Assessment of the impact of wetland changes on carbon storage in coastal urban agglomerations from 1990 to 2035 in support of SDG15.1.

The quantitative assessment and spatial representation of wetland carbon storage, which play a critical role in the global carbon cycle and human production, can provide useful data and knowledge for decision-making in achieving sustainable development goals (SDGs). Currently, human activities and climate change impacts pose a challenge for the assessment of wetland carbon storage in coastal urban clusters. We proposed a "past-present-future" long time series refined wetland carbon storage assessment model using Guangxi Beibu Gulf (GBG) and Guangdong, Hong Kong, Macao and the Greater Bay Area (GBA) as the study area. The CLUE-S and InVEST models were coupled to conduct a comparative analysis of the spatial and temporal changes in wetland carbon storage and the spatial identification of damages from 1990 to 2035 and finally explore the sensitivity of wetland changes to carbon storage and quantitatively assess the SDG15.1 target. The results showed that (1) both urban clusters are characterized by many reservoirs/farming ponds, large river areas and few lakes. 1990-2035 rivers, shallow waters and mudflats have a decreasing trend to be distributed in the middle of their respective regions, mangroves are on an increasing trend, GBG is mainly distributed in the Maowei Sea and GBA is mainly distributed in Shenzhen Bay. (2) Wetland carbon storage of the two urban clusters show an overall fluctuating downward trend, with rivers, lakes and beaches all showing a downward trend. The multiyear average carbon storage of the GBG are 3.2 times higher than those of the GBA. In ecological protection scenario (EPS) policy planning, it is reasonable to help wetland carbon sequestration in coastal urban clusters. (3) The trend of wetland change from 1990 to 2020 was positive for carbon storage. The rate of recovery of wetland carbon stocks is lower in GBA than in GBG under the natural increase scenario (NIS) and the ecological protection scenario (EPS). The economic development scenario (EDS) contributes least to the realisation of SDG15.1 for the coastal urban agglomeration. The ecological protection scenario (EPS) contributes the most to the realisation of SDG15.1 for the coastal urban agglomeration.

Evaluation of future wetland changes under optimal scenarios and land degradation neutrality analysis in the Guangdong-Hong Kong-Macao Greater Bay Area.

Wetlands are one of the most productive ecosystems on Earth and are also focused on by the Sustainable Development Goals (SDGs). However, global wetlands have suffered from considerable degradation due to rapid urbanization and climate change. To support wetland protection and SDG reporting, we predicted future wetland changes and assessed land degradation neutrality (LDN) from 2020 to 2035 under four scenarios in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). A simulation model combining random forest (RF), CLUE-S and multi-objective programming (MOP) methods was developed to predict wetland patterns under the natural increase scenario (NIS), economic development scenario (EDS), ecological protection and restoration scenario (ERPS) and harmonious development scenario (HDS). The simulation results indicated that the integration of RF and CLUE-S achieved good simulation accuracy, with OA over 0.86 and kappa indices over 0.79. From 2020 to 2035, the mangrove, tidal flat and agricultural pond increased while the coastal shallow water decreased under all scenarios. The river decreased under NIS and EDS, while increased under ERPS and HDS. The Reservoir decreased under NIS, while increased under the remaining scenarios. Among scenarios, the EDS had the largest built-up land and agricultural pond, and the ERPS had the largest forest and grassland. The HDS was a coordinated scenario that balanced economic development and ecological protection. Its natural wetlands were almost equal to these of ERPS, and its built-up land and cropland were almost equal to these of EDS. Then, the land degradation and SDG 15.3.1 indicators were calculated to support the LDN target. From 2020 to 2035, the ERPS had a smallest gap of 705.51 km2 from the LDN target, following the HDS, EDS and NIS. The SDG 15.3.1 indicator was lowest under the ERPS, with a value of 0.85 %. Our study could offer strong support for urban sustainable development and SDGs reporting.

Regulation of fast and slow light characteristics of the add-drop ring-resonator employing an assisted ring.

We demonstrate theoretically and experimentally that the fast and slow light characteristics of the add-drop ring-resonator (ADRR) can be regulated by introducing an assisted ring. This novel geometry is named ring-assisted add-drop ring-resonator (RA-ADRR). When the assisted ring is under-coupled, the fast and slow light characteristics of through and drop ports of the RA-ADRR will be reversed, which is different from the coupled resonator induced transparency (CRIT) studied previously. With the decrease of loss, the dispersion peak (dip) of the two ports will grow up towards the opposite directions and finally the inversion occurs. Meanwhile, we find that by increasing the circumference of the assisted ring, the dispersion of the two ports could be improved proportionally. The experimental results show that the maximum group delays of the through and drop ports are 115 ns and -485 ns, respectively. This novel phenomenon could greatly enhance the sensitivity of slow light interferometers and also has potential applications in optical communication, network, filtering and switching.

Continuous Dynamics Monitoring of Multi-Lake Water Extent Using a Spatial and Temporal Adaptive Fusion Method Based on Two Sets of MODIS Products.

Due to the widespread presence of noise, such as clouds and cloud shadows, continuous, high spatiotemporal-resolution dynamic monitoring of lake water extents is still limited using remote sensing data. This study aims to take an approach to mapping continuous time series of highly-accurate lake water extents. Four lakes from diverse regions of China were selected as cases. In order to reduce the impact of noise and ensure high spatial and temporal resolution of the final results, two sets of MODIS products (including MOD09A1 and MOD13Q1) are used to extract water bodies. This approach mainly comprises preliminary classification, post processing and data fusion. The preliminary classification used the Random Forest (RF) classifier to efficiently and automatically obtain the initial classification results. Post-processing is implemented to repair the classification results affected by noise as much as possible. The processed results of the two sets of products are fused by using the Homologous Data-Based Spatial and Temporal Adaptive Fusion Method (HDSTAFM), which reduces the effect of noise and also improve the temporal and spatial resolution for the final water results. We determined the accuracy using Landsat-based water results, and the values of overall accuracy (OA), user's accuracy (UA), producer's accuracy (PA), and kappa coefficients (KC) are mostly greater than 0.9. Good correlation was achieved for a time series of water area and altimetry data, obtained by multiple satellites, and also for water-level data selected from hydrological stations.

Detection of the spatial patterns of water storage variation over China in recent 70 years.

Terrestrial water storage (TWS) variation is crucial for global hydrological cycles and water resources management under climatic changes. In the previous studies, changes in water storage of some part of China have been studied with GRACE data in recent ten years. However, the spatial pattern of changes in water storage over China may be different in a long period. Here, we aimed to present long-term spatial patterns of TWS over China between 1948 to 2015 by unique Global Land Data Assimilation System Version 2 data and identify possible factors to water storage changes. The results revealed that the inner-annual variations in TWS of China exhibited remarkable downward trends with decreased rate of 0.1 cm/yr. Meanwhile, we found that spatial patterns of TWS in China can be divided into three distinct sub-regions of TWS region with increased, TWS region with decreased, TWS region with insignificant variation. The Northeast had decreased trends (-0.05 cm/yr) due to climate change and anthropogenic activities. Urban expansion is a non-ignorable factor to TWS reduction in Jing-Jin-Ji region (r = 0.61); the west had increased from 1948 to 2015 (0.03 cm/yr) due to precipitation increased and recharge by glacier melt; the south had insignificant trends and TWS varied with precipitation (r = 0.78).

Evaluation of the Vegetation Coverage Resilience in Areas Damaged by the Wenchuan Earthquake Based on MODIS-EVI Data.

The concept of resilience was integrated into post-earthquake ecological restoration assessments in 10 counties heavily impacted by the 2008 Wenchuan earthquake. Ecological resilience was defined as the time interval required for the vegetation coverage to recover to pre-earthquake levels in damaged areas. MODIS-EVI data from May to August in 2000 to 2016 were used to calculate the ecological resilience by fitting the curve of recovery rate (RR) versus time. The following conclusions were reached: (1) An area of 424.1 km² sustained vegetation damage. (2) The vegetation recovery was found to be linear based on the statistical analysis of the most common components of the damaged areas; consequently, linear fitting was used to estimate the resilience. (3) In terms of vegetation coverage, 44.2% of the damaged areas have already recovered. The vast majority of damaged areas are predicted to achieve vegetation recovery by 2022, but 5.3% of the damaged areas will not recover within this time period and have no resilience. (4) The management of damaged areas near roads, rivers and mining operations, especially at elevations of 2000-2500 m, slopes greater than 30°, and precipitation levels greater than 1200 mm, should be prioritized in the future. (5) The innovations of this study include the method used to extract earthquake-related vegetation damage and the prediction of vegetation succession based on resilience.

[Spectral Study of LaCl3 in Aqueous Solutions].

In this study, the Raman and fluorescence spectra of LaCl3 solution were studied with theoretical calculation and spectroscopic experiments. Based on B3LYP method of density functional theory, with the 6-31G(D,P)+Def2-SV (P) based on the group level the lanthanum chloride solution of micro cluster structure is calculated. The results show that the micro cluster molecules tend to form a 9 coordination structure, which verifies the feasibility of the method. Theoretical and experimental Raman values are compared to the basic consistent. The addition of LaCl(3) leads to the increase of the peak intensity of the Raman spectra in the 300~600 cm-1 range, which may be caused by the superposition of the La-O vibration and the rocking peaks of O­H in aqueous solutions; In the 3 000~4 000 cm(-1) range, the peak of lanthanum chloride solution is narrow compared with water, which may be caused by the stretching vibration of O­H in lanthanum hydrate. Fluorescence emission spectra at 350 nm appear obvious new peak, the good linearity was obtained between the peak intensity and the concentrations, and a rapid method for the quantitative analysis of lanthanum chloride solution from the angle of the complex is also realized. On the same basis set level calculated fluorescence emission center of clusters, in the range of allowable error, the theoretical calculation and the experimental spectra are basically consistent, and the new peak of the experimental spectra are identified.

Assessing Nebraska playa wetland inundation status during 1985-2015 using Landsat data and Google Earth Engine.

Playa wetlands in Nebraska provide globally important habitats for migratory waterfowl. Inundation condition is an important indicator of playa wetland functionality. However, there is a lack of long-term continuous monitoring records for playa wetlands. The objective of this study was to determine a suitable index for Landsat images to map the playa inundation status in March and April during 1985-2015. Four types of spectral indices-negative normalized vegetation index, Normalized Difference Water Index (NDWI), modified NDWI, and Tasseled Cap Wetness-Greenness Difference (TCWGD)-were evaluated to detect playa inundation conditions from Landsat images. The results indicate that the TCWGD is the most suitable index for distinguishing playa inundation status. By using Landsat images and Google Earth Engine, we mapped the spring inundation condition of Nebraska playas during 1985-2015. The results show that the total inundated areas were 176.79 km2 in spring migratory season, representing 18.92% of the total area of playa wetlands. There were 9898 wetlands inundated at least once in either March or April during the past 30 years, representing 29.41% of a total of 33,659 historical wetlands. After comparing the historical hydric soil footprints and the inundated areas, the results indicate that the hydrological conditions of the majority of playas in Nebraska have changed. The inundated wetlands are candidates for protection and/or partial restoration, and the un-inundated wetlands need more attention for wetland restoration. Wetlands in areas enrolled in conservation easements had a significantly high level of playa inundation status than non-conserved wetlands during spring migratory seasons in the past decades.These conservation easements only count for 4.29% of the total footprint areas, but they have contributed 20.82% of the inundation areas in Nebraska during the past 30 years.

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

Use RUSLE2 model to assess the impact of soil erosion on playa inundation and hydrophyte conditions in the Rainwater Basin, Nebraska.

Playas in the Rainwater Basin region in Nebraska are globally important wetlands that are continuously threatened by culturally accelerated sedimentation. Using annual habitat survey data and wetland vegetation inventories, inundation and hydrophyte community distributions were evaluated for properties under different types of conservation status. Annual soil erosion rates from surrounding watersheds were calculated to estimate sediment accumulated rates using the Revised Universal Soil Loss Equation 2 (RUSLE2). The slope-length component of the RUSLE2 was derived from 2009 light detection and ranging (LiDAR) data after the methods described by Van Remortel (Computers & Geosciences 30:1043-1053, 2004). Wetlands enrolled in conservation programs were inundated more and were dominated to a greater degree by hydrophytes than wetlands not enrolled in these programs. The mean estimated soil erosion rate at the Rainwater Basin landscape level was 4.67 tons/ha/year, and the mean estimated sediment accumulation depth for public watersheds was estimated as 0.19 cm/year. Without appropriate conservation actions, the current inundated acres and wetland acres growing hydrophytes would be further reduced by sediment accumulation. The results illustrated the importance of conservation programs to protect wetlands.

[[Zn-(CH3CH2OH)n]2+ Clusters Probed with Fluorescence Spectroscopy and Computation].

In this paper the cluster structures of [Zn-(CH3CH2OH)n]2+ have been investigated with spectroscopic experiment and theoretical calculation. According to the fluorescence spectroscopy experiments, the fluorescence peak of ethanol molecules was found between 275~330 nm. A new peak appeared between 350~380 nm after the metal ions (Zn2+) was added into ethanol solution due to the generation of new clusters of molecules, and the original fluorescence peak of ethanol molecules became weak owing to the destroyed structure of ethanol molecules induced by Zn2+. The cluster structures of Zn2+ in water solution were investigated by using different methods. By comparing the results, a more accurate and fast B3LYP method of DFT was found and applied to optimize the possible structures of [Zn-(CH3CH2OH)n]2+. The results suggested that the first solvation shell of the system is up to six ethanol molecules, and thermodynamic parameters also shows the six kinds of molecular clusters which are likely in the solution. Moreover compared the theoretical fluorescence spectroscopy with experimental fluorescence spectroscopy, new clusters [Zn-(CH3CH2OH)n]2+ have been generated, with [Zn-(CH3CH2OH)n]2+(n=1~3) as main constructions.

[The Change Detection of High Spatial Resolution Remotely Sensed Imagery Based on OB-HMAD Algorithm and Spectral Features].

The high spatial resolution remotely sensed imagery has abundant detailed information of earth surface, and the multi-temporal change detection for the high resolution remotely sensed imagery can realize the variations of geographical unit. In terms of the high spatial resolution remotely sensed imagery, the traditional remote sensing change detection algorithms have obvious defects. In this paper, learning from the object-based image analysis idea, we proposed a semi-automatic threshold selection algorithm named OB-HMAD (object-based-hybrid-MAD), on the basis of object-based image analysis and multivariate alternative detection algorithm (MAD), which used the spectral features of remotely sensed imagery into the field of object-based change detection. Additionally, OB-HMAD algorithm has been compared with other the threshold segmentation algorithms by the change detection experiment. Firstly, we obtained the image object by the multi-solution segmentation algorithm. Secondly, we got the object-based difference image object using MAD and minimum noise fraction rotation (MNF) for improving the SNR of the image object. Then, the change objects or area are classified using histogram curvature analysis (HCA) method for the semi-automatic threshold selection, which determined the threshold by calculated the maximum value of curvature of the histogram, so the HCA algorithm has better automation than other threshold segmentation algorithms. Finally, the change detection results are validated using confusion matrix with the field sample data. Worldview-2 imagery of 2012 and 2013 in case study of Beijing were used to validate the proposed OB-HMAD algorithm. The experiment results indicated that OB-HMAD algorithm which integrated the multi-channel spectral information could be effectively used in multi-temporal high resolution remotely sensed imagery change detection, and it has basically solved the "salt and pepper" problem which always exists in the pixel-based change detection, and has mitigated the impact of building shadows and geometric registration error, and has improved the overall accuracy and kappa coefficient than other change detection algorithm, but it has more undetected error. By compared with the SNR of image object, we know that the MNF transformation could effectively improve to concentrate the change information.

[Case-control study on spinal leveraging manipulation and medicine for the treatment of degenerative scoliosis].

OBJECTIVE: To compare clinical effects of spinal leveraging manipulation and medicine for the treatment of degenerative scoliosis in pain and function. METHODS: From July 2010 to June 2013, 38 patients with degenerative scoliosis were randomly divided into spinal leveraging manipulation group and medicine group by coin tossing. In manipulation group, there were 9 males and 11 females aged from 58 to 74 years old with an average of (66.63±7.73), the courses of diseases ranged from 3 to 8 months with an average of (5.65±2.58), spinal leveraging manipulation(following meridian to straighten tendon,relieving spasm, osteopathy and massage, clearing and activating the channels and collaterals) were performed for 30 min, once a day, 4 days for a period treatment, totally 9 courses. In medicine group, there were 8 males and 10 females aged from 57 to 70 years old with an average of (63.51±6.61) the courses of diseases ranged from 3 to 5 months with an average of (4.82±1.43), celecoxib with eperisone hydrochloride were orally taken, 4 days for a period treatment, totally 9 courses. VAS score, Cobb angle and ODI score were measured. RESULTS: After treatment, VAS score in manipulation group was (5.38±0.99), (6.36±1.31) in medicine group,and had significant meaning (t=2.618, P<0.05); there was significant differences in Cobb angle between manipulation group (16.51±4.89)° and medicine group (19.85±5.03) °(t=2.074,P<0.05); and had obviously meaning in ODI score between manipulation group (20.20±2.93) and medicine group (26.01±3.11) (t=5.592, P<0.05). CONCLUSION: Spinal leveraging manipulation for degenerative scoliosis could regulate muscle balance on both side of spine, correct coronal imbalances in spine, recover normal sequence of spine, reduce and remove opperssion and stimulation of nerve root, relieve pain in leg and waist and further improve quality of life.

miR-335 inhibits the proliferation and invasion of clear cell renal cell carcinoma cells through direct suppression of BCL-W.

Increasing evidence has demonstrated that small non-coding microRNAs (miRNAs) play important roles in cancer development and progression. Recent studies have shown that microRNA-335 (miR-335) functions as an oncogene or a tumor suppressor in various human cancer types, but its role in clear cell renal cell carcinoma (ccRCC) remains poorly understood. In our study, we firstly found that the expression level of miR-335 was significantly downregulated in ccRCC tissues versus corresponding non-tumor tissues and the low expression of miR-335 was significantly associated with lymph node metastasis, larger tumor size, and poor T stage. Then, we found that overexpression of miR-335 significantly suppressed the proliferation and invasion of 786-O and CaKi-1 ccRCC cell lines. We subsequently found that miR-335 could interact with the 3'-untranslated regions (3'UTR) of B-cell CLL/lymphoma 2 like 2 (BCL-W or BCL2L2) messenger RNA (mRNA) and repress its expression. In addition, re-expression of BCL-W (without the 3'UTR) could partially abrogate the miR-335-induced 786-O and CaKi-1 ccRCC cell proliferation and invasion inhibition. Furthermore, we found that expression patterns of miR-335 were inversely correlated with those of BCL-W mRNA in ccRCC tissues. Taken together, these results indicate that miR-335 acts as a novel tumor suppressor to regulate ccRCC cell proliferation and invasion through downregulation of BCL-W expression.

[Comparison of red edge parameters of winter wheat canopy under late frost stress].

In the present study, late frost experiments were implemented under a range of subfreezing temperatures (-1 - -9 degrees C) by using a field movable climate chamber (FMCC) and a cold climate chamber, respectively. Based on the spectra of winter wheat canopy measured at noon on the first day after the frost experiments, red edge parameters REP, Dr, SDr, Dr(min), Dr/Dr(min) and Dr/SDr were extracted using maximum first derivative spectrum method (FD), linear four-point interpolation method (FPI), polynomial fitting method (POLY), inverted Gaussian fitting method (IG) and linear extrapolation technique (LE), respectively. The capacity of the red edge parameters to detect late frost stress was explicated from the aspects of the early, sensitivity and stability through correlation analysis, linear regression modeling and fluctuation analysis. The result indicates that except for REP calculated from FPI and IG method in Experiment 1, REP from the other methods was correlated with frost temperatures (P < 0.05). Thereinto, significant levels (P) of POLY and LE methods all reached 0.01. Except for POLY method in Experiment 2, Dr/SDr from the other methods were all significantly correlated with frost temperatures (P < 0.01). REP showed a trend to shift to short-wave band with decreasing temperatures. The lower the temperature, the more obvious the trend is. Of all the REP, REP calculated by LE method had the highest correlation with frost temperatures which indicated that LE method is the best for REP extraction. In Experiment 1 and 2, only Dr(min) and Dr/Dr(min), calculated by FD method simultaneously achieved the requirements for the early (their correlations with frost temperatures showed a significant level P < 0.01), sensitivity (abso- lute value of the slope of fluctuation coefficient is greater than 2.0) and stability (their correlations with frost temperatures al- ways keep a consistent direction). Dr/SDr calculated from FD and IG methods always had a low sensitivity in Experiment 2. In Experiment 1, the sensitivity of Dr/SDr from FD was moderate and IG was high. REP calculated from LE method had a lowest sensitivity in the two experiments. Totally, Dr(min) and Dr/Dr(min) calculated by FD method have the strongest detection capacity for frost temperature, which will be helpful to conducting the research on early diagnosis of late frost injury to winter wheat.

An integrated use of topography with RSI in gully mapping, Shandong Peninsula, China.

Taking the Quickbird optical satellite imagery of the small watershed of Beiyanzigou valley of Qixia city, Shandong province, as the study data, we proposed a new method by using a fused image of topography with remote sensing imagery (RSI) to achieve a high precision interpretation of gully edge lines. The technique first transformed remote sensing imagery into HSV color space from RGB color space. Then the slope threshold values of gully edge line and gully thalweg were gained through field survey and the slope data were segmented using thresholding, respectively. Based on the fused image in combination with gully thalweg thresholding vectors, the gully thalweg thresholding vectors were amended. Lastly, the gully edge line might be interpreted based on the amended gully thalweg vectors, fused image, gully edge line thresholding vectors, and slope data. A testing region was selected in the study area to assess the accuracy. Then accuracy assessment of the gully information interpreted by both interpreting remote sensing imagery only and the fused image was performed using the deviation, kappa coefficient, and overall accuracy of error matrix. Compared with interpreting remote sensing imagery only, the overall accuracy and kappa coefficient are increased by 24.080% and 264.364%, respectively. The average deviations of gully head and gully edge line are reduced by 60.448% and 67.406%, respectively. The test results show the thematic and the positional accuracy of gully interpreted by new method are significantly higher. Finally, the error sources for interpretation accuracy by the two methods were analyzed.

A quantitative study of gully erosion based on object-oriented analysis techniques: a case study in Beiyanzikou catchment of Qixia, Shandong, China.

This paper took a subregion in a small watershed gully system at Beiyanzikou catchment of Qixia, China, as a study and, using object-orientated image analysis (OBIA), extracted shoulder line of gullies from high spatial resolution digital orthophoto map (DOM) aerial photographs. Next, it proposed an accuracy assessment method based on the adjacent distance between the boundary classified by remote sensing and points measured by RTK-GPS along the shoulder line of gullies. Finally, the original surface was fitted using linear regression in accordance with the elevation of two extracted edges of experimental gullies, named Gully 1 and Gully 2, and the erosion volume was calculated. The results indicate that OBIA can effectively extract information of gullies; average range difference between points field measured along the edge of gullies and classified boundary is 0.3166 m, with variance of 0.2116 m. The erosion area and volume of two gullies are 2141.6250 m(2), 5074.1790 m(3) and 1316.1250 m(2), 1591.5784 m(3), respectively. The results of the study provide a new method for the quantitative study of small gully erosion.

[Change of vegetation net primary productivity in Yellow River watersheds from 2001 to 2010 and its climatic driving factors analysis].

Based on the MODIS-NDVI remotely sensed imagery, this paper analyzed the spatial distribution of vegetation net primary production (NPP) calculated by CASA model in Yellow River watersheds from 2001 to 2010. Associated with the temperature and precipitation data in the same period, this article respectively analyzed the change trends of vegetation NPP in six ecosystems with different spatial and temporal scales, and the relationship between NPP and climate factors. The results indicated that in terms of spatial scale, the vegetation NPP gradually reduced from northwest to southeast, the average of annual NPP was 108.53 Tg C, and the spatial distribution of vegetation NPP was highly related with the land cover types. In terms of temporal scale, the vegetation NPP gradually increased from 2001 to 2010, but this change trend had large differences in these regions. On annual level, the vegetation NPP had no significant correlation with climate factors, but precipitation and temperature had considerable impacts on the vegetation NPP on monthly level. The correlations between NPP and climate factors were different in different ecosystems, so did the time lag effect of the climate factors. The air temperature response of the NPP variation was relatively sensitive in forest ecosystem and the precipitation response was significant in grassland and wetland ecosystems. Additionally, the precipitation response of the NPP variation in grassland ecosystem had time lag effect and so did the air temperature response in desert ecosystem.

[Prediction of methane emission of paddy field based on the support vector regression model].

The methane emission data of paddy fields was obtained by using the static chamber and gas chromatography, and six parameters including atmospheric temperature, soil temperature at 5 cm depth, pH of soil, Eh of soil, soil moisture and ground biomass were selected as the primary influencing factors of methane emission. The support vector regression (epsilon-SVR) model was built on the optimization of structural risk minimization, and the parameters of the epsilon-SVR model were optimized using Leave-one-out Cross Validation (LOOCV). The prediction accuracy of model was evaluated by k-fold cross validation with the mean relative error (MRE) and the root mean square error (RMSE). In addition, the accuracy of the epsilon-SVR model was analyzed by comparison with the Back Propagation-Artificial Neural Network (BP-ANN) model. The results indicated that the predicted value of the epsilon-SVR model with the parameters C and epsilon optimized by LOOCV was in good agreement with the measured value, and the average MRE of test samples was 44% and the average RMSE was 16.21 mg x (m2 x h)(-1) in the process of 11-fold cross validation. Compared with the BP-ANN model, the correlation coefficient was 0.863, and all the indicators were better. It demonstrated that the 8-SVR model could be applied to the prediction of methane emission of paddy fields.

Increased expression of ALDH1A1 protein is associated with poor prognosis in clear cell renal cell carcinoma.

Aldehyde dehydrogenase 1A1 (ALDH1A1) has been characterized as a cancer stem cell marker in different types of tumors. It plays a key role in various biological processes in tumor, including cell proliferation, invasion and chemoresistance. Recently, ALDH1A1 has been described as a prognostic marker in various tumors. In this study, we detected the expression of ALDH1A1 in 95 clear cell renal cell carcinoma (ccRCC) by immunohistochemistry and correlated it with clinicopathological parameters and prognosis. We further explored the correlation of ALDH1A1 expression to proliferation, invasion and drug sensitivity of renal cancer cell in vitro by silencing of ALDH1A1 in A498 renal cell line. ALDH1A1 protein showed high expression in 53 of 95 cases of ccRCC (56.8 %), which was significantly higher than that in normal tissues (5/23, 21.7 %). ALDH1A1 overexpression was significantly associated with tumor stage (P = 0.000), recurrence (P = 0.000), tumor size (P = 0.000) and vascular invasion (P = 0.023). The Kaplan-Meier survival analysis demonstrated that ALDH1A1 overexpression was significantly associated with shorter recurrence-free survival and overall survival (P = 0.003 and P = 0.008, respectively). Multivariate analysis demonstrated that ALDH1A1 was an independent prognostic factor for patients with ccRCC. Experiments in vitro further showed ALDH1A1 played an essential role in proliferation, invasion and drug sensitivity of renal cancer cell. In conclusion, ALDH1A1 might be a potential molecular marker in ccRCC, which provided us with a new therapeutic target in ccRCC.