Effects of variance-function misspecification in analysis of longitudinal data

The approach of generalized estimating equations (GEE) is based on the framework of generalized linear models but allows for specification of a working matrix for modeling within-subject correlations. The variance is often assumed to be a known function of the mean. This article investigates the impacts of misspecifying the variance function on estimators of the mean parameters for quantitative responses. Our numerical studies indicate that (1) correct specification of the variance function can improve the estimation efficiency even if the correlation structure is misspecified; (2) misspecification of the variance function impacts much more on estimators for within-cluster covariates than for cluster-level covariates; and (3) if the variance function is misspecified, correct choice of the correlation structure may not necessarily improve estimation efficiency. We illustrate impacts of different variance functions using a real data set from cow growth.     

On the accuracy of analytical potentials: comment on ‘Accurate ab initio calculation of the Ar–CF4 intermolecular potential energy surface’

In a recent study of the Ar–CF₄ intermolecular interaction potential [Shen C-C, Chang R-Y. Accurate ab initio calculation of the Ar–CF₄ intermolecular potential energy surface. Mol Sim. 2010;36:1111–1122], Shen and Chang (SC) illustrated how the use of bond functions can improve the accuracy and basis-set saturation of electronic structure calculations employing perturbation and coupled-cluster theory. SC then used these ab initio data to derive analytic potential energy functions for use in chemical dynamics simulations. We critically examine these analytic potentials and comment on their usage in such simulations. Our analysis highlights the need for care and global validation when deriving analytic potential energy functions.     

Increasing the potency of neutralizing single-domain antibodies by functionalization with a CD11b/CD18 binding domain

Recombinant single domain antibodies (nanobodies) constitute an attractive alternative for the production of neutralizing therapeutic agents. Their small size warrants rapid bioavailability and fast penetration to sites of toxin uptake, but also rapid renal clearance, which negatively affects their performance. In this work, we present a new strategy to drastically improve the neutralizing potency of single domain antibodies based on their fusion to a second nanobody specific for the complement receptor CD11b/CD18 (Mac-1). These bispecific antibodies retain a small size (?30 kDa), but acquire effector functions that promote the elimination of the toxin-immunocomplexes. The principle was demonstrated in a mouse model of lethal toxicity with tetanus toxin. Three anti-tetanus toxin nanobodies were selected and characterized in terms of overlapping epitopes and inhibition of toxin binding to neuron gangliosides. Bispecific constructs of the most promising monodomain antibodies were built using anti Mac-1, CD45 and MHC II nanobodies. When co-administered with the toxin, all bispecific antibodies showed higher toxin-neutralizing capacity than the monomeric ones, but only their fusion to the anti-endocytic receptor Mac-1 nanobody allowed the mice to survive a 10-fold lethal dose. In a model of delayed neutralization of the toxin, the anti- Mac-1 bispecific antibodies outperformed a sheep anti-toxin polyclonal IgG that had shown similar neutralization potency in the co-administration experiments. This strategy should have widespread application in the development of nanobody-based neutralizing therapeutics, which can be produced economically and more safely than conventional antisera.     

A novel method for predicting and using distance constraints of high accuracy for refining protein structure prediction

The principal bottleneck in protein structure prediction is the refinement of models from lower accuracies to the resolution observed by experiment. We developed a novel constraints‐based refinement method that identifies a high number of accurate input constraints from initial models and rebuilds them using restrained torsion angle dynamics (rTAD). We previously created a Bayesian statistics‐based residue‐specific all‐atom probability discriminatory function (RAPDF) to discriminate native‐like models by measuring the probability of accuracy for atom type distances within a given model. Here, we exploit RAPDF to score (i.e., filter) constraints from initial predictions that may or may not be close to a native‐like state, obtain consensus of top scoring constraints amongst five initial models, and compile sets with no redundant residue pair constraints. We find that this method consistently produces a large and highly accurate set of distance constraints from which to build refinement models. We further optimize the balance between accuracy and coverage of constraints by producing multiple structure sets using different constraint distance cutoffs, and note that the cutoff governs spatially near versus distant effects in model generation. This complete procedure of deriving distance constraints for rTAD simulations improves the quality of initial predictions significantly in all cases evaluated by us. Our procedure represents a significant step in solving the protein structure prediction and refinement problem, by enabling the use of consensus constraints, RAPDF, and rTAD for protein structure modeling and refinement. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

凋落物的树种多样性与杉木人工林土壤生态功能

通过模拟试验和野外调查对凋落物的树种多样性与杉木人工林土壤生态功能之间关系进行了研究。林分取样调查的结果表明 ,有两个树种凋落物覆盖的几个杉阔混交林土壤脲酶和蔗糖酶活性均明显高于只有杉木凋落物覆盖的杉木纯林土壤 ,酸性磷酸酶活性也呈现相同的变化趋势 ,这 3种土壤酶活性均以具有凋落物种类最多的次生常绿阔叶林土壤最高。除了土壤酶活性升高之外 ,杉阔混交林的土壤有机质和全氮含量也明显高于仅有一种杉木凋落物覆盖的杉木纯林土壤。采用杉木叶凋落物与不同阔叶树种凋落物处理土壤的模拟试验结果表明 ,在凋落物投放量和 (1 5NH4 ) 2 SO4 施用量相同的控制条件下 ,随着投放的凋落物树种组成的增加 ,土壤中 1 5N的残留量也随之增加 ,而其损失量却随之减少 ;土壤中杉木幼树对于 1 5N的吸收量以及杉木幼树的单株鲜重也随着处理凋落物组成树种的增加而增加 ,具有不同树种数量的凋落物处理之间差异显著 (p<0 .0 5 )。可见随着凋落物树种多样性的增加 ,不仅土壤有机质和全氮含量这两个基本的质量指标得到明显改善 ,而且土壤酶活性、土壤养分保蓄功能以及保证幼树良好生长等的生态功能明显改善     

Estimation of separable direct and indirect effects in continuous time

Many research questions involve time-to-event outcomes that can be prevented from occurring due to competing events. In these settings, we must be careful about the causal interpretation of classical statistical estimands. In particular, estimands on the hazard scale, such as ratios of cause-specific or subdistribution hazards, are fundamentally hard to interpret causally. Estimands on the risk scale, such as contrasts of cumulative incidence functions, do have a clear causal interpretation, but they only capture the total effect of the treatment on the event of interest; that is, effects both through and outside of the competing event. To disentangle causal treatment effects on the event of interest and competing events, the separable direct and indirect effects were recently introduced. Here we provide new results on the estimation of direct and indirect separable effects in continuous time. In particular, we derive the nonparametric influence function in continuous time and use it to construct an estimator that has certain robustness properties. We also propose a simple estimator based on semiparametric models for the two cause-specific hazard functions. We describe the asymptotic properties of these estimators and present results from simulation studies, suggesting that the estimators behave satisfactorily in finite samples. Finally, we reanalyze the prostate cancer trial from Stensrud et al. (2020).     

Cholecalciferol (vitamin D 3) improves cognitive dysfunction and reduces inflammation in a rat fatty liver model of metabolic syndrome

Aim

The aim of this study was to examine the effects of cholecalciferol on systemic inflammation and memory in the setting of fatty liver disease in rats.

Materials and methods

To induce the development of fatty liver disease, the rats were fed a 35% fructose solution over 8 weeks. Group I (n = 6) was designated as the control group and fed with standard rat chow. Group II (n = 6) was provided with, standard rat chow, and 0.3 μg/kg/day of oral cholecalciferol over a duration of 2 weeks. In addition to standard rat chow, group III (n = 6) and group IV (n = 6) were given 4 mL of the 35% fructose solution per day via oral gavage for 8 weeks. However, group IV was also given 0.3 μg/kg/day of oral cholecalciferol over 2 weeks. After the treatment period, passive avoidance tasks were performed by all groups. The liver and brain were harvested for subsequent biochemical and histopathologic analyses.

Key findings

The development of fatty liver extends the memory latency period of passively avoiding tasks after 1 trial. Moreover, there were increases in brain TNF-α and plasma MDA levels according to two-way analysis of variance. Cholecalciferol supplementation decreased the latency period of passively avoiding tasks in rats with hepatosteatosis, and also significantly reduced brain TNF-α and plasma MDA levels.

Significance

Fatty liver may contribute to the development of systemic inflammation, which affects cognition and causes deficits in memory; however, the anti-inflammatory and antioxidant properties of vitamin D may improve the cognitive function of rats with hepatosteatosis.     

黏蛋白1基因敲除小鼠模型的构建

黏蛋白1(MUC1)属黏蛋白家族成员,分布于上皮细胞膜表面,由于在免疫炎症反应以及肿瘤发生中的重要作用而日益受到重视.为了进一步深入研究MUC1的生物学功能,构建了Muc1基因敲除小鼠模型.首先,根据小鼠Muc1基因组序列设计基因剔除策略,将2个loxP位点分别插在外显子2和3两侧,构建基因剔除载体Muc1-ABRLFn-pBR322.以电穿孔方法将载体导入胚胎干细胞(ES细胞),用G418和更昔洛韦进行正负筛选获得4个同源重组的ES细胞克隆.挑选其中一个阳性ES克隆行囊胚显微注射,获得16只嵌合率大于50%的雄鼠;其次,利用嵌合雄鼠与C57BL/6J野生型雌鼠交配后获得11只floxP杂合子小鼠(10雄1雌),通过杂合子小鼠回交,并进一步与EⅡa-Cre小鼠交配,最终成功得到Muc1全身敲除小鼠,其中纯合子小鼠未出现胚胎致死现象.初步表型观察未发现Muc1基因敲除相关器官组织结构的异常改变.本研究为MUC1的生物学功能的挖掘,尤其是MUC1在肿瘤发生转移中的作用机制的揭示提供了实验平台.     

Expected utility maximization and selection of stable plant cultivars

Summary In most plant breeding programs, selection of the best commercially suitable cultivars for a target group of environments is based on information obtained from evaluation trials cultivated in a sample of environments. Information on the performance of cultivars collected in a sample of environments can only be approximate and, consequently, selection of the best cultivar involves choosing among cultivars that respond uncertainly in many environments. The agronomic and/or economic value of a cultivar across environments may be considered the general or overall utility of the cultivar. Data from a sample of environments therefore provides only an estimate of any cultivar's overall utility, with the overall goal of selection among all cultivars being the maximization of the expected utility. Within this frame-work, expected utility maximization, an approach to decision making that has been well developed in the disciplines of economics and statistics, can assist the plant breeder in making such decisions. This research was initiated (1) to determine how expected utility maximization might be used to develop indices that are useful for selecting broadly adapted plant cultivars, and (2) to determine how the breeder's preferences might affect choice of the best cultivar. The data used in this research were from USDA Regional Soybean Tests. The results indicated that expected utility maximization, which explicitly incorporates into the selection rule the plant breeder's preferences regarding stability, can be a useful aid in the selection of stable plant cultivars.     

Shapes and functions of species–area curves (II): a review of new models and parameterizations

This paper has extended and updated my earlier list and analysis of candidate models used in theoretical modelling and empirical examination of species–area relationships (SARs). I have also reviewed trivariate models that can be applied to include a second independent variable (in addition to area) and discussed extensively the justifications for fitting curves to SARs and the choice of model. There is also a summary of the characteristics of several new candidate models, especially extended power models, logarithmic models and parameterizations of the negative-exponential family and the logistic family. I have, moreover, examined the characteristics and shapes of trivariate linear, logarithmic and power models, including combination variables and interaction terms. The choice of models according to best fit may conflict with problems of non-normality or heteroscedasticity. The need to compare parameter estimates between data sets should also affect model choice. With few data points and large scatter, models with few parameters are often preferable. With narrow-scale windows, even inflexible models such as the power model and the logarithmic model may produce good fits, whereas with wider-scale windows where inflexible models do not fit well, more flexible models such as the second persistence (P2) model and the cumulative Weibull distribution may be preferable. When extrapolations and expected shapes are important, one should consider models with expected shapes, e.g. the power model for sample area curves and the P2 model for isolate curves. The choice of trivariate models poses special challenges, which one can more effectively evaluate by inspecting graphical plots.