Spatial Analysis Research Articles

Is the TCA cycle malate dehydrogenase-citrate synthase metabolon an illusion?

This review discusses the intriguing yet controversial concept of metabolons, focusing on the malate dehydrogenase-citrate synthase (MDH-CISY) metabolon as a model. Metabolons are multienzyme complexes composed of enzymes that catalyze sequential reactions in metabolic pathways. Metabolons have been proposed to enhance metabolic pathway efficiency by facilitating substrate channeling. However, there is skepticism about the presence of metabolons and their functionality in physiological conditions in vivo. We address the skepticism by reviewing compelling evidence supporting the existence of the MDH-CISY metabolon and highlighting its potential functions in cellular metabolism. The electrostatic interaction between MDH and CISY and the intermediate oxaloacetate, channeled within the metabolon, has been demonstrated using various experimental techniques, including protein-protein interaction assays, isotope dilution studies, and enzyme coupling assays. Regardless of the wealth of in vitro evidence, further validation is required to elucidate the functionality of MDH-CISY metabolons in living systems using advanced structural and spatial analysis techniques.

Exploring spatial analysis of the voting patterns in the Afghanistan president elections of 2019

Exploring spatial analysis of the voting patterns in the Afghanistan president elections of 2019

The impact of regional disparities on the availability of meningococcal vaccines in the US

BackgroundIn the United States (US), three types of vaccines are available to prevent invasive meningococcal disease (IMD), a severe and potentially fatal infection: quadrivalent conjugate vaccines against serogroups A, C, W, Y (MenACWY), and monovalent vaccines against serogroup B (MenB) as well as a newly licensed pentavalent vaccine (MenABCWY) protecting against serogroup A, B, C, W, and Y. The CDC’s Advisory Committee on Immunization Practices (ACIP) routinely recommends MenACWY vaccine for all 11- to 12-year-olds with a booster dose at 16 years. MenB vaccination is recommended based on shared clinical decision-making (SCDM) for 16- to 23-year-olds. Recently, the pentavalent meningococcal vaccine (MenABCWY) was recommended by the ACIP. Meningococcal vaccine uptake is suboptimal across the country, particularly among individuals with lower socioeconomic status (SES), despite these recommendations. The objective of the spatial analyses was to assess the relationship between stocking of MenACWY and MenB vaccines, area-level SES, and state-level policies.MethodsThe number of MenACWY and MenB doses stocked by vaccinators was obtained from IQVIA and the CDC’s Vaccine for Children (VFC) program and compiled into a county-level dataset from 2016 to 2019. SES, as measured using the CDC’s Social Vulnerability Index (SVI), state-level school recommendations, and universal purchasing programs were among the main county-level covariates included to control for factors likely influencing stocking. Data were stratified by public and private market. Bayesian spatial regression models were developed to quantify the variations in rates of stocking and the relative rates of stocking of both vaccines.ResultsAfter accounting for county-level characteristics, lower SES counties tended to have fewer doses of MenB relative to MenACWY on both public and private markets. Lower SES counties tended to have more supply of public vs. private doses. Universal purchasing programs had a strong effect on the markets for both vaccines shifting nearly all doses to the public market. School vaccination strategy was key for improving stocking rates.ConclusionsOverall, the results show that MenACWY has greater stock relative to MenB across the US. This difference is exacerbated in vulnerable areas without school entry requirements for vaccination and results in inequity of vaccine availability. Beyond state-level policy and SES differences, SCDM recommendations may be a contributing factor, although this was not directly assessed by our model.

Assessment of spatial cyclone surge susceptibility through GIS-based AHP multi-criteria analysis and frequency ratio: a case study from the Bangladesh coast

Tropical cyclones, including surge inundation, are a common event in the coastal regions of Bangladesh. The surge washes out the area within a very short period and remains in flooded condition for several days. Spatial analysis to understand the surge susceptibility level can assist the cyclone management system. Surge susceptibility analysis could be one of the most essential parts of disaster risk reduction through which cyclone vulnerability can be minimized. A Geographic Information Systems-based analytical hierarchy process (AHP) multi-criteria analysis and bivariate frequency ratio (FR) techniques were conducted to understand the surge susceptibility level of a cyclone-prone area on the Bangladesh coast. A total of 10 criteria were considered influential to surge flooding, i.e. Topographic Wetness Index, elevation, wind velocity, slope, distance from sea and rivers, drainage density, Land Use and Land Cover, Normalized Difference Vegetation Index, precipitation, and soil types. The final surge susceptibility maps were categorized into five classes, i.e. very low, low, moderate, high, and very high. Conferring to these susceptibility classes, policymakers can make decisions for future land use management and disaster risk reduction activities. According to this research, AHP showed better precision (Receiver Operating Characteristic) than FR for surge susceptibility prediction on the Bangladesh coast.

Assessing the Impact of Land Use and Land Cover Changes on Surface Temperature Dynamics Using Google Earth Engine: A Case Study of Tlemcen Municipality, Northwestern Algeria (1989–2019)

Changes in land use and land cover (LULC) have a significant impact on urban planning and environmental dynamics, especially in regions experiencing rapid urbanization. In this context, by leveraging the Google Earth Engine (GEE), this study evaluates the effects of land use and land cover modifications on surface temperature in a semi-arid zone of northwestern Algeria between 1989 and 2019. Through the analysis of Landsat images on GEE, indices such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference latent heat index (NDLI) were extracted, and the random forest and split window algorithms were used for supervised classification and surface temperature estimation. The multi-index approach combining the Normalized Difference Tillage Index (NDTI), NDBI, and NDVI resulted in kappa coefficients ranging from 0.96 to 0.98. The spatial and temporal analysis of surface temperature revealed an increase of 4 to 6 degrees across the four classes (urban, barren land, vegetation, and forest). The Google Earth Engine approach facilitated detailed spatial and temporal analysis, aiding in understanding surface temperature evolution at various scales. This ability to conduct large-scale and long-term analysis is essential for understanding trends and impacts of land use changes at regional and global levels.

Analyzing water level variability in Odisha: insights from multi-year data and spatial analysis

A comprehensive analysis of long-term water level trends is essential for freshwater sustainability. Given that Odisha heavily relies on agriculture, the monitoring and management of groundwater and its fluctuations are imperative for ensuring future sustainability in the state. Here, we analyzed the trend in Groundwater using water level data for a 30-year period (1990–2020) for the entire Odisha region. Moreover, to determine the long term variability, critical zones of future groundwater variability and controlling parameters of the water level change, we used spatio-temporal water level data of 746 locations. Water level rise of coastal districts during post-monsoon (POM), corresponds to the intensity of rainfall received, thus rising, however other districts of Odisha, showing decline in water level during the same season is due to shortage of rainfall, increase in population at a sudden, and over pumping due to industrial activities. Similarly, during pre-monsoon (PRM), water level shows an increasing trend in hard rock terrain of Odisha implying rabi crop irrigation, high density drainage network and lesser population density. Feature selection techniques were used in this study to know the parameters controlling most to this water level fluctuation in the entire Odisha state. Precipitation followed by landuse & landcover, lithology and population density are controlling the most for the long term water level change. Drainage, elevation, lithology and slope are positively related to the water level change while others are negatively related. It is also inferred that the districts like Mayurbhanj, Sundargarh, Keonjhar, Kandhamal, Boudh, Dhenkanal, Gajapati, Koraput and Kalahandi contain most of the high critical zone concerning future availability of groundwater while most of the coastal regions are safe.

Spatial Interactions of a City-Region Using GIS and Survey-based Data

The concept of spatial interaction deduced from Newton’s first law of motion, which has been extended from interactions between two points of elements e.g., two cities to interactions between other points of other elements in a system, e.g., more than two cities, can be applied to elucidate settlements spatial interaction at the regional scale, particularly a city-region as a system. Elucidating the levels of spatial interactions in city-regions is crucial to support decision-making processes in regional development planning and policies, which in turn contributes to sustainable regional developments if implemented fully. The current study, therefore, combined Geographic Information Systems and survey-based data to evaluate the level of spatial interactions of a city-region as a system to support sustainable regional development planning and policies using the defined Abuja city-region as a case study. Data were collected through a questionnaire survey of residents, land cover maps that were produced using Remote Sensing processes of satellite image classification, and urban and regional land use plans. An integrated analysis was deployed, including descriptive statistics and spatial analysis through extraction, digitization, overlay, clipping, and geometrical calculation processes. Key findings showed that the work location for most residents in satellite settlements is Abuja city. Also, the study showed that while Lugbe, Kubwa, and Old Karu settlements have the highest level of spatial interaction with Abuja city, Kuje and Gwagwalada settlements have the lowest level of spatial interaction. The findings and their associated implications of the current study are useful to support decision-makers in planning and policies for the spatial distribution of urban infrastructures across settlements that make up defined city-regions in Sub-Saharan Africa in particular and in the Global South in general

SARS-CoV-2 seroprevalence among Beninese pregnant women in the third year of the pandemic

BackgroundPregnant women are a vulnerable population to COVID-19 given an increased susceptibility to severe SARS-CoV-2 infection and pregnancy complications. However, few SARS-CoV-2 serological surveys have been performed among this population to assess the extent of the infection in sub-Saharan countries. The objectives of this study were to determine SARS-CoV-2 seroprevalence among Beninese pregnant women, to identify spatial seropositivity clusters and to analyse factors associated with the infection.MethodsA cross-sectional study including women in their third trimester of pregnancy attending the antenatal care (ANC) clinics at Allada (south Benin) and Natitingou (north Benin) was conducted. Rapid diagnostic tests (RDT) for detection of IgG/IgM against the SARS-CoV-2 spike protein were performed using capillary blood. Seroprevalence of SARS-CoV-2 antibodies and associations between SARS-CoV-2 serostatus and maternal characteristics were analyzed by multivariate logistic regression. Spatial analyses were performed using the spatial scan statistics to identify spatial clusters of SARS-CoV-2 infection.ResultsA total of 861 pregnant women were enrolled between May 4 and June 29, 2022. 58/861 (6.7%) participants reported having received COVID-19 vaccine. None of the participants had been diagnosed with COVID-19 during their pregnancy. SARS-CoV-2 antibodies were detected in 607/802 (75.7%; 95% CI 72.56%–78.62%) of unvaccinated participants. Several urban and rural spatial clusters of SARS-CoV-2 cases were identified in Allada and one urban spatial cluster was identified in Natitingou. Unvaccinated participants from Allada with at least one previous morbidity were at a three-times higher risk of presenting SARS-CoV-2 antibodies (OR = 2.89; 95%CI 1.19%-7.00%).ConclusionThree out of four pregnant women had SARS-CoV-2 antibodies, suggesting a high virus circulation among pregnant women in Benin, while COVID-19 vaccination coverage was low. Pregnant women with comorbidities may be at increased risk of SARS-CoV-2 infection. This population should be prioritized for COVID-19 diagnosis and vaccination in order to prevent its deleterious effects.Trial registrationNCT06170320 (retrospectively registered on December 21, 2023).

Short‐Term Sea Fog Area Forecast: A New Data Set and Deep Learning Approach

AbstractPrompt and precise forecast of sea fog regions ensures maritime navigational safety. This paper establishes a prompt and precise forecast of sea fog regions that ensure maritime navigational safety. This paper establishes a multivariable sea fog forecast (MV‐SFF) data set and proposes a deep learning‐based forecast method named rich‐element aggregated (REA) for short‐term forecasts. The MV‐SFF data set contains 122 sea fog events from 2010 to 2020. Each event in the MV‐SFF data set includes meteorological variables from reanalysis data and geostationary ocean color imager satellite images captured hourly on the day of the sea fog occurrence. This data set aims to comprehensively utilize meteorological elements and remote sensing observations to predict the spatial changes of sea fog areas in the next 7 hours. The proposed REA model can extract and integrate features from historical meteorological elements of different types, times, and spatial locations. In addition, REA utilizes a position‐aware edge detection mechanism to locate the exact position of sea fog. We perform a seven‐hour ahead forecast starting from 09:16 local time and show promising forecasting results, which demonstrate the fog area forecast ability of our model. Compared with existing advanced deep learning networks, the REA model is superior in performance and stability.

Housing price determinants in Ecuador: a spatial hedonic analysis

Purpose This paper aims to compare the efficiency of spatial and nonspatial hedonic price models in capturing housing submarkets dynamics for cities in developing countries. This study expects to contribute to a better understanding of the housing price determinants from both nonspatial and spatial perspectives. In addition, this paper fills a gap in the literature on the study of housing prices from a spatial perspective in Latin American cities. Design/methodology/approach This study uses a comparative analysis between an ordinary least squares regression and a geographical weighted regression, GWR. The study also assesses the performance of two distinct data sources: the city’s cadastral records and a real estate sales web portal. Findings The results suggest that compared to the traditional regression model, the spatial regression models are more effective at capturing housing market variations on a fine scale. Moreover, they reveal interesting findings on the spatial varying, sometimes contradictory effects of some housing attributes on housing prices in different areas of the city, suggesting the potential impact from segregation. Research limitations/implications The availability of data on housing prices and characteristics in Latin American cities is fragmented and complex. The level of detail, granularity and coverage is not consistent over time. For this reason, this study combines and compares data sets from official and unofficial sources in an effort to close this gap. Likewise, the socioeconomic variables that come from the census must be carefully analyzed, knowing the historical context in which they were constructed, what they represent and their interpretation. Practical implications This paper suggests that despite the improvement on the spatial models, the selection of a specific one should always be based on the diagnosis of it as it highly depends on the data used and the objectives of the study. Originality/value This study enriches the limited body of literature on spatial hedonic price models of housing in Latin American cities. It also shed light on the importance of spatial approaches to identify complex housing submarkets.

Application of Remote Sensing and Spatial Fuzzy Multi-criteria Decision Analysis to Identify Potential Dust Sources in Lake Urmia Basin, Northwest Iran

Application of Remote Sensing and Spatial Fuzzy Multi-criteria Decision Analysis to Identify Potential Dust Sources in Lake Urmia Basin, Northwest Iran

Algorithm for Evaluating the Difficulty of Land Consolidation Using Cadastral Data

Optimum planning and effective land consolidation, widely discussed by contemporary authors, is a response to the perceivable need to modernise global agriculture to ensure the community’s food security and create steady, sustainable development in rural areas. Adequate leveraging of agricultural policy instruments requires setting a correct strategic direction, including allocating available funds and considering the technical feasibility of the adopted assumptions. The selection of relevant methods to ensure the efficient and complete accomplishment of the anticipated results should follow a rational analysis of the actual work complexity. This paper presents an innovative, proprietary method for evaluating the difficulty of potential land consolidation using a standardised cadastral data set. The designed tool, which relies on automated algorithms applied in a GIS environment, provides accurate data describing the expected land consolidation complexity at individual stages of the procedure. Detailed and current information on land ownership, use, and farm geometry processed using efficient spatial and statistical analysis methods provides transparent and unambiguous results. The proposed solution was used in developing the difficulty assessment of land consolidation in 58 villages of the Strzyżów district in southeastern Poland.

Semantic guidance incremental network for efficiency video super-resolution

AbstractIn video streaming, bandwidth constraints significantly affect client-side video quality. Addressing this, deep neural networks offer a promising avenue for implementing video super-resolution (VSR) at the user end, leveraging advancements in modern hardware, including mobile devices. The principal challenge in VSR is the computational intensity involved in processing temporal/spatial video data. Conventional methods, uniformly processing entire scenes, often result in inefficient resource allocation. This is evident in the over-processing of simpler regions and insufficient attention to complex regions, leading to edge artifacts in merged regions. Our innovative approach employs semantic segmentation and spatial frequency-based categorization to divide each video frame into regions of varying complexity: simple, medium, and complex. These are then processed through an efficient incremental model, optimizing computational resources. A key innovation is the sparse temporal/spatial feature transformation layer, which mitigates edge artifacts and ensures seamless integration of regional features, enhancing the naturalness of the super-resolution outcome. Experimental results demonstrate that our method significantly boosts VSR efficiency while maintaining effectiveness. This marks a notable advancement in streaming video technology, optimizing video quality with reduced computational demands. This approach, featuring semantic segmentation, spatial frequency analysis, and an incremental network structure, represents a substantial improvement over traditional VSR methodologies, addressing the core challenges of efficiency and quality in high-resolution video streaming.

Differential spatial plasticity response in common bean (Phaseolus vulgaris L.) root architecture under water stress is driven by increased root diameter, surface area and volume at deeper layers

Root plasticity enables plants to adapt to spatial and temporal changes in soil resources. In this study, 40 common bean genotypes evaluated for two root and shoot traits under irrigated and water stress. Three genotypes WB-216, WB-N-2, and WB-966 with contrasting plasticity responses were used for in-depth study. Highest positive plasticity for most root traits was found in case of WB-N2 and WB-216, whereas, WB-966 had negative plasticity for all the traits recorded. In terms of spatial plasticity for root traits in three root length sections, WB-216 was positively plastic for root diameter with progressive decrease from top to bottom sections. WB-N2 had positive plasticity values for root diameter, root surface area and root volume. WB-966 had negative plasticity for all the traits. For WB-216, the root diameter increased under drought in S1 but was almost same in bottom sections. In case of WB-N2, there was increase in root diameter in S2 and S3, but for WB-966, root diameter decreased in all sections. Similar trend was observed in all three genotypes for root surface area and volume. We report that major drivers of spatial plasticity of root architectural traits are increased root diameter, surface area and volume at deeper layers.

Brain white matter microstructural alterations in Behcet's syndrome correlate with cognitive impairment and disease severity: A diffusion tensor imaging study

ObjectivesTo evaluate the microstructural integrity of brain white matter tracts in patients with Neuro-Behcet's syndrome (NBS) and Behcet's syndrome (BS) without neurological manifestations using diffusion tensor imaging (DTI) and to investigate potential utility of DTI as a surrogate biomarker of neurocognitive functioning and disease severity. MethodsThis cross-sectional study comprised 34 NBS patients and 32 BS patients without neurological involvement, identified based on the International Study Group of the Behcet's disease (ISGBD) and the International Consensus Recommendation (ICR) criteria, as well as 33 healthy controls. Cognitive functions, including attention, memory, language, abstraction, executive control, visuospatial skills, and sensorimotor performance were assessed using standardized questionnaires. DTI data were analyzed using tract-based spatial statistics (TBSS) and automated probabilistic tractography to investigate inter-group differences. Subsequently, correlations between tensor-derived parameters of white matter tracts, neurocognitive test scores, and disease severity measures were examined. ResultsDTI revealed decreased fractional anisotropy and increased radial diffusivity, mean diffusivity, and axial diffusivity in both supratentorial and infratentorial white matter in NBS patients, indicating widespread loss of microstructural integrity. Moreover, this loss of integrity was also observed in BS patients without neurological manifestations, albeit to a lesser extent. In NBS patients, certain white matter tracts, including cingulum bundle, were associated with poor cognitive performance across multiple domains and disease severity. DiscussionDTI findings might potentially serve as a neuroimaging marker to predict the extent of neurocognitive impairment and disease severity associated with central nervous system involvement in BS.

SpatialGE: A user-friendly web application to democratize spatial transcriptomics analysis.

Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential is often underutilized due to the advanced data analysis and programming skills required. To address this, we present spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provides a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enables comparative analysis among samples and supports various ST technologies. We demonstrate the utility of spatialGE through its application in studying the tumor microenvironment of melanoma brain metastasis and Merkel cell carcinoma. Our results highlight the ability of spatialGE to identify spatial gene expression patterns and enrichments, providing valuable insights into the tumor microenvironment and its utility in democratizing ST data analysis for the wider scientific community.

Analysis of spatiotemporal variation in land surface temperature on the Tibetan Plateau from 2001 to 2020

ABSTRACT With global climate warming intensifying, the Land Surface Temperature (LST) variation on the Tibetan Plateau (TP) has become a hot topic in environmental studies. This letter employs spatial analysis to identify LST distribution characteristics, the Pettitt test to detect abrupt changes, and Fast Fourier Transform (FFT) to analyse periodic variations. Analysing mean annual and seasonal LST from 2001 to 2020, the results indicate the following: (1) Over the past 20 years, the TP’s LST increased slowly at 0.08°C per decade, with significant seasonal variations. Warming trends dominate in spring, summer, and autumn, shifting from the northern to the southwestern plateau. Autumn shows the most significant warming at 0.31°C per decade, while winter shows a cooling trend at −0.46°C per decade. (2) The Pettitt test reveals no statistically significant abrupt changes in mean annual and seasonal LST, though potential change points were detected in 2013 for mean annual LST, and in 2005, 2006, 2014, and 2017 for summer, spring, autumn, and winter, respectively. This indicates relative stability in interannual temperature changes, with some asynchronous seasonal variations. FFT analysis indicates dominant short-term variability with a period of 3.33 years and secondary cycles of 10 and 5 years.

Slow recovery rates and spatial aggregation of Triatoma infestans populations in an area with high pyrethroid resistance in the Argentine Chaco

BackgroundThe emergence of pyrethroid resistance has threatened the elimination of Triatoma infestans from the Gran Chaco ecoregion. We investigated the status and spatial distribution of house infestation with T. infestans and its main determinants in Castelli, a municipality of the Argentine Chaco with record levels of triatomine pyrethroid resistance, persistent infestation over 2005–2014, and limited or no control actions over 2015–2020.MethodsWe conducted a 2-year longitudinal survey to assess triatomine infestation by timed manual searches in a well-defined rural section of Castelli including 14 villages and 234 inhabited houses in 2018 (baseline) and 2020, collected housing and sociodemographic data by on-site inspection and a tailored questionnaire, and synthetized these data into three indices generated by multiple correspondence analysis.ResultsThe overall prevalence of house infestation in 2018 (33.8%) and 2020 (31.6%) virtually matched the historical estimates for the period 2005–2014 (33.7%) under recurrent pyrethroid sprays. While mean peridomestic infestation remained the same (26.4–26.7%) between 2018 and 2020, domestic infestation slightly decreased from 12.2 to 8.3%. Key triatomine habitats were storerooms, domiciles, kitchens, and structures occupied by chickens. Local spatial analysis showed significant aggregation of infestation and bug abundance in five villages, four of which had very high pyrethroid resistance approximately over 2010–2013, suggesting persistent infestations over space-time. House bug abundance within the hotspots consistently exceeded the estimates recorded in other villages. Multiple regression analysis revealed that the presence and relative abundance of T. infestans in domiciles were strongly and negatively associated with indices for household preventive practices (pesticide use) and housing quality. Questionnaire-derived information showed extensive use of pyrethroids associated with livestock raising and concomitant spillover treatment of dogs and (peri) domestic premises.ConclusionsTriatoma infestans populations in an area with high pyrethroid resistance showed slow recovery and propagation rates despite limited or marginal control actions over a 5-year period. Consistent with these patterns, independent experiments confirmed the lower fitness of pyrethroid-resistant triatomines in Castelli compared with susceptible conspecifics. Targeting hotspots and pyrethroid-resistant foci with appropriate house modification measures and judicious application of alternative insecticides with adequate toxicity profiles are needed to suppress resistant triatomine populations and prevent their eventual regional spread.Graphical

The Role and Value of Children in the Ancient Near East During the 3rd Millennium BC: A View from Archaeological and Textual Sources

ABSTRACT In an attempt to reconstruct the role and value of childhood in 3rd millennium BC Near Eastern societies, this article discusses specific archaeological aspects related to the material culture of children, integrating notions provided by contemporary cuneiform sources. In particular, burial contexts from selected sites of the Euphrates Valley will be analysed since they offer a large set of tangible data to investigate the remains of children and the artefacts associated with them. In this respect simple statistics and spatial analysis of burials is presented, ultimately trying to reconstruct the value accorded to children by adults through the lens of the funerary sphere.

High-Speed Clearing and High-Resolution Staining for Analysis of Various Markers for Neurons and Vessels.

Tissue clearing enables deep imaging in various tissues by increasing the transparency of tissues, but there were limitations of immunostaining of the large-volume tissues such as the whole brain. Here, we cleared and immune-stained whole mouse brain tissues using a novel clearing technique termed high-speed clearing and high-resolution staining (HCHS). We observed neural structures within the cleared brains using both a confocal microscope and a light-sheet fluorescence microscope (LSFM). The reconstructed 3D images were analyzed using a computational reconstruction algorithm. Various neural structures were well observed in three-dimensional (3D) images of the cleared brains from Gad-green fluorescent protein (GFP) mice and Thy 1-yellow fluorescent protein (YFP) mice. The intrinsic fluorescence signals of both transgenic mice were preserved after HCHS. In addition, large-scale 3D imaging of brains, immune-stained by the HCHS method using a mild detergent-based solution, allowed for the global topological analysis of several neuronal markers such as c-Fos, neuronal nuclear protein (NeuN), Microtubule-associated protein 2 (Map2), Tuj1, glial fibrillary acidic protein (GFAP), and tyrosine hydroxylase (TH) in various anatomical regions in the whole mouse brain tissues. Finally, through comparisons with various existing tissue clearing methodologies such as CUBIC, Visikol, and 3DISCO, it was confirmed that the HCHS methodology results in relatively less tissue deformation and higher fluorescence retention. In conclusion, the development of 3D imaging based on novel tissue-clearing techniques (HCHS) will enable detailed spatial analysis of neural and vascular networks present within the brain.