Reviews

500 Computing Tips for Teachers and Lecturers by Phil Race and Steve McDowell, London: Kogan Page, 1996. ISBN: 0–7494–1931–8. 135 pages, paperback. £15.99

Living alone, loneliness and lack of emotional support as predictors of suicide and self-harm: A nine-year follow up of the UK Biobank cohort

Background: The association between loneliness and suicide is poorly understood. We investigated how living alone, loneliness and emotional support were related to suicide and self-harm in a longitudinal design. Methods: Between 2006 and 2010 UK Biobank recruited and assessed in detail over 0.5 million people in middle age. Data were linked to prospective hospital admission and mortality records. Adjusted Cox regression models were used to investigate relationships between living arrangements, loneliness and emotional support, and both suicide and self-harm as outcomes. Results: For men, both living alone (Hazard Ratio (HR) 2.16, 95%CI 1.51-3.09) and living with non-partners (HR 1.80, 95%CI 1.08-3.00) were associated with death by suicide, independently of loneliness, which had a modest relationship with suicide (HR 1.43, 95%CI 0.1.01-2.03). For women, there was no evidence that living arrangements, loneliness or emotional support were associated with death by suicide. Associations between living alone and self-harm were explained by health for women, and by health, loneliness and emotional support for men. In fully adjusted models, loneliness was associated with hospital admissions for self-harm in both women (HR 1.89, 95%CI 1.57-2.28) and men (HR 1.74, 95%CI 1.40-2.16). Limitations: Loneliness and emotional support were operationalized using single item measures. Conclusions: For men - but not for women - living alone or living with a non-partner increased the risk of suicide, a finding not explained by subjective loneliness. Overall, loneliness may be more important as a risk factor for self-harm than for suicide. Loneliness also appears to lessen the protective associations of cohabitation

A case study application of machine-learning for the detection of greenhouse gas emission sources

Conclusively linking local, episodic enhancements in greenhouse gas concentrations to a specific emission source can be challenging, particularly when faced with multiple proximal sources of emissions and variable meteorology, and in the absence of co-emitted tracer gases. This study demonstrates and evaluates the efficacy of using machine-learning tools to detect episodic emissions of methane (CH4) from a shale gas extraction facility in Lancashire (United Kingdom). Two machine-learning tools (rmweather and Prophet) were trained using a two-year climatological baseline dataset collected prior to gas extraction operations at the facility. The baseline dataset consisted of high-precision trace gas concentrations and meteorological data, sampled at 1 Hz continuously between 2016 and 2019. The models showed good overall predictive capacity for baseline CH4 concentrations, with R2 values of 0.85 and 0.76 under optimised training conditions for rmweather and Prophet, respectively. CH4 concentrations were then forecast for an 18-month period from the onset of operations at the shale gas facility (in 2018). Forecast values were compared with true measurements to detect anomalous deviations that may indicate the presence of new emission events associated with the operational facility. Both models successfully detected two periods in which CH4 emissions were known to have occurred (December 2018 and January 2019) via anomalous deviations between modelled and measured concentrations. This work demonstrates the application of machine-learning models for the detection of CH4 emission events from newly built industrial sources, when used in combination with real-time atmospheric monitoring and a baseline dataset collected prior to installation

Carotid intima-media thickness novel loci, sex-specific effects, and genetic correlations with obesity and glucometabolic traits in UK Biobank

Objective: Atherosclerosis is the underlying cause of most cardiovascular disease, but mechanisms underlying atherosclerosis are incompletely understood. Ultrasound measurement of the carotid intima-media thickness (cIMT) can be used to measure vascular remodeling, which is indicative of atherosclerosis. Genome-wide association studies have identified many genetic loci associated with cIMT, but heterogeneity of measurements collected by many small cohorts have been a major limitation in these efforts. Here, we conducted genome-wide association analyses in UKB (UK Biobank; N=22 179), the largest single study with consistent cIMT measurements. Approach and Results: We used BOLT-LMM to run linear regression of cIMT in UKB, adjusted for age, sex, and genotyping chip. In white British participants, we identified 5 novel loci associated with cIMT and replicated most previously reported loci. In the first sex-specific analyses of cIMT, we identified a locus on chromosome 5, associated with cIMT in women only and highlight VCAN as a good candidate gene at this locus. Genetic correlations with body mass index and glucometabolic traits were also observed. Two loci influenced risk of ischemic heart disease. Conclusions: These findings replicate previously reported associations, highlight novel biology, and provide new directions for investigating the sex differences observed in cardiovascular disease presentation and progression

Identification of novel genome-wide associations for suicidality in UK Biobank, genetic correlation with psychiatric disorders and polygenic association with completed suicide

Background: Suicide is a major issue for global public health. Suicidality describes a broad spectrum of thoughts and behaviours, some of which are common in the general population. Although suicide results from a complex interaction of multiple social and psychological factors, predisposition to suicidality is at least partly genetic. Methods: Ordinal genome-wide association study of suicidality in the UK Biobank cohort comparing: ‘no suicidality’ controls (N = 83,557); ‘thoughts that life was not worth living’ (N = 21,063); ‘ever contemplated self-harm’ (N = 13,038); ‘act of deliberate self-harm in the past’ (N = 2498); and ‘previous suicide attempt’ (N = 2666). Outcomes: We identified three novel genome-wide significant loci for suicidality (on chromosomes nine, 11 and 13) and moderate-to-strong genetic correlations between suicidality and a range of psychiatric disorders, most notably depression (rg 0·81). Interpretation: These findings provide new information about genetic variants relating to increased risk of suicidal thoughts and behaviours. Future work should assess the extent to which polygenic risk scores for suicidality, in combination with non-genetic risk factors, may be useful for stratified approaches to suicide prevention at a population level

Environmental baseline monitoring for shale gas development in the UK: identification and geochemical characterisation of local source emissions of methane to atmosphere

Baseline mobile surveys of methane sources using vehicle-mounted instruments have been performed in the Fylde and Ryedale regions of Northern England over the 2016–19 period around proposed unconventional (shale) gas extraction sites. The aim was to identify and characterise methane sources ahead of hydraulically fractured shale gas extraction in the area around drilling sites. This allows a potential additional source of emissions to atmosphere to be readily distinguished from adjacent sources, should gas production take place. The surveys have used ethane:methane (C2:C1) ratios to separate combustion, thermogenic gas and biogenic sources. Sample collection of source plumes followed by high precision δ13C analysis of methane, to separate and isotopically characterise sources, adds additional biogenic source distinction between active and closed landfills, and ruminant eructations from manure. The surveys show that both drill sites and adjacent fixed monitoring sites have cow barns and gas network pipeline leaks as sources of methane within a 1 km range. These two sources are readily separated by isotopes (δ13C of −67 to −58‰ for barns, compared to −43 to −39‰ for gas leaks), and ethane:methane ratios (0.05 for gas leaks). Under a well-mixed daytime atmospheric boundary layer these sources are generally detectable as above baseline elevations up to 100 m downwind for gas leaks and up to 500 m downwind for populated cow barns. It is considered that careful analysis of these proxies for unconventional production gas, if and when available, will allow any fugitive emissions from operations to be distinguished from surrounding sources