Analysing the Global and Local Spatial Associations of Medical Resources Across Wuhan City Using POI Data

Background: There is a sharp contradiction between the supply and demand of medical resources in the provincial capitals of China. Understanding the spatial patterns of medical resources and identifying their spatial association and heterogeneity is a prerequisite to ensuring that limited resources are allocated fairly and optimally, which, along with improvements to urban residents’ quality of life, is a key aim of healthy city planning. However, the existing studies on medical resources pattern mainly focus on their spatial distribution and evolution characteristics, and lack the analyses of the spatial co-location between medical resources from the global and local perspectives. It is worth noting that the research on the spatial relationship between medical resources is an important way to realize the spatial equity and operation efficiency of urban medical resources. Methods: Localized colocation quotient (LCLQ) analysis has been used successfully to measure directional spatial associations and heterogeneity between categorical point data. Using point of interest (POI) data and the LCLQ method, this paper presents the first analysis of spatial patterns and directional spatial associations between six medical resources across Wuhan city. Results: (1) Pharmacies, clinics and community hospitals show “multicentre + multicircle”, “centre + axis + dot” and “banded” distribution characteristics, respectively, but specialized hospitals and general hospitals present “single core” and “double core” modes. (2) Overall, medical resources show agglomeration characteristics. The degrees of spatial agglomeration of the five medical resources, are ranked from high to low as follows: pharmacy, clinic, community hospital, special hospital, general hospital and 3A hospital. (3) Although pharmacies, clinics, and community hospitals of basic medical resources are interdependent, specialized hospitals, general hospitals and 3A hospitals of professional medical resources are also interdependent; furthermore, basic medical resources and professional medical resources are mutually exclusive. Conclusions: Government and urban planners should pay great attention to the spatial distribution characteristics and association intensity of medical resources when formulating relevant policies. The findings of this study contribute to health equity and health policy discussions around basic medical services and professional medical services

Estimates of daily ground-level NO2 concentrations in China based on big data and machine learning approaches

Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants. However, current ground-level NO2 concentration data are lack of either high-resolution coverage or full coverage national wide, due to the poor quality of source data and the computing power of the models. To our knowledge, this study is the first to estimate the ground-level NO2 concentration in China with national coverage as well as relatively high spatiotemporal resolution (0.25 degree; daily intervals) over the newest past 6 years (2013-2018). We advanced a Random Forest model integrated K-means (RF-K) for the estimates with multi-source parameters. Besides meteorological parameters, satellite retrievals parameters, we also, for the first time, introduce socio-economic parameters to assess the impact by human activities. The results show that: (1) the RF-K model we developed shows better prediction performance than other models, with cross-validation R2 = 0.64 (MAPE = 34.78%). (2) The annual average concentration of NO2 in China showed a weak increasing trend . While in the economic zones such as Beijing-Tianjin-Hebei region, Yangtze River Delta, and Pearl River Delta, the NO2 concentration there even decreased or remained unchanged, especially in spring. Our dataset has verified that pollutant controlling targets have been achieved in these areas. With mapping daily nationwide ground-level NO2 concentrations, this study provides timely data with high quality for air quality management for China. We provide a universal model framework to quickly generate a timely national atmospheric pollutants concentration map with a high spatial-temporal resolution, based on improved machine learning methods

Phenological Shifts of the Deciduous Forests and Their Responses to Climate Variations in North America

Forests play a vital role in sequestering carbon dioxide from the atmosphere. Vegetation phenology is sensitive to climate changes and natural environments. Exploring the patterns in phenological events of the forests can provide useful insights for understanding the dynamics of vegetation growth and their responses to climate variations. Deciduous forest in North America is an important part of global forests. Here we apply time-series remote sensing imagery to map the critical dates of vegetation phenological events, including the start of season (SOS), end of season (EOS), and growth length (GL) of the deciduous forests in North America during the past two decades. The findings show that the SOS and EOS present considerable spatial and temporal variations. Earlier SOS, delayed EOS, and therefore extended GL are detected in a large part of the study area from temporal trend analysis over the years, though the magnitude of the trend varies at different locations. The phenological events are found to correlate to the environmental factors and the impact on the vegetation phenology from the factors is location-dependent. The findings confirm that the phenology of the deciduous forests in North America is updated such as advanced SOS and delayed EOS in the last two decades and the climate variations are likely among the driving forces for the updates. Considering that previous studies warn that shifts in vegetation phenology could reverse the role of forests as net emitters or net sinks, we suggest that forest management should be strengthened to forests that experience significant changes in the phenological events

Autophagy activity and expression pattern of autophagy-related markers in the podocytes of patients with lupus nephritis: association with pathological classification

Objective: To identify the significance of autophagy in lupus nephritis (LN). Methods: The number of autophagosomes in podocytes was counted and the expression of multiple molecular markers associated with autophagy was evaluated in LN specimens: in renal biopsy specimens from 90 patients with LN and 15 healthy controls, autophagosomes in podocytes were counted using transmission electron microscopy and the expression levels of four autophage related proteins including Beclin-1, microtubule-associated protein light chain 3 (LC3), autophagy-related gene 7 (Atg7), and UNC-51-like kinase 1 (ULK1) were measured using immunohistochemistry. Results: The number of autophagosomes in patients with LN types III, IV, and combined V–IV type were significantly higher than in controls (p < 0.0001; p < 0.0001; p = 0.009, respectively). However, the autophagosomes numbers in patients with II and V types LN were significantly lower than controls (both p < 0.0001). Various levels of marker expression were identified, and they correlated significantly with LN pathology classifications. Moreover, the percentage of marker expression in LN types III, IV and V-IV were significantly higher than controls (p < 0.05), while that in types II and V were lower than controls, although the difference for LC3 and ULK1 was not statistically significant. Conclusions: Autophagy activity and expression pattern of autophagy-related markers in podocytes were significantly positively correlated with LN of types III, IV, and V–IV, but negatively correlated with II and V types. Therefore autophagy could be a useful predictor of LN pathology type, and be informative and helpful in the development of treatment strategies in clinical settings

A Rapid and High-Precision Mountain Vertex Extraction Method Based on Hotspot Analysis Clustering and Improved Eight-Connected Extraction Algorithms for Digital Elevation Models

Digital Elevation Model (DEM)-based mountain vertex extraction is one of the most useful DEM applications, providing important information to properly characterize topographic features. Current vertex-extraction techniques have considerable limitations, such as yielding low-accuracy results and generating false mountain vertices. To overcome these limitations, a new approach is proposed that combines Hotspot Analysis Clustering and the Improved Eight-Connected Extraction algorithms that would quickly and accurately provide the location and elevation of mountain vertices. The use of the elevation-based Hotspot Analysis Clustering Algorithm allows the fast partitioning of the mountain vertex area, which significantly reduces data and considerably improves the efficiency of mountain vertex extraction. The algorithm also minimizes false mountain vertices, which can be problematic in valleys, ridges, and other rugged terrains. The Eight-Connected Extraction Algorithm also hastens the precise determination of vertex location and elevation, providing a better balance between accuracy and efficiency in vertex extraction. The proposed approach was used and tested on seven different datasets and was compared against traditional vertex extraction methods. The results of the quantitative evaluation show that the proposed approach yielded higher efficiency, considerably minimized the occurrence of invalid points, and generated higher vertex extraction accuracy compared to other traditional methods

A rapid and high-precision mountain vertex extraction method based on hotspot analysis clustering and improved eight-connected extraction algorithms for digital elevation models

Digital Elevation Model (DEM)-based mountain vertex extraction is one of the most useful DEM applications, providing important information to properly characterize topographic features. Current vertex-extraction techniques have considerable limitations, such as yielding low-accuracy results and generating false mountain vertices. To overcome these limitations, a new approach is proposed that combines Hotspot Analysis Clustering and the Improved Eight-Connected Extraction algorithms that would quickly and accurately provide the location and elevation of mountain vertices. The use of the elevation-based Hotspot Analysis Clustering Algorithm allows the fast partitioning of the mountain vertex area, which significantly reduces data and considerably improves the efficiency of mountain vertex extraction. The algorithm also minimizes false mountain vertices, which can be problematic in valleys, ridges, and other rugged terrains. The Eight-Connected Extraction Algorithm also hastens the precise determination of vertex location and elevation, providing a better balance between accuracy and efficiency in vertex extraction. The proposed approach was used and tested on seven different datasets and was compared against traditional vertex extraction methods. The results of the quantitative evaluation show that the proposed approach yielded higher efficiency, considerably minimized the occurrence of invalid points, and generated higher vertex extraction accuracy compared to other traditional methods.</p

Identification of endonuclease domain-containing 1 as a novel tumor suppressor in prostate cancer

Abstract Background Endonuclease domain containing 1 (ENDOD1) is implicated in tumorigenesis and aggressiveness of multiple tumors. In this study, we aimed to investigate the role of ENDOD1 in prostate cancer (PCa). Methods Immunohistochemistry were performed in 30 cases of benign prostatic hyperplasia (BPH) and 50 cases of PCa to identify its association with clinicopathological characteristics. Real-time PCR and western blot were used to detect ENDOD1 mRNA and protein expression in normal prostatic epithelial and PCa cell lines. MTT assays were employed to determine the effect of cell proliferation. Flow cytometry was used to explore the cell cycle distribution and apoptotic effects. Transwell migration and invasion assays were done to evaluate changes in the ability of cell migration and invasion. Results Immunoreactivity scores of ENDOD1 showed no statistical difference between BPH and low-grade PCa, whereas lower immunostaining scores were observed in high-grade compared with low-grade PCa. Real-time PCR data indicated that ENDOD1 mRNA expression was markedly increased in LNCaP and 22Rv1 cells and decreased in PC3 and DU145 cells compared to the normal epithelial cells RWPE1. Western blot showed that androgen-sensitive LNCaP cells had the highest protein expression level of ENDOD1, whereas castration-resistant PCa cell lines PC3 and DU145 had significantly lower protein levels. Meanwhile, overexpression of ENDOD1 suppressed cell proliferation, induced G0/G1 cell cycle arrest and inhibited cell migration and invasion. Conversely, siRNA-mediated silencing of ENDOD1 promoted cell proliferation, migration and invasion. No apoptotic effects occurred upon manipulation of ENDOD1 expression. Conclusion Our results indicate that ENDOD1 is a novel tumor suppressor in PCa, which may be employed as a new drug target of preventing progression to metastatic castration-resistant prostate cancer

A rapid and high-precision mountain vertex extraction method based on hotspot analysis clustering and improved eight-connected extraction algorithms for digital elevation models

Digital Elevation Model (DEM)-based mountain vertex extraction is one of the most useful DEM applications, providing important information to properly characterize topographic features. Current vertex-extraction techniques have considerable limitations, such as yielding low-accuracy results and generating false mountain vertices. To overcome these limitations, a new approach is proposed that combines Hotspot Analysis Clustering and the Improved Eight-Connected Extraction algorithms that would quickly and accurately provide the location and elevation of mountain vertices. The use of the elevation-based Hotspot Analysis Clustering Algorithm allows the fast partitioning of the mountain vertex area, which significantly reduces data and considerably improves the efficiency of mountain vertex extraction. The algorithm also minimizes false mountain vertices, which can be problematic in valleys, ridges, and other rugged terrains. The Eight-Connected Extraction Algorithm also hastens the precise determination of vertex location and elevation, providing a better balance between accuracy and efficiency in vertex extraction. The proposed approach was used and tested on seven different datasets and was compared against traditional vertex extraction methods. The results of the quantitative evaluation show that the proposed approach yielded higher efficiency, considerably minimized the occurrence of invalid points, and generated higher vertex extraction accuracy compared to other traditional methods.Optical and Laser Remote Sensin