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| 1 | 利用光学遥感数据、GIS及人工神经网络模型分析区域滑坡灾害(英文)显示文摘用光学遥感数据和地理信息系统(GIS)分析了马来西亚Selangor地区的滑坡灾害。通过遥感图像解译和野外调查,在研究区内确定出滑坡发生区。通过GIS和图像处理,建立了一个集地形、地质和遥感图像等多种信息的空间数据库。滑坡发生的因素主要为:地形坡度、地形方位、地形曲率及与排水设备距离;岩性及与线性构造距离;TM图像解译得到的植被覆盖情况;Landsat图像解译得到的植被指数;降水量。通过建立人工神经网络模型对这些因素进行分析后得到滑坡灾害图:由反向传播训练方法确定每个因素的权重值,然后用该权重值计算出滑坡灾害指数,最后用GIS工具生成滑坡灾害图。用遥感解译和野外观测确定出的滑坡位置资料验证了滑坡灾害图,准确率为82.92%。结果表明推测的滑坡灾害图与滑坡实际发生区域足够吻合。 | Biswajeet Pradhan Saro Lee | 2007 | 地学前缘2007，14，6： | 29 |
| 2 | Inflammatory bowel diseases: A disease (s) of modern times? Is incidence still increasing?显示文摘Inflammatory bowel diseases (IBD) are a heterogeneous group of diseases, not always easy to diagnose, even more difficult to classify, and diagnostic criteria are not always uniform. Well done population-based studies are not abundant, and so comparisons among different geographical areas or populations are not always very reliable. In this article, we have reviewed epidemiological studies available on the world’s population while making a critical review of published data. | Cristina Saro Gismera Beatriz Sicilia Aladrén | 2008 | World Journal of Gastroenterology2008，14，36： | 17 |
| 3 | Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran显示文摘The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies. | Phuong Thao Thi Ngo Mahdi Panahi Khabat Khosravi Omid Ghorbanzadeh Narges Kariminejad Artemi Cerda Saro Lee | 2021 | Geoscience Frontiers2021，12，2： | 13 |
| 4 | Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer显示文摘As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency. | Wei Chen Xi Chen Jianbing Peng Mahdi Panahi Saro Lee | 2021 | Geoscience Frontiers2021，12，1： | 9 |
| 5 | Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression(SVR)with GWO,BAT and COA algorithms显示文摘In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)datasets.The landslide susceptibility map was produced using 14 causative factors.We applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive accuracy.The resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility modeling.SVR-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of 0.23046.The three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance. | Abdul-Lateef Balogun Fatemeh Rezaie Quoc Bao Pham Ljubomir Gigović Siniša Drobnjak Yusuf AAina Mahdi Panahi Shamsudeen Temitope Yekeen Saro Lee | 2021 | Geoscience Frontiers2021，12，3： | 6 |
| 6 | Deep learning neural networks for spatially explicit prediction of flash flood probability显示文摘Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two architectures of deep learning neural networks,namely convolutional neural networks(CNN)and recurrent neural networks(RNN),for spatially explicit prediction and mapping of flash flood probability.To develop and validate the predictive models,a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed.The step-wise weight assessment ratio analysis(SWARA)was employed to investigate the spatial interplay between floods and different influencing factors.The CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics technique.The results showed that the CNN model(AUC=0.832,RMSE=0.144)performed slightly better than the RNN model(AUC=0.814,RMSE=0.181)in predicting future floods.Further,these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study area.This study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province,and the resulting probability maps can be used for the development of mitigation plans in response to the future floods.The general policy implication of our study suggests that design,implementation,and verification of flood early warning systems should be directed to approximately 40%of the land area characterized by high and very susceptibility to flooding. | Mahdi Panahi Abolfazl Jaafari Ataollah Shirzadi Himan Shahabi Omid Rahmati Ebrahim Omidvar Saro Lee Dieu Tien Bui | 2021 | Geoscience Frontiers2021，12，3： | 4 |
| 7 | Fundamental electronic structure and multiatomic bonding in 13 biocompatible high-entropy alloys显示文摘High-entropy alloys(HEAs)have attracted great attention due to their many unique properties and potential applications.The nature of interatomic interactions in this unique class of complex multicomponent alloys is not fully developed or understood.We report a theoretical modeling technique to enable in-depth analysis of their electronic structures and interatomic bonding,and predict HEA properties based on the use of the quantum mechanical metrics,the total bond order density(TBOD)and the partial bond order density(PBOD).Application to 13 biocompatible multicomponent HEAs yields many new and insightful results,including the inadequacy of using the valence electron count,quantification of large lattice distortion,validation of mechanical properties with experiment data,modeling porosity to reduce Young’s modulus.This work outlines a road map for the rational design of HEAs for biomedical applications. | Wai-Yim Ching Saro San Jamieson Brechtl Ridwan Sakidja Miqin Zhang Peter K.Liaw | 2020 | npj Computational Materials2020，，1： | 2 |
| 8 | Differential expression of genes involved in the epigenetic regulation of cell identity in normal human mammary cell commitment and differentiation显示文摘The establishment and maintenance of mammary epithelial cell identity depends on the activity of a group of proteins, collectively called maintenance proteins, that act as epigenetic regulators of gene transcription through DNA methylation, histone modification, and chromatin remodeling. Increasing evidence indicates that dysregulation of these crucial proteins may disrupt epithelial cell integrity and trigger breast tumor initiation. Therefore, we explored in silico the expression pattern of a panel of 369 genes known to be involved in the establishment and maintenance of epithelial cell identity and mammary gland remodeling in cell subpopulations isolated from normal human mammary tissue and selectively enriched in their content of bipotent progenitors, committed luminal progenitors, and differentiated myoepithelial or differentiated luminal cells. The results indicated that, compared to bipotent cells, differentiated myoepithelial and luminal subpopulations were both characterized by the differential expression of 4 genes involved in cell identity maintenance: CBX6 and PCGF2, encoding proteins belonging to the Polycomb group, and SMARCD3 and SMARCE1, encoding proteins belonging to the Trithorax group. In addition to these common genes, the myoepithelial phenotype was associated with the differential expression of HDAC1, which encodes histone deacetylase 1, whereas the luminal phenotype was associated with the differential expression of SMARCA4 and HAT1, which encode a Trithorax protein and histone acetylase 1, respectively. The luminal compartment was further characterized by the overexpression of ALDH1A3 and GATA3, and the down-regulation of NOTCH4 and CCNB1, with the latter suggesting a block in cell cycle progression at the G2 phase. In contrast, myoepithelial differentiation was associated with the overexpression of MYC and the down-regulation of CCNE1, with the latter suggesting a block in cell cycle progression at the G1 phase. | Danila Coradini Patrizia Boracchi Saro Oriana Elia Biganzoli Federico Ambrogi | 2014 | Chinese Journal of Cancer2014，33，10： | 2 |
| 9 | Epithelial cell polarity and tumorigenesis: new perspectives for cancer detection and treatment显示文摘房间房间粘附和房间极性的损失通常与他们的侵略在上皮的起源和相互关联的肿瘤被观察进转移的邻近的纸巾和形成。成长证据显示房间极性和房间房间粘附的损失可能也在癌症的早舞台是重要的。在这评论的第一部分,我们描出建立并且维持并且在肿瘤讨论房间极性和顶端的 junctional 建筑群部件的参与上皮的纸巾的极性的机制的当前的理解致病。在第二部分,我们在癌症诊断和预后探讨房间极性和 junctional 建筑群部件的临床的意义。最后,我们在癌症的治疗作为治疗学的目标探索他们的潜在的使用。 | Danila CORADINI Claudia CASARSA Saro ORIANA | 2011 | Acta Pharmacologica Sinica2011，32，5： | 2 |
| 10 | Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM显示文摘The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)techniques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model’s result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area. | Alireza Arabameri Fatemeh Rezaie Subodh Chandra Pal Artemi Cerda Asish Saha Rabin Chakrabortty Saro Lee | 2021 | Geoscience Frontiers2021，12，6： | 2 |
| 11 | chronic hepatitis B reactivation following infliximab therapy in Crohn's disease patients : need for primary prophylaxis 显示文摘 | ESTEVE M SARO C GONZALEZ-HUIX F | 2004 | Gut2004，53，9： | 1 |
| 12 | Epidemiology of chronic inflammatory bowel disease in Gijon, Asturias显示文摘 | Saro Gismera C Lacort Fernandez M Arguelles Fernandez G | 2001 | Gastroenterol Hepatol2001，24，5： | 1 |
| 13 | LIBS applications in the aluminium, glass and steel industries; LIBS 2004显示文摘 | De Saro R | 2004 | Laser In- duced Plasma Spectroscopy and Applications2004，，： | 1 |
| 14 | Second tri- mester Doppler ultrasound screening of the uterine arteries differentiates between subsequent normal and poor out- comes of hypertensive pregnancy: two different patho- physiological entities? 显示文摘 | Aardema M W Saro M C Lander M | 2004 | Clin Sci (Lond)2004，106，4： | 1 |
| 15 | Application of an eviden- tial belief function model in landslide susceptibility map- ping显示文摘 | Omar F A Biswajeet P Saro L | 2012 | Computers & Geosciences2012，，44： | 1 |
| 16 | Application of a fu~y operator to susceptibility estimations of coal mine subsidence in Taebaek City, Korea显示文摘 | Jong - Kuk Choi Ki - Dong Kim Saro Lee | 2010 | Environment Earth Science2010，59，5： | 1 |
| 17 | Development of GIS-based geological hazard information system and its application for landslide analysis in Korea显示文摘 | Saro Lee Ueechan Choi | | 0，，03： | 1 |
| 18 | Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS显示文摘 | LEE Saro | 2004 | Environmental Management2004，34，2： | 1 |
| 19 | Ovarian triggering in clomiphene citrate stimulated cycle; human chorionic gonadotropin versus a gonadotropin releasing hormone agonist显示文摘 | Schmidt-saros C Kaplan DR Saros P | 1995 | J Assist Reprod Genet1995，12，2： | 1 |
| 20 | C-reaction protein and morphology in patients with acute myocardial infarction显示文摘 | Saro T Tanaka A Namba M | 2003 | Circulation2003，108，3： | 1 |
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