BACKGROUND Helicobacter pylori(H.pylori)stands as the predominant infectious agent linked to the onset of gastritis,peptic ulcer diseases,and gastric cancer(GC).Identified as the exclusive bacterial factor associated ...BACKGROUND Helicobacter pylori(H.pylori)stands as the predominant infectious agent linked to the onset of gastritis,peptic ulcer diseases,and gastric cancer(GC).Identified as the exclusive bacterial factor associated with the onset of GC,it is classified as a group 1 carcinogen by the World Health Organization.The elimination of H.pylori plays a crucial role in the primary prevention of GC.While the prevalence has declined in recent decades,H.pylori infection is still highly prevalent in China,accounting for a significant part of the disease burden of GC.Therefore,updated prevalence information for H.pylori infection,especially regional and demographic variations in China,is an important basis for the design of targeted strategies that will be effective for the prevention of GC and application of policies for H.pylori control.AIM To methodically evaluate the occurrence of H.pylori infection throughout China and establish a reference point for subsequent investigations.METHODS A systematic review and meta-analysis was conducted following established guidelines,as detailed in our methodology section.RESULTS Our review synthesized data from 152 studies,covering a sample of 763827 individuals,314423 of whom were infected with H.pylori.We evaluated infection rates in China's Mainland and the combined prevalence of H.pylori was 42.8%(95%CI:40.7-44.9).Subgroup analysis indicated the highest prevalence in Northwest China at 51.3%(95%CI:45.6-56.9),and in Qinghai Province,the prevalence reached 60.2%(95%CI:46.5-73.9).The urea breath test,which recorded the highest infection rate,showed a prevalence of 43.7%(95%CI:41.4-46.0).No notable differences in infection rates were observed between genders.Notably,the prevalence among the elderly was significantly higher at 44.5%(95%CI:41.9-47.1),compared to children,who showed a prevalence of 27.5%(95%CI:19.58-34.7).CONCLUSION Between 2014 and 2023,the prevalence of H.pylori infection in China decreased to 42.8%,down from the previous decade.However,the infection rates vary considerably across different geographical areas,among various populations,and by detection methods employed.展开更多
This review summarizes the research outcomes and findings documented in 45 journal papers using a shared tunnel boring machine(TBM)dataset for performance prediction and boring efficiency optimization using machine le...This review summarizes the research outcomes and findings documented in 45 journal papers using a shared tunnel boring machine(TBM)dataset for performance prediction and boring efficiency optimization using machine learning methods.The big dataset was col-lected during the Yinsong water diversion project construction in China,covering the tunnel excavation of a 20 km-section with 199 items of monitoring metrics taken with an interval of one second.The research papers were the result of a call for contributions during a TBM machine learning contest in 2019 and covered a variety of topics related to the intelligent construction of TBM.This review com-prises two parts.Part I is concerned with the data processing,feature extraction,and machine learning methods applied by the contrib-utors.The review finds that the data-driven and knowledge-driven approaches in extracting important features applied by various authors are diversified,requiring further studies to achieve commonly accepted criteria.The techniques for cleaning and amending the raw data adopted by the contributors were summarized,indicating some highlights such as the importance of sufficiently high fre-quency of data acquisition(higher than 1 second),classification and standardization for the data preprocessing process,and the appro-priate selections of features in a boring cycle.The review finds that both supervised and unsupervised machine learning methods have been utilized by various researchers.The ensemble and deep learning methods have found wide applications.Part I highlights the impor-tant features of the individual methods applied by the contributors,including the structures of the algorithm,selection of hyperparam-eters,and model validation approaches.展开更多
This review discusses the application scenarios of the machine learning-supported performance prediction and the optimization effi-ciency of tunnel boring machines(TBMs).The rock mass quality ratings,which are based o...This review discusses the application scenarios of the machine learning-supported performance prediction and the optimization effi-ciency of tunnel boring machines(TBMs).The rock mass quality ratings,which are based on the Chinese code for geological survey,were used to provide"labels"suitable for supervised learning.As a result,the generation of machine prediction for rock mass grades reason-ably agreed with the ground truth documented in geological maps.In contrast,the main operational parameters,i.e.,thrust and torque,can be reasonably predicted based on historical data.Consequently,18 collapse sections of the Yinsong project have been successfully predicted by several researchers.Preliminary studies on the selection of the optimal penetration rate and cost were conducted.This review also presents a summary of the main achievements in response to the initiatives of the Lotus Pool Contest in China.For the first time,large and well-documented TBM performance data has been shared for joint scientific research.Moreover,the review discusses the technical problems that require further study and the perspectives in the future development of intelligent TBM construction based on big data and machine learning.展开更多
Plant antimicrobial peptides are a very large family of antimicrobial peptides,which have strong resistance to various pathogenic microorganisms,especially fungi.With the increasing use of antibiotics,the problems cau...Plant antimicrobial peptides are a very large family of antimicrobial peptides,which have strong resistance to various pathogenic microorganisms,especially fungi.With the increasing use of antibiotics,the problems caused by antibiotics,including antibiotic residues and pathogen resistance,are becoming more and more prominent.The research on antimicrobial peptides as new antibiotic substitutes is also a hot spot.This article introduces the action sites and antibacterial mechanisms of several plant antimicrobial peptides,as well as the application of plant antimicrobial peptides in the fields of medicine,agriculture,and food preservation.展开更多
Rock condition perception based on tunnel boring machine(TBM)data is of great importance for not only ensuring tunnel boring safety but also improving construction efficiency.The prediction of TBM boring responses(i.e...Rock condition perception based on tunnel boring machine(TBM)data is of great importance for not only ensuring tunnel boring safety but also improving construction efficiency.The prediction of TBM boring responses(i.e.,torque and total thrust of the cutterhead)largely determines the reliability of rock condition perception.In this paper,a new architecture of a two-dimensional convolutional neural network(2D-CNN)with a dual-input strategy is proposed to predict the TBM responses.The TBM Lot 3 of the Yinsong project in Jilin province,China,is taken as the case study in this paper.Two types of models that follow different learning strategies are compared:one is defined as the point model,which only learns data of the stable phase,and the other is defined as the line model,which learns data from both the loading and stable boring phases.The line model is further improved by the weighted loss function method.The results indicate that the strategy of learning data from both the loading phase and stable boring phase and increasing the weight of samples from the stable phase is shown to be optimal in predicting TBM boring responses.In terms of learning strategies,the line model can learn the influence of active control parameters on passive response parameters,but the point model cannot.In terms of machine learning algorithms,2D-CNN has the best performance,with R2 values of 0.865 and 0.923 for torque and total thrust,respectively.The proposed line model can overcome the problem that the traditional model failed to learn the influence of control parameters.Such a model can provide a solid base for the timely optimization of the control parameters in TBM boring process.展开更多
基金Supported by The Zhongyuan Famous Doctor,No.ZYYCYU202012119Scientific Research Special Project of Traditional Chinese Medicine in Henan Province,No.2024ZY2004Scientific Research Special Project of the National TCM Inheritance and Innovation Center of Henan Provincial Health Commission,No.2023ZXZX1093.
文摘BACKGROUND Helicobacter pylori(H.pylori)stands as the predominant infectious agent linked to the onset of gastritis,peptic ulcer diseases,and gastric cancer(GC).Identified as the exclusive bacterial factor associated with the onset of GC,it is classified as a group 1 carcinogen by the World Health Organization.The elimination of H.pylori plays a crucial role in the primary prevention of GC.While the prevalence has declined in recent decades,H.pylori infection is still highly prevalent in China,accounting for a significant part of the disease burden of GC.Therefore,updated prevalence information for H.pylori infection,especially regional and demographic variations in China,is an important basis for the design of targeted strategies that will be effective for the prevention of GC and application of policies for H.pylori control.AIM To methodically evaluate the occurrence of H.pylori infection throughout China and establish a reference point for subsequent investigations.METHODS A systematic review and meta-analysis was conducted following established guidelines,as detailed in our methodology section.RESULTS Our review synthesized data from 152 studies,covering a sample of 763827 individuals,314423 of whom were infected with H.pylori.We evaluated infection rates in China's Mainland and the combined prevalence of H.pylori was 42.8%(95%CI:40.7-44.9).Subgroup analysis indicated the highest prevalence in Northwest China at 51.3%(95%CI:45.6-56.9),and in Qinghai Province,the prevalence reached 60.2%(95%CI:46.5-73.9).The urea breath test,which recorded the highest infection rate,showed a prevalence of 43.7%(95%CI:41.4-46.0).No notable differences in infection rates were observed between genders.Notably,the prevalence among the elderly was significantly higher at 44.5%(95%CI:41.9-47.1),compared to children,who showed a prevalence of 27.5%(95%CI:19.58-34.7).CONCLUSION Between 2014 and 2023,the prevalence of H.pylori infection in China decreased to 42.8%,down from the previous decade.However,the infection rates vary considerably across different geographical areas,among various populations,and by detection methods employed.
基金supported by the National Key R&D Program of China(Grant No.2018YFB1702504)the National Natural Science Foundation of China(Grant Nos.52179121,51879284)+3 种基金the State Key Laboratory of Simulations and Regulation of Water Cycle in River Basin,China(Grant No.SKL2022ZD05)the IWHR Research&Development Support Program,China(Grant No.GE0145B012021)the Natural Science Foundation of Shaanxi Province,China(Grant No.2021JLM-50)the National Key R&D Program of China(Grant No.2022YFE0200400).
文摘This review summarizes the research outcomes and findings documented in 45 journal papers using a shared tunnel boring machine(TBM)dataset for performance prediction and boring efficiency optimization using machine learning methods.The big dataset was col-lected during the Yinsong water diversion project construction in China,covering the tunnel excavation of a 20 km-section with 199 items of monitoring metrics taken with an interval of one second.The research papers were the result of a call for contributions during a TBM machine learning contest in 2019 and covered a variety of topics related to the intelligent construction of TBM.This review com-prises two parts.Part I is concerned with the data processing,feature extraction,and machine learning methods applied by the contrib-utors.The review finds that the data-driven and knowledge-driven approaches in extracting important features applied by various authors are diversified,requiring further studies to achieve commonly accepted criteria.The techniques for cleaning and amending the raw data adopted by the contributors were summarized,indicating some highlights such as the importance of sufficiently high fre-quency of data acquisition(higher than 1 second),classification and standardization for the data preprocessing process,and the appro-priate selections of features in a boring cycle.The review finds that both supervised and unsupervised machine learning methods have been utilized by various researchers.The ensemble and deep learning methods have found wide applications.Part I highlights the impor-tant features of the individual methods applied by the contributors,including the structures of the algorithm,selection of hyperparam-eters,and model validation approaches.
基金supported by grants from the National Key R&D Program of China(Grant No.2018YFB1702504)the National Natural Science Foundation of China(Grant Nos.52179121,51879284)+3 种基金the State Key Laboratory of Simulations and Regulation of Water Cycle in River Basin,China(Grant No.SKL2022ZD05)the IWHR Research&Development Support Program,China(Grant No.GE0145B012021)the Natural Science Foundation of Shaanxi Province,China(Grant No.2021JLM-50)the National Key R&D Program of China(Grant No.2022YFE0200400).
文摘This review discusses the application scenarios of the machine learning-supported performance prediction and the optimization effi-ciency of tunnel boring machines(TBMs).The rock mass quality ratings,which are based on the Chinese code for geological survey,were used to provide"labels"suitable for supervised learning.As a result,the generation of machine prediction for rock mass grades reason-ably agreed with the ground truth documented in geological maps.In contrast,the main operational parameters,i.e.,thrust and torque,can be reasonably predicted based on historical data.Consequently,18 collapse sections of the Yinsong project have been successfully predicted by several researchers.Preliminary studies on the selection of the optimal penetration rate and cost were conducted.This review also presents a summary of the main achievements in response to the initiatives of the Lotus Pool Contest in China.For the first time,large and well-documented TBM performance data has been shared for joint scientific research.Moreover,the review discusses the technical problems that require further study and the perspectives in the future development of intelligent TBM construction based on big data and machine learning.
文摘Plant antimicrobial peptides are a very large family of antimicrobial peptides,which have strong resistance to various pathogenic microorganisms,especially fungi.With the increasing use of antibiotics,the problems caused by antibiotics,including antibiotic residues and pathogen resistance,are becoming more and more prominent.The research on antimicrobial peptides as new antibiotic substitutes is also a hot spot.This article introduces the action sites and antibacterial mechanisms of several plant antimicrobial peptides,as well as the application of plant antimicrobial peptides in the fields of medicine,agriculture,and food preservation.
基金supported by the National Key R&D Program of China(Grant No.2022YFE0200400)the Natural Science Foundation of China(Grant No.52025094)+1 种基金In addition,we sincerely give our thanks to the data support from the National Program on Key Basic Research Project(973 Program,Grant No.2015CB058100)of China,China Railway Engineering Equipment Group Corporationthe Survey and Design Institute of Water Conservancy of Jilin Province,China.
文摘Rock condition perception based on tunnel boring machine(TBM)data is of great importance for not only ensuring tunnel boring safety but also improving construction efficiency.The prediction of TBM boring responses(i.e.,torque and total thrust of the cutterhead)largely determines the reliability of rock condition perception.In this paper,a new architecture of a two-dimensional convolutional neural network(2D-CNN)with a dual-input strategy is proposed to predict the TBM responses.The TBM Lot 3 of the Yinsong project in Jilin province,China,is taken as the case study in this paper.Two types of models that follow different learning strategies are compared:one is defined as the point model,which only learns data of the stable phase,and the other is defined as the line model,which learns data from both the loading and stable boring phases.The line model is further improved by the weighted loss function method.The results indicate that the strategy of learning data from both the loading phase and stable boring phase and increasing the weight of samples from the stable phase is shown to be optimal in predicting TBM boring responses.In terms of learning strategies,the line model can learn the influence of active control parameters on passive response parameters,but the point model cannot.In terms of machine learning algorithms,2D-CNN has the best performance,with R2 values of 0.865 and 0.923 for torque and total thrust,respectively.The proposed line model can overcome the problem that the traditional model failed to learn the influence of control parameters.Such a model can provide a solid base for the timely optimization of the control parameters in TBM boring process.
