Assessment of extravascular extracellular space fraction in human melanoma xenografts by DCE-MRI and kinetic modeling

Tumor aggressiveness and response to therapy are influenced by the extravascular extracellular space fraction (EESF) of the malignant tissue. The EESF may, therefore, be an important prognostic parameter for cancer patients. The aim of this study was to investigate whether gadopentetate dimeglumine (Gd-DTPA)-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to assess the EESF of tumors. Amelanotic human melanoma xenografts (A-07, R-18) were used as preclinical models of human cancer. Images of E.F (E is the initial extraction fraction of Gd-DTPA and F is perfusion) and lambda (the partition coefficient of Gd-DTPA) were obtained by Kety analysis of DCE-MRI data. Our study was based on the hypothesis that lambda is governed by the EESF and is not influenced significantly by microvascular density (MVD) or blood perfusion. To test this hypothesis, we searched for correlations between lambda and E.F, MVD or EESF by comparing lambda images with E.F images, histological preparations from the imaged tissue and the radial heterogeneity in EESF obtained by invasive imaging. Positive correlations were found between lambda and EESF. Thus, median lambda was larger in A-07 tumors than in R-18 tumors by a factor of 4.2 (P<.00001), consistent with the histological observation that EESF is approximately fourfold larger in A-07 tumors than in R-18 tumors. The radial heterogeneity in lambda in A-07 and R-18 tumors was almost identical to the radial heterogeneity in EESF. Moreover, lambda was larger in tissue regions with high EESF than in tissue regions with low EESF in A-07 tumors (P=.048). On the other hand, significant correlations between lambda and MVD or E.F could not be detected. Consequently, Kety analysis of Gd-DTPA-based DCE-MRI series of xenografted tumors provides lambda images that primarily reflect the EESF of the tissue.     

Comparison of analytical and numerical analysis of the reference region model for DCE-MRI

This study compared three methods for analyzing DCE-MRI data with a reference region (RR) model: a linear least-square fitting with numerical analysis (LLSQ-N), a nonlinear least-square fitting with numerical analysis (NLSQ-N), and an analytical analysis (NLSQ-A). The accuracy and precision of estimating the pharmacokinetic parameter ratios K_R and V_R, where K_R is defined as a ratio between the two volume transfer constants, K^trans,TOI and K^trans,RR, and V_R is the ratio between the two extracellular extravascular volumes, v_e,TOI and v_e,RR, were assessed using simulations under various signal-to-noise ratios (SNRs) and temporal resolutions (4, 6, 30, and 60 s). When no noise was added, the simulations showed that the mean percent error (MPE) for the estimated K_R and V_R using the LLSQ-N and NLSQ-N methods ranged from 1.2% to 31.6% with various temporal resolutions while the NLSQ-A method maintained a very high accuracy (< 1.0×10^− 4 %) regardless of the temporal resolution. The simulation also indicated that the LLSQ-N and NLSQ-N methods appear to underestimate the parameter ratios more than the NLSQ-A method. In addition, seven in vivo DCE-MRI datasets from spontaneously occurring canine brain tumors were analyzed with each method. Results for the in vivo study showed that K_R (ranging from 0.63 to 3.11) and V_R (ranging from 2.82 to 19.16) for the NLSQ-A method were both higher than results for the other two methods (K_R ranging from 0.01 to 1.29 and V_R ranging from 1.48 to 19.59). A temporal downsampling experiment showed that the averaged percent error for the NLSQ-A method (8.45%) was lower than the other two methods (22.97% for LLSQ-N and 65.02% for NLSQ-N) for K_R, and the averaged percent error for the NLSQ-A method (6.33%) was lower than the other two methods (6.57% for LLSQ-N and 13.66% for NLSQ-N) for V_R. Using simulations, we showed that the NLSQ-A method can estimate the ratios of pharmacokinetic parameters more accurately and precisely than the NLSQ-N and LLSQ-N methods over various SNRs and temporal resolutions. All simulations were validated with in vivo DCE MRI data.     

EAST运动斯塔克效应诊断数据处理中GPU并行化加速算法的研究

在EAST装置单道运动斯塔克效应(MSE)诊断系统数据处理中,采用CPU(中央处理器)+GPU(图形处理器)异构化模型,实现了数字谐波分析(DHA)算法的并行化加速计算。由CPU完成数据的加载及简单的数学计算,由GPU实现DHA算法的傅里叶正、逆变换及滤波等并行化计算,与串行算法相比,获得了2000倍以上的加速,可以满足MSE诊断实验期间及时数据处理的要求。     

Classification of breast mass lesions using model-based analysis of the characteristic kinetic curve derived from fuzzy c-means clustering

The purpose of this study is to evaluate the diagnostic efficacy of the representative characteristic kinetic curve of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) extracted by fuzzy c-means (FCM) clustering for the discrimination of benign and malignant breast tumors using a novel computer-aided diagnosis (CAD) system. About the research data set, DCE-MRIs of 132 solid breast masses with definite histopathologic diagnosis (63 benign and 69 malignant) were used in this study. At first, the tumor region was automatically segmented using the region growing method based on the integrated color map formed by the combination of kinetic and area under curve color map. Then, the FCM clustering was used to identify the time-signal curve with the larger initial enhancement inside the segmented region as the representative kinetic curve, and then the parameters of the Tofts pharmacokinetic model for the representative kinetic curve were compared with conventional curve analysis (maximal enhancement, time to peak, uptake rate and washout rate) for each mass. The results were analyzed with a receiver operating characteristic curve and Student's t test to evaluate the classification performance. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the combined model-based parameters of the extracted kinetic curve from FCM clustering were 86.36% (114/132), 85.51% (59/69), 87.30% (55/63), 88.06% (59/67) and 84.62% (55/65), better than those from a conventional curve analysis. The A(Z) value was 0.9154 for Tofts model-based parametric features, better than that for conventional curve analysis (0.8673), for discriminating malignant and benign lesions. In conclusion, model-based analysis of the characteristic kinetic curve of breast mass derived from FCM clustering provides effective lesion classification. This approach has potential in the development of a CAD system for DCE breast MRI.     

Extraction of single-particle mean kinetic energy from macroscopic thermodynamic data

We have devised a simple and practical method for the extraction of single-particle mean kinetic energy from the Helmholtz free energy, when this quantity is known for a pair of isotopes of the element under investigation. This approach is originated from a property of the mass derivative of the Helmholtz free energy, but makes use of a mass incremental ratio evaluated for the aforementioned isotopes. The transformation from mass incremental ratio to mass derivative is done using a self-consistent method based on two approximations, the latter of which is quite surprising and has been thoroughly tested. Practical calculations on solid and liquid H₂ and D₂, where the center-of-mass mean kinetic energy is known from reliable quantum simulation techniques, are accomplished, obtaining a remarkable agreement, in spite of a slight overestimate for the calculated data set over the simulated one. The last part of the study is devoted to mixed systems, like water and ice, where thermodynamic data are abundant and proton mean kinetic energy has been measured via deep inelastic neutron scattering. Here results are less accurate than earlier, with an overestimate of about 9-10%, and, in addition, no substantial variation going from ice to liquid water. Further investigations on the H₂O/D₂O system are suggested.     

Acoustic voice analysis in patients with Parkinson''s disease treated with dopaminergic drugs

To quantify several acoustic features of the voice in patients withParkinson's disease (PD), 41 patients and 28 age and sex-matched controls were studied. PD severity was assessed with the Unified PD Rating Scale (UPDRS) and the Hoehn and Yahr staging. The Computerized Speech Lab 4300 program (Kay Elemetrics) was used. Two seconds of a sustained /a/ and a sentence were captured with a microphone and laryngograph equipment. Measures included fundamental frequency (FO), frequency perturbation (fitter), intensity perturbation (shimmer), and harmonic/noise ratio (H/N) of the vowel /a/, and frequency and intensity variability of a sentence, phonational range, dynamic range at the natural frequency, maximum phonational time and s/z ratio. All subjects underwent indirect laryngoscopy and/or laryngeal fibroscopy. When compared with controls, PD patients showed higher jitter, lower H/N ratio, lower frequency and intensity variability of the sentence, and lower phonational range and reported a higher frequency of the presence of low voice-intensity, monopitch, voice arrests, and struggle. These features seem to be unaffected by the duration and severity of the disease.     

激光等离子体物理实验数据格式研究

 鉴于现行激光等离子体物理实验数据属于无格式的原始数据，从数据分析的角度出发，在研究总结“星光”和“神光”实验数据的基础上，合理规划了激光等离子体物理实验数据文件格式，并据此重新编写了数据采集程序和数据格式转换程序。叙述了新格式数据文件的优点及应用。     

Application of generalized linear filters in data analysis

It is shown that a useful generalized linear filterW can be constructed from experimental data. The data are divided into many experiments and this ensemble is used to calculate the autocorrelation functions which appear inW. In turn, from this filter one determines a Hamiltonian . The eigenvectors and eigenvalues of this Hamiltonian are evaluated. For a good experiment there is one small eigenvalue, and the rest are 1. TheW so determined usefully reduces the noise in a new data set. The presence of two or more small eigenvalues indicates that the experimental data contains more than a single signal. The action ofW on selected members of the ensemble, and/or new data sets, extracts the different signals with, again, a useful noise reduction. Both computer simulations and real positron annihilation data are used to illustrate these development.This paper is dedicated to Philippe Choquard on the occasion of this 65th birthday.     

HPM效应实验中区间删失数据的处理与统计分析

在HPM效应实验中经常可以获得区间删失数据,为了能够合理利用这类数据对HPM效应进行有效分析,需要对它进行处理。根据电子器件的微波失效机理和实验现象,基于插值方法的思想,在充分利用删失数据信息情况下建立了不同阶插值精度的数据处理方法。理论分析可知,高阶精度处理方法要优于低阶精度方法。此外,根据构建的区间删失数据,通过统计分析可知,处理后数据与原始数据在统计意义上没有显著差异,可用于HPM效应研究,为数据的可靠分析提供了有利支撑。     

Long-range correlation analysis of urban traffic data

<正>This paper investigates urban traffic data by analysing the long-range correlation with detrended fluctuation analysis.Through a large number of real data collected by the travel time detection system in Beijing,the variation of flow in different time periods and intersections is studied.According to the long-range correlation in different time scales, it mainly discusses the effect of intersection location in road net,people activity customs and special traffic controls on urban traffic flow.As demonstrated by the obtained results,the urban traffic flow represents three-phase characters similar to highway traffic.Moreover,compared by the two groups of data obtained before and after the special traffic restrictions(vehicles with special numbered plates only run in a special workday) enforcement,it indicates that the rules not only reduce the flow but also avoid irregular fluctuation.     

Micromagnetic recording field analysis of fast-switching single-pole-type heads for bit-patterned media

A Landau–Lifshitz–Gilbert (LLG) micromagnetic analysis of the recording field of single-pole-type (SPT) heads was carried out. The whole volume comprising the SPT head and the double-layered medium was treated micromagnetically using the finite-difference method with cubic cells as small as 5 nm, giving a total number of cells of more than 10.8 million. A parallelized fast Fourier transform (FFT) method was used to solve this large-scale problem. Dynamic recording fields were calculated for various head structures and head materials. The timing (synchronization) between the dynamic head field and land location in bit-patterned media (BPM) is discussed and the design methodology is discussed for a fast-switching SPT head.     

Artificial neural networks applied to the analysis of synchrotron nuclear resonant scattering data

The capabilities of artificial neural networks (ANNs) have been investigated for the analysis of nuclear resonant scattering (NRS) data obtained at a synchrotron source. The major advantage of ANNs over conventional analysis methods is that, after an initial training phase, the analysis is fully automatic and practically instantaneous, which allows for a direct intervention of the experimentalist on‐site. This is particularly interesting for NRS experiments, where large amounts of data are obtained in very short time intervals and where the conventional analysis method may become quite time‐consuming and complicated. To test the capability of ANNs for the automation of the NRS data analysis, a neural network was trained and applied to the specific case of an Fe/Cr multilayer. It was shown how the hyperfine field parameters of the system could be extracted from the experimental NRS spectra. The reliability and accuracy of the ANN was verified by comparing the output of the network with the results obtained by conventional data analysis.     

Longitudinal brain imaging of five malignant glioma patients treated with bevacizumab using susceptibility-weighted magnetic resonance imaging at 7 T

Malignant glioma is a rare tumor type characterized by prominent vascular proliferation. Antiangiogenic therapy with the monoclonal antibody bevacizumab is considered as a promising therapeutic strategy, although the effect on tumor vascularization is unclear. High-field susceptibility-weighted imaging (SWI) visualizes the microvasculature and may contribute to the investigation of antiangiogenic therapy responses in gliomas. We prospectively studied five adult malignant glioma patients treated with bevacizumab-containing regimens. In each patient, we performed three 7-T SWI and T1-weighted imaging investigations (baseline and 2 and 4 weeks after the start of bevacizumab treatment). In addition, we imaged a postmortem brain of a patient with glioblastoma using 7-T SWI and performed detailed histopathological analysis. We observed almost total resolution of brain edema in three of five patients after initiation of bevacizumab therapy. In one case with rapid increase of the lesion size despite bevacizumab therapy, SWI showed progressive increase of irregular hypointense structures, most likely corresponding to increasing amounts of pathological microvasculature. In one case with progressive neurological decline, 7-T images showed multiple intratumoral microhemorrhages after the first bevacizumab application. Correlation of postmortem neuroimaging with histopathology confirmed that SWI-positive structures correspond to tumor vasculature. The experience from our case series indicates that longitudinal 7-T SWI seems to be an appropriate method for investigation of changes in brain tumor vascularization over time under antiangiogenic therapy.     

Maximum likelihood estimation for second level fMRI data analysis with expectation trust region algorithm

The trust region method which originated from the Levenberg–Marquardt (LM) algorithm for mixed effect model estimation are considered in the context of second level functional magnetic resonance imaging (fMRI) data analysis. We first present the mathematical and optimization details of the method for the mixed effect model analysis, then we compare the proposed methods with the conventional expectation-maximization (EM) algorithm based on a series of datasets (synthetic and real human fMRI datasets). From simulation studies, we found a higher damping factor for the LM algorithm is better than lower damping factor for the fMRI data analysis. More importantly, in most cases, the expectation trust region algorithm is superior to the EM algorithm in terms of accuracy if the random effect variance is large. We also compare these algorithms on real human datasets which comprise repeated measures of fMRI in phased-encoded and random block experiment designs. We observed that the proposed method is faster in computation and robust to Gaussian noise for the fMRI analysis. The advantages and limitations of the suggested methods are discussed.     

Development and exploration of a new methodology for the fitting and analysis of XAS data

A new data analysis methodology for X‐ray absorption near‐edge spectroscopy (XANES) is introduced and tested using several examples. The methodology has been implemented within the context of a new Matlab‐based program discussed in a companion related article [Delgado‐Jaime et al. (2010), J. Synchrotron Rad. 17 , 132–137]. The approach makes use of a Monte Carlo search method to seek appropriate starting points for a fit model, allowing for the generation of a large number of independent fits with minimal user‐induced bias. The applicability of this methodology is tested using various data sets on the Cl K‐edge XAS data for tetragonal CuCl₄^2?, a common reference compound used for calibration and covalency estimation in M—Cl bonds. A new background model function that effectively blends together background profiles with spectral features is an important component of the discussed methodology. The development of a robust evaluation function to fit multiple‐edge data is discussed and the implications regarding standard approaches to data analysis are discussed and explored within these examples.     

Statistical analysis of gene regulatory networks reconstructed from gene expression data of lung cancer

Recently, inferring gene regulatory network from large-scale gene expression data has been considered as an important effort to understand the life system in whole. In this paper, for the purpose of getting further information about lung cancer, a gene regulatory network of lung cancer is reconstructed from gene expression data. In this network, vertices represent genes and edges between any two vertices represent their co-regulatory relationships. It is found that this network has some characteristics which are shared by most cellular networks of health lives, such as power-law, small-world behaviors. On the other hand, it also presents some features which are obviously different from other networks, such as assortative mixing. In the last section of this paper, the significance of these findings in the context of biological processes of lung cancer is discussed.     

变分同化结合广义变分最佳分析对微波散射计资料进行海面风场反演

针对微波散射计资料的海面风场反演,本文首先利用QuikSCAT卫星散射计的近实时资料,借助荷兰皇家气象学会的SeaWinds散射计数据处理模式,在不同风速和海域的情况下,采用多解方案结合二维变分同化方法进行风向的模糊去除,达到消除解的不唯一性的目的,且对4种反演结果（低速风、中速风、高速风和近海岸风）与美国国家海洋和大气管理局卫星资料中心的近实时风场数据的对比分析,说明了该技术的有效性.在此基础上,对反演得到的中速风场利用广义变分最佳分析方法进行了调整,调整后的风场结构有了进一步的改善.此方法可为我国散射计资料反演海面风场的研究提供新的思路和方法. 关键词： 多解方案 二维变分同化 广义变分最佳分析 正则化方法     

变分同化结合广义变分最佳分析对微波散射计资料进行海面风场反演

针对微波散射计资料的海面风场反演,本文首先利用QuikSCAT卫星散射计的近实时资料,借助荷兰皇家气象学会的SeaWinds散射计数据处理模式,在不同风速和海域的情况下,采用多解方案结合二维变分同化方法进行风向的模糊去除,达到消除解的不唯一性的目的,且对4种反演结果（低速风、中速风、高速风和近海岸风）与美国国家海洋和大气管理局卫星资料中心的近实时风场数据的对比分析,说明了该技术的有效性.在此基础上,对反演得到的中速风场利用广义变分最佳分析方法进行了调整,调整后的风场结构有了进一步的改善.此方法可为我国散射计资料反演海面风场的研究提供新的思路和方法.     

Correlation dimension: A pivotal statistic for non-constrained realizations of composite hypotheses in surrogate data analysis

Currently surrogate data analysis can be used to determine if data is consistent with various linear systems, or something else (a nonlinear system). In this paper we propose an extension of these methods in an attempt to make more specific classifications within the class of nonlinear systems.

In the method of surrogate data one estimates the probability distribution of values of a test statistic for a set of experimental data under the assumption that the data is consistent with a given hypothesis. If the probability distribution of the test statistic is different for different dynamical systems consistent with the hypothesis, one must ensure that the surrogate generation technique generates surrogate data that are a good approximation to the data. This is often achieved with a careful choice of surrogate generation method and for noise driven linear surrogates such methods are commonly used.

This paper argues that, in many cases (particularly for nonlinear hypotheses), it is easier to select a test statistic for which the probability distribution of test statistic values is the same for all systems consistent with the hypothesis. For most linear hypotheses one can use a reliable estimator of a dynamic invariant of the underlying class of processes. For more complex, nonlinear hypothesis it requires suitable restatement (or cautious statement) of the hypothesis. Using such statistics one can build nonlinear models of the data and apply the methods of surrogate data to determine if the data is consistent with a simulation from a broad class of models. These ideas are illustrated with estimates of probability distribution functions for correlation dimension estimates of experimental and artificial data, and linear and nonlinear hypotheses.