Soil erosion control (SEC), carbon sequestration (CAS), and soil moisture (SMO) strongly interact in the semi-arid Loess Plateau. Since SMO has supportive effects on SEC and CAS, it can be considered as ecosystem service (ES), and there is an immediate need to coordinate the relationships among these ecosystem services (ESs) to promote the sustainability of vegetation recovery. In this study, we quantified the ESs, ES trade-offs, and the environmental factors in 151 sample plots in the Ansai watershed, and we used a redundancy analysis (RDA) to clarify the effects of environmental factors on these ESs and their trade-offs. The results were as follows: (1) the general trend in the SEC of vegetation types was Robinia pseudoacacia (CH)>native grass (NG)>small arbor (ST)>Hippophae rhamnoides (SJ)>artificial grass (AG)>Caragana korshinskii (NT)>apple orchard (GY)>crop (CP); the CAS trend was CH>SJ>NT>AG>CP>ST>GY>NG; and the SMO trend was CP>NG>GY>AG>SJ>ST>CH>NT. (2) For SEC-SMO trade-offs, the influence of vegetation type, altitude, silt and sand composition was dominant. The arrangement of NG, AG, and SJ could decrease the extent of the trade-offs. (3) For CAS-SMO trade-offs, vegetation coverage and types were the dominant factors, but the effects were not complex. The extent of these trade-offs was lowest for NT, and that for SJ was the second lowest. (4) Considering the relationships among the three ESs, SJ was the most appropriate afforestation plant. Combing the vegetation types, slope position, slope gradient, and soil properties could regulate these ES relationships. The dominant factors influencing ES trade-offs varied among the different soil layers, so we must consider the corresponding influencing factors to regulate ESs. Moreover, manual management measures were also important for coordinating the ES relationships. Our research provides a better understanding of the mechanisms influencing the relationships among ESs.

Long non-coding RNAs play critical roles in tumour progression. Through analysis of publicly available genomic datasets, we found that MIR22HG, the host gene of microRNAs miR-22-3p and miR-22-5p, is ranked among the most dysregulated long non-coding RNAs in glioblastoma. The main purpose of this work was to determine the impact of MIR22HG on glioblastoma growth and invasion and to elucidate its mechanistic function. The MIR22HG/miR-22 axis was highly expressed in glioblastoma as well as in glioma stem-like cells compared to normal neural stem cells. In glioblastoma, increased expression of MIR22HG is associated with poor prognosis. Through a number of functional studies, we show that MIR22HG silencing inhibits the Wnt/β-catenin signalling pathway through loss of miR-22-3p and -5p. This leads to attenuated cell proliferation, invasion and in vivo tumour growth. We further show that two genes, SFRP2 and PCDH15, are direct targets of miR-22-3p and -5p and inhibit Wnt signalling in glioblastoma. Finally, based on the 3D structure of the pre-miR-22, we identified a specific small-molecule inhibitor, AC1L6JTK, that inhibits the enzyme Dicer to block processing of pre-miR-22 into mature miR-22. AC1L6JTK treatment caused an inhibition of tumour growth in vivo. Our findings show that MIR22HG is a critical inducer of the Wnt/β-catenin signalling pathway, and that its targeting may represent a novel therapeutic strategy in glioblastoma patients.

Understanding the regime shifts of social-ecological systems (SES) and their local and spillover effects over a long time frame is important for future sustainability. We provide a perspective of processes unfolding over time to identify the regime shifts of a SES based on changes in the relationships between SES components while also addressing their drivers and local and spillover effects. The applicability of this approach has been demonstrated by analyzing the evolution over the past 1000 years of the SES in China’s Loess Plateau (LP). Five evolutionary phases were identified: “fast expansion of cultivation,” “slow expansion of cultivation,” “landscape engineering for higher production,” “transition from cultivation to ecological conservation,” and “revegetation for environment.” Our study establishes empirical links between the state (phase) of a SES to its drivers and effects. Lessons of single-goal driven and locally focused SES management in the LP, which did not consider these links, have important implications to long-term planning and policy formulation of SES.

BackgroundOcular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial.MethodsIn this paper, we develop an effective cost-sensitive deep residual convolutional neural network (CS-ResCNN) classifier to diagnose ophthalmic diseases using retro-illumination images. First, the regions of interest (crystalline lens) are automatically identified via twice-applied Canny detection and Hough transformation. Then, the localized zones are fed into the CS-ResCNN to extract high-level features for subsequent use in automatic diagnosis. Second, the impacts of cost factors on the CS-ResCNN are further analyzed using a grid-search procedure to verify that our proposed system is robust and efficient.ResultsQualitative analyses and quantitative experimental results demonstrate that our proposed method outperforms other conventional approaches and offers exceptional mean accuracy (92.24%), specificity (93.19%), sensitivity (89.66%) and AUC (97.11%) results. Moreover, the sensitivity of the CS-ResCNN is enhanced by over 13.6% compared to the native CNN method.ConclusionOur study provides a practical strategy for addressing imbalanced ophthalmological datasets and has the potential to be applied to other medical images. The developed and deployed CS-ResCNN could serve as computer-aided diagnosis software for ophthalmologists in clinical application.Electronic supplementary materialThe online version of this article (10.1186/s12938-017-0420-1) contains supplementary material, which is available to authorized users.

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