Effects of soil conditions and drought on egg hatching and larval survival of the clover root weevil (Sitona lepidus)

Soil-dwelling insect herbivores are significant pests in many managed ecosystems. Because eggs and larvae are difficult to observe, mathematical models have been developed to predict life-cycle events occurring in the soil. To date, these models have incorporated very little empirical information about how soil and drought conditions interact to shape these processes. This study investigated how soil temperature (10, 15, 20 and 25 °C), water content (0.02 (air dried), 0.10 and 0.25 g g−1) and pH (5, 7 and 9) interactively affected egg hatching and early larval lifespan of the clover root weevil (Sitona lepidus Gyllenhal, Coleoptera: Curculionidae). Eggs developed over 3.5 times faster at 25 °C compared with 10 °C (hatching after 40.1 and 11.5 days, respectively). The effect of drought on S. lepidus eggs was investigated by exposing eggs to drought conditions before wetting the soil (2–12 days later) at four temperatures. No eggs hatched in dry soil, suggesting that S. lepidus eggs require water to remain viable. Eggs hatched significantly sooner in slightly acidic soil (pH 5) compared with soils with higher pH values. There was also a significant interaction between soil temperature, pH and soil water content. Egg viability was significantly reduced by exposure to drought. When exposed to 2–6 days of drought, egg viability was 80–100% at all temperatures but fell to 50% after 12 days exposure at 10 °C and did not hatch at all at 20 °C and above. Drought exposure also increased hatching time of viable eggs. The effects of soil conditions on unfed larvae were less influential, except for soil temperature which significantly reduced larval longevity by 57% when reared at 25 °C compared with 10 °C (4.1 and 9.7 days, respectively). The effects of soil conditions on S. lepidus eggs and larvae are discussed in the context of global climate change and how such empirically based information could be useful for refining existing mathematical models of these processes.

A semi-Markov multistate model for estimation of the mean quality-adjusted survival for non-progressive processes

We discuss the estimation of the expected value of the quality-adjusted survival, based on multistate models. We generalize an earlier work, considering the sojourn times in health states are not identically distributed, for a given vector of covariates. Approaches based on semiparametric and parametric (exponential and Weibull distributions) methodologies are considered. A simulation study is conducted to evaluate the performance of the proposed estimator and the jackknife resampling method is used to estimate the variance of such estimator. An application to a real data set is also included.

Digging effort in leaf-cutting ant queens (Atta sexdens rubropilosa) and its effects on survival and colony growth during the claustral phase

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Evaluation of cytokines concentration and percentage of survival of rabies virus-infected mice submitted to anti-rabies Vero-cell propagated vaccine and P-acnes

Previously, survival of rabies infection was shown to correlate with low IL-6 serum concentration in mice subjected to post-exposure treatment with the Fuenzalida Palacios rabies vaccine in conjunction with the immunomodulator Propionibacterium acnes, previously Corynebacterium parvum. Considering the substitution of the Fuenzalida Palacios rabies vaccine by the Vero cell raised anti-rabies vaccine in almost all countries, the objective of this work was to evaluate the survival and cytokine serum concentration of rabies virus-infected mice treated with P. acnes in conjunction with or the anti-rabies-VERO vaccine. For this, Swiss mice were experimentally infected with street rabies virus and subjected to vaccine and/or P. acnes following infection. Animals were killed at different times and serum was collected to evaluate cytokines. The greatest survival was observed in animals given one or two does of P. acnes in the absence of vaccination. Animals given anti-rabies VERO vaccine alone or with three doses of P. acnes had the second highest survival rate. The group that had the highest percentage of mortality also had the highest IL-6 concentration on the 10th day, a time correlating with clinical symptoms of the animals. The results reinforce the inefficacy of anti-rabies vaccine in only one dose as a post-exposure treatment irrespective of the type of vaccine used, the immunomodulation activity of P. acnes in rabies post-exposure treatment and suggest a role for IL-6 in rabies virus pathogenesis. (c) 2006 Elsevier B.V. All rights reserved.

Increased interleukin-10 associated with low IL-6 concentration correlated with greater survival rates in mice infected by rabies virus vaccinated against it and immunomodulated with P-acnes

Macrophage activity, cytokines serum concentration, serum neutralizing antibodies and lethality by rabies were evaluated in swiss mice experimentally infected with street rabies virus and submitted or not to antirabies vaccination and immunomodulation with P. acnes. Animals were killed at different times and serum was collected in order to evaluate cytokines concentration; peritonial and splenic macrophages were collected for macrophage activity evaluation. Greater survival rates higher IL-10 and low IL-6 serum concentration were observed in vaccinated animals treated using P. acnes. (C) 2004 Elsevier Ltd. All rights reserved.

Likelihood approximations and discrete models for tied survival data

Ties among event times are often recorded in survival studies. For example, in a two week laboratory study where event times are measured in days, ties are very likely to occur. The proportional hazards model might be used in this setting using an approximated partial likelihood function. This approximation works well when the number of ties is small. on the other hand, discrete regression models are suggested when the data are heavily tied. However, in many situations it is not clear which approach should be used in practice. In this work, empirical guidelines based on Monte Carlo simulations are provided. These recommendations are based on a measure of the amount of tied data present and the mean square error. An example illustrates the proposed criterion.

One step forward: contrasting the effects of Toe clipping and PIT tagging on frog survival and recapture probability

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Hatching, survival and deformities of piracanjuba (Brycon orbignyanus) embryos subjected to different cooling protocols

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

THE LOG-BURR XII REGRESSION MODEL FOR GROUPED SURVIVAL DATA

The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.

Predictors of long-term survival among first-ever ischemic and hemorrhagic stroke in a Brazilian stroke cohort

Abstract Background Few studies have examined both ischemic and hemorrhagic stroke to identify prognostic factors associated to long-term stroke survival. We investigated long-term survival and predictors that could adversely influence ischemic and hemorrhagic first-ever stroke prognosis. Methods We prospectively ascertained 665 consecutive first-ever ischemic and hemorrhagic stroke cases from “The Study of Stroke Mortality and Morbidity” (The EMMA Study) in a community hospital in São Paulo, Brazil. We evaluated cardiovascular risk factors and sociodemographic characteristics (age, gender, race and educational level). Results We found a lower survival rate among hemorrhagic cases compared to ischemic stroke cases at the end of 4 years of follow-up (52% vs. 44%, p = 0.04). The risk of death was two times higher among people with ischemic stroke without formal education. Also, we found consistently higher risk of death for diabetics with ischemic stroke (HR = 1.45; 95% CI = 1.07-1.97) compared to no diabetics. As expected, age equally influenced on the high risk of poor survival, regardless of stroke subtype. Conclusions For ischemic stroke, the lack of formal education and diabetes were significant independent predictors of poor long-term survival.

Robust Inferences For Covariate Effects On Survival Time With Censored Linear Regression Models

Various inference procedures for linear regression models with censored failure times have been studied extensively. Recent developments on efficient algorithms to implement these procedures enhance the practical usage of such models in survival analysis. In this article, we present robust inferences for certain covariate effects on the failure time in the presence of "nuisance" confounders under a semiparametric, partial linear regression setting. Specifically, the estimation procedures for the regression coefficients of interest are derived from a working linear model and are valid even when the function of the confounders in the model is not correctly specified. The new proposals are illustrated with two examples and their validity for cases with practical sample sizes is demonstrated via a simulation study.

Analyzing Panel Count Data with Informative Observation Times

In this paper, we study panel count data with informative observation times. We assume nonparametric and semiparametric proportional rate models for the underlying recurrent event process, where the form of the baseline rate function is left unspecified and a subject-specific frailty variable inflates or deflates the rate function multiplicatively. The proposed models allow the recurrent event processes and observation times to be correlated through their connections with the unobserved frailty; moreover, the distributions of both the frailty variable and observation times are considered as nuisance parameters. The baseline rate function and the regression parameters are estimated by maximizing a conditional likelihood function of observed event counts and solving estimation equations. Large sample properties of the proposed estimators are studied. Numerical studies demonstrate that the proposed estimation procedures perform well for moderate sample sizes. An application to a bladder tumor study is presented to illustrate the use of the proposed methods.

Analyzing Bivariate Survival Data with Interval Sampling and Application to Cancer Epidemiology

In medical follow-up studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as the outcomes to identify the progression of a disease. In cancer studies interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme where the first failure event (cancer onset) is identified within a calendar time interval, the time of the initiating event (birth) can be retrospectively confirmed, and the occurrence of the second event (death) is observed sub ject to right censoring. To analyze this type of bivariate failure time data, it is important to recognize the presence of bias arising due to interval sampling. In this paper, nonparametric and semiparametric methods are developed to analyze the bivariate survival data with interval sampling under stationary and semi-stationary conditions. Numerical studies demonstrate the proposed estimating approaches perform well with practical sample sizes in different simulated models. We apply the proposed methods to SEER ovarian cancer registry data for illustration of the methods and theory.

Quality of life and quality-adjusted survival (Q-TWiST) in patients receiving dose-intensive or standard dose chemotherapy for high-risk primary breast cancer

Quality of life (QL) is an important consideration when comparing adjuvant therapies for early breast cancer, especially if they differ substantially in toxicity. We evaluated QL and Q-TWiST among patients randomised to adjuvant dose-intensive epirubicin and cyclophosphamide administered with filgrastim and progenitor cell support (DI-EC) or standard-dose anthracycline-based chemotherapy (SD-CT). We estimated the duration of chemotherapy toxicity (TOX), time without disease symptoms and toxicity (TWiST), and time following relapse (REL). Patients scored QL indicators. Mean durations for the three transition times were weighted with patient reported utilities to obtain mean Q-TWiST. Patients receiving DI-EC reported worse QL during TOX, especially treatment burden (month 3: P<0.01), but a faster recovery 3 months following chemotherapy than patients receiving SD-CT, for example, less coping effort (P<0.01). Average Q-TWiST was 1.8 months longer for patients receiving DI-EC (95% CI, -2.5 to 6.1). Q-TWiST favoured DI-EC for most values of utilities attached to TOX and REL. Despite greater initial toxicity, quality-adjusted survival was similar or better with dose-intensive treatment as compared to standard treatment. Thus, QL considerations should not be prohibitive if future intensive therapies show superior efficacy.

Differences in Time to Disease Progression Do Not Predict for Cancer-specific Survival in Patients Receiving Immediate or Deferred Androgen-deprivation Therapy for Prostate Cancer: Final Results of EORTC Randomized Trial 30891 with 12 Years of Follow-up

BACKGROUND Trials assessing the benefit of immediate androgen-deprivation therapy (ADT) for treating prostate cancer (PCa) have often done so based on differences in detectable prostate-specific antigen (PSA) relapse or metastatic disease rates at a specific time after randomization. OBJECTIVE Based on the long-term results of European Organization for Research and Treatment of Cancer (EORTC) trial 30891, we questioned if differences in time to progression predict for survival differences. DESIGN, SETTING, AND PARTICIPANTS EORTC trial 30891 compared immediate ADT (n=492) with orchiectomy or luteinizing hormone-releasing hormone analog with deferred ADT (n=493) initiated upon symptomatic disease progression or life-threatening complications in randomly assigned T0-4 N0-2 M0 PCa patients. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Time to first objective progression (documented metastases, ureteric obstruction, not PSA rise) and time to objective castration-resistant progressive disease were compared as well as PCa mortality and overall survival. RESULTS AND LIMITATIONS After a median of 12.8 yr, 769 of the 985 patients had died (78%), 269 of PCa (27%). For patients receiving deferred ADT, the overall treatment time was 31% of that for patients on immediate ADT. Deferred ADT was significantly worse than immediate ADT for time to first objective disease progression (p<0.0001; 10-yr progression rates 42% vs 30%). However, time to objective castration-resistant disease after deferred ADT did not differ significantly (p=0.42) from that after immediate ADT. In addition, PCa mortality did not differ significantly, except in patients with aggressive PCa resulting in death within 3-5 yr after diagnosis. Deferred ADT was inferior to immediate ADT in terms of overall survival (hazard ratio: 1.21; 95% confidence interval, 1.05-1.39; p [noninferiority]=0.72, p [difference] = 0.0085). CONCLUSIONS This study shows that if hormonal manipulation is used at different times during the disease course, differences in time to first disease progression cannot predict differences in disease-specific survival. A deferred ADT policy may substantially reduce the time on treatment, but it is not suitable for patients with rapidly progressing disease.