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
Dysregulation of neurotransmitter metabolism in the central nervous system contributes to mood disorders such as depression, anxiety, and post–traumatic stress disorder. Monoamines and amino acids are important types...Dysregulation of neurotransmitter metabolism in the central nervous system contributes to mood disorders such as depression, anxiety, and post–traumatic stress disorder. Monoamines and amino acids are important types of neurotransmitters. Our previous results have shown that disco-interacting protein 2 homolog A(Dip2a) knockout mice exhibit brain development disorders and abnormal amino acid metabolism in serum. This suggests that DIP2A is involved in the metabolism of amino acid–associated neurotransmitters. Therefore, we performed targeted neurotransmitter metabolomics analysis and found that Dip2a deficiency caused abnormal metabolism of tryptophan and thyroxine in the basolateral amygdala and medial prefrontal cortex. In addition, acute restraint stress induced a decrease in 5-hydroxytryptamine in the basolateral amygdala. Additionally, Dip2a was abundantly expressed in excitatory neurons of the basolateral amygdala, and deletion of Dip2a in these neurons resulted in hopelessness-like behavior in the tail suspension test. Altogether, these findings demonstrate that DIP2A in the basolateral amygdala may be involved in the regulation of stress susceptibility. This provides critical evidence implicating a role of DIP2A in affective disorders.展开更多
Breast cancer(BC)is now the most common cancer and the fifth leading cause of cancer-associated mortality among women in China1.Germline pathogenic variants(PVs)of BC susceptibility genes,such as the well-known BRCA1/...Breast cancer(BC)is now the most common cancer and the fifth leading cause of cancer-associated mortality among women in China1.Germline pathogenic variants(PVs)of BC susceptibility genes,such as the well-known BRCA1/2 genes,increase the risk of BC and other cancers(ovarian and pancreatic cancer)^(2,3).Recent studies have demonstrated substantial benefits of poly(adenosine diphosphate ribose)polymerase inhibitors in the treatment of BC patients who carry BRCA1/2 PVs^(4).展开更多
Landslides are significant natural geological hazards.Landslide susceptibility evaluation involves the quantitative assessment and prediction of potential landslide locations and their probabilities.Research has explo...Landslides are significant natural geological hazards.Landslide susceptibility evaluation involves the quantitative assessment and prediction of potential landslide locations and their probabilities.Research has explored susceptibility assessment methods based on spatial-scale analysis.This evaluation integrates two models—global and local scale—using a CNN model and a PSO-CNN coupled model.Key aspects include selecting evaluation factors and optimizing model parameters for landslide susceptibility at different scales.A major focus of current landslide research is utilizing prediction results to enhance prevention and control measures.展开更多
Flooding remains one of the most destructive natural disasters,posing significant risks to both human lives and infrastructure.In India,where a large area is susceptible to flood hazards,the importance of accurate flo...Flooding remains one of the most destructive natural disasters,posing significant risks to both human lives and infrastructure.In India,where a large area is susceptible to flood hazards,the importance of accurate flood frequency analysis(FFA)and flood susceptibility mapping cannot be overstated.This study focuses on the Haora River basin in Tripura,a region prone to frequent flooding due to a combination of natural and anthropogenic factors.This study evaluates the suitability of the Log-Pearson Type Ⅲ(LP-Ⅲ)and Gumbel Extreme Value-1(EV-1)distributions for estimating peak discharges and delineates floodsusceptible zones in the Haora River basin,Tripura.Using 40 years of peak discharge data(1984-2023),the LP-Ⅲ distribution was identified as the most appropriate model based on goodness-of-fit tests.Flood susceptibility mapping,integrating 16 thematic layers through the Analytical Hierarchy Process,identified 8%,64%,and 26%of the area as high,moderate,and low susceptibility zones,respectively,with a model success rate of 0.81.The findings highlight the need for improved flood management strategies,such as enhancing river capacity and constructing flood spill channels.These insights are critical for designing targeted flood mitigation measures in the Haora basin and other flood-prone regions.展开更多
The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenz...The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.展开更多
Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calcu...Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide susceptibility evaluation models,in this study,a method of landslide hazard susceptibility evaluation is proposed by combining SBAS-InSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar)and SSA-BP(Sparrow Search Algorithm-Back Propagation)neural network algorithm.The SBAS-InSAR technology is adopted to identify potential landslide hazards in the study area,update the cataloging data of landslide hazards,and 11 evaluation factors are chosen for constructing the SSA-BP model for training and validation.Baihetan Reservoir area is selected as a case study for validation.As indicated by the results,the application of SBAS-InSAR technology,combined with both ascending and descending orbit data,effectively addresses the incomplete identification of landslide hazards caused by geometric distortion of single orbit SAR data(e.g.,shadow,overlay,and perspective contraction)in deep canyon areas,thereby enabling the acquisition of up-to-date landslide hazard data.Moreover,in comparison to the conventional BP(Back Propagation)algorithm,the accuracy of the model constructed by the SSA-BP algorithm exhibits a significant increase,with mean squared error and mean absolute error reduced by 0.0142 and 0.0607,respectively.Additionally,during the process of susceptibility evaluation,the SSA-BP model effectively circumvents the issue of considerable manual interventions in calculating the weight of evaluation factors.The area under the curve of this model reaches 0.909,surpassing BP(0.835),random forest(0.792),and the information value method(0.699).The risk of landslide occurrence in the Baihetan Reservoir area is positively correlated with slope,surface temperature,and deformation rate,while it is negatively correlated with fault distance and normalized difference vegetation index.Geological lithology exerts minimal influence on the occurrence of landslides,with the risk being low in forest land and high in grassland.The method proposed in this study provides a useful reference for disaster prevention and mitigation departments to perform landslide hazard susceptibility evaluations in deep canyon areas under complex geological conditions.展开更多
The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study are...The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. Regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-R technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic Roughness Index(TRI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). Results were validated through the Receiver Operator Characteristics(ROC) curve. Results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-R propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-R validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure.展开更多
In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken a...In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.展开更多
The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable ...The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30e110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.展开更多
Landslide susceptibility mapping is an integral part of geological hazard analysis.Recently,the emphasis of many studies has been on data-driven models,notably those derived from machine learning,owing to their aptitu...Landslide susceptibility mapping is an integral part of geological hazard analysis.Recently,the emphasis of many studies has been on data-driven models,notably those derived from machine learning,owing to their aptitude for tackling complex non-linear problems.However,the prevailing models often disregard qualitative research,leading to limited interpretability and mistakes in extracting negative samples,i.e.inaccurate non-landslide samples.In this study,Scoops 3D(a three-dimensional slope stability analysis tool)was utilized to conduct a qualitative assessment of slope stability in the Yunyang section of the Three Gorges Reservoir area.The depth of the bedrock was predicted utilizing a Convolutional Neural Network(CNN),incorporating local boreholes and building on the insights from prior research.The Random Forest(RF)algorithm was subsequently used to execute a data-driven landslide susceptibility analysis.The proposed methodology demonstrated a notable increase of 29.25%in the evaluation metric,the area under the receiver operating characteristic curve(ROC-AUC),outperforming the prevailing benchmark model.Furthermore,the landslide susceptibility map generated by the proposed model demonstrated superior interpretability.This result not only validates the effectiveness of amalgamating mathematical and mechanistic insights for such analyses,but it also carries substantial academic and practical implications.展开更多
Coronavirus disease 2019(COVID-19)is a highly infectious disease caused by a novel human coronavirus called severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Diabetes is a well-known risk factor for infectio...Coronavirus disease 2019(COVID-19)is a highly infectious disease caused by a novel human coronavirus called severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Diabetes is a well-known risk factor for infectious diseases with high prevalence and increased severity.Here,we elucidated the possible factors for the increased vulnerability of diabetic patients to SARS-CoV-2 infection and the more severe COVID-19 illness.The worsened prognosis of patients with both COVID-19 and diabetes may be attributable to host receptor angiotensinconverting enzyme 2-assisted viral uptake.Moreover,insulin resistance is often associated with impaired mucosal and skin barrier integrity,resulting in microbiota dysbiosis,which increases susceptibility to viral infections.It may also be associated with higher levels of pro-inflammatory cytokines resulting from an impaired immune system in diabetics,inducing a cytokine storm and excessive inflammation.This review describes diabetes mellitus and its complications,explains the risk factors,such as disease characteristics and patient lifestyle,which may contribute to the high susceptibility of diabetic patients to COVID-19,and discusses preventive and therapeutic strategies for COVID-19-positive diabetic patients.展开更多
Coral sandy soils widely exist in coral island reefs and seashores in tropical and subtropical regions.Due to the unique marine depositional environment of coral sandy soils,the engineering characteristics and respons...Coral sandy soils widely exist in coral island reefs and seashores in tropical and subtropical regions.Due to the unique marine depositional environment of coral sandy soils,the engineering characteristics and responses of these soils subjected to monotonic and cyclic loadings have been a subject of intense interest among the geotechnical and earthquake engineering communities.This paper critically reviews the progress of experimental investigations on the undrained behavior of coral sandy soils under monotonic and cyclic loadings over the last three decades.The focus of coverage includes the contractive-dilative behavior,the pattern of excess pore-water pressure(EPWP)generation and the liquefaction mechanism and liquefaction resistance,the small-strain shear modulus and strain-dependent shear modulus and damping,the cyclic softening feature,and the anisotropic characteristics of undrained responses of saturated coral sandy soils.In particular,the advances made in the past decades are reviewed from the following aspects:(1)the characterization of factors that impact the mechanism and patterns of EPWP build-up;(2)the identification of liquefaction triggering in terms of the apparent viscosity and the average flow coefficient;(3)the establishment of the invariable form of strain-based,stress-based,or energy-based EPWP ratio formulas and the unique relationship between the new proxy of liquefaction resistance and the number of cycles required to reach liquefaction;(4)the establishment of the invariable form of the predictive formulas of small strain modulus and strain-dependent shear modulus;and(5)the investigation on the effects of stress-induced anisotropy on liquefaction susceptibility and dynamic deformation characteristics.Insights gained through the critical review of these advances in the past decades offer a perspective for future research to further resolve the fundamental issues concerning the liquefaction mechanism and responses of coral sandy sites subjected to cyclic loadings associated with seismic events in marine environments.展开更多
The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper ...The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.展开更多
The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques we...The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.展开更多
Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Co...Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.展开更多
Objective:Nucleotide excision repair(NER)plays a vital role in maintaining genome stability,and the effect of NER gene polymorphisms on hepatoblastoma susceptibility is still under investigation.This study aimed to ev...Objective:Nucleotide excision repair(NER)plays a vital role in maintaining genome stability,and the effect of NER gene polymorphisms on hepatoblastoma susceptibility is still under investigation.This study aimed to evaluate the relationship between NER gene polymorphisms and the risk of hepatoblastoma in Eastern Chinese Han children.Methods:In this five-center case-control study,we enrolled 966 subjects from East China(193 hepatoblastoma patients and 773 healthy controls).The TaqMan method was used to genotype 19 single nucleotide polymorphisms(SNPs)in NER pathway genes,including ERCC1,XPA,XPC,XPD,XPF,and XPG.Then,multivariate logistic regression analysis was performed,and odds ratios(ORs)and 95%confidence intervals(95%CIs)were utilized to assess the strength of associations.Results:Three SNPs were related to hepatoblastoma risk.XPC rs2229090 and XPD rs3810366 significantly contributed to hepatoblastoma risk according to the dominant model(adjusted OR=1.49,95%CI=1.07−2.08,P=0.019;adjusted OR=1.66,95%CI=1.12−2.45,P=0.012,respectively).However,XPD rs238406 conferred a significantly decreased risk of hepatoblastoma under the dominant model(adjusted OR=0.68,95%CI=0.49−0.95;P=0.024).Stratified analysis demonstrated that these significant associations were more prominent in certain subgroups.Moreover,there was evidence of functional implications of these significant SNPs suggested by online expression quantitative trait loci(eQTLs)and splicing quantitative trait loci(sQTLs)analysis.Conclusions:In summary,NER pathway gene polymorphisms(XPC rs2229090,XPD rs3810366,and XPD rs238406)are significantly associated with hepatoblastoma risk,and further research is required to verify these findings.展开更多
It has been reported that C-type lectins(CTLs),which are pattern recognition receptors of the insect innate immunity response,may compete with Cry toxin for the receptor alkaline phosphatase to decrease its toxicity i...It has been reported that C-type lectins(CTLs),which are pattern recognition receptors of the insect innate immunity response,may compete with Cry toxin for the receptor alkaline phosphatase to decrease its toxicity in insects.However,to date,which CTLs affect larval susceptibility to Bt in Spodoptera exigua is not clear.In this study,33 CTL genes were identified from S.exigua.Based on the number of carbohydrate-recognition domains(CRDs)and the domain architectures,they were classified into three groups:(1)nineteen CTL-S(single-CRD),(2)eight immulectin(dual-CRD)and(3)six CTL-X(CRD with other domains).RT-qPCR analysis revealed that expression levels of SeCTL-S15,IML-4 and CTL-X6 were upregulated after challenge with Bt and Cry1Ab.Tissue and developmental stage expression analysis showed that only SeCTL-S15 was mainly expressed in the midgut and larva,respectively.Knockdown of SeCTL-S15 significantly increased Bt susceptibility,as indicated by reduced survival and larval weight.These results suggest that CTL-S15 might play a vital role in the low susceptibility of larvae to Bt in S.exigua.Our results provide new insights into CTL function in insects.展开更多
Landslide susceptibility assessment is an essential tool for disaster prevention and management. In areas with multiple fault zones, the impact of fault zone on slope stability cannot be disregarded. This study perfor...Landslide susceptibility assessment is an essential tool for disaster prevention and management. In areas with multiple fault zones, the impact of fault zone on slope stability cannot be disregarded. This study performed qualitative analysis of fault zones and proposed a zoning method to assess the landslide susceptibility in Chengkou County, Chongqing Municipality, China. The region within a distance of 1 km from the faults was designated as sub-zone A, while the remaining area was labeled as sub-zone B. To accomplish the assessment, a dataset comprising 388 historical landslides and 388 non-landslide points was used to train the random forest model. 10-fold cross-validation was utilized to select the training and testing datasets for the model. The results of the models were analyzed and discussed, with a focus on model performance and prediction uncertainty. By implementing the proposed division strategy based on fault zone, the accuracy, precision, recall, F-score, and AUC of both two sub-zones surpassed those of the whole region. In comparison to the results obtained for the whole region, sub-zone B exhibited an increase in AUC by 6.15%, while sub-zone A demonstrated a corresponding increase of 1.66%. Moreover, the results of 100 random realizations indicated that the division strategy has little effect on the prediction uncertainty. This study introduces a novel approach to enhance the prediction accuracy of the landslide susceptibility mapping model in areas with multiple fault zones.展开更多
Background: Despite a significant decline in neonatal deaths in the last 20 years (5 million in 1990 to 2.4 million in 2019), the risk of death is still high, especially in developing countries. In Tanzania, neonatal ...Background: Despite a significant decline in neonatal deaths in the last 20 years (5 million in 1990 to 2.4 million in 2019), the risk of death is still high, especially in developing countries. In Tanzania, neonatal sepsis is the third leading cause of neonatal death, accounting for 25% of all deaths. The rising global threat of antimicrobial resistance and the rising burden of neonatal death due to neonatal sepsis have been of great concern and have delayed progress toward reaching SDG goal 3.2 by 2030. This study aims to determine the bacteriological profile, antibiotic susceptibility patterns, and predictors of bacteremia among neonates with clinical sepsis at KCMC Hospital in Northern Tanzania. Methodology: This study had a cross-sectional design conducted at KCMC Hospital, Northern Tanzania. The study population was neonates admitted to the neonatal unit at KCMC Hospital. Data were collected using questionnaires and blood cultures from neonates. Frequencies and proportions were used to summarize categorical variables, while continuous variables were summarized using mean and standard deviation. The frequencies and proportions of bacteria isolated and the antimicrobial susceptibility results were analyzed and compared using Pearson’s chi-square test and Fisher’s exact test where applicable. Modified Poisson regression model was used to determine factors associated with positive blood culture. Results: Out of 411 neonates with a clinical diagnosis of neonatal sepsis, 175 (42.9%) had positive blood cultures. Gram-positive bacteria were most frequently isolated at 52.3%, and gram-negative bacteria were 47.7%. Coagulase-negative Staphylococcus (30.7%) and Staphylococcus aureus (19.9%) were the predominant gram-positive isolates. Gram-negative isolates were Klebsiella spp 47 (26.7%), E. coli 10 (5.7%), and Citrobacter spp 10 (5.1%). The gram-positive isolates were sensitive to vancomycin, piperacillin/tazobactam, and ceftazidime, whereas the gram-negative were sensitive to amikacin, meropenem, and vancomycin. The study did not find statistically significant associations between clinical factors and positive blood cultures in bacteremia. Conclusion: Gram-positive bacteria are the dominant pathogens in early-onset and late-onset neonatal sepsis. High levels of resistance to ampicillin and ceftriaxone and moderate resistance to gentamycin were observed in both gram-positive and gram-negative bacteria. Gram-positive organisms exhibit better susceptibility rates to vancomycin and ciprofloxacin, while gram-negative micro-organisms are more sensitive to amikacin and meropenem. An effective initial treatment approach for neonatal sepsis would involve a combination of drugs.展开更多
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supported by the STI 2030—Major Projects 2021ZD0204000,No.2021ZD0204003 (to XZ)the National Natural Science Foundation of China,Nos.32170973 (to XZ),32071018 (to ZH)。
文摘Dysregulation of neurotransmitter metabolism in the central nervous system contributes to mood disorders such as depression, anxiety, and post–traumatic stress disorder. Monoamines and amino acids are important types of neurotransmitters. Our previous results have shown that disco-interacting protein 2 homolog A(Dip2a) knockout mice exhibit brain development disorders and abnormal amino acid metabolism in serum. This suggests that DIP2A is involved in the metabolism of amino acid–associated neurotransmitters. Therefore, we performed targeted neurotransmitter metabolomics analysis and found that Dip2a deficiency caused abnormal metabolism of tryptophan and thyroxine in the basolateral amygdala and medial prefrontal cortex. In addition, acute restraint stress induced a decrease in 5-hydroxytryptamine in the basolateral amygdala. Additionally, Dip2a was abundantly expressed in excitatory neurons of the basolateral amygdala, and deletion of Dip2a in these neurons resulted in hopelessness-like behavior in the tail suspension test. Altogether, these findings demonstrate that DIP2A in the basolateral amygdala may be involved in the regulation of stress susceptibility. This provides critical evidence implicating a role of DIP2A in affective disorders.
基金supported by grants from the National Natural Science Foundation of China(Grant Nos.82272932,81974422,and 92359201).
文摘Breast cancer(BC)is now the most common cancer and the fifth leading cause of cancer-associated mortality among women in China1.Germline pathogenic variants(PVs)of BC susceptibility genes,such as the well-known BRCA1/2 genes,increase the risk of BC and other cancers(ovarian and pancreatic cancer)^(2,3).Recent studies have demonstrated substantial benefits of poly(adenosine diphosphate ribose)polymerase inhibitors in the treatment of BC patients who carry BRCA1/2 PVs^(4).
基金Jiangxi Provincial Innovative Training Project“Post-earthquake Landslide Risk Evaluation Study under Spatial Scale Modelling”(Project No.:S202311318050)。
文摘Landslides are significant natural geological hazards.Landslide susceptibility evaluation involves the quantitative assessment and prediction of potential landslide locations and their probabilities.Research has explored susceptibility assessment methods based on spatial-scale analysis.This evaluation integrates two models—global and local scale—using a CNN model and a PSO-CNN coupled model.Key aspects include selecting evaluation factors and optimizing model parameters for landslide susceptibility at different scales.A major focus of current landslide research is utilizing prediction results to enhance prevention and control measures.
文摘Flooding remains one of the most destructive natural disasters,posing significant risks to both human lives and infrastructure.In India,where a large area is susceptible to flood hazards,the importance of accurate flood frequency analysis(FFA)and flood susceptibility mapping cannot be overstated.This study focuses on the Haora River basin in Tripura,a region prone to frequent flooding due to a combination of natural and anthropogenic factors.This study evaluates the suitability of the Log-Pearson Type Ⅲ(LP-Ⅲ)and Gumbel Extreme Value-1(EV-1)distributions for estimating peak discharges and delineates floodsusceptible zones in the Haora River basin,Tripura.Using 40 years of peak discharge data(1984-2023),the LP-Ⅲ distribution was identified as the most appropriate model based on goodness-of-fit tests.Flood susceptibility mapping,integrating 16 thematic layers through the Analytical Hierarchy Process,identified 8%,64%,and 26%of the area as high,moderate,and low susceptibility zones,respectively,with a model success rate of 0.81.The findings highlight the need for improved flood management strategies,such as enhancing river capacity and constructing flood spill channels.These insights are critical for designing targeted flood mitigation measures in the Haora basin and other flood-prone regions.
文摘The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.
基金funded by the National Natural Science Foundation of China(Grant No.41861134008)Muhammad Asif Khan academician workstation of Yunnan Province(Grant No.202105AF150076)+6 种基金General program of Yunnan Province Science and Technology Department(Grant No.202105AF150076)Key Project of Natural Science Foundation of Yunnan Province(Grant No.202101AS070019)Key R&D Program of Yunnan Province(Grant No.202003AC100002)General Program of basic research plan of Yunnan Province(Grant No.202001AT070059)Major scientific and technological projects of Yunnan Province:Research on Key Technologies of ecological environment monitoring and intelligent management of natural resources in Yunnan(No:202202AD080010)“Study on High-Level Hidden Landslide Identification Based on Multi-Source Data”of Key Laboratory of Early Rapid Identification,Prevention and Control of Geological Diseases in Traffic Corridor of High Intensity Earthquake Mountainous Area of Yunnan Province(KLGDTC-2021-02)Guizhou Scientific and Technology Fund(QKHJ-ZK[2023]YB 193).
文摘Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide susceptibility evaluation models,in this study,a method of landslide hazard susceptibility evaluation is proposed by combining SBAS-InSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar)and SSA-BP(Sparrow Search Algorithm-Back Propagation)neural network algorithm.The SBAS-InSAR technology is adopted to identify potential landslide hazards in the study area,update the cataloging data of landslide hazards,and 11 evaluation factors are chosen for constructing the SSA-BP model for training and validation.Baihetan Reservoir area is selected as a case study for validation.As indicated by the results,the application of SBAS-InSAR technology,combined with both ascending and descending orbit data,effectively addresses the incomplete identification of landslide hazards caused by geometric distortion of single orbit SAR data(e.g.,shadow,overlay,and perspective contraction)in deep canyon areas,thereby enabling the acquisition of up-to-date landslide hazard data.Moreover,in comparison to the conventional BP(Back Propagation)algorithm,the accuracy of the model constructed by the SSA-BP algorithm exhibits a significant increase,with mean squared error and mean absolute error reduced by 0.0142 and 0.0607,respectively.Additionally,during the process of susceptibility evaluation,the SSA-BP model effectively circumvents the issue of considerable manual interventions in calculating the weight of evaluation factors.The area under the curve of this model reaches 0.909,surpassing BP(0.835),random forest(0.792),and the information value method(0.699).The risk of landslide occurrence in the Baihetan Reservoir area is positively correlated with slope,surface temperature,and deformation rate,while it is negatively correlated with fault distance and normalized difference vegetation index.Geological lithology exerts minimal influence on the occurrence of landslides,with the risk being low in forest land and high in grassland.The method proposed in this study provides a useful reference for disaster prevention and mitigation departments to perform landslide hazard susceptibility evaluations in deep canyon areas under complex geological conditions.
基金financially supported by the Higher Education Commission of Pakistan (HEC) grant under National Research Program for Universities (NRPU) with No: (20-14681/NRPU/R&D/HEC/20212021)。
文摘The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. Regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-R technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic Roughness Index(TRI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). Results were validated through the Receiver Operator Characteristics(ROC) curve. Results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-R propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-R validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure.
基金This work is funded by the National Natural Science Foundation of China(Grant Nos.42377164 and 52079062)the National Science Fund for Distinguished Young Scholars of China(Grant No.52222905).
文摘In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.
基金the National Natural Science Foundation of China(Grant Nos.42377164 and 52079062)the Interdisciplinary Innovation Fund of Natural Science,Nanchang University(Grant No.9167-28220007-YB2107).
文摘The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30e110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.
基金funded by the Sichuan Transportation Science and Technology Project(Grant No.2018-ZL-01)High-end Foreign Expert Introduction program(Grant No.G2022165004L)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.HZ2021001).
文摘Landslide susceptibility mapping is an integral part of geological hazard analysis.Recently,the emphasis of many studies has been on data-driven models,notably those derived from machine learning,owing to their aptitude for tackling complex non-linear problems.However,the prevailing models often disregard qualitative research,leading to limited interpretability and mistakes in extracting negative samples,i.e.inaccurate non-landslide samples.In this study,Scoops 3D(a three-dimensional slope stability analysis tool)was utilized to conduct a qualitative assessment of slope stability in the Yunyang section of the Three Gorges Reservoir area.The depth of the bedrock was predicted utilizing a Convolutional Neural Network(CNN),incorporating local boreholes and building on the insights from prior research.The Random Forest(RF)algorithm was subsequently used to execute a data-driven landslide susceptibility analysis.The proposed methodology demonstrated a notable increase of 29.25%in the evaluation metric,the area under the receiver operating characteristic curve(ROC-AUC),outperforming the prevailing benchmark model.Furthermore,the landslide susceptibility map generated by the proposed model demonstrated superior interpretability.This result not only validates the effectiveness of amalgamating mathematical and mechanistic insights for such analyses,but it also carries substantial academic and practical implications.
基金Supported by Scientific Research Foundation of Shanghai Municipal Health Commission of Changning District,No.20234Y038.
文摘Coronavirus disease 2019(COVID-19)is a highly infectious disease caused by a novel human coronavirus called severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Diabetes is a well-known risk factor for infectious diseases with high prevalence and increased severity.Here,we elucidated the possible factors for the increased vulnerability of diabetic patients to SARS-CoV-2 infection and the more severe COVID-19 illness.The worsened prognosis of patients with both COVID-19 and diabetes may be attributable to host receptor angiotensinconverting enzyme 2-assisted viral uptake.Moreover,insulin resistance is often associated with impaired mucosal and skin barrier integrity,resulting in microbiota dysbiosis,which increases susceptibility to viral infections.It may also be associated with higher levels of pro-inflammatory cytokines resulting from an impaired immune system in diabetics,inducing a cytokine storm and excessive inflammation.This review describes diabetes mellitus and its complications,explains the risk factors,such as disease characteristics and patient lifestyle,which may contribute to the high susceptibility of diabetic patients to COVID-19,and discusses preventive and therapeutic strategies for COVID-19-positive diabetic patients.
基金National Natural Science Foundation of China under Grant No.52278503。
文摘Coral sandy soils widely exist in coral island reefs and seashores in tropical and subtropical regions.Due to the unique marine depositional environment of coral sandy soils,the engineering characteristics and responses of these soils subjected to monotonic and cyclic loadings have been a subject of intense interest among the geotechnical and earthquake engineering communities.This paper critically reviews the progress of experimental investigations on the undrained behavior of coral sandy soils under monotonic and cyclic loadings over the last three decades.The focus of coverage includes the contractive-dilative behavior,the pattern of excess pore-water pressure(EPWP)generation and the liquefaction mechanism and liquefaction resistance,the small-strain shear modulus and strain-dependent shear modulus and damping,the cyclic softening feature,and the anisotropic characteristics of undrained responses of saturated coral sandy soils.In particular,the advances made in the past decades are reviewed from the following aspects:(1)the characterization of factors that impact the mechanism and patterns of EPWP build-up;(2)the identification of liquefaction triggering in terms of the apparent viscosity and the average flow coefficient;(3)the establishment of the invariable form of strain-based,stress-based,or energy-based EPWP ratio formulas and the unique relationship between the new proxy of liquefaction resistance and the number of cycles required to reach liquefaction;(4)the establishment of the invariable form of the predictive formulas of small strain modulus and strain-dependent shear modulus;and(5)the investigation on the effects of stress-induced anisotropy on liquefaction susceptibility and dynamic deformation characteristics.Insights gained through the critical review of these advances in the past decades offer a perspective for future research to further resolve the fundamental issues concerning the liquefaction mechanism and responses of coral sandy sites subjected to cyclic loadings associated with seismic events in marine environments.
文摘The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(Grant no.2019QZKK0904)Natural Science Foundation of Hebei Province(Grant no.D2022403032)S&T Program of Hebei(Grant no.E2021403001).
文摘The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.
基金supported by the projects of the China Geological Survey(DD20221729,DD20190291)Zhuhai Urban Geological Survey(including informatization)(MZCD–2201–008).
文摘Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.
基金supported by grants from the Innovation and Cultivation Fund Project of the Seventh Medical Center,PLA General Hospital(No.QZX-2023-7)Postdoctoral Science Foundation of China(No.2021M691649)Postdoctoral Science Foundation of Jiangsu Province(No.2021K524C).
文摘Objective:Nucleotide excision repair(NER)plays a vital role in maintaining genome stability,and the effect of NER gene polymorphisms on hepatoblastoma susceptibility is still under investigation.This study aimed to evaluate the relationship between NER gene polymorphisms and the risk of hepatoblastoma in Eastern Chinese Han children.Methods:In this five-center case-control study,we enrolled 966 subjects from East China(193 hepatoblastoma patients and 773 healthy controls).The TaqMan method was used to genotype 19 single nucleotide polymorphisms(SNPs)in NER pathway genes,including ERCC1,XPA,XPC,XPD,XPF,and XPG.Then,multivariate logistic regression analysis was performed,and odds ratios(ORs)and 95%confidence intervals(95%CIs)were utilized to assess the strength of associations.Results:Three SNPs were related to hepatoblastoma risk.XPC rs2229090 and XPD rs3810366 significantly contributed to hepatoblastoma risk according to the dominant model(adjusted OR=1.49,95%CI=1.07−2.08,P=0.019;adjusted OR=1.66,95%CI=1.12−2.45,P=0.012,respectively).However,XPD rs238406 conferred a significantly decreased risk of hepatoblastoma under the dominant model(adjusted OR=0.68,95%CI=0.49−0.95;P=0.024).Stratified analysis demonstrated that these significant associations were more prominent in certain subgroups.Moreover,there was evidence of functional implications of these significant SNPs suggested by online expression quantitative trait loci(eQTLs)and splicing quantitative trait loci(sQTLs)analysis.Conclusions:In summary,NER pathway gene polymorphisms(XPC rs2229090,XPD rs3810366,and XPD rs238406)are significantly associated with hepatoblastoma risk,and further research is required to verify these findings.
基金supported by the National Key Research and Development Program of China(2022YFD1401200)the National Natural Science Foundation of China(32172397).
文摘It has been reported that C-type lectins(CTLs),which are pattern recognition receptors of the insect innate immunity response,may compete with Cry toxin for the receptor alkaline phosphatase to decrease its toxicity in insects.However,to date,which CTLs affect larval susceptibility to Bt in Spodoptera exigua is not clear.In this study,33 CTL genes were identified from S.exigua.Based on the number of carbohydrate-recognition domains(CRDs)and the domain architectures,they were classified into three groups:(1)nineteen CTL-S(single-CRD),(2)eight immulectin(dual-CRD)and(3)six CTL-X(CRD with other domains).RT-qPCR analysis revealed that expression levels of SeCTL-S15,IML-4 and CTL-X6 were upregulated after challenge with Bt and Cry1Ab.Tissue and developmental stage expression analysis showed that only SeCTL-S15 was mainly expressed in the midgut and larva,respectively.Knockdown of SeCTL-S15 significantly increased Bt susceptibility,as indicated by reduced survival and larval weight.These results suggest that CTL-S15 might play a vital role in the low susceptibility of larvae to Bt in S.exigua.Our results provide new insights into CTL function in insects.
基金Postdoctoral Research Foundation of China (2021M700608)Natural Science Foundation Project of Chongqing, Chongqing Science and Technology Commission (cstc2021jcyj-bsh0047)+1 种基金Scientific Project Supported by the Bureau of Planning and Natural Resources, Chongqing (2301DH09002)Sichuan Transportation Science and Technology Project (2018ZL-01)。
文摘Landslide susceptibility assessment is an essential tool for disaster prevention and management. In areas with multiple fault zones, the impact of fault zone on slope stability cannot be disregarded. This study performed qualitative analysis of fault zones and proposed a zoning method to assess the landslide susceptibility in Chengkou County, Chongqing Municipality, China. The region within a distance of 1 km from the faults was designated as sub-zone A, while the remaining area was labeled as sub-zone B. To accomplish the assessment, a dataset comprising 388 historical landslides and 388 non-landslide points was used to train the random forest model. 10-fold cross-validation was utilized to select the training and testing datasets for the model. The results of the models were analyzed and discussed, with a focus on model performance and prediction uncertainty. By implementing the proposed division strategy based on fault zone, the accuracy, precision, recall, F-score, and AUC of both two sub-zones surpassed those of the whole region. In comparison to the results obtained for the whole region, sub-zone B exhibited an increase in AUC by 6.15%, while sub-zone A demonstrated a corresponding increase of 1.66%. Moreover, the results of 100 random realizations indicated that the division strategy has little effect on the prediction uncertainty. This study introduces a novel approach to enhance the prediction accuracy of the landslide susceptibility mapping model in areas with multiple fault zones.
文摘Background: Despite a significant decline in neonatal deaths in the last 20 years (5 million in 1990 to 2.4 million in 2019), the risk of death is still high, especially in developing countries. In Tanzania, neonatal sepsis is the third leading cause of neonatal death, accounting for 25% of all deaths. The rising global threat of antimicrobial resistance and the rising burden of neonatal death due to neonatal sepsis have been of great concern and have delayed progress toward reaching SDG goal 3.2 by 2030. This study aims to determine the bacteriological profile, antibiotic susceptibility patterns, and predictors of bacteremia among neonates with clinical sepsis at KCMC Hospital in Northern Tanzania. Methodology: This study had a cross-sectional design conducted at KCMC Hospital, Northern Tanzania. The study population was neonates admitted to the neonatal unit at KCMC Hospital. Data were collected using questionnaires and blood cultures from neonates. Frequencies and proportions were used to summarize categorical variables, while continuous variables were summarized using mean and standard deviation. The frequencies and proportions of bacteria isolated and the antimicrobial susceptibility results were analyzed and compared using Pearson’s chi-square test and Fisher’s exact test where applicable. Modified Poisson regression model was used to determine factors associated with positive blood culture. Results: Out of 411 neonates with a clinical diagnosis of neonatal sepsis, 175 (42.9%) had positive blood cultures. Gram-positive bacteria were most frequently isolated at 52.3%, and gram-negative bacteria were 47.7%. Coagulase-negative Staphylococcus (30.7%) and Staphylococcus aureus (19.9%) were the predominant gram-positive isolates. Gram-negative isolates were Klebsiella spp 47 (26.7%), E. coli 10 (5.7%), and Citrobacter spp 10 (5.1%). The gram-positive isolates were sensitive to vancomycin, piperacillin/tazobactam, and ceftazidime, whereas the gram-negative were sensitive to amikacin, meropenem, and vancomycin. The study did not find statistically significant associations between clinical factors and positive blood cultures in bacteremia. Conclusion: Gram-positive bacteria are the dominant pathogens in early-onset and late-onset neonatal sepsis. High levels of resistance to ampicillin and ceftriaxone and moderate resistance to gentamycin were observed in both gram-positive and gram-negative bacteria. Gram-positive organisms exhibit better susceptibility rates to vancomycin and ciprofloxacin, while gram-negative micro-organisms are more sensitive to amikacin and meropenem. An effective initial treatment approach for neonatal sepsis would involve a combination of drugs.
