Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automaticall...Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.展开更多
Accurate assessment of seismic landslide susceptibility is crucial for disaster prevention and emergency decision-making.Although machine learning methods have been widely applied in this field,they exhibit a strong d...Accurate assessment of seismic landslide susceptibility is crucial for disaster prevention and emergency decision-making.Although machine learning methods have been widely applied in this field,they exhibit a strong dependence on large quantities of highquality samples,resulting in significantly low prediction accuracy of existing studies under data-scarce or crossregional prediction scenarios,which fail to meet practical application requirements.To address this issue,this study proposes an intelligent prediction model integrating transfer learning and a sampling optimization strategy,aiming to enhance the accuracy and applicability of seismic landslide susceptibility assessment.The model first improves the sample collection method through the sampling optimization strategy to enhance the precision and representativeness of training samples.This not only ensures the accuracy of origin area training but also further strengthens the model's predictive ability in the target area.Subsequently,it incorporates Transfer Component Analysis(TCA)to overcome the differences in environmental characteristics between the origin area and target area,and couples TCA with the Light GBM algorithm to construct the TCA-Light GBM model,realizing the assessment of seismic landslide susceptibility in sample-free areas.Validated through case studies of the Jiuzhaigou and Luding earthquakes,the results demonstrate that the proposed TCALight GBM transfer learning method exhibits excellent applicability in seismic landslide susceptibility prediction.After optimization with the TCA algorithm,the model's prediction performance in the target domain is significantly improved,with the AUC value increasing from 0.719 to 0.827,representing an increase of approximately 15.02%.This indicates that TCA technology can effectively alleviate the feature distribution discrepancy between the source domain and target domain,enhancing the model's generalization ability.The method is particularly suitable for scenarios with data scarcity and cross-regional prediction and can provide reliable technical support for the emergency response and risk prevention and control of seismic hazards.展开更多
Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering acti...Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.展开更多
This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment mo...This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment models.First,the cumulative probability method revealed that a low probability(15%)of geologic hazards between any two geologic hazard points occurred outside a buffer zone with a radius of 2297 m(i.e.,the distance threshold).The training dataset was established,consisting of negative samples(non-hazard points)randomly generated based on the distance threshold,positive samples(i.e.,historical hazards),and 13 conditioning factors.Then,models were built using five machine learning algorithms,namely random forest(RF),gradient boosting decision tree(GBDT),naive Bayes(NB),logistic regression(LR),and support vector machine(SVM).The comprehensive performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and overall accuracy(OA)as indicators,revealing that RF exhibited the best performance,with OA and AUC values of 2.7127 and 0.981,respectively.Furthermore,the machine learning models constructed by considering the distance threshold outperformed those built using the unoptimized dataset.The characteristic factors were ranked using the mutual information method,with their scores decreasing in the order of rainfall(0.1616),altitude(0.06),normalized difference vegetation index(NDVI;0.04),and distance from roads(0.03).Finally,the geologic hazard susceptibility classification was assessed using the natural breaks method combined with a clustering algorithm.The results indicate that the clustering algorithm exhibited higher classification accuracy than the natural breaks method.The findings of this study demonstrate that the proposed model optimization scheme can provide a scientific basis for the prevention and control of geologic hazards.展开更多
The susceptibility of ore particles to electrical breakdown plays a critical role for high voltage pulse(HVP)breakage,yet its quantitative characterization still lacks deep understanding.Two indicators,namely breakdow...The susceptibility of ore particles to electrical breakdown plays a critical role for high voltage pulse(HVP)breakage,yet its quantitative characterization still lacks deep understanding.Two indicators,namely breakdown delay time(T_(d))and breakdown strength(E_(b))were compared,based on analysis on the two breakdown modes namely wavefront mode and post-wave mode.It was found that T_(d) is more suitable to characterize the susceptibility of ore particles to electrical breakdown in HVP breakage than E_(b).A probabilistic model based on the Weibull distribution is developed to describe the relation of breakdown probability to T_(d).Regression analyses were conducted to investigate how operating parameters and particle properties influence Td and size reduction degree of ore particles in HVP breakage.The regressed models demonstrate potential capability to predict metallic minerals content and HVP breakage degree based on operating parameters and particle properties.展开更多
Staphylococcus aureus(S.aureus)is the third most common pathogen causing 10.6%of bacterial foodborne illnesses in China in 2021[1].Heat-stable Staphylococcal Enterotoxins(SEs)produced by S.aureus are the main contribu...Staphylococcus aureus(S.aureus)is the third most common pathogen causing 10.6%of bacterial foodborne illnesses in China in 2021[1].Heat-stable Staphylococcal Enterotoxins(SEs)produced by S.aureus are the main contributors to staphylococcal food poisoning(SFP),causing vomiting,diarrhea,abdominal pain,headache,muscle cramps,and other acute gastroenteritis symptoms.More than 25 SEs and staphylococcal enterotoxin-like toxins(SE/s)have been described and which together comprise a superfamily of pyrogenic toxin superantigens(SAgs)[2].展开更多
The effect from the interaction of the alternating current(AC)magnetic field with kilogram-level test mass(TM)limits the detectivity of the TianQin space-based gravitational wave detection.The quantifed effect require...The effect from the interaction of the alternating current(AC)magnetic field with kilogram-level test mass(TM)limits the detectivity of the TianQin space-based gravitational wave detection.The quantifed effect requires the determination of the AC magnetic susceptibilityχ(f)of the TM.A torque method is proposed to measure theχ(f)of kg-level samples at the mHz band with a precision of 1×10^(-7).Combined with our previous work[Phys.Rev.Appl.18044010(2022)],the general frequency-dependent susceptibility of the alloy cube with side length L and electrical conductivityσis determined asχ(f)=χ0+(0.24±0.01)σμ0L^(2)f from 0.1 mHz to 1 Hz.The determination is helpful for the preliminary estimation of the in-band eddy current efect in the TianQin noise budget.The technique can be adopted to accurately measureχ(f)of the actual TM in other precision experiments,where the magnetic noise is a signifcant detection limit.展开更多
The 2019 Typhoon Lekima triggered extensive landslides in Zhejiang Province.To explore the impact of typhoon paths on the distribution of landslide susceptibility,this study proposes a spatiotemporal zoning assessment...The 2019 Typhoon Lekima triggered extensive landslides in Zhejiang Province.To explore the impact of typhoon paths on the distribution of landslide susceptibility,this study proposes a spatiotemporal zoning assessment framework based on typhoon paths and inner rainbands.According to the typhoon landing path and its rainfall impact range,the study area is divided into the typhoon event period(TEP)and the annual non-typhoon period(ANP).The model uses 14 environmental factors,with the only difference between TEP and ANP being the rainfall index:TEP uses 48-hour rainfall during the typhoon,while ANP uses multi-year average annual rainfall.Modeling and comparative analysis were conducted using six machine learning models including random forest(RF)and support vector machine(SVM).The results show that the distribution pattern of high-risk landslide areas during TEP is significantly correlated with typhoon intensity:when the intensity is level 12,high-risk areas are radially distributed;at levels 10-11,they tend to concentrate asymmetrically along the coast;and when the intensity drops to below level 9,the overall susceptibility decreases significantly.During ANP,the distribution of landslides is relatively uniform with no obvious spatial concentration.Analysis on the factor contribution rate indicates that the rainfall weight in TEP is as high as 32.1%,making it the dominant factor;in ANP,the rainfall weight drops to 13.6%while the influence of factors such as slope and topographic wetness index increases,revealing differences in landslide formation mechanisms between the two periods.This study demonstrates that the spatiotemporal zoning method based on typhoon paths can effectively characterize the spatial susceptibility patterns of landslides and improve disaster identification capabilities under extreme weather conditions.The finally generated annual susceptibility zoning map divides the study area into four types of risk regions,providing a reference for dynamic monitoring and differentiated risk management of landslides in typhoon-prone areas.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain c...Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.展开更多
Identifying the factors that contribute to individual susceptibility to cancer is essential for both prevention and treatment.The advancement of biotechnologies,particularly next-generation sequencing,has accelerated ...Identifying the factors that contribute to individual susceptibility to cancer is essential for both prevention and treatment.The advancement of biotechnologies,particularly next-generation sequencing,has accelerated the discovery of genetic variants linked to cancer susceptibility.While hundreds of cancer-susceptibility genes have been identified,they only explain a small fraction of the overall cancer risk,a phenomenon known as"missing heritability".Despite progress,even considering factors such as epistasis,epigenetics,and gene-environment interactions,the missing heritability remains unresolved.Recent research has revealed that an individual's microbiome composition plays a significant role in cancer susceptibility through several mechanisms,such as modulating immune cell activity and influencing the presence or removal of environmental carcinogens.In this review,we examine the multifaceted roles of the microbiome in cancer risk and explore gene-microbiome and environment-microbiome interactions that may contribute to cancer susceptibility.Additionally,we highlight the importance of experimental models,such as collaborative cross mice,and advanced analytical tools,like artificial intelligence,in identifying microbial factors associated with cancer risk.Understanding these microbial determinants can open new avenues for interventions aimed at reducing cancer risk and guide the development of more effective cancer treatments.展开更多
Landslide susceptibility evaluation plays an important role in disaster prevention and reduction.Feature-based transfer learning(TL)is an effective method for solving landslide susceptibility mapping(LSM)in target reg...Landslide susceptibility evaluation plays an important role in disaster prevention and reduction.Feature-based transfer learning(TL)is an effective method for solving landslide susceptibility mapping(LSM)in target regions with no available samples.However,as the study area expands,the distribution of land-slide types and triggering mechanisms becomes more diverse,leading to performance degradation in models relying on landslide evaluation knowledge from a single source domain due to domain feature shift.To address this,this study proposes a Multi-source Domain Adaptation Convolutional Neural Network(MDACNN),which combines the landslide prediction knowledge learned from two source domains to perform cross-regional LSM in complex large-scale areas.The method is validated through case studies in three regions located in southeastern coastal China and compared with single-source domain TL models(TCA-based models).The results demonstrate that MDACNN effectively integrates transfer knowledge from multiple source domains to learn diverse landslide-triggering mechanisms,thereby significantly reducing prediction bias inherent to single-source domain TL models,achieving an average improvement of 16.58%across all metrics.Moreover,the landslide susceptibility maps gener-ated by MDACNN accurately quantify the spatial distribution of landslide risks in the target area,provid-ing a powerful scientific and technological tool for landslide disaster management and prevention.展开更多
Research on the application of machine learning(ML)models to landslide susceptibility assessments has gained popularity in recent years,with a focus primarily on topographic factors derived from digital elevation mode...Research on the application of machine learning(ML)models to landslide susceptibility assessments has gained popularity in recent years,with a focus primarily on topographic factors derived from digital elevation models(DEMs).However,few studies have focused on the explanatory effects of these factors on different models,i.e.whether DEM-based factors affect different models in the same way.This study investigated whether different ML models could yield consistent interpretations of DEM-based factors using explanatory algorithms.Six ML models,including a support vector machine,a neural network,extreme gradient boosting,a random forest,linear regression,and K-nearest neighbors,were trained and evaluated on five geospatial datasets derived from different DEMs.Each dataset contained eight DEM-based and six non-DEM-based factors from 8912 landslide samples.Model performance was assessed using accuracy,precision,recall rate,F1-score,kappa coefficient,and receiver operating characteristic curves.Explanatory analyses,including Shapley additive explanations and partial dependence plots,were also employed to investigate the effects of topographic factors on landslide susceptibility.The results indicate that DEM-based factors consistently influenced different ML models across the datasets.Furthermore,tree-based models outperformed the other models in almost all datasets,while the most suitable DEMs were obtained from Copernicus and TanDEM-X.In addition,the concave surface without potholes on steep slopes are ideal topographic conditions for landslide formation in the study area.This study can benefit the wider landslide research community by clarifying how topographic factors affect ML models.展开更多
Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with region...Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.展开更多
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
Infrastructure in mountainous regions is particularly vulnerable when exposed to socio-natural hazards associated with extreme events,especially flood events involving the transport of large volumes of sediment and wo...Infrastructure in mountainous regions is particularly vulnerable when exposed to socio-natural hazards associated with extreme events,especially flood events involving the transport of large volumes of sediment and woody debris.In this context,understanding how such processes affect the structural stability of bridges is crucial for effective risk management and the planning of resilient infrastructure.This study examines the impacts of river floods,including large wood and sediment transport,on the“El Blanco Bridge”over the Blanco River in Chaitén,Chilean Patagonia,and the resulting susceptibility of the structure.The 2D Iber model,which solves the shallow water equations,was employed to simulate different flood scenarios as bi-phasic flows(i.e.,water,inorganic and organic sediments,the latter are referred to as large wood,LW),evaluating the hydrodynamic loadings(i.e.pressure distributions and forces)on piers and their susceptibility to sliding,overturning and scouring.Critical flood scenarios that could pose a potential risk of infrastructure failure were identified by separately determining the associated peak discharge,sediment transport rates,LW loads and bed elevation changes.Compared to clear water flows,LW transport resulted in a reduction of the factor of safety against overturning and sliding,indicating higher hydrodynamic loads on the exposed structure.When sediment transport was considered,increasing flood flows slightly augmented maximum scour depth at the base of the piers.This study underscores the significance of hydrodynamic modeling of the Blanco River for natural risk management,and highlights the importance of considering LW transport when quantifying the safety of structures,especially in catchments where easily transportable LW sources may be found(e.g.,in catchments following fires or volcanic eruptions).展开更多
With the rapid urbanization process,ground collapses caused by anthropogenic activities occur frequently.Accurate susceptibility mapping is of great significance for disaster prevention and control.In this study,1198 ...With the rapid urbanization process,ground collapses caused by anthropogenic activities occur frequently.Accurate susceptibility mapping is of great significance for disaster prevention and control.In this study,1198 ground collapse cases in Shenzhen from 2017 to 2020 were collected.Eight effective factors(elevation,relief,clay proportion,average annual precipitation,distance from water,land use type,building density,and road density)were selected to construct the evaluation index system.Ground collapse susceptibility was analyzed and mapped using the normalized frequency ratio(NFR),logistic regression(LR),and NFR-LR coupling models.Finally,the result rationality and performance of the three models were compared through frequency ratio(FR)and ROC curve.The results indicate that all three models can effectively evaluate the ground collapse susceptibility(AUC>0.7),and the NFR-LR model result is more rational and has the best performance(AUC=0.791).The very high and high susceptibility zones cover a total area of 545.68 km^(2) and involve Nanshan,Luohu,and Futian District,as well as some areas of Baoan,Guangming,and Longgang District.The ground collapses in Shenzhen mainly occurred in the built-up areas,and the greater intensity of anthropogenic activities,the more susceptible to the disaster.展开更多
Amid growing typhoon risks driven by climate change with projected shifts in precipitation intensity and temperature patterns,Taiwan faces increasing challenges in flood risk.In response,this study proposes a geograph...Amid growing typhoon risks driven by climate change with projected shifts in precipitation intensity and temperature patterns,Taiwan faces increasing challenges in flood risk.In response,this study proposes a geographic information system(GIS)-based artificial intelligence(AI)model to assess flood susceptibility in Keelung City,integrating geospatial and hydrometeorological data collected during Typhoon Krathon(2024).The model employs the random forest(RF)algorithm,using seven environmental variables excluding average elevation,slope,topographic wetness index(TWI),frequency of cumulative rainfall threshold exceedance,normalized difference vegetation index(NDVI),flow accumulation,and drainage density,with the number of flood events per unit area as the output.The RF model demonstrates high accuracy,achieving the accuracy of 97.45%.Feature importance indicates that NDVI is the most critical predictor,followed by flow accumulation,TWI,and rainfall frequency.Furthermore,under the IPCC AR5 RCP8.5 scenarios,projected 50-year return period rainfall in Keelung City increases by 42.40%-64.95%under+2℃to+4℃warming.These projections were integrated into the RF model to simulate future flood susceptibility.Results indicate two districts in the study area face the greatest increase in flood risk,emphasizing the need for targeted climate adaptation in vulnerable urban areas.展开更多
Various genetic association studies have identified numerous single nucleotide polymorphisms(SNPs)associated with nasopharyngeal carcinoma(NPC)risk.However,these studies have predominantly focused on common variants,l...Various genetic association studies have identified numerous single nucleotide polymorphisms(SNPs)associated with nasopharyngeal carcinoma(NPC)risk.However,these studies have predominantly focused on common variants,leaving the contribution of rare variants to the“missing heritability”largely unexplored.Here,we integrate genotyping data from 3925 NPC cases and 15,048 healthy controls to identify a rare SNP,rs141121474,resulting in a Glu510Lys mutation in KLHDC4 gene linked to increased NPC risk.Subsequent analyses reveal that KLHDC4 is highly expressed in NPC and correlates with poorer prognosis.Functional characterizations demonstrate that KLHDC4 acts as an oncogene in NPC cells,enhancing their migratory and metastatic capabilities,with these effects being further augmented by the Glu510Lys mutation.Mechanistically,the Glu510Lys mutant exhibits increased interaction with Vimentin compared to the wild-type KLHDC4(KLHDC4-WT),leading to elevated Vimentin protein stability and modulation of the epithelial-mesenchymal transition process,thereby promoting tumor metastasis.Moreover,Vimentin knockdown significantly mitigates the oncogenic effects induced by overexpression of both KLHDC4-WT and the Glu510Lys variant.Collectively,our findings highlight the critical role of the rare KLHDC4 variant rs141121474 in NPC progression and propose its potential as a diagnostic and therapeutic target for NPC patients.展开更多
The rice stem borer,Chilo suppressalis(Walker)(Lepidoptera:Crambidae),is one of the most serious pests in rice-growing areas,and it has developed resistance to most insecticides currently used in the field.Cyproflanil...The rice stem borer,Chilo suppressalis(Walker)(Lepidoptera:Crambidae),is one of the most serious pests in rice-growing areas,and it has developed resistance to most insecticides currently used in the field.Cyproflanilide is a novel meta-diamide insecticide that has shown high activities to multiple pests.Evaluating the risk of resistance to cyproflanilide in C.suppressalis is necessary for its preventive resistance management.Here we established the baseline susceptibility of C.suppressalis to cyproflanilide by the rice-seedling dipping method and topical application,and the LC_(50) and LD_(50) values were 0.026 mg L^(-1) and 0.122 ng/larva,respectively.The LC_(50) values of cyproflanilide in 37 field populations ranged from 0.012 to 0.061 mg L^(-1),and 25 field populations exhibited resistance to chlorantraniliprole with the highest LC_(50) value of 3,770.059 mg L^(-1).In addition,a logistic distribution model analysis indicated that only 0.048 mg L^(-1) of cyproflanilide was required to kill 90% field chlorantraniliprole-resistant populations of C.suppressalis,compared to 2,087.764 mg L^(-1) of chlorantraniliprole for a similar level of control.Resistance screening over 19 generations did not result in resistance to cyproflanilide(RR=3.1-fold).The realized heritability(h^(2))of resistance was estimated as 0.067 by using threshold trait analysis,suggesting a low risk of cyproflanilide resistance development in susceptible strains.The Cypro-SEL population(F_(10))had no obvious fitness cost(relative fitness=0.96),and no significant changes in sensitivity to seven tested insecticides.These findings suggested that cyproflanilide is a promising insecticide for the management of chlorantraniliprole-resistant C.suppressalis.Moreover,this integrated risk assessment provides scientific application guidelines for the sustainable resistance management of cyproflanilide for controlling C.suppressalis.展开更多
文摘Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.
文摘Accurate assessment of seismic landslide susceptibility is crucial for disaster prevention and emergency decision-making.Although machine learning methods have been widely applied in this field,they exhibit a strong dependence on large quantities of highquality samples,resulting in significantly low prediction accuracy of existing studies under data-scarce or crossregional prediction scenarios,which fail to meet practical application requirements.To address this issue,this study proposes an intelligent prediction model integrating transfer learning and a sampling optimization strategy,aiming to enhance the accuracy and applicability of seismic landslide susceptibility assessment.The model first improves the sample collection method through the sampling optimization strategy to enhance the precision and representativeness of training samples.This not only ensures the accuracy of origin area training but also further strengthens the model's predictive ability in the target area.Subsequently,it incorporates Transfer Component Analysis(TCA)to overcome the differences in environmental characteristics between the origin area and target area,and couples TCA with the Light GBM algorithm to construct the TCA-Light GBM model,realizing the assessment of seismic landslide susceptibility in sample-free areas.Validated through case studies of the Jiuzhaigou and Luding earthquakes,the results demonstrate that the proposed TCALight GBM transfer learning method exhibits excellent applicability in seismic landslide susceptibility prediction.After optimization with the TCA algorithm,the model's prediction performance in the target domain is significantly improved,with the AUC value increasing from 0.719 to 0.827,representing an increase of approximately 15.02%.This indicates that TCA technology can effectively alleviate the feature distribution discrepancy between the source domain and target domain,enhancing the model's generalization ability.The method is particularly suitable for scenarios with data scarcity and cross-regional prediction and can provide reliable technical support for the emergency response and risk prevention and control of seismic hazards.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3007201)the National Natural Science Foundation of China(Grant No.42377161)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB 2024ZR03).
文摘Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.
基金supported by a project entitled Loess Plateau Region-Watershed-Slope Geological Hazard Multi-Scale Collaborative Intelligent Early Warning System of the National Key R&D Program of China(2022YFC3003404)a project of the Shaanxi Youth Science and Technology Star(2021KJXX-87)public welfare geological survey projects of Shaanxi Institute of Geologic Survey(20180301,201918,202103,and 202413)。
文摘This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment models.First,the cumulative probability method revealed that a low probability(15%)of geologic hazards between any two geologic hazard points occurred outside a buffer zone with a radius of 2297 m(i.e.,the distance threshold).The training dataset was established,consisting of negative samples(non-hazard points)randomly generated based on the distance threshold,positive samples(i.e.,historical hazards),and 13 conditioning factors.Then,models were built using five machine learning algorithms,namely random forest(RF),gradient boosting decision tree(GBDT),naive Bayes(NB),logistic regression(LR),and support vector machine(SVM).The comprehensive performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and overall accuracy(OA)as indicators,revealing that RF exhibited the best performance,with OA and AUC values of 2.7127 and 0.981,respectively.Furthermore,the machine learning models constructed by considering the distance threshold outperformed those built using the unoptimized dataset.The characteristic factors were ranked using the mutual information method,with their scores decreasing in the order of rainfall(0.1616),altitude(0.06),normalized difference vegetation index(NDVI;0.04),and distance from roads(0.03).Finally,the geologic hazard susceptibility classification was assessed using the natural breaks method combined with a clustering algorithm.The results indicate that the clustering algorithm exhibited higher classification accuracy than the natural breaks method.The findings of this study demonstrate that the proposed model optimization scheme can provide a scientific basis for the prevention and control of geologic hazards.
基金The financial supports from National Natural Science Foundation of China(Nos.52574313,52204272 and 52074091)to this project。
文摘The susceptibility of ore particles to electrical breakdown plays a critical role for high voltage pulse(HVP)breakage,yet its quantitative characterization still lacks deep understanding.Two indicators,namely breakdown delay time(T_(d))and breakdown strength(E_(b))were compared,based on analysis on the two breakdown modes namely wavefront mode and post-wave mode.It was found that T_(d) is more suitable to characterize the susceptibility of ore particles to electrical breakdown in HVP breakage than E_(b).A probabilistic model based on the Weibull distribution is developed to describe the relation of breakdown probability to T_(d).Regression analyses were conducted to investigate how operating parameters and particle properties influence Td and size reduction degree of ore particles in HVP breakage.The regressed models demonstrate potential capability to predict metallic minerals content and HVP breakage degree based on operating parameters and particle properties.
基金supported by the Ministry of Science and Technology of the People’s Republic of China(2022YFD1800400).
文摘Staphylococcus aureus(S.aureus)is the third most common pathogen causing 10.6%of bacterial foodborne illnesses in China in 2021[1].Heat-stable Staphylococcal Enterotoxins(SEs)produced by S.aureus are the main contributors to staphylococcal food poisoning(SFP),causing vomiting,diarrhea,abdominal pain,headache,muscle cramps,and other acute gastroenteritis symptoms.More than 25 SEs and staphylococcal enterotoxin-like toxins(SE/s)have been described and which together comprise a superfamily of pyrogenic toxin superantigens(SAgs)[2].
基金supported by the National Key R&D Program of China(Grant No.2020YFC2200500)the Key Laboratory of Tian Qin Project(Sun Yat-sen University),Ministry of Education+1 种基金the National Natural Science Foundation of China(Grant Nos.12075325,12005308,and 11605065)the Doctoral Research Foundation Project of Hubei University of Arts and Science(Grant No.kyqdf2059017)。
文摘The effect from the interaction of the alternating current(AC)magnetic field with kilogram-level test mass(TM)limits the detectivity of the TianQin space-based gravitational wave detection.The quantifed effect requires the determination of the AC magnetic susceptibilityχ(f)of the TM.A torque method is proposed to measure theχ(f)of kg-level samples at the mHz band with a precision of 1×10^(-7).Combined with our previous work[Phys.Rev.Appl.18044010(2022)],the general frequency-dependent susceptibility of the alloy cube with side length L and electrical conductivityσis determined asχ(f)=χ0+(0.24±0.01)σμ0L^(2)f from 0.1 mHz to 1 Hz.The determination is helpful for the preliminary estimation of the in-band eddy current efect in the TianQin noise budget.The technique can be adopted to accurately measureχ(f)of the actual TM in other precision experiments,where the magnetic noise is a signifcant detection limit.
基金supported by the project of National Natural Science Foundation of China(Grant No.42371203 and U21A2032)the project Financial Fund of Sichuan Institute of Geological Survey(SCIGSCZDXM-2024008)+1 种基金Sichuan Provincial Science and Technology Department Program Funding(No.2025YFHZ0010)Science and Technology Program of Aba City(NO.R24YYJSYJ0001)。
文摘The 2019 Typhoon Lekima triggered extensive landslides in Zhejiang Province.To explore the impact of typhoon paths on the distribution of landslide susceptibility,this study proposes a spatiotemporal zoning assessment framework based on typhoon paths and inner rainbands.According to the typhoon landing path and its rainfall impact range,the study area is divided into the typhoon event period(TEP)and the annual non-typhoon period(ANP).The model uses 14 environmental factors,with the only difference between TEP and ANP being the rainfall index:TEP uses 48-hour rainfall during the typhoon,while ANP uses multi-year average annual rainfall.Modeling and comparative analysis were conducted using six machine learning models including random forest(RF)and support vector machine(SVM).The results show that the distribution pattern of high-risk landslide areas during TEP is significantly correlated with typhoon intensity:when the intensity is level 12,high-risk areas are radially distributed;at levels 10-11,they tend to concentrate asymmetrically along the coast;and when the intensity drops to below level 9,the overall susceptibility decreases significantly.During ANP,the distribution of landslides is relatively uniform with no obvious spatial concentration.Analysis on the factor contribution rate indicates that the rainfall weight in TEP is as high as 32.1%,making it the dominant factor;in ANP,the rainfall weight drops to 13.6%while the influence of factors such as slope and topographic wetness index increases,revealing differences in landslide formation mechanisms between the two periods.This study demonstrates that the spatiotemporal zoning method based on typhoon paths can effectively characterize the spatial susceptibility patterns of landslides and improve disaster identification capabilities under extreme weather conditions.The finally generated annual susceptibility zoning map divides the study area into four types of risk regions,providing a reference for dynamic monitoring and differentiated risk management of landslides in typhoon-prone areas.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金funded by the Natural Science Foundation of China(Grant Nos.42377164 and 41972280)the Badong National Observation and Research Station of Geohazards(Grant No.BNORSG-202305).
文摘Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.
基金Supported by The United States Department of Defense Breast Cancer Research Program,No.BC190820the National Institutes of Health,No.R01ES031322.
文摘Identifying the factors that contribute to individual susceptibility to cancer is essential for both prevention and treatment.The advancement of biotechnologies,particularly next-generation sequencing,has accelerated the discovery of genetic variants linked to cancer susceptibility.While hundreds of cancer-susceptibility genes have been identified,they only explain a small fraction of the overall cancer risk,a phenomenon known as"missing heritability".Despite progress,even considering factors such as epistasis,epigenetics,and gene-environment interactions,the missing heritability remains unresolved.Recent research has revealed that an individual's microbiome composition plays a significant role in cancer susceptibility through several mechanisms,such as modulating immune cell activity and influencing the presence or removal of environmental carcinogens.In this review,we examine the multifaceted roles of the microbiome in cancer risk and explore gene-microbiome and environment-microbiome interactions that may contribute to cancer susceptibility.Additionally,we highlight the importance of experimental models,such as collaborative cross mice,and advanced analytical tools,like artificial intelligence,in identifying microbial factors associated with cancer risk.Understanding these microbial determinants can open new avenues for interventions aimed at reducing cancer risk and guide the development of more effective cancer treatments.
基金the National Natural Science Foundation of China(Grant No.42301002,and 52109118)Fujian Provincial Water Resources Science and Technology Project(Grant No.MSK202524)Guidance fund for Science and Technology Program,Fujian province(Grant No.2024Y0002).
文摘Landslide susceptibility evaluation plays an important role in disaster prevention and reduction.Feature-based transfer learning(TL)is an effective method for solving landslide susceptibility mapping(LSM)in target regions with no available samples.However,as the study area expands,the distribution of land-slide types and triggering mechanisms becomes more diverse,leading to performance degradation in models relying on landslide evaluation knowledge from a single source domain due to domain feature shift.To address this,this study proposes a Multi-source Domain Adaptation Convolutional Neural Network(MDACNN),which combines the landslide prediction knowledge learned from two source domains to perform cross-regional LSM in complex large-scale areas.The method is validated through case studies in three regions located in southeastern coastal China and compared with single-source domain TL models(TCA-based models).The results demonstrate that MDACNN effectively integrates transfer knowledge from multiple source domains to learn diverse landslide-triggering mechanisms,thereby significantly reducing prediction bias inherent to single-source domain TL models,achieving an average improvement of 16.58%across all metrics.Moreover,the landslide susceptibility maps gener-ated by MDACNN accurately quantify the spatial distribution of landslide risks in the target area,provid-ing a powerful scientific and technological tool for landslide disaster management and prevention.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3003205)the Chengdu University of Technology Postgraduate Innovative Cultivation Program(Grant No.10800-000510-01-022)+1 种基金the Sichuan Science and Technology Program(Grant No.2025ZNSFSC1206)the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project(Grant No.SKLGP2023Z026).
文摘Research on the application of machine learning(ML)models to landslide susceptibility assessments has gained popularity in recent years,with a focus primarily on topographic factors derived from digital elevation models(DEMs).However,few studies have focused on the explanatory effects of these factors on different models,i.e.whether DEM-based factors affect different models in the same way.This study investigated whether different ML models could yield consistent interpretations of DEM-based factors using explanatory algorithms.Six ML models,including a support vector machine,a neural network,extreme gradient boosting,a random forest,linear regression,and K-nearest neighbors,were trained and evaluated on five geospatial datasets derived from different DEMs.Each dataset contained eight DEM-based and six non-DEM-based factors from 8912 landslide samples.Model performance was assessed using accuracy,precision,recall rate,F1-score,kappa coefficient,and receiver operating characteristic curves.Explanatory analyses,including Shapley additive explanations and partial dependence plots,were also employed to investigate the effects of topographic factors on landslide susceptibility.The results indicate that DEM-based factors consistently influenced different ML models across the datasets.Furthermore,tree-based models outperformed the other models in almost all datasets,while the most suitable DEMs were obtained from Copernicus and TanDEM-X.In addition,the concave surface without potholes on steep slopes are ideal topographic conditions for landslide formation in the study area.This study can benefit the wider landslide research community by clarifying how topographic factors affect ML models.
基金funding support from the National Natural Science Foundation of China(Grant Nos.U22A20594,52079045)Hong-Zhi Cui acknowledges the financial support of the China Scholarship Council(Grant No.CSC:202206710014)for his research at Universitat Politecnica de Catalunya,Barcelona.
文摘Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.
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funded by the ANID Fondecyt Nr.1200091"Unravelling the dynamics and impacts of sediment-laden flows in urban areas in southern Chile as a basis for innovative adaptation(SEDIMPACT)"by principal investigator Bruno Mazzorana.
文摘Infrastructure in mountainous regions is particularly vulnerable when exposed to socio-natural hazards associated with extreme events,especially flood events involving the transport of large volumes of sediment and woody debris.In this context,understanding how such processes affect the structural stability of bridges is crucial for effective risk management and the planning of resilient infrastructure.This study examines the impacts of river floods,including large wood and sediment transport,on the“El Blanco Bridge”over the Blanco River in Chaitén,Chilean Patagonia,and the resulting susceptibility of the structure.The 2D Iber model,which solves the shallow water equations,was employed to simulate different flood scenarios as bi-phasic flows(i.e.,water,inorganic and organic sediments,the latter are referred to as large wood,LW),evaluating the hydrodynamic loadings(i.e.pressure distributions and forces)on piers and their susceptibility to sliding,overturning and scouring.Critical flood scenarios that could pose a potential risk of infrastructure failure were identified by separately determining the associated peak discharge,sediment transport rates,LW loads and bed elevation changes.Compared to clear water flows,LW transport resulted in a reduction of the factor of safety against overturning and sliding,indicating higher hydrodynamic loads on the exposed structure.When sediment transport was considered,increasing flood flows slightly augmented maximum scour depth at the base of the piers.This study underscores the significance of hydrodynamic modeling of the Blanco River for natural risk management,and highlights the importance of considering LW transport when quantifying the safety of structures,especially in catchments where easily transportable LW sources may be found(e.g.,in catchments following fires or volcanic eruptions).
基金jointed supported by the National Natural Science Foundation of China(Nos.41920104007,41731284)。
文摘With the rapid urbanization process,ground collapses caused by anthropogenic activities occur frequently.Accurate susceptibility mapping is of great significance for disaster prevention and control.In this study,1198 ground collapse cases in Shenzhen from 2017 to 2020 were collected.Eight effective factors(elevation,relief,clay proportion,average annual precipitation,distance from water,land use type,building density,and road density)were selected to construct the evaluation index system.Ground collapse susceptibility was analyzed and mapped using the normalized frequency ratio(NFR),logistic regression(LR),and NFR-LR coupling models.Finally,the result rationality and performance of the three models were compared through frequency ratio(FR)and ROC curve.The results indicate that all three models can effectively evaluate the ground collapse susceptibility(AUC>0.7),and the NFR-LR model result is more rational and has the best performance(AUC=0.791).The very high and high susceptibility zones cover a total area of 545.68 km^(2) and involve Nanshan,Luohu,and Futian District,as well as some areas of Baoan,Guangming,and Longgang District.The ground collapses in Shenzhen mainly occurred in the built-up areas,and the greater intensity of anthropogenic activities,the more susceptible to the disaster.
基金supported by the National Science and Technology Council(NSTC),Taiwan(NSTC 114-2119-M-019-003).
文摘Amid growing typhoon risks driven by climate change with projected shifts in precipitation intensity and temperature patterns,Taiwan faces increasing challenges in flood risk.In response,this study proposes a geographic information system(GIS)-based artificial intelligence(AI)model to assess flood susceptibility in Keelung City,integrating geospatial and hydrometeorological data collected during Typhoon Krathon(2024).The model employs the random forest(RF)algorithm,using seven environmental variables excluding average elevation,slope,topographic wetness index(TWI),frequency of cumulative rainfall threshold exceedance,normalized difference vegetation index(NDVI),flow accumulation,and drainage density,with the number of flood events per unit area as the output.The RF model demonstrates high accuracy,achieving the accuracy of 97.45%.Feature importance indicates that NDVI is the most critical predictor,followed by flow accumulation,TWI,and rainfall frequency.Furthermore,under the IPCC AR5 RCP8.5 scenarios,projected 50-year return period rainfall in Keelung City increases by 42.40%-64.95%under+2℃to+4℃warming.These projections were integrated into the RF model to simulate future flood susceptibility.Results indicate two districts in the study area face the greatest increase in flood risk,emphasizing the need for targeted climate adaptation in vulnerable urban areas.
基金supported by the National Natural Science Foundation(82261160657,82102490,and 81572781)the Guangdong Basic and Applied Basic Research Foundation(2024A1515013061)+2 种基金the Sci-Tech Project Foundation of Guangzhou City(2023A04J2141)Chang Jiang Scholars Program(J.-X.B.)the Hong Kong Research Grant Council(RGC)Theme-based Research Scheme Funds(T12-703/22-R and T12-703/23-N).
文摘Various genetic association studies have identified numerous single nucleotide polymorphisms(SNPs)associated with nasopharyngeal carcinoma(NPC)risk.However,these studies have predominantly focused on common variants,leaving the contribution of rare variants to the“missing heritability”largely unexplored.Here,we integrate genotyping data from 3925 NPC cases and 15,048 healthy controls to identify a rare SNP,rs141121474,resulting in a Glu510Lys mutation in KLHDC4 gene linked to increased NPC risk.Subsequent analyses reveal that KLHDC4 is highly expressed in NPC and correlates with poorer prognosis.Functional characterizations demonstrate that KLHDC4 acts as an oncogene in NPC cells,enhancing their migratory and metastatic capabilities,with these effects being further augmented by the Glu510Lys mutation.Mechanistically,the Glu510Lys mutant exhibits increased interaction with Vimentin compared to the wild-type KLHDC4(KLHDC4-WT),leading to elevated Vimentin protein stability and modulation of the epithelial-mesenchymal transition process,thereby promoting tumor metastasis.Moreover,Vimentin knockdown significantly mitigates the oncogenic effects induced by overexpression of both KLHDC4-WT and the Glu510Lys variant.Collectively,our findings highlight the critical role of the rare KLHDC4 variant rs141121474 in NPC progression and propose its potential as a diagnostic and therapeutic target for NPC patients.
基金funded by the National Key Research and Developmental Program of China(2022YFD1700200).
文摘The rice stem borer,Chilo suppressalis(Walker)(Lepidoptera:Crambidae),is one of the most serious pests in rice-growing areas,and it has developed resistance to most insecticides currently used in the field.Cyproflanilide is a novel meta-diamide insecticide that has shown high activities to multiple pests.Evaluating the risk of resistance to cyproflanilide in C.suppressalis is necessary for its preventive resistance management.Here we established the baseline susceptibility of C.suppressalis to cyproflanilide by the rice-seedling dipping method and topical application,and the LC_(50) and LD_(50) values were 0.026 mg L^(-1) and 0.122 ng/larva,respectively.The LC_(50) values of cyproflanilide in 37 field populations ranged from 0.012 to 0.061 mg L^(-1),and 25 field populations exhibited resistance to chlorantraniliprole with the highest LC_(50) value of 3,770.059 mg L^(-1).In addition,a logistic distribution model analysis indicated that only 0.048 mg L^(-1) of cyproflanilide was required to kill 90% field chlorantraniliprole-resistant populations of C.suppressalis,compared to 2,087.764 mg L^(-1) of chlorantraniliprole for a similar level of control.Resistance screening over 19 generations did not result in resistance to cyproflanilide(RR=3.1-fold).The realized heritability(h^(2))of resistance was estimated as 0.067 by using threshold trait analysis,suggesting a low risk of cyproflanilide resistance development in susceptible strains.The Cypro-SEL population(F_(10))had no obvious fitness cost(relative fitness=0.96),and no significant changes in sensitivity to seven tested insecticides.These findings suggested that cyproflanilide is a promising insecticide for the management of chlorantraniliprole-resistant C.suppressalis.Moreover,this integrated risk assessment provides scientific application guidelines for the sustainable resistance management of cyproflanilide for controlling C.suppressalis.
