[Temporal and Spatial Variation Characteristics of Heavy Metal in Atmospheric Deposition in China from 2000 to 2018].

Owing to the significant impact of heavy metals in atmospheric deposition on soil, clear knowledge on the present situation and temporal and spatial variation in fluxes of heavy metals in atmospheric deposition all around China is urgently needed. In this study, we collected 99 published papers on deposition fluxes of heavy metals from 2001 to 2021 based on the CNKI and Web of Science database and extracted 718 to 1672 monitoring points from these papers. The Meta-analysis method was used to calculate the weighted average of deposition fluxes of heavy metals, and the spatial-temporal characteristics in different periods from 2000 to 2018 were studied by subgroup analysis, which compared the differences between different types of areas, such as agricultural and rural areas and urban and industrial areas. The results showed that the annual fluxes of heavy metals in atmospheric deposition[mg·(m2·a)-1] in China were as follows:Zn (96.75)>Pb (23.37)>Cu (12.77)>Cr (11.04)>Ni (6.61)>As (2.97)>Cd (0.48)>Hg (0.05). Overall, the estimated value of deposition fluxes in China from 2000 to 2018 was higher than that of rural areas in England from 1995 to 1998. The deposition fluxes in industrial areas and urban areas were much higher than those in the agricultural and rural areas, especially the industrial areas where the heavy metal pollution was more serious. The deposition fluxes of As and Cd in the Changsha-Zhuzhou-Xiangtan area were relatively high, whereas the atmospheric deposition of heavy metals in Northeast China, the Pearl River Delta, and North China Plain was more serious than that in the other areas. In the past 20 years, the annual deposition fluxes of Cd fluctuated around the overall average, without an obviously declining trend, whereas the deposition fluxes of Cd in the urban, agricultural, and rural areas showed a trend of growth. These results suggested that precise and risk control measures of atmospheric emissions should be established based on the characteristics of regional industrial structure, which should cover all levels, all types, and all regions. In addition, more restrictive measures should be taken to solve the current problem caused by the higher deposition flux of Cd in atmospheric deposition.

Differential expression of glial cell line-derived neurotrophic factor splice variants in the mouse brain.

Glial cell line-derived neurotrophic factor (GDNF) plays a critical role in neuronal survival and function. GDNF has two major splice variants in the brain, α-pro-GDNF and ß-pro-GDNF, and both isoforms have strong neuroprotective effects on dopamine neurons. However, the expression of the GDNF splice variants in dopaminergic neurons in the brain remains unclear. Therefore, in this study, we investigated the mRNA and protein expression of α- and ß-pro-GDNF in the mouse brain by real-time quantitative polymerase chain reaction, using splice variant-specific primers, and western blot analysis. At the mRNA level, ß-pro-GDNF expression was significantly greater than that of α-pro-GDNF in the mouse brain. In contrast, at the protein level, α-pro-GDNF expression was markedly greater than that of ß-pro-GDNF. To clarify the mechanism underlying this inverse relationship in mRNA and protein expression levels of the GDNF splice variants, we analyzed the expression of sorting protein-related receptor with A-type repeats (SorLA) by real-time quantitative polymerase chain reaction. At the mRNA level, SorLA was positively associated with ß-pro-GDNF expression, but not with α-pro-GDNF expression. This suggests that the differential expression of α- and ß-pro-GDNF in the mouse brain is related to SorLA expression. As a sorting protein, SorLA could contribute to the inverse relationship among the mRNA and protein levels of the GDNF isoforms. This study was approved by the Animal Ethics Committee of Xuzhou Medical University, China on July 14, 2016.

MiRNAs Mediate GDNF-Induced Proliferation and Migration of Glioma Cells.

BACKGROUND/AIMS: Glial cell line-derived neurotrophic factor (GDNF) is an important factor promoting invasive glioma growth. This study was performed to reveal a unique mechanism of glioma cell proliferation and migration. METHODS: Human U251 glioma cells were used to screen the optimal GDNF concentration and treatment time to stimulate proliferation and migration. MicroRNA (MiRNA) expression profiles were detected by microarray and confirmed by real-time polymerase chain reaction (PCR). The target genes of differentially expressed miRNAs were predicted by miRWalk, and those targeted by multiple miRNAs were screened with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A regulatory miRNA network was constructed using ingenuity pathway analysis (IPA). Target gene expression of differentially expressed miRNAs was examined by real-time PCR or mRNA microarray. RESULTS: The results show that 50 ng/mL GDNF for 24 h significantly promotes U251 glioma cell proliferation and migration (P < 0.05). Seven miRNAs (hsa-miR-194-5p, hsa-miR-152-3p, hsa-miR-205-5p, hsa-miR-629-5p, hsa-miR-3609, hsa-miR-183-5p, and hsa-miR-487b-3p) were significantly up-regulated after GDNF treatment (P < 0.05). These miRNAs are primarily involved in signal transduction, cell adhesion and cell cycle through mitogen-activated protein kinase (MAPK) signaling, focal adhesion and glioma signal pathways. Five of these miRNAs (hsa-miR-194-5p, hsa-miR-152-3p, hsa-miR-205-5p, hsa-miR-183-5p, and hsa-miR-487b-3p) co-regulate TP53 and Akt. mRNA expression levels of four genes co-targeted by two or more up-regulated miRNAs were significantly decreased after GDNF treatment (P < 0.05). CONCLUSION: GDNF treatment of U251 glioma cells significantly increased the expression of seven miRNAs involved in cell adhesion and the cell cycle.

Egr-1 and RNA POL II facilitate glioma cell GDNF transcription induced by histone hyperacetylation in promoter II.

The specific mechanisms for epigenetic regulation of gene transcription remain to be elucidated. We previously demonstrated that hyperacetylation of histone H3K9 in promoter II of glioma cells promotes high transcription of the glial cell line-derived neurotrophic factor (GDNF) gene. This hyperacetylation significantly enhanced Egr-1 binding and increased the recruitment of RNA polymerase II (RNA POL II) to that region (P < 0.05). Egr-1 expression was abnormally increased in C6 glioma cells. Further overexpression of Egr-1 significantly increased Egr-1 binding to GDNF promoter II, while increasing RNA POL II recruitment, thus increasing GDNF transcription (P < 0.01). When the acetylation of H3K9 in the Egr-1 binding site was significantly reduced by the histone acetyltransferase (HAT) inhibitor curcumin, binding of Egr-1 to GDNF promoter II, RNA POL II recruitment, and GDNF mRNA expression were significantly downregulated (P < 0.01). Moreover, curcumin attenuated the effects of Egr-1 overexpression on Egr-1 binding, RNA POL II recruitment, and GDNF transcription (P < 0.01). Egr-1 and RNA POL II co-existed in the nucleus of C6 glioma cells, with overlapping regions, but they were not bound to each other. In conclusion, highly expressed Egr-1 may be involved in the recruitment of RNA POL II in GDNF promoter II in a non-binding manner, and thereby involved in regulating GDNF transcription in high-grade glioma cells. This regulation is dependent on histone hyperacetylation in GDNF promoter II.

[Estimation of organic matter content of north fluvo-aquic soil based on the coupling model of wavelet transform and partial least squares].

For improving the estimation accuracy of soil organic matter content of the north fluvo-aquic soil, wavelet transform technology is introduced. The soil samples were collected from Tongzhou district and Shunyi district in Beijing city. And the data source is from soil hyperspectral data obtained under laboratory condition. First, discrete wavelet transform efficiently decomposes hyperspectral into approximate coefficients and detail coefficients. Then, the correlation between approximate coefficients, detail coefficients and organic matter content was analyzed, and the sensitive bands of the organic matter were screened. Finally, models were established to estimate the soil organic content by using the partial least squares regression (PLSR). Results show that the NIR bands made more contributions than the visible band in estimating organic matter content models; the ability of approximate coefficients to estimate organic matter content is better than that of detail coefficients; The estimation precision of the detail coefficients fir soil organic matter content decreases with the spectral resolution being lower; Compared with the commonly used three types of soil spectral reflectance transforms, the wavelet transform can improve the estimation ability of soil spectral fir organic content; The accuracy of the best model established by the approximate coefficients or detail coefficients is higher, and the coefficient of determination (R2) and the root mean square error (RMSE) of the best model for approximate coefficients are 0.722 and 0.221, respectively. The R2 and RMSE of the best model for detail coefficients are 0.670 and 0.255, respectively.

[Monitoring freeze stress levels on winter wheat from hyperspectral reflectance data using principal component analysis].

In order to detect the freeze injury stress level of winter wheat growing in natural environment fast and accurately, the present paper takes winter wheat as experimental object. First winter wheat canopy hyperspectral data were treated with resampling smooth Second hyperspectral data were analyzed based on principal components analysis (PCA), a freeze injury inversion model was established, stems survival rate was dependent, and principal components of spectral data were chosen as independent variables. Third, the precision of the model was testified. The result showed that the freeze injury inversion model based on 6 principal components can estimate the winter wheat freeze injury accurately with the coefficient of determination (R2) of 0. 697 5, root mean square error (RMSE) of 0. 184 2, and the accuracy of 0. 697 5. And the model was verified. It can be concluded that the PCA technology has been shown to be very promising in detecting winter wheat freeze injury effectively, and provide important reference for detecting other stress on crop.

[Inversion of organic matter content of the north fluvo-aquic soil based on hyperspectral and multi-spectra].

The present study aims to assess the feasibility of multi-spectral data in monitoring soil organic matter content. The data source comes from hyperspectral measured under laboratory condition, and simulated multi-spectral data from the hyperspectral. According to the reflectance response functions of Landsat TM and HJ-CCD (the Environment and Disaster Reduction Small Satellites, HJ), the hyperspectra were resampled for the corresponding bands of multi-spectral sensors. The correlation between hyperspectral, simulated reflectance spectra and organic matter content was calculated, and used to extract the sensitive bands of the organic matter in the north fluvo-aquic soil. The partial least square regression (PLSR) method was used to establish experiential models to estimate soil organic matter content. Both root mean squared error (RMSE) and coefficient of the determination (R2) were introduced to test the precision and stability of the modes. Results demonstrate that compared with the hyperspectral data, the best model established by simulated multi-spectral data gives a good result for organic matter content, with R2=0.586, and RMSE=0.280. Therefore, using multi-spectral data to predict tide soil organic matter content is feasible.

[Change of LAI and spectral response for rice under flood and waterlogging stress].

In order to provide the foundational theoretical support for flood loss estimation of rice with RS, the change of leaf area index (LAI) and canopy spectral response during four developmental stages and three waterlogging depths were studied, and the LAI estimation model was established with spectra characteristics parameter using regression analysis method. The results show that LAI value decreases as water depth increases in tillering, jointing and heading stages, and LAI value under complete submergence decreased by 36. 36% than CK in jointing stages. "Double-Peak" presented in the canopy first derivative spectra of 680-760 nm where the red edge parameters existed, and the main peak is located in the 724-737 nm with 701 and 718 nm exhibiting secondary peak. With water depth increasing, "Triple-Peak" emerges especially. The red edge position moves to long-wavelength direction in each developmental stage. Blue shift of red edge amplitude and red edge area was detected in tillering, jointing and filling stages, while red shift appeared in heading stage. The relationship between spectra characteristics parameters and LAI were investigated during 4 growth stages, results were not consistently significant at any wavelengths, and the leaf area indices were significantly correlative to the spectra parameters before heading stage, so the spectra parameters before heading stage can be used to estimate the leaf area indices, and a regression model based on parameter D(lambda737)/D(lambda718) was recommended. Therefore the variation range of LAI for rice could response to the stress intensity directly, and the regression model LAI= 3. 138(D(lambda737)/D(lambda718))-0. 806 can precisely estimate the leaf area index under flooding and waterlogging stress.

[Monitoring stripe rust of winter wheat using PHI based on sensitive bands].

Forty six points representing different severity degree of stripe rust were established in winter wheat field. The canopy reflectance was collected by an ASD hand-held spectrometer at each point. Meanwhile, the diseases index was investigated. These data were used for the following analysis. Firstly, the relationships between diseases index and reflectance of bands in the range of 300-1500 nm were analyzed. The sensitive bands were selected for stripe rust detecting. Secondly, considering the character of PHI image, red bands (620-718 nm) and near infrared bands (770-805 nm) were assigned as the best bands. Finally, the mean reflectance of red bands (620-718 nm) and near infrared bands (770-805 nm) was calculated respectively to construct the reverse model with the observed diseases indexes: DI = 19.241 R1 - 2.20667 R2 + 12.2744. With this model, the severity degree of stripe rust of winter wheat was monitored successfully in PHI image.

[The analysis of consistency between HJ-1B and Landsat 5 TM for retrieving LST based on the single-channel algorithm].

To ascertain whether the thermal infrared image of HJ-1B which has the similar sensor parameter and setting to Landsat 5 TM6 image is applicable for retrieving the land surface temperature (LST), a comparison of retrieved LST between two types of sensors was conducted. Two scenes of thermal infrared images that came from different sensors were acquired in 5th, Apr 2009, which covered the same region in Beijing. To retrieve LST, a generalized single-channel algorithm developed by Jiménez-Muñoz and Sobrino was applied. The LST of study area for both images was thus generated. Based on the LST mapping results and corresponding statistics, an apparent trend could be observed which indicated the consistency in both LST value and its spatial distribution. Consequently, the performance of HJ-IB IRS serving as the data source for LST retrieval was assessed and illustrated in this study. Besides, a high temporal resolution as well as wide swath of the HJ-IRS data suggested its potential in application.

[Remote sensing resource monitoring on Atractylodes lancea].

Remote sensing technology was used for investigation of the resources of Atractylodes lancea. Firstly, the general situation of Jiangshu Maoshan and A. lancea in Maoshan was introduced; Secondly, the methods of remote sensing on the resource of the wild drugs were explained. Thirdly, the TM images were interpret according to the differences of the objects reflex spectrum, and growth environments in Damao mountain, Ermao mountain and Xiaomao mountain were divided into different sub-areas according to the results of the field investigations. Finally, the resource of A. lancea in Jiangshu Maoshan was estimated.

[Study on dynamical changes of Ginkgo biloba resources in Pizhou city of Jiangsu Province base on RS and GIS].

OBJECTIVE: To analyse the dynamical changes of the Ginkgo biloba's resources from 2001 to 2006, in Pizhou city, Jiangshu province by useing spatial analytical function of GIS and RS technology. METHOD: Use the GIS and RS technology, extracted the information of G. biloba by scientific investigation, researched the spatial distribution and dynamical changes of G. biloba based on landsat 5 TM: the Apr. 3rd, 2001; Jan. 16th, 2005; July 30th, 2006. RESULT: Ginkgo biloba's resource was 1.61 x 10(5) hm2 in 2001, 1.84 x 10(5) hm2 in 2005, 1.88 x 10(5) hm2 in 2006. CONCLUSION: Ginkgo biloba's resource rised from 1.61 x 10(5) hm2 to 1.88 x 10(5) hm2 from 2001 to 2006, showed the gradually rise.

[Optimizing management on degraded grassland in Xilin River Basin based on ecological risk assessment].

By the methods of remote sensing (RS) and geographic information system (GIS), and based on the estimations of degradation degree, risk degree and easy-restoration degree of degraded grasslands, an ecological management index (EMI) model of grassland was established to approach the practical ways of optimizing management of degraded grassland. A case study in the Xilin River Basin of Inner Mongolia showed that this model could quantitatively analyze the degradation degree, risk degree and easy-restoration degree of the grasslands under different optimizing management levels, which was of significance for applying rational measures with pertinence, and beneficial to the optimal allocation of resources during the management of degraded grassland. The EMI model could integrate most concerned information, which made it applicable widely.