Spatial Structure Research Articles

Cloning and functional characterization of the pinoresinol-lariciresinol reductase gene IiPLR2 in Isatis indigotica

The pinoresinol-lariciresinol reductase (PLR), a crucial enzyme in the biosynthesis of lignans in plants, catalyzes a two-step reaction to produce lariciresinol and secoisolariciresinol. Lignans such as lariciresinol are the effective components of traditional Chinese medicine Radix Isatidis in exerting antiviral activity. In order to study the function of the key enzyme PLR in the biosynthesis of lariciresinol in Isatis indigotica, the original plant of Radix Isatidis, IiPLR2 was cloned from I. indigotica, with a full length of 954 bp, encoding 317 amino acids. Multiple sequence alignment showed that IiPLR2 contained a conserved nicotinamide adenine dinucleotide phosphate (NADPH)-binding motif. The phylogenetic tree showcased that IiPLR2 shared the same clade with AtPrR1 from Arabidopsis thaliana. The prokaryotic expression vector pET32a-IiPLR2 was constructed and then transformed into Escherichia coli BL21(DE3) competent cells for protein expression. The purified enzyme IiPLR2 could catalyze the conversion of pinoresinol to lariciresinol and the conversion of lariciresinol to secoisolariciresinol. The cloning, sequencing, and catalytic functional analysis of IiPLR2 in this study enrich the understanding of this kind of functional proteins in I. indigotica and supplement the biosynthesis pathways of lignans. Moreover, this study provides a functional module for further research on metabolic regulation and synthetic biology and lays a foundation for comprehensively revealing the relationship between the spatial structures and catalytic functions of such proteins.

Spatial information allows inference of the prevalence of direct cell-to-cell viral infection.

The role of direct cell-to-cell spread in viral infections-where virions spread between host and susceptible cells without needing to be secreted into the extracellular environment-has come to be understood as essential to the dynamics of medically significant viruses like hepatitis C and influenza. Recent work in both the experimental and mathematical modelling literature has attempted to quantify the prevalence of cell-to-cell infection compared to the conventional free virus route using a variety of methods and experimental data. However, estimates are subject to significant uncertainty and moreover rely on data collected by inhibiting one mode of infection by either chemical or physical factors, which may influence the other mode of infection to an extent which is difficult to quantify. In this work, we conduct a simulation-estimation study to probe the practical identifiability of the proportion of cell-to-cell infection, using two standard mathematical models and synthetic data that would likely be realistic to obtain in the laboratory. We show that this quantity cannot be estimated using non-spatial data alone, and that the collection of a data which describes the spatial structure of the infection is necessary to infer the proportion of cell-to-cell infection. Our results provide guidance for the design of relevant experiments and mathematical tools for accurately inferring the prevalence of cell-to-cell infection in in vitro and in vivo contexts.

The hospitalisation risk of chronic circulatory and respiratory diseases associated with coal mining in the general population in Queensland, Australia

Queensland is the main coal mining state in Australia where populations in coal-mining areas have been historically exposed to coal mining emissions. Although a higher risk of chronic circulatory and respiratory diseases has been associated with coal mining globally, few studies have investigated these associations in the Queensland general population. This study estimates the association of coal production with hospitalisations for chronic circulatory and respiratory diseases in Queensland considering spatial and temporal variations during 1997–2014.An ecological analysis used a Bayesian hierarchical spatiotemporal model to estimate the association of coal production with standardised rates of each, chronic circulatory and respiratory diseases, adjusting for sociodemographic factors and considering the spatial structure of Queensland's statistical areas (SA2) in the 18-year period. Two specifications; with and without a space-time interaction effect were compared using the integrated nested Laplace approximation -INLA approach. The posterior mean of the best fit model was used to map the spatial, temporal and spatiotemporal trends of risk.The analysis considered 2,831,121 hospitalisation records. Coalmining was associated with a 4 % (2.4—5.5) higher risk of hospitalisation for chronic respiratory diseases in the model with a space-time interaction effect which had the best fit. An emerging higher risk of either chronic circulatory and respiratory diseases was identified in eastern areas and some coal-mining areas in central and southeast Queensland. There were important disparities in the spatiotemporal trend of risk between coal -and non-coalmining areas for each, chronic circulatory and respiratory diseases.Coalmining is associated with an increased risk of chronic respiratory diseases in the Queensland general population. Bayesian spatiotemporal analyses are robust methods to identify environmental determinants of morbidity in exposed populations. This methodology helps identifying at-risk populations which can be useful to support decision-making in health. Future research is required to investigate the causality links between coalmining and these diseases.

The whole-process tracking method of stable state transformation for multistable tensegrity based on Levenberg-Marquardt method

The tensegrity structure is prestressed spatial structure composed of rods and cables. Its multistable phenomenon brings great research potential in energy storage and robotic. In order to completely analyze the changes of configurations and mechanical property in stable state transformation (SST) processes of multistable tensegrity structures, a whole-process tracking (WPT) method based on Levenberg-Marquardt method is proposed. Three numerical examples are calculated, and the WPT is successfully realized, which proves the feasibility and universality of proposed WPT method. According to the calculation results, the variation of the driving force and elastic strain energy during SST are further summarized. Alternatively, different types of driving-paths are investigated, the results show that mechanical property during SST could be adjusted effectively by modifying the driving-path. The proposed WPT method and analysis results provides insights for designing and implementing multistable tensegrity structures.

Nonlinear oscillation of elastic sagged cable using refined mode truncation: A comparative study

A nonlinear sagged cable, due to its initial curvature, leads to various challenges of empirical mode truncation used by routine Galerkin method when constructing reduced-order model. It is recently elucidated that ( Guo and Rega, 2023a ), the key for refined mode truncation (and thus for correct nonlinear dynamics prediction) is to first eliminate low-order nonlinear terms of spatial continuous structures. This paper focuses on refined truncation of nonlinear sagged cable by leveraging the recent low-order elimination perspective, which is realized by a normal form development. Further comparative studies for both primary resonant and two-to-one internally resonant dynamics of the sagged cable, including nonlinear frequency responses, backbone curves, and Poincaré mapping, demonstrate notable differences between the two different types of models built by either routine or refined truncation, which confirms necessity of the refined mode truncation used for geometrically nonlinear structures like sagged cables.

On Evaluation of Spatial Structure based on Sound Localization

On Evaluation of Spatial Structure based on Sound Localization

Predicting the Integrated Fire Resistance of Wildland–Urban Interface Plant Communities by Spatial Structure Analysis Learning for Shanghai, China

Abstract: Fire is a prevalent hazard that poses a significant risk to public safety and societal progress. The continuous expansion of densely populated urban areas, exacerbated by global warming and the increasing intensification of urban heat islands, has led to a notable increase in the frequency and severity of fires worldwide. Incorporating measures to withstand different types of calamities has always been a crucial aspect of urban infrastructure. Well-designed plant communities play a pivotal role as a component of green space systems in addressing climate-related challenges, effectively mitigating the occurrence and spread of fires. This study conducted field research on 21 sites in the green belt around Shanghai, China, quantifying tree morphological indexes and coordinate positions. The spatial structure attributes of different plant communities were analyzed by principal component analysis, CRITIC weighting approach, and stepwise regression analysis to build a comprehensive fire resistance prediction model. Through this research, the relationship between community spatial structures and fire resistance was explored. A systematic construction of a prediction model based on community spatial structures for fire resistance was undertaken, and the fire resistance performance could be quickly judged by easily measured tree morphological indexes, providing valuable insights for the dynamic prediction of fire resistance. According to the evaluation and ranking conducted by the prediction model, the Celtis sinensis, Sapindus saponaria, Osmanthus fragrans, Koelreuteria paniculata, and Distylium racemosum + Populus euramericana ‘I-214’ communities exhibited a high level of fire resistance. On the other hand, the Koelreuteria bipinnata + Ligustrum lucidum, Ginkgo biloba + Camphora officinarum + Ligustrum lucidum, and Ligustrum lucidum + Sapindus saponaria communities obtained lower scores and were positioned lower in the ranking. It is emphasized that the integration of monitoring and regulation is essential to ensure the ecological integrity and well-being of green areas in the Wildland–Urban Interface.

Stand spatial structural diversity: Developing and validating a novel index

Numerous studies related to the diversity-productivity relationships have mainly focused on the effects of species diversity, function diversity, and nonspatial structural diversity (e.g., variations of diameter), but have overlooked the significant roles of spatial structural diversity. Therefore, the motivation of the present study was to develop a comprehensive index of stand spatial structural diversity (H′) by integrating the strengths of the Shannon-Wiener index and the quaternion joint probability distribution of commonly used neighborhood-based parameters, namely the uniform angle index (W), mingling index (M), diameter dominance index (U) and crowdedness index (C). The application abilities of the index were tested using ten 100 m ×100 m sample plots that were distributed in three different climate zones [4 plots in Greater Khingan Mountains (GKM), 2 plots in Lesser Khingan Mountains (LKM), 4 plots in Changbai Mountains (CBM)] in Heilongjiang Province, northeast China. Meanwhile, the potential driving factors on stand spatial structural diversity were also detected using stepwise regression and hierarchical partitioning techniques based on 3000 simulated stands. The results indicated that the average values of H′ at GKM, LKM, and CBM were 0.8436, 0.8079, and 0.8098, suggesting that the stand spatial structural diversities of the forests in GKM were notably higher than those in LKM and CBM. The characteristics of M, C, and W emerged as the three most influential factors driving stand spatial structural diversity. This study contributes significantly not only by providing an effective index for measuring stand spatial structural diversity, but also by streamlining the complex decision processes associated with optimizing stand spatial structure.

Supramolecular chemistry in cyclodextrin inclusion complexes: The formation rules of terpenes/β-cyclodextrin inclusion complexes

Supramolecular chemistry of cyclodextrins (CDs) inclusion complexes is a hot research topic at present, but current studies often focus on the host-guest interaction between CDs and hydrophobic substances, while ignoring the self-assembly behavior of the host and guest during the inclusion process. Based on the spatial structures, six representative terpenes were selected in this study, and the stoichiometric ratio between terpenes and β-cyclodextrin (β-CD) was investigated by isothermal titration calorimetry (ITC) method. Meanwhile, the constructed inclusion complexes (ICs) were characterized by nuclear magnetic resonance, X-ray diffraction, scanning electron microscope, etc. On this basis, the assembly process of β-CD and terpenes was investigated firstly by molecular simulation method, and the inclusion process between β-CD and terpenes was further studied in terms of the formation of individual ICs and ICs aggregates. Results showed that hydrophobic interaction and van der Waals force were the main driving forces of the reaction between terpenes and β-CD, and the formation of ICs, β-CD molecule aggregation, terpene aggregation, and ICs aggregation occurred simultaneously during the inclusion process of terpenes and β-CD. To sum up, this study may provide certain thinking and reference for the subsequent supramolecular chemical research on CDs.

Growth Motivation of Urban Agglomerations in Multiscale Spatial Structures from the Perspective of Synergy Theory

Growth Motivation of Urban Agglomerations in Multiscale Spatial Structures from the Perspective of Synergy Theory

Image-based machine learning and cluster analysis for urban road network: employing Orange for codeless visual programming

ABSTRACT In the pursuit of sustainable urban development, it is imperative to address strategies for optimizing and harmonizing the configuration of urban spatial layouts, especially in response to rapid urbanization. Urban road networks play a pivotal role in shaping intricate urban spatial patterns as they intricately weave throughout the cityscape, significantly influencing land utilization, spatial arrangements, and the mobility of residents. The urban environment is a complex system teeming with multifaceted human activities. With the continuous evolution of advanced data and computational technologies, it is now feasible to construct intricate models for an in-depth exploration of urban spatial structures. In this study, we constructed an analytical framework that leveraged road network image recognition, multi-scale influence factors analysis, and predictions using codeless visual programming. Within this framework, deep learning and unsupervised clustering are applied to complete the hierarchical clustering of road networks and exploit the intrinsic association between cities. Meanwhile, hierarchical clustering and natural and humanistic attributes of cities are connected to investigate the strength of different attributes on specific clusters. The research has the following results: (1) In addition to the alignment of urban road network clustering outcomes, quantitative unevenness and geographical heterogeneity are also observed. (2) Multiple attributes have a complicated impact on urban space, including city size, and socioeconomic and geographic characteristics. Among these factors, socio-economic variables emerge as the principal drivers influencing urban spatial structure. (3) The research enhances the model’s generalization capability and predictive accuracy. As attributes increase and model learning improves, both the Area Under Curve (AUC) and F-1 scores exhibit continuous enhancements, ultimately achieving error-free predictions. The study’s findings offer valuable insights into urban spatial structures and systems, supporting urban decision-makers in addressing challenges associated with urban growth.

Higher‐order dissimilarity in biodiversity: Identifying dissimilarities of spatial or temporal dissimilarity structures

Higher‐order dissimilarity in biodiversity: Identifying dissimilarities of spatial or temporal dissimilarity structures

Self-shaping free-form spatial structure with large-deformable bunched CFRP rods

Based on the bending-active concept, this paper presents a self-shaping system with bunched carbon fiber-reinforced polymer (CFRP) rods to construct complex curved surfaces, establishing an overall design and construction process. Such component consists of a bunch of slender and easily bendable CFRP rods that are preformed and then bounded by connecting joints. Then, those components form free-form spatial structures. The proposed system offers several advantages: strong designability, low transportation cost, short construction period, polished appearance and low maintenance requirements during service. For component design, to determine the shape of such component, a two-stage mechanical analysis is proposed based on the large deformation theory and finite element analysis (FEA): a form-finding process to determine the deformed component shape through its cross-sections and boundary-load conditions; and a reverse form-finding process to obtain these above-mentioned parameters from a given component shape. The reliability of both methods is verified through in-plane bending tests. A design demonstration of a saddle-shaped curved spatial structure is performed using this method. Overall, the proposed system can guide the actual construction projects of complex curved surfaces with self-shaping bunched CFRP rods.

Unerfüllte Behandlungswünsche jüngerer und älterer Menschen in sozial deprivierten Räumen: eine qualitative Interviewstudie im Ruhrgebiet

IntroductionUnmet health care needs are considered a key indicator of equity in access to health care. For younger people, they can lead to poorer health outcomes in adulthood, for older people, they are associated with an increased risk of mortality. Unmet needs were therefore investigated as part of a research project on „Improving the health-related life situation of young and old people in the Ruhr area.“ MethodUnmet health care needs were surveyed with the help of semi-structured guideline interviews with younger and older people in the Ruhr area (n=29). Due to the spatial and social structure of the study region, the aim was to recruit especially people with a low subjective social status (SSS) for the study. The interviews were recorded, transcribed, and subjected to a qualitative content analysis and a supplementary frequency analysis. The reporting is based on the Standards for Qualitative Research (SRQR). ResultsThe respondents reported deficiencies in treatment and doctor-patient communication with GPs and specialists. The feeling of not being taken seriously due to age is cited several times by both age groups as a reason for unmet health care needs. Waiting times are particularly relevant in the case of specialist consultations, while co-payments for services mainly play a role in dentistry and orthodontics. Unmet needs are primarily reported by people with a medium SSS. Overall, almost two thirds of the respondents stated that they have or have had at least one unmet need. Participants with a low SSS report fewer unmet needs and do not mention any financial aspects as a cause. Explanations for this range from lower health literacy to the fear of being discredited. ConclusionsThe results provide indications of deficits in regional health care provision for younger and older people in the Ruhr region. Corresponding fields of action result from the areas of care and reasons identified as relevant for unmet health care needs. The reported findings also provide a basis for differentiated quantitative surveys with representative samples.

Specific glycine-dependent enzyme motion determines the potency of conformation selective inhibitors of threonyl-tRNA synthetase

The function of proteins depends on their correct structure and proper dynamics. Understanding the dynamics of target proteins facilitates drug design and development. However, dynamic information is often hidden in the spatial structure of proteins. It is important but difficult to identify the specific residues that play a decisive role in protein dynamics. Here, we report that a critical glycine residue (Gly463) dominates the motion of threonyl-tRNA synthetase (ThrRS) and the sensitivity of the enzyme to antibiotics. Obafluorin (OB), a natural antibiotic, is a novel covalent inhibitor of ThrRS. The binding of OB induces a large conformational change in ThrRS. Through five crystal structures, biochemical and biophysical analyses, and computational simulations, we found that Gly463 plays an important role in the dynamics of ThrRS. Mutating this flexible residue into more rigid residues did not damage the enzyme’s three-dimensional structure but significantly improved the thermal stability of the enzyme and suppressed its ability to change conformation. These mutations cause resistance of ThrRS to antibiotics that are conformationally selective, such as OB and borrelidin. This work not only elucidates the molecular mechanism of the self-resistance of OB-producing Pseudomonas fluorescens but also emphasizes the importance of backbone kinetics for aminoacyl-tRNA synthetase-targeting drug development.

Exploring sensitive area in the whole pacific for two types of El Niño predictions and their implication for targeted observations

Based on the initial errors in the whole Pacific that are most likely to affect the predictions of two types of El Niño events, the sensitive area of ocean temperature in the whole Pacific for El Niño prediction starting from January is identified by using the Geophysical Fluid Dynamic Laboratory CM2P1, a fully coupled global climate model. The error growth analysis and the numerical experiments illustrated that, the initial ocean temperature in the Victoria mode (VM) region in the North Pacific will affect the intensity predictions of the CP-El Niño while that in the subsurface layer of the west equatorial Pacific and the surface layer of southeast Pacific will modulate the spatial structure predictions of CP-El Niño. But for EP-El Niño, the former plus the surface layer of the equatorial central eastern pacific will modulate the spatial structure predictions of the event while the latter is shown to be more effective in predictions of the intensity of the event. Furthermore, if targeted observations are conducted in the sensitive area of the whole Pacific, the El Niño prediction skills, including intensity and spatial structure predictions, could be greatly improved for both EP- and CP-El Niño events. Neither the sensitive area of subtropical Pacific nor the tropical Pacific can be precluded as accurate indicators when forecasting particular flavors and the intensity of El Niño events.

Diffractionless transmission of optical beams through a four-level atomic system affected by a plasmonic nanostructure

A nondiffracting propagation of an optical beam through a four-level double-V-type quantum system near a plasmonic nanostructure is investigated. We study the linear absorption and dispersion properties of the quantum system as it interacts with two laser fields. We discuss the effect of the control beam with a Laguerre-Gaussian (LG) profile on the focusing of the probe beam in the presence of a plasmonic nanostructure. An appropriately selected control beam excites one transition of the atomic system and generates a spatially varying refraction index modulation for a weak probe beam that couples to the other transition. We demonstrate that placing a plasmonic nanostructure at a nanometer distance from the atomic system and using a control field with the spatial structure leads to the diffraction-less propagation of the probe beam through the atomic system. Also, it is shown that the optical properties and probe beam focusing can be controlled by adjusting the distance of the plasmonic nanostructure from the atomic system. The proposed all-optical waveguide with high contrast and transmission can be used to implement applications such as image transfer through the medium and image processing.

The Spread of Tibetan Buddhism in Mongolia from the 16th to the 17th Century: The Spatial Formation of the World Heritage Site Erdene Zuu Monastery

Erdene Zuu is the oldest extant Buddhist temple in the country of Mongolia, founded following the reintroduction of Tibetan Buddhism to Inner Mongolia in the sixteenth century. The subject of this paper is the building activities of the sixteenth and seventeenth centuries, particularly of the complex centering on Gurban Zuu (Three Temples), which are the main buildings of Erdene Zuu. The author first confirms Gurban Zuu’s ground plan based on measurements, and then interprets the “black-ink inscription” discovered on the ridge purlin of the Central Buddha Hall. This complex is then compared with Inner Mongolian Buddhist temples of the same period. Finally, the author studies whether or not the spatial structure of the temple architecture of the Mongolian Empire of the thirteenth and fourteenth centuries was continued at Erdene Zuu, and analyzes the position that Erdene Zuu occupied in the Tibetan Buddhist sphere. This comparative study investigates the origins of Erdene Zuu’s architectural spatial composition within East Asia.

Neuroimaging meta regression for coordinate based meta analysis data with a spatial model.

Coordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In this article, we propose a generative coordinate-based meta-regression (CBMR) framework to approximate a smooth activation intensity function and investigate the effect of study-level covariates (e.g. year of publication, sample size). We employ a spline parameterization to model the spatial structure of brain activation and consider four stochastic models for modeling the random variation in foci. To examine the validity of CBMR, we estimate brain activation on 20 meta-analytic datasets, conduct spatial homogeneity tests at the voxel level, and compare the results to those generated by existing kernel-based and model-based approaches. Keywords: generalized linear models; meta-analysis; spatial statistics; statistical modeling.

Exploring the spatial trade-off effects of green space on older people’s physical inactivity: Evidence from Shanghai

Given that green space patterns and population density often show opposing trends over space, this could lead to trade-off (distance/population decay) relationships. Consequently, this paper utilized the Study on Global Ageing and Adult Health (SAGE) data from Shanghai, China, to investigate whether there is a spatial trade-off in the health effects of green spaces on people’s inactivity. Our findings confirmed the trade-off effects of green spaces in reducing sedentary time, using interaction terms to highlight how the health impacts of green spaces vary across different population densities and may thus lead to inconsistent relationships between green space and health outcomes. Generally, the ability of green spaces to affect sedentary time decreased as population density increases, particularly regarding park proximity and the Normalized Difference Vegetation Index (NDVI). Our research also revealed that the trade-off in health benefits from green spaces was influenced by the type of green space. For instance, park proximity, park area, and NDVI showed trade-off effects, with higher population densities enhancing the impact of NDVI on increasing sedentary time (β = 133.871), despite NDVI itself decreasing with a rise in population density (r = -0.68). Conversely, the Green Vegetation Index (GVI) did not demonstrate such an effect. The unequal spatial distribution and composition of green space may further exacerbate health inequities. Therefore, this paper recommends that future provisioning of green spaces should take into account the local urban spatial structure.