Although the study was small owing to recruitment difficulties, it still provides evidence of effectiveness of melatonin in children with sleep difficulties and ASD, which we predict a larger study would confirm.

SummaryBackgroundStaphylococcus aureus bacteraemia is a common cause of severe community-acquired and hospital-acquired infection worldwide. We tested the hypothesis that adjunctive rifampicin would reduce bacteriologically confirmed treatment failure or disease recurrence, or death, by enhancing early S aureus killing, sterilising infected foci and blood faster, and reducing risks of dissemination and metastatic infection.MethodsIn this multicentre, randomised, double-blind, placebo-controlled trial, adults (≥18 years) with S aureus bacteraemia who had received ≤96 h of active antibiotic therapy were recruited from 29 UK hospitals. Patients were randomly assigned (1:1) via a computer-generated sequential randomisation list to receive 2 weeks of adjunctive rifampicin (600 mg or 900 mg per day according to weight, oral or intravenous) versus identical placebo, together with standard antibiotic therapy. Randomisation was stratified by centre. Patients, investigators, and those caring for the patients were masked to group allocation. The primary outcome was time to bacteriologically confirmed treatment failure or disease recurrence, or death (all-cause), from randomisation to 12 weeks, adjudicated by an independent review committee masked to the treatment. Analysis was intention to treat. This trial was registered, number ISRCTN37666216, and is closed to new participants.FindingsBetween Dec 10, 2012, and Oct 25, 2016, 758 eligible participants were randomly assigned: 370 to rifampicin and 388 to placebo. 485 (64%) participants had community-acquired S aureus infections, and 132 (17%) had nosocomial S aureus infections. 47 (6%) had meticillin-resistant infections. 301 (40%) participants had an initial deep infection focus. Standard antibiotics were given for 29 (IQR 18–45) days; 619 (82%) participants received flucloxacillin. By week 12, 62 (17%) of participants who received rifampicin versus 71 (18%) who received placebo experienced treatment failure or disease recurrence, or died (absolute risk difference −1·4%, 95% CI −7·0 to 4·3; hazard ratio 0·96, 0·68–1·35, p=0·81). From randomisation to 12 weeks, no evidence of differences in serious (p=0·17) or grade 3–4 (p=0·36) adverse events were observed; however, 63 (17%) participants in the rifampicin group versus 39 (10%) in the placebo group had antibiotic or trial drug-modifying adverse events (p=0·004), and 24 (6%) versus six (2%) had drug interactions (p=0·0005).InterpretationAdjunctive rifampicin provided no overall benefit over standard antibiotic therapy in adults with S aureus bacteraemia.FundingUK National Institute for Health Research Health Technology Assessment.

Management accounting researchers have been slow to explore the empirical implications of the “manager effect” on management control choices. We critique the “manager effect” literature and identify research opportunities for management accounting researchers. Since the publication of Bertrand and Schoar's (2003) seminal paper, which shows that individual managers have an effect on firm behavior, a large and growing body of accounting and finance research has used publicly available data to identify individual manager effects on a variety of firm outcomes. Management accounting researchers can add significant value to this research; for example, by exploring the control choices that a firm makes to mitigate the adverse consequences associated with some managerial characteristics. In this critique we first identify some of the theoretical and methodological challenges associated with the “manager effects” research and second identify opportunities for management accounting researchers to explore these effects while overcoming some of the limitations.

Credit ratings are an important metric for business managers and a contributor to economic growth. Forecasting such ratings might be a suitable application of big data analytics. As machine learning is one of the foundations of intelligent big data analytics, this chapter presents a comparative analysis of traditional statistical models and popular machine learning models for the prediction of Moody's long-term corporate debt ratings. Machine learning techniques such as artificial neural networks, support vector machines, and random forests generally outperformed their traditional counterparts in terms of both overall accuracy and the Kappa statistic. The parametric models may be hindered by missing variables and restrictive assumptions about the underlying distributions in the data. This chapter reveals the relative effectiveness of non-parametric big data analytics to model a complex process that frequently arises in business, specifically determining credit ratings.

In this paper we discuss how operating system design and implementation influences the methodology for computer forensics investigations, with the focus on forensic acquisition of memory. In theory the operating system could support such investigations both in terms of tools for analysis of data and by making the system data readily accessible for analysis. Conventional operating systems such as Windows and UNIX derivatives offer some memory-related tools that are geared towards the analysis of system crashes, rather than forensic investigations. In this paper we demonstrate how techniques developed for persistent operating systems, where lifetime of data is independent of the method of its creation and storage, could support computer forensics investigations delivering higher efficiency and accuracy. It is proposed that some of the features offered by persistent systems could be built into conventional operating systems to make illicit activities easier to identify and analyse. We further propose a new technique for forensically sound acquisition of memory based on the persistence paradigm.