Bayesian estimation of two-part joint models for a longitudinal semicontinuous biomarker and a terminal event with INLA: Interests for cancer clinical trial evaluation

Two-part joint models for a longitudinal semicontinuous biomarker and a terminal event have been recently introduced based on frequentist estimation. The biomarker distribution is decomposed into a probability of positive value and the expected value among positive values. Shared random effects can represent the association structure between the biomarker and the terminal event. The computational burden increases compared to standard joint models with a single regression model for the biomarker. In this context, the frequentist estimation implemented in the R package frailtypack can be challenging for complex models (i.e., a large number of parameters and dimension of the random effects). As an alternative, we propose a Bayesian estimation of two-part joint models based on the Integrated Nested Laplace Approximation (INLA) algorithm to alleviate the computational burden and fit more complex models. Our simulation studies confirm that INLA provides accurate approximation of posterior estimates and to reduced computation time and variability of estimates compared to frailtypack in the situations considered. We contrast the Bayesian and frequentist approaches in the analysis of two randomized cancer clinical trials (GERCOR and PRIME studies), where INLA has a reduced variability for the association between the biomarker and the risk of event. Moreover, the Bayesian approach was able to characterize subgroups of patients associated with different responses to treatment in the PRIME study. Our study suggests that the Bayesian approach using the INLA algorithm enables to fit complex joint models that might be of interest in a wide range of clinical applications.     

Bias-reduced estimators of conditional odds ratios in matched case-control studies with unmatched confounding

We study bias-reduced estimators of exponentially transformed parameters in general linear models (GLMs) and show how they can be used to obtain bias-reduced conditional (or unconditional) odds ratios in matched case-control studies. Two options are considered and compared: the explicit approach and the implicit approach. The implicit approach is based on the modified score function where bias-reduced estimates are obtained by using iterative procedures to solve the modified score equations. The explicit approach is shown to be a one-step approximation of this iterative procedure. To apply these approaches for the conditional analysis of matched case-control studies, with potentially unmatched confounding and with several exposures, we utilize the relation between the conditional likelihood and the likelihood of the unconditional logit binomial GLM for matched pairs and Cox partial likelihood for matched sets with appropriately setup data. The properties of the estimators are evaluated by using a large Monte Carlo simulation study and an illustration of a real dataset is shown. Researchers reporting the results on the exponentiated scale should use bias-reduced estimators since otherwise the effects can be under or overestimated, where the magnitude of the bias is especially large in studies with smaller sample sizes.     

Structure-Activity Relationship of Methyl 4-Aminobenzoate Derivatives as Being Drug Candidate Targeting Glutathione Related Enzymes: in Vitro and in Silico Approaches

A thiol compound, glutathione, is essential for healthy cell defence against xenobiotics and oxidative stress. Glutathione reductase (GR) and glutathione S-transferase (GST) are two glutathione-related enzymes that function in the antioxidant and the detoxification systems. In this study, potential inhibitory effects of methyl 4-aminobenzoate derivatives on GR and GST were examined in vitro. GR and GST were isolated from human erythrocytes with 7.63 EU/mg protein and 5.66 EU/mg protein specific activity, respectively. It was found that compound 1 (methyl 4-amino-3-bromo-5-fluorobenzoate with K_i value of 0.325±0.012 μM) and compound 5 (methyl 4-amino-2-nitrobenzoate with K_i value of 92.41±22.26 μM) inhibited GR and GST stronger than other derivatives. Furthermore, a computer-aided method was used to predict the binding affinities of derivatives, ADME characteristics, and toxicities. Derivatives 4 (methyl 4-amino-2-bromobenzoate) and 6 (methyl 4-amino-2-chlorobenzoate) were estimated to have the lowest binding energies into GR and GST receptors, respectively according to results of in silico studies.     

山东省假性肥大型肌营养不良的RFLP分析 Ⅰ．可疑携带者的基因型确定

本文选用10个DMD基因内部和桥探针,对山东省9个地区的21个DMD／BMD家系的173名成员进行RFLP分析,其中可疑携带者55人,多数家系应用1－3个探针,有基因重组家庭选用4－5个探针,便可完成多态分析,RFLP分析可以确定85．45％的可疑携带者（≥95％可信限）,即13人确定为携带者,30人排除携带者,另有4人风险大幅提高,通过这些探针的群体多态检测和不同家庭结构和类型的应用分析,我们提出了在中国人群中DMD／SMD基因诊断的基本程序。     

ploidyfrost: Reference-free estimation of ploidy level from whole genome sequencing data based on de Bruijn graphs

Polyploidy is ubiquitous and its consequences are complex and variable. A change of ploidy level generally influences genetic diversity and results in morphological, physiological and ecological differences between cells or organisms with different ploidy levels. To avoid cumbersome experiments and take advantage of the less biased information provided by the vast amounts of genome sequencing data, computational tools for ploidy estimation are urgently needed. Until now, although a few such tools have been developed, many aspects of this estimation, such as the requirement of a reference genome, the lack of informative results and objective inferences, and the influence of false positives from errors and repeats, need further improvement. We have developed ploidyfrost , a de Bruijn graph-based method, to estimate ploidy levels from whole genome sequencing data sets without a reference genome. ploidyfrost provides a visual representation of allele frequency distribution generated using the ggplot2 package as well as quantitative results using the Gaussian mixture model. In addition, it takes advantage of colouring information encoded in coloured de Bruijn graphs to analyse multiple samples simultaneously and to flexibly filter putative false positives. We evaluated the performance of ploidyfrost by analysing highly heterozygous or repetitive samples of Cyclocarya paliurus and a complex allooctoploid sample of Fragaria × ananassa. Moreover, we demonstrated that the accuracy of analysis results can be improved by constraining a threshold such as Cramér's V coefficient on variant features, which may significantly reduce the side effects of sequencing errors and annoying repeats on the graphical structure constructed.     

Differences in Body Image and Depression Among Obese Women With and Without Binge Eating Disorder

Obese individuals with binge eating disorder (BED) differ from obese non-binge eating (NBE) individuals in a number of clinically relevant ways. This study examined attitudinal responses to various measures of body image in women seeking obesity treatment, by comparing NBE participants (n=80) to those with BED (n=48). It was hypothesized that women with BED would demonstrate greater attitudinal disturbance of body image compared to NBE individuals. It was further hypothesized that significant differences between groups would remain after statistically controlling for degree of depression. Consistent with the primary hypothesis, BED participants reported significantly increased attitudinal disturbance in body dissatisfaction and size perception compared to NBE participants. Although shared variance was observed between measures of depression and body image on some items, several aspects of increased body image disturbance remained after statistically controlling for depression. Treatment implications and recommendations for future research are discussed.     

升流厌氧污泥层反应器动力学模型

用碘离子作示踪剂,采用矩形脉冲示踪法测定升流厌氧污泥层(UASB)反应器的流动分布。建立了申级返混加沟流模型。模型简单,能够反映反应器流动分布,具有较强的拟合能力和良好的适用性。运用流动模型和Monod方程,建立了UASB反应器稳态模型,并对模型参数进行了估计。通过灵敏度分析,进水基质浓度S。,废水流量Q,最大比基质降解速率,μmax 对出水基质浓度有较大影响。在稳态模型的基础上又建立了UASB反应器动态模态,利用此模型,对出水基质浓度序列Se,和产气量序列Qg进行计算预测,平均偏差分别5．40％和7．46％,标准偏差分别为7．02％和9．66％。     

矩阵的加号逆理论在参数估计中的应用

本文从矩阵的加号逆理论出发,根据求矛盾线性方程组最佳逼近解的方法,建立起一种新的参数估计方法,同时给出了显著性检验方法,这种方法更简单更精确．     

生物种群动态微分方程模型参数估计方法

本文以数值分析和最优化技术的有机结合为基础,提出了一种新的对动态微分方程模型直接进行数据拟合和参数估计方法,并以Logistic微分方程、生物种间竞争关系微分方程以及一种复合形态的Logistic微分方程为例进行了数据拟合试验．结果表明,该方法对各种动态微分方程模型均能进行最优拟合分析并求解其参数．同时发现,以前有的作者〔1,2,3,4,5〕提出的方法所得到的参数估计值存在系统误差且误差较大．     

Thermodynamical model for insertion and aggregate binding of caffeine to the homopolymer poly(riboadenylate) and model choice by data analysis

This paper describes the model used to estimate the parameters of caffeine-poly(riboadenylate) (poly(A)) interactions from corresponding 1H-NMR measurements. The model of insertion and aggregate binding describes the non-cooperative insertion of a molecule C into an interspace between two monomers of a homopolymer in competition with aggregate binding. It contains two binding constants, K1 for insertion and K2 for the interaction of monomeric A units of the polymer with C molecules in bound aggregates, and two cooperativity parameters, Kcc for stacking of C molecules within aggregates and tau which is thought to be due to conformational adaptation of the polymer to those bound aggregates which cover more than one A unit. In contrast to other models, the size of a binding site (within the aggregates) is less than one monomeric unit, with n denoting the maximum number of C molecules per A unit in bound aggregates. The model is developed for general n by means of the method of sequence-generating functions. For n = 2 and n = 3, the correctness of the model treatment was checked by the matrix method. The model is applicable to the binding of aggregates to homopolymers, which are flexible enough to fit their structure to the aggregates.