A Simple,Flexible Failure Model

A new failure model is introduced in the form of a four-parameter nonlinear differential equation, with failure probability as the dependent variable and failure time as the independent variable. The first parameter characterizes the location, the second the scale, and the other two the shape of the model. The type of the accompanying hazard function is immediately read off the shape parameters. The new model approximates the classical failure models with rather high precision, but also models cases where the failure density is skewed to the left. It can be used to analyze survival data objectively, based on the shape of the failure distribution. The computation of quantiles and moments is easy and fast. Nonlinear regression methods are used to estimate parameter values.     

Effect of new founders on retention of gene diversity in captive populations: A formalization of the nucleus population concept

A nucleus population is a small captive population genetically supported by periodic importation of wild caught animals. Periodic importation will allow nucleus populations to maintain the same amount of gene diversity as larger captive populations that do not import wild caught animals. The function of nucleus populations as envisioned by the IUCN/SSC Captive Breeding Specialist Group (CBSG) is to make additional captive space available for endangered taxa not currently maintained in captivity. In this article, mathematical models are developed to assess the effectiveness of the nucleus population concept in reducing the population sizes necessary to maintain appreciable amounts of gene diversity in captive populations. It is shown that the Nucleus I population concept, as defined and promoted by the CBSG, requires an importation rate 10–20 times greater than they have indicated. Whereas nucleus populations are not appropriate for maintenance of significant amounts of gene diversity in long-term breeding programs, small populations can be valuable for research, education, and reintroduction projects with short-term goals. Decisions have to be made on which of the many endangered taxa will be maintained and for what purposes, if captive breeding is to be an effective component of species conservation. © 1993 Wiley-Liss, Inc.     

Interactions of target population size,population parameters,and program management on viability of captive populations

When established conservation programs expand and evolve, management practices may become inconsistent with program goals. In the past decade, the American Zoo and Aquarium Association expanded species conservation programs by increasing the number of Species Survival Plans (SSP) and establishing more than 300 new Population Management Plan (PMP) programs. However, limited space in captive breeding facilities forces a competition among SSPs and less intensively managed PMPs. Regional Collection Plans establish priorities and allocate space accordingly by setting target population size for each species; species of high conservation priority (SSPs) are allocated space at the expense of lower priority species (PMPs). Because population size and genetic composition interact to impact population viability, target population size is a significant factor to a population’s prospects for long‐term survival. We examined four population parameters (current population size, target population size, current gene diversity, and mean generation time) for 46 mammalian SSPs and 17 PMPs. Relative to SSPs, PMPs combine smaller current and target population sizes, lower levels of current gene diversity, and shorter mean generation times than SSPs. Thus, the average PMP population can expect to lose gene diversity more rapidly than the average SSP population. PMPs are projected to lose 10% or more of their founding gene diversity, within only 2 years. In contrast, the average SSP population is projected to lose 10% in 40 years. Populations with small current or target population sizes require intensive management to avoid extinction. More intensive genetic management of populations typically designated as PMPs, through recruitment of potential founders and equalization of founder representation, could increase gene diversity and improve viability. Less rigorous population management should be reserved for populations whose long‐term survival is either secure or that can be readily replenished from the wild. Because PMP populations need intense genetic management similar to that currently in effect for SSPs, there should be neither a management‐level distinction between programs nor an arbitrary difference in space allocated to programs. Zoo Biol 20:169–183, 2001. © 2001 Wiley‐Liss, Inc.     

Microorganisms surviving for 5300 years

Abstract Recently, the well-preserved corpse of a prehistoric man with an age of approximately 5300 years bp was discovered in the Central European Alps. Analysis of materials associated with the individual has revealed the presence of microorganisms which are believed to have survived since the time of death. So far, two fungi have been isolated and identified as species of the genera Chaetomium and Absidia , respectively. In addition, we have obtained one bacterial isolate which we have identified as a Streptomyces species. Our findings demonstrate that microorganisms can remain viable under appropriate circumstances for thousands of years. The isolates may enable us to study evolutionary trends within microorganisms.     

A method to measure predictive ability of an injury risk curve using an observation-adjusted area under the receiver operating characteristic curve

Area under the receiver operating characteristic curve (AROC) is commonly used to choose a biomechanical metric from which to construct an injury risk curve (IRC). However, AROC may not handle censored datasets adequately. Survival analysis creates robust estimates of IRCs which accommodate censored data. We present an observation-adjusted ROC (oaROC) which uses the survival-based IRC to estimate the AROC. We verified and evaluated this method using simulated datasets of different censoring statuses and sample sizes. For a dataset with 1000 left and right censored observations, the median AROC closely approached the oaROC_True, or the oaROC calculated using an assumed “true” IRC, differing by a fraction of a percent, 0.1%. Using simulated datasets with various censoring, we found that oaROC converged onto oaROC_True in all cases. For datasets with right and non-censored observations, AROC did not converge onto oaROC_True. oaROC for datasets with only non-censored observations converged the fastest, and for a dataset with 10 observations, the median oaROC differed from oaROC_True by 2.74% while the corresponding median AROC with left and right censored data differed from oaROC_True by 9.74%. We also calculated the AROC and oaROC for a published side impact dataset, and differences between the two methods ranged between −24.08% and 24.55% depending on metric. Overall, when compared with AROC, we found oaROC performs equivalently for doubly censored data, better for non-censored data, and can accommodate more types of data than AROC. While more validation is needed, the results indicate that oaROC is a viable alternative which can be incorporated into the metric selection process for IRCs.     

Performance of the marginal structural models under various scenarios of incomplete marker's values: A simulation study

Marginal structural models (MSMs) have been proposed for estimating a treatment's effect, in the presence of time‐dependent confounding. We aimed to evaluate the performance of the Cox MSM in the presence of missing data and to explore methods to adjust for missingness. We simulated data with a continuous time‐dependent confounder and a binary treatment. We explored two classes of missing data: (i) missed visits, which resemble clinical cohort studies; (ii) missing confounder's values, which correspond to interval cohort studies. Missing data were generated under various mechanisms. In the first class, the source of the bias was the extreme treatment weights. Truncation or normalization improved estimation. Therefore, particular attention must be paid to the distribution of weights, and truncation or normalization should be applied if extreme weights are noticed. In the second case, bias was due to the misspecification of the treatment model. Last observation carried forward (LOCF), multiple imputation (MI), and inverse probability of missingness weighting (IPMW) were used to correct for the missingness. We found that alternatives, especially the IPMW method, perform better than the classic LOCF method. Nevertheless, in situations with high marker's variance and rarely recorded measurements none of the examined method adequately corrected the bias.