Quantile regression models with multivariate failure time data

As an alternative to the mean regression model, the quantile regression model has been studied extensively with independent failure time data. However, due to natural or artificial clustering, it is common to encounter multivariate failure time data in biomedical research where the intracluster correlation needs to be accounted for appropriately. For right-censored correlated survival data, we investigate the quantile regression model and adapt an estimating equation approach for parameter estimation under the working independence assumption, as well as a weighted version for enhancing the efficiency. We show that the parameter estimates are consistent and asymptotically follow normal distributions. The variance estimation using asymptotic approximation involves nonparametric functional density estimation. We employ the bootstrap and perturbation resampling methods for the estimation of the variance-covariance matrix. We examine the proposed method for finite sample sizes through simulation studies, and illustrate it with data from a clinical trial on otitis media.     

Optimization of harvesting age in an integral age-dependent model of population dynamics

The paper deals with optimal control in a linear integral age-dependent model of population dynamics. A problem for maximizing the harvesting return on a finite time horizon is formulated and analyzed. The optimal controls are the harvesting age and the rate of population removal by harvesting. The gradient and necessary condition for an extremum are derived. A qualitative analysis of the problem is provided. The model shows the presence of a zero-investment period. A preliminary asymptotic analysis indicates possible turnpike properties of the optimal harvesting age. Biological interpretation of all results is provided.     

Effect of temperature and guanidine hydrochloride on ferrocytochrome<Emphasis Type="Italic"> c</Emphasis> at neutral pH

Thermally denatured horse heart ferrocytochrome c (ferrocyt c) has been characterized using absorption spectroscopy, differential scanning calorimetry (DSC) and viscometry at pH 7.0. DSC experiments have yielded the transition temperature of denaturant-free ferrocyt c unfolding as 100.6±0.3 °C, indicating an extremely high stability of the protein. The presence of guanidine hydrochloride (GdnHCl) facilitated estimation of the structural features of thermally unfolded ferrocyt c. The stability of the protein, expressed by G _D at 25 °C, is 59±5 kJ mol^–1 (DSC) and 65±6 kJ mol^–1 (absorption spectroscopy). An absorption spectrum of ferrocyt c demonstrates that the heme occurs in the high-spin state at extreme denaturing conditions (94 °C, 6.6 M GdnHCl). Absorption spectroscopy, using heme as a probe, shows that thermal denaturation of ferrocyt c occurs as a transition from a native low-spin (Met80/His18) to a high-spin disordered state with involvement of non-native, low-spin (bis-His) species.Abbreviations CD circular dichroism - cyt c cytochrome c - DSC differential scanning calorimetry - ferricyt c ferricytochrome c - ferrocyt c ferrocytochrome c - GdnHCl guanidine hydrochloride - NHE normal hydrogen electrode     

An analytical model for the ergometer rowing: inverse multibody dynamics analysis

A model for the ergometer rowing exercise is presented in this paper. From the quantitative observations of a particular trajectory (motion), the model is used to determine the moment of the forces produced by the muscles about each joint. These forces are evaluated according to the continuous system of equations of motion. An inverse dynamics analysis is performed in order to predict the joint torques developed by the muscles during the execution of the task. An elementary multibody mechanical system is used as an example to discuss the assumptions and procedures adopted.     

Multigrid Block Preconditioning for a Coupled System of Partial Differential Equations Modeling the Electrical Activity in the Heart

The electrical activity of the heart may be modeled with a system of partial differential equations (PDEs) known as the bidomain model. Computer simulations based on these equations may become a helpful tool to understand the relationship between changes in the electrical field and various heart diseases. Because of the rapid variations in the electrical field, sufficiently accurate simulations require a fine-scale discretization of the equations. For realistic geometries this leads to a large number of grid points and consequently large linear systems to be solved for each time step. In this paper, we present a fully coupled discretization of the bidomain model, leading to a block structured linear system. We take advantage of the block structure to construct an efficient preconditioner for the linear system, by combining multigrid with an operator splitting technique.     

Sequential adaptive variables and subject selection for GEE methods

Modeling correlated or highly stratified multiple-response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time-consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process—such as the active-learning methods found in the machine-learning literature—will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method.     

广义神经传播方程非协调类Wilson元的超收敛分析及外推

在半离散格式下研究了广义神经传播方程的非协调类Wilson有限元方法．利用该单元相容误差比协调误差高一阶的特殊性质和双线性元的高精度分析技巧,得到了相应的超逼近性质和超收敛结果．进一步地,构造了一个新的外推格式,并借助于该单元相容误差比协调误差高两阶的特殊性质,由此导出了能量模意义下具有O（h3）阶的外推效果．     

Analysis of the length distribution of amyloid fibrils by centrifugal sedimentation

The aggregation of normally soluble peptides and proteins into amyloid fibrils is a process associated with a wide range of pathological conditions, including Alzheimer's and Parkinson's diseases. It has become apparent that aggregates of different sizes possess markedly different biological effects, with aggregates of lower relative molecular weight being associated with stronger neurotoxicity. Yet, although many approaches exist to measure the total mass concentration of aggregates, the ability to probe the length distribution of growing aggregates in solution has remained more elusive. In this work, we applied a differential centrifugation technique to measure the sedimentation coefficients of amyloid fibrils produced during the aggregation process of the amyloid β (M1–42) peptide (Aβ42). The centrifugal method has the advantage of providing structural information on the fibril distribution directly in solution and affording a short analysis time with respect to alternative imaging and analytical centrifugation approaches. We show that under quiescent conditions interactions between Aβ42 fibrils lead to lateral association and to the formation of entangled clusters. By contrast, aggregation under shaking generates a population of filaments characterized by shorter lengths. The results, which have been validated by cryogenic transmission electron microscopy (cryo-TEM) analysis, highlight the important role that fibril–fibril assembly can play in the deposition of aggregation-prone peptides.     

Stability,sensitivity and uncertainty rates in the flow equations of ecological models

In previous work by the authors about dynamic system modeling, basic ecosystems concepts and their application to ecological modeling theory were formalized. Measuring how a variable effects certain processes leads to improvements in dynamic systems modelling and facilitates the author’s study of system diversity in which model sensitivity is a key theme. Initially, some variables and their numeric data are used for modeling. Predictions from the constructed models depend on these data. Generic study of sensitivity aims to show to what degree model behavior is altered by modification of some specific data. If small variations cause important modifications in the model’s global behavior then the model is very sensitive in relation to the variables used. If uncertain systems are considered, then it is important to submit the system to extreme situations and analyze its behavior. In this article, some indexes of uncertainty will be defined in order to determine the variables' influence in the case of extreme changes. This will permit analysis of the system’s sensitivity in relation to several simulations.