Analysis of Hereditary Stability and Disease Susceptivity of sFat-1 Transgenic Pigs

[Objective] This study aimed to investigate the hereditary stability of sFat-1 transgenic pigs and the differences in disease susceptivity between sFat-1 transgenic pigs and non-transgenic pigs. [Method] The integration of sFat-1 gene in pigs was detected by PCR; the infection of transgenic pig to pseudorabies, leptospirosis, swine dysentery, brucellosis, Mycobacterium tuberculosis, rotavirus and mycoplasma hyopneumoniae was detected by using ELISA and PCR. [Result] The positive ratio of F3 generation sFat-1 transgenic pigs was 18.5%; the susceptivity of positive sFat- 1 transgenic and negative pigs to seven infectious diseases showed no significant difference. [Conclusion] Exogenous gene in sFat-1 transgenic pigs can not be stably inherited. The overall physical condition of positive transgenic and negative pigs was similar.

How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? d A catchment-scale case study from China

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.

Landslide hazard susceptibility evaluation based on SBAS-InSAR technology and SSA-BP neural network algorithm:A case study of Baihetan Reservoir Area

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.

A Discussion on the Flexible Regulation Capacity Requirements of China’s Power System

A high proportion of variable renewable energy(VRE)is one of the most significant characteristics of China’s future power system under the"dual carbon"target.However,wind and solar power units are more uncontrollable and less supportive for power system stability than traditional thermal power units,due to their susceptibility to the weather and the grid connection of power electronics.Therefore,as the capacity and generation of VRE grow rapidly and even dominate the power structure,the power system’s ability to deal with disturbances will continue to decrease.

Liquefaction susceptibility and deformation characteristics of saturated coral sandy soils subjected to cyclic loadings-a critical review

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.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

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.

Diabetes and susceptibility to COVID-19:Risk factors and preventive and therapeutic strategies

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.

Effect of physiological heterogeneity of E. coli population on antibiotic susceptivity test

According to the instantaneous growth rate (dN/dt) of E. coli CVCC249 growing in batch culture, the entire growth progress was distinguished into four phases: accelerating growth phase, constant growth phase, decelerating growth phase; declining phase, in each of which obvious variation in physiological; biochemical properties was detected, including total DNA, total protein,; MTT-dehydrogenase activity, etc., that led to difference in their antibiotic susceptivity. Antibiotic susceptivity of the population sampled from each phase was tested by Concentration-killing Curve (CKC) approach following the formula N=N 0/{1+exp[r·(x-BC 50)]}, showing as normal distribution at the individual cell level for an internal population, in which the median bactericidal concentration BC 50 represents the mean level of susceptivity, while the bactericidal span BC 199=(2lnN 0)/r indicates the variation degree of the antibiotic susceptivity. Furthermore, tested by CKC approach, the antibiotic susceptivity of E. coli CVCC249 population in each physiological phase to gentamicin or enoxacin was various: susceptivity of the population in the constant growth phase; declining phase all increased compared with that in the accelerating growth phase for gentamicin but declined for enoxacin. The primary investigations revealed that the physiological phase should be taken into account in the context of antibiotic susceptivity; research into antimicrobial mechanism. However there are few reports concerned with this study. Further research using different kinds of antibiotics with synchronized continuous culture of different bacterial strains is required.

Assessment of large-scale multiple forest disturbance susceptibilities with AutoML framework: an Izmir Regional Forest Directorate case

Disturbances such as forest fires,intense winds,and insect damage exert strong impacts on forest ecosystems by shaping their structure and growth dynamics,with contributions from climate change.Consequently,there is a need for reliable and operational methods to monitor and map these disturbances for the development of suitable management strategies.While susceptibility assessment using machine learning methods has increased,most studies have focused on a single disturbance.Moreover,there has been limited exploration of the use of“Automated Machine Learning(AutoML)”in the literature.In this study,susceptibility assessment for multiple forest disturbances(fires,insect damage,and wind damage)was conducted using the PyCaret AutoML framework in the Izmir Regional Forest Directorate(RFD)in Turkey.The AutoML framework compared 14 machine learning algorithms and ranked the best models based on AUC(area under the curve)values.The extra tree classifier(ET)algorithm was selected for modeling the susceptibility of each disturbance due to its good performance(AUC values>0.98).The study evaluated susceptibilities for both individual and multiple disturbances,creating a total of four susceptibility maps using fifteen driving factors in the assessment.According to the results,82.5%of forested areas in the Izmir RFD are susceptible to multiple disturbances at high and very high levels.Additionally,a potential forest disturbances map was created,revealing that 15.6%of forested areas in the Izmir RFD may experience no damage from the disturbances considered,while 54.2%could face damage from all three disturbances.The SHAP(Shapley Additive exPlanations)methodology was applied to evaluate the importance of features on prediction and the nonlinear relationship between explanatory features and susceptibility to disturbance.

Preliminary study of metabonomic changes during the progression of atherosclerosis in miniature pigs

Background:To explore potential biomarkers for early diagnosis of atherosclerosis(AS)and provide basic data for further research on AS,the characteristics of serum metabolomics during the progression of AS in mini-pigs were observed dynamically.Methods:An AS model in Bama miniature pigs was established by a high-cholesterol and high-fat diet.Fasting serum samples were collected monthly for metabolomics and serum lipid detection.At the end of the treatment period,pathological analysis of the abdominal aorta and coronary artery was performed to evaluate the lesions of AS,thereby distinguishing the susceptibility of mini-pigs to AS.The metabolomics was de-tected using a high-resolution untargeted metabolomic approach.Statistical analysis was used to identify metabolites associated with AS susceptibility.Results:Based on pathological analysis,mini-pigs were divided into two groups:a susceptible group(n=3)and a non-susceptible group(n=6).A total of 1318 metabo-lites were identified,with significant shifting of metabolic profiles over time in both groups.Dynamic monitoring analysis highlighted 57 metabolites that exhibited an ob-vious trend of differential changes between two groups with the advance of time.The KEGG(Kyoto Encyclopedia of Genes and Genomes)pathway enrichment analysis in-dicated significant disorders in cholesterol metabolism,primary bile acid metabolism,histidine metabolism,as well as taurine and hypotaurine metabolism.Conclusions:During the progression of AS in mini-pigs induced by high-cholesterol/high-fat diet,the alterations in serum metabolic profile exhibited a time-dependent pattern,accompanied by notable disturbances in lipid metabolism,cholesterol me-tabolism,and amino acid metabolism.These metabolites may become potential bio-markers for early diagnosis of AS.

GIS-based prediction method of shallow landslides induced by heavy rainfall in large mountainous areas

Rainwater runoff that does not infiltrate the soil during heavy rainfall may increase slope instability. The effect of runoff is usually neglected in conventional rainfall-induced slope failure analysis to simplify the model. To analyze the effect of runoff on slope stability, this study simultaneously simulated the effects of surface runoff and rainfall infiltration on bank slopes in the Three Gorges Reservoir Area. A shallow slope failure method that can be used to analyze runoff was proposed based on the modified Green-Ampt model, the simplified Saint-Venant model, and the infinite slope model. In this model, the modified Green–Ampt model was used to estimate the rainfall infiltration capacity and the wetting front depth. The eight-flow(D8) method and the simplified Saint-Venant model were selected to estimate the distribution of runoff. By considering the wetting front depth as the slip surface depth, the factor of safety of the slope could be determined using the infinite slope stability model. A comparison of the different models reveals that runoff can escalate the instability of certain slopes, causing stable slopes to become unstable. Comparison of the unstable areas obtained from the simulation with the actual landslide sites shows that the model proposed in this study can successfully predict landslides at these sites. The slope instability assessment model proposed in this study offers an alternative approach for estimating high-risk areas in large mountainous regions.

Isolation,identification,and virulence gene analysis of pathogenic Aeromonas dhakensis in Macrobrachium rosenbergii and histopathological observation

To identify the cause of mass mortality of adult Macrobrachium rosenbergii in a farm in Gaoyou City,Jiangsu Province,China,a dominant strain named DKQ-1 was isolated from the hepatopancreas of dying M.rosenbergii and identified as Aeromonas dhakensis by purification culture,biochemical characterization,and 16S rRNA and gyrB gene sequence analysis.The results of the challenge test revealed that the strain was highly pathogenic and the 50%lethal dose(LD_(50))in 72 h to M.rosenbergii was 1.54×10^(5)CFU/mL.The amplification results of virulence genes show that strain DKQ-1 carried 9 virulence genes,including ascV,aexT,aer,act,lip,ompAI,gcaT,acg,and exu,supporting the strong virulence of strain DKQ-1 to M.rosenbergii.Histopathological observation of the hepatopancreas,gills,and intestines indicated that DKQ-1 injection into M.rosenbergii could cause serious tissue damage,which further supported the strong virulence of this strain.In addition,a drug susceptibility test revealed that strain DKQ-1 was sensitive to 16 kinds of antibiotics,resistant to 9 kinds of antibiotics,and had intermediate resistance to spectinomycin and kanamycin.This study is the first report of A.dhakensis isolated from M.rosenbergii and provided a reference for the pathogen identification of bacterial diseases in M.rosenbergii,and for the prevention and treatment caused by A.dhakensis.

A Comprehensive Study of the Association between LEPR Gene rs1137101 Variant and Risk of Digestive System Cancers

Objective The leptin receptor,encoded by the LEPR gene,is involved in tumorigenesis.A potential functional variant of LEPR,rs1137101(Gln223Arg),has been extensively investigated for its contribution to the risk of digestive system(DS)cancers,but results remain conflicting rather than conclusive.Here,we performed a case–control study and subsequent meta-analysis to examine the association between rs1137101 and DS cancer risk.Methods A total of 1,727 patients with cancer(gastric/liver/colorectal:460/480/787)and 800 healthy controls were recruited.Genotyping of rs1137101 was conducted using a polymerase chain reactionrestriction fragment length polymorphism(PCR-RFLP)assay and confirmed using Sanger sequencing.Twenty-four eligible studies were included in the meta-analysis.Results After Bonferroni correction,the case–control study revealed that rs1137101 was significantly associated with the risk of liver cancer in the Hubei Chinese population.The meta-analysis suggested that rs1137101 is significantly associated with the risk of overall DS,gastric,and liver cancer in the Chinese population.Conclusion The LEPR rs1137101 variant may be a genetic biomarker for susceptibility to DS cancers(especially liver and gastric cancer)in the Chinese population.

Induced CTL-S15 gene expression by Bacillus thuringiensis declines susceptibility in Spodoptera exigua

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.

Nucleotide excision repair gene polymorphisms and hepatoblastoma susceptibility in Eastern Chinese children:A five-center case-control study

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.

Autophagy Alleviates Cold Exposure-induced Tight Junction Injury in Murine Ileum

Cold exposure is a pervasive stressor in the polar and subpolar regions,exerting both acute and chronic effects on individuals.This environmental factor is known to induce physiological stress,compromise immune response efficacy,and increase susceptibility to various diseases.Chronic cold exposure,characterized by repetitive nonconsecutive exposure to suboptimal temperatures over an extended duration.

Hydrothermal alteration of the surface volcanic rocks at the Acoculco geothermal field,Mexico:a multi-parametric approach

The studies on hydrothermal alteration-induced eff ects in surface and subsurface rocks provide useful information in the characterization and exploitation of a geothermal reservoir.Generally,these studies are based on traditional,and reliable methods like petrography(primary and secondary minerals,and grade of alteration),and geochemistry(mobility of elements,changes in mass and concentration of elements,and fluid inclusions).Recently,apart from these established methods,some methods based on the geochemical(Chemical Index of Alteration,CIA;Weathering Index of Parkar,WIP;Loss on Ignition,LOI;and Sulfur,S)and rock magnetic properties(magnetic susceptibility,χlf;and percentage frequency-dependent susceptibility,χfd%)are also being applied in the identification of whether a rock is an altered or a fresh one.The Acoculco Geothermal Field(AGF),Mexico,is characterized by high temperature and very low permeability,and it is considered a promissory Enhanced Geothermal System.The following changes are observed in the rocks as a result of an increase in hydrothermal alteration:(1)an increase in CIA,LOI,and S values,and a decrease in WIP;(2)an increase in quartz and quartz polymorph minerals(silicification),and clay minerals(argillization);and(3)decrease inχlf values.At AGF,the most altered surface acid rocks are characterized by entirely quartz and its polymorphs,and clay minerals.The present study also indicates the applicability of the binary plots of major elements(felsic vs mafic component)and rock magnetic parameters(χlf vs.χfd%).The rock withχfd%value of 2-10 andχlf value<0.5×10^(-6)m^(3) kg^(-1)indicate the presence of single domain and stable single domain grains,which in turn suggests that it is an altered rock.These methods are simple to apply,rapid,reliable,and have the potential to become eff ective tools for the identifi cation of hydrothermally altered rocks during the initial stage of geothermal exploration.

Physics-informed optimization for a data-driven approach in landslide susceptibility evaluation

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.

Machine learning solution for regional landslide susceptibility based on fault zone division strategy

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.

Modelling of debris-flow susceptibility and propagation: a case study from Northwest Himalaya

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.