Selenium persistency and speciation in the tissues of lambs following the withdrawal of dietary high-dose selenium-enriched yeast

The objective was to determine the concentration of total selenium (Se) and the proportion of total Se comprised as selenomethionine (SeMet) and selenocysteine (SeCys) in post mortem tissues of lambs in the six weeks period following the withdrawal of a diet containing high dose selenized yeast (SY), derived from a specific strain of Saccharomyces cerevisae CNCM (Collection Nationale de Culture de Micro-organism) I-3060. Thirty Texel x Suffolk lambs used in this study had previously received diets (91 days) containing either high dose SY (HSY; 6.30 mg Se/kg DM) or an unsupplemented control (C; 0.13 mg Se/kg DM). Following the period of supplementation all lambs were then offered a complete pelleted diet, without additional Se (0.15 mg Se/kg DM), for 42 days. At enrollment and 21 and 42 days later, five lambs from each treatment were blood sampled, euthanased and samples of heart, liver, kidney and skeletal muscle (Longissimus Dorsi and Psoas Major) tissue were retained. Total Se concentration in whole blood and tissues was significantly (P < 0.001) higher in HSY lambs at all time points that had previously received long term exposure to high dietary concentrations of SY. The distribution of total Se and the proportions of total Se comprised as SeMet and SeCys differed between tissues, treatment and time points. Total Se was greatest in HSY liver and kidney (22.64 and 18.96 mg Se/kg DM, respectively) and SeCys comprised the greatest proportion of total Se. Conversely, cardiac and skeletal muscle (Longissimus Dorsi and Psoas Major) tissues had lower total Se concentration (10.80, 7.02 and 7.82 mg Se/kg DM, respectively) and SeMet was the predominant selenized amino acid. Rates of Se clearance in HSY liver (307 µg Se/day) and kidney (238 µg Se/day) were higher compared with HSY cardiac tissue (120 µg Se/day) and skeletal muscle (20 µg Se/day). In conclusion differences in Se clearance rates were different between tissue types, reflecting the relative metabolic activity of each tissue, and appear to be dependant upon the proportions of total Se comprised as either SeMet or SeCys.

Selenium supplementation of lactating dairy cows: effects on milk production and total selenium content and speciation in blood, milk and cheese

Forty-multiparous Holstein cows were used in a 16-wk continuous design study to determine the effects of either selenium (Se) source, selenized yeast (SY) (derived from a specific strain of Saccharomyces cerevisiae CNCM I-3060 Sel-Plex®) or sodium selenite (SS), or inclusion rate of SY on Se concentration and speciation in blood, milk and cheese. Cows received ad libitum a TMR with 1:1 forage:concentrate ratio on a dry matter (DM) basis. There were four diets (T1-T4) which differed only in either source or dose of Se additive. Estimated total dietary Se for T1 (no supplement), T2 (SS), T3 (SY) and T4 (SY) was 0.16, 0.30, 0.30 and 0.45 mg/kg DM, respectively. Blood and milk samples were taken at 28 day intervals and at each time point there were positive linear effects of SY on Se concentration in blood and milk. At day 112 blood and milk Se values for T1-T4 were 177, 208, 248, 279 ± 6.6 and 24, 38, 57, 72 ± 3.7 ng/g fresh material, respectively and indicate improved uptake and incorporation of Se from SY. While selenocysteine (SeCys) was the main selenised amino acid in blood its concentration was not markedly affected by treatment, but the proportion of total Se as selenomethionine (SeMet) increased with increasing inclusion rate of SY. In milk, there were no marked treatment effects on SeCys content, but Se source had a marked effect on the proportion of total Se as SeMet. At day 112 replacing SS (T2) with SY (T3) increased the SeMet concentration of milk from 36 to 111 ng Se/g and its concentration increased further to 157 ng Se/g as the inclusion rate of SY increased further (T4) to provide 0.45 mg Se/kg TMR. Neither Se source nor inclusion rate effected the keeping quality of milk. At day 112, milk from T1, T2, and T3 was made into a hard cheese and Se source had a marked effect on total Se and the proportion of total Se comprised as either SeMet or SeCys. Replacing SS (T2) with SY (T3) increased total Se, SeMet and SeCys content from 180 to 340 ng Se/g, 57 to 153 ng Se/g and 52 to 92 ng Se/g, respectively. Key words: dairy cow, milk and cheese, selenomethionine, selenocysteine, milk keeping quality

Speciation in fig wasps

There are over 700 species of fig trees in the tropics and several thousand species of fig wasps are associated with their syconia (inflorescences). These wasps comprise a monophyletic family of fig pollinators and several diverse lineages of non-pollinating wasps. The pollinator larvae gall fig flowers, while larvae of non-pollinating species either initiate their own galls or parasitise the galls of other wasps. A single fig species has 1-4 pollinator species and also hosts up to 30 non-pollinating wasp species. Most wasps show a high degree of host plant specificity and are known from only a single fig species. However, in some cases wasps may be shared across closely related fig species. There is impressive morphological coevolution between figs and fig wasps and this, combined with a high degree of partner specificity, led to the expectation that figs and pollinators have cospeciated extensively. Comparison of deep phylogenies supports long-term codivergence of figs and pollinators, but also suggests that some host shifts have occurred. Phylogenies of more closely related species do not match perfectly and may even be incongruent, suggesting significant roles for processes other than strict cospeciation. Combined with recent evidence on host specificity patterns, this suggests that pollinator wasps may often speciate by host shifts between closely related figs, or by duplication (the wasp speciates but the fig doesn't). The frequencies and biological details of these different modes of speciation invite further study. Far less is known about speciation in non-pollinating fig wasps. Some lineages have probably coevolved with figs and pollinators for most of the evolutionary history of the symbiosis, while others appear to be more recent colonisers. Many species appear to be highly host plant specific, but those that lay eggs through the fig wall without entering the syconium (the majority of species) may be subject to fewer constraints on host-shifting than pollinators. There is evidence for substantial host shifting in at least one gens, but also evidence for ecological speciation on the same host plant by niche shifts in other cases. Finally, recent work has begun to address the issue of “community phylogeny” and provided evidence for long-term co-divergence of multiple pollinating and non-pollinating wasp lineages with their host figs.

The effect of consumption volume on profile and liking of oral nutritional supplements of varied sweetness: sequential profiling and boredom tests

Oral nutrition supplements (ONS) are routinely prescribed to those with, or at risk of, malnutrition. Previous research identified poor compliance due to taste and sweetness. This paper investigates taste and hedonic liking of ONS, of varying sweetness and metallic levels, over consumption volume; an important consideration as patients are prescribed large volumes of ONS daily. A sequential descriptive profile was developed to determine the perception of sensory attributes over repeat consumption of ONS. Changes in liking of ONS following repeat consumption were characterised by a boredom test. Certain flavour (metallic taste, soya milk flavour) and mouthfeel (mouthdrying, mouthcoating) attributes built up over increased consumption volume (p 0.002). Hedonic liking data from two cohorts, healthy older volunteers (n = 32, median age 73) and patients (n = 28, median age 85), suggested such build-up was disliked. Efforts made to improve the palatability of ONS must take account of the build up of taste and mouthfeel characteristics over increased consumption volume.

An adaptive approach to implementing bivariate group sequential clinical trial designs

In clinical trials, situations often arise where more than one response from each patient is of interest; and it is required that any decision to stop the study be based upon some or all of these measures simultaneously. Theory for the design of sequential experiments with simultaneous bivariate responses is described by Jennison and Turnbull (Jennison, C., Turnbull, B. W. (1993). Group sequential tests for bivariate response: interim analyses of clinical trials with both efficacy and safety endpoints. Biometrics 49:741-752) and Cook and Farewell (Cook, R. J., Farewell, V. T. (1994). Guidelines for monitoring efficacy and toxicity responses in clinical trials. Biometrics 50:1146-1152) in the context of one efficacy and one safety response. These expositions are in terms of normally distributed data with known covariance. The methods proposed require specification of the correlation, ρ between test statistics monitored as part of the sequential test. It can be difficult to quantify ρ and previous authors have suggested simply taking the lowest plausible value, as this will guarantee power. This paper begins with an illustration of the effect that inappropriate specification of ρ can have on the preservation of trial error rates. It is shown that both the type I error and the power can be adversely affected. As a possible solution to this problem, formulas are provided for the calculation of correlation from data collected as part of the trial. An adaptive approach is proposed and evaluated that makes use of these formulas and an example is provided to illustrate the method. Attention is restricted to the bivariate case for ease of computation, although the formulas derived are applicable in the general multivariate case.

Point estimates and confidence regions for sequential trials involving selection

A number of authors have proposed clinical trial designs involving the comparison of several experimental treatments with a control treatment in two or more stages. At the end of the first stage, the most promising experimental treatment is selected, and all other experimental treatments are dropped from the trial. Provided it is good enough, the selected experimental treatment is then compared with the control treatment in one or more subsequent stages. The analysis of data from such a trial is problematic because of the treatment selection and the possibility of stopping at interim analyses. These aspects lead to bias in the maximum-likelihood estimate of the advantage of the selected experimental treatment over the control and to inaccurate coverage for the associated confidence interval. In this paper, we evaluate the bias of the maximum-likelihood estimate and propose a bias-adjusted estimate. We also propose an approach to the construction of a confidence region for the vector of advantages of the experimental treatments over the control based on an ordering of the sample space. These regions are shown to have accurate coverage, although they are also shown to be necessarily unbounded. Confidence intervals for the advantage of the selected treatment are obtained from the confidence regions and are shown to have more accurate coverage than the standard confidence interval based upon the maximum-likelihood estimate and its asymptotic standard error.

Sequential designs for phase III clinical trials incorporating treatment selection

Most statistical methodology for phase III clinical trials focuses on the comparison of a single experimental treatment with a control. An increasing desire to reduce the time before regulatory approval of a new drug is sought has led to development of two-stage or sequential designs for trials that combine the definitive analysis associated with phase III with the treatment selection element of a phase II study. In this paper we consider a trial in which the most promising of a number of experimental treatments is selected at the first interim analysis. This considerably reduces the computational load associated with the construction of stopping boundaries compared to the approach proposed by Follman, Proschan and Geller (Biometrics 1994; 50: 325-336). The computational requirement does not exceed that for the sequential comparison of a single experimental treatment with a control. Existing methods are extended in two ways. First, the use of the efficient score as a test statistic makes the analysis of binary, normal or failure-time data, as well as adjustment for covariates or stratification straightforward. Second, the question of trial power is also considered, enabling the determination of sample size required to give specified power. Copyright © 2003 John Wiley & Sons, Ltd.

The sequential analysis of repeated binary responses: a score test for the case of three time points

In this paper a robust method is developed for the analysis of data consisting of repeated binary observations taken at up to three fixed time points on each subject. The primary objective is to compare outcomes at the last time point, using earlier observations to predict this for subjects with incomplete records. A score test is derived. The method is developed for application to sequential clinical trials, as at interim analyses there will be many incomplete records occurring in non-informative patterns. Motivation for the methodology comes from experience with clinical trials in stroke and head injury, and data from one such trial is used to illustrate the approach. Extensions to more than three time points and to allow for stratification are discussed. Copyright © 2005 John Wiley & Sons, Ltd.

Incorporating data received after a sequential trial has stopped into the final analysis: implementation and comparison of methods

In a sequential clinical trial, accrual of data on patients often continues after the stopping criterion for the study has been met. This is termed “overrunning.” Overrunning occurs mainly when the primary response from each patient is measured after some extended observation period. The objective of this article is to compare two methods of allowing for overrunning. In particular, simulation studies are reported that assess the two procedures in terms of how well they maintain the intended type I error rate. The effect on power resulting from the incorporation of “overrunning data” using the two procedures is evaluated.

An adaptive group sequential design for phase II/III clinical trials that select a single treatment from several

There is increasing interest in combining Phases II and III of clinical development into a single trial in which one of a small number of competing experimental treatments is ultimately selected and where a valid comparison is made between this treatment and the control treatment. Such a trial usually proceeds in stages, with the least promising experimental treatments dropped as soon as possible. In this paper we present a highly flexible design that uses adaptive group sequential methodology to monitor an order statistic. By using this approach, it is possible to design a trial which can have any number of stages, begins with any number of experimental treatments, and permits any number of these to continue at any stage. The test statistic used is based upon efficient scores, so the method can be easily applied to binary, ordinal, failure time, or normally distributed outcomes. The method is illustrated with an example, and simulations are conducted to investigate its type I error rate and power under a range of scenarios.

A practical comparison of group-sequential and adaptive designs

Sequential methods provide a formal framework by which clinical trial data can be monitored as they accumulate. The results from interim analyses can be used either to modify the design of the remainder of the trial or to stop the trial as soon as sufficient evidence of either the presence or absence of a treatment effect is available. The circumstances under which the trial will be stopped with a claim of superiority for the experimental treatment, must, however, be determined in advance so as to control the overall type I error rate. One approach to calculating the stopping rule is the group-sequential method. A relatively recent alternative to group-sequential approaches is the adaptive design method. This latter approach provides considerable flexibility in changes to the design of a clinical trial at an interim point. However, a criticism is that the method by which evidence from different parts of the trial is combined means that a final comparison of treatments is not based on a sufficient statistic for the treatment difference, suggesting that the method may lack power. The aim of this paper is to compare two adaptive design approaches with the group-sequential approach. We first compare the form of the stopping boundaries obtained using the different methods. We then focus on a comparison of the power of the different trials when they are designed so as to be as similar as possible. We conclude that all methods acceptably control type I error rate and power when the sample size is modified based on a variance estimate, provided no interim analysis is so small that the asymptotic properties of the test statistic no longer hold. In the latter case, the group-sequential approach is to be preferred. Provided that asymptotic assumptions hold, the adaptive design approaches control the type I error rate even if the sample size is adjusted on the basis of an estimate of the treatment effect, showing that the adaptive designs allow more modifications than the group-sequential method.