Many parts of the world experience frequent and severe droughts. Summer drought can significantly reduce primary productivity and carbon sequestration capacity. The impacts of spring droughts, however, have received much less attention. A severe and sustained spring drought occurred in southwestern China in 2010. Here we examine the influence of this spring drought on the primary productivity of terrestrial ecosystems using data on climate, vegetation greenness and productivity. We first assess the spatial extent, duration and severity of the drought using precipitation data and the Palmer drought severity index. We then examine the impacts of the drought on terrestrial ecosystems using satellite data for the period 2000-2010.Our results show that the spring drought substantially reduced the enhanced vegetation index (EVI) and gross primary productivity (GPP) during spring 2010 (March-May). Both EVI and GPP also substantially declined in the summer and did not fully recover from the drought stress until August. The drought reduced regional annual GPP and net primary productivity (NPP) in 2010 by 65 and 46 Tg C yr −1 , respectively. Both annual GPP and NPP in 2010 were the lowest over the period 2000-2010. The negative effects of the drought on annual primary productivity were partly offset by the remarkably high productivity in August and September caused by the exceptionally wet conditions in late summer and early fall and the farming practices adopted to mitigate drought effects. Our results show that, like summer droughts, spring droughts can also have significant impacts on vegetation productivity and terrestrial carbon cycling.

Recently, traditional Chinese medicine and medicinal herbs have attracted more attentions worldwide for its anti-tumor efficacy. Celastrol and Triptolide, two active components extracted from the Chinese herb Tripterygium wilfordii Hook F (known as Lei Gong Teng or Thunder of God Vine), have shown anti-tumor effects. Celastrol was identified as a natural 26 s proteasome inhibitor which promotes cell apoptosis and inhibits tumor growth. The effect and mechanism of Triptolide on prostate cancer (PCa) is not well studied. Here we demonstrated that Triptolide, more potent than Celastrol, inhibited cell growth and induced cell death in LNCaP and PC-3 cell lines. Triptolide also significantly inhibited the xenografted PC-3 tumor growth in nude mice. Moreover, Triptolide induced PCa cell apoptosis through caspases activation and PARP cleavage. Unbalance between SUMOylation and deSUMOylation was reported to play an important role in PCa progression. SUMO-specific protease 1 (SENP1) was thought to be a potential marker and therapeutical target of PCa. Importantly, we observed that Triptolide down-regulated SENP1 expression in both mRNA and protein levels in dose-dependent and time-dependent manners, resulting in an enhanced cellular SUMOylation in PCa cells. Meanwhile, Triptolide decreased AR and c-Jun expression at similar manners, and suppressed AR and c-Jun transcription activity. Furthermore, knockdown or ectopic SENP1, c-Jun and AR expression in PCa cells inhibited the Triptolide anti-PCa effects. Taken together, our data suggest that Triptolide is a natural compound with potential therapeutic value for PCa. Its anti-tumor activity may be attributed to mechanisms involving down-regulation of SENP1 that restores SUMOylation and deSUMOyaltion balance and negative regulation of AR and c-Jun expression that inhibits the AR and c-Jun mediated transcription in PCa.

Abstract. The Qinghai-Tibetan Plateau has been experiencing a distinct warming trend, and climate warming has a direct and quick impact on the alpine grassland ecosystem. We detected the greenness trend of the grasslands in the plateau using Moderate Resolution Imaging Spectroradiometer data from 2000 to 2009. Weather station data were used to explore the climatic drivers for vegetation greenness variations. The results demonstrated that the region-wide averaged normalized difference vegetation index (NDVI) increased at a rate of 0.036 yr −1 . Approximately 20% of the vegetation areas, which were primarily located in the northeastern plateau, exhibited significant NDVI increase trend (p-value <0.05). Only 4% of the vegetated area showed significant decrease trends, which were mostly in the central and southwestern plateau. A strong positive relationship between NDVI and precipitation, especially in the northeastern plateau, suggested that precipitation was a favorable factor for the grassland NDVI. Negative correlations between NDVI and temperature, especially in the southern plateau, indicated that higher temperature adversely affected the grassland growth. Although a warming climate was expected to be beneficial to the vegetation growth in cold regions, the grasslands in the central and southwestern plateau showed a decrease in trends influenced by increased temperature coupled with decreased precipitation.

Global land mapping of satellite-observed CO2 total columns using spatio-Global land mapping of satellite-observed CO2 total columns using spatiotemporal geostatistics temporal geostatistics

Agricultural greenhouses (AGs) are an important facility for the development of modern agriculture. Accurately and effectively detecting AGs is a necessity for the strategic planning of modern agriculture. With the advent of deep learning algorithms, various convolutional neural network (CNN)-based models have been proposed for object detection with high spatial resolution images. In this paper, we conducted a comparative assessment of the three well-established CNN-based models, which are Faster R-CNN, You Look Only Once-v3 (YOLO v3), and Single Shot Multi-Box Detector (SSD) for detecting AGs. The transfer learning and fine-tuning approaches were implemented to train models. Accuracy and efficiency evaluation results show that YOLO v3 achieved the best performance according to the average precision (mAP), frames per second (FPS) metrics and visual inspection. The SSD demonstrated an advantage in detection speed with an FPS twice higher than Faster R-CNN, although their mAP is close on the test set. The trained models were also applied to two independent test sets, which proved that these models have a certain transability and the higher resolution images are significant for accuracy improvement. Our study suggests YOLO v3 with superiorities in both accuracy and computational efficiency can be applied to detect AGs using high-resolution satellite images operationally.

Long-term changes in the water budget of lakes in the Tibetan Plateau due to climate change are of great interest not only for the importance of water management, but also for the critical challenge due to the lack of observations. In this paper, the water budget of Nam Co Lake during 1980-2010 is simulated using a dynamical monthly water balance model. The simulated lake level is in good agreement with field investigations and the remotely sensed lake level. The long-term hydrological simulation shows that from 1980 to 2010, lake level rose from 4718.34 to 4724.93 m, accompanied by an increase of lake water storage volume from 77.33 3 10 9 to 83.66 3 10 9 m 3 . For the net lake level rise (5.93 m) during the period 1980-2010, the proportional contributions of rainfall-runoff, glacier melt, precipitation on the lake, lake percolation, and evaporation are 104.7%, 56.6%, 41.7%, 222.2%, and 280.9%, respectively. A positive but diminishing annual water surplus is found in Nam Co Lake, implying a continuous but slowing rise in lake level as a hydrological consequence of climate change.

Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO2 emissions. Quantifying anthropogenic CO2 emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO2 concentration. In this study, we propose an approach for estimating anthropogenic CO2 emissions by an artificial neural network using column-average dry air mole fraction of CO2 (XCO2) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO2 anomalies (dXCO2) derived from XCO2 and anthropogenic emission data during 2010–2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO2 in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO2 emissions especially in the areas with high anthropogenic CO2 emissions. Our results indicate that XCO2 data from satellite observations can be applied in estimating anthropogenic CO2 emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO2 uptake and emissions, from satellite observations.

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