Autoantibodies from patients with kidney allograft vasculopathy stimulate a proinflammatory switch in endothelial cells and monocytes mediated via GPCR-directed PAR1-TNF-α signaling

Non-HLA-directed regulatory autoantibodies (RABs) are known to target G-protein coupled receptors (GPCRs) and thereby contribute to kidney transplant vasculopathy and failure. However, the detailed underlying signaling mechanisms in human microvascular endothelial cells (HMECs) and immune cells need to be clarified in more detail. In this study, we compared the immune stimulatory effects and concomitant intracellular and extracellular signaling mechanisms of immunoglobulin G (IgG)-fractions from kidney transplant patients with allograft vasculopathy (KTx-IgG), to that from patients without vasculopathy, or matched healthy controls (Con-IgG). We found that KTx-IgG from patients with vasculopathy, but not KTx-IgG from patients without vasculopathy or Con-IgG, elicits HMEC activation and subsequent upregulation and secretion of tumor necrosis factor alpha (TNF-alpha) from HMECs, which was amplified in the presence of the protease-activated thrombin receptor 1 (PAR1) activator thrombin, but could be omitted by selectively blocking the PAR1 receptor. The amount and activity of the TNF-alpha secreted by HMECs stimulated with KTx-IgG from patients with vasculopathy was sufficient to induce subsequent THP-1 monocytic cell activation. Furthermore, AP-1/c-FOS, was identified as crucial transcription factor complex controlling the KTx-IgG-induced endothelial TNF-alpha synthesis, and mircoRNA-let-7f-5p as a regulatory element in modulating the underlying signaling cascade. In conclusion, exposure of HMECs to KTx-IgG from patients with allograft vasculopathy, but not KTx-IgG from patients without vasculopathy or healthy Con-IgG, triggers signaling through the PAR1-AP-1/c-FOS-miRNA-let7-axis, to control TNF-alpha gene transcription and TNF-alpha-induced monocyte activation. These observations offer a greater mechanistic understanding of endothelial cells and subsequent immune cell activation in the clinical setting of transplant vasculopathy that can eventually lead to transplant failure, irrespective of alloantigen-directed responses

Autoantibodies from patients with kidney allograft vasculopathy stimulate a proinflammatory switch in endothelial cells and monocytes mediated via GPCR-directed PAR1-TNF-α signaling

Non-HLA-directed regulatory autoantibodies (RABs) are known to target G-protein coupled receptors (GPCRs) and thereby contribute to kidney transplant vasculopathy and failure. However, the detailed underlying signaling mechanisms in human microvascular endothelial cells (HMECs) and immune cells need to be clarified in more detail. In this study, we compared the immune stimulatory effects and concomitant intracellular and extracellular signaling mechanisms of immunoglobulin G (IgG)-fractions from kidney transplant patients with allograft vasculopathy (KTx-IgG), to that from patients without vasculopathy, or matched healthy controls (Con-IgG). We found that KTx-IgG from patients with vasculopathy, but not KTx-IgG from patients without vasculopathy or Con-IgG, elicits HMEC activation and subsequent upregulation and secretion of tumor necrosis factor alpha (TNF-α) from HMECs, which was amplified in the presence of the protease-activated thrombin receptor 1 (PAR1) activator thrombin, but could be omitted by selectively blocking the PAR1 receptor. The amount and activity of the TNF-α secreted by HMECs stimulated with KTx-IgG from patients with vasculopathy was sufficient to induce subsequent THP-1 monocytic cell activation. Furthermore, AP-1/c-FOS, was identified as crucial transcription factor complex controlling the KTx-IgG-induced endothelial TNF-α synthesis, and mircoRNA-let-7f-5p as a regulatory element in modulating the underlying signaling cascade. In conclusion, exposure of HMECs to KTx-IgG from patients with allograft vasculopathy, but not KTx-IgG from patients without vasculopathy or healthy Con-IgG, triggers signaling through the PAR1-AP-1/c-FOS-miRNA-let7-axis, to control TNF-α gene transcription and TNF-α-induced monocyte activation. These observations offer a greater mechanistic understanding of endothelial cells and subsequent immune cell activation in the clinical setting of transplant vasculopathy that can eventually lead to transplant failure, irrespective of alloantigen-directed responses

The impact of inflammation on bone mass in children

Bone is a dynamic tissue. Skeletal bone integrity is maintained through bone modeling and remodeling. The mechanisms underlying this bone mass regulation are complex and interrelated. An imbalance in the regulation of bone remodeling through bone resorption and bone formation results in bone loss. Chronic inflammation influences bone mass regulation. Inflammation-related bone disorders share many common mechanisms of bone loss. These mechanisms are ultimately mediated through the uncoupling of bone remodeling. Cachexia, physical inactivity, pro-inflammatory cytokines, as well as iatrogenic factors related to effects of immunosuppression are some of the common mechanisms. Recently, cytokine signaling through the central nervous system has been investigated for its potential role in bone mass dysregulation in inflammatory conditions. Growing research on the molecular mechanisms involved in inflammation-induced bone loss may lead to more selective therapeutic targeting of these pathological signaling pathways

A Spatio-Temporal Brightness Temperature Prediction Method for Forest Fire Detection with MODIS Data: A Case Study in San Diego

Early detection of forest fire is helpful for monitoring the spread of fire promptly, minimizing the loss of forests, wild animals, human life, and economy. The performance of brightness temperature (BT) prediction determines the accuracy of fire detection. Great efforts have been made on BT prediction model building, but there still remains some uncertainty. Based on the widely used contextual BT prediction model (CM) and temporal-contextual BT prediction model (TCM), we proposed a spatio-temporal contextual BT prediction model (STCM), which involves historical images to contrast the BT correlation matrix between the pixel to be predicted and its background pixels within a dynamic window, and the spatial distance factor was introduced to modify the BT correlation matrix. We applied the STCM to a fire-prone area in San Diego, California, US, and compared it with CM and TCM. We found that the average RMSE of STCM was 12.54% and 9.12% lower than that of CM and TCM, and the standard deviation of RMSE calculated by STCM was reduced by 12.04% and 15.57% compared with CM and TCM, respectively. In addition, the bias of STCM was concentrated around zero and the range of bias of STCM was 88.7% and 15.3% lower than that of CM and TCM, respectively. The results demonstrated that the STCM can be used to obtain the highest BT prediction accuracy and most robust performance, followed by TCM, and CM performed worst. Our research on the BT prediction of potential fire pixels is helpful for improving the fire detection accuracy and is potentially useful for the prediction of other environmental variables with high spatial and temporal autocorrelation. However, the requirement of high-quality continuous data will limit the application of STCM in cloudy and rainy areas

A Zoning Earthquake Casualty Prediction Model Based on Machine Learning

The evaluation of mortality in earthquake-stricken areas is vital for the emergency response during rescue operations. Hence, an effective and universal approach for accurately predicting the number of casualties due to an earthquake is needed. To obtain a precise casualty prediction method that can be applied to regions with different geographical environments, a spatial division method based on regional differences and a zoning casualty prediction method based on support vector regression (SVR) are proposed in this study. This study comprises three parts: (1) evaluating the importance of influential features on seismic fatality based on random forest to select indicators for the prediction model; (2) dividing the study area into different grades of risk zones with a strata fault line dataset and WorldPop population dataset; and (3) developing a zoning support vector regression model (Z-SVR) with optimal parameters that is suitable for different risk areas. We selected 30 historical earthquakes that occurred in China’s mainland from 1950 to 2017 to examine the prediction performance of Z-SVR and compared its performance with those of other widely used machine learning methods. The results show that Z-SVR outperformed the other machine learning methods and can further enhance the accuracy of casualty prediction

Study of the Seasonal Effect of Building Shadows on Urban Land Surface Temperatures Based on Remote Sensing Data

Building shadows (BSs) frequently occur in urban areas, and their area and distribution display strong seasonal variations that significantly influence the urban land surface temperature (LST). However, it remains unclear how BSs affect the LST at the city scale because it is difficult to extract the shaded area at the subpixel scale and to connect such areas with the LST at the pixel scale. In this study, we combined the sun angle, building height, building footprint and building occlusion to extract the seasonal spatial distribution of BSs in the central area of Beijing. The effect of BSs on the LST was analyzed using LST retrieved from Landsat-8 thermal infrared sensor data. First, the relationship between the LST patch fragmentation and proportion of BSs in the sample areas was modeled without vegetation. Then, we quantitatively studied the mitigated intensity of the LST in pure impervious surfaces (IS) and vegetation pixels covered by BSs; next, we analyzed the LST sensitivity of these pixels to BSs. The results showed that the existence of BSs influences the fragmentation of the low LST patches strongly from summer to winter. On the other hand, pure IS pixels totally covered by BSs experienced a greater cooling effect, with 3.16 K on 10 July, and the lowest cooling occurred between 14 and 25 December, with a mean of 1.24 K. Without considering the relationship in winter, the LST is nonlinearly correlated to the building shadows ratio (BSR) in pixels, and an approximate 10% increase in the BSR resulted in decreases in the LST of approximately 0.33 K (mean of 16 April and 10 May), 0.37 K (10 July) and 0.24 K (28 September) for pure IS pixels, and 0.18 K, 0.20 K and 0.15 K, respectively, for pure vegetation pixels. Further analysis indicates that the LST of pure IS pixels is more sensitive to BSs than that of vegetation because the self-regulation mechanism of vegetation reduces the cooling effect of BSs. These findings can help urban planners understand the cooling characteristics of BSs and design suitable urban forms to resist urban heat islands (UHIs)

Mapping Population Distribution with High Spatiotemporal Resolution in Beijing Using Baidu Heat Map Data

Population distribution data with high spatiotemporal resolution are of significant value and fundamental to many application areas, such as public health, urban planning, environmental change, and disaster management. However, such data are still not widely available due to the limited knowledge of complex human activity patterns. The emergence of location-based service big data provides additional opportunities to solve this problem. In this study, we integrated ambient population data, nighttime light data, and building volume data; innovatively proposed a spatial downscaling framework for Baidu heat map data during work time and sleep time; and mapped the population distribution with high spatiotemporal resolution (i.e., hourly, 100 m) in Beijing. Finally, we validated the generated population distribution maps with high spatiotemporal resolution using the highest-quality validation data (i.e., mobile signaling data). The relevant results indicate that our proposed spatial downscaling framework for both work time and sleep time has high accuracy, that the distribution of the population in Beijing on a regular weekday shows “centripetal centralization at daytime, centrifugal dispersion at night” spatiotemporal variation characteristics, that the interaction between the purpose of residents’ activities and the spatial functional differences leads to the spatiotemporal evolution of the population distribution, and that China’s “surgical control and dynamic zero COVID-19” epidemic policy was strongly implemented. In addition, our proposed spatial downscaling framework can be transferred to other regions, which is of value for governmental emergency measures and for studies about human risks to environmental issues

High-Precision Population Spatialization in Metropolises Based on Ensemble Learning: A Case Study of Beijing, China

Accurate spatial population distribution information, especially for metropolises, is of significant value and is fundamental to many application areas such as public health, urban development planning and disaster assessment management. Random forest is the most widely used model in population spatialization studies. However, a reliable model for accurately mapping the spatial distribution of metropolitan populations is still lacking due to the inherent limitations of the random forest model and the complexity of the population spatialization problem. In this study, we integrate gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and support vector regression (SVR) through ensemble learning algorithm stacking to construct a novel population spatialization model we name GXLS-Stacking. We integrate socioeconomic data that enhance the characterization of the population’s spatial distribution (e.g., point-of-interest data, building outline data with height, artificial impervious surface data, etc.) and natural environmental data with a combination of census data to train the model to generate a high-precision gridded population density map with a 100 m spatial resolution for Beijing in 2020. Finally, the generated gridded population density map is validated at the pixel level using the highest resolution validation data (i.e., community household registration data) in the current study. The results show that the GXLS-Stacking model can predict the population’s spatial distribution with high precision (R2 = 0.8004, MAE = 34.67 persons/hectare, RMSE = 54.92 persons/hectare), and its overall performance is not only better than the four individual models but also better than the random forest model. Compared to the natural environmental features, a city’s socioeconomic features are more capable in characterizing the spatial distribution of the population and the intensity of human activities. In addition, the gridded population density map obtained by the GXLS-Stacking model can provide highly accurate information on the population’s spatial distribution and can be used to analyze the spatial patterns of metropolitan population density. Moreover, the GXLS-Stacking model has the ability to be generalized to metropolises with comprehensive and high-quality data, whether in China or in other countries. Furthermore, for small and medium-sized cities, our modeling process can still provide an effective reference for their population spatialization methods