Modelling the consequences of targeted selective treatment strategies on performance and emergence of anthelmintic resistance amongst grazing calves

The development of anthelmintic resistance by helminths can be slowed by maintaining refugia on pasture or in untreated hosts. Targeted selective treatments (TST) may achieve this through the treatment only of individuals that would benefit most from anthelmintic, according to certain criteria. However TST consequences on cattle are uncertain, mainly due to difficulties of comparison between alternative strategies. We developed a mathematical model to compare: 1) the most ‘beneficial’ indicator for treatment selection and 2) the method of selection of calves exposed to Ostertagia ostertagi, i.e. treating a fixed percentage of the population with the lowest (or highest) indicator values versus treating individuals who exceed (or are below) a given indicator threshold. The indicators evaluated were average daily gain (ADG), faecal egg counts (FEC), plasma pepsinogen, combined FEC and plasma pepsinogen, versus random selection of individuals. Treatment success was assessed in terms of benefit per R (BPR), the ratio of average benefit in weight gain to change in frequency of resistance alleles R (relative to an untreated population). The optimal indicator in terms of BPR for fixed percentages of calves treated was plasma pepsinogen and the worst ADG; in the latter case treatment was applied to some individuals who were not in need of treatment. The reverse was found when calves were treated according to threshold criteria, with ADG being the best target indicator for treatment. This was also the most beneficial strategy overall, with a significantly higher BPR value than any other strategy, but its degree of success depended on the chosen threshold of the indicator. The study shows strong support for TST, with all strategies showing improvements on calves treated selectively, compared with whole-herd treatment at 3, 8, 13 weeks post-turnout. The developed model appeared capable of assessing the consequences of other TST strategies on calf populations

Modelling the impacts of pasture contamination and stocking rate for the development of targeted selective treatment strategies for Ostertagia ostertagi infection in calves

A simulation study was carried out to assess whether variation in pasture contamination or stocking rate impact upon the optimal design of targeted selective treatment (TST) strategies. Two methods of TST implementation were considered: 1) treatment of a fixed percentage of a herd according to a given phenotypic trait, or 2) treatment of individuals that exceeded a threshold value for a given phenotypic trait. Four phenotypic traits, on which to base treatment were considered: 1) average daily bodyweight gain, 2) faecal egg count, 3) plasma pepsinogen, or 4) random selection. Each implementation method (fixed percentage or threshold treatment) and determinant criteria (phenotypic trait) was assessed in terms of benefit per R (BPR), the ratio of average benefit in weight gain to change in frequency of resistance alleles R (relative to an untreated population). The impact of pasture contamination on optimal TST strategy design was investigated by setting the initial pasture contamination to 100, 200 or 500 O. ostertagi L3/kg DM herbage; stocking rate was investigated at a low (3calves/ha), conventional (5 calves/ha) or high (7 calves/ha) stocking rates. When treating a fixed percentage of the herd, treatments according to plasma pepsinogen or random selection were identified as the most beneficial (i.e. resulted in the greatest BPR) for all levels of initial pasture contamination and all stocking rates. Conversely when treatments were administered according to threshold values ADG was most beneficial, and was identified as the best TST strategy (i.e. resulted in the greatest overall BPR) for all levels of initial pasture contamination and all stocking rates

A stochastic model to investigate the effects of control strategies on calves exposed to Ostertagia ostertagi

Predicting the effectiveness of parasite control strategies requires accounting for the responses of individual hosts and the epidemiology of parasite supra- and infra-populations. The first objective was to develop a stochastic model that predicted the parasitological interactions within a group of first season grazing calves challenged by Ostertagia ostertagi, by considering phenotypic variation amongst the calves and variation in parasite infra-population. Model behaviour was assessed using variations in parasite supra-population and calf stocking rate. The model showed the initial pasture infection level to have little impact on parasitological output traits, such as worm burdens and FEC, or overall performance of calves, whereas increasing stocking rate had a disproportionately large effect on both parasitological and performance traits. Model predictions were compared with published data taken from experiments on common control strategies, such as reducing stocking rates, the ‘dose and move’ strategy and strategic treatment with anthelmintic at specific times. Model predictions showed in most cases reasonable agreement with observations, supporting model robustness. The stochastic model developed is flexible, with the potential to predict the consequences of other nematode control strategies, such as targeted selective treatments on groups of grazing calves

Transient and sustained bacterial adaptation following repeated sublethal exposure to microbicides and a novel human antimicrobial peptide

Microbicides (biocides) play an important role in the prevention and treatment of infections. While there is currently little evidence for in-use treatment failures attributable to acquired reductions in microbicide susceptibility, the susceptibility of some bacteria can be reduced by sublethal laboratory exposure to certain agents. In this investigation, a range of environmental bacterial isolates (11 genera, 18 species) were repeatedly exposed to four microbicides (cetrimide, chlorhexidine, polyhexamethylene biguanide [PHMB], and triclosan) and a cationic apolipoprotein E-derived antimicrobial peptide (apoEdpL-W) using a previously validated exposure system. Susceptibilities (MICs and minimum bactericidal concentrations [MBCs]) were determined before and after 10 passages (P10) in the presence of an antimicrobial and then after a further 10 passages without an antimicrobial to determine the stability of any adaptations. Bacteria exhibiting >4-fold increases in MBCs were further examined for alterations in biofilm-forming ability. Following microbicide exposure, ≥4-fold decreases in susceptibility (MIC or MBC) occurred for cetrimide (5/18 bacteria), apoEdpL-W (7/18), chlorhexidine (8/18), PHMB (8/18), and triclosan (11/18). Of the 34 ≥4-fold increases in the MICs, 15 were fully reversible, 13 were partially reversible, and 6 were nonreversible. Of the 26 ≥4-fold increases in the MBCs, 7 were fully reversible, 14 were partially reversible, and 5 were nonreversible. Significant decreases in biofilm formation in P10 strains occurred for apoEdpL-W (1/18 bacteria), chlorhexidine (1/18), and triclosan (2/18), while significant increases occurred for apoEdpL-W (1/18), triclosan (1/18), and chlorhexidine (2/18). These data indicate that the stability of induced changes in microbicide susceptibility varies but may be sustained for some combinations of a bacterium and a microbicide

Developments in automated flexible gauging and the uncertainty associated with comparative coordinate measurement

Traditional manufacturing uses coordinate measuring machines (CMMs) or component-specific gauging for in-process and post-process inspection. In assessing the fitness for purpose of these measuring systems, it is necessary to evaluate the uncertainty associated with CMM measurement. However, this is not straightforward since the measurement results are subject to a large range of factors including systematic and environmental effects that are difficult to quantify. In addition, machine tool errors and thermal effects of the machine and component can have a significant impact on the comparison between on-machine measurement, in-process measurement and post-process inspection. Coordinate measurements can also be made in a gauging/comparator mode in which measurements of a work piece are compared with those of a calibrated master artefact, and many of the difficulties associated with evaluating the measurement uncertainties are avoided since many of the systematic effects cancel out. Therefore, the use of flexible gauging either as part of an automated or manually-served workflow is particularly beneficial

Evaluation of automated flexible gauge performance using experimental designs

An essential part of assessing whether a measurement or gauging system meets its intended purpose is to estimate the measurement uncertainties. This paper employs the design of experiments (DOE) approach to implement a practical analysis of measurement uncertainty of Renishaw Equator automated flexible gauge. The factors of interest are measurement strategy, part location, and environmental effects. The experimental results show the ability of the versatile gauge to effectively meet its measurement capability in both discrete-point probing and scanning measuring modes within its whole measuring volume and, in particular, at high scanning speeds and under workshop conditions

Fluorescent IGF-II analogues for FRET-based investigations into the binding of IGF-II to the IGF-1R

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material.The interaction of IGF-II with the insulin receptor (IR) and type 1 insulin-like growth factor receptor (IGF-1R) has recently been identified as potential therapeutic target for the treatment of cancer. Understanding the interactions of IGF-II with these receptors is required for the development of potential anticancer therapeutics. This work describes an efficient convergent synthesis of native IGF-II and two nonnative IGF-II analogues with coumarin fluorescent probes incorporated at residues 19 and 28. These fluorescent analogues bind with nanomolar affinities to the IGF-1R and are suitable for use in fluorescence resonance energy transfer (FRET) studies. From these studies the F19Cou IGF-II and F28Cou IGF-II proteins were identified as good probes for investigating the binding interactions of IGF-II with the IGF-1R and its other high affinity binding partners

Keck Spectroscopy of Candidate Proto-globular Clusters in NGC 1275

Keck spectroscopy of 5 proto-globular cluster candidates in NGC 1275 has been combined with HST WFPC2 photometry to explore the nature and origin of these objects and discriminate between merger and cooling flow scenarios for globular cluster formation. The objects we have studied are not HII regions, but rather star clusters, yet their integrated spectral properties do not resemble young or intermediate age Magellanic Cloud clusters or Milky Way open clusters. The clusters' Balmer absorption appears to be too strong to be consistent with any of the standard Bruzual & Charlot evolutionary models at any metallicity. If these models are adopted, an IMF which is skewed to high masses provides a better fit to the data. A truncated IMF with a mass range of 2-3 Mo reproduces the observed Balmer equivalent widths and colors at about 450 Myr. Formation in a continuous cooling flow appears to be ruled out since the age of the clusters is much larger than the cooling time, the spatial scale of the clusters is much smaller than the cooling flow radius, and the deduced star formation rate in the cooling flow favors a steep rather than a flat IMF. A merger would have to produce clusters only in the central few kpc, presumably from gas in the merging galaxies which was channeled rapidly to the center. Widespread shocks in merging galaxies cannot have produced these clusters. If these objects are confirmed to have a relatively flat, or truncated, IMF it is unclear whether or not they will evolve into objects we would regard as bona fide globular clusters.Comment: 30 pages (AAS two column style, including 9 tables and 7 figures) to appear in the AJ (August issue), also available at http://www.ucolick.org/~mkissler/Sages/sages.html (with a full resolution Fig.1) Revised Version: previous posted version was an uncorrect ealier iteration, parts of the text, tables and figures changed. The overall conclusions remain unchange

Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures

Linear mixed models are commonly used in analyzing stepped-wedge cluster randomized trials (SW-CRTs). A key consideration for analyzing a SW-CRT is accounting for the potentially complex correlation structure, which can be achieved by specifying a random effects structure. Common random effects structures for a SW-CRT include random intercept, random cluster-by-period, and discrete-time decay. Recently, more complex structures, such as the random intervention structure, have been proposed. In practice, specifying appropriate random effects can be challenging. Robust variance estimators (RVE) may be applied to linear mixed models to provide consistent estimators of standard errors of fixed effect parameters in the presence of random-effects misspecification. However, there has been no empirical investigation of RVE for SW-CRT. In this paper, we first review five RVEs (both standard and small-sample bias-corrected RVEs) that are available for linear mixed models. We then describe a comprehensive simulation study to examine the performance of these RVEs for SW-CRTs with a continuous outcome under different data generators. For each data generator, we investigate whether the use of a RVE with either the random intercept model or the random cluster-by-period model is sufficient to provide valid statistical inference for fixed effect parameters, when these working models are subject to misspecification. Our results indicate that the random intercept and random cluster-by-period models with RVEs performed similarly. The CR3 RVE estimator, coupled with the number of clusters minus two degrees of freedom correction, consistently gave the best coverage results, but could be slightly anti-conservative when the number of clusters was below 16. We summarize the implications of our results for linear mixed model analysis of SW-CRTs in practice.Comment: Correct figure legend and table Typo

Shrub expansion in tundra ecosystems: dynamics, impacts and research priorities

Part of Focus on Dynamics of Arctic and Sub-Arctic Vegetation Recent research using repeat photography, long-term ecological monitoring and dendrochronology has documented shrub expansion in arctic, high-latitude and alpine tundra ecosystems. Here, we (1) synthesize these findings, (2) present a conceptual framework that identifies mechanisms and constraints on shrub increase, (3) explore causes, feedbacks and implications of the increased shrub cover in tundra ecosystems, and (4) address potential lines of investigation for future research. Satellite observations from around the circumpolar Arctic, showing increased productivity, measured as changes in 'greenness', have coincided with a general rise in high-latitude air temperatures and have been partly attributed to increases in shrub cover. Studies indicate that warming temperatures, changes in snow cover, altered disturbance regimes as a result of permafrost thaw, tundra fires, and anthropogenic activities or changes in herbivory intensity are all contributing to observed changes in shrub abundance. A large-scale increase in shrub cover will change the structure of tundra ecosystems and alter energy fluxes, regional climate, soil–atmosphere exchange of water, carbon and nutrients, and ecological interactions between species. In order to project future rates of shrub expansion and understand the feedbacks to ecosystem and climate processes, future research should investigate the species or trait-specific responses of shrubs to climate change including: (1) the temperature sensitivity of shrub growth, (2) factors controlling the recruitment of new individuals, and (3) the relative influence of the positive and negative feedbacks involved in shrub expansion