A comprehensive non‐invasive framework for automated evaluation of acute renal transplant rejection using DCE‐MRI

The objective was to develop a novel and automated comprehensive framework for the non‐invasive identification and classification of kidney non‐rejection and acute rejection transplants using 2D dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI). The proposed approach consists of four steps. First, kidney objects are segmented from the surrounding structures with a geometric deformable model. Second, a non‐rigid registration approach is employed to account for any local kidney deformation. In the third step, the cortex of the kidney is extracted in order to determine dynamic agent delivery, since it is the cortex that is primarily affected by the perfusion deficits that underlie the pathophysiology of acute rejection. Finally, we use an analytical function‐based model to fit the dynamic contrast agent kinetic curves in order to determine possible rejection candidates. Five features that map the data from the original data space to the feature space are chosen with a k‐nearest‐neighbor (KNN) classifier to distinguish between acute rejection and non‐rejection transplants. Our study includes 50 transplant patients divided into two groups: 27 patients with stable kidney function and the remainder with impaired kidney function. All of the patients underwent DCE‐MRI, while the patients in the impaired group also underwent ultrasound‐guided fine needle biopsy. We extracted the kidney objects and the renal cortex from DCE‐MRI for accurate medical evaluation with an accuracy of 0.97 ± 0.02 and 0.90 ± 0.03, respectively, using the Dice similarity metric. In a cohort of 50 participants, our framework classified all cases correctly (100%) as rejection or non‐rejection transplant candidates, which is comparable to the gold standard of biopsy but without the associated deleterious side‐effects. Both the 95% confidence interval (CI) statistic and the receiver operating characteristic (ROC) analysis document the ability to separate rejection and non‐rejection groups. The average plateau (AP) signal magnitude and the gamma‐variate model functional parameter α have the best individual discriminating characteristics. Copyright © 2013 John Wiley & Sons, Ltd.     

Re-examination of the potential step chronoamperometry method through numerical inversion of Laplace transforms. II. Application examples

In the first part of this work, we proposed the so-called GS-, FE- and FT-PSCA algorithms to investigate a wide range of electrode geometry and chemical, electrochemical and one-dimensional mass-transport processes by the potential-step chronoamperometry (PSCA) method. Each algorithm provides a full explicit formulation of Faradaic current with respect to time, applied potential and electrochemical parameters (initial concentrations, geometrical parameter(s), standard potential, electrochemical and chemical rate constants and diffusion coefficients). Application examples relative to diffusion equations with spherical electrode geometry are presented in this second article by way of illustration of the potentialities of GS- and FT-PSCA algorithms. First, the case of one-step electrochemical reactions at spherical and hemispherical electrodes of any radius is investigated when both implicated species are soluble in the electrolytic solution. The numerical solution obtained from GS-PSCA algorithm is favourably compared to previous results in the electrochemical literature. Next, we use the FT-PSCA algorithm for investigating spherical diffusion with amalgamation reaction. Finally, the characteristic features of chronoamperograms and sampled-current voltammograms pertaining to the above reaction are examined and discussed.     

Determining sequence length or content in zero, one, and two dimensions

High-throughput assays are essential for the practical application of mutation detection in medicine and research. Moreover, such assays should produce informative data of high quality that have a low-error rate and a low cost. Unfortunately, this is not currently the case. Instead, we typically witness legions of people reviewing imperfect data at astronomical expense yielding uncertain results. To address this problem, for the past decade we have been developing methods that exploit the inherent quantitative nature of DNA experiments. By generating high-quality data, careful DNA-signal quantification permits robust analysis for determining true alleles and certainty measures. We will explore several assays and methods. In a one-dimensional readout, short tandem repeat (STR) data display interesting artifacts. Even with high-quality data, PCR artifacts such as stutter and relative amplification can confound correct or automated scoring. However, by appropriate mathematical analysis, these artifacts can be essentially removed from the data. The result is fully automated data scoring, quality assessment, and new types of DNA analysis. These approaches enable the accurate analysis of pooled DNA samples, for both genetic and forensic applications. On a two-dimensional surface (comprised of zero-dimensional spots) one can perform assays of extremely high-throughput at low cost. The question is how to determine DNA sequence length or content from nonelectrophoretic intensity data. Here again, mathematical analysis of highly quantitative data provides a solution. We will discuss new lab assays that can produce data containing such information; mathematical transformation then determines DNA length or content.     

粘弹性条件下槽道板结构的准静态弯曲变形

引入小挠度薄板理论,将槽道板结构简化作支承于地基上的薄板,基于对应性原理和数值逆变换方法,以微槽道换热器为例,解析计算了槽道板结构的粘弹性弯曲变形和粘弹性弯曲应力,并与有限元模拟结果进行对比,由此验证用该解析计算方法分析承压槽道板结构粘弹性能的可行性与准确性。结果表明:解析计算结果与有限元模拟结果基本一致,该方法可作为承压槽道板结构设备力学设计的有效方法。     

Pharmacokinetic curve fitting using numerical inverse Laplace transformation

The combination of the nonlinear regression program ADAPT II with Talbot's method of numerical Laplace transformation, that allows parameter estimation when the model function is given only in the Laplace domain, is described and successfully applied to pharmacokinetic problems. The accuracy and precision of the method has been found satisfactory; its performance is comparable to that achieved in parameter estimation based on functions defined in the time domain.     

Detecting interacting genetic loci with effects on quantitative traits where the nature and order of the interaction are unknown

Standard techniques for single marker quantitative trait mapping perform poorly in detecting complex interacting genetic influences. When a genetic marker interacts with other genetic markers and/or environmental factors to influence a quantitative trait, a sample of individuals will show different effects according to their exposure to other interacting factors. This paper presents a Bayesian mixture model, which effectively models heterogeneous genetic effects apparent at a single marker. We compute approximate Bayes factors which provide an efficient strategy for screening genetic markers (genome‐wide) for evidence of a heterogeneous effect on a quantitative trait. We present a simulation study which demonstrates that the approximation is good and provide a real data example which identifies a population‐specific genetic effect on gene expression in the HapMap CEU and YRI populations. We advocate the use of the model as a strategy for identifying candidate interacting markers without any knowledge of the nature or order of the interaction. The source of heterogeneity can be modeled as an extension. Genet. Epidemiol. 34: 299–308, 2010. © 2009 Wiley‐Liss, Inc.     

Analysis of arterial–venous blood concentration difference of drugs based on recirculatory theory with fast inverse laplace transform (FILT)

An arterial and venous blood (or plasma) concentration difference of drugs across the lung of rats was evaluated based on the recirculatory concept. The recirculatory system is given by the combination of the transfer functions for the pulmonary and the systemic circulations and is described by a Laplace-transformed equation, i.e., an image equation. For the manipulation of the image equations, the fast inverse Laplace transform (FILT) was adopted and MULTI(FILT) was used for the simultaneous curve fitting to estimate the pharmacokinetic parameters in the recirculatory model. Metoprolol as a test drug and cephalexin as a control drag were infused, respectively into the femoral vein for 30 min, and arterial and venous blood samples were collected simultaneously through the cannula at the femoral artery and at right atrium during and after the infusion. Exponential functions were assumed for the weight functions through both the pulmonary and systemic circulations. Results of the curve fitting showed that the single-pass extraction ratio through the pulmonary circulation (E_p)of meloprolol was about 0.2, whereas that of cephalexin was negligible. The mean transit times through the pulmonary circulation (¯t_p of metoprolol and cephalexin were both about 0.5 min, which is small. The singlepass extraction ratios through the systemic circulation (E_s)of metoprolol and cephalexin were both about 0.1. and the mean transit times through the systemic circulation (¯t_s were 11.5 min and 8.2 min, respectively.     

Significant precision improvement for temperature mapping.

MR temperature measurements are important for applications such as the evaluation of thermal therapies and radiofrequency (RF) coil heating effects. In this work the spherical mean value (SMV) method has been applied to significantly improve the precision of MR temperature mapping in a homogeneous gel phantom. Temperature-increase maps of the phantom were obtained with three-dimensional (3D) MR phase difference mapping after heating with the RF coil. The temperature-increase distribution in most regions in the phantom is a harmonic function with the mean value property. Based on this property, the precision of temperature-increase maps was improved up to sixfold with the SMV method. Comparison of this method with conventional smoothing, further precision improvement, and the in vivo application of the SMV method are discussed.     

Identifying the most likely contributors to a Y-STR mixture using the discrete Laplace method

In some crime cases, the male part of the DNA in a stain can only be analysed using Y chromosomal markers, e.g. Y-STRs. This may be the case in e.g. rape cases, where the male components can only be detected as Y-STR profiles, because the fraction of male DNA is much smaller than that of female DNA, which can mask the male results when autosomal STRs are investigated. Sometimes, mixtures of Y-STRs are observed, e.g. in rape cases with multiple offenders. In such cases, Y-STR mixture analysis is required, e.g. by mixture deconvolution, to deduce the most likely DNA profiles from the contributors.We demonstrate how the discrete Laplace method can be used to separate a two person Y-STR mixture, where the Y-STR profiles of the true contributors are not present in the reference dataset, which is often the case for Y-STR profiles in real case work. We also briefly discuss how to calculate the weight of the evidence using the likelihood ratio principle when a suspect's Y-STR profile fits into a two person mixture. We used three datasets with between 7 and 21 Y-STR loci: Denmark (n = 181), Somalia (n = 201) and Germany (n = 3443). The Danish dataset with 21 loci was truncated to 15 and 10 loci to examine the effect of the number of loci. For each of these datasets, an out of sample simulation study was performed: A total of 550 mixtures were composed by randomly sampling two haplotypes, h₁ and h₂, from the dataset.We then used the discrete Laplace method on the remaining data (excluding h₁ and h₂) to rank the contributor pairs by the product of the contributors’ estimated haplotype frequencies. Successful separation of mixtures (defined by the observation that the true contributor pair was among the 10 most likely contributor pairs) was found in 42–52% of the cases for 21 loci, 69–75% for 15 loci and 92–99% for 10 loci or less depending on the dataset and how the discrete Laplace model was chosen. Y-STR mixtures with many loci are difficult to separate, but even haplotypes with 21 Y-STR loci can be separated.