In regression, despite being both aimed at estimating the Mean Squared Prediction Error (MSPE), Akaike’s Final Prediction Error (FPE) and the Generalized Cross Validation (GCV) selection criteria are usually derived ...In regression, despite being both aimed at estimating the Mean Squared Prediction Error (MSPE), Akaike’s Final Prediction Error (FPE) and the Generalized Cross Validation (GCV) selection criteria are usually derived from two quite different perspectives. Here, settling on the most commonly accepted definition of the MSPE as the expectation of the squared prediction error loss, we provide theoretical expressions for it, valid for any linear model (LM) fitter, be it under random or non random designs. Specializing these MSPE expressions for each of them, we are able to derive closed formulas of the MSPE for some of the most popular LM fitters: Ordinary Least Squares (OLS), with or without a full column rank design matrix;Ordinary and Generalized Ridge regression, the latter embedding smoothing splines fitting. For each of these LM fitters, we then deduce a computable estimate of the MSPE which turns out to coincide with Akaike’s FPE. Using a slight variation, we similarly get a class of MSPE estimates coinciding with the classical GCV formula for those same LM fitters.展开更多
A method for fast l-fold cross validation is proposed for the regularized extreme learning machine(RELM). The computational time of fast l-fold cross validation increases as the fold number decreases, which is opposit...A method for fast l-fold cross validation is proposed for the regularized extreme learning machine(RELM). The computational time of fast l-fold cross validation increases as the fold number decreases, which is opposite to that of naive l-fold cross validation. As opposed to naive l-fold cross validation, fast l-fold cross validation takes the advantage in terms of computational time, especially for the large fold number such as l > 20. To corroborate the efficacy and feasibility of fast l-fold cross validation,experiments on five benchmark regression data sets are evaluated.展开更多
Background: A random multiple-regression model that simultaneously fit all allele substitution effects for additive markers or haplotypes as uncorrelated random effects was proposed for Best Linear Unbiased Prediction...Background: A random multiple-regression model that simultaneously fit all allele substitution effects for additive markers or haplotypes as uncorrelated random effects was proposed for Best Linear Unbiased Prediction, using whole-genome data. Leave-one-out cross validation can be used to quantify the predictive ability of a statistical model.Methods: Naive application of Leave-one-out cross validation is computationally intensive because the training and validation analyses need to be repeated n times, once for each observation. Efficient Leave-one-out cross validation strategies are presented here, requiring little more effort than a single analysis.Results: Efficient Leave-one-out cross validation strategies is 786 times faster than the naive application for a simulated dataset with 1,000 observations and 10,000 markers and 99 times faster with 1,000 observations and 100 markers. These efficiencies relative to the naive approach using the same model will increase with increases in the number of observations.Conclusions: Efficient Leave-one-out cross validation strategies are presented here, requiring little more effort than a single analysis.展开更多
Support vector machine (SVM) has been successfully applied for classification in this paper. This paper discussed the basic principle of the SVM at first, and then SVM classifier with polynomial kernel and the Gaussia...Support vector machine (SVM) has been successfully applied for classification in this paper. This paper discussed the basic principle of the SVM at first, and then SVM classifier with polynomial kernel and the Gaussian radial basis function kernel are choosen to determine pupils who have difficulties in writing. The 10-fold cross-validation method for training and validating is introduced. The aim of this paper is to compare the performance of support vector machine with RBF and polynomial kernel used for classifying pupils with or without handwriting difficulties. Experimental results showed that the performance of SVM with RBF kernel is better than the one with polynomial kernel.展开更多
A method of analysis for the simultaneous determination of methylisothiazolinone (MI), methylchloroisothiazolinone (CMI), benzisothiazolinone (BIT) and Bronopol (BNP) in washing-up liquid was established. The method c...A method of analysis for the simultaneous determination of methylisothiazolinone (MI), methylchloroisothiazolinone (CMI), benzisothiazolinone (BIT) and Bronopol (BNP) in washing-up liquid was established. The method consisted of a gradient HPLC analysis at three different wavelengths. The four compounds could be analyzed with good precision and accuracy.展开更多
The state-selective cross section data are useful for understanding and modeling the x-ray emission in celestial observations.In the present work,using the cold target recoil ion momentum spectroscopy,for the first ti...The state-selective cross section data are useful for understanding and modeling the x-ray emission in celestial observations.In the present work,using the cold target recoil ion momentum spectroscopy,for the first time we investigated the state-selective single electron capture processes for S^(q+)–He and H_(2)(q=11–15)collision systems at an impact energy of q×20 keV and obtained the relative state-selective cross sections.The results indicate that only a few principal quantum states of the projectile energy level are populated in a single electron capture process.In particular,the increase of the projectile charge state leads to the population of the states with higher principal quantum numbers.It is also shown that the experimental averaged n-shell populations are reproduced well by the over-barrier model.The database is openly available in Science Data Bank at 10.57760/sciencedb.j00113.00091.展开更多
To accelerate the practicality of electromagnetic railguns,it is necessary to use a combination of threedimensional numerical simulation and experiments to study the mechanism of bore damage.In this paper,a three-dime...To accelerate the practicality of electromagnetic railguns,it is necessary to use a combination of threedimensional numerical simulation and experiments to study the mechanism of bore damage.In this paper,a three-dimensional numerical model of the augmented railgun with four parallel unconventional rails is introduced to simulate the internal ballistic process and realize the multi-physics field coupling calculation of the rail gun,and a test experiment of a medium-caliber electromagnetic launcher powered by pulse formation network(PFN)is carried out.Various test methods such as spectrometer,fiber grating and high-speed camera are used to test several parameters such as muzzle initial velocity,transient magnetic field strength and stress-strain of rail.Combining the simulation results and experimental data,the damage condition of the contact surface is analyzed.展开更多
According to the design specifications,the construction of extended piles involves traversing the tunnel’s upper region and extending to the underlying rock layer.To address this challenge,a subterranean transfer str...According to the design specifications,the construction of extended piles involves traversing the tunnel’s upper region and extending to the underlying rock layer.To address this challenge,a subterranean transfer structure spanning multiple subway tunnels was proposed.Deliberating on the function of piles in the transfer structure as springs with axial and bending stiffness,and taking into account the force balance and deformation coordination conditions of beams and plates within the transfer structure,we established a simplified mechanical model that incorporates soil stratification by combining it with the Winkler elastic foundation beam model.The resolved established simplifiedmechanicalmodel employed finite difference technology and the Newton-Simpsonmethod,elucidating the mechanical mechanism of the transfer structure.The research findings suggest that the load carried by the upper structural columns can be transferred to the pile foundation beneath the beams through the transfer structure,subsequently reaching the deep soil layer and ensuring minimal impact on adjacent tunnels.The established simplified analysis method can be used for stress analysis of the transfer structure,concurrently considering soil stratification,pile foundation behavior,and plate action.The pile length,pile section size,and beam section size within the transfer structure should account for the characteristics of the upper load,ensuring an even distribution of the beam bending moment.展开更多
A tracking stability control problem for the vertical electric stabilization system of moving tank based on adaptive robust servo control is addressed.This paper mainly focuses on two types of possibly fast timevaryin...A tracking stability control problem for the vertical electric stabilization system of moving tank based on adaptive robust servo control is addressed.This paper mainly focuses on two types of possibly fast timevarying but bounded uncertainty within the vertical electric stabilization system:model parameter uncertainty and uncertain nonlinearity.First,the vertical electric stabilization system is constructed as an uncertain nonlinear dynamic system that can reflect the practical mechanics transfer process of the system.Second,the dynamical equation in the form of state space is established by designing the angular tracking error.Third,the comprehensive parameter of system uncertainty is designed to estimate the most conservative effects of uncertainty.Finally,an adaptive robust servo control which can effectively handle the combined effects of complex nonlinearity and uncertainty is proposed.The feasibility of the proposed control strategy under the practical physical condition is validated through the tests on the experimental platform.This paper pioneers the introduction of the internal nonlinearity and uncertainty of the vertical electric stabilization system into the settlement of the tracking stability control problem,and validates the advanced servo control strategy through experiment for the first time.展开更多
Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation...Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation metric for image classifier models and apply it to the CT image classification of lung cancer. A convolutional neural network is employed as the deep neural network (DNN) image classifier, with the residual network (ResNet) 50 chosen as the DNN archi-tecture. The image data used comprise a lung CT image set. Two classification models are built from datasets with varying amounts of data, and lung cancer is categorized into four classes using 10-fold cross-validation. Furthermore, we employ t-distributed stochastic neighbor embedding to visually explain the data distribution after classification. Experimental results demonstrate that cross en-tropy is a highly useful metric for evaluating the reliability of image classifier models. It is noted that for a more comprehensive evaluation of model perfor-mance, combining with other evaluation metrics is considered essential. .展开更多
The neutron-induced total cross sections of natural lead have been measured in a wide energy range(0.3 eV-20 MeV)on the back-streaming white neutron beamline(Back-n)at the China Spallation Neutron Source.Neutron energ...The neutron-induced total cross sections of natural lead have been measured in a wide energy range(0.3 eV-20 MeV)on the back-streaming white neutron beamline(Back-n)at the China Spallation Neutron Source.Neutron energy was determined by the neutron total cross-section spectrometer using the time-of-flight technique.A fast multi-cell fission chamber was used as the neutron detector,and a 10-mm-thick high-purity natural lead sample was employed for the neutron transmission measurements.The on-beam background was determined using Co,In,Ag,and Cd filters.The excitation function of ^(nat)Pb(n,tot)reaction below 20 MeV was calculated using the TALYS-1.96 nuclear-reaction modeling program.The present results were compared with previous results,the evaluated data available in the five major evaluated nuclear data libraries(i.e.,ENDF/B-VIII.0,JEFF-3.3,JENDL-5,CENDL-3.2,and BROND-3.1),and the theoretical calculation curve.Good agreement was found between the new results and those of previous experiments and with the theoretical curves in the corresponding region.This measurement obtained the neutron total cross section of natural lead with good accuracy over a wide energy range and added experimental data in the resonance energy range.This provides more reliable experimental data for nuclear engineering design and nuclear data evaluation of lead.展开更多
Background:Aspergillus fumigatus(Af)is one of the most ubiquitous fungi and its infection potency is suggested to be strongly controlled by the host genetic back-ground.The aim of this study was to search for candidat...Background:Aspergillus fumigatus(Af)is one of the most ubiquitous fungi and its infection potency is suggested to be strongly controlled by the host genetic back-ground.The aim of this study was to search for candidate genes associated with host susceptibility to Aspergillus fumigatus(Af)using an RNAseq approach in CC lines and hepatic gene expression.Methods:We studied 31 male mice from 25 CC lines at 8 weeks old;the mice were infected with Af.Liver tissues were extracted from these mice 5 days post-infection,and next-generation RNA-sequencing(RNAseq)was performed.The GENE-E analysis platform was used to generate a clustered heat map matrix.Results:Significant variation in body weight changes between CC lines was ob-served.Hepatic gene expression revealed 12 top prioritized candidate genes differ-entially expressed in resistant versus susceptible mice based on body weight changes.Interestingly,three candidate genes are located within genomic intervals of the previ-ously mapped quantitative trait loci(QTL),including Gm16270 and Stox1 on chromo-some 10 and Gm11033 on chromosome 8.Conclusions:Our findings emphasize the CC mouse model's power in fine mapping the genetic components underlying susceptibility towards Af.As a next step,eQTL analysis will be performed for our RNA-Seq data.Suggested candidate genes from our study will be further assessed with a human cohort with aspergillosis.展开更多
Statistical machine learning models should be evaluated and validated before putting to work.Conventional k-fold Monte Carlo cross-validation(MCCV)procedure uses a pseudo-random sequence to partition instances into k ...Statistical machine learning models should be evaluated and validated before putting to work.Conventional k-fold Monte Carlo cross-validation(MCCV)procedure uses a pseudo-random sequence to partition instances into k subsets,which usually causes subsampling bias,inflates generalization errors and jeopardizes the reliability and effectiveness of cross-validation.Based on ordered systematic sampling theory in statistics and low-discrepancy sequence theory in number theory,we propose a new k-fold cross-validation procedure by replacing a pseudo-random sequence with a best-discrepancy sequence,which ensures low subsampling bias and leads to more precise expected-prediction-error(EPE)estimates.Experiments with 156 benchmark datasets and three classifiers(logistic regression,decision tree and na?ve bayes)show that in general,our cross-validation procedure can extrude subsampling bias in the MCCV by lowering the EPE around 7.18%and the variances around 26.73%.In comparison,the stratified MCCV can reduce the EPE and variances of the MCCV around 1.58%and 11.85%,respectively.The leave-one-out(LOO)can lower the EPE around 2.50%but its variances are much higher than the any other cross-validation(CV)procedure.The computational time of our cross-validation procedure is just 8.64%of the MCCV,8.67%of the stratified MCCV and 16.72%of the LOO.Experiments also show that our approach is more beneficial for datasets characterized by relatively small size and large aspect ratio.This makes our approach particularly pertinent when solving bioscience classification problems.Our proposed systematic subsampling technique could be generalized to other machine learning algorithms that involve random subsampling mechanism.展开更多
Frequentist model averaging has received much attention from econometricians and statisticians in recent years.A key problem with frequentist model average estimators is the choice of weights.This paper develops a new...Frequentist model averaging has received much attention from econometricians and statisticians in recent years.A key problem with frequentist model average estimators is the choice of weights.This paper develops a new approach of choosing weights based on an approximation of generalized cross validation.The resultant least squares model average estimators are proved to be asymptotically optimal in the sense of achieving the lowest possible squared errors.Especially,the optimality is built under both discrete and continuous weigh sets.Compared with the existing approach based on Mallows criterion,the conditions required for the asymptotic optimality of the proposed method are more reasonable.Simulation studies and real data application show good performance of the proposed estimators.展开更多
为了实现提高产量和抵抗病害等能力的目的,需要提高育种水平,通过设计交差验证(Cross-Validation)实验进行大豆基因型和表型数据的分组处理,根据数据的个体和mark的数量进行合理分配,采用gBLUP(genomic Best Linear Unbiased Prediction...为了实现提高产量和抵抗病害等能力的目的,需要提高育种水平,通过设计交差验证(Cross-Validation)实验进行大豆基因型和表型数据的分组处理,根据数据的个体和mark的数量进行合理分配,采用gBLUP(genomic Best Linear Unbiased Prediction)方法进行表型预测。根据对大豆数据多个性状通过不同分组的对比来得到精确值的范围,为后续的育种分析提供依据。对于只有大豆基因型数据而没有表型数据的情况,需要模拟表型,根据设定遗传力和模拟位点的个数(NQTN)进行模拟,然后再进行不同分组获取精准值,这样扩大了大豆数据的预测灵活性。展开更多
文摘In regression, despite being both aimed at estimating the Mean Squared Prediction Error (MSPE), Akaike’s Final Prediction Error (FPE) and the Generalized Cross Validation (GCV) selection criteria are usually derived from two quite different perspectives. Here, settling on the most commonly accepted definition of the MSPE as the expectation of the squared prediction error loss, we provide theoretical expressions for it, valid for any linear model (LM) fitter, be it under random or non random designs. Specializing these MSPE expressions for each of them, we are able to derive closed formulas of the MSPE for some of the most popular LM fitters: Ordinary Least Squares (OLS), with or without a full column rank design matrix;Ordinary and Generalized Ridge regression, the latter embedding smoothing splines fitting. For each of these LM fitters, we then deduce a computable estimate of the MSPE which turns out to coincide with Akaike’s FPE. Using a slight variation, we similarly get a class of MSPE estimates coinciding with the classical GCV formula for those same LM fitters.
基金supported by the National Natural Science Foundation of China(51006052)the NUST Outstanding Scholar Supporting Program
文摘A method for fast l-fold cross validation is proposed for the regularized extreme learning machine(RELM). The computational time of fast l-fold cross validation increases as the fold number decreases, which is opposite to that of naive l-fold cross validation. As opposed to naive l-fold cross validation, fast l-fold cross validation takes the advantage in terms of computational time, especially for the large fold number such as l > 20. To corroborate the efficacy and feasibility of fast l-fold cross validation,experiments on five benchmark regression data sets are evaluated.
基金supported by the US Department of Agriculture,Agriculture and Food Research Initiative National Institute of Food and Agriculture Competitive grant no.2015-67015-22947
文摘Background: A random multiple-regression model that simultaneously fit all allele substitution effects for additive markers or haplotypes as uncorrelated random effects was proposed for Best Linear Unbiased Prediction, using whole-genome data. Leave-one-out cross validation can be used to quantify the predictive ability of a statistical model.Methods: Naive application of Leave-one-out cross validation is computationally intensive because the training and validation analyses need to be repeated n times, once for each observation. Efficient Leave-one-out cross validation strategies are presented here, requiring little more effort than a single analysis.Results: Efficient Leave-one-out cross validation strategies is 786 times faster than the naive application for a simulated dataset with 1,000 observations and 10,000 markers and 99 times faster with 1,000 observations and 100 markers. These efficiencies relative to the naive approach using the same model will increase with increases in the number of observations.Conclusions: Efficient Leave-one-out cross validation strategies are presented here, requiring little more effort than a single analysis.
文摘Support vector machine (SVM) has been successfully applied for classification in this paper. This paper discussed the basic principle of the SVM at first, and then SVM classifier with polynomial kernel and the Gaussian radial basis function kernel are choosen to determine pupils who have difficulties in writing. The 10-fold cross-validation method for training and validating is introduced. The aim of this paper is to compare the performance of support vector machine with RBF and polynomial kernel used for classifying pupils with or without handwriting difficulties. Experimental results showed that the performance of SVM with RBF kernel is better than the one with polynomial kernel.
文摘A method of analysis for the simultaneous determination of methylisothiazolinone (MI), methylchloroisothiazolinone (CMI), benzisothiazolinone (BIT) and Bronopol (BNP) in washing-up liquid was established. The method consisted of a gradient HPLC analysis at three different wavelengths. The four compounds could be analyzed with good precision and accuracy.
基金Project supported by the National Key Research and Development Program of China(Grant No.2017YFA0402400)the National Natural Science Foundation of China(Grant Nos.11974358 and 11934004)+1 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB34020000)the Heavy Ion Research Facility in Lanzhou(HIRFL).
文摘The state-selective cross section data are useful for understanding and modeling the x-ray emission in celestial observations.In the present work,using the cold target recoil ion momentum spectroscopy,for the first time we investigated the state-selective single electron capture processes for S^(q+)–He and H_(2)(q=11–15)collision systems at an impact energy of q×20 keV and obtained the relative state-selective cross sections.The results indicate that only a few principal quantum states of the projectile energy level are populated in a single electron capture process.In particular,the increase of the projectile charge state leads to the population of the states with higher principal quantum numbers.It is also shown that the experimental averaged n-shell populations are reproduced well by the over-barrier model.The database is openly available in Science Data Bank at 10.57760/sciencedb.j00113.00091.
文摘To accelerate the practicality of electromagnetic railguns,it is necessary to use a combination of threedimensional numerical simulation and experiments to study the mechanism of bore damage.In this paper,a three-dimensional numerical model of the augmented railgun with four parallel unconventional rails is introduced to simulate the internal ballistic process and realize the multi-physics field coupling calculation of the rail gun,and a test experiment of a medium-caliber electromagnetic launcher powered by pulse formation network(PFN)is carried out.Various test methods such as spectrometer,fiber grating and high-speed camera are used to test several parameters such as muzzle initial velocity,transient magnetic field strength and stress-strain of rail.Combining the simulation results and experimental data,the damage condition of the contact surface is analyzed.
基金supported by the Construction and Scientific Research Project of the Zhejiang Provincial Department of Housing and Urban-Rural Development(No.2021K126,Granted byM.J.,Long,URL:https://jst.zj.gov.cn/)the ScientificResearch Project of ChinaConstruction 4th Engineering Bureau(No.CSCEC4B-2022-KTA-10,Granted by Z.C.,Bai,URL:https://4 bur.cscec.com/)+2 种基金the Scientific Research Project of China Construction 4th Engineering Bureau(No.CSCEC4B-2023-KTA-10,Granted by D.J.,Geng,URL:https://4bur.cscec.com/)the Natural Science Foundation of Hubei Province(No.2022CFD055,Granted by N.,Dai,URL:https://kjt.hubei.gov.cn/)the National Key Research and Development Program of China under Grant No.2022YFC3803002.
文摘According to the design specifications,the construction of extended piles involves traversing the tunnel’s upper region and extending to the underlying rock layer.To address this challenge,a subterranean transfer structure spanning multiple subway tunnels was proposed.Deliberating on the function of piles in the transfer structure as springs with axial and bending stiffness,and taking into account the force balance and deformation coordination conditions of beams and plates within the transfer structure,we established a simplified mechanical model that incorporates soil stratification by combining it with the Winkler elastic foundation beam model.The resolved established simplifiedmechanicalmodel employed finite difference technology and the Newton-Simpsonmethod,elucidating the mechanical mechanism of the transfer structure.The research findings suggest that the load carried by the upper structural columns can be transferred to the pile foundation beneath the beams through the transfer structure,subsequently reaching the deep soil layer and ensuring minimal impact on adjacent tunnels.The established simplified analysis method can be used for stress analysis of the transfer structure,concurrently considering soil stratification,pile foundation behavior,and plate action.The pile length,pile section size,and beam section size within the transfer structure should account for the characteristics of the upper load,ensuring an even distribution of the beam bending moment.
基金supported in part by the Nation Natural Science Foundation of China under Grant No.52175099China Postdoctoral Science Foundation under Grant No.2020M671494Jiangsu Planned Projects for Postdoctoral Research Funds under Grant No.2020Z179。
文摘A tracking stability control problem for the vertical electric stabilization system of moving tank based on adaptive robust servo control is addressed.This paper mainly focuses on two types of possibly fast timevarying but bounded uncertainty within the vertical electric stabilization system:model parameter uncertainty and uncertain nonlinearity.First,the vertical electric stabilization system is constructed as an uncertain nonlinear dynamic system that can reflect the practical mechanics transfer process of the system.Second,the dynamical equation in the form of state space is established by designing the angular tracking error.Third,the comprehensive parameter of system uncertainty is designed to estimate the most conservative effects of uncertainty.Finally,an adaptive robust servo control which can effectively handle the combined effects of complex nonlinearity and uncertainty is proposed.The feasibility of the proposed control strategy under the practical physical condition is validated through the tests on the experimental platform.This paper pioneers the introduction of the internal nonlinearity and uncertainty of the vertical electric stabilization system into the settlement of the tracking stability control problem,and validates the advanced servo control strategy through experiment for the first time.
文摘Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation metric for image classifier models and apply it to the CT image classification of lung cancer. A convolutional neural network is employed as the deep neural network (DNN) image classifier, with the residual network (ResNet) 50 chosen as the DNN archi-tecture. The image data used comprise a lung CT image set. Two classification models are built from datasets with varying amounts of data, and lung cancer is categorized into four classes using 10-fold cross-validation. Furthermore, we employ t-distributed stochastic neighbor embedding to visually explain the data distribution after classification. Experimental results demonstrate that cross en-tropy is a highly useful metric for evaluating the reliability of image classifier models. It is noted that for a more comprehensive evaluation of model perfor-mance, combining with other evaluation metrics is considered essential. .
基金This work is supported by the National Natural Science Foundation of China(No.12375296)the Key Laboratory of Nuclear Data Foundation(No.JCKY2022201C153)+2 种基金the Natural Science Foundation of Hunan Province of China(Nos.2021JJ40444,2020RC3054)the Youth Innovation Promotion Association CAS(No.2023014)the National Key Research and Development Plan(No.2022YFA1603303).
文摘The neutron-induced total cross sections of natural lead have been measured in a wide energy range(0.3 eV-20 MeV)on the back-streaming white neutron beamline(Back-n)at the China Spallation Neutron Source.Neutron energy was determined by the neutron total cross-section spectrometer using the time-of-flight technique.A fast multi-cell fission chamber was used as the neutron detector,and a 10-mm-thick high-purity natural lead sample was employed for the neutron transmission measurements.The on-beam background was determined using Co,In,Ag,and Cd filters.The excitation function of ^(nat)Pb(n,tot)reaction below 20 MeV was calculated using the TALYS-1.96 nuclear-reaction modeling program.The present results were compared with previous results,the evaluated data available in the five major evaluated nuclear data libraries(i.e.,ENDF/B-VIII.0,JEFF-3.3,JENDL-5,CENDL-3.2,and BROND-3.1),and the theoretical calculation curve.Good agreement was found between the new results and those of previous experiments and with the theoretical curves in the corresponding region.This measurement obtained the neutron total cross section of natural lead with good accuracy over a wide energy range and added experimental data in the resonance energy range.This provides more reliable experimental data for nuclear engineering design and nuclear data evaluation of lead.
基金European Sequencing and Genotyping Institutes(ESGI),Grant/Award Number:075491/Z/04,085906/Z/08/Z and 090532/Z/09/ZTel-Aviv University(TAU)。
文摘Background:Aspergillus fumigatus(Af)is one of the most ubiquitous fungi and its infection potency is suggested to be strongly controlled by the host genetic back-ground.The aim of this study was to search for candidate genes associated with host susceptibility to Aspergillus fumigatus(Af)using an RNAseq approach in CC lines and hepatic gene expression.Methods:We studied 31 male mice from 25 CC lines at 8 weeks old;the mice were infected with Af.Liver tissues were extracted from these mice 5 days post-infection,and next-generation RNA-sequencing(RNAseq)was performed.The GENE-E analysis platform was used to generate a clustered heat map matrix.Results:Significant variation in body weight changes between CC lines was ob-served.Hepatic gene expression revealed 12 top prioritized candidate genes differ-entially expressed in resistant versus susceptible mice based on body weight changes.Interestingly,three candidate genes are located within genomic intervals of the previ-ously mapped quantitative trait loci(QTL),including Gm16270 and Stox1 on chromo-some 10 and Gm11033 on chromosome 8.Conclusions:Our findings emphasize the CC mouse model's power in fine mapping the genetic components underlying susceptibility towards Af.As a next step,eQTL analysis will be performed for our RNA-Seq data.Suggested candidate genes from our study will be further assessed with a human cohort with aspergillosis.
基金supported by the National Natural Science Foundation of China[grant number 42275025]the Youth Innovation Promotion Association of the Chinese Academy of Sciences[grant number 2023084].
基金supported by the Qilu Youth Scholar Project of Shandong Universitysupported by National Natural Science Foundation of China(Grant No.11531008)+1 种基金the Ministry of Education of China(Grant No.IRT16R43)the Taishan Scholar Project of Shandong Province。
文摘Statistical machine learning models should be evaluated and validated before putting to work.Conventional k-fold Monte Carlo cross-validation(MCCV)procedure uses a pseudo-random sequence to partition instances into k subsets,which usually causes subsampling bias,inflates generalization errors and jeopardizes the reliability and effectiveness of cross-validation.Based on ordered systematic sampling theory in statistics and low-discrepancy sequence theory in number theory,we propose a new k-fold cross-validation procedure by replacing a pseudo-random sequence with a best-discrepancy sequence,which ensures low subsampling bias and leads to more precise expected-prediction-error(EPE)estimates.Experiments with 156 benchmark datasets and three classifiers(logistic regression,decision tree and na?ve bayes)show that in general,our cross-validation procedure can extrude subsampling bias in the MCCV by lowering the EPE around 7.18%and the variances around 26.73%.In comparison,the stratified MCCV can reduce the EPE and variances of the MCCV around 1.58%and 11.85%,respectively.The leave-one-out(LOO)can lower the EPE around 2.50%but its variances are much higher than the any other cross-validation(CV)procedure.The computational time of our cross-validation procedure is just 8.64%of the MCCV,8.67%of the stratified MCCV and 16.72%of the LOO.Experiments also show that our approach is more beneficial for datasets characterized by relatively small size and large aspect ratio.This makes our approach particularly pertinent when solving bioscience classification problems.Our proposed systematic subsampling technique could be generalized to other machine learning algorithms that involve random subsampling mechanism.
基金by National Key R&D Program of China(2020AAA0105200)the Ministry of Science and Technology of China(Grant no.2016YFB0502301)+1 种基金the National Natural Science Foundation of China(Grant nos.11871294,12031016,11971323,71925007,72042019,72091212 and 12001559)a joint grant from the Academy for Multidisciplinary Studies,Capital Normal University.
文摘Frequentist model averaging has received much attention from econometricians and statisticians in recent years.A key problem with frequentist model average estimators is the choice of weights.This paper develops a new approach of choosing weights based on an approximation of generalized cross validation.The resultant least squares model average estimators are proved to be asymptotically optimal in the sense of achieving the lowest possible squared errors.Especially,the optimality is built under both discrete and continuous weigh sets.Compared with the existing approach based on Mallows criterion,the conditions required for the asymptotic optimality of the proposed method are more reasonable.Simulation studies and real data application show good performance of the proposed estimators.
文摘为了实现提高产量和抵抗病害等能力的目的,需要提高育种水平,通过设计交差验证(Cross-Validation)实验进行大豆基因型和表型数据的分组处理,根据数据的个体和mark的数量进行合理分配,采用gBLUP(genomic Best Linear Unbiased Prediction)方法进行表型预测。根据对大豆数据多个性状通过不同分组的对比来得到精确值的范围,为后续的育种分析提供依据。对于只有大豆基因型数据而没有表型数据的情况,需要模拟表型,根据设定遗传力和模拟位点的个数(NQTN)进行模拟,然后再进行不同分组获取精准值,这样扩大了大豆数据的预测灵活性。
