Commercial phosphor-converted white LEDs(pc-WLEDs)face two inherent limitations,namely blue light hazard and low color rendering index,due to the use of blue LEDs as excitation source.To address these challenges,viole...Commercial phosphor-converted white LEDs(pc-WLEDs)face two inherent limitations,namely blue light hazard and low color rendering index,due to the use of blue LEDs as excitation source.To address these challenges,violet LEDs are proposed as an alternative solution.Currently,phosphors that can be efficiently excited by violet light(with wavelengths from 400 to 420 nm)remain under development still.In this study,we utilize large language models to construct a comprehensive database of Eu^(2+)and Ce^(3+)doped phosphors for discovering novel violet-excited phosphors.A total of 822 phosphor data entries,including elemental compositions,crystal structures and excitation/emission wavelengths,have been extracted and validated from 9551 research papers.Compared with Ce^(3+)doped phosphors,the Eu^(2+)are in general more suited for violet-excited phosphors,as well as red-emitting phosphors.In particular,Eu^(2+)doped nitrides and sulfides are worth of exploration for violet-excited phosphors.This database is expected to be useful in the future development of phosphors for pc-WLEDs based on artificial intelligence methods.The datasets in this article are listed in Science Data Bank at http://doi.org/10.57760/sciencedb.34314.展开更多
tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years f...tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years from accumulating studies.However,repositories for cataloging the detailed information on tsRNA–disease associations are scarce.In this study,we provide a tsRNADisease database by integrating experimentally and computationally supported tsRNA–disease associations from manual curation of literatures and other related resources.tsRNADisease contains 5571 manually curated associations between 4759 tsRNAs and 166 diseases with experimental evidence from 346 studies.In addition,it also contains 5013 predicted associations between 1297 tsRNAs and 111 diseases.tsRNADisease provides a user-friendly interface to browse,retrieve,and download data conveniently.This database can improve our understanding of tsRNA deregulation in diseases and serve as a valuable resource for investigating the mechanism of disease-related tsRNAs.tsRNADisease is freely available at http://www.compgenelab.info/tsRNADisease.展开更多
Classifying job offers into occupational categories is a fundamental task in human resource information systems,as it improves and streamlines indexing,search,and matching between openings and job seekers.Comprehensiv...Classifying job offers into occupational categories is a fundamental task in human resource information systems,as it improves and streamlines indexing,search,and matching between openings and job seekers.Comprehensive occupational databases such as O∗NET or ESCO provide detailed taxonomies of interrelated positions that can be leveraged to align the textual content of postings with occupational categories,thereby facilitating standardization,cross-system interoperability,and access to metadata for each occupation(e.g.,tasks,knowledge,skills,and abilities).In this work,we explore the effectiveness of fine-tuning existing language models(LMs)to classify job offers with occupational descriptors from O∗NET.This enables a more precise assessment of candidate suitability by identifying the specific knowledge and skills required for each position,and helps automate recruitment processes by mitigating human bias and subjectivity in candidate selection.We evaluate three representative BERT-like models:BERT,RoBERTa,and DeBERTa.BERT serves as the baseline encoder-only architecture;RoBERTa incorporates advances in pretraining objectives and data scale;and DeBERTa introduces architectural improvements through disentangled attention mechanisms.The best performance was achieved with the DeBERTa model,although the other models also produced strong results,and no statistically significant differences were observed acrossmodels.We also find that these models typically reach optimal performance after only a few training epochs,and that training with smaller,balanced datasets is effective.Consequently,comparable results can be obtained with models that require fewer computational resources and less training time,facilitating deployment and practical use.展开更多
Frontiers of Nursing is a nursing academic journal,CN 14-1395/R,ISSN 2097-5368.Size A4,quarterly publication,color printing,public offerings at home and abroad which is managed by the First Hospital of Shanxi Medical ...Frontiers of Nursing is a nursing academic journal,CN 14-1395/R,ISSN 2097-5368.Size A4,quarterly publication,color printing,public offerings at home and abroad which is managed by the First Hospital of Shanxi Medical University,sponsored by Shanxi Nursing Association and Shanxi Medical Periodical Press Co.,Ltd.Frontiers of Nursing has been indexed by databases including Scopus,Google Scholar,DOAJ,Baidu Scholar,CNKI,VIP,Wanfang,Chaoxing,and so on.展开更多
Sepsis poses a serious threat to patient survival,making timely risk assessment crucial.Predicting in-hospital mortality based on clinical indicators can aid in making better clinical decisions.Previous studies have f...Sepsis poses a serious threat to patient survival,making timely risk assessment crucial.Predicting in-hospital mortality based on clinical indicators can aid in making better clinical decisions.Previous studies have focused on classifier selection but lacked a comprehensive analysis of feature selection and data preprocessing.This study optimized machine learning models for sepsis mortality prediction by:(1)comprehensively comparing feature selection and classification methods to identify the best combination,(2)building a high-performing model with fewer features,and(3)identifying key clinically relevant indicators.Methods:Using the MIMIC-III sepsis cohort,we conducted a comprehensive analysis to determine the optimal model,including data preprocessing,data balance,classifier selection,and feature selection.Feature importance was further analyzed to identify the key predictors of in-hospital mortality.Results:The proposed Synthetic Minority Oversampling Technique-Random Forest Recursive Feature Elimination-Extreme Gradient Boosting(SMOTE-(RF-RFE)-XGB)model achieved high predictive performance with a mean Area Under the Curve(AUC)of 0.8507,while reducing the number of features from 78 to 39.Compared to other feature selection methods evaluated in this study and those reported in related literature,Random Forest Recursive Feature Elimination(RF-RFE)offers the best trade-off between accuracy,feature compactness,and stability.Additionally,feature importance rankings consistently identified Acute Physiology Score Ⅲ(APS Ⅲ),Ventilation on First Day,and Depression as the top three most influential predictors,besides the Length of Stay in ICU and Hospital.Conclusions:This study addresses key gaps by conducting a comprehensive evaluation of classifiers and feature selection methods for predicting in-hospital mortality in patients with sepsis.The proposed SMOTE-(RFRFE)-XGB model achieved a high predictive performance and stability with a compact feature set.APS III,Ventilation on First Day,and Depression were consistently identified as key predictors besides Length of Stay in ICU and Hospital.展开更多
The increasing complexity of China’s electricity market creates substantial challenges for settlement automation,data consistency,and operational scalability.Existing provincial settlement systems are fragmented,lack...The increasing complexity of China’s electricity market creates substantial challenges for settlement automation,data consistency,and operational scalability.Existing provincial settlement systems are fragmented,lack a unified data structure,and depend heavily on manual intervention to process high-frequency and retroactive transactions.To address these limitations,a graph-based unified settlement framework is proposed to enhance automation,flexibility,and adaptability in electricity market settlements.A flexible attribute-graph model is employed to represent heterogeneousmulti-market data,enabling standardized integration,rapid querying,and seamless adaptation to evolving business requirements.An extensible operator library is designed to support configurable settlement rules,and a suite of modular tools—including dataset generation,formula configuration,billing templates,and task scheduling—facilitates end-to-end automated settlement processing.A robust refund-clearing mechanism is further incorporated,utilizing sandbox execution,data-version snapshots,dynamic lineage tracing,and real-time changecapture technologies to enable rapid and accurate recalculations under dynamic policy and data revisions.Case studies based on real-world data from regional Chinese markets validate the effectiveness of the proposed approach,demonstrating marked improvements in computational efficiency,system robustness,and automation.Moreover,enhanced settlement accuracy and high temporal granularity improve price-signal fidelity,promote cost-reflective tariffs,and incentivize energy-efficient and demand-responsive behavior among market participants.The method not only supports equitable and transparent market operations but also provides a generalizable,scalable foundation for modern electricity settlement platforms in increasingly complex and dynamic market environments.展开更多
This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to...This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.展开更多
Large language model-based(LLM-based)text-to-SQL methods have achieved important progress in generating SQL queries for real-world applications.When confronted with table content-aware questions in real-world scenario...Large language model-based(LLM-based)text-to-SQL methods have achieved important progress in generating SQL queries for real-world applications.When confronted with table content-aware questions in real-world scenarios,ambiguous data content keywords and nonexistent database schema column names within the question lead to the poor performance of existing methods.To solve this problem,we propose a novel approach towards table content-aware text-to-SQL with self-retrieval(TCSR-SQL).It leverages LLM's in-context learning capability to extract data content keywords within the question and infer possible related database schema,which is used to generate Seed SQL to fuzz search databases.The search results are further used to confirm the encoding knowledge with the designed encoding knowledge table,including column names and exact stored content values used in the SQL.The encoding knowledge is sent to obtain the final Precise SQL following multirounds of generation-execution-revision process.To validate our approach,we introduce a table-content-aware,questionrelated benchmark dataset,containing 2115 question-SQL pairs.Comprehensive experiments conducted on this benchmark demonstrate the remarkable performance of TCSR-SQL,achieving an improvement of at least 27.8%in execution accuracy compared to other state-of-the-art methods.展开更多
Baosteel Technical Research has been indexed in many databases such as ProQuest CSA,Реферативныйжурнал(VINITI),China National Knowledge Infrastructure(CNKI),Wanfang Data,VIP Information,Airiti Librar...Baosteel Technical Research has been indexed in many databases such as ProQuest CSA,Реферативныйжурнал(VINITI),China National Knowledge Infrastructure(CNKI),Wanfang Data,VIP Information,Airiti Library,and Superstar Journals Database.These databases have obtained permission to digitally copy,compile,distribute,and disseminate the works of Baosteel Technical Research within their networks.The copyright usage fees are covered by the remuneration paid to the authors by Baosteel Technical Research.All authors submitting articles for publication in Baosteel Technical Research are hereby requested to conform to the above arrangement.If anyone has any objections,please inform regarding the same while submitting the manuscript so that the article is appropriately handled by Baosteel Technical Research.展开更多
Primary liver cancer (PLC) is a major global healthchallenge, ranking as the sixth most common andthird most fatal malignancy worldwide, according toGLOBOCAN 2022 estimates[1]. This high mortalityrate underscores the ...Primary liver cancer (PLC) is a major global healthchallenge, ranking as the sixth most common andthird most fatal malignancy worldwide, according toGLOBOCAN 2022 estimates[1]. This high mortalityrate underscores the aggressive nature of thedisease and the significant burden it places on globalhealthcare systems. Although primary preventionremains the cornerstone of liver cancer control,improving outcomes for patients already diagnosedis equally critical for mitigating the impact of thedisease.展开更多
Background:The purpose of this study was to analyze and classify adverse drug events(ADEs)related to ceftazidime/avibactam reported in the Food and Drug Administration Adverse Event Reporting System(FAERS)database and...Background:The purpose of this study was to analyze and classify adverse drug events(ADEs)related to ceftazidime/avibactam reported in the Food and Drug Administration Adverse Event Reporting System(FAERS)database and to evaluate their potential safety signals since the drug’s market introduction.Methods:This analysis systematically extracted and filtered FAERS data for ceftazidime/avibactam from its market launch in 2015 to the last quarter of 2024,utilizing the Medical Dictionary for Regulatory Activities(MedDRA)terminology for ADE recoding.The analysis employed the reporting odds ratio(ROR)method to assess the strength of ADE signals and to identify significant diseases associated with infections,the hepatobiliary system,the urinary system,and the nervous system.Results:A review of 540 adverse reaction reports revealed significant signals of adverse effects related to infections,hepatobiliary disorders,urinary system issues,and neurological impairments,including pathogen resistance,liver and kidney function impairment,encephalopathy,thrombocytopenia,and toxic epidermal necrolysis.However,these issues require further clinical attention.Conclusion:Ceftazidime/avibactam is associated with a range of adverse reactions,necessitating enhanced clinical monitoring,particularly in patients with underlying liver or kidney dysfunction.Continuous risk assessment and vigilant monitoring are critical for its clinical use.However,this study is limited by inherent reporting biases and confounders associated with the spontaneous reporting database(FAERS).Future research should validate these signals through prospective cohort and mechanistic studies and explore personalized risk management strategies for high-risk populations.展开更多
Research into metamorphism plays a pivotal role in reconstructing the evolution of continent,particularly through the study of ancient rocks that are highly susceptible to metamorphic alterations due to multiple tecto...Research into metamorphism plays a pivotal role in reconstructing the evolution of continent,particularly through the study of ancient rocks that are highly susceptible to metamorphic alterations due to multiple tectonic activities.In the big data era,the establishment of new data platforms and the application of big data methods have become a focus for metamorphic rocks.Significant progress has been made in creating specialized databases,compiling comprehensive datasets,and utilizing data analytics to address complex scientific questions.However,many existing databases are inadequate in meeting the specific requirements of metamorphic research,resulting from a substantial amount of valuable data remaining uncollected.Therefore,constructing new databases that can cope with the development of the data era is necessary.This article provides an extensive review of existing databases related to metamorphic rocks and discusses data-driven studies in this.Accordingly,several crucial factors that need to be taken into consideration in the establishment of specialized metamorphic databases are identified,aiming to leverage data-driven applications to achieve broader scientific objectives in metamorphic research.展开更多
In the year 1971,the world’s biggest structural biology collaboration name—The Research Collaboratory for Structural Bioinformatics(RCSB),was formed to gather all the structural biologists at a single platform and t...In the year 1971,the world’s biggest structural biology collaboration name—The Research Collaboratory for Structural Bioinformatics(RCSB),was formed to gather all the structural biologists at a single platform and then extended out to be the world’s most extensive structural data repository named RCSB-Protein Data Bank(PDB)(https://www.rcsb.org/)that has provided the service for more than 50 years and continues its legacy for the discoveries and repositories for structural data.The RCSB has evolved from being a collaboratory network to a full-fledged database and tool with a huge list of protein structures,nucleic acid-containing structures,ModelArchive,and AlphaFold structures,and the best is that it is expanding day by day with computational advancement with tools and visual experiences.In this review article,we have discussed how RCSB has been a successful collaboratory network,its expansion in each decade,and how it has helped the ground-breaking research.The PDB tools that are helping the researchers,yearly data deposition,validation,processing,and suggestions that can help the developer improve for upcoming years are also discussed.This review will help future researchers understand the complete history of RCSB and its developments in each decade and how various future collaborative networks can be developed in various scientific areas and can be successful by keeping RCSB as a case study.展开更多
Objective ZW10 interacting kinetochore protein(ZWINT)has been demonstrated to play a pivotal role in the growth,invasion,and migration of cancers.Nevertheless,whether the expression levels of ZWINT are significantly c...Objective ZW10 interacting kinetochore protein(ZWINT)has been demonstrated to play a pivotal role in the growth,invasion,and migration of cancers.Nevertheless,whether the expression levels of ZWINT are significantly correlated with clinicopathological characteristics and prognostic outcomes of patients with breast cancer remains elusive.This study systematically investigated the clinical significance of ZWINT expression in breast cancer through integrated molecular subtyping and survival analysis.Methods We systematically characterized the spatial expression pattern of ZWINT across various breast cancer subtypes and assessed its prognostic significance using an integrated bioinformatics approach that involved multi-omics analysis.The approach included the Breast Cancer Gene-Expression Miner v5.1(bc-GenExMiner v5.1),TNMplot,MuTarget,PrognoScan database,and Database for Annotation,Visualization,and Integrated Discovery(DAVID).Results Our analysis revealed consistent upregulation of ZWINT mRNA and protein expression across distinct clinicopathological subtypes of breast cancer.ZWINT overexpression demonstrated significant co-occurrence with truncating mutations in cadherin 1(CDH1)and tumor protein p53(TP53),suggesting potential functional crosstalk in tumor progression pathways.The overexpression of ZWINT correlated with adverse clinical outcomes,showing 48%increased mortality risk(overall survival:HR 1.48,95%CI 1.23–1.79),66%higher recurrence probability(relapse-free survival:1.66,95%CI 1.50–1.84),and 63%elevated metastasis risk(distant metastasis-free survival:HR 1.63,95%CI 1.39–1.90).Multivariate Cox regression incorporating TNM staging and molecular subtypes confirmed ZWINT as an independent prognostic determinant(P<0.001,Harrell’s C-index=0.7827),which was validated through bootstrap resampling(1000 iterations).Conclusion ZWINT may serve as a potential biomarker for prognosis and a possible therapeutic target alongside TP53/CDH1 in breast cancer.展开更多
BACKGROUND Ampullary adenocarcinoma is a rare malignant tumor of the gastrointestinal tract.Currently,only a few cases have been reported,resulting in limited information on survival.AIM To develop a dynamic nomogram ...BACKGROUND Ampullary adenocarcinoma is a rare malignant tumor of the gastrointestinal tract.Currently,only a few cases have been reported,resulting in limited information on survival.AIM To develop a dynamic nomogram using internal and external validation to predict survival in patients with ampullary adenocarcinoma.METHODS Data were sourced from the surveillance,epidemiology,and end results stat database.The patients in the database were randomized in a 7:3 ratio into training and validation groups.Using Cox regression univariate and multivariate analyses in the training group,we identified independent risk factors for overall survival and cancer-specific survival to develop the nomogram.The nomogram was validated with a cohort of patients from the First Affiliated Hospital of the Army Medical University.RESULTS For overall and cancer-specific survival,12(sex,age,race,lymph node ratio,tumor size,chemotherapy,surgical modality,T stage,tumor differentiation,brain metastasis,lung metastasis,and extension)and 6(age;surveillance,epidemiology,and end results stage;lymph node ratio;chemotherapy;surgical modality;and tumor differentiation)independent risk factors,respectively,were incorporated into the nomogram.The area under the curve values at 1,3,and 5 years,respectively,were 0.807,0.842,and 0.826 for overall survival and 0.816,0.835,and 0.841 for cancer-specific survival.The internal and external validation cohorts indicated good consistency of the nomogram.CONCLUSION The dynamic nomogram offers robust predictive efficacy for the overall and cancer-specific survival of ampullary adenocarcinoma.展开更多
Objective:Near vision loss(NVL)is one of the leading causes of visual impairment worldwide,exerting a profound impact on individual quality of life and socio-economic development.This study aims to analyze the burden ...Objective:Near vision loss(NVL)is one of the leading causes of visual impairment worldwide,exerting a profound impact on individual quality of life and socio-economic development.This study aims to analyze the burden of NVL in China by sex and age groups from 1990 to 2021 and to project trends over the next 15 years.Methods:Using data from the Global Burden of Disease(GBD)2021 database,we conducted descriptive analyses of NVL prevalence in China,calculated age-standardized prevalence rates(ASPR)and age-standardized disability-adjusted life years rates(ASDR)to compare burden differences between sexes and age groups,and applied an autoregressive integrated moving average(ARIMA)model to predict NVL trends for the next 15 years.The model selection was based on best-fit criteria to ensure reliable projections.Results:From 1990 to 2021,China’s ASPR of NVL rose from 10096.24/100000 to 15624.54/100000,and ASDR increased from 101.75/100000 to 158.75/100000.In 2021,ASPR(16551.70/100000)and ASDR(167.69/100000)were higher among females than males(14686.21/100000 and 149.76/100000,respectively).China ranked highest globally in both NVL cases and disability-adjusted life years(DALYs),with female burden significantly exceeding male burden.Projections indicated this trend and sex gap will persist until 2036.Compared with 1990,the prevalence cases and DALYs increased by 239.20%and 238.82%,respectively in 2021,with the highest burden among females and the 55−59 age group.The ARIMA model predicted continued increases in prevalence and DALYs by 2036,with females maintaining a higher burden than males.Conclusion:This study reveals a marked increase in the NVL burden in China and predicts continued growth in the coming years.Public health policies should prioritize NVL prevention and control,with special attention to women and middle-aged populations to mitigate long-term societal and health impacts.展开更多
基金National Key Research and Development Program of China(2021YFB3500501)。
文摘Commercial phosphor-converted white LEDs(pc-WLEDs)face two inherent limitations,namely blue light hazard and low color rendering index,due to the use of blue LEDs as excitation source.To address these challenges,violet LEDs are proposed as an alternative solution.Currently,phosphors that can be efficiently excited by violet light(with wavelengths from 400 to 420 nm)remain under development still.In this study,we utilize large language models to construct a comprehensive database of Eu^(2+)and Ce^(3+)doped phosphors for discovering novel violet-excited phosphors.A total of 822 phosphor data entries,including elemental compositions,crystal structures and excitation/emission wavelengths,have been extracted and validated from 9551 research papers.Compared with Ce^(3+)doped phosphors,the Eu^(2+)are in general more suited for violet-excited phosphors,as well as red-emitting phosphors.In particular,Eu^(2+)doped nitrides and sulfides are worth of exploration for violet-excited phosphors.This database is expected to be useful in the future development of phosphors for pc-WLEDs based on artificial intelligence methods.The datasets in this article are listed in Science Data Bank at http://doi.org/10.57760/sciencedb.34314.
基金supported by the National Natural Science Foundation of China(91959106)the Foundation of the Shanghai Municipal Education Commission(24RGZNC02)+4 种基金Shanghai Key Laboratory of Intelligent Information Processing,Fudan University(IIPL-2025-RD3-02)Key University Science Research Project of Anhui Province(2023AH030108)Climbing Peak Training Program for Innovative Technology team of Yijishan Hospital,Wannan Medical College(PF201904)Peak Training Program for Scientific Research of Yijishan Hospital,Wannan Medical College(GF2019G15)the talent project of the First Affiliated Hospital of Wannan Medical College(Yijishan Hospital of Wannan Medical College)(YR202422).
文摘tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years from accumulating studies.However,repositories for cataloging the detailed information on tsRNA–disease associations are scarce.In this study,we provide a tsRNADisease database by integrating experimentally and computationally supported tsRNA–disease associations from manual curation of literatures and other related resources.tsRNADisease contains 5571 manually curated associations between 4759 tsRNAs and 166 diseases with experimental evidence from 346 studies.In addition,it also contains 5013 predicted associations between 1297 tsRNAs and 111 diseases.tsRNADisease provides a user-friendly interface to browse,retrieve,and download data conveniently.This database can improve our understanding of tsRNA deregulation in diseases and serve as a valuable resource for investigating the mechanism of disease-related tsRNAs.tsRNADisease is freely available at http://www.compgenelab.info/tsRNADisease.
文摘Classifying job offers into occupational categories is a fundamental task in human resource information systems,as it improves and streamlines indexing,search,and matching between openings and job seekers.Comprehensive occupational databases such as O∗NET or ESCO provide detailed taxonomies of interrelated positions that can be leveraged to align the textual content of postings with occupational categories,thereby facilitating standardization,cross-system interoperability,and access to metadata for each occupation(e.g.,tasks,knowledge,skills,and abilities).In this work,we explore the effectiveness of fine-tuning existing language models(LMs)to classify job offers with occupational descriptors from O∗NET.This enables a more precise assessment of candidate suitability by identifying the specific knowledge and skills required for each position,and helps automate recruitment processes by mitigating human bias and subjectivity in candidate selection.We evaluate three representative BERT-like models:BERT,RoBERTa,and DeBERTa.BERT serves as the baseline encoder-only architecture;RoBERTa incorporates advances in pretraining objectives and data scale;and DeBERTa introduces architectural improvements through disentangled attention mechanisms.The best performance was achieved with the DeBERTa model,although the other models also produced strong results,and no statistically significant differences were observed acrossmodels.We also find that these models typically reach optimal performance after only a few training epochs,and that training with smaller,balanced datasets is effective.Consequently,comparable results can be obtained with models that require fewer computational resources and less training time,facilitating deployment and practical use.
文摘Frontiers of Nursing is a nursing academic journal,CN 14-1395/R,ISSN 2097-5368.Size A4,quarterly publication,color printing,public offerings at home and abroad which is managed by the First Hospital of Shanxi Medical University,sponsored by Shanxi Nursing Association and Shanxi Medical Periodical Press Co.,Ltd.Frontiers of Nursing has been indexed by databases including Scopus,Google Scholar,DOAJ,Baidu Scholar,CNKI,VIP,Wanfang,Chaoxing,and so on.
文摘Sepsis poses a serious threat to patient survival,making timely risk assessment crucial.Predicting in-hospital mortality based on clinical indicators can aid in making better clinical decisions.Previous studies have focused on classifier selection but lacked a comprehensive analysis of feature selection and data preprocessing.This study optimized machine learning models for sepsis mortality prediction by:(1)comprehensively comparing feature selection and classification methods to identify the best combination,(2)building a high-performing model with fewer features,and(3)identifying key clinically relevant indicators.Methods:Using the MIMIC-III sepsis cohort,we conducted a comprehensive analysis to determine the optimal model,including data preprocessing,data balance,classifier selection,and feature selection.Feature importance was further analyzed to identify the key predictors of in-hospital mortality.Results:The proposed Synthetic Minority Oversampling Technique-Random Forest Recursive Feature Elimination-Extreme Gradient Boosting(SMOTE-(RF-RFE)-XGB)model achieved high predictive performance with a mean Area Under the Curve(AUC)of 0.8507,while reducing the number of features from 78 to 39.Compared to other feature selection methods evaluated in this study and those reported in related literature,Random Forest Recursive Feature Elimination(RF-RFE)offers the best trade-off between accuracy,feature compactness,and stability.Additionally,feature importance rankings consistently identified Acute Physiology Score Ⅲ(APS Ⅲ),Ventilation on First Day,and Depression as the top three most influential predictors,besides the Length of Stay in ICU and Hospital.Conclusions:This study addresses key gaps by conducting a comprehensive evaluation of classifiers and feature selection methods for predicting in-hospital mortality in patients with sepsis.The proposed SMOTE-(RFRFE)-XGB model achieved a high predictive performance and stability with a compact feature set.APS III,Ventilation on First Day,and Depression were consistently identified as key predictors besides Length of Stay in ICU and Hospital.
基金funded by the Science and Technology Project of State Grid Corporation of China(5108-202355437A-3-2-ZN).
文摘The increasing complexity of China’s electricity market creates substantial challenges for settlement automation,data consistency,and operational scalability.Existing provincial settlement systems are fragmented,lack a unified data structure,and depend heavily on manual intervention to process high-frequency and retroactive transactions.To address these limitations,a graph-based unified settlement framework is proposed to enhance automation,flexibility,and adaptability in electricity market settlements.A flexible attribute-graph model is employed to represent heterogeneousmulti-market data,enabling standardized integration,rapid querying,and seamless adaptation to evolving business requirements.An extensible operator library is designed to support configurable settlement rules,and a suite of modular tools—including dataset generation,formula configuration,billing templates,and task scheduling—facilitates end-to-end automated settlement processing.A robust refund-clearing mechanism is further incorporated,utilizing sandbox execution,data-version snapshots,dynamic lineage tracing,and real-time changecapture technologies to enable rapid and accurate recalculations under dynamic policy and data revisions.Case studies based on real-world data from regional Chinese markets validate the effectiveness of the proposed approach,demonstrating marked improvements in computational efficiency,system robustness,and automation.Moreover,enhanced settlement accuracy and high temporal granularity improve price-signal fidelity,promote cost-reflective tariffs,and incentivize energy-efficient and demand-responsive behavior among market participants.The method not only supports equitable and transparent market operations but also provides a generalizable,scalable foundation for modern electricity settlement platforms in increasingly complex and dynamic market environments.
基金Supported by Japan Society for the Promotion of Science,No.24K11935.
文摘This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.
基金supported by the National Key Research and Development Program of China under(Grant 2023YFB3106504)Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies under(Grant 2022B1212010005)+2 种基金the Major Key Project of PCL under(Grant PCL2023A09)Shenzhen Science and Technology Program under(Grants ZDSYS20210623091809029 and RCBS20221008093131089)the project of Guangdong Power Grid Co.Ltd.under(Grants 037800KC23090005 and GD-KJXM20231042).
文摘Large language model-based(LLM-based)text-to-SQL methods have achieved important progress in generating SQL queries for real-world applications.When confronted with table content-aware questions in real-world scenarios,ambiguous data content keywords and nonexistent database schema column names within the question lead to the poor performance of existing methods.To solve this problem,we propose a novel approach towards table content-aware text-to-SQL with self-retrieval(TCSR-SQL).It leverages LLM's in-context learning capability to extract data content keywords within the question and infer possible related database schema,which is used to generate Seed SQL to fuzz search databases.The search results are further used to confirm the encoding knowledge with the designed encoding knowledge table,including column names and exact stored content values used in the SQL.The encoding knowledge is sent to obtain the final Precise SQL following multirounds of generation-execution-revision process.To validate our approach,we introduce a table-content-aware,questionrelated benchmark dataset,containing 2115 question-SQL pairs.Comprehensive experiments conducted on this benchmark demonstrate the remarkable performance of TCSR-SQL,achieving an improvement of at least 27.8%in execution accuracy compared to other state-of-the-art methods.
文摘Baosteel Technical Research has been indexed in many databases such as ProQuest CSA,Реферативныйжурнал(VINITI),China National Knowledge Infrastructure(CNKI),Wanfang Data,VIP Information,Airiti Library,and Superstar Journals Database.These databases have obtained permission to digitally copy,compile,distribute,and disseminate the works of Baosteel Technical Research within their networks.The copyright usage fees are covered by the remuneration paid to the authors by Baosteel Technical Research.All authors submitting articles for publication in Baosteel Technical Research are hereby requested to conform to the above arrangement.If anyone has any objections,please inform regarding the same while submitting the manuscript so that the article is appropriately handled by Baosteel Technical Research.
基金National Key Project of Research and Development Program of China[2021YFC2500404].
文摘Primary liver cancer (PLC) is a major global healthchallenge, ranking as the sixth most common andthird most fatal malignancy worldwide, according toGLOBOCAN 2022 estimates[1]. This high mortalityrate underscores the aggressive nature of thedisease and the significant burden it places on globalhealthcare systems. Although primary preventionremains the cornerstone of liver cancer control,improving outcomes for patients already diagnosedis equally critical for mitigating the impact of thedisease.
基金Intramural Project of The First Affiliated Hospital of Guangxi University of Chinese Medicine(2018QN008).
文摘Background:The purpose of this study was to analyze and classify adverse drug events(ADEs)related to ceftazidime/avibactam reported in the Food and Drug Administration Adverse Event Reporting System(FAERS)database and to evaluate their potential safety signals since the drug’s market introduction.Methods:This analysis systematically extracted and filtered FAERS data for ceftazidime/avibactam from its market launch in 2015 to the last quarter of 2024,utilizing the Medical Dictionary for Regulatory Activities(MedDRA)terminology for ADE recoding.The analysis employed the reporting odds ratio(ROR)method to assess the strength of ADE signals and to identify significant diseases associated with infections,the hepatobiliary system,the urinary system,and the nervous system.Results:A review of 540 adverse reaction reports revealed significant signals of adverse effects related to infections,hepatobiliary disorders,urinary system issues,and neurological impairments,including pathogen resistance,liver and kidney function impairment,encephalopathy,thrombocytopenia,and toxic epidermal necrolysis.However,these issues require further clinical attention.Conclusion:Ceftazidime/avibactam is associated with a range of adverse reactions,necessitating enhanced clinical monitoring,particularly in patients with underlying liver or kidney dysfunction.Continuous risk assessment and vigilant monitoring are critical for its clinical use.However,this study is limited by inherent reporting biases and confounders associated with the spontaneous reporting database(FAERS).Future research should validate these signals through prospective cohort and mechanistic studies and explore personalized risk management strategies for high-risk populations.
基金funded by the National Natural Science Foundation of China(No.42220104008)。
文摘Research into metamorphism plays a pivotal role in reconstructing the evolution of continent,particularly through the study of ancient rocks that are highly susceptible to metamorphic alterations due to multiple tectonic activities.In the big data era,the establishment of new data platforms and the application of big data methods have become a focus for metamorphic rocks.Significant progress has been made in creating specialized databases,compiling comprehensive datasets,and utilizing data analytics to address complex scientific questions.However,many existing databases are inadequate in meeting the specific requirements of metamorphic research,resulting from a substantial amount of valuable data remaining uncollected.Therefore,constructing new databases that can cope with the development of the data era is necessary.This article provides an extensive review of existing databases related to metamorphic rocks and discusses data-driven studies in this.Accordingly,several crucial factors that need to be taken into consideration in the establishment of specialized metamorphic databases are identified,aiming to leverage data-driven applications to achieve broader scientific objectives in metamorphic research.
文摘In the year 1971,the world’s biggest structural biology collaboration name—The Research Collaboratory for Structural Bioinformatics(RCSB),was formed to gather all the structural biologists at a single platform and then extended out to be the world’s most extensive structural data repository named RCSB-Protein Data Bank(PDB)(https://www.rcsb.org/)that has provided the service for more than 50 years and continues its legacy for the discoveries and repositories for structural data.The RCSB has evolved from being a collaboratory network to a full-fledged database and tool with a huge list of protein structures,nucleic acid-containing structures,ModelArchive,and AlphaFold structures,and the best is that it is expanding day by day with computational advancement with tools and visual experiences.In this review article,we have discussed how RCSB has been a successful collaboratory network,its expansion in each decade,and how it has helped the ground-breaking research.The PDB tools that are helping the researchers,yearly data deposition,validation,processing,and suggestions that can help the developer improve for upcoming years are also discussed.This review will help future researchers understand the complete history of RCSB and its developments in each decade and how various future collaborative networks can be developed in various scientific areas and can be successful by keeping RCSB as a case study.
基金supported by the Research Project of Maternal and Child Health Hospital of Hubei Province(No.2023SFYM008)Key Project of Hubei Provincial Natural Science Foundation(No.JCZRLH202500304).
文摘Objective ZW10 interacting kinetochore protein(ZWINT)has been demonstrated to play a pivotal role in the growth,invasion,and migration of cancers.Nevertheless,whether the expression levels of ZWINT are significantly correlated with clinicopathological characteristics and prognostic outcomes of patients with breast cancer remains elusive.This study systematically investigated the clinical significance of ZWINT expression in breast cancer through integrated molecular subtyping and survival analysis.Methods We systematically characterized the spatial expression pattern of ZWINT across various breast cancer subtypes and assessed its prognostic significance using an integrated bioinformatics approach that involved multi-omics analysis.The approach included the Breast Cancer Gene-Expression Miner v5.1(bc-GenExMiner v5.1),TNMplot,MuTarget,PrognoScan database,and Database for Annotation,Visualization,and Integrated Discovery(DAVID).Results Our analysis revealed consistent upregulation of ZWINT mRNA and protein expression across distinct clinicopathological subtypes of breast cancer.ZWINT overexpression demonstrated significant co-occurrence with truncating mutations in cadherin 1(CDH1)and tumor protein p53(TP53),suggesting potential functional crosstalk in tumor progression pathways.The overexpression of ZWINT correlated with adverse clinical outcomes,showing 48%increased mortality risk(overall survival:HR 1.48,95%CI 1.23–1.79),66%higher recurrence probability(relapse-free survival:1.66,95%CI 1.50–1.84),and 63%elevated metastasis risk(distant metastasis-free survival:HR 1.63,95%CI 1.39–1.90).Multivariate Cox regression incorporating TNM staging and molecular subtypes confirmed ZWINT as an independent prognostic determinant(P<0.001,Harrell’s C-index=0.7827),which was validated through bootstrap resampling(1000 iterations).Conclusion ZWINT may serve as a potential biomarker for prognosis and a possible therapeutic target alongside TP53/CDH1 in breast cancer.
基金Supported by the Appropriate Technology Promotion Program in Chongqing,No.2023jstg005.
文摘BACKGROUND Ampullary adenocarcinoma is a rare malignant tumor of the gastrointestinal tract.Currently,only a few cases have been reported,resulting in limited information on survival.AIM To develop a dynamic nomogram using internal and external validation to predict survival in patients with ampullary adenocarcinoma.METHODS Data were sourced from the surveillance,epidemiology,and end results stat database.The patients in the database were randomized in a 7:3 ratio into training and validation groups.Using Cox regression univariate and multivariate analyses in the training group,we identified independent risk factors for overall survival and cancer-specific survival to develop the nomogram.The nomogram was validated with a cohort of patients from the First Affiliated Hospital of the Army Medical University.RESULTS For overall and cancer-specific survival,12(sex,age,race,lymph node ratio,tumor size,chemotherapy,surgical modality,T stage,tumor differentiation,brain metastasis,lung metastasis,and extension)and 6(age;surveillance,epidemiology,and end results stage;lymph node ratio;chemotherapy;surgical modality;and tumor differentiation)independent risk factors,respectively,were incorporated into the nomogram.The area under the curve values at 1,3,and 5 years,respectively,were 0.807,0.842,and 0.826 for overall survival and 0.816,0.835,and 0.841 for cancer-specific survival.The internal and external validation cohorts indicated good consistency of the nomogram.CONCLUSION The dynamic nomogram offers robust predictive efficacy for the overall and cancer-specific survival of ampullary adenocarcinoma.
基金supported by the Natural Science Foundation of Hunan Province(2023JJ30817)Hunan Provincial Natural Science Foundation-Hengyang City Joint Fund Project(2025JJ70129)+1 种基金Changsha Natural Science Foundation(kq2403057)China。
文摘Objective:Near vision loss(NVL)is one of the leading causes of visual impairment worldwide,exerting a profound impact on individual quality of life and socio-economic development.This study aims to analyze the burden of NVL in China by sex and age groups from 1990 to 2021 and to project trends over the next 15 years.Methods:Using data from the Global Burden of Disease(GBD)2021 database,we conducted descriptive analyses of NVL prevalence in China,calculated age-standardized prevalence rates(ASPR)and age-standardized disability-adjusted life years rates(ASDR)to compare burden differences between sexes and age groups,and applied an autoregressive integrated moving average(ARIMA)model to predict NVL trends for the next 15 years.The model selection was based on best-fit criteria to ensure reliable projections.Results:From 1990 to 2021,China’s ASPR of NVL rose from 10096.24/100000 to 15624.54/100000,and ASDR increased from 101.75/100000 to 158.75/100000.In 2021,ASPR(16551.70/100000)and ASDR(167.69/100000)were higher among females than males(14686.21/100000 and 149.76/100000,respectively).China ranked highest globally in both NVL cases and disability-adjusted life years(DALYs),with female burden significantly exceeding male burden.Projections indicated this trend and sex gap will persist until 2036.Compared with 1990,the prevalence cases and DALYs increased by 239.20%and 238.82%,respectively in 2021,with the highest burden among females and the 55−59 age group.The ARIMA model predicted continued increases in prevalence and DALYs by 2036,with females maintaining a higher burden than males.Conclusion:This study reveals a marked increase in the NVL burden in China and predicts continued growth in the coming years.Public health policies should prioritize NVL prevention and control,with special attention to women and middle-aged populations to mitigate long-term societal and health impacts.
