Machine Learning in Nuclear Physics

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques.Comment: Comments are welcom

Silk garments plus standard care compared with standard care for treating eczema in children: a randomised controlled, observer blind, pragmatic trial (CLOTHES Trial)

Background The role of clothing in the management of eczema (syn. atopic dermatitis, atopic eczema) is poorly understood. This trial evaluated the effectiveness and cost-effectiveness of silk garments (in addition to standard care) for the management of eczema in children with moderate to severe disease. Methods and findings This was a parallel group randomised controlled, observer-blind trial. Children aged 1 to 15 years with moderate to severe eczema were recruited from secondary care and the community in five UK centres. Participants were allocated using on-line randomisation (1:1) to standard care, or standard care plus silk garments; stratified by age and recruiting centre. Silk garments were worn for 6 months. Primary outcome (eczema severity) was assessed at baseline, 2, 4 and 6 months, by nurses blinded to treatment allocation using the Eczema Area and Severity Index (EASI), which was log-transformed for analysis (intention-to-treat analysis). Safety outcome: number of skin infections. Three hundred children were randomised (26th Nov 2013 to 5th May 2015): 42% girls, 79% white, mean age 5 years. Primary analysis included 282/300 (94%) children (n = 141 in each group). The garments were worn more often at night than in the day (median of 81% of nights (25th to 75th centile 57% to 96%) and 34% of days (25th to 75th centile 10% to 76%)). Geometric mean EASI scores at baseline, 2, 4 and 6 months were 9·2, 6·4, 5·8, 5·4 for silk clothing and 8·4, 6·6, 6·0, 5·4 for standard care. There was no evidence of any difference between the groups in EASI score averaged over all follow up visits adjusted for baseline EASI score, age and centre (adjusted ratio of geometric means: 0·95, 95% CI 0·85 to 1·07). This confidence interval is equivalent to a difference of -1·5 to 0·5 in the original EASI scale units which is not clinically important. Skin infections occurred in 36/142 (25%) and 39/141 (28%) for silk clothing and standard care respectively. Even if the small observed treatment effect was genuine, the incremental cost per QALY was £56,881 in the base case analysis from an NHS perspective, suggesting that silk garments are unlikely to be cost-effective within currently accepted thresholds. Main limitations: whilst minimising detection bias, use of an objective primary outcome may have underestimated treatment effects. Conclusions Silk clothing is unlikely to provide additional benefit over standard care in children with moderate to severe eczema

Volunteer Bias in Recruitment, Retention, and Blood Sample Donation in a Randomised Controlled Trial Involving Mothers and Their Children at Six Months and Two Years: A Longitudinal Analysis

BACKGROUND: The vulnerability of clinical trials to volunteer bias is under-reported. Volunteer bias is systematic error due to differences between those who choose to participate in studies and those who do not. METHODS AND RESULTS: This paper extends the applications of the concept of volunteer bias by using data from a trial of probiotic supplementation for childhood atopy in healthy dyads to explore 1) differences between a) trial participants and aggregated data from publicly available databases b) participants and non-participants as the trial progressed 2) impact on trial findings of weighting data according to deprivation (Townsend) fifths in the sample and target populations. 1) a) Recruits (n = 454) were less deprived than the target population, matched for area of residence and delivery dates (n = 6,893) (mean [SD] deprivation scores 0.09[4.21] and 0.79[4.08], t = 3.44, df = 511, p<0.001). b) i) As the trial progressed, representation of the most deprived decreased. These participants and smokers were less likely to be retained at 6 months (n = 430[95%]) (OR 0.29,0.13-0.67 and 0.20,0.09-0.46), and 2 years (n = 380[84%]) (aOR 0.68,0.50-0.93 and 0.55,0.28-1.09), and consent to infant blood sample donation (n = 220[48%]) (aOR 0.72,0.57-0.92 and 0.43,0.22-0.83). ii) Mothers interested in probiotics or research or reporting infants' adverse events or rashes were more likely to attend research clinics and consent to skin-prick testing. Mothers participating to help children were more likely to consent to infant blood sample donation. 2) In one trial outcome, atopic eczema, the intervention had a positive effect only in the over-represented, least deprived group. Here, data weighting attenuated risk reduction from 6.9%(0.9-13.1%) to 4.6%(-1.4-+10.5%), and OR from 0.40(0.18-0.91) to 0.56(0.26-1.21). Other findings were unchanged. CONCLUSIONS: Potential for volunteer bias intensified during the trial, due to non-participation of the most deprived and smokers. However, these were not the only predictors of non-participation. Data weighting quantified volunteer bias and modified one important trial outcome. TRIAL REGISTRATION: This randomised, double blind, parallel group, placebo controlled trial is registered with the International Standard Randomised Controlled Trials Register, Number (ISRCTN) 26287422. Registered title: Probiotics in the prevention of atopy in infants and children

Colloquium: Machine learning in nuclear physics

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Colloquium provides a snapshot of nuclear physics research, which has been transformed by machine learning techniques