Landslide risk zoning in Ruijin, Jiangxi, China

Abstract. Landslides are one of the major geohazards threatening human society. This study was aimed at conducting such a hazard risk prediction and zoning based on an efficient machine learning approach, Random Forest (RF), for Ruijin, Jiangxi, China. Multiple geospatial and geo-environmental data such as land cover, NDVI, landform, rainfall, stratigraphic lithology, proximity to faults, to roads and to rivers, depth of the weathered crust, etc., were utilized in this research. After pre-processing, including digitization, linear feature buffering and value assignment, 19 hazard-causative factors were eventually produced and converted into raster to constitute a 19-band geo-environmental dataset. 155 observed landslides that had truly taken places in the past 10 years were utilized to establish a vector layer. 70 % of the disaster sites (points) were randomly selected to compose a training set (TS) and the remained 30 % to form a validation set (VS). A number of non-risk samples were identified in low slope (

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Landslide Geo-Hazard Risk Mapping Using Logistic Regression Modeling in Guixi, Jiangxi, China

Sustainability ◽

10.3390/su13094830 ◽

2021 ◽

Vol 13 (9) ◽

pp. 4830

Author(s):

Wenchao Huangfu ◽

Weicheng Wu ◽

Xiaoting Zhou ◽

Ziyu Lin ◽

Guiliang Zhang ◽

...

Keyword(s):

Logistic Regression ◽

Landslide Risk ◽

Risk Modeling ◽

Prevention Measures ◽

Training Set ◽

Reliable Prediction ◽

Landslide Occurrence ◽

Hazard Risk ◽

Causative Factors ◽

Validation Set

Reliable prediction of landslide occurrence is important for hazard risk reduction and prevention. Taking Guixi in northeast Jiangxi as an example, this research aimed to conduct such a landslide risk assessment using a multiple logistic regression (MLR) algorithm. Field-investigated landslides and non-landslide sites were converted into polygons. We randomly generated 50,000 sampling points to intersect these polygons and the intersected points were divided into two parts, a training set (TS) and a validation set (VT) in a ratio of 7 to 3. Thirteen geo-environmental factors, including elevation, slope, and distance from roads were employed as hazard-causative factors, which were intersected by the TS to create the random point (RP)-based dataset. The next step was to compute the certainty factor (CF) of each factor to constitute a CF-based dataset. MLR was applied to the two datasets for landslide risk modeling. The probability of landslides was then calculated in each pixel, and risk maps were produced. The overall accuracy of these two models versus VS was 91.5% and 90.4% with a Kappa coefficient of 0.814 and 0.782, respectively. The RP-based MLR modeling achieved more reliable predictions and its risk map seems more plausible for providing technical support for implementing disaster prevention measures in Guixi.

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Mapping Landslide Hazard Risk Using Random Forest Algorithm in Guixi, Jiangxi, China

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9110695 ◽

2020 ◽

Vol 9 (11) ◽

pp. 695

Author(s):

Yang Zhang ◽

Weicheng Wu ◽

Yaozu Qin ◽

Ziyu Lin ◽

Guiliang Zhang ◽

...

Keyword(s):

Human Life ◽

Landslide Hazard ◽

Landslide Risk ◽

Disaster Prevention ◽

Risk Modeling ◽

Prevention Measures ◽

Training Set ◽

Hazard Risk ◽

Calculated Probability ◽

Validation Set

Landslide hazards affect the security of human life and property. Mapping the spatial distribution of landslide hazard risk is critical for decision-makers to implement disaster prevention measures. This study aimed to predict and zone landslide hazard risk, using Guixi County in eastern Jiangxi, China, as an example. An integrated dataset composed of 21 geo-information layers, including lithology, rainfall, altitude, slope, distances to faults, roads and rivers, and thickness of the weathering crust, was used to achieve the aim. Non-digital layers were digitized and assigned weights based on their landslide propensity. Landslide locations and non-risk zones (flat areas) were both vectorized as polygons and randomly divided into two groups to create a training set (70%) and a validation set (30%). Using this training set, the Random Forests (RF) algorithm, which is known for its accurate prediction, was applied to the integrated dataset for risk modeling. The results were assessed against the validation set. Overall accuracy of 91.23% and Kappa Coefficient of 0.82 were obtained. The calculated probability for each pixel was consequently graded into different zones for risk mapping. Hence, we conclude that landslide risk zoning using the RF algorithm can serve as a pertinent reference for local government in their disaster prevention and early warning measures.

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Discriminating Malignancy in Thyroid Nodules: The Nomogram Versus the Kwak and ACR TI-RADS

Otolaryngology ◽

10.1177/0194599820939071 ◽

2020 ◽

Vol 163 (6) ◽

pp. 1156-1165

Author(s):

Juan Xiao ◽

Qiang Xiao ◽

Wei Cong ◽

Ting Li ◽

Shouluan Ding ◽

...

Keyword(s):

Thyroid Nodules ◽

Characteristic Curve ◽

Area Under The Curve ◽

Diagnostic Study ◽

Diagnostic Efficiency ◽

Training Set ◽

Multivariable Logistic Regression Model ◽

Predictive Values ◽

Validation Set ◽

Sensitivity Specificity

Objective To develop an easy-to-use nomogram for discrimination of malignant thyroid nodules and to compare diagnostic efficiency with the Kwak and American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Study Design Retrospective diagnostic study. Setting The Second Hospital of Shandong University. Subjects and Methods From March 2017 to April 2019, 792 patients with 1940 thyroid nodules were included into the training set; from May 2019 to December 2019, 174 patients with 389 nodules were included into the validation set. Multivariable logistic regression model was used to develop a nomogram for discriminating malignant nodules. To compare the diagnostic performance of the nomogram with the Kwak and ACR TI-RADS, the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values were calculated. Results The nomogram consisted of 7 factors: composition, orientation, echogenicity, border, margin, extrathyroidal extension, and calcification. In the training set, for all nodules, the area under the curve (AUC) for the nomogram was 0.844, which was higher than the Kwak TI-RADS (0.826, P = .008) and the ACR TI-RADS (0.810, P < .001). For the 822 nodules >1 cm, the AUC of the nomogram was 0.891, which was higher than the Kwak TI-RADS (0.852, P < .001) and the ACR TI-RADS (0.853, P < .001). In the validation set, the AUC of the nomogram was also higher than the Kwak and ACR TI-RADS ( P < .05), each in the whole series and separately for nodules >1 or ≤1 cm. Conclusions When compared with the Kwak and ACR TI-RADS, the nomogram had a better performance in discriminating malignant thyroid nodules.

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POS0859 DEEP PHENOTYPING OF DERMATOMYOSITIS BASED ON LIPID FERROPTOSIS-RELATED GENES BY MACHINE LEARNING

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.2323 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 684.1-684

Author(s):

J. Q. Zhang ◽

S. X. Zhang ◽

R. Zhao ◽

J. Qiao ◽

M. T. Qiu ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

Expression Profiles ◽

Inflammatory Myopathy ◽

Shanxi Province ◽

Survival Prediction ◽

Training Set ◽

Redox Biology ◽

Score Model ◽

Validation Set

Background:Dermatomyositis (DM) is an idiopathic inflammatory myopathy with heterogeneous clinical manifestation that raise challenges regarding diagnosis and therapy1. Ferroptosis is a newly discovered form of regulated cell death that is the nexus between metabolism, redox biology, and rheumatic immune diseases2. However, how ferroptosis maintains the balance of lymphocyte T cells and affect disease activity in DM is unclear.Objectives:To investigate an ferroptosis-related multiple gene expression signature for classification by assessing the global gene expression profile, and calculate the lymphocyte T cells status in the different subsets.Methods:Gene expression profiles of skeletal muscle from DM samples were acquired from GEO database. GSE143323 (30 patients and 20 HCs) was selected as the training set. The GSE3307 contained 21 DM patients and was selected as the validation set. The 60 ferroptosis genes were obtained from previous literature3. The intersection of the global gene and ferroptosis genes was considered the set of significant G-Ferroptosis genes for further analysis. The “NMF” (R-package) was applied as an unsupervised clustering method for sample classification by using G-Ferroptosis genes expression microarray data from the training datasets. An ferroptosis score model was constructed. The performance of the ferroptosis genes-based risk score model constructed by the DM training set was validated in the batch-1 and batch-2 DM sets. Normalized ferroptosis genes training data was used to compare the ssGSEA scores of gene sets between the high risk and low risk group. The statistical software package R (version 4.0.3) was used for all analyses. P value < 0.05 were considered statistically significant.Results:We selected 54 significant G-Ferroptosis genes for further analysis in training set. There were 2 distinct subtypes (high-ferroptosis-score groups and low-ferroptosis-score groups) identified in G-Ferroptosis genes cohort which were also identified in validation datasets (Fig.1A, C, D). Metallothionein 1G (MT1G) was a characteristic gene of low-ferroptosis-score group. The characteristic genes of high-ferroptosis-score group were acyl-CoA synthetase family member 2(ACSF2) and aconitase 1(ACO1) (Fig.1B). Patients in high-ferroptosis-score group had a lower level of Tregs compared with that of low-ferroptosis-score patients in both training and validation set (P <0.05, Fig.1E).Conclusion:The biological process of ferroptosis is associated with the lever of Tregs, suggesting the process of ferroptosis may be involved in the disease progression of DM. Identificating ferroptosis-related features for DM might provide a new idea for clinical treatment.References:[1]DeWane ME, Waldman R, Lu J. Dermatomyositis: Clinical features and pathogenesis. Journal of the American Academy of Dermatology 2020;82(2):267-81. doi: 10.1016/j.jaad.2019.06.1309 [published Online First: 2019/07/08].[2]Liang C, Zhang X, Yang M, et al. Recent Progress in Ferroptosis Inducers for Cancer Therapy. Advanced materials (Deerfield Beach, Fla) 2019;31(51):e1904197. doi: 10.1002/adma.201904197 [published Online First: 2019/10/09].[3]Liang JY, Wang DS, Lin HC, et al. A Novel Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Hepatocellular Carcinoma. International journal of biological sciences 2020;16(13):2430-41. doi: 10.7150/ijbs.45050 [published Online First: 2020/08/08].Acknowledgements:This project was supported by National Science Foundation of China (82001740).Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features

Journal of Personalized Medicine ◽

10.3390/jpm11010008 ◽

2020 ◽

Vol 11 (1) ◽

pp. 8

Author(s):

Claudia-Gabriela Moldovanu ◽

Bianca Boca ◽

Andrei Lebovici ◽

Attila Tamas-Szora ◽

Diana Sorina Feier ◽

...

Keyword(s):

Computed Tomography ◽

Renal Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Predictive Ability ◽

Nuclear Grade ◽

Training Set ◽

Cell Renal Cell Carcinoma ◽

Validation Set ◽

Sensitivity Specificity

Nuclear grade is important for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). This study aimed to determine the ability of preoperative four-phase multiphasic multidetector computed tomography (MDCT)-based radiomics features to predict the WHO/ISUP nuclear grade. In all 102 patients with histologically confirmed ccRCC, the training set (n = 62) and validation set (n = 40) were randomly assigned. In both datasets, patients were categorized according to the WHO/ISUP grading system into low-grade ccRCC (grades 1 and 2) and high-grade ccRCC (grades 3 and 4). The feature selection process consisted of three steps, including least absolute shrinkage and selection operator (LASSO) regression analysis, and the radiomics scores were developed using 48 radiomics features (10 in the unenhanced phase, 17 in the corticomedullary (CM) phase, 14 in the nephrographic (NP) phase, and 7 in the excretory phase). The radiomics score (Rad-Score) derived from the CM phase achieved the best predictive ability, with a sensitivity, specificity, and an area under the curve (AUC) of 90.91%, 95.00%, and 0.97 in the training set. In the validation set, the Rad-Score derived from the NP phase achieved the best predictive ability, with a sensitivity, specificity, and an AUC of 72.73%, 85.30%, and 0.84. We constructed a complex model, adding the radiomics score for each of the phases to the clinicoradiological characteristics, and found significantly better performance in the discrimination of the nuclear grades of ccRCCs in all MDCT phases. The highest AUC of 0.99 (95% CI, 0.92–1.00, p < 0.0001) was demonstrated for the CM phase. Our results showed that the MDCT radiomics features may play a role as potential imaging biomarkers to preoperatively predict the WHO/ISUP grade of ccRCCs.

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EEG Feature Fusion for Person Identification Using Efficient Machine Learning Approach

10.1109/picict53635.2021.00029 ◽

2021 ◽

Author(s):

Zaid Abdi Alkareem Alyasseri ◽

Mohammed Azmi Al-Betar ◽

Mohammed A. Awadallah ◽

Sharif Naser Makhadmeh ◽

Osama Ahmad Alomari ◽

...

Keyword(s):

Machine Learning ◽

Feature Fusion ◽

Learning Approach ◽

Person Identification ◽

Machine Learning Approach ◽

Efficient Machine

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Efficient machine learning approach for volunteer eye-blink detection in real-time using webcam

Expert Systems with Applications ◽

10.1016/j.eswa.2021.116073 ◽

2021 ◽

pp. 116073

Author(s):

Paulo Augusto de Lima Medeiros ◽

Gabriel Vinícius Souza da Silva ◽

Felipe Ricardo dos Santos Fernandes ◽

Ignacio Sánchez-Gendriz ◽

Hertz Wilton Castro Lins ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Learning Approach ◽

Eye Blink ◽

Machine Learning Approach ◽

Blink Detection ◽

Efficient Machine

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Application of Multi-Scale Fusion Attention U-Net to Segment the Thyroid Gland on CT Localization Images for Radiotherapy

10.21203/rs.3.rs-949323/v1 ◽

2021 ◽

Author(s):

Xiaobo Wen ◽

Biao Zhao ◽

Meifang Yuan ◽

Jinzhi Li ◽

Mengzhen Sun ◽

...

Keyword(s):

Thyroid Gland ◽

Clinical Work ◽

Similarity Coefficient ◽

Dice Similarity Coefficient ◽

Training Set ◽

Data Set ◽

Test Set ◽

Noise Interference ◽

Multi Scale ◽

Validation Set

Abstract Objectives: To explore the performance of Multi-scale Fusion Attention U-net (MSFA-U-net) in thyroid gland segmentation on CT localization images for radiotherapy. Methods: CT localization images for radiotherapy of 80 patients with breast cancer or head and neck tumors were selected; label images were manually delineated by experienced radiologists. The data set was randomly divided into the training set (n=60), the validation set (n=10), and the test set (n=10). Data expansion was performed in the training set, and the performance of the MSFA-U-net model was evaluated using the evaluation indicators Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), positive predictive value (PPV), sensitivity (SE), and Hausdorff distance (HD). Results: With the MSFA-U-net model, the DSC, JSC, PPV, SE, and HD indexes of the segmented thyroid gland in the test set were 0.8967±0.0935, 0.8219±0.1115, 0.9065±0.0940, 0.8979±0.1104, and 2.3922±0.5423, respectively. Compared with U-net, HR-net, and Attention U-net, MSFA-U-net showed that DSC increased by 0.052, 0.0376, and 0.0346 respectively; JSC increased by 0.0569, 0.0805, and 0.0433, respectively; SE increased by 0.0361, 0.1091, and 0.0831, respectively; and HD increased by −0.208, −0.1952, and −0.0548, respectively. The test set image results showed that the thyroid edges segmented by the MSFA-U-net model were closer to the standard thyroid delineated by the experts, in comparison with those segmented by the other three models. Moreover, the edges were smoother, over-anti-noise interference was stronger, and oversegmentation and undersegmentation were reduced. Conclusion: The MSFA-U-net model can meet basic clinical requirements and improve the efficiency of physicians' clinical work.

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An Integrating of Clinical, Pathological, and Radiomic Approach to Predict the Microsatellite Instability in Rectal Carcinoma

10.21203/rs.3.rs-957494/v1 ◽

2021 ◽

Author(s):

Hang Yuan ◽

Peng Yu ◽

Jiankai Li ◽

Niping Song ◽

Zi'ang Wan ◽

...

Keyword(s):

Microsatellite Instability ◽

Rectal Carcinoma ◽

Logistic Models ◽

Integrative Model ◽

Training Set ◽

Node Metastasis ◽

Pathological Characteristics ◽

The Status ◽

Validation Set ◽

Operator Curve

Abstract Objective: To develop an integrative model with clinical, pathological, and radiomic characteristics to predict the status of microsatellite instability (MSI) in rectal carcinoma (RC). Methods: A cohort of 788 RCs with 97 high MSI status (MSI-H) and 691 microsatellite stable status (MSS) were enrolled. The clinical and pathological characteristics were recorded. The radiomic features were calculated after segmentation of volume of interests and then patients were divided into the training set and validation set with a random proportion of 7:3. The logistic models of simple clinical characteristics (LM-Clin), pathological characteristics (LM-Patho), and radiomic features (LM-Radio) were constructed to distinguish MSI-H from MSS. The relevant radiomic score was calculated. Finally, a integrative nomogram (LM-Nomo) including significant clinical, pathological characteristics, and radiomics was developed. The area under receiver operator curve (AUC) was calculated to evaluate the efficacy of prediction. Results: The AUC of simple LM-Clin including variables of CEA and hypertension and LM-Patho including characteristics of gross type and lymph node metastasis ratio (LNR) was 0.584 (95%CI, 0.549-0.619) and 0.585 (95%CI, 0.550-0.619), which was lower than that of LM-Radio including 12 radiomic features with AUC of 0.737 (95%CI, 0.675-0.799). The LM-Nomo contained CEA, hypertension, LNR, and radiomic score, and the AUC was 0.757 (95%CI, 0.726-0.787). Conclusion: The AUCs of LM-Clin and LM-Patho were disappointing and lower than that of LM-Radio. The LM-Nomo demonstrated the best performance in predicting MSI-H status.

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A Radiomics nomogram for preoperative predicting recurrence of low grade gliomas based on multiparametric MRI

10.21203/rs.3.rs-345383/v1 ◽

2021 ◽

Author(s):

Zhenhua Wang ◽

Xinlan Xiao ◽

Zhaotao Zhang ◽

Keng He ◽

Peipei Pang ◽

...

Keyword(s):

Multivariate Logistic Regression Analysis ◽

Low Grade ◽

Training Set ◽

Decision Curve Analysis ◽

Logistic Analysis ◽

Contrast Enhanced ◽

Fluid Attenuated Inversion Recovery ◽

Validation Set ◽

The Individual ◽

Good Agreement

Abstract Objective To develop a radiomics nomogram to predict the recurrence of Low grade glioma(LGG) after their first surgery; Methods A retrospective analysis of pathological, clinical and Magnetic resonance image(MRI) of LGG patients who underwent surgery and had a recurrence between 2017 and 2020 in our hospital was performed. After a rigorous selection,64 patients were eligible and enrolled in the study(22 cases were with recurrent gliomas),which was randomly assigned in a 7:3 ratio to either the training set and validation set; T1WI,T2WI fluid-attenuated-inversion-recovery(T2WI-FLAIR) and contrast-enhanced T1-weighted(T1CE) sequences, 396 radiomics features were extracted from each image sequence, minimum-redundancy maximum-relevancy(mRMR) alone or combining with univariate logistic analysis were used for features screening, the screened features were performed by multivariate logistic regression analysis and developed a predictive model both in training set and validation set; Receiver operating characteristic(ROC) curve, calibration curve, and decision curve analysis(DCA) were used to assess the performance of each model. Results The radiomics nomogram derived from three MRI sequence yielded an ideal performance than the individual ones, the AUC in the training set and validation set were 0.966 and 0.93 respectively, 95% confidence interval(95%CI) were 0.949-0.99 and 0.905-0.973 respectively; the calibration curves indicated good agreement between the predictive and the actual probability. The DCA demonstrated that a combination of three sequences had more favorable clinical predictive value than single sequence imaging. Conclusion Our multiparametric radiomics nomogram could be an efficient and accurate tool for predicting the recurrence of LGG after its first resection.

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