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Ecological restoration is an effective method for mitigating environmental degradation, controlling water loss, and diminishing soil erosion. Long-term ecological restoration projects are critical for sustainable regional economic and ecological development. To understand the ecological transition from 1982 to 2013 in the Loess Plateau (LP), this study analyzed the spatiotemporal variability of vegetation using the satellite-derived Normalized Difference Vegetation Index (NDVI) dataset, known as the third-generation Global Inventory Modeling and Mapping Studies (GIMMS3g) dataset. Furthermore, the relationships between NDVI, climate change, and the “Grain for Green” Program (GGP) were assessed. At the regional scale, the average growing season, spring, summer, and autumn NDVI showed a significant increase from 1982 to 2013, 1982 to 1999, and 2000 to 2013, most notably in the latter period. At the pixel scale, the average growing season, spring, summer, and autumn NDVI decreased from the southeast to the northwest LP during the three time periods. In most areas in the LP, this parameter increased during the three periods, especially in 2000–2013. Moreover, increasing precipitation resulted in an improvement in vegetation cover. The average NDVI in the growing season was positively (negatively) correlated with precipitation in the north (south) LP during 1982–2013. In terms of human activity, our results indicate a strong correlation between the cumulative afforestation area and NDVI for 1998–2013, with r = 0.73, n = 16, and P

In recent years, depth and width Convolution Neural Networks (CNNs) have achieved significant results in various fields of machine vision. To be honest, deploying the depth models to devices directly is Incredible. Reducing the width of the framework is an effective strategy to make the model more slender. In this paper, we propose a channel pruning method to simultaneously accelerate and compress deep CNNs while maintaining their accuracy. First, a pre-trained CNN model is evaluated layer-by-layer according to the hybrid statistics-based criterion. The negative-score channel and the corresponding filters are discarded. For the performance damaged by channel pruning, knowledge distillation is adopted to fine-tune to achieve stronger generalization ability. Experiments on the ILSVRC-12 benchmark demonstrate the effectiveness of our method when applying to the popular CNN framework. We achieve $3.79\times$ speed-up and $19.1\times$ compression baselines on VGG-16, $3.6\times$ acceleration and $5.5\times$ compression on Darknet (fully convolutional network), both with a little accuracy decrease. Even for modern networks like ResNet, we achieve $2.1\times$ speed-up and $2.2\times$ compression, suffering only 0.4% accuracy loss.

Purpose This study aims to analyze the changes in the ecosystem service value (ESV) in response to land use and contribute significantly to ecological construction and sustainable development. Design/methodology/approach The present study was conducted in the upper Zhanghe River region based on the moderate-resolution imaging spectroradiometer Land Cover Type product MCD12Q1 by using geographic information system (GIS) methods to process and re-classify the land-use data and using the Chinese ESV equivalent weight factors to investigate changes in land use and resulting changes in ESV between 2001 and 2013. Findings The results showed significant fluctuations in ESV between 2001 and 2013: there was a decline in ESV from 2001 to 2004, followed by a gradual rise after 2004, and the overall ESV exceeded 2001 levels by the end of 2013. However, the pattern of ESV change differed across geographic locations, and each administrative region contributed differently to the overall trend. The analysis confirmed that the land-use change was closely related to the change in its ESV, and the coefficients of sensitivity of ESV for all types of land use were less than one, indicating that the coefficient value of ESV lacked elasticity. Research limitations/implications Therefore, to promote sustainable development in the upper Zhanghe River region, ESV should be taken into consideration when planning land use, especially for land types with high ESV, such as water bodies and forestlands. Originality/value The results can provide scientific support for the sustainable development of the ecological, economic and societal aspects of the upper Zhanghe River region. In addition, county-level administrative divisions were set as the basic research unit for the analysis and discussion of ESV changes in each unit within the research period and its impact on the overall ESV of the entire area to lay down a foundation for the analysis of the ESV spatial dynamic distribution in the entire research site.

Xiaoliang Chen,1,* Yueying Yang,2,* Yong Cao,1,* Changjun Wu,3 Shuxian Wu,3 Zhan Su,1 Hongwei Jin,1 Dongli Wang,1 Gengxin Zhang,3 Wei Fan,4 Jinbo Lin,5 Yunhong Zeng,3 Dongsheng Hu6 1The Center for Chronic Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, China; 2Science and Education Department, Shenzhen School of the Affiliated High School of Renmin University of China, Shenzhen, China; 3Department of Oncology, Guangming District People’s Hospital of Shenzhen, Shenzhen, China; 4Division of Digestive and Liver Disease, Cedars-Sinai Medical Center, Los Angeles, CA, USA; 5Department of Oncology, Longgang Central Hospital of Shenzhen Affiliated to Zunyi Medical College, Shenzhen, China; 6School of Medicine, Shenzhen University, Shenzhen, China *These authors contributed equally to this work Purpose: Long noncoding RNA PVT1 is dysregulated in some human tumors and has been found to increase the risk of tumor progression and poor prognosis. This study aimed to reanalyze the effect of PVT1 on tumorous prognosis.Materials and methods: The effect of PVT1 on metastasis and survival were analyzed by univariate logistic regression and Cox proportional hazards model for 32 types of cancer in the Cancer Genome Atlas database (TCGA), and the relationship between PVT1 level and expression of relative genes was assessed by Pearson correlation analysis. RevMan5.3 and STATA14.0 were used to estimate pooled effects of PVT1 on cancer prognosis with data from TCGA and published studies.Results: In TCGA data, high PVT1 expression tended to increase the risk of TNM progression and decreased the overall survival (OS) time in most of cancers. The pooled effect of PVT1 on TNM (pooled-OR=1.46, 95% CI: 1.29–1.65) and OS (pooled HR=1.32, 95% CI: 1.22–1.43), calculated from 37 and 48 cohorts, identified that high PVT1 expression promoted the metastasis and poor prognosis of cancer. Furthermore, the pooled ORs of 2.77 (95% CI: 1.65–4.66), 4.32 (95% CI: 1.99–9.36), 1.35 (95% CI: 1.01–1.80), 1.62 (95% CI: 1.21–2.18) and 1.48 (95% CI: 1.02–2.15) provided evidence that PVT1 played a role in lymph node metastasis, depth of invasion, distant metastasis, differentiation and lymphatic invasion; while the expression of 24 identified target genes was significantly associated with PVT1 level, and high PVT1 expression dependently decreased the OS time under the influence of co-expression genes (OR=1.29, 95% CI: 1.25–1.32) in high-throughput RNA sequencing merging data. In addition, the expression of PVT1 could be upregulated by smoking, with the pooled OR being 1.09 (95% CI 1.01–1.16).Conclusion: PVT1 is a dependent biomarker for tumorous prognosis surveillance. However, the reference value of PVT1 needs further study. Keywords: PVT1, Cancer, biomarker, survive, metastasis&nbsp