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Monitoring long-term biomass dynamics in drylands is of great importance for many environmental applications including land degradation and global carbon cycle modeling. Biomass has extensively been estimated based on the normalized difference vegetation index (NDVI) as a measure of the vegetation greenness. The vegetation optical depth (VOD) derived from satellite passive microwave observations is mainly sensitive to the water content in total aboveground vegetation layer. VOD therefore provides a complementary data source to NDVI for monitoring biomass dynamics in drylands, yet further evaluations based on ground measurements are needed for an improved understanding of the potential advantages. In this study, we assess the capability of a long-term VOD dataset (1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) to capture the temporal and spatial variability of in situ measured green biomass (herbaceous mass and woody plant foliage mass) in the semi-arid Senegalese Sahel. Results show that the magnitude and peak time of VOD are sensitive to the woody plant foliage whereas NDVI seasonality is primarily governed by the green herbaceous vegetation stratum in the study area. Moreover, VOD is found to be more robust against typical NDVI drawbacks of saturation effect and dependence on plant structure (herbaceous and woody compositions) across the study area when used as a proxy for vegetation productivity. Finally, both VOD and NDVI well reflect the spatial and inter-annual dynamics of the in situ green biomass data; however, the seasonal metrics showing the highest degree of explained variance differ between the two data sources. While the observations in October (period of in situ data collection) perform best for VOD (r 2 = 0.88), the small growing season integral (sensitive to recurrent vegetation) have the highest correlations for NDVI (r 2 = 0.90). Overall, in spite of the coarse resolution, the study shows that VOD is an efficient proxy for estimating green biomass of the entire vegetation stratum in the semi-arid Sahel and likely also in other dryland areas.

Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone’s acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals.

The usage of polymer composites in various engineering fields has increased. However, the long-term service performance of such materials under aggressive conditions is still poorly understood, which limits the development of safe and economically effective designs. In this study, the aging of an epoxy resin and its carbon fiber-reinforced polymer (CFRP) composites upon immersion in water, acidic, and alkaline solutions was evaluated at different temperatures. The service life of the CFRP composites under various conditions could be predicted by the Arrhenius theory. The thermal and mechanical analysis results indicated that the CFRP composites were more vulnerable to HCl owing to the higher moisture absorption and diffusion of HCl into their cracks. The scanning electron microscopy results showed that the polymer matrix was damaged and degraded. Therefore, to allow long-term application, CFRP composites must be protected from acidic environments.

Cartilage injury is difficult to repair since the cartilage tissue lacks self-restoration ability. Improved formation of chondrocytes differentiated from the mesenchymal stem cells (MSC) by genetic regulation is a potentially promising therapeutic option. SOX9 is a critical transcription factor for mesenchymal condensation prior to chondrogenesis. Previous studies demonstrated that several microRNAs (miRNAs or miRs) play a critical role in the chondrogenic differentiation of MSCs. However, the interactional relations between miR-30a and SOX9 during chondrogenic differentiation of MSCs need to be further elucidated. In the present study, human bone marrow-derived mesenchymal stem cells have been isolated and induced into chondrogenic differentiation to imitate the cartilage formation in vitro. Additionally, the expression levels of several miRNAs that were reported to interact with the SOX9 3′untranslated region (UTR) were examined by using reverse transcription-quantitative PCR. The interactional relations between candidate miRNAs and SOX9 were verified with the transfection of a miRNA mimic or inhibitor and a luciferase reporter gene assay. The results indicate that miR-30a and miR-195 were consistently increased during MSC chondrogenic differentiation. Additionally, the binding of miR-30a to the SOX9 3UTR was verified. Then, the authors upregulated the expression of miR-30a and found that MSC chondrogenic differentiation was inhibited. Taken together, the results of the present study demonstrate that miR-30a has a negative regulatory effect on MSC chondrogenic differentiation by targeting SOX9. Advances in epigenetic regulating methods will likely be the future of systemic treatment of cartilage injury.

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Abstract:The increasingly intensive and extensive coal mining activities on the Loess Plateau pose a threat to the fragile local ecosystems. Quantifying the effects of coal mining activities on environmental conditions is of great interest for restoring and managing the local ecosystems and resources. This paper generates dense NDVI (Normalized Difference Vegetation Index) time series between 2000 and 2011 at a spatial resolution of 30 m by blending Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) data using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and further evaluates its capability for mapping vegetation trends around a typical coalfield on the Loss Plateau. Synthetic NDVI images were generated using (1) STARFM-generated NIR (near infrared) and red band reflectance data (scheme 1) and (2) Landsat and MODIS NDVI images directly as inputs for STARFM (scheme 2). By comparing the synthetic NDVI images with the corresponding Landsat NDVI, we found that scheme 2 consistently OPEN ACCESSRemote Sens. 2013, 5 4256 generated better results (0.70 < R 2 < 0.76) than scheme 1 (0.56 < R 2 < 0.70) in this study area. Trend analysis was then performed with the synthetic dense NDVI time series and the annual maximum NDVI (NDVI max ) time series. The accuracy of these trends was evaluated by comparing to those from the corresponding MODIS time series, and it was concluded that both the trends from synthetic/MODIS NDVI dense time series and synthetic/MODIS NDVI max time series (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) were highly consistent. Compared to trends from MODIS time series, trends from synthetic time series are better able to capture fine scale vegetation changes. STARFM-generated synthetic NDVI time series could be used to quantify the effects of mining activities on vegetation, but the test areas should be selected with caution, as the trends derived from synthetic and MODIS time series may be significantly different in some areas.

A repetitive basic-acidic motif in U1-70K is critical for its phase separation and aggregation.