Colonization of the large bowel of healthy infants by Clostridium difficile was studied. Feces were collected from five breast-fed aand five formula-fed infants throughout the first year of life, and levels of C. difficile were quantitated. Three breast-fed and five formula-fed infants were colonized for periods of between 8 and 42 weeks, and another infant harbored the organism only during week 1. Colonization of breast-fed infants commenced before or during weaning, with levels reaching 10(3) to 10(5) organisms per g of wet feces. Colonization of formula-fed infants commenced before solid foods were given, with levels of 10(3) to 10(7) organisms per g of wet feces. Isolates from eight of the babies were shown to produce cytotoxin in vitro. Single fecal specimens from 60 more children aged up to 4 years were also examined, and it was found that the carriage rate of C. difficile fell sharply after 1 year of age, although in the second year it was still higher than in adults. These findings are discussed in relation to the microbial ecology of the large bowel and the paradox that levels of C. difficile in the large bowel of healthy infants are similar to those causing pseudomembranous colitis in patients.

Ethical behavior encompasses actions that benefit both self and society. This means that tackling antimicrobial resistance (AMR) becomes an ethical obligation, because the prospect of declining anti-infectives affects everyone. Without preventive action, loss of drugs that have saved lives over the past century, will condemn ourselves, people we know, and people we don’t know, to unacceptable risk of untreatable infection. Policies aimed at extending antimicrobial life should be considered within an ethical framework, in order to balance the choice, range, and quality of drugs against stewardship activities. Conserving availability and effectiveness for future use should not compromise today’s patients. Practices such as antimicrobial prophylaxis for healthy people ‘at risk’ should receive full debate. There are additional ethical considerations for AMR involving veterinary care, agriculture, and relevant bio-industries. Restrictions for farmers potentially threaten the quality and quantity of food production with economic consequences. Antibiotics for companion animals do not necessarily spare those used for humans. While low-income countries cannot afford much-needed drugs, pharmaceutical companies are reluctant to develop novel agents for short-term return only. Public demand encourages over-the-counter, internet, black market, and counterfeit drugs, all of which compromise international control. Prescribers themselves require educational support to balance therapeutic choice against collateral damage to both body and environment. Predicted mortality due to AMR provides justification for international co-operation, commitment and investment to support surveillance and stewardship along with development of novel antimicrobial drugs. Ethical arguments for, and against, control of antimicrobial resistance strategies are presented and discussed in this review.

Multi-fidelity meta-modelling has become a popular means of efficiently distributing computational resource across various levels of simulation fidelity to obtain numerically accurate predictions of an expensive function. Such techniques have significant potential within an engineering design paradigm incorporating either many-query analyses or outer-loop applications.This paper presents a hybrid parametric/non-parametric information correction method incorporating the sequential application of several distinct stages within an artificial neural network based surrogate framework. The proposed methodology may be used to correct any domain encompassing set of low-fidelity input/output correspondence using a small subset of high-fidelity samples. A global surrogate can then be generated via a doubleloop ANN hyper-parameter selection and training procedure.To demonstrate the effectiveness of the proposed metamodelling approach, the aerodynamic response prediction of a parametrized waverider-based re-entry vehicle is examined. Results suggest that the incorporation of multiple corrective stages leveraging low-fidelity data can offer significant improvements in computational efficiency when modelling the expensive highfidelity function compared with single stage correction. The costs to achieve global accuracy are examined and compared across single/multi-stage variants, with consideration given to surrogate construction and evaluation. Results are compared both with the low-fidelity approximation and a surrogate of the 'true' response built using only the high-fidelity samples available to the corrective method.

This paper presents a multi-fidelity meta-modelling and model management framework designed to efficiently incorporate increased levels of simulation fidelity from multiple, competing sources into early-stage multidisciplinary design optimisation scenarios. Phase specific/invariant low-fidelity physics-based subsystem models are adaptively corrected via iterative sampling of high(er)-fidelity simulators. The correction process is decomposed into several distinct parametric/non-parametric stages, each leveraging alternate aspects of the available model responses. Globally approximating surrogates are constructed at each degree of fidelity (low, mid, and high) via an automated hyper-parameter selection and training procedure. The resulting hierarchy drives the optimisation process, with local refinement managed according to a confidence-based multi-response adaptive sampling procedure, with bias given to global parameter sensitivities. An application of this approach is demonstrated via the aerodynamic response prediction of a parametrized re-entry vehicle, subjected to a static/dynamic parameter optimisation for three separate single-objective problems. It is found that the proposed data correction process facilitates increased efficiency in attaining a desired approximation accuracy relative to a single-fidelity equivalent model. When applied within the proposed multi-fidelity management framework, clear convergence to the objective optimum is observed for each examined design optimisation scenario, outperforming an equivalent single-fidelity approach in terms of computational efficiency and solution variability.