Acute retinal injury and the relationship between nerve growth factor, Notch1 transcription and short-lived dedifferentiation transient changes of mammalian Müller cells

AbstractOur aim is to define related molecular events on how dormant Müller glia cells re-enter the cell cycle, proliferate and produce new retinal neurons from initial injury to glial scar formation. Sodium iodate (NaIO3) was used to induce acute retinal injury. Long-Evans rats were administered with NaIO3 or phosphate-buffered saline by intraperitoneal injection. The proliferation, dedifferentiation and neurogenesis of Müller cells were analyzed by double-labeled fluorescence immunohistochemistry with primary antibodies – against Müller cells and specific cell markers. Possible molecules that limit the regenerative potential of Müller cells were also determined by immunofluorescence staining, quantitative RT-PCR, protein array, ELISA and Western blot. In the first 3–7days after NaIO3 administration, Müller cells were activated and underwent a fate switch, including transient proliferation, dedifferentiation and neurogenesis. Nerve growth factor (NGF) signaling concomitantly increased with the downregulation of p27Kip1 in Müller cells, which may promote Müller cells to re-enter the cell cycle. The transient increase of NGF signaling and the transient decrease of Notch signaling inhibited Hes1, which might enhance the neuronal differentiation of dedifferentiated Müller cells and suppress gliosis. Upregulated Notch and decreased NGF expressions limit dedifferentiation and neurogenesis, but induces retinal Müller cell gliosis at a later stage. We conclude that transient NGF upregulation and Notch1 downregulation may activate the transient proliferation, dedifferentiation and neurogenesis of Müller cells during NaIO3-induced acute retinal injury; which could be a therapeutic target for overcoming Müller cell gliosis. Such therapy could be potentially used for treating retinal-related diseases

BEV-LaneDet: a Simple and Effective 3D Lane Detection Baseline

3D lane detection which plays a crucial role in vehicle routing, has recently been a rapidly developing topic in autonomous driving. Previous works struggle with practicality due to their complicated spatial transformations and inflexible representations of 3D lanes. Faced with the issues, our work proposes an efficient and robust monocular 3D lane detection called BEV-LaneDet with three main contributions. First, we introduce the Virtual Camera that unifies the in/extrinsic parameters of cameras mounted on different vehicles to guarantee the consistency of the spatial relationship among cameras. It can effectively promote the learning procedure due to the unified visual space. We secondly propose a simple but efficient 3D lane representation called Key-Points Representation. This module is more suitable to represent the complicated and diverse 3D lane structures. At last, we present a light-weight and chip-friendly spatial transformation module named Spatial Transformation Pyramid to transform multiscale front-view features into BEV features. Experimental results demonstrate that our work outperforms the state-of-the-art approaches in terms of F-Score, being 10.6% higher on the OpenLane dataset and 5.9% higher on the Apollo 3D synthetic dataset, with a speed of 185 FPS. The source code will released at https://github.com/gigo-team/bev_lane_det.Comment: Accepted by CVPR202

The Response of Net Primary Production to Climate Change: A Case Study in the 400 mm Annual Precipitation Fluctuation Zone in China

The regions in China that intersect the 400 mm annual precipitation line are especially ecologically sensitive and extremely vulnerable to anthropogenic activities. However, in the context of climate change, the response of vegetation Net Primary Production (NPP) in this region has not been scientifically studied in depth. NPP suffers from the comprehensive effect of multiple climatic factors, and how to eliminate the effect of interfering variables in the correlation analysis of NPP and target variables (temperature or precipitation) is the major challenge in the study of NPP influencing factors. The correlation coefficient between NPP and target variable was calculated by ignoring other variables that also had a large impact on NPP. This increased the uncertainty of research results. Therefore, in this study, the second-order partial correlation analysis method was used to analyze the correlation between NPP and target variables by controlling other variables. This can effectively decrease the uncertainty of analysis results. In this paper, the univariate linear regression, coefficient of variation, and Hurst index estimation were used to study the spatial and temporal variations in NPP and analyze whether the NPP seasonal and annual variability will persist into the future. The results show the following: (i) The spatial distribution of NPP correlated with precipitation and had a gradually decreasing trend from southeast to northwest. From 2000 to 2015, the NPP in the study area had a general upward trend, with a small variation in its range. (ii) Areas with negative partial correlation coefficients between NPP and precipitation are consistent with the areas with more abundant water resources. The partial correlation coefficient between the NPP and the Land Surface Temperature (LST) was positive for 52.64% of the total study area. Finally, the prediction of the persistence of NPP variation into the future showed significant differences on varying time scales. On an annual scale, NPP was predicted to persist for 46% of the study area. On a seasonal scale, NPP in autumn was predicted to account for 49.92%, followed by spring (25.67%), summer (13.40%), and winter (6.75%)

Inequality and Influencing Factors of Spatial Accessibility of Medical Facilities in Rural Areas of China: A Case Study of Henan Province

The equalization of medical services has received increasing attention, and improving the accessibility of medical facilities in rural areas is key for the realization of fairness with regard to medical services. This study studies the rural areas of Henan Province, China, and uses unincorporated villages as the basic unit. The spatial pattern of accessibility in rural areas was comprehensively analyzed via geographic information system spatial analysis and coefficient of variation. The spatial heterogeneity of relevant influencing factors was assessed by using the geographically weighted regression model. The results show that: (1) The distance cost of medical treatment in rural areas is normally distributed, and most areas are within a range of 2−6 km. (2) The accessibility in rural areas has clear spatial differences, is significantly affected by terrain, and shows characteristics of significant spatial agglomeration. The eastern and central regions have good spatial accessibility, while the western regions have poor spatial accessibility. Furthermore, regions with poor accessibility are mainly located in mountainous areas. (3) The spatial equilibrium of accessibility follows a pattern of gradual deterioration from east to west. The better accessibility-unbalanced type is mostly located in the center of Henan Province, while the poor accessibility-unbalanced type is concentrated in mountainous areas. (4) The area, elevation, residential density, and per capita industrial output are positively correlated with spatial accessibility, while road network density and population density are negatively correlated

Asymmetric seasonal daytime and nighttime warming and its effects on vegetation in the Loess Plateau.

Over the period 1982-2015, temperatures have exhibited an asymmetric warming pattern diurnally, as well as seasonally across the Loess Plateau. However, very limited research has studied the implications and effects of such seasonally heterogeneous warming across the Loess Plateau. In this study, we also analyzed the time series trends and seasonal spatial patterns of the maximum (Tmax) and minimum (Tmin) temperatures and evaluated how different vegetation responded to daytime and nighttime warming in the Loess Plateau from 1982 to 2015 based on the NDVI and meteorological parameters (precipitation or temperature). We found that Tmax and Tmin significantly increased throughout the years except for Tmax in autumn, and the diurnal asymmetric warming showed some striking seasonal differences. For example, the increasing rates of Tmin in spring, summer, autumn, and winter were 0.75, 1.20, 1.88, and 1.10 times larger than that of Tmax, respectively. NDVI showed significantly positive correlation with Tmax and Tmin in spring and winter, while NDVI presented significantly positive correlation with Tmin in summer and Tmax in autumn across entire Loess Plateau. Furthermore, we also discovered diverse seasonal responses in terms of vegetation types to daytime and nighttime warming. For instance, Spring NDVI showed significantly positive partial correlations with Tmax and Tmin. In summer, grasslands and wetlands merely displayed significantly positive partial correlations with Tmin. Cultivated land presented significantly positive partial correlation between the NDVI and Tmax (Tmin) in autumn. In winter, cultivated land, forest, and grassland exhibited significantly positive partial correlation with Tmax and Tmin, while only wetland showed a significantly positive partial correlation with Tmax. Our results demonstrated responses of vegetation to climate extremes and enhance a better understanding of the seasonally different responses of vegetation under global climate change at different scale

Influence of Urbanization Factors on Surface Urban Heat Island Intensity: A Comparison of Countries at Different Developmental Phases

Urbanization is a global problem with demographic trends. The urban heat island plays a dominant role in local climate systems. Despite existing efforts to understand the impacts of multiple urbanization factors on the urban heat island globally, very little is known about the attribution of urban heat island magnitude to urbanization in different locations or developmental phases. In this study, based on global land surface temperature data, urban spatial domain data, gross domestic product (GDP), and population data, we analyzed the influence of multiple urbanization factors on global surface urban heat island intensity (SUHII). We also tentatively compared the abovementioned factors between different regions across the globe, especially between China and the USA, the largest countries that are experiencing or have experienced rapid urbanization in recent decades. The results showed that global SUHII had remarkable spatial heterogeneity due to the geographical and socioeconomic variation between cities. There was a significant correlation between SUHII and population as well as GDP in global cities. Moreover, this study suggested that the impacts of population on SUHII might be stronger in the early stages of urbanization, and the GDP factor would become a critical factor at a certain development level. The urban area also had non-ignorable impacts on SUHII, while the correlation between SUHII and urban shape was relatively weak. All these may imply that the best approach to slow down SUHII is to find other solutions, e.g., optimize the spatial configuration of urban internal landscapes, when the urbanization reaches a high level

Value Assessment of Health Losses Caused by PM_2.5 Pollution in Cities of Atmospheric Pollution Transmission Channel in the Beijing–Tianjin–Hebei Region, China

A set of exposure–response coefficients between fine particulate matter (PM2.5) pollution and different health endpoints were determined through the meta-analysis method based on 2254 studies collected from the Web of Science database. With data including remotely-sensed PM2.5 concentration, demographic data, health data, and survey data, a Poisson regression model was used to assess the health losses and their economic value caused by PM2.5 pollution in cities of atmospheric pollution transmission channel in the Beijing–Tianjin–Hebei region, China. The results showed the following: (1) Significant exposure–response relationships existed between PM2.5 pollution and a set of health endpoints, including all-cause death, death from circulatory disease, death from respiratory disease, death from lung cancer, hospitalization for circulatory disease, hospitalization for respiratory disease, and outpatient emergency treatment. Each increase of 10 μg/m3 in PM2.5 concentration led to an increase of 5.69% (95% CI (confidence interval): 4.12%, 7.85%), 6.88% (95% CI: 4.94%, 9.58%), 4.71% (95% CI: 2.93%, 7.57%), 9.53% (95% CI: 6.84%, 13.28%), 5.33% (95% CI: 3.90%, 7.27%), 5.50% (95% CI: 4.09%, 7.38%), and 6.35% (95% CI: 4.71%, 8.56%) for above-mentioned health endpoints, respectively. (2) PM2.5 pollution posed a serious threat to residents’ health. In 2016, the number of deaths, hospitalizations, and outpatient emergency visits induced by PM2.5 pollution in cities of atmospheric pollution transmission channel in the Beijing–Tianjin–Hebei region reached 309,643, 1,867,240, and 47,655,405, respectively, accounting for 28.36%, 27.02% and 30.13% of the total number of deaths, hospitalizations, and outpatient emergency visits, respectively. (3) The economic value of health losses due to PM2.5 pollution in the study area was approximately $28.1 billion, accounting for 1.52% of the gross domestic product. The economic value of health losses was higher in Beijing, Tianjin, Shijiazhuang, Zhengzhou, Handan, Baoding, and Cangzhou, but lower in Taiyuan, Yangquan, Changzhi, Jincheng, and Hebi

The Spatio-Temporal Characteristics and Influencing Factors of Covid-19 Spread in Shenzhen, China—An Analysis Based on 417 Cases

The global pandemic of COVID-19 has made it the focus of current attention. At present, the law of COVID-19 spread in cities is not clear. Cities have long been difficult areas for epidemic prevention and control because of the high population density, high mobility of people, and high frequency of contacts. This paper analyzed case information for 417 patients with COVID-19 in Shenzhen, China. The nearest neighbor index method, kernel density method, and the standard deviation ellipse method were used to analyze the spatio-temporal characteristics of the COVID-19 spread in Shenzhen. The factors influencing that spread were then explored using the multiple linear regression method. The results show that: (1) The development of COVID-19 epidemic situation in Shenzhen occurred in three stages. The patients showed significant hysteresis from the onset of symptoms to hospitalization and then to diagnosis. Prior to 27 January, there was a relatively long time interval between the onset of symptoms and hospitalization for COVID-19; the interval decreased thereafter. (2) The epidemic site (the place where the patient stays during the onset of the disease) showed an agglomeration in space. The degree of agglomeration constantly increased across the three time nodes of 31 January, 14 February, and 22 February. The epidemic sites formed a “core area” in terms of spatial distribution and spread along the “northwest–southeast” direction of the city. (3) Economic and social factors significantly impacted the spread of COVID-19, while environmental factors have not played a significant role

Estimating Relations of Vegetation, Climate Change, and Human Activity: A Case Study in the 400 mm Annual Precipitation Fluctuation Zone, China

The 400 mm annual precipitation fluctuation zone (75°55′–127°6′E and 26°55′–53°6′N) is located in central and western China, which is a transition area from traditional agricultural to animal husbandry. It is extremely sensitive to climatic changes. The corresponding changes of the ecosystem, represented by vegetation, under the dual influences of climate change and human activities are important issues in the study of the regional ecological environment. Based on the Savitzky–Golay (S–G) filtering method, the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Differential Vegetation Index (NDVI) dataset (NDVI3g) was reconstructed in this paper. Sen’s slope estimation, Mann–Kendall (M–K), multiple regression residual analysis, and the Hurst index were used to quantify the impacts of climate change and human activities on vegetation; in addition, the future persistence characteristics of the vegetation changes trend were analyzed. Vegetation changes in the study area had an obvious spatio-temporal heterogeneity. On an annual scale, the vegetation increased considerably, with a growth rate of 0.50%/10a. The multi-year mean value of NDVI and growth rate of cultivated land were the highest, followed by the forest land and grassland. On a seasonal scale, the vegetation cover increased most significantly in autumn, followed by spring and summer. In the southeastern and central parts of the study area, the vegetation cover increased significantly (P < 0.05), while it decreased significantly in the northeastern and southwestern parts. In summer, the NDVI value of all vegetation types (cultivated land, forest land and grassland) reached the maximum. The change rate of NDVI value for cultivated land reached the highest in autumn (1.57%/10a), forest land reached the highest in spring (1.15%/10a), and grassland reached the highest in autumn (0.49%/10a). The NDVI of cultivated land increased in all seasons, while forest land (−0.31%/10a) and grassland (−0.009%/10a) decreased in winter. Partial correlation analysis between vegetation and precipitation, temperature found that the areas with positive correlation accounted for 66.29% and 55.05% of the total area, respectively. Under the influence of climate change alone, 62.79% of the study area showed an increasing tendency, among which 46.79% showed a significant upward trend (P < 0.05). The NDVI decreased in 37.21% of the regions and decreased significantly in 14.88% of the regions (P < 0.05). Under the influence of human activities alone, the vegetation in the study area showed an upward trend in 59.61%, with a significant increase in 41.35% (P < 0.05), a downward trend in 40.39%, and a significant downward trend in 7.95% (P < 0.05). Vegetation growth is highly unstable and prone to drastic changes, depending on the environmental conditions