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Diabetic retinopathy (DR) is a sight-threatening complication of diabetes. IRS-1 was predicted to be the target gene of microRNA-126 (miR-126). The present study was designed to illustrate the involvement of miR-126 in the regulation of DR via targeting IRS-1. The present study revealed that the expression of miR-126 was significantly decreased while IRS-1 expression was increased in endothelial cells (ECs) and retinal pericytes (RPs) from a DR mouse model compared with healthy controls. Furthermore, a luciferase reporter assay confirmed the interaction between miR-126 and IRS-1. Following transfection with anmiR-126 mimic or miR-126 inhibitor, overexpression of miR-126 was demonstrated to suppress the invasion and viability of ECs and RPs and to inhibit the IRS-1 and phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) pathway protein expression levels, with inhibition of miR-126 leading to reverse results. Furthermore, transfection with small interfering RNA targeting IRS-1 altered the miR-126-induced effects observed in ECs, indicating that miR-126 may suppress angiogenesis in DR via inhibition ofIRS-1 expression. Taken together, the results of the present study suggested that miR-126 affected the expression of IRS-1, resulting in downregulated expression of PI3K/Akt pathway proteins, and also suppressed cell invasion and viability. These results may provide a potential therapeutic strategy for DR. [ABSTRACT FROM AUTHOR]

Accurately modeling the land surface phenology based on satellite data is very important to the study of vegetation ecological dynamics and the related ecosystem process. In this study, we developed a Sigmoid curve (S-curve) function by integrating an asymmetric Gaussian function and a logistic function to fit the leaf area index (LAI) curve. We applied the resulting asymptotic lines and the curvature extrema to derive the vegetation phenophases of germination, green-up, maturity, senescence, defoliation and dormancy. The new proposed S-curve function has been tested in a specific area (Shangdong Province, China), characterized by a specific pattern in leaf area index (LAI) time course due to the dominant presence of crops. The function has not yet received any global testing. The identified phenophases were validated against measurement stations in Shandong Province. (i) From the site-scale comparison, we find that the detected phenophases using the S-curve (SC) algorithm are more consistent with the observations than using the logistic (LC) algorithm and the asymmetric Gaussian (AG) algorithm, especially for the germination and dormancy. The phenological recognition rates (PRRs) of the SC algorithm are obviously higher than those of two other algorithms. The S-curve function fits the LAI curve much better than the logistic function and asymmetric Gaussian function; (ii) The retrieval results of the SC algorithm are reliable and in close proximity to the green-up observed data whether using the AVHRR LAI or the improved MODIS LAI. Three inversion algorithms shows the retrieval results based on AVHRR LAI are all later than based on improved MODIS LAI. The bias statistics reveal that the retrieval results based on the AVHRR LAI datasets are more reasonable than based on the improved MODIS LAI datasets. Overall, the S-curve algorithm has the advantage of deriving vegetation phenophases across time and space as compared to the LC algorithm and the AG algorithm. With the SC algorithm, the vegetation phenophases can be extracted more effectively. [ABSTRACT FROM AUTHOR]

Climate change inevitably leads to changes in hydrothermal circulation. However, thermal-hydrologic exchanging caused by land cover change has also undergone ineligible changes. Therefore, studying the comprehensive effects of climate and land cover changes on land surface water and heat exchanges enables us to well understand the formation mechanism of regional climate and predict climate change with fewer uncertainties. This study investigated the land surface thermal-hydrologic exchange across southern China for the next 40 years using a land surface model (ecosystem-atmosphere simulation scheme (EASS)). Our findings are summarized as follows. (i) Spatiotemporal variation patterns of sensible heat flux (H) and evapotranspiration (ET) under the land cover scenarios (A2a or B2a) and climate change scenario (A1B) are unanimous. (ii) Both H and ET take on a single peak pattern, and the peak occurs in June or July. (iii) Based on the regional interannual variability analysis, H displays a downward trend (10%) and ET presents an increasing trend (15%). (iv)The annual average H and ET would, respectively, increase and decrease by about 10% when woodland converts to the cultivated land. Through this study, we recognize that land surface water and heat exchanges are affected greatly by the future climate change as well as land cover change. [ABSTRACT FROM AUTHOR]