Lin Chen

INPLASY202240071 Efficacy and safety of Wuling Powder in the treatment of patients with diabetic nephropathy: a systematic review and meta-analysis Keywords: Wuling Powder; diabetic nephropathy. Do…

The dependence of water use efficiency (WUE) on changes in land cover types is crucial for understanding of long-term water availability and assessment of water-saving strategies. Investigating the impact of land cover types on ecosystem WUE has important implications when revealing water dynamics and land management. However, the determination of WUE and its dominant factors have always been subject to high data dependency and large calculation consumption within large basins. This paper proposes a framework for processing actual evapotranspiration (AET) and WUE calculation by coupling the Maximum Entropy Production (MEP) method with the Google Earth Engine (GEE). By employing the proposed framework and three data sources available in the GEE platform, results for actual ET and WUE from 2001 to 2020 were obtained in the Yellow River Basin (YRB). The results show that the proposed framework provides an acceptable estimation of actual ET via validation with Eddy Covariance flux sites in the YRB. The calculated WUE values varied greatly in different sub-basins within the YRB, indicating a cumulative growth rate of about 56% during the past 20 years. The dominant factor that led to these changes was the transition from Grasslands into other land-use types. Our results suggest that the use of the GEE platform coupled with the MEP method offers new possibilities for advancing understanding of water exchange and water resource management.

Subsequently to the publication of the above article, and a Corrigendum that has already been published with the intention of showing the corrected version of Fig. 5 (DOI: 10.3892/ijo.2021.5213; published online on May 5, 2021), the authors have realized that only the data for Fig. 5C needed to be corrected with respect to the original figure published in the article, and there was no need to have presented replacement data for Fig. 5E in the previous Corrigendum. The further revised version of Fig. 5, featuring the corrected data for Fig. 5C, is shown on the next page. All the authors agree to this Corrigendum. Note that the revisions made to this figure do not adversely affect the results reported in the paper, or the conclusions stated therein. The authors regret that Fig. 5 was not presented in its finally revised form in the previous Corrigendum, and offer their apologies to the Editor and to the readers of the Journal. [the original article was published in International Journal of Oncology 54: 479‑490, 2019; DOI: 10.3892/ijo.2018.4659]

Deep learning has achieved great success in various areas and its success is closely linked to the availability of massive data. But in general, a large dataset could include sensitive data and therefore the model should have the capability to avoid privacy leakage. To achieve this aim, many works apply the famous privacy framework named differential privacy into deep learning to preserve privacy. In this article, we propose a novel perturbed iterative gradient descent optimization (PIGDO) algorithm and prove that this algorithm satisfies the differential privacy. Besides, we propose a modified moments accountant (MMA) method to conduct the privacy analysis and obtain a tighter bound of privacy loss compared with the original moments accountant method. A number of experiments demonstrate that our optimization algorithm can not only improve the model accuracy and training speed, but also achieve better privacy guarantees over the state-of-the-art algorithm while reaching the equivalent accuracy. We provide codes for all of our experiments in https://github.com/CGCL-codes/DPDLIGDO.git .