Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced mach...Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning algorithm.To assess aviation safety and identify the causes of incidents, a classification model with light gradient boosting machine (LGBM)based on the aviation safety reporting system (ASRS) has been developed. It is improved by k-fold cross-validation with hybrid sampling model (HSCV), which may boost classification performance and maintain data balance. The results show that employing the LGBM-HSCV model can significantly improve accuracy while alleviating data imbalance. Vertical comparison with other cross-validation (CV) methods and lateral comparison with different fold times comprise the comparative approach. Aside from the comparison, two further CV approaches based on the improved method in this study are discussed:one with a different sampling and folding order, and the other with more CV. According to the assessment indices with different methods, the LGBMHSCV model proposed here is effective at detecting incident causes. The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data processing, and the model’s accurate identification of civil aviation incident causes can assist to improve civil aviation safety.展开更多
In deriving a regression model analysts often have to use variable selection, despite of problems introduced by data- dependent model building. Resampling approaches are proposed to handle some of the critical issues....In deriving a regression model analysts often have to use variable selection, despite of problems introduced by data- dependent model building. Resampling approaches are proposed to handle some of the critical issues. In order to assess and compare several strategies, we will conduct a simulation study with 15 predictors and a complex correlation structure in the linear regression model. Using sample sizes of 100 and 400 and estimates of the residual variance corresponding to R2 of 0.50 and 0.71, we consider 4 scenarios with varying amount of information. We also consider two examples with 24 and 13 predictors, respectively. We will discuss the value of cross-validation, shrinkage and backward elimination (BE) with varying significance level. We will assess whether 2-step approaches using global or parameterwise shrinkage (PWSF) can improve selected models and will compare results to models derived with the LASSO procedure. Beside of MSE we will use model sparsity and further criteria for model assessment. The amount of information in the data has an influence on the selected models and the comparison of the procedures. None of the approaches was best in all scenarios. The performance of backward elimination with a suitably chosen significance level was not worse compared to the LASSO and BE models selected were much sparser, an important advantage for interpretation and transportability. Compared to global shrinkage, PWSF had better performance. Provided that the amount of information is not too small, we conclude that BE followed by PWSF is a suitable approach when variable selection is a key part of data analysis.展开更多
For the nonparametric regression model Y-ni = g(x(ni)) + epsilon(ni)i = 1, ..., n, with regularly spaced nonrandom design, the authors study the behavior of the nonlinear wavelet estimator of g(x). When the threshold ...For the nonparametric regression model Y-ni = g(x(ni)) + epsilon(ni)i = 1, ..., n, with regularly spaced nonrandom design, the authors study the behavior of the nonlinear wavelet estimator of g(x). When the threshold and truncation parameters are chosen by cross-validation on the everage squared error, strong consistency for the case of dyadic sample size and moment consistency for arbitrary sample size are established under some regular conditions.展开更多
Paraneoplastic neurological syndrome refers to certain malignant tumors that have affected the distant nervous system and caused corresponding dysfunction in the absence of tumor metastasis.Patients with this syndrome...Paraneoplastic neurological syndrome refers to certain malignant tumors that have affected the distant nervous system and caused corresponding dysfunction in the absence of tumor metastasis.Patients with this syndrome produce multiple antibodies,each targeting a different antigen and causing different symptoms and signs.The CV2/collapsin response mediator protein 5(CRMP5)antibody is a major antibody of this type.It damages the nervous system,which often manifests as limbic encephalitis,chorea,ocular manifestation,cerebellar ataxia,myelopathy,and peripheral neuropathy.Detecting CV2/CRMP5 antibody is crucial for the clinical diagnosis of paraneoplastic neurological syndrome,and anti-tumor and immunological therapies can help to alleviate symptoms and improve prognosis.However,because of the low incidence of this disease,few repo rts and no reviews have been published about it so far.This article intends to review the research on CV2/CRMP5antibody-associated paraneoplastic neurological syndrome and summarize its clinical features to help clinicians comprehensively understand the disease.Additionally,this review discusses the curre nt challenges that this disease poses,and the application prospects of new detection and diagnostic techniques in the field of paraneoplastic neurological syndrom e,including CV2/CRMP5-associated paraneoplastic neurological syndrome,in recent years.展开更多
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,pre...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.展开更多
[Objectives]To optimize the solid-state fermentation process of Flos Sophorae Immaturus by Penicillium with Sophora japonica cv.jinhuai as raw material.[Methods]The fermentation conditions were optimized by single fac...[Objectives]To optimize the solid-state fermentation process of Flos Sophorae Immaturus by Penicillium with Sophora japonica cv.jinhuai as raw material.[Methods]The fermentation conditions were optimized by single factor experiment and response surface methodology with quercetin content as the dependent variable.[Results]According to the established model,the optimal fermentation process of Flos Sophorae Immaturus was obtained as follows:temperature 29.97℃,time 6.88 d,rotation speed 180.86 rpm,inoculation amount 3.93 mL,and the expected content of quercetin was 34.8053 mg/g.Based on this,the fermentation parameters were adjusted,and the actual content was 33.67 mg/g,which was close to the predicted value.[Conclusions]The optimization of fermentation process of Flos Sophorae Immaturus by response surface methodology provides a reference for the development and utilization of this medicinal material.展开更多
目的探究有腹部手术史病人应用安全性关键视角(critical view of safety,CVS)技术理念行急诊腹腔镜胆囊切除术(LC)的应用价值。方法回顾性分析既往有腹部手术史接受急诊LC手术患者48例,依据患者手术是否施行CVS理念将患者分为CVS组和非...目的探究有腹部手术史病人应用安全性关键视角(critical view of safety,CVS)技术理念行急诊腹腔镜胆囊切除术(LC)的应用价值。方法回顾性分析既往有腹部手术史接受急诊LC手术患者48例,依据患者手术是否施行CVS理念将患者分为CVS组和非CVS组。其中CVS组28例,非CVS组20例,分析患者手术时间、术中出血量、术中胆管损伤、临近脏器损伤、胃肠功能恢复时间、总住院时间、术后腹腔引流量、术后拔管时间、总住院费用、术后出血、术后胆漏发生率等情况。结果所有患者均顺利完成手术,两组患者术中出血量、术后胃肠功能恢复时间、术后出血、总住院费用、总住院时间等无明显差异;CVS组手术时间稍长于非CVS组,而术后腹腔引流量、术后拔管时间CVS组显著低于非CVS组,差异有统计学意义(P<0.05);此外,CVS组发生胆管损伤、临近脏器损伤、术后胆漏等并发症发生率显著低于非CVS组,差异有统计学意义(P<0.05)。结论CVS技术理念的应用可有效提高有腹部手术史患者LC手术的安全性,尤其对于急诊手术患者,可显著降低术后并发症发生率,该理念的应用有助于促进患者康复、提高手术安全性。展开更多
基金supported by the National Natural Science Foundation of China Civil Aviation Joint Fund (U1833110)Research on the Dual Prevention Mechanism and Intelligent Management Technology f or Civil Aviation Safety Risks (YK23-03-05)。
文摘Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning algorithm.To assess aviation safety and identify the causes of incidents, a classification model with light gradient boosting machine (LGBM)based on the aviation safety reporting system (ASRS) has been developed. It is improved by k-fold cross-validation with hybrid sampling model (HSCV), which may boost classification performance and maintain data balance. The results show that employing the LGBM-HSCV model can significantly improve accuracy while alleviating data imbalance. Vertical comparison with other cross-validation (CV) methods and lateral comparison with different fold times comprise the comparative approach. Aside from the comparison, two further CV approaches based on the improved method in this study are discussed:one with a different sampling and folding order, and the other with more CV. According to the assessment indices with different methods, the LGBMHSCV model proposed here is effective at detecting incident causes. The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data processing, and the model’s accurate identification of civil aviation incident causes can assist to improve civil aviation safety.
文摘In deriving a regression model analysts often have to use variable selection, despite of problems introduced by data- dependent model building. Resampling approaches are proposed to handle some of the critical issues. In order to assess and compare several strategies, we will conduct a simulation study with 15 predictors and a complex correlation structure in the linear regression model. Using sample sizes of 100 and 400 and estimates of the residual variance corresponding to R2 of 0.50 and 0.71, we consider 4 scenarios with varying amount of information. We also consider two examples with 24 and 13 predictors, respectively. We will discuss the value of cross-validation, shrinkage and backward elimination (BE) with varying significance level. We will assess whether 2-step approaches using global or parameterwise shrinkage (PWSF) can improve selected models and will compare results to models derived with the LASSO procedure. Beside of MSE we will use model sparsity and further criteria for model assessment. The amount of information in the data has an influence on the selected models and the comparison of the procedures. None of the approaches was best in all scenarios. The performance of backward elimination with a suitably chosen significance level was not worse compared to the LASSO and BE models selected were much sparser, an important advantage for interpretation and transportability. Compared to global shrinkage, PWSF had better performance. Provided that the amount of information is not too small, we conclude that BE followed by PWSF is a suitable approach when variable selection is a key part of data analysis.
文摘For the nonparametric regression model Y-ni = g(x(ni)) + epsilon(ni)i = 1, ..., n, with regularly spaced nonrandom design, the authors study the behavior of the nonlinear wavelet estimator of g(x). When the threshold and truncation parameters are chosen by cross-validation on the everage squared error, strong consistency for the case of dyadic sample size and moment consistency for arbitrary sample size are established under some regular conditions.
基金National Natural Science Foundation of China,No.U1604181Henan Province Key R&D and Promotion Special Project (Science and Technology Tackle),No.212102310834+1 种基金Henan Medical Education Research Project,No.Wjlx2020531the Joint project of Medical Science and Technology Research Program of Henan Province,No.LHGJ20190078 (all to JW)。
文摘Paraneoplastic neurological syndrome refers to certain malignant tumors that have affected the distant nervous system and caused corresponding dysfunction in the absence of tumor metastasis.Patients with this syndrome produce multiple antibodies,each targeting a different antigen and causing different symptoms and signs.The CV2/collapsin response mediator protein 5(CRMP5)antibody is a major antibody of this type.It damages the nervous system,which often manifests as limbic encephalitis,chorea,ocular manifestation,cerebellar ataxia,myelopathy,and peripheral neuropathy.Detecting CV2/CRMP5 antibody is crucial for the clinical diagnosis of paraneoplastic neurological syndrome,and anti-tumor and immunological therapies can help to alleviate symptoms and improve prognosis.However,because of the low incidence of this disease,few repo rts and no reviews have been published about it so far.This article intends to review the research on CV2/CRMP5antibody-associated paraneoplastic neurological syndrome and summarize its clinical features to help clinicians comprehensively understand the disease.Additionally,this review discusses the curre nt challenges that this disease poses,and the application prospects of new detection and diagnostic techniques in the field of paraneoplastic neurological syndrom e,including CV2/CRMP5-associated paraneoplastic neurological syndrome,in recent years.
基金supported in part by National Sciences Foundation of China grant ( 11672001)Jiangsu Province Science and Technology Agency grant ( BE2016785)supported in part by Postgraduate Research & Practice Innovation Program of Jiangsu Province grant ( KYCX18_0156)
文摘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.
基金Supported by Guilin Scientific Research and Technology Development Program(20210202-1,2020011203-1,2020011203-2)Open Project of Guangxi Key Laboratory of Cancer Immunology and Microenvironment Regulation(2022KF005)+1 种基金Guangxi Science and Technology Major Project(Guike AA22096020)Fund for Central Guiding Local Science and Technology Development(ZY20230102).
文摘[Objectives]To optimize the solid-state fermentation process of Flos Sophorae Immaturus by Penicillium with Sophora japonica cv.jinhuai as raw material.[Methods]The fermentation conditions were optimized by single factor experiment and response surface methodology with quercetin content as the dependent variable.[Results]According to the established model,the optimal fermentation process of Flos Sophorae Immaturus was obtained as follows:temperature 29.97℃,time 6.88 d,rotation speed 180.86 rpm,inoculation amount 3.93 mL,and the expected content of quercetin was 34.8053 mg/g.Based on this,the fermentation parameters were adjusted,and the actual content was 33.67 mg/g,which was close to the predicted value.[Conclusions]The optimization of fermentation process of Flos Sophorae Immaturus by response surface methodology provides a reference for the development and utilization of this medicinal material.
文摘目的探究有腹部手术史病人应用安全性关键视角(critical view of safety,CVS)技术理念行急诊腹腔镜胆囊切除术(LC)的应用价值。方法回顾性分析既往有腹部手术史接受急诊LC手术患者48例,依据患者手术是否施行CVS理念将患者分为CVS组和非CVS组。其中CVS组28例,非CVS组20例,分析患者手术时间、术中出血量、术中胆管损伤、临近脏器损伤、胃肠功能恢复时间、总住院时间、术后腹腔引流量、术后拔管时间、总住院费用、术后出血、术后胆漏发生率等情况。结果所有患者均顺利完成手术,两组患者术中出血量、术后胃肠功能恢复时间、术后出血、总住院费用、总住院时间等无明显差异;CVS组手术时间稍长于非CVS组,而术后腹腔引流量、术后拔管时间CVS组显著低于非CVS组,差异有统计学意义(P<0.05);此外,CVS组发生胆管损伤、临近脏器损伤、术后胆漏等并发症发生率显著低于非CVS组,差异有统计学意义(P<0.05)。结论CVS技术理念的应用可有效提高有腹部手术史患者LC手术的安全性,尤其对于急诊手术患者,可显著降低术后并发症发生率,该理念的应用有助于促进患者康复、提高手术安全性。
