Use of external rates in nested case-control studies with application to the international radiation study of cervical cancer patients.

A method is proposed for analysis of nested case-control studies that combines the matched comparison of covariate values between cases and controls and a comparison of the observed numbers of cases in the nesting cohort with expected numbers based on external rates and average relative risks estimated from the controls. The former comparison is based on the conditional likelihood for matched case-control studies and the latter on the unconditional likelihood for Poisson regression. It is shown that the two likelihoods are orthogonal and that their product is an estimator of the full survival likelihood that would have been obtained on the total cohort, had complete covariate data been available. Parameter estimation and significance tests follow in the usual way by maximizing this product likelihood. The method is illustrated using data on leukemia following irradiation for cervical cancer. In this study, the original cohort study showed a clear excess of leukemia in the first 15 years after exposure, but it was not feasible to obtain dose estimates on the entire cohort. However, the subsequent nested case-control study failed to demonstrate significant differences between alternative dose-response relations and effects of time-related modifiers. The combined analysis allows much clearer discrimination between alternative dose-time-response models.     

A Model of Photon Cell Killing Based on the Spatio-Temporal Clustering of DNA Damage in Higher Order Chromatin Structures

We present a new approach to model dose rate effects on cell killing after photon radiation based on the spatio-temporal clustering of DNA double strand breaks (DSBs) within higher order chromatin structures of approximately 1–2 Mbp size, so called giant loops. The main concept of this approach consists of a distinction of two classes of lesions, isolated and clustered DSBs, characterized by the number of double strand breaks induced in a giant loop. We assume a low lethality and fast component of repair for isolated DSBs and a high lethality and slow component of repair for clustered DSBs. With appropriate rates, the temporal transition between the different lesion classes is expressed in terms of five differential equations. These allow formulating the dynamics involved in the competition of damage induction and repair for arbitrary dose rates and fractionation schemes. Final cell survival probabilities are computable with a cell line specific set of three parameters: The lethality for isolated DSBs, the lethality for clustered DSBs and the half-life time of isolated DSBs.By comparison with larger sets of published experimental data it is demonstrated that the model describes the cell line dependent response to treatments using either continuous irradiation at a constant dose rate or to split dose irradiation well. Furthermore, an analytic investigation of the formulation concerning single fraction treatments with constant dose rates in the limiting cases of extremely high or low dose rates is presented. The approach is consistent with the Linear-Quadratic model extended by the Lea-Catcheside factor up to the second moment in dose. Finally, it is shown that the model correctly predicts empirical findings about the dose rate dependence of incidence probabilities for deterministic radiation effects like pneumonitis and the bone marrow syndrome. These findings further support the general concepts on which the approach is based.     

Nicardipine diminished equinatoxin II-induced decrease of coronary flow in isolated rat and pig hearts

Equinatoxin II (EqT II) is a basic, cardiotoxic polypeptide. The vasoconstrictory effect of the toxin on isolated porcine coronary arteries was diminished by nicardipine, an L-type calcium channel antagonist. A comparison was made of the effects of EqT II alone and EqT II in the presence of nicardipine on the coronary flow in porcine and rat hearts isolated according to Langendorff's method. In both models EqT II decreased coronary flow in a dose-dependent manner and there were no statistically significant differences between the two models (p>0.05). However, 1 M nicardipine diminished the effects of EqT II on coronary flow in isolated porcine hearts more than in isolated rat hearts (p<0.05). The results suggest that the activation of L-type calcium channels is one of the mechanisms involved in the lowering of coronary flow induced by EqT II.     

A semiparametric approach for the two-sample comparison of survival times with long-term survivors

In the two-sample comparison of survival times with long-term survivors, the overall difference between the two distributions reflects differences occurring in early follow-up for susceptible subjects and in long-term follow-up for nonsusceptible subjects. In this setting, we propose statistics for testing (i) no overall, (ii) no short-term, and (iii) no long-term difference between the two distributions to be compared. The statistics are derived as follows. A semiparametric model is defined that characterizes a short-term effect and a long-term effect. By approximating this model about no difference in early survival, a time-dependent proportional hazards model is obtained. The statistics are obtained from this working model. The asymptotic distributions of the statistics for testing no overall or no short-term effects are ascertained, while that of the statistic for testing no long-term effect is valid only when the short-term effect is small. Simulation studies investigate the power properties of the proposed tests for different configurations. The results show the interesting behavior of the proposed tests for situations where a short-term effect is expected. An example investigating the impact of progesterone receptors status on local tumor relapse for patients with early breast cancer illustrates the use of the proposed tests.     

Dimension reduction in survival regressions with censored data via an imputed spline approach

Dimension reduction methods have been proposed for regression analysis with predictors of high dimension, but have not received much attention on the problems with censored data. In this article, we present an iterative imputed spline approach based on principal Hessian directions (PHD) for censored survival data in order to reduce the dimension of predictors without requiring a prespecified parametric model. Our proposal is to replace the right-censored survival time with its conditional expectation for adjusting the censoring effect by using the Kaplan-Meier estimator and an adaptive polynomial spline regression in the residual imputation. A sparse estimation strategy is incorporated in our approach to enhance the interpretation of variable selection. This approach can be implemented in not only PHD, but also other methods developed for estimating the central mean subspace. Simulation studies with right-censored data are conducted for the imputed spline approach to PHD (IS-PHD) in comparison with two methods of sliced inverse regression, minimum average variance estimation, and naive PHD in ignorance of censoring. The results demonstrate that the proposed IS-PHD method is particularly useful for survival time responses approximating symmetric or bending structures. Illustrative applications to two real data sets are also presented.     

Characterization of low populated peptide helical structures in solution by means of NMR proton conformational shifts

A NOE independent NMR method is proposed to characterize unambiguously residues involved in low populated isolated peptide helices. The method is based on the comparison of amide and H alpha chemical shift changes originated upon the addition of stabilizing or denaturing agents with true helical conformational shifts that have been measured for the first time using an isolated model peptide helix, the one formed by Ac-(Leu-Lys-Lys-Leu)3-NHEt in aqueous solution.     

Joint modeling of longitudinal and survival data via a common frailty

We develop a joint model for the analysis of longitudinal and survival data in the presence of data clustering. We use a mixed effects model for the repeated measures that incorporates both subject- and cluster-level random effects, with subjects nested within clusters. A Cox frailty model is used for the survival model in order to accommodate the clustering. We then link the two responses via the common cluster-level random effects, or frailties. This model allows us to simultaneously evaluate the effect of covariates on the two types of responses, while accounting for both the relationship between the responses and data clustering. The model was motivated by a study of end-stage renal disease patients undergoing hemodialysis, where we wished to evaluate the effect of iron treatment on both the patients' hemoglobin levels and survival times, with the patients clustered by enrollment site.     

Rethinking ecological inference: density dependence in reef fishes

We use a meta‐analysis of density dependence in reef fishes to evaluate how ecologists approach detection, inference, and estimation. We compared two groups of studies: those that detected effects of density on survival and those that did not. Distinctions between these groups have spawned heated debate about the processes that affect fish dynamics. Per capita effects of density were similar between the two groups, although total effects (and hence ambient density) were greater in studies that detected density effects. The majority of the variation in effects of density was not resolved by the classification of studies based on the authors' conclusions. These results suggest (1) that standard inferences based on null hypothesis tests may miss important sources of variation in effects and give rise to unnecessary debate; and (2) that estimation of effect sizes and model parameters (including their uncertainty) is a powerful alternative to detection of ecological processes.     

Adaptation of Listeria monocytogenes in a simulated cheese medium: effects on virulence using the Galleria mellonella infection model

The aim of this study was to evaluate the effect of the acid and salt adaptation in a cheese‐based medium on the virulence potential of Listeria monocytogenes strains isolated from cheese and dairy processing environment using the Galleria mellonella model. Four L. monocytogenes strains were exposed to a cheese‐based medium in conditions of induction of an acid tolerance response and osmotolerance response (pH 5·5 and 3·5% w/v NaCl) and injected in G. mellonella insects. The survival of insects and the L. monocytogenes growth kinetics in insects were evaluated. The gene expression of hly, actA and inlA genes was determined by real‐time PCR. The adapted cells of two dairy strains showed reduced insect mortality (P < 0·05) in comparison with nonadapted cells. Listeria monocytogenes Scott A was the least virulent, whereas the cheese isolate C882 caused the highest insect mortality, and no differences (P > 0·05) was found between adapted and nonadapted cells. The gene expression results evidenced an overexpression of virulence genes in cheese‐based medium, but not in simulated insect‐induced conditions. Our results suggest that adaptation to low pH and salt in a cheese‐based medium can affect the virulence of L. monocytogenes, but this effect is strain dependent.

Significance and Impact of the Study

In this study, the impact of adaptation to low pH and salt in a cheese‐based medium on L. monocytogenes virulence was tested using the Wax Moth G. mellonella model. This model allowed the differentiation of the virulence potential between the L. monocytogenes strains. The effect of adaptation on virulence is strain dependent. The G. mellonella model revealed to be a prompt method to test food‐related factors on L. monocytogenes virulence.     

Flexible Estimation of Differences in Treatment-Specific Recurrent Event Means in the Presence of a Terminating Event

Summary .  In this article, we consider the setting where the event of interest can occur repeatedly for the same subject (i.e., a recurrent event; e.g., hospitalization) and may be stopped permanently by a terminating event (e.g., death). Among the different ways to model recurrent/terminal event data, the marginal mean (i.e., averaging over the survival distribution) is of primary interest from a public health or health economics perspective. Often, the difference between treatment-specific recurrent event means will not be constant over time, particularly when treatment-specific differences in survival exist. In such cases, it makes more sense to quantify treatment effect based on the cumulative difference in the recurrent event means, as opposed to the instantaneous difference in the rates. We propose a method that compares treatments by separately estimating the survival probabilities and recurrent event rates given survival, then integrating to get the mean number of events. The proposed method combines an additive model for the conditional recurrent event rate and a proportional hazards model for the terminating event hazard. The treatment effects on survival and on recurrent event rate among survivors are estimated in constructing our measure and explain the mechanism generating the difference under study. The example that motivates this research is the repeated occurrence of hospitalization among kidney transplant recipients, where the effect of expanded criteria donor (ECD) compared to non-ECD kidney transplantation on the mean number of hospitalizations is of interest.     

Semiparametric estimation of random effects using the Cox model based on the EM algorithm.

Consider a survival experiment where individuals within a certain subset of the population share a common, unobservable, random frailty. Such a frailty could be an unobservable genetic or early environmental effect if individuals were in sibling groups or an environmental effect if individuals were grouped by households. Suppose that if the frailty, omega, is known, the Cox proportional hazards model for the observable covariates is valid with the consequence of the random effect being a multiplicative factor on the hazard rate. Assuming tht the random frailties follow a gamma distribution, estimates of the fixed and random effects are obtained by using an EM algorithm based on a profile likelihood construction. The method developed is applied to the Framingham Heart Study to examine the risks of smoking and cholesterol levels, adjusting for potential random effects.     

Confidence Intervals for the Difference of Median Survival Times Using the Stratified Cox Proportional Hazards Model

The stratified Cox proportional hazards model is introduced to incorporate covariates and involve nonproportional treatment effect of two groups into the analysis and then the confidence interval estimators for the difference in median survival times of two treatments in stratified Cox model are proposed. The one is based on baseline survival functions of two groups, and the other on average survival functions of two groups. I illustrate the proposed methods with an example from a study conducted by the Radiation Therapy Oncology Group in cancer of the mouth and throat. Simulations are carried out to investigate the small‐sample properties of proposed methods in terms of coverage rates.     

Random regression models for detection of gene by environment interaction

Two random regression models, where the effect of a putative QTL was regressed on an environmental gradient, are described. The first model estimates the correlation between intercept and slope of the random regression, while the other model restricts this correlation to 1 or -1, which is expected under a bi-allelic QTL model. The random regression models were compared to a model assuming no gene by environment interactions. The comparison was done with regards to the models ability to detect QTL, to position them accurately and to detect possible QTL by environment interactions. A simulation study based on a granddaughter design was conducted, and QTL were assumed, either by assigning an effect independent of the environment or as a linear function of a simulated environmental gradient. It was concluded that the random regression models were suitable for detection of QTL effects, in the presence and absence of interactions with environmental gradients. Fixing the correlation between intercept and slope of the random regression had a positive effect on power when the QTL effects re-ranked between environments.     

A method for radioprotective effect evaluation

The method here proposed is based on the prevention of photodynamic yeast cell damage by substances possessing radioprotective activity.The photodynamic yeast inactivation was achieved with Toluidine Blue as the photosensitizer and white exciting light. The model radioprotectors tested, namely, WR2721 [S-2(3-aminopropylamino)ethylphosphorothioic acid] and AET [2-aminoethylisothiouronium bromide], were applied at concentrations ranging from 0 to 200 mg/L. Under the conditions of photodynamic damage WR2721 and AET demonstrated a protective effect as evaluated by the enhancement of the cell survival and colony formation. The protection achieved by AET was more effective. The dependence of the protective effect on the concentration of both agents was linear in the low concentrations range.Experiments with radioprotective preparations of yeast origin demonstrated a similar relationship between the concentration and cell survival.These results indicate that the prevention of the photodynamic yeast cell damage by radioprotectors can find an application as a method for determination of radioprotective effects demonstrated by biotechnologically obtained substances in the course of their production and purification.     

Modeling multivariate survival data by a semiparametric random effects proportional odds model

In this article, the focus is on the analysis of multivariate survival time data with various types of dependence structures. Examples of multivariate survival data include clustered data and repeated measurements from the same subject, such as the interrecurrence times of cancer tumors. A random effect semiparametric proportional odds model is proposed as an alternative to the proportional hazards model. The distribution of the random effects is assumed to be multivariate normal and the random effect is assumed to act additively to the baseline log-odds function. This class of models, which includes the usual shared random effects model, the additive variance components model, and the dynamic random effects model as special cases, is highly flexible and is capable of modeling a wide range of multivariate survival data. A unified estimation procedure is proposed to estimate the regression and dependence parameters simultaneously by means of a marginal-likelihood approach. Unlike the fully parametric case, the regression parameter estimate is not sensitive to the choice of correlation structure of the random effects. The marginal likelihood is approximated by the Monte Carlo method. Simulation studies are carried out to investigate the performance of the proposed method. The proposed method is applied to two well-known data sets, including clustered data and recurrent event times data.     

Genetic diversity and fitness in peripheral and central populations of the European tree frog Hyla arborea

Genetic diversity is expected to decrease in small and isolated populations as a consequence of founder effects, bottlenecks, inbreeding and genetic drift. In this study we analyse temporal and spatial effects on genetic variation and progeny viability of the European tree frog (Hyla arborea) at two scales. First, the Swedish distribution has been isolated from the continental distribution for more than 8000 thousand years, and secondly, within Sweden, recent habitat alterations that have taken place during this century have increased isolation between local populations. Genetic variation and progeny survival in relation to isolation was studied within the entire Swedish distribution of the tree frog. Allozyme electrophoresis analysis of froglets, sampled across the Swedish distribution, revealed a low overall genetic variation (1.06 alleles/locus) at the protein level in comparison with continental populations (1.54-1.68 alleles/locus). However, egg hatchability (97%) and early larval survival (95%) were not lower than in other parts of the tree frog distribution or in other anuran species. Within the Swedish distribution, early larval survival was lower in isolated breeding ponds than in more central ones. However, no differences in genetic variation were found in relation to isolation. Polymorphism was detected only at a single locus, and was restricted geographically to the eastern part of the Swedish distribution. Bottlenecks due to climatic changes and fragmentation of suitable habitat (primarily natural pastures with ponds) are suggested as possible causes of the low genetic diversity of the Swedish tree frog population.     

烟盲蝽用芝麻饲养的效果及捕食能力评估

烟盲蝽Nesidiocoris tenuis是一种杂食性天敌昆虫。本研究通过比较芝麻饲养和补充烟粉虱Bemisia tabaci猎物饲养烟盲蝽的生物学特征,评估了用芝麻人工饲养烟盲蝽的饲养效果和该种群对自然猎物的捕食能力。结果显示,用芝麻饲养的烟盲蝽若虫发育历期较长,成虫后足胫节长度较小。然而,烟盲蝽的雌性比例、存活率、成虫生殖力以及对烟粉虱和烟蚜的捕食量没有显著差异。两个种群连续繁殖10代时,芝麻饲养的烟盲蝽种群的内禀增长率为0.2152,与补充猎物饲养的烟盲蝽种群的内禀增长率0.2398相近。以上结果表明,仅用芝麻提供营养可以进行烟盲蝽的人工饲养,并且这种方法为烟盲蝽的规模化饲养和基于芝麻种植的田间饲养模式的建立提供了参考依据。     

Influence of environmental variability on anchovy early life stages (Engraulis encrasicolus) in two different areas of the Central Mediterranean Sea

The survival of early stages of small pelagic fish species (e.g. Engraulis encrasicolus) is highly dependent on environmental conditions in both spawning and nursery areas. Knowing the relationship between the mortality rates of the early stages and the environment may help to study and model recruitment fluctuations. During the summer of 2006, two consecutive oceanographic cruises were carried out in the Central Mediterranean sea (CMED) in two different areas: the western Libyan waters and the Sicilian?CMaltese waters. For the first time a nearly synoptic comparison between the two border areas of the CMED is performed. In spite of a higher overall egg density in the Sicilian?CMaltese waters, there was a higher density of anchovy larvae in Libyan waters. The comparison between the oceanographic datasets singled out different circulation patterns and different characteristics of water masses, which helped to explain the differences in density of the spawning products in the two areas. A Lagrangian transport model was used to evaluate the effects of major oceanographic features on offshore egg and larval transport. The results of the model simulations and the nutritional conditions, as indicated by lipid, carbohydrate and protein contents, support the hypothesis that the western Libyan waters may represent a more favourable nursery ground compared to the Sicilian?CMaltese waters in terms of environmental conditions and food availability.     

Fitness costs and maternal effects associated with resistance to transgenic cotton in the pink bollworm (Lepidoptera: Gelechiidae).

Transgenic cotton producing a Bacillus thuringiensis (Bt) toxin is widely used for controlling the pink bollworm, Perctinophora gossypiella (Saunders). We compared performance of pink bollworm strains resistant to Bt cotton with performance of their susceptible counterparts on non-Bt cotton. We found fitness costs that reduced survival on non-Bt cotton by an average of 51.5% in two resistant strains relative to the susceptible strains. The survival cost was recessive in one set of crosses between a resistant strain and the susceptible strain from which it was derived. However, crosses involving an unrelated resistant and susceptible strain indicated that the survival cost could be dominant. Development time on non-Bt cotton did not differ between the two related resistant and susceptible strains. A slight recessive cost affecting development time was suggested by comparison of the unrelated resistant and susceptible strains. Maternal effects transmitted by parents that had eaten Bt-treated artificial diet as larvae had negative effects on embryogenesis, adult fertility, or both, and reduced the ability of neonates to enter cotton bolls. These results provide further evidence that fitness costs associated with the evolution of resistance to Bt cotton are substantial in the pink bollworm.     

Neighbourhood effects influence drought‐induced mortality of savanna trees in Australia

Questions: The following hypotheses of neighbourhood effects on drought‐induced mortality are evaluated: (A) drought‐induced stem death is randomly distributed in space, (B) stems are predisposed to drought‐induced death through negative density‐dependent effects and (C) stems are predisposed to drought‐induced death due to local deficits in plant available resources. Location: Central Queensland, Australia. Methods: Recent mass mortality of woody stems was surveyed and mapped in three 1.21‐ha quadrats within Eucalyptus melanophloia‐dominated savanna. A multi‐faceted analytical approach was adopted including spatial pattern analyses, two logistic regressions of neighbourhood density effects on survival and spatial autocorrelation analyses of model residuals. Results: Mortality was concentrated in stems ≤15‐cm diameter at breast height (DBH). Survival was aggregated or random in quadrats 1 and 3 and random o regular in quadrat 2. Small neighbour density had a negative effect on survival in all quadrats. In addition, the second model identified a positive relationship between survival and living neighbour density in quadrat 3 (indicating a resource patch effect), but a negative relationship in quadrat 2 (density effect). Analysis of model residuals showed that neighbour density explained mortality equally well across quadrat 2, but not across quadrats 1 and 3. Conclusions: There was evidence in support of hypotheses B (neighbour density) and C (resource heterogeneity). We found strong support for an interaction between microsite quality and neighbourhood stem densities, and suggest that this interaction is driven by plant available water.