Evolutionary timescale of monocots determined by the fossilized birth‐death model using a large number of fossil records

Although the phylogenetic relationships between monocot orders are sufficiently understood, a timescale of their evolution is needed. Several studies on molecular clock dating are available, but their results have been biased by their calibration schemes. Recently, the fossilized birth‐death model, a type of Bayesian dating method, was proposed, and it does not require prior calibration and allows the use all available fossils. Using this model, we conducted divergence‐time estimations of monocots to explore their evolutionary timeline without calibration bias. This is the first application of this model to seed plants. The dataset contained the matK and rbcL chloroplast genes of 118 monocot genera covering all extant orders. We employed information from 247 monocot fossils, which exceeded previous dating analyses that used a maximum of 12 monocot fossils. The crown group of monocots was dated to approximately the Late Jurassic–Early Cretaceous periods, and most extant monocot orders were estimated to diverge throughout the Early Cretaceous. Our results overlapped with the divergence time of insect lineages, such as beetles and flies, suggesting an association with pollinators in early monocot evolution. In addition, we proposed three new orders based on divergence time: Orchidales separated from Asparagales and Tofieldiales and Arales separated from Aslimatales.     

Exploring the power of Bayesian birth‐death skyline models to detect mass extinction events from phylogenies with only extant taxa

Mass extinction events (MEEs), defined as significant losses of species diversity in significantly short time periods, have attracted the attention of biologists because of their link to major environmental change. MEEs have traditionally been studied through the fossil record, but the development of birth‐death models has made it possible to detect their signature based on extant‐taxa phylogenies. Most birth‐death models consider MEEs as instantaneous events where a high proportion of species are simultaneously removed from the tree (“single pulse” approach), in contrast to the paleontological record, where MEEs have a time duration. Here, we explore the power of a Bayesian Birth‐Death Skyline (BDSKY) model to detect the signature of MEEs through changes in extinction rates under a “time‐slice” approach. In this approach, MEEs are time intervals where the extinction rate is greater than the speciation rate. Results showed BDSKY can detect and locate MEEs but that precision and accuracy depend on the phylogeny's size and MEE intensity. Comparisons of BDSKY with the single‐pulse Bayesian model, CoMET, showed a similar frequency of Type II error and neither model exhibited Type I error. However, while CoMET performed better in detecting and locating MEEs for smaller phylogenies, BDSKY showed higher accuracy in estimating extinction and speciation rates.     

Logistic regression when covariates are random effects from a non‐linear mixed model

In many studies, the association of longitudinal measurements of a continuous response and a binary outcome are often of interest. A convenient framework for this type of problems is the joint model, which is formulated to investigate the association between a binary outcome and features of longitudinal measurements through a common set of latent random effects. The joint model, which is the focus of this article, is a logistic regression model with covariates defined as the individual‐specific random effects in a non‐linear mixed‐effects model (NLMEM) for the longitudinal measurements. We discuss different estimation procedures, which include two‐stage, best linear unbiased predictors, and various numerical integration techniques. The proposed methods are illustrated using a real data set where the objective is to study the association between longitudinal hormone levels and the pregnancy outcome in a group of young women. The numerical performance of the estimating methods is also evaluated by means of simulation.     

Parameter estimation and model selection for Neyman-Scott point processes

This paper proposes an approximative method for maximum likelihood estimation of parameters of Neyman-Scott and similar point processes. It is based on the point pattern resulting from forming all difference points of pairs of points in the window of observation. The intensity function of this constructed point process can be expressed in terms of second-order characteristics of the original process. This opens the way to parameter estimation, if the difference pattern is treated as a non-homogeneous Poisson process. The computational feasibility and accuracy of this approach is examined by means of simulated data. Furthermore, the method is applied to two biological data sets. For these data, various cluster process models are considered and compared with respect to their goodness-of-fit.     

Saddlepoint Approximations to the Moments of Multitype Age‐Dependent Branching Processes,with Applications

Summary This article proposes saddlepoint approximations to the expectation and variance–covariance function of multitype age‐dependent branching processes. The proposed approximations are found accurate, easy to implement, and much faster to compute than by simulating the process. Multiple applications are presented, including the analyses of clonal data on the generation of oligodendrocytes from their immediate progenitor cells, and on the proliferation of Hela cells. New estimators are also constructed to analyze clonal data. The proposed methods are finally used to approximate the distribution of the generation, which has recently found several applications in cell biology.     

Copula‐based semiparametric models for spatiotemporal data

The joint analysis of spatial and temporal processes poses computational challenges due to the data's high dimensionality. Furthermore, such data are commonly non‐Gaussian. In this paper, we introduce a copula‐based spatiotemporal model for analyzing spatiotemporal data and propose a semiparametric estimator. The proposed algorithm is computationally simple, since it models the marginal distribution and the spatiotemporal dependence separately. Instead of assuming a parametric distribution, the proposed method models the marginal distributions nonparametrically and thus offers more flexibility. The method also provides a convenient way to construct both point and interval predictions at new times and locations, based on the estimated conditional quantiles. Through a simulation study and an analysis of wind speeds observed along the border between Oregon and Washington, we show that our method produces more accurate point and interval predictions for skewed data than those based on normality assumptions.     

Validation of delay‐multiply‐and‐standard‐deviation weighting factor for improved photoacoustic imaging of sentinel lymph node

Delay‐and‐sum (DAS) is one of the most common algorithms used to construct the photoacoustic images due to its low complexity. However, it results in images with high sidelobes and low resolution. Delay‐and‐standard‐deviation (DASD) weighting factor can improve the contrast of the images compared to DAS. However, it still suffers from high sidelobes. In this work, a new weighting factor, named delay‐multiply‐and‐standard‐deviation (DMASD) is introduced to enhance the contrast of the reconstructed images compared to other mentioned methods. In the proposed method, the SD of the mutual multiplied delayed signals are calculated, normalized and multiplied to DAS beamformed data. The results show that DMASD improves the signal‐to‐noise‐ratio about 19.29 and 7.3 dB compared to DAS and DASD, respectively, for in vivo imaging of the sentinel lymph node. Moreover, the contrast ratio is improved by the DMASD about 23.61 and 10.81 dB compared to DAS and DASD, respectively.      

Two kinds of physician‐assisted death

I argue that the concept ‘physician‐assisted suicide’ covers two procedures that should be distinguished: giving someone access to humane means to end his own life, and taking co‐responsibility for the safe and effective execution of that plan. In the first section I explain the distinction, in the following sections I show why it is important. To begin with I argue that we should expect the laws that permit these two kinds of ‘assistance’ to be different in their justificatory structure. Laws that permit giving access only presuppose that the right to self‐determination implies a right to suicide, but laws that permit doctors to take co‐responsibility may have to appeal to a principle of mercy or beneficence. Actually this difference in justificatory structure can to some extent be found in existing regulatory systems, though far from consistently. Finally I argue that if one recognizes a right to suicide, as Oregon and other American states implicitly do, and as the European Court of Human Rights has recently done explicitly, one is committed to permit the first kind of ‘assistance’ under some conditions.     

Analysis of a high‐throughput cone‐and‐plate apparatus for the application of defined spatiotemporal flow to cultured cells

The shear stresses derived from blood flow regulate many aspects of vascular and immunobiology. In vitro studies on the shear stress‐mediated mechanobiology of endothelial cells have been carried out using systems analogous to the cone‐and‐plate viscometer in which a rotating, low‐angle cone applies fluid shear stress to cells grown on an underlying, flat culture surface. We recently developed a device that could perform high‐throughput studies on shear‐mediated mechanobiology through the rotation of cone‐tipped shafts in a standard 96‐well culture plate. Here, we present a model of the three‐dimensional flow within the culture wells with a rotating, cone‐tipped shaft. Using this model we examined the effects of modifying the design parameters of the system to allow the device to create a variety of flow profiles. We first examined the case of steady‐state flow with the shaft rotating at constant angular velocity. By varying the angular velocity and distance of the cone from the underlying plate we were able to create flow profiles with controlled shear stress gradients in the radial direction within the plate. These findings indicate that both linear and non‐linear spatial distributions in shear stress can be created across the bottom of the culture plate. In the transition and “parallel shaft” regions of the system, the angular velocities needed to provide high levels of physiological shear stress (5 Pa) created intermediate Reynolds number Taylor‐Couette flow. In some cases, this led to the development of a flow regime in which stable helical vortices were created within the well. We also examined the system under oscillatory and pulsatile motion of the shaft and demonstrated minimal time lag between the rotation of the cone and the shear stress on the cell culture surface. Biotechnol. Bioeng. 2013; 110: 1782–1793. © 2013 Wiley Periodicals, Inc.     

GDF‐15 is a better complimentary marker for risk stratification of arrhythmic death in non‐ischaemic,dilated cardiomyopathy than soluble ST2

Growth differentiation factor (GDF)‐15 and soluble ST2 (sST2) are established prognostic markers in acute and chronic heart failure. Assessment of these biomarkers might improve arrhythmic risk stratification of patients with non‐ischaemic, dilated cardiomyopathy (DCM) based on left ventricular ejection fraction (LVEF). We studied the prognostic value of GDF‐15 and sST2 for prediction of arrhythmic death (AD) and all‐cause mortality in patients with DCM. We prospectively enrolled 52 patients with DCM and LVEF ≤ 50%. Primary end‐points were time to AD or resuscitated cardiac arrest (RCA), and secondary end‐point was all‐cause mortality. The median follow‐up time was 7 years. A cardiac death was observed in 20 patients, where 10 patients had an AD and 2 patients had a RCA. One patient died a non‐cardiac death. GDF‐15, but not sST2, was associated with increased risk of the AD/RCA with a hazard ratio (HR) of 2.1 (95% CI = 1.1‐4.3; P = .031). GDF‐15 remained an independent predictor of AD/RCA after adjustment for LVEF with adjusted HR of 2.2 (95% CI = 1.1‐4.5; P = .028). Both GDF‐15 and sST2 were independent predictors of all‐cause mortality (adjusted HR = 2.4; 95% CI = 1.4‐4.2; P = .003 vs HR = 1.6; 95% CI = 1.05‐2.7; P = .030). In a model including GDF‐15, sST2, LVEF and NYHA functional class, only GDF‐15 was significantly associated with the secondary end‐point (adjusted HR = 2.2; 95% CI = 1.05‐5.2; P = .038). GDF‐15 is superior to sST2 in prediction of fatal arrhythmic events and all‐cause mortality in DCM. Assessment of GDF‐15 could provide additional information on top of LVEF and help identifying patients at risk of arrhythmic death.     

Pattern‐Mixture Zero‐Inflated Mixed Models for Longitudinal Unbalanced Count Data with Excessive Zeros

Analysis of longitudinal data with excessive zeros has gained increasing attention in recent years; however, current approaches to the analysis of longitudinal data with excessive zeros have primarily focused on balanced data. Dropouts are common in longitudinal studies; therefore, the analysis of the resulting unbalanced data is complicated by the missing mechanism. Our study is motivated by the analysis of longitudinal skin cancer count data presented by Greenberg, Baron, Stukel, Stevens, Mandel, Spencer, Elias, Lowe, Nierenberg, Bayrd, Vance, Freeman, Clendenning, Kwan, and the Skin Cancer Prevention Study Group[New England Journal of Medicine 323 , 789–795]. The data consist of a large number of zero responses (83% of the observations) as well as a substantial amount of dropout (about 52% of the observations). To account for both excessive zeros and dropout patterns, we propose a pattern‐mixture zero‐inflated model with compound Poisson random effects for the unbalanced longitudinal skin cancer data. We also incorporate an autoregressive of order 1 correlation structure in the model to capture longitudinal correlation of the count responses. A quasi‐likelihood approach has been developed in the estimation of our model. We illustrated the method with analysis of the longitudinal skin cancer data.