Deep Spatial Properties (DeepSP). DeepSP directly predicts SAP and SCM scores in different domains of antibody variable regions based solely on their sequences without performing MD simulations. The linear correlation coefficient between DeepSP scores and MD-derived scores for 30 properties achieved values between 0.76 and 0.96 with an average of 0.87. DeepSP descriptors were employed as features to build machine learning models to predict the aggregation rate of 21 antibodies, and the performance is similar to the results obtained from the previous study using MD simulations. This result demonstrates that the DeepSP approach significantly reduces the computational time required compared to MD simulations. The DeepSP model enables the rapid generation of 30 structural properties that can also be used as features in other research to train machine learning models for predicting various antibody stability using sequences only. DeepSP is freely available as an online tool via https://deepspwebapp.onrender.com and the codes and parameters are freely available at https://github.com/Lailabcode/DeepSP.

The resolution of face perception varies systematically across the visual field.

Visual abilities tend to vary predictably across the visual field-for simple low-level stimuli, visibility is better along the horizontal vs. vertical meridian and in the lower vs. upper visual field. In contrast, face perception abilities have been reported to show either distinct or entirely idiosyncratic patterns of variation in peripheral vision, suggesting a dissociation between the spatial properties of low- and higher-level vision. To assess this link more clearly, we extended methods used in low-level vision to develop an acuity test for face perception, measuring the smallest size at which facial gender can be reliably judged in peripheral vision. In 3 experiments, we show the characteristic inversion effect, with better acuity for upright faces than inverted, demonstrating the engagement of high-level face-selective processes in peripheral vision. We also observe a clear advantage for gender acuity on the horizontal vs. vertical meridian and a smaller-but-consistent lower- vs. upper-field advantage. These visual field variations match those of low-level vision, indicating that higher-level face processing abilities either inherit or actively maintain the characteristic patterns of spatial selectivity found in early vision. The commonality of these spatial variations throughout the visual hierarchy means that the location of faces in our visual field systematically influences our perception of them.

Inerter-controlled topological interface states in locally resonant lattices with beyond-nearest neighbor coupling

This paper explores the emergence of topological interface states in one-dimensional locally resonant lattices incorporating inerters in both nearest neighbor (NN) and beyond-nearest neighbor (BNN) coupling. The investigation focuses on the unique wave propagation characteristics of these lattices, particularly the presence and behavior of interface states. The non-trivial topological behavior due to broken inversion symmetry within the unit cell of the locally resonant lattice is comprehensively investigated in the presence of inerters in NN and BNN coupling. The emerging interface states in the supercell analysis exhibit specific spatial and frequency localization properties due to inerter-based BNN interactions. Additionally, the study demonstrates the ability of inerter elements with weak inertance to control the frequency of interface states while maintaining the fundamental topological properties of the lattice. The identified topological interface states in lattices with BNN coupling present an opportunity for designing diverse devices, such as waveguides, filters, sensors, and energy harvesting systems. Overall, this research enhances our comprehension of topological phenomena in inerter-based locally resonant lattices with BNN interactions and introduces possibilities for creating robust and versatile devices based on topologically protected edge/interface states.

Метафоризация детерминативных глагольных композитов с параметрическими компонентами “hoch” и “nieder” в немецком языке

This paper deals with a study of the metaphorical meanings represented by complex verbal phrases with a definitional component of a parametric adjective. The research aims to reveal the associative and imaginative properties of parametric adjectives, on the basis of which metaphorical transfers are formed. The scientific novelty of the research lies in the fact that spatial properties of German adjectives “hoch” and “nieder” are considered for the first time through the prism of figurative perception of space. This inspires us to broaden our knowledge not only about the nominal aspects of these phrases but also how the size attributes of material objects are being restructured in the mindset of a human and how certain abstract phenomena and notions are cognized in a parametric plan. The study detected that the parametric adjectives in the analyzed composite verbs vary largely with the degree of their semantic metaphorization. The core of the imagery of these units contains the re-conceptualization (re-thinking) of spatial relations, which leads to the appearance of new properties and attributes of imagination used to nominate non-spatial sense categories.

Inferring failure coupling strength in complex networks through generative models

ABSTRACT The failure propagation process has always been a research hotspot in the field of complex system reliability. Most of the research studies focus on the inference of network structure, ignoring one of the most critical parameters for network reliability, failure coupling strength. Here, we develop a generative model describing the failure process to infer the failure coupling strength. Using this model, we generate the node failure sequences under different coupling strength and analyze the spatial properties of failure propagation. We find the failed nodes tend to appear near the previously failed nodes with increasing coupling strength. Based on our generative model, we propose a Bayesian inference method to infer the failure coupling strength from observed system instantaneous failure state data. We find the inferred values are close to the true values for small failure coupling strength. As failure coupling strength increases, error comes from the limits of failure propagation spatial data. We apply this Bayesian inference method to actual biological networks and infer the network failure coupling strength. Our proposed Bayesian inference method helps analyze the hidden failure mechanism from actual scenarios.

Neural processing in the primary auditory cortex following cholinergic lesions of the basal forebrain in ferrets

Cortical acetylcholine (ACh) release has been linked to various cognitive functions, including perceptual learning. We have previously shown that cortical cholinergic innervation is necessary for accurate sound localization in ferrets, as well as for their ability to adapt with training to altered spatial cues. To explore whether these behavioral deficits are associated with changes in the response properties of cortical neurons, we recorded neural activity in the primary auditory cortex (A1) of anesthetized ferrets in which cholinergic inputs had been reduced by making bilateral injections of the immunotoxin ME20.4-SAP in the nucleus basalis (NB) prior to training the animals. The pattern of spontaneous activity of A1 units recorded in the ferrets with cholinergic lesions (NB ACh–) was similar to that in controls, although the proportion of burst-type units was significantly lower. Depletion of ACh also resulted in more synchronous activity in A1. No changes in thresholds, frequency tuning or in the distribution of characteristic frequencies were found in these animals. When tested with normal acoustic inputs, the spatial sensitivity of A1 neurons in the NB ACh– ferrets and the distribution of their preferred interaural level differences also closely resembled those found in control animals, indicating that these properties had not been altered by sound localization training with one ear occluded. Simulating the animals’ previous experience with a virtual earplug in one ear reduced the contralateral preference of A1 units in both groups, but caused azimuth sensitivity to change in slightly different ways, which may reflect the modest adaptation observed in the NB ACh– group. These results show that while ACh is required for behavioral adaptation to altered spatial cues, it is not required for maintenance of the spectral and spatial response properties of A1 neurons.

Modulation of the neuronal response in human primary visual cortex by re-entrant projections during retinal input processing as manifest in the visual evoked potential

Initial deflections in the visual evoked potential (VEP) reflect the neuronal process of extracting features from the retinal input; a process not modulated by re-entrant projections. Later deflections in the VEP reflect the neuronal process of combining features into an object, a process referred to as ‘object closure’ and modulated by re-entrant projections. Our earlier work indicated that the VEP reflects independent neuronal responses processing temporal – and spatial luminance contrast and that these responses arise from an interaction between forward and re-entrant input. In this earlier work, changing the temporal luminance contrast property of a stimulus altered its spatial luminance contrast property. We recorded the VEP in 12 volunteers viewing image pairs of a windmill, regular dartboard or an RMS dartboard rotated by either Π/4, Π/2, 3Π/4 or Π radians with respect to each other. The windmill and regular dartboard had identical white to black ratio, while the two dartboards identical contrast edges per unit area. Rotation varied temporal luminance contrast of a stimulus without affecting its spatial luminance contrast. N75, P100, N135 and P240 amplitude and latency were compared and a source localisation and temporal frequency analysis performed. P100 amplitude signals a neuronal response processing temporal luminance contrast that is modulated by re-entrant projections with fast axonal conduction velocities. N135 and P240 signal the neuronal response processing spatial luminance contrast and is modulated by re-entrant projections with slow axonal conduction velocities. The dorsal stream is interconnected by fast axonal conduction velocities, the ventral stream by slow axonal conduction velocities.

Investigating the mechanisms of sustainable urban land renewal: A perspective of spatial development rights

Urban renewal, as an important means of promoting sustainable urbanization in the stock era, is essentially a process of redistributing land appreciation benefits caused by spatial development rights. However, current literature mostly analyzes the traditional two-dimensional renewal model from the perspective of market mechanisms, which leads to the inability of urban land renewal mechanisms to adapt to sustainable urban development. Therefore, taking Shenzhen as an example, our study aims to understand the spatial mechanism of urban land renewal from a three-dimensional perspective of spatial development rights. Using geographically weighted regression and two-stage least squares regression, we found that the objective of value analysis has shifted from the common minimum unit of land to local space. Moreover, affected by the surrounding environment and various supporting public service facilities, the existence of spatial heterogeneity leads to significant differences in the value of the different functional space within same geographical ranges. The influence intensity of economy, society, and environment shows a decreasing pattern in space from the center of the region to the outer circle of the region. We not only construct a sustainable urban land renewal framework based on three-dimensional spatial property rights, but also distinguish the mechanisms and endogenous issues for different functional spaces. Our study could make a great difference in adjustment of urban land development rights and value acquisition.