The single-event susceptibility of three silicon carbide(SiC)metal-oxide-semiconductor field-effect transistor(MOSFET)power devices structures(planar,trench and double trench)is researched by the technology computer-a...The single-event susceptibility of three silicon carbide(SiC)metal-oxide-semiconductor field-effect transistor(MOSFET)power devices structures(planar,trench and double trench)is researched by the technology computer-aided design(TCAD)simulation.Comparative analysis of the heavy-ion irradiation effects on three device structures reveals distinct susceptibility characteristics.The gate oxide region is identified as the most sensitive position in planar devices,while trench and doubletrench structures exhibit no localized sensitive regions.Furthermore,the single-event susceptibility demonstrates strong depth dependence across all three structures,with enhanced vulnerability observed at greater ion penetration depths.展开更多
Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to ev...Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to evaluate debris flow susceptibility and associated hazards across Tajikistan.A dataset comprising 405 documented debris flow points and 14 influencing factors,encompassing geological,climatic-hydrological,and anthropogenic variables,was established.Three machine learning algorithms—Random Forest,Support Vector Machine(SVM),and Multi-layer Perceptron—were applied to generate susceptibility maps and delineate debris flow risk zones.The results indicate that the areas of higher and high susceptibility accounted for 20.43%and 4.41%of the national area,respectively,and were predominantly concentrated along the Zeravshan and Vakhsh river basins.Among the evaluated models,SVM model demonstrated the highest predictive performance.Beyond conventional topographic and environmental controls,drought conditions were identified as a critical factor influencing debris flow occurrence within the arid and semi-arid mountainous regions of Tajikistan.These findings provide a scientific basis for regional debris flow risk management and disaster mitigation planning,and offer practical guidance for selecting conditioning factors in machine-learning-based susceptibility assessments in other dry mountainous environments.展开更多
Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility ar...Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk.This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest(RF),Gradient Boosting Regression Trees(GBRT),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost)—to generate susceptibility maps.The Shapley additive explanation(SHAP)method was applied to quantify factor importance and explore their nonlinear effects.The results showed that:(1)CatBoost was the best-performing model(CA=0.938,AUC=0.980)in assessing landslide susceptibility,with altitude emerging as the most significant factor,followed by distance to roads and earthquake sites,precipitation,and slope;(2)the SHAP method revealed critical nonlinear thresholds,demonstrating that historical landslides were concentrated at mid-altitudes(1400-4000 m)and decreased markedly above 4000 m,with a parallel reduction in probability beyond 700 m from roads;and(3)landslide-prone areas,comprising 13%of the QTP,were concentrated in the southeastern and northeastern parts of the plateau.By integrating machine learning and SHAP analysis,this study revealed landslide hazard-prone areas and their driving factors,providing insights to support disaster management strategies and sustainable regional planning.展开更多
AIM:To conduct a genetic analysis of Han-Chinese patients with isolated congenital ptosis(ICP)and identify the genetic variants related to the condition.METHODS:Sixty-five unrelated patients with ICP were enrolled.Com...AIM:To conduct a genetic analysis of Han-Chinese patients with isolated congenital ptosis(ICP)and identify the genetic variants related to the condition.METHODS:Sixty-five unrelated patients with ICP were enrolled.Comprehensive clinical examinations,whole exome sequencing(WES),and Sanger sequencing were used to reveal the potential genetic causes.Combined with public and in-house control databases,multiple bioinformatics prediction tools,and conservation analysis,the potential variants were further analyzed.AlphaFold 3,an accurate modelling prediction tool,was utilized to generate three-dimensional structural models of both wild-type and mutated proteins.RESULTS:Three novel heterozygous variants in the zinc finger homeobox 4 gene(ZFHX4),c.5145C>A(p.N1715K),c.10382C>T(p.A3461V),and c.10795G>A(p.A3599T),were identified in three patients,respectively.Bioinformatics analyses suggested that these variants are likely to exert deleterious effects,supporting their potential involvement in the pathogenesis of ptosis.CONCLUSION:The novel heterozygous ZFHX4 variants are identified as disease-associated variants in three patients with ptosis,suggesting that ZFHX4 may be a disease-causing gene for autosomal dominant ICP with incomplete penetrance or a susceptibility gene.These findings expand the variant spectrum of ZFHX4,improve understanding of the pathogenesis of ZFHX4-related ptosis,and may contribute to the genetic counseling and disease management,as well as the development of experimental treatments.展开更多
Accurate assessment of seismic landslide susceptibility is crucial for disaster prevention and emergency decision-making.Although machine learning methods have been widely applied in this field,they exhibit a strong d...Accurate assessment of seismic landslide susceptibility is crucial for disaster prevention and emergency decision-making.Although machine learning methods have been widely applied in this field,they exhibit a strong dependence on large quantities of highquality samples,resulting in significantly low prediction accuracy of existing studies under data-scarce or crossregional prediction scenarios,which fail to meet practical application requirements.To address this issue,this study proposes an intelligent prediction model integrating transfer learning and a sampling optimization strategy,aiming to enhance the accuracy and applicability of seismic landslide susceptibility assessment.The model first improves the sample collection method through the sampling optimization strategy to enhance the precision and representativeness of training samples.This not only ensures the accuracy of origin area training but also further strengthens the model's predictive ability in the target area.Subsequently,it incorporates Transfer Component Analysis(TCA)to overcome the differences in environmental characteristics between the origin area and target area,and couples TCA with the Light GBM algorithm to construct the TCA-Light GBM model,realizing the assessment of seismic landslide susceptibility in sample-free areas.Validated through case studies of the Jiuzhaigou and Luding earthquakes,the results demonstrate that the proposed TCALight GBM transfer learning method exhibits excellent applicability in seismic landslide susceptibility prediction.After optimization with the TCA algorithm,the model's prediction performance in the target domain is significantly improved,with the AUC value increasing from 0.719 to 0.827,representing an increase of approximately 15.02%.This indicates that TCA technology can effectively alleviate the feature distribution discrepancy between the source domain and target domain,enhancing the model's generalization ability.The method is particularly suitable for scenarios with data scarcity and cross-regional prediction and can provide reliable technical support for the emergency response and risk prevention and control of seismic hazards.展开更多
Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automaticall...Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.展开更多
The susceptibility of ore particles to electrical breakdown plays a critical role for high voltage pulse(HVP)breakage,yet its quantitative characterization still lacks deep understanding.Two indicators,namely breakdow...The susceptibility of ore particles to electrical breakdown plays a critical role for high voltage pulse(HVP)breakage,yet its quantitative characterization still lacks deep understanding.Two indicators,namely breakdown delay time(T_(d))and breakdown strength(E_(b))were compared,based on analysis on the two breakdown modes namely wavefront mode and post-wave mode.It was found that T_(d) is more suitable to characterize the susceptibility of ore particles to electrical breakdown in HVP breakage than E_(b).A probabilistic model based on the Weibull distribution is developed to describe the relation of breakdown probability to T_(d).Regression analyses were conducted to investigate how operating parameters and particle properties influence Td and size reduction degree of ore particles in HVP breakage.The regressed models demonstrate potential capability to predict metallic minerals content and HVP breakage degree based on operating parameters and particle properties.展开更多
Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering acti...Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.展开更多
This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment mo...This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment models.First,the cumulative probability method revealed that a low probability(15%)of geologic hazards between any two geologic hazard points occurred outside a buffer zone with a radius of 2297 m(i.e.,the distance threshold).The training dataset was established,consisting of negative samples(non-hazard points)randomly generated based on the distance threshold,positive samples(i.e.,historical hazards),and 13 conditioning factors.Then,models were built using five machine learning algorithms,namely random forest(RF),gradient boosting decision tree(GBDT),naive Bayes(NB),logistic regression(LR),and support vector machine(SVM).The comprehensive performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and overall accuracy(OA)as indicators,revealing that RF exhibited the best performance,with OA and AUC values of 2.7127 and 0.981,respectively.Furthermore,the machine learning models constructed by considering the distance threshold outperformed those built using the unoptimized dataset.The characteristic factors were ranked using the mutual information method,with their scores decreasing in the order of rainfall(0.1616),altitude(0.06),normalized difference vegetation index(NDVI;0.04),and distance from roads(0.03).Finally,the geologic hazard susceptibility classification was assessed using the natural breaks method combined with a clustering algorithm.The results indicate that the clustering algorithm exhibited higher classification accuracy than the natural breaks method.The findings of this study demonstrate that the proposed model optimization scheme can provide a scientific basis for the prevention and control of geologic hazards.展开更多
Mesenchymal stromal cell transplantation is an effective and promising approach for treating various systemic and diffuse diseases.However,the biological characteristics of transplanted mesenchymal stromal cells in hu...Mesenchymal stromal cell transplantation is an effective and promising approach for treating various systemic and diffuse diseases.However,the biological characteristics of transplanted mesenchymal stromal cells in humans remain unclear,including cell viability,distribution,migration,and fate.Conventional cell tracing methods cannot be used in the clinic.The use of superparamagnetic iron oxide nanoparticles as contrast agents allows for the observation of transplanted cells using magnetic resonance imaging.In 2016,the National Medical Products Administration of China approved a new superparamagnetic iron oxide nanoparticle,Ruicun,for use as a contrast agent in clinical trials.In the present study,an acute hemi-transection spinal cord injury model was established in beagle dogs.The injury was then treated by transplantation of Ruicun-labeled mesenchymal stromal cells.The results indicated that Ruicunlabeled mesenchymal stromal cells repaired damaged spinal cord fibers and partially restored neurological function in animals with acute spinal cord injury.T2*-weighted imaging revealed low signal areas on both sides of the injured spinal cord.The results of quantitative susceptibility mapping with ultrashort echo time sequences indicated that Ruicun-labeled mesenchymal stromal cells persisted stably within the injured spinal cord for over 4 weeks.These findings suggest that magnetic resonance imaging has the potential to effectively track the migration of Ruicun-labeled mesenchymal stromal cells and assess their ability to repair spinal cord injury.展开更多
Staphylococcus aureus(S.aureus)is the third most common pathogen causing 10.6%of bacterial foodborne illnesses in China in 2021[1].Heat-stable Staphylococcal Enterotoxins(SEs)produced by S.aureus are the main contribu...Staphylococcus aureus(S.aureus)is the third most common pathogen causing 10.6%of bacterial foodborne illnesses in China in 2021[1].Heat-stable Staphylococcal Enterotoxins(SEs)produced by S.aureus are the main contributors to staphylococcal food poisoning(SFP),causing vomiting,diarrhea,abdominal pain,headache,muscle cramps,and other acute gastroenteritis symptoms.More than 25 SEs and staphylococcal enterotoxin-like toxins(SE/s)have been described and which together comprise a superfamily of pyrogenic toxin superantigens(SAgs)[2].展开更多
BACKGROUND Transforming growth factor-β(TGF-β)superfamily plays an important role in tumor progression and metastasis.Activin A receptor type 1C(ACVR1C)is a TGF-βtype I receptor that is involved in tumorigenesis th...BACKGROUND Transforming growth factor-β(TGF-β)superfamily plays an important role in tumor progression and metastasis.Activin A receptor type 1C(ACVR1C)is a TGF-βtype I receptor that is involved in tumorigenesis through binding to dif-ferent ligands.AIM To evaluate the correlation between single nucleotide polymorphisms(SNPs)of ACVR1C and susceptibility to esophageal squamous cell carcinoma(ESCC)in Chinese Han population.METHODS In this hospital-based cohort study,1043 ESCC patients and 1143 healthy controls were enrolled.Five SNPs(rs4664229,rs4556933,rs77886248,rs77263459,rs6734630)of ACVR1C were assessed by the ligation detection reaction method.Hardy-Weinberg equilibrium test,genetic model analysis,stratified analysis,linkage disequi-librium test,and haplotype analysis were conducted.RESULTS Participants carrying ACVR1C rs4556933 GA mutant had significantly decreased risk of ESCC,and those with rs77886248 TA mutant were related with higher risk,especially in older male smokers.In the haplotype analysis,ACVR1C Trs4664229Ars4556933Trs77886248Crs77263459Ars6734630 increased risk of ESCC,while Trs4664229Grs4556933Trs77886248Crs77263459Ars6734630 was associated with lower susceptibility to ESCC.CONCLUSION ACVR1C rs4556933 and rs77886248 SNPs were associated with the susceptibility to ESCC,which could provide a potential target for early diagnosis and treatment of ESCC in Chinese Han population.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain c...Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.展开更多
Wildfires significantly disrupt the physical and hydrologic conditions of the environment,leading to vegetation loss and altered surface geo-material properties.These complex dynamics promote post-fire gully erosion,y...Wildfires significantly disrupt the physical and hydrologic conditions of the environment,leading to vegetation loss and altered surface geo-material properties.These complex dynamics promote post-fire gully erosion,yet the key conditioning factors(e.g.,topography,hydrology)remain insufficiently understood.This study proposes a novel artificial intelligence(AI)framework that integrates four machine learning(ML)models with Shapley Additive Explanations(SHAP)method,offering a hierarchical perspective from global to local on the dominant factors controlling gully distribution in wildfireaffected areas.In a case study of Xiangjiao catchment burned on March 28,2020,in Muli County in Sichuan Province of Southwest China,we derived 21 geoenvironmental factors to assess the susceptibility of post-fire gully erosion using logistic regression(LR),support vector machine(SVM),random forest(RF),and convolutional neural network(CNN)models.SHAP-based model interpretation revealed eight key conditioning factors:topographic position index(TPI),topographic wetness index(TWI),distance to stream,mean annual precipitation,differenced normalized burn ratio(d NBR),land use/cover,soil type,and distance to road.Comparative model evaluation demonstrated that reduced-variable models incorporating these dominant factors achieved accuracy comparable to that of the initial-variable models,with AUC values exceeding 0.868 across all ML algorithms.These findings provide critical insights into gully erosion behavior in wildfire-affected areas,supporting the decision-making process behind environmental management and hazard mitigation.展开更多
Recent studies have shown a noticeable increase in global Helicobacter pylori(H.pylori)resistance,with clarithromycin resistance surpassing 15%in various areas.However,inadequate epidemiological monitoring,especially ...Recent studies have shown a noticeable increase in global Helicobacter pylori(H.pylori)resistance,with clarithromycin resistance surpassing 15%in various areas.However,inadequate epidemiological monitoring,especially in developing countries,and the absence of uniform testing methods lead to discrepancies between regions and a possible underestimation of resistance levels.The complexity of treating H.pylori is driven by its highly dynamic genome,which is prone to frequent mutations contributing to phenotypical resistance.The usual course of action in empirical treatment involves using a combination of various drugs simultaneously,leading to significant resistance selection pressure and potential side effects.The emergence of H.pylori strains resistant to multiple drugs is closely tied to failures in first-line treatment,highlighting the need to prevent further resistance by using optimal initial empirical therapy or regimens guided by antibiotic susceptibility testing,requiring a collection of mixed samples and multiple isolates for accurate assessment.The emergence of new treatments like potassium-competitive acid blockers offers a hopeful approach to decrease antimicrobial usage while still ensuring effectiveness in comparison to traditional therapies with proton pump inhibitors.Additionally,the use of probiotics is under investigation to identify specific strains and formulations that may mitigate therapy-associated adverse effects.展开更多
Landslide susceptibility evaluation plays an important role in disaster prevention and reduction.Feature-based transfer learning(TL)is an effective method for solving landslide susceptibility mapping(LSM)in target reg...Landslide susceptibility evaluation plays an important role in disaster prevention and reduction.Feature-based transfer learning(TL)is an effective method for solving landslide susceptibility mapping(LSM)in target regions with no available samples.However,as the study area expands,the distribution of land-slide types and triggering mechanisms becomes more diverse,leading to performance degradation in models relying on landslide evaluation knowledge from a single source domain due to domain feature shift.To address this,this study proposes a Multi-source Domain Adaptation Convolutional Neural Network(MDACNN),which combines the landslide prediction knowledge learned from two source domains to perform cross-regional LSM in complex large-scale areas.The method is validated through case studies in three regions located in southeastern coastal China and compared with single-source domain TL models(TCA-based models).The results demonstrate that MDACNN effectively integrates transfer knowledge from multiple source domains to learn diverse landslide-triggering mechanisms,thereby significantly reducing prediction bias inherent to single-source domain TL models,achieving an average improvement of 16.58%across all metrics.Moreover,the landslide susceptibility maps gener-ated by MDACNN accurately quantify the spatial distribution of landslide risks in the target area,provid-ing a powerful scientific and technological tool for landslide disaster management and prevention.展开更多
Research on the application of machine learning(ML)models to landslide susceptibility assessments has gained popularity in recent years,with a focus primarily on topographic factors derived from digital elevation mode...Research on the application of machine learning(ML)models to landslide susceptibility assessments has gained popularity in recent years,with a focus primarily on topographic factors derived from digital elevation models(DEMs).However,few studies have focused on the explanatory effects of these factors on different models,i.e.whether DEM-based factors affect different models in the same way.This study investigated whether different ML models could yield consistent interpretations of DEM-based factors using explanatory algorithms.Six ML models,including a support vector machine,a neural network,extreme gradient boosting,a random forest,linear regression,and K-nearest neighbors,were trained and evaluated on five geospatial datasets derived from different DEMs.Each dataset contained eight DEM-based and six non-DEM-based factors from 8912 landslide samples.Model performance was assessed using accuracy,precision,recall rate,F1-score,kappa coefficient,and receiver operating characteristic curves.Explanatory analyses,including Shapley additive explanations and partial dependence plots,were also employed to investigate the effects of topographic factors on landslide susceptibility.The results indicate that DEM-based factors consistently influenced different ML models across the datasets.Furthermore,tree-based models outperformed the other models in almost all datasets,while the most suitable DEMs were obtained from Copernicus and TanDEM-X.In addition,the concave surface without potholes on steep slopes are ideal topographic conditions for landslide formation in the study area.This study can benefit the wider landslide research community by clarifying how topographic factors affect ML models.展开更多
Identifying the factors that contribute to individual susceptibility to cancer is essential for both prevention and treatment.The advancement of biotechnologies,particularly next-generation sequencing,has accelerated ...Identifying the factors that contribute to individual susceptibility to cancer is essential for both prevention and treatment.The advancement of biotechnologies,particularly next-generation sequencing,has accelerated the discovery of genetic variants linked to cancer susceptibility.While hundreds of cancer-susceptibility genes have been identified,they only explain a small fraction of the overall cancer risk,a phenomenon known as"missing heritability".Despite progress,even considering factors such as epistasis,epigenetics,and gene-environment interactions,the missing heritability remains unresolved.Recent research has revealed that an individual's microbiome composition plays a significant role in cancer susceptibility through several mechanisms,such as modulating immune cell activity and influencing the presence or removal of environmental carcinogens.In this review,we examine the multifaceted roles of the microbiome in cancer risk and explore gene-microbiome and environment-microbiome interactions that may contribute to cancer susceptibility.Additionally,we highlight the importance of experimental models,such as collaborative cross mice,and advanced analytical tools,like artificial intelligence,in identifying microbial factors associated with cancer risk.Understanding these microbial determinants can open new avenues for interventions aimed at reducing cancer risk and guide the development of more effective cancer treatments.展开更多
Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with region...Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.展开更多
基金National Key Research and Development Program of China(2023YFA1609000)National Natural Science Foundation of China(62474190,U22B2043,U2267210)。
文摘The single-event susceptibility of three silicon carbide(SiC)metal-oxide-semiconductor field-effect transistor(MOSFET)power devices structures(planar,trench and double trench)is researched by the technology computer-aided design(TCAD)simulation.Comparative analysis of the heavy-ion irradiation effects on three device structures reveals distinct susceptibility characteristics.The gate oxide region is identified as the most sensitive position in planar devices,while trench and doubletrench structures exhibit no localized sensitive regions.Furthermore,the single-event susceptibility demonstrates strong depth dependence across all three structures,with enhanced vulnerability observed at greater ion penetration depths.
基金supported by the National Natural Science Foundation of China(42361144880)the Science and Technology Program of Xizang Autonomous Region,China(XZ202402ZD0001)the Qinghai Province Basic Research Program Project,China(2024-ZJ-904).
文摘Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to evaluate debris flow susceptibility and associated hazards across Tajikistan.A dataset comprising 405 documented debris flow points and 14 influencing factors,encompassing geological,climatic-hydrological,and anthropogenic variables,was established.Three machine learning algorithms—Random Forest,Support Vector Machine(SVM),and Multi-layer Perceptron—were applied to generate susceptibility maps and delineate debris flow risk zones.The results indicate that the areas of higher and high susceptibility accounted for 20.43%and 4.41%of the national area,respectively,and were predominantly concentrated along the Zeravshan and Vakhsh river basins.Among the evaluated models,SVM model demonstrated the highest predictive performance.Beyond conventional topographic and environmental controls,drought conditions were identified as a critical factor influencing debris flow occurrence within the arid and semi-arid mountainous regions of Tajikistan.These findings provide a scientific basis for regional debris flow risk management and disaster mitigation planning,and offer practical guidance for selecting conditioning factors in machine-learning-based susceptibility assessments in other dry mountainous environments.
基金The National Key Research and Development Program of China,No.2023YFC3206601。
文摘Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk.This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest(RF),Gradient Boosting Regression Trees(GBRT),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost)—to generate susceptibility maps.The Shapley additive explanation(SHAP)method was applied to quantify factor importance and explore their nonlinear effects.The results showed that:(1)CatBoost was the best-performing model(CA=0.938,AUC=0.980)in assessing landslide susceptibility,with altitude emerging as the most significant factor,followed by distance to roads and earthquake sites,precipitation,and slope;(2)the SHAP method revealed critical nonlinear thresholds,demonstrating that historical landslides were concentrated at mid-altitudes(1400-4000 m)and decreased markedly above 4000 m,with a parallel reduction in probability beyond 700 m from roads;and(3)landslide-prone areas,comprising 13%of the QTP,were concentrated in the southeastern and northeastern parts of the plateau.By integrating machine learning and SHAP analysis,this study revealed landslide hazard-prone areas and their driving factors,providing insights to support disaster management strategies and sustainable regional planning.
基金Supported by the National Natural Science Foundation of China(No.81873686)Natural Science Foundation of Hunan Province(No.2023JJ30715)+4 种基金Scientific Research Project of Hunan Provincial Health Commission(No.A202303018385)Health Research Project of Hunan Provincial Health Commission(No.W20243024)Distinguished Professor of the Lotus Scholars Award Program of Hunan ProvinceSublimation Scholars Project of Central South UniversityWisdom Accumulation and Talent Cultivation Project of the Third Xiangya Hospital of Central South University(No.YX202109).
文摘AIM:To conduct a genetic analysis of Han-Chinese patients with isolated congenital ptosis(ICP)and identify the genetic variants related to the condition.METHODS:Sixty-five unrelated patients with ICP were enrolled.Comprehensive clinical examinations,whole exome sequencing(WES),and Sanger sequencing were used to reveal the potential genetic causes.Combined with public and in-house control databases,multiple bioinformatics prediction tools,and conservation analysis,the potential variants were further analyzed.AlphaFold 3,an accurate modelling prediction tool,was utilized to generate three-dimensional structural models of both wild-type and mutated proteins.RESULTS:Three novel heterozygous variants in the zinc finger homeobox 4 gene(ZFHX4),c.5145C>A(p.N1715K),c.10382C>T(p.A3461V),and c.10795G>A(p.A3599T),were identified in three patients,respectively.Bioinformatics analyses suggested that these variants are likely to exert deleterious effects,supporting their potential involvement in the pathogenesis of ptosis.CONCLUSION:The novel heterozygous ZFHX4 variants are identified as disease-associated variants in three patients with ptosis,suggesting that ZFHX4 may be a disease-causing gene for autosomal dominant ICP with incomplete penetrance or a susceptibility gene.These findings expand the variant spectrum of ZFHX4,improve understanding of the pathogenesis of ZFHX4-related ptosis,and may contribute to the genetic counseling and disease management,as well as the development of experimental treatments.
文摘Accurate assessment of seismic landslide susceptibility is crucial for disaster prevention and emergency decision-making.Although machine learning methods have been widely applied in this field,they exhibit a strong dependence on large quantities of highquality samples,resulting in significantly low prediction accuracy of existing studies under data-scarce or crossregional prediction scenarios,which fail to meet practical application requirements.To address this issue,this study proposes an intelligent prediction model integrating transfer learning and a sampling optimization strategy,aiming to enhance the accuracy and applicability of seismic landslide susceptibility assessment.The model first improves the sample collection method through the sampling optimization strategy to enhance the precision and representativeness of training samples.This not only ensures the accuracy of origin area training but also further strengthens the model's predictive ability in the target area.Subsequently,it incorporates Transfer Component Analysis(TCA)to overcome the differences in environmental characteristics between the origin area and target area,and couples TCA with the Light GBM algorithm to construct the TCA-Light GBM model,realizing the assessment of seismic landslide susceptibility in sample-free areas.Validated through case studies of the Jiuzhaigou and Luding earthquakes,the results demonstrate that the proposed TCALight GBM transfer learning method exhibits excellent applicability in seismic landslide susceptibility prediction.After optimization with the TCA algorithm,the model's prediction performance in the target domain is significantly improved,with the AUC value increasing from 0.719 to 0.827,representing an increase of approximately 15.02%.This indicates that TCA technology can effectively alleviate the feature distribution discrepancy between the source domain and target domain,enhancing the model's generalization ability.The method is particularly suitable for scenarios with data scarcity and cross-regional prediction and can provide reliable technical support for the emergency response and risk prevention and control of seismic hazards.
文摘Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.
基金The financial supports from National Natural Science Foundation of China(Nos.52574313,52204272 and 52074091)to this project。
文摘The susceptibility of ore particles to electrical breakdown plays a critical role for high voltage pulse(HVP)breakage,yet its quantitative characterization still lacks deep understanding.Two indicators,namely breakdown delay time(T_(d))and breakdown strength(E_(b))were compared,based on analysis on the two breakdown modes namely wavefront mode and post-wave mode.It was found that T_(d) is more suitable to characterize the susceptibility of ore particles to electrical breakdown in HVP breakage than E_(b).A probabilistic model based on the Weibull distribution is developed to describe the relation of breakdown probability to T_(d).Regression analyses were conducted to investigate how operating parameters and particle properties influence Td and size reduction degree of ore particles in HVP breakage.The regressed models demonstrate potential capability to predict metallic minerals content and HVP breakage degree based on operating parameters and particle properties.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3007201)the National Natural Science Foundation of China(Grant No.42377161)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB 2024ZR03).
文摘Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.
基金supported by a project entitled Loess Plateau Region-Watershed-Slope Geological Hazard Multi-Scale Collaborative Intelligent Early Warning System of the National Key R&D Program of China(2022YFC3003404)a project of the Shaanxi Youth Science and Technology Star(2021KJXX-87)public welfare geological survey projects of Shaanxi Institute of Geologic Survey(20180301,201918,202103,and 202413)。
文摘This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment models.First,the cumulative probability method revealed that a low probability(15%)of geologic hazards between any two geologic hazard points occurred outside a buffer zone with a radius of 2297 m(i.e.,the distance threshold).The training dataset was established,consisting of negative samples(non-hazard points)randomly generated based on the distance threshold,positive samples(i.e.,historical hazards),and 13 conditioning factors.Then,models were built using five machine learning algorithms,namely random forest(RF),gradient boosting decision tree(GBDT),naive Bayes(NB),logistic regression(LR),and support vector machine(SVM).The comprehensive performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and overall accuracy(OA)as indicators,revealing that RF exhibited the best performance,with OA and AUC values of 2.7127 and 0.981,respectively.Furthermore,the machine learning models constructed by considering the distance threshold outperformed those built using the unoptimized dataset.The characteristic factors were ranked using the mutual information method,with their scores decreasing in the order of rainfall(0.1616),altitude(0.06),normalized difference vegetation index(NDVI;0.04),and distance from roads(0.03).Finally,the geologic hazard susceptibility classification was assessed using the natural breaks method combined with a clustering algorithm.The results indicate that the clustering algorithm exhibited higher classification accuracy than the natural breaks method.The findings of this study demonstrate that the proposed model optimization scheme can provide a scientific basis for the prevention and control of geologic hazards.
基金supported by the National Key R&D Program of China,Nos.2017YFA0104302(to NG and XM)and 2017YFA0104304(to BW and ZZ)
文摘Mesenchymal stromal cell transplantation is an effective and promising approach for treating various systemic and diffuse diseases.However,the biological characteristics of transplanted mesenchymal stromal cells in humans remain unclear,including cell viability,distribution,migration,and fate.Conventional cell tracing methods cannot be used in the clinic.The use of superparamagnetic iron oxide nanoparticles as contrast agents allows for the observation of transplanted cells using magnetic resonance imaging.In 2016,the National Medical Products Administration of China approved a new superparamagnetic iron oxide nanoparticle,Ruicun,for use as a contrast agent in clinical trials.In the present study,an acute hemi-transection spinal cord injury model was established in beagle dogs.The injury was then treated by transplantation of Ruicun-labeled mesenchymal stromal cells.The results indicated that Ruicunlabeled mesenchymal stromal cells repaired damaged spinal cord fibers and partially restored neurological function in animals with acute spinal cord injury.T2*-weighted imaging revealed low signal areas on both sides of the injured spinal cord.The results of quantitative susceptibility mapping with ultrashort echo time sequences indicated that Ruicun-labeled mesenchymal stromal cells persisted stably within the injured spinal cord for over 4 weeks.These findings suggest that magnetic resonance imaging has the potential to effectively track the migration of Ruicun-labeled mesenchymal stromal cells and assess their ability to repair spinal cord injury.
基金supported by the Ministry of Science and Technology of the People’s Republic of China(2022YFD1800400).
文摘Staphylococcus aureus(S.aureus)is the third most common pathogen causing 10.6%of bacterial foodborne illnesses in China in 2021[1].Heat-stable Staphylococcal Enterotoxins(SEs)produced by S.aureus are the main contributors to staphylococcal food poisoning(SFP),causing vomiting,diarrhea,abdominal pain,headache,muscle cramps,and other acute gastroenteritis symptoms.More than 25 SEs and staphylococcal enterotoxin-like toxins(SE/s)have been described and which together comprise a superfamily of pyrogenic toxin superantigens(SAgs)[2].
基金Supported by The National Natural Science Foundation of China,No.82350127 and No.82241013the Shanghai Natural Science Foundation,No.20ZR1411600+2 种基金the Shanghai Shenkang Hospital Development Center,No.SHDC2020CR4039the Bethune Ethicon Excellent Surgery Foundation,No.CESS2021TC04Xuhui District Medical Research Project of Shanghai,No.SHXH201805.
文摘BACKGROUND Transforming growth factor-β(TGF-β)superfamily plays an important role in tumor progression and metastasis.Activin A receptor type 1C(ACVR1C)is a TGF-βtype I receptor that is involved in tumorigenesis through binding to dif-ferent ligands.AIM To evaluate the correlation between single nucleotide polymorphisms(SNPs)of ACVR1C and susceptibility to esophageal squamous cell carcinoma(ESCC)in Chinese Han population.METHODS In this hospital-based cohort study,1043 ESCC patients and 1143 healthy controls were enrolled.Five SNPs(rs4664229,rs4556933,rs77886248,rs77263459,rs6734630)of ACVR1C were assessed by the ligation detection reaction method.Hardy-Weinberg equilibrium test,genetic model analysis,stratified analysis,linkage disequi-librium test,and haplotype analysis were conducted.RESULTS Participants carrying ACVR1C rs4556933 GA mutant had significantly decreased risk of ESCC,and those with rs77886248 TA mutant were related with higher risk,especially in older male smokers.In the haplotype analysis,ACVR1C Trs4664229Ars4556933Trs77886248Crs77263459Ars6734630 increased risk of ESCC,while Trs4664229Grs4556933Trs77886248Crs77263459Ars6734630 was associated with lower susceptibility to ESCC.CONCLUSION ACVR1C rs4556933 and rs77886248 SNPs were associated with the susceptibility to ESCC,which could provide a potential target for early diagnosis and treatment of ESCC in Chinese Han population.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金funded by the Natural Science Foundation of China(Grant Nos.42377164 and 41972280)the Badong National Observation and Research Station of Geohazards(Grant No.BNORSG-202305).
文摘Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.
基金the National Natural Science Foundation of China(42377170,42407212)the National Funded Postdoctoral Researcher Program(GZB20230606)+3 种基金the Postdoctoral Research Foundation of China(2024M752679)the Sichuan Natural Science Foundation(2025ZNSFSC1205)the National Key R&D Program of China(2022YFC3005704)the Sichuan Province Science and Technology Support Program(2024NSFSC0100)。
文摘Wildfires significantly disrupt the physical and hydrologic conditions of the environment,leading to vegetation loss and altered surface geo-material properties.These complex dynamics promote post-fire gully erosion,yet the key conditioning factors(e.g.,topography,hydrology)remain insufficiently understood.This study proposes a novel artificial intelligence(AI)framework that integrates four machine learning(ML)models with Shapley Additive Explanations(SHAP)method,offering a hierarchical perspective from global to local on the dominant factors controlling gully distribution in wildfireaffected areas.In a case study of Xiangjiao catchment burned on March 28,2020,in Muli County in Sichuan Province of Southwest China,we derived 21 geoenvironmental factors to assess the susceptibility of post-fire gully erosion using logistic regression(LR),support vector machine(SVM),random forest(RF),and convolutional neural network(CNN)models.SHAP-based model interpretation revealed eight key conditioning factors:topographic position index(TPI),topographic wetness index(TWI),distance to stream,mean annual precipitation,differenced normalized burn ratio(d NBR),land use/cover,soil type,and distance to road.Comparative model evaluation demonstrated that reduced-variable models incorporating these dominant factors achieved accuracy comparable to that of the initial-variable models,with AUC values exceeding 0.868 across all ML algorithms.These findings provide critical insights into gully erosion behavior in wildfire-affected areas,supporting the decision-making process behind environmental management and hazard mitigation.
基金Supported by the Industrial Technological Initiation Scholarship of National Council for Scientific and Technological Development,CNPq,Brazil,No.0932204294929829 and No.7414780530977345the Scientific Initiation Scholarship Programme(PIBIC)of National Council for Scientific and Technological Development,CNPq,Brazil,No.5763023359532159,No.6472982965854452,and No.7340128440641417+2 种基金the Scientific Initiation Scholarship Programme(PIBIC)of Bahia State Research Support Foundation,FAPESB,Brazil,No.19.573.301.5418the PERMANECER Programme of Pro-Rectory of Student Assistance at Federal University of Bahia,No.R8EZ-4V4W-6LQX-5LC8the CNPq Research Productivity Fellow,No.4357511882624145.
文摘Recent studies have shown a noticeable increase in global Helicobacter pylori(H.pylori)resistance,with clarithromycin resistance surpassing 15%in various areas.However,inadequate epidemiological monitoring,especially in developing countries,and the absence of uniform testing methods lead to discrepancies between regions and a possible underestimation of resistance levels.The complexity of treating H.pylori is driven by its highly dynamic genome,which is prone to frequent mutations contributing to phenotypical resistance.The usual course of action in empirical treatment involves using a combination of various drugs simultaneously,leading to significant resistance selection pressure and potential side effects.The emergence of H.pylori strains resistant to multiple drugs is closely tied to failures in first-line treatment,highlighting the need to prevent further resistance by using optimal initial empirical therapy or regimens guided by antibiotic susceptibility testing,requiring a collection of mixed samples and multiple isolates for accurate assessment.The emergence of new treatments like potassium-competitive acid blockers offers a hopeful approach to decrease antimicrobial usage while still ensuring effectiveness in comparison to traditional therapies with proton pump inhibitors.Additionally,the use of probiotics is under investigation to identify specific strains and formulations that may mitigate therapy-associated adverse effects.
基金the National Natural Science Foundation of China(Grant No.42301002,and 52109118)Fujian Provincial Water Resources Science and Technology Project(Grant No.MSK202524)Guidance fund for Science and Technology Program,Fujian province(Grant No.2024Y0002).
文摘Landslide susceptibility evaluation plays an important role in disaster prevention and reduction.Feature-based transfer learning(TL)is an effective method for solving landslide susceptibility mapping(LSM)in target regions with no available samples.However,as the study area expands,the distribution of land-slide types and triggering mechanisms becomes more diverse,leading to performance degradation in models relying on landslide evaluation knowledge from a single source domain due to domain feature shift.To address this,this study proposes a Multi-source Domain Adaptation Convolutional Neural Network(MDACNN),which combines the landslide prediction knowledge learned from two source domains to perform cross-regional LSM in complex large-scale areas.The method is validated through case studies in three regions located in southeastern coastal China and compared with single-source domain TL models(TCA-based models).The results demonstrate that MDACNN effectively integrates transfer knowledge from multiple source domains to learn diverse landslide-triggering mechanisms,thereby significantly reducing prediction bias inherent to single-source domain TL models,achieving an average improvement of 16.58%across all metrics.Moreover,the landslide susceptibility maps gener-ated by MDACNN accurately quantify the spatial distribution of landslide risks in the target area,provid-ing a powerful scientific and technological tool for landslide disaster management and prevention.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3003205)the Chengdu University of Technology Postgraduate Innovative Cultivation Program(Grant No.10800-000510-01-022)+1 种基金the Sichuan Science and Technology Program(Grant No.2025ZNSFSC1206)the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project(Grant No.SKLGP2023Z026).
文摘Research on the application of machine learning(ML)models to landslide susceptibility assessments has gained popularity in recent years,with a focus primarily on topographic factors derived from digital elevation models(DEMs).However,few studies have focused on the explanatory effects of these factors on different models,i.e.whether DEM-based factors affect different models in the same way.This study investigated whether different ML models could yield consistent interpretations of DEM-based factors using explanatory algorithms.Six ML models,including a support vector machine,a neural network,extreme gradient boosting,a random forest,linear regression,and K-nearest neighbors,were trained and evaluated on five geospatial datasets derived from different DEMs.Each dataset contained eight DEM-based and six non-DEM-based factors from 8912 landslide samples.Model performance was assessed using accuracy,precision,recall rate,F1-score,kappa coefficient,and receiver operating characteristic curves.Explanatory analyses,including Shapley additive explanations and partial dependence plots,were also employed to investigate the effects of topographic factors on landslide susceptibility.The results indicate that DEM-based factors consistently influenced different ML models across the datasets.Furthermore,tree-based models outperformed the other models in almost all datasets,while the most suitable DEMs were obtained from Copernicus and TanDEM-X.In addition,the concave surface without potholes on steep slopes are ideal topographic conditions for landslide formation in the study area.This study can benefit the wider landslide research community by clarifying how topographic factors affect ML models.
基金Supported by The United States Department of Defense Breast Cancer Research Program,No.BC190820the National Institutes of Health,No.R01ES031322.
文摘Identifying the factors that contribute to individual susceptibility to cancer is essential for both prevention and treatment.The advancement of biotechnologies,particularly next-generation sequencing,has accelerated the discovery of genetic variants linked to cancer susceptibility.While hundreds of cancer-susceptibility genes have been identified,they only explain a small fraction of the overall cancer risk,a phenomenon known as"missing heritability".Despite progress,even considering factors such as epistasis,epigenetics,and gene-environment interactions,the missing heritability remains unresolved.Recent research has revealed that an individual's microbiome composition plays a significant role in cancer susceptibility through several mechanisms,such as modulating immune cell activity and influencing the presence or removal of environmental carcinogens.In this review,we examine the multifaceted roles of the microbiome in cancer risk and explore gene-microbiome and environment-microbiome interactions that may contribute to cancer susceptibility.Additionally,we highlight the importance of experimental models,such as collaborative cross mice,and advanced analytical tools,like artificial intelligence,in identifying microbial factors associated with cancer risk.Understanding these microbial determinants can open new avenues for interventions aimed at reducing cancer risk and guide the development of more effective cancer treatments.
基金funding support from the National Natural Science Foundation of China(Grant Nos.U22A20594,52079045)Hong-Zhi Cui acknowledges the financial support of the China Scholarship Council(Grant No.CSC:202206710014)for his research at Universitat Politecnica de Catalunya,Barcelona.
文摘Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.
