Correlation between diabetic retinopathy and Helicobacter pylori infection: a cross-sectional retrospective study

AIM:To explore the correlation between diabetic retinopathy(DR)and Helicobacter pylori(Hp)infection,based on data from a physical examination population.METHODS:This cross-sectional retrospective analysis included data of 73824 health examination participants from December 2018 to December 2019.Participants were divided into the diabetic group and non-diabetic group,nondiabetic retinopathy(NDR)group,non-proliferative diabetic retinopathy(NPDR)group,proliferative diabetic retinopathy(PDR)group,and Hp infection group.Gender,age,body mass index(BMI),systolic blood pressure(SBP),diastolic blood pressure(DBP),fasting plasma glucose(FPG),glycated hemoglobin A1c(HbA1c),triglycerides(TG),total cholesterol(TC),low-density lipoprotein cholesterol(LDL-C),high-density lipoprotein cholesterol(HDL-C),and Hp data were recorded to compare the degree of DR lesions and Hp infection.Logistic regression analysis was used to evaluate the correlation between DR and Hp infection.RESULTS:There was a statistically significant difference between the diabetic and non-diabetic group(χ2=94.17,P<0.0001).Logistic regression analysis showed that male sex,age,BMI,SBP,TG,LDL-C,and Hp infection were independent risk factors for DR.There was no correlation between the degree of DR lesions and Hp infection(ρ=-0.00339,P=0.7753).Age[odds ratio(OR)=1.035,95%CI:1.024,1.046,P<0.0001]and SBP(OR=1.009,95%CI:1.004,1.015,P=0.0013)were independent risk factors for the degree of DR.CONCLUSION:There is a significant correlation between DR and Hp infection in the physical examination population.Hp infection is a risk factor for DR,and there is no significant difference between Hp infection and DR of different pathological degrees.Actively eradicating Hp may be of help to prevent DR.

New insight into fracturing characterization of shale under cyclic soft stimulation:A lab-scale investigation

The cyclic soft stimulation(CSS)is a new method of reservoir reforming for which the mechanism of fracturing crack propagation is ambiguous with regard to the alternating fluid pressure.This study aims to provide a comprehensive understanding of the fracturing mechanical characterizations of CSS under different magnitudes and amplitudes of the alternating fluid pressure.Acoustic emission(AE)is recorded to investigate the damage evolution under CSS based on the b value analysis of AE.Experimental results reveal the difference of pressure in a crack under different cyclic fluid pressure conditions.The AE results show that the maximum radiated energy under CSS tends to be reduced with the increase in the amplitude and magnitude of the alternating fluid pressure.The finishing crucial touch is that the crack extending criterion under CSS is proposed,which combines the injection parameters,the rock properties and in-situ stress.According to the crack extending criterion,the fluctuation fluid pressure causes the reduction of a critical crack extending pressure,and the CSS causes the crack to initiate and propagate under low fluid pressure.Under a higher-value magnitude of alternating fluid pressure,the cyclic times of CSS is less for the crack initiation.In supplement to the crack extending criterion,a distinct relationship between the radiated energy and the cyclic fluid pressure also is established based on the energy dissipation criterion.These new findings provide an insight into the determination of crack extending criterion under CSS for efficiently implementing shale fracturing.

Privacy-Preserving Collaborative Filtering Algorithm Based on Local Differential Privacy

Mobile edge computing(MEC)is an emerging technolohgy that extends cloud computing to the edge of a network.MEC has been applied to a variety of services.Specially,MEC can help to reduce network delay and improve the service quality of recommendation systems.In a MEC-based recommendation system,users’rating data are collected and analyzed by the edge servers.If the servers behave dishonestly or break down,users’privacy may be disclosed.To solve this issue,we design a recommendation framework that applies local differential privacy(LDP)to collaborative filtering.In the proposed framework,users’rating data are perturbed to satisfy LDP and then released to the edge servers.The edge servers perform partial computing task by using the perturbed data.The cloud computing center computes the similarity between items by using the computing results generated by edge servers.We propose a data perturbation method to protect user’s original rating values,where the Harmony mechanism is modified so as to preserve the accuracy of similarity computation.And to enhance the protection of privacy,we propose two methods to protect both users’rating values and rating behaviors.Experimental results on real-world data demonstrate that the proposed methods perform better than existing differentially private recommendation methods.

Universal materials model of deep-learning density functional theory Hamiltonian

Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence,but how to achieve this fantastic and challenging objective remains elusive.Here,we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian(Deep H),enabling computational modeling of the complicated structure-property relationship of materials in general.By constructing a large materials database and substantially improving the Deep H method,we obtain a universal materials model of Deep H capable of handling diverse elemental compositions and material structures,achieving remarkable accuracy in predicting material properties.We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models.This work not only demonstrates the concept of Deep H's universal materials model but also lays the groundwork for developing large materials models,opening up significant opportunities for advancing artificial intelligencedriven materials discovery.

满足本地化差分隐私的推荐系统中隐私预算的优化设置

推荐系统可帮助用户从众多的数据中发现用户所需数据,与此同时,上传用户原始数据给服务器也可能泄露用户隐私.本文使用本地化差分隐私技术为推荐系统中的用户数据提供隐私保护.在本地化差分隐私模型中,隐私预算控制用户数据的隐私保护程度,较高的隐私预算通常意味着较高的分析准确性.为在最小化隐私损失的同时最大化推荐准确性,我们将隐私预算设置问题建模为多臂赌博机问题,并提出基于置信度上界的学习策略帮助用户选择最优的隐私预算.考虑到用户对不同数据的敏感程度不同,我们对学习策略进行了改进.真实数据集上的实验结果表明,所提策略可以帮助用户选出合适的隐私预算,可有效提高用户的累计收益.