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National Key Programme for Developing Basic Science [2009CB421105]; Chinese Academy of Sciences [KSCX1-YW-09-01]

The summer day-by-day precipitation data of 97 meteorological stations on the Qinghai-Tibet Plateau from 1961 to 2004 were selected to analyze the temporal-spatial distribution through accumulated variance, correlation analysis, regression analysis, empirical orthogonal function, power spectrum function and spatial analysis tools of GIS. The result showed that summer precipitation occupied a relatively high proportion in the area with less annual precipitation on the Plateau and the correlation between summer precipitation and annual precipitation was strong. The altitude of these stations and summer precipitation tendency presented stronger positive correlation below 2000 m, with correlation value up to 0.604 (α=0.01). The subtracting tendency values between 1961–1983 and 1984–2004 at five altitude ranges (2000–2500 m, 2500–3000 m, 3500–4000 m, 4000–4500 m and above 4500 m) were above zero and accounted for 71.4% of the total. Using empirical orthogonal function, summer precipitation could be roughly divided into three precipitation pattern fields: the Southeast Plateau Pattern Field, the Northeast Plateau Pattern field and the Three Rivers’ Headstream Regions Pattern Field. The former two ones had a reverse value from the north to the south and opposite line was along 35°N. The potential cycles of the three pattern fields were 5.33a, 21.33a and 2.17a respectively, tested by the confidence probability of 90%. The station altitudes and summer precipitation potential cycles presented strong negative correlation in the stations above 4500 m, with correlation value of −0.626 (α=0.01). In Three Rivers Headstream Regions summer precipitation cycle decreased as the altitude rose in the stations above 3500 m and increased as the altitude rose in those below 3500 m. The empirical orthogonal function analysis in June precipitation, July precipitation and August precipitation showed that the June precipitation pattern field was similar to the July’s, in which southern Plateau was positive and northern Plateau negative. But positive value area in July precipitation pattern field was obviously less than June’s. The August pattern field was totally opposite to June’s and July’s. The positive area in August pattern field jumped from the southern Plateau to the northern Plateau.

In this paper the normalized difference vegetation index (NDVI) and land surface temperature (Ts) derived from National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) 10-day composite data were adopted to construct an NDVI–Ts space, from which the temperature–vegetation dryness index (TVDI) is suggested. Soil moisture status in all of China from March to May 2000 was evaluated. To minimize the effect caused by spatial heterogeneity of climatic parameters, TVDI was calculated for each of the three agricultural climatic regions in China. The results show that severe drought was mainly distributed in northwestern China and part of north and south China; the area of severe drought increased from about 67 × 104 km2 in March to 126 × 104 km2 in May. To evaluate the efficiency of TVDI in soil moisture monitoring, the spatial pattern of TVDI was first compared with and verified by the measured soil moisture from more than 90 weather stations in China by the linear r...

Remote sensing is widely applied in the study of terrestrial primary production and the global carbon cycle. The researches on the spatial heterogeneity in images with different sensors and resolutions would improve the application of remote sensing. In this study two sites on alpine meadow grassland in Qinghai, China, which have distinct fractal vegetation cover, were used to test and analyze differences between Normalized Difference Vegetation Index (NDVI) and enhanced vegetation index (EVI) derived from the Huanjing (HJ) and Landsat Thematic Mapper (TM) sensors. The results showed that: 1) NDVI estimated from HJ were smaller than the corresponding values from TM at the two sites whereas EVI were almost the same for the two sensors. 2) The overall variance represented by HJ data was consistently about half of that of Landsat TM although their nominal pixel size is approximately 30m for both sensors. The overall variance from EVI is greater than that from NDVI. The difference of the range between the two sensors is about 6 pixels at 30m resolution. The difference of the range in which there is not more corrective between two vegetation indices is about 1 pixel. 3) The sill decreased when pixel size increased from 30m to 1km, and then decreased very quickly when pixel size is changed to 250m from 30m or 90m but slowly when changed from 250m to 500m. HJ can capture this spatial heterogeneity to some extent and this study provides foundations for the use of the sensor for validation of net primary productivity estimates obtained from ecosystem process models.

The spatial scaling of satellite data is faced widely and inevitably in remote sensing applications for the spatial heterogeneity of ecosystems. In this study variogram analysis was used to evaluate the spatial variability and the scale effects of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from Huanjing (that is, environment satellite sensor in Chinese, HJ-1A/B), Landsat-5 Thematic Mapper (TM), Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m, 500 m, 1 km, and the field sampled above-ground biomass (AGB). Results show that the overall spatial variance decreased when pixel size increased from 30 m (HJ and TM) to 1 km (MODIS) at the area of 10 km × 10 km. The value of 1 or 3×3 pixels approximately represent the above-ground biomass from the cyclic sampling design. This indicates that the HJ data can be used to retrieve the biomass and its scaling-up for its performance comparable with Landsat TM data, though both sensors were applicable than that of MODIS. Further the method to scale-up is a fundament approach to the validation and application of MODIS products and ecosystem model’s outputs on regional scale.

Fragile X syndrome, one of the most common forms of inherited mental retardation, is caused by expansion of the CGG repeat in the 5′-untranslated region of the X-linked Fmr1 gene, which results in transcriptional silencing and loss of expression of its encoded protein FMRP. The loss of FMRP increases proliferation and alters fate specification in adult neural progenitor cells (aNPCs). However, little is known about Fmr1 mRNA regulation at the transcriptional and post-transcriptional levels. In the present study, we report that miR-130b regulated Fmr1 expression by directly targeting its 3′-untranslated region (3′ UTR). Up-regulation of miR-130b in mouse embryonic neural progenitor cells (eNPCs) decreased Fmr1 expression, markedly increased eNPC proliferation and altered the differentiation tendency of eNPCs, suggesting that antagonizing miR-130b may be a new therapeutic entry point for treating Fragile X syndrome.

Light-use efficiency (LUE) is used commonly with remotely sensed and meteorological data to monitor terrestrial ecosystem primary production (NPP). Studying the variation in space of LUE is helpful to obtain a deep understanding of the carbon cycles. Spatial pattern of LUE in subtropics evergreen coniferous based on NPP were studied at local level. The results showed that the average of LUE was 0.71 g·MJ−1. LUE tracked closely with NPP and NPP gradually decreased from southeast to northwest in subtropics evergreen coniferous. In a linear regression, longitude, latitude, and precipitation accounted for approximately 50.5 % of the variability in LUE. LUE showed single-peaked curve followed with the increase of precipitation. The highest LUE was reached when the precipitation is about 1200 ∼ 1400 mm. Spatial patterns of the LUE were discussed based on the observation data, which provides very crucial data for further correction, improvement and evaluation of LUE model.

Monitoring crop growth status and yields using remote sensing data have been a challenges both in estimating the growing parameters and quantifying the seasonal changes. Traditionally, NOAA AVHRR data was applied to estimate and predict crop yields with statistical correlation methods. However, its spatial resolution of 8-km is not satisfying in monitoring crop growth on the site level. The launch of TERRA with moderate resolution imaging spectroradiometer (MODIS) instruments onboard began a new era in remote sensing of the Earth system which is providing a series of products of unparalleled quality and sophistication for the observation and biophysical monitoring of the terrestrial environment. Crop growth models simulate biophysical processes in the soil-crop-atmospheric system provide a continuous description of crop growth and development. Combining a growth model with the input parameters derived from remote sensing data provides spatial integrity as well as a real-time "calibration" of model parameters. A field study was conducted to evaluate the applicability of the 8-day MODIS leaf area index (LAI) data product in operational assessment of wheat growth condition and yields in the region of Yucheng, ShanDong Province, in China. The MODIS LAI product were used to compared with the DSSAT LAI--the output of crop simulation model (DSSAT) and the observed LAI. The MODIS LAI corresponded comparatively well with the DSSAT LAI in the early stage which have been tested well with the observed LAI, however in the later wheat growing stage, there are still some difference between the MODIS LAI and observed LAI. Limitations of this study and its conclusions are also discussed.

The determination of terrestrial ecosystem carbon source/sink spatial pattern is becoming one of the hottest problems and many environment politics focus on it. As a new tool for terrestrial ecosystem carbon modelling at large scale from field plot, to region, to global, remote sensing is applied to initialize, drive, and validate the model, combined with geophysics information system (GIS) and computer modelling. Carbon flux models with remote sensing data as input may be classified as light use efficiency model, process model, and eco-physiological model based on “big leaf” hypothesis. The model generally includes two parts: NPP and soil respiration model to estimate carbon flux based on the principle that the carbon flux of ecosystem equal NPP minus heterogeneity respiration (soil respiration). Remote sensing, however, is more applied in NPP modeling but little in soil respiration estimation. The latter mostly based on relationship between soil respiration and soil temperature and is highly developed. Since remote sensing is applied to retrieve land surface temperature (LST) with infrared waveband, a hypothesis was put forward, that is, land surface temperature retrieved from infrared waveband can substitute soil temperature to estimate soil respiration. The hypothesis was validated with a field experiment and result was given in this article. The experiment located in a winter wheat field at Quzhou experiment station, Hebei province, China, from Apr 19 to May 20, in 2002. The soil respiration rate was measured with CID photosynthesis system, and canopy infrared temperature, soil surface temperature were measured respectively at same time. The station provided us soil moisture content data of whole growth of winter wheat. The result shows that the soil CO 2 efflux from winter wheat field is -0.03~1.38μmolm -2 s -1 . Its diurnal variation is well fitted with univariate quartic curve. Its variation in winter wheat heading growth period well coincide with temperature and soil moisture content. The Pearson correlation analysis shows that, on the averaged sense, for a day, soil CO 2 efflux significantly correlated with the temperature of the air (T air ), the soil surface (T sur ), the averaged thermal (T inf ) temperature respectively at the p-level

The primary purpose of this study was to estimate the boundary between vegetated and non-vegetated areas and to assess the condition of desertification in central Asia and western China located in arid and semiarid regions. Remote sensing data used in this study are a time-series of 10-day maximum Normalized Difference Vegetation Index (NDVI) composites derived from Global Area Coverage of Advanced Very High Resolution Radiometer (AVHRR) from 1982 to 2000. Taking place and development of desertification in the arid and semiarid regions directly influence the density and growth status of vegetation, making surface vegetation a most important indicator to desertification assessment. Vegetation is very sparse in desert and therefore onset of green-up in the desert was undetectable with AVHRR NDVI data. The occurrence of onset of green-up, as determined with time series NDVI data was used to identify desert and non-desert areas. The coefficient of variation (CoV) of the monthly NDVI (maximum-value composite) is used as a parameter to characterize the changes of vegetation in this work. The CoV can be used to compare the amount of variation in different sets of samples data. Changes in the value of the pixel-level CoV over time can be interpreted as a measure of vegetative biomass change over that time. The method to detect and quantify changes in CoV values for each pixel over 20-year period for which data was available is based on linear regression. If the CoV values exhibit a statistically significant decrease over time, it is possible to conclude that the area imaged in that pixel is under desertification.

The scarcity of tissue-specific stem cells and the complexity of their surrounding environment have made molecular characterization of these cells particularly challenging. Through single-cell transcriptome and weighted gene co-expression network analysis (WGCNA), we uncovered molecular properties of CD133(+)/GFAP(-) ependymal (E) cells in the adult mouse forebrain neurogenic zone. Surprisingly, prominent hub genes of the gene network unique to ependymal CD133(+)/GFAP(-) quiescent cells were enriched for immune-responsive genes, as well as genes encoding receptors for angiogenic factors. Administration of vascular endothelial growth factor (VEGF) activated CD133(+) ependymal neural stem cells (NSCs), lining not only the lateral but also the fourth ventricles and, together with basic fibroblast growth factor (bFGF), elicited subsequent neural lineage differentiation and migration. This study revealed the existence of dormant ependymal NSCs throughout the ventricular surface of the CNS, as well as signals abundant after injury for their activation.