Using Multiple Risk Factors and Generalized Linear Mixed Models with 5-Fold Cross-Validation Strategy for Optimal Carotid Plaque Progression Prediction

Background Cardiovascular diseases are closely linked to atherosclerotic plaque development and rupture.Plaque progression prediction is of fundamental significance to cardiovascular research and disease diagnosis,prevention,and treatment.Generalized linear mixed models(GLMM)is an extension of linear model for categorical responses while considering the correlation among observations.Methods Magnetic resonance image(MRI)data of carotid atheroscleroticplaques were acquired from 20 patients with consent obtained and 3D thin-layer models were constructed to calculate plaque stress and strain for plaque progression prediction.Data for ten morphological and biomechanical risk factors included wall thickness(WT),lipid percent(LP),minimum cap thickness(MinCT),plaque area(PA),plaque burden(PB),lumen area(LA),maximum plaque wall stress(MPWS),maximum plaque wall strain(MPWSn),average plaque wall stress(APWS),and average plaque wall strain(APWSn)were extracted from all slices for analysis.Wall thickness increase(WTI),plaque burden increase(PBI)and plaque area increase(PAI) were chosen as three measures for plaque progression.Generalized linear mixed models(GLMM)with 5-fold cross-validation strategy were used to calculate prediction accuracy for each predictor and identify optimal predictor with the highest prediction accuracy defined as sum of sensitivity and specificity.All 201 MRI slices were randomly divided into 4 training subgroups and 1 verification subgroup.The training subgroups were used for model fitting,and the verification subgroup was used to estimate the model.All combinations(total1023)of 10 risk factors were feed to GLMM and the prediction accuracy of each predictor were selected from the point on the ROC(receiver operating characteristic)curve with the highest sum of specificity and sensitivity.Results LA was the best single predictor for PBI with the highest prediction accuracy(1.360 1),and the area under of the ROC curve(AUC)is0.654 0,followed by APWSn(1.336 3)with AUC=0.6342.The optimal predictor among all possible combinations for PBI was the combination of LA,PA,LP,WT,MPWS and MPWSn with prediction accuracy=1.414 6(AUC=0.715 8).LA was once again the best single predictor for PAI with the highest prediction accuracy(1.184 6)with AUC=0.606 4,followed by MPWSn(1. 183 2)with AUC=0.6084.The combination of PA,PB,WT,MPWS,MPWSn and APWSn gave the best prediction accuracy(1.302 5)for PAI,and the AUC value is 0.6657.PA was the best single predictor for WTI with highest prediction accuracy(1.288 7)with AUC=0.641 5,followed by WT(1.254 0),with AUC=0.6097.The combination of PA,PB,WT,LP,MinCT,MPWS and MPWS was the best predictor for WTI with prediction accuracy as 1.314 0,with AUC=0.6552.This indicated that PBI was a more predictable measure than WTI and PAI. The combinational predictors improved prediction accuracy by 9.95%,4.01%and 1.96%over the best single predictors for PAI,PBI and WTI(AUC values improved by9.78%,9.45%,and 2.14%),respectively.Conclusions The use of GLMM with 5-fold cross-validation strategy combining both morphological and biomechanical risk factors could potentially improve the accuracy of carotid plaque progression prediction.This study suggests that a linear combination of multiple predictors can provide potential improvement to existing plaque assessment schemes.

Morphology cluster and prediction of growth of human brain pyramidal neurons

Predicting neuron growth is valuable to understand the morphology of neurons, thus it is helpful in the research of neuron classification. This study sought to propose a new method of predicting the growth of human neurons using 1 907 sets of data in human brain pyramidal neurons obtained from the website of NeuroMorpho.Org. First, we analyzed neurons in a morphology field and used an expectation-maximization algorithm to specify the neurons into six clusters. Second, naive Bayes classifier was used to verify the accuracy of the expectation-maximization algorithm. Experiment results proved that the cluster groups here were efficient and feasible. Finally, a new method to rank the six expectation-maximization algorithm clustered classes was used in predicting the growth of human pyramidal neurons.

Nonclassical Plane-crystallographic Groups and Their Applications Ⅲ

Six kinds of nonclassical periodic lattices with locally 10-fold rotational symmetries are proposed. They can be delineated via nonclassical plane-crystallographic groups. The projections on the planes of corresponding unit cells consisting of embedding polyhedra generate the periodic lattices, respectively. The Fourier-transform patterns of the periodiclattices have almost perfect 10-fold rotational symmetries, which are very similar to those displaying in the electron-diffraction patterns of so-called quasicrystals.

MCS HOG Features and SVM Based Handwritten Digit Recognition System

Digit Recognition is an essential element of the process of scanning and converting documents into electronic format. In this work, a new Multiple-Cell Size (MCS) approach is being proposed for utilizing Histogram of Oriented Gradient (HOG) features and a Support Vector Machine (SVM) based classifier for efficient classification of Handwritten Digits. The HOG based technique is sensitive to the cell size selection used in the relevant feature extraction computations. Hence a new MCS approach has been used to perform HOG analysis and compute the HOG features. The system has been tested on the Benchmark MNIST Digit Database of handwritten digits and a classification accuracy of 99.36% has been achieved using an Independent Test set strategy. A Cross-Validation analysis of the classification system has also been performed using the 10-Fold Cross-Validation strategy and a 10-Fold classification accuracy of 99.26% has been obtained. The classification performance of the proposed system is superior to existing techniques using complex procedures since it has achieved at par or better results using simple operations in both the Feature Space and in the Classifier Space. The plots of the system’s Confusion Matrix and the Receiver Operating Characteristics (ROC) show evidence of the superior performance of the proposed new MCS HOG and SVM based digit classification system.

An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping:Application in Two Areas of Three Gorges Reservoir,China

Numerous researches have been published on the application of landslide susceptibility assessment models;however,they were only applied in the same areas as the models were originated,the effect of applying the models to other areas than the origin of the models has not been explored.This study is purposed to develop an optimized random forest(RF)model with best ratios of positive-to-negative cells and 10-fold cross-validation for landslide susceptibility mapping(LSM),and then explore its generalization ability not only in the area where the model is originated but also in area other than the origin of the model.Two typical counties(Fengjie County and Wushan County)in the Three Gorges Reservoir area,China,which have the same terrain and geological conditions,were selected as an example.To begin with,landslide inventory was prepared based on field investigations,satellite images,and historical records,and 1522 landslides were then identified in Fengjie County.22 landslide-conditioning factors under the influence of topography,geology,environmental conditions,and human activities were prepared.Then,combined with 10-fold cross-validation,three typical ratios of positive-to-negative cells,i.e.,1:1,1:5,and 1:10,were adopted for comparative analyses.An optimized RF model(Fengjie-based model)with the best ratios of positive-to-negative cells and 10-fold cross-validation was constructed.Finally,the Fengjie-based model was applied to Fengjie County and Wushan County,and the confusion matrix and area under the receiver operating characteristic(ROC)curve value(AUC)were used to estimate the accuracy.The Fengjie-based model delivered high stability and predictive capability in Fengjie County,indicating a great generalization ability of the model to the area where the model is originated.The LSM in Wushan County generated by the Fengjie-based model had a reasonable reference value,indicating the Fengjiebased model had a great generalization ability in area other than the origin of the model.The Fengjiebased model in this study could be applied in other similar areas/countries with the same terrain and geological conditions,and a LSM may be generated without collecting landslide information for modeling,so as to reduce workload and improve efficiency in practice.