Autonomous recovery in componentized Internet applications

In this paper we show how to reduce downtime of J2EE applications by rapidly and automatically recovering from transient and intermittent software failures, without requiring application modifications. Our prototype combines three application-agnostic techniques: macroanalysis for fault detection and localization, microrebooting for rapid recovery, and external management of recovery actions. The individual techniques are autonomous and work across a wide range of componentized Internet applications, making them well-suited to the rapidly changing software of Internet services. The proposed framework has been integrated with JBoss, an open-source J2EE application server. Our prototype provides an execution platform that can automatically recover J2EE applications within seconds of the manifestation of a fault. Our system can provide a subset of a system's active end users with the illusion of continuous uptime, in spite of failures occurring behind the scenes, even when there is no functional redundancy in the system.     

Compound regressive models for family data.

The regressive models for the analysis of family data are extended to include cases in which the within-sibship covariation may exceed that implied by the class A regressive model, but for which birth order is not required. In addition to specified major genes, if any, and common parental phenotypes, the excess within-sibship covariation may come from a common cumulative risk from unspecified factors such as a shared environment, and other genes. The within-sibship cumulative risk has a probability distribution in the population. The sib-sib correlation (more generally within-sibship statistical dependence) is equal for all pairs within a given sibship. The compound regressive model is thus a version of the class D regressive model with the property of within-sibship interchangeability. The work is motivated here by comparing and contrasting the Elston-Stewart algorithm and the Morton-MacLean algorithm for the mixed model of inheritance. This points the way to derive practical algorithms for the compound regressive models proposed, with easy extensions to pedigrees of arbitrary structure, and to multilocus problems.     

On Bivariate Cumulative Damage Models With Application

Bivariate cumulative damage models are proposed where the responses given the damages are independent random variables. The bivariate damage process can be either bivariate Poisson or bivariate gamma. A bivariate continuous cumulative damage model is investigated in which the responses given the damages have gamma distributions. In this case evaluation of the joint density function and bivariate tail probability function is facilitated by expanding the gamma distributions of the conditional responses by Laguerre polynomials. This approach also leads to evaluation of associated survival models. Moments and estimating equations are discussed. In addition, a bivariate discrete cumulative damage model is investigated in which the responses given the damages have a distribution chosen from a class that includes the negative binomial, the Neyman Type‐A, the Polya‐Aeppli, and the Lagrangian Poisson. Probabilities are obtained from recursive formulas which do not involve cancellation error as all quantities are non‐negative. Moments and estimating equations are presented for these models also. The continuous and the discrete models are applied to describe the rise of systolic and diastolic blood pressure with age.     

空间约束背景下海岸带湿地保护边界研究

湿地保护边界划定是维护湿地可持续发展的基础前提,也是协调湿地保护与农地利用、城镇发展土地配置与空间布局的必经途径。研究从湿地保护边界的"源-汇"生态景观互动视角着眼,明晰了湿地保护源及保护优先等级,建构了空间约束评价与生态断界来刻画汇景观空间层次分异,借助最小累积阻力模型测度与划分了湿地保护边界及其类型。研究发现:(1)基于多重因素的空间约束评价与湿地生态断界的划分,通过最小阻力模型有效地刻画外部因子集成对湿地保护的限制及差异,以量化方式描绘湿地保护所处阻力最小地带,为湿地保护边界划分提供了新的思路。(2)杭州湾南岸生态断界与中高约束的区域占据研究区52.69%比例,无约束地域仅为12.79%,奠定了综合因素空间约束强势的基本格局。加之低度约束区域占据34.53%,显示杭州湾南岸区域多介于高低空间约束性势转换或过渡阶段,压缩了湿地保护的空间。(3)研究划分湿地保护边界范围与缓冲区带为49.11、24.07km~2,面积总和仅占据杭州湾南岸5.47%,湿地保护拓展和弹性调整空间极为有限。除生态断界提供湿地保护的天然边界外,湿地保护界限表现为保育林区、农地延伸型和水源保护型三类。     

Partitioning Detectability Components in Populations Subject to Within-Season Temporary Emigration Using Binomial Mixture Models

Detectability of individual animals is highly variable and nearly always < 1; imperfect detection must be accounted for to reliably estimate population sizes and trends. Hierarchical models can simultaneously estimate abundance and effective detection probability, but there are several different mechanisms that cause variation in detectability. Neglecting temporary emigration can lead to biased population estimates because availability and conditional detection probability are confounded. In this study, we extend previous hierarchical binomial mixture models to account for multiple sources of variation in detectability. The state process of the hierarchical model describes ecological mechanisms that generate spatial and temporal patterns in abundance, while the observation model accounts for the imperfect nature of counting individuals due to temporary emigration and false absences. We illustrate our model’s potential advantages, including the allowance of temporary emigration between sampling periods, with a case study of southern red-backed salamanders Plethodon serratus. We fit our model and a standard binomial mixture model to counts of terrestrial salamanders surveyed at 40 sites during 3–5 surveys each spring and fall 2010–2012. Our models generated similar parameter estimates to standard binomial mixture models. Aspect was the best predictor of salamander abundance in our case study; abundance increased as aspect became more northeasterly. Increased time-since-rainfall strongly decreased salamander surface activity (i.e. availability for sampling), while higher amounts of woody cover objects and rocks increased conditional detection probability (i.e. probability of capture, given an animal is exposed to sampling). By explicitly accounting for both components of detectability, we increased congruence between our statistical modeling and our ecological understanding of the system. We stress the importance of choosing survey locations and protocols that maximize species availability and conditional detection probability to increase population parameter estimate reliability.     

Evolution under Spatially Heterogeneous Selection in Solid Tumors

Spatial genetic and phenotypic diversity within solid tumors has been well documented. Nevertheless, how this heterogeneity affects temporal dynamics of tumorigenesis has not been rigorously examined because solid tumors do not evolve as the standard population genetic model due to the spatial constraint. We therefore, propose a neutral spatial (NS) model whereby the mutation accumulation increases toward the periphery; the genealogical relationship is spatially determined and the selection efficacy is blunted (due to kin competition). In this model, neutral mutations are accrued and spatially distributed in manners different from those of advantageous mutations. Importantly, the distinctions could be blurred in the conventional model. To test the NS model, we performed a three-dimensional multiple microsampling of two hepatocellular carcinomas. Whole-genome sequencing (WGS) revealed a 2-fold increase in mutations going from the center to the periphery. The operation of natural selection can then be tested by examining the spatially determined clonal relationships and the clonal sizes. Due to limited migration, only the expansion of highly advantageous clones can sweep through a large part of the tumor to reveal the selective advantages. Hence, even multiregional sampling can only reveal a fraction of fitness differences in solid tumors. Our results suggest that the NS patterns are crucial for testing the influence of natural selection during tumorigenesis, especially for small solid tumors.     

State-dependent inactivation of the Kv3 potassium channel.

Inactivation of Kv3 (Kv1.3) delayed rectifier potassium channels was studied in the Xenopus oocyte expression system. These channels inactivate slowly during a long depolarizing pulse. In addition, inactivation accumulates in response to a series of short depolarizing pulses (cumulative inactivation), although no significant inactivation occurs within each short pulse. The extent of cumulative inactivation does not depend on the voltage during the depolarizing pulse, but it does vary in a biphasic manner as a function of the interpulse duration. Furthermore, the rate of cumulative inactivation is influenced by changing the rate of deactivation. These data are consistent with a model in which Kv3 channel inactivation is a state-dependent and voltage-independent process. Macroscopic and single channel experiments indicate that inactivation can occur from a closed (silent) state before channel opening. That is, channels need not open to inactivate. The transition that leads to the inactivated state from the silent state is, in fact, severalfold faster then the observed inactivation of current during long depolarizing pulses. Long pulse-induced inactivation appears to be slow, because its rate is limited by the probability that channels are in the open state, rather than in the silent state from which they can inactivate. External potassium and external calcium ions alter the rates of cumulative and long pulse-induced inactivation, suggesting that antagonistic potassium and calcium binding steps are involved in the normal gating of the channel.     

An energy-based model of optimal feeding-territory size

An energy-based model of feeding territoriality is described. The model predicts an optimal territory size where the territory holder's net energy intake is maximized, on a daily or seasonal time scale. Simulated effects on optimal territory size of animal size, food availability, and competitor density are in general agreement with observed relationships in a wide variety of animals. When salmonid data are used to estimate the model parameter values, predicted territory sizes are similar to those actually observed. When emigration is allowed, the model predicts that territoriality, through individual selection alone, can regulate population size, but that this regulation breaks down when initial densities exceed some threshold value. Sensitivity analysis shows optimal territory size to be most affected by those parameters influencing food intake and energy expenditure. Some alternative criteria for optimization are also discussed. An animal maximizing net foraging efficiency has a smaller territory than one maximizing net energy, but the effects of animal size, food availability, and competitor density on territory size are the same in either case.     

The role of variance in capped-rate stochastic growth models with external mortality

Techniques from queueing theory can be used to model the growth of individuals in a stochastic environment where the growth rate cannot exceed some physiological maximum. In the simplifying case where there are no metabolic costs and the individual has an unlimited gut size, a general theory for the probability of reaching a target weight is presented. The role of environmental variance is examined in two different cases; randomly varying prey size and clustering in the prey arrival rate. It is argued that when food is scarce environmental stochasticity is beneficial to the individual but when food is abundant it can lower the chance of survival. The growth and recruitment of larval fish is used as an example.     

Towards a better model of the effect of prostrate vegetation cover on wind erosion

Vegetation cover is the key to controlling wind erosion. A brief review of wind erosion/cover models is outlined. Fryrear's (1985) soil cover (wheat stubble) model was evaluated against field wind tunnel results from far south-west N.S.W. Fryrear's equation over estimated the soil loss compared to field wind tunnel results.Fryrear's model failed to provide meaningful results at low cover levels with the soil loss ratio, SLR>1 for percent soil cover, % SC<6. A single parameter exponential model was fitted to the wind tunnel data which ensured that SLR did not exceed 1 for 100% SC. Even with this improvement, the exponential model has drawbacks.Results suggest that the SLR is sensitive to wind velocity and that SLR goes to 0 well before % SC=100. A method for approximating the threshold wind velocity required to initiate erosion for various cover levels is described. Using the recurrence interval for a prescribed wind velocity, the probability of erosion hazard for a field can be determined.It is the authors belief that the wind tunnel is underestimating the occurrence of wind erosion events in this study. Three reasons why the wind tunnel may be underestimating erosion events are given.     

Assessing putative interlocus sexual conflict in Drosophila melanogaster using experimental evolution

The theoretical foundation of sexually antagonistic coevolution is that females suffer a net fitness cost through their interactions with males. The empirical prediction is that direct costs to female lifetime fecundity will exceed indirect benefits despite a possible increase in the genetic quality of offspring. Although direct costs of males have been repeatedly shown, to date no study has comprehensively tested whether females are compensated for this direct harm through indirect benefits. Here we use experimental evolution to show that a mutation giving Drosophila melanogaster females nearly complete resistance to the direct costs of male courtship and remating, but which also excluded almost all indirect benefits, is strongly favoured by selection. We estimated the selection coefficient favouring the resistance allele to be +20%. These results demonstrate that any indirect benefits that females accrued were not sufficient to counter-balance the direct costs of males, and reinforce a large body of past studies by verifying interlocus sexual conflict in this model system.     

Genetic and environmental factors in the trade‐off between sexual and asexual reproduction of a rare clonal angiosperm

Understanding the factors that influence the trade‐off between flowering and clonal growth in angiosperms is of great interest to botanists, plant ecologists and evolutionary biologists. Being able, ultimately, to manipulate these factors has potentially wide‐ranging applications in conservation biology, horticulture and agriculture. A rare, native Australian species, Melaleuca deanei, displays a low frequency of flowering and failed seedling recruitment, with persistence and expansion in the field predominantly via clonal root suckering. A decade of studies into its reproductive biology and ecology reveal both genetic and environmental factors reducing sexual success within its populations. Five factors influencing the trade‐off from sexual reproduction to clonal propagation are distilled and discussed in this work: 1. environmental constraint on sexual success; 2. small population size/low mate availability; 3. an obligate outcrossing breeding system; 4. somatic mutations in older clones; and 5. phylogenetic constraint. The work is potentially informative to the life cycles and management of other clonal angiosperms, be they rare and endangered; highly fecund weedy species; or economically important species. Methods to test each factor in future studies are proposed.     

Borrowing strength with nonexchangeable priors over subpopulations

We introduce a nonparametric Bayesian model for a phase II clinical trial with patients presenting different subtypes of the disease under study. The objective is to estimate the success probability of an experimental therapy for each subtype. We consider the case when small sample sizes require extensive borrowing of information across subtypes, but the subtypes are not a priori exchangeable. The lack of a priori exchangeability hinders the straightforward use of traditional hierarchical models to implement borrowing of strength across disease subtypes. We introduce instead a random partition model for the set of disease subtypes. This is a variation of the product partition model that allows us to model a nonexchangeable prior structure. Like a hierarchical model, the proposed clustering approach considers all observations, across all disease subtypes, to estimate individual success probabilities. But in contrast to standard hierarchical models, the model considers disease subtypes a priori nonexchangeable. This implies that when assessing the success probability for a particular type our model borrows more information from the outcome of the patients sharing the same prognosis than from the others. Our data arise from a phase II clinical trial of patients with sarcoma, a rare type of cancer affecting connective or supportive tissues and soft tissue (e.g., cartilage and fat). Each patient presents one subtype of the disease and subtypes are grouped by good, intermediate, and poor prognosis. The prior model should respect the varying prognosis across disease subtypes. The practical motivation for the proposed approach is that the number of accrued patients within each disease subtype is small. Thus it is not possible to carry out a clinical study of possible new therapies for rare conditions, because it would be impossible to plan for sufficiently large sample size to achieve the desired power. We carry out a simulation study to compare the proposed model with a model that assumes similar success probabilities for all subtypes with the same prognosis, i.e., a fixed partition of subtypes by prognosis. When the assumption is satisfied the two models perform comparably. But the proposed model outperforms the competing model when the assumption is incorrect.     

Genome wide association studies in presence of misclassified binary responses

Background

Misclassification has been shown to have a high prevalence in binary responses in both livestock and human populations. Leaving these errors uncorrected before analyses will have a negative impact on the overall goal of genome-wide association studies (GWAS) including reducing predictive power. A liability threshold model that contemplates misclassification was developed to assess the effects of mis-diagnostic errors on GWAS. Four simulated scenarios of case–control datasets were generated. Each dataset consisted of 2000 individuals and was analyzed with varying odds ratios of the influential SNPs and misclassification rates of 5% and 10%.

Results

Analyses of binary responses subject to misclassification resulted in underestimation of influential SNPs and failed to estimate the true magnitude and direction of the effects. Once the misclassification algorithm was applied there was a 12% to 29% increase in accuracy, and a substantial reduction in bias. The proposed method was able to capture the majority of the most significant SNPs that were not identified in the analysis of the misclassified data. In fact, in one of the simulation scenarios, 33% of the influential SNPs were not identified using the misclassified data, compared with the analysis using the data without misclassification. However, using the proposed method, only 13% were not identified. Furthermore, the proposed method was able to identify with high probability a large portion of the truly misclassified observations.

Conclusions

The proposed model provides a statistical tool to correct or at least attenuate the negative effects of misclassified binary responses in GWAS. Across different levels of misclassification probability as well as odds ratios of significant SNPs, the model proved to be robust. In fact, SNP effects, and misclassification probability were accurately estimated and the truly misclassified observations were identified with high probabilities compared to non-misclassified responses. This study was limited to situations where the misclassification probability was assumed to be the same in cases and controls which is not always the case based on real human disease data. Thus, it is of interest to evaluate the performance of the proposed model in that situation which is the current focus of our research.

     

基于成串刺激计算立即释放囊泡池（RRP）大小的改进方法

突触囊泡的立即释放囊泡池（RRP）概念已被广泛用于突触传递的分析. 基于这些囊泡池中囊泡性质是均匀的假设，通过外推成串刺激累积诱发的突触后兴奋性电流，已经开发了几种确定RRP大小的方法. 然而，使用不同刺激频率确定这些成串刺激得到的RRP大小结果不同. 这种频率依赖性显示了这些估算方法的不完备性，与RRP的定义相矛盾. 因此，我们提出了基于成串刺激计算RRP大小的改进算法. 假设RRP的填充率正比于RRP释放的部分，并且矫正RRP的未使用部分，给出RRP释放过程的完整数学描述，得到具体的解析结果. 与已知的两种常用方法做比较，该方法很好地描述了RRP的释放和填充过程，得到了比较良好的RRP大小和囊泡释放概率大小的评估. 该方法不受刺激频率的条件限制，可以很好地适用于不能给予高频刺激的细胞.     

The Fundamental Constraint on the evolution of culture

This paper argues that there is a general constraint on the evolution of culture. This constraint – what I am calling the Fundamental Constraint – must be satisfied in order for a cultural system to be adaptive. The Fundamental Constraint is this: for culture to be adaptive there must be a positive correlation between the fitness of cultural variants and their fitness impact on the organisms adopting those variants. Two ways of satisfying the Fundamental Constraint are introduced, structural solutions and evaluative solutions. Because of the limitations on these solutions, this constraint helps explain why there is not more culture in nature, why the culture that does exist has the form it has, and why complex, cumulative culture is restricted to the human species.     

Optimal infection strategies: should macroparasites hedge their bets?

Despite considerable research into the mechanisms that lead to the persistence of parasites, the huge diversity of macroparasite transmission strategies observed both within and among species has yet to be explained. This may be because questions of parasite persistence are typically addressed at the population level, even though observed transmission rates are determined by infection events at the level of the individual parasite. To help overcome this disparity, a simple model is developed to explore the optimal infection strategy for a macroparasite under a range of selection pressures. The model calculates the fitness of the parasite by considering explicitly the probability of the individual infective stages surviving and infecting. The optimal strategy is highly sensitive to the rate of host availability and, considering the parasite's fitness, it is often preferable to have sub-maximal infectivity to maximise survival during periods of host absence. An important finding is that when parasites are faced with unpredictable conditions such as the time of host availability, the optimum strategy may be to produce offspring that differ in their infection strategies. By spreading the risk in this way, known as bet hedging, parasites can increase the chances that at least some of their offspring will infect successfully. This potential for variation in infection strategies has not been considered explicitly before and may have wide reaching implications for current epidemiology theory.     

Two-sample Kolmogorov-Smirnov test for truncated data

A nonparametric statistical test to compare two cumulative frequency distribution functions is presented that can be used even when both samples include censored data, as is often the case when comparing the survival of two groups of laboratory animals under conditions in which the experiment is terminated before all the animals die. (Such a design can produce considerable savings and is to be recommended.) The program calculates exact probabilities for both the one-sided and two-sided alternatives to the null hypothesis, applicable to the case of equal group size, as well as the corresponding general asymptotic values; a continuity correction is employed that markedly improves the asymptotic approximation. Expressions are stated in terms of two different but related statistics, and the one that utilizes more information in any particular set of data is selected for the probability calculations. All basic equations and definitions are provided.     

On Estimation of Relative Risk in Case Control Studies

An estimator of relative risk in a case control study has been proposed in terms of observed cell frequencies and the probability of disease. The bias of the usual estimator i.e odds ratio as compared to the new estimator has been workedout. The expression of Mean Square Error of proposed estimator has been derived in situations where probability of disease is exactly known and when it is estimated through an independent survey. It has been observed that there is a serious error using odds ratio as an estimate of relative risk when probability of disease is not negligible. In such situations the proposed estimator can be used with advantage.     

Augmented Inverse Probability Weighted Estimator for Cox Missing Covariate Regression

This article investigates an augmented inverse selection probability weighted estimator for Cox regression parameter estimation when covariate variables are incomplete. This estimator extends the Horvitz and Thompson (1952, Journal of the American Statistical Association 47, 663-685) weighted estimator. This estimator is doubly robust because it is consistent as long as either the selection probability model or the joint distribution of covariates is correctly specified. The augmentation term of the estimating equation depends on the baseline cumulative hazard and on a conditional distribution that can be implemented by using an EM-type algorithm. This method is compared with some previously proposed estimators via simulation studies. The method is applied to a real example.