185 research outputs found

    Geoscience for Sustainable Futures : our journey towards impact

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    This report describes the BGS Geoscience for Sustainable Futures (GSF) National Capability (NC)/Official Development Assistance (ODA) programme. It used ODA funding to advance collaborative geoscience research and innovation to address challenges in lower- and middle-income countries. We describe the programme’s progress towards achieving lasting change (‘impact’) in the contexts where activities were carried out. These activities were organised into projects under three research platforms (RPs) focusing on different development issues and working in different regions: • RP1: integrated resource management in eastern Africa (addressing the UN’s Sustainable Development Goals (SDGs) 2, 3, 6 and 12) • RP2: resilience of Asian cities (SDGs 6, 11 and 13) • RP3: global geological risk (SDGs 1, 9 and 11) For each platform, a ‘Theory of Change’ (ToC) approach was used to plan and navigate the pathway to impact. This approach requires first analysing the institutional and environmental context of the location being considered, so that an impact objective may be defined. From this, the behaviour and capacity changes (‘outcomes’) that are expected to lead to that impact are identified. Those outcomes will rely on outputs such as reports, policy briefing notes, databases and web portals, which are produced by various activities, such as workshops, research and conference attendance. An idealised ToC is shown in Figure 1. The theories of change developed during the programme in this report are significantly more complex. As of 27 May 2022, the programme’s outputs include: • 96 peer-review journal articles • 42 reports • 8 book chapters • 15 maps • 14 technical products, databases and datasets • 4 webtools Around 200 engagement activities have also been undertaken. To assess how much progress the RPs have made towards producing outcomes and impact, ‘impact narratives’ were prepared for each project and analysed to determine the types of outcome that had emerged or were emerging as of August 2021: • conceptual: changes in knowledge, understanding and attitudes • capacity (skills) strengthening: increased ability (of individuals and organisations) to conduct similar work in future • enduring connectivity: changes to the existence and strength of networks of people and one-to-one relationships, and organisations who understand and can make use of the research • instrumental: changes in policy and practice While it is possible to point to many positive outcomes arising from the GSF programme (Table 1), we recognise the journey to impact is not yet over. The knowledge, skills and relationships developed will potentially contribute to further outcomes and impact after the programme ends. Maximising this potential impact will depend on the steps taken by BGS staff and our partners in the coming months and years. Numerous lessons have been learned during the programme about doing impact-focused research and using ToC. They point to the need for greater input from partners in: • developing theories of change • detailed stakeholder mapping • emphasising knowledge brokering activities • clear indicators of progress towards impact that can be monitored both during a project and after it end

    Void-Assisted Ion-Paired Proton Transfer at Water-Ionic Liquid Interfaces.

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    At the water-trihexyl(tetradecyl)phosphonium tris(pentafluoroethyl)trifluorophosphate ([P14,6,6,6 ][FAP]) ionic liquid interface, the unusual electrochemical transfer behavior of protons (H(+) ) and deuterium ions (D(+) ) was identified. Alkali metal cations (such as Li(+) , Na(+) , K(+) ) did not undergo this transfer. H(+) /D(+) transfers were assisted by the hydrophobic counter anion of the ionic liquid, [FAP](-) , resulting in the formation of a mixed capacitive layer from the filling of the latent voids within the anisotropic ionic liquid structure. This phenomenon could impact areas such as proton-coupled electron transfers, fuel cells, and hydrogen storage where ionic liquids are used as aprotic solvents

    Whitefield News

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    File includes: January 2017 Volume 4, Issue 7 February 2017 Volume 4, Issue 8 March 2017 Volume 4, Issue 9 April 2017 Volume 4, Issue 10 May 2017 Volume 4, Issue 11 June 2017 Volume 4, Issue 12 July 2017 Volume 5, Issue 1 August 2017 Volume 5, Issue 2 September 2017 Volume 5, Issue 3 October 2017 Volume 5, Issue 4 November 2017 Volume 5, Issue 5 December 2017 Volume 5, Issue

    Whitefield News

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    File includes: January 2016 Volume 3, Issue 7 February 2016 Volume 3, Issue 8 March 2016 Volume 3, Issue 9 April 2016 Volume 3, Issue 10 May 2016 Volume 3, Issue 11 June 2016 Volume 3, Issue 12 July 2016 Volume 4, Issue 1 August 2016 Volume 4, Issue 2 September 2016, Volume 4, Issue 3 October 2016, Volume 4, Issue 4 November 2016, Volume 4, Issue 5 December 2016, Volume 4, Issue

    Genetic variation at CHRNA5-CHRNA3-CHRNB4 interacts with smoking status to influence body mass index

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    Background Cigarette smoking is associated with lower body mass index (BMI), and a commonly cited reason for unwillingness to quit smoking is a concern about weight gain. Common variation in the CHRNA5-CHRNA3-CHRNB4 gene region (chromosome 15q25) is robustly associated with smoking quantity in smokers, but its association with BMI is unknown. We hypothesized that genotype would accurately reflect smoking exposure and that, if smoking were causally related to weight, it would be associated with BMI in smokers, but not in never smokers. Methods We stratified nine European study samples by smoking status and, in each stratum, analysed the association between genotype of the 15q25 SNP, rs1051730, and BMI. We meta-analysed the results (n = 24 198) and then tested for a genotype × smoking status interaction. Results There was no evidence of association between BMI and genotype in the never smokers {difference per T-allele: 0.05 kg/m2 [95% confidence interval (95% CI): −0.05 to 0.18]; P = 0.25}. However, in ever smokers, each additional smoking-related T-allele was associated with a 0.23 kg/m2 (95% CI: 0.13-0.31) lower BMI (P = 8 × 10−6). The effect size was larger in current [0.33 kg/m2 lower BMI per T-allele (95% CI: 0.18-0.48); P = 6 × 10−5], than in former smokers [0.16 kg/m2 (95% CI: 0.03-0.29); P = 0.01]. There was strong evidence of genotype × smoking interaction (P = 0.0001). Conclusions Smoking status modifies the association between the 15q25 variant and BMI, which strengthens evidence that smoking exposure is causally associated with reduced BMI. Smoking cessation initiatives might be more successful if they include support to maintain a healthy BM

    Sixty-five common genetic variants and prediction of type 2 diabetes.

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    We developed a 65 type 2 diabetes (T2D) variant-weighted gene score to examine the impact on T2D risk assessment in a U.K.-based consortium of prospective studies, with subjects initially free from T2D (N = 13,294; 37.3% women; mean age 58.5 [38-99] years). We compared the performance of the gene score with the phenotypically derived Framingham Offspring Study T2D risk model and then the two in combination. Over the median 10 years of follow-up, 804 participants developed T2D. The odds ratio for T2D (top vs. bottom quintiles of gene score) was 2.70 (95% CI 2.12-3.43). With a 10% false-positive rate, the genetic score alone detected 19.9% incident cases, the Framingham risk model 30.7%, and together 37.3%. The respective area under the receiver operator characteristic curves were 0.60 (95% CI 0.58-0.62), 0.75 (95% CI 0.73 to 0.77), and 0.76 (95% CI 0.75 to 0.78). The combined risk score net reclassification improvement (NRI) was 8.1% (5.0 to 11.2; P = 3.31 × 10(-7)). While BMI stratification into tertiles influenced the NRI (BMI ≤24.5 kg/m(2), 27.6% [95% CI 17.7-37.5], P = 4.82 × 10(-8); 24.5-27.5 kg/m(2), 11.6% [95% CI 5.8-17.4], P = 9.88 × 10(-5); >27.5 kg/m(2), 2.6% [95% CI -1.4 to 6.6], P = 0.20), age categories did not. The addition of the gene score to a phenotypic risk model leads to a potentially clinically important improvement in discrimination of incident T2D

    Is disrupted sleep a risk factor for Alzheimer's disease?:Evidence from a two-sample Mendelian randomization analysis

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    Background It is established that Alzheimer’s disease (AD) patients experience sleep disruption. However, it remains unknown whether disruption in the quantity, quality or timing of sleep is a risk factor for the onset of AD. Methods We used the largest published genome-wide association studies of self-reported and accelerometer-measured sleep traits (chronotype, duration, fragmentation, insomnia, daytime napping and daytime sleepiness), and AD. Mendelian randomization (MR) was used to estimate the causal effect of self-reported and accelerometer-measured sleep parameters on AD risk. Results Overall, there was little evidence to support a causal effect of sleep traits on AD risk. There was some suggestive evidence that self-reported daytime napping was associated with lower AD risk [odds ratio (OR): 0.70, 95% confidence interval (CI): 0.50–0.99). Some other sleep traits (accelerometer-measured ‘eveningness’ and sleep duration, and self-reported daytime sleepiness) had ORs of a similar magnitude to daytime napping, but were less precisely estimated. Conclusions Overall, we found very limited evidence to support a causal effect of sleep traits on AD risk. Our findings provide tentative evidence that daytime napping may reduce AD risk. Given that this is the first MR study of multiple self-report and objective sleep traits on AD risk, findings should be replicated using independent samples when such data become available

    Antidepressant treatment with sertraline for adults with depressive symptoms in primary care : the PANDA research programme including RCT

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    Background Despite a growing number of prescriptions for antidepressants (over 70 million in 2018), there is uncertainty about when people with depression might benefit from antidepressant medication and concern that antidepressants are prescribed unnecessarily. Objectives The main objective of the PANDA (What are the indications for Prescribing ANtiDepressAnts that will lead to a clinical benefit?) research programme was to provide more guidance about when antidepressants are likely to benefit people with depression. We aimed to estimate the minimal clinically important difference for commonly used self-administered scales for depression and anxiety, and to understand more about how patients respond to such assessments. We carried out an observational study of patients with depressive symptoms and a placebo-controlled randomised controlled trial of sertraline versus placebo to estimate the treatment effect in UK primary care. The hypothesis was that the severity and duration of symptoms were related to treatment response. Design The programme consisted of three phases. The first phase relied on the secondary analysis of existing data extracted from published trials. The second phase was the PANDA cohort study of patients with depressive symptoms who presented to primary care and were followed up 2, 4 and 6 weeks after a baseline assessment. Both quantitative and qualitative methods were used in the analysis. The third phase was a multicentre randomised placebo-controlled double-blind trial of sertraline versus placebo in patients presenting to primary care with depressive symptoms. Setting UK primary care in Bristol, London, Liverpool and York. Participants Patients aged 18–74 years who were experiencing depressive symptoms in primary care. Eligibility for the PANDA randomised controlled trial included that there was uncertainty about the benefits about treatment with an antidepressant. Interventions In the PANDA randomised controlled trial, patients were individually randomised to 100 mg daily of sertraline or an identical placebo. The PANDA cohort study was an observational study. Main outcome measures Depressive symptoms measured using the Patient Health Questionnaire were the primary outcome for the randomised controlled trial. Other outcomes included anxiety symptoms using the Generalised Anxiety Disorder-7; depressive symptoms using the Beck Depression Inventory, version 2; health-related quality of life; self-reported improvement; and cost-effectiveness. Results The secondary analysis of existing randomised controlled trials [GENetic and clinical Predictors Of treatment response in Depression (GenPod), TREAting Depression with physical activity (TREAD) and Clinical effectiveness and cost-effectiveness of cognitive Behavioural Therapy as an adjunct to pharmacotherapy for treatment-resistant depression in primary care (CoBalT)] found evidence that the minimal clinically important difference increased as the initial severity of depressive symptoms rose. Our estimates of minimal clinically important difference were a 17% and 18% reduction in Beck Depression Inventory scores for GenPod and TREAD, respectively. In CoBalT, a 32% reduction corresponded to the minimal clinically important difference but the participants in this study had depression that had not responded to antidepressants. In the PANDA study cohort, and from our analyses in existing data, we found that the minimal clinically important difference varies considerably with the initial severity of depressive and anxiety symptoms. Expressing the minimal clinically important difference as a percentage reduction reduces this variation at higher scores, but at low scores the percentage reduction increased substantially. The results from the qualitative studies pointed out many limitations of the Patient Health Questionnaire-9 items in assessing change and recovery from depression. In the PANDA randomised controlled trial, there was no evidence that sertraline resulted in a reduction in depressive symptoms within 6 weeks of randomisation, but there was some evidence of a reduction by 12 weeks. However, sertraline led to a reduction in anxiety symptoms, an improvement of mental health-related quality of life and an increased likelihood of reporting improvement. The mean Patient Health Questionnaire-9 items score at 6 weeks was 7.98 (standard deviation 5.63) in the sertraline group and 8.76 (standard deviation 5.86) in the placebo group (5% relative reduction, 95% confidence interval –7% to 15%; p = 0.41). Of the secondary outcomes, there was strong evidence that sertraline reduced anxiety symptoms (Generalised Anxiety Disorder-7 score reduced by 17% (95% confidence interval 9% to 25%; p = 0.00005). Sertraline had a high probability (> 90%) of being cost-effective at 12 weeks. The PANDA randomised controlled trial found no evidence that treatment response or cost-effectiveness was related to severity or duration of depressive symptoms. The minimal clinically important difference estimates suggested that sertraline’s effect on anxiety, but not on depression, was likely to be clinically important. Limitations The results from the randomised controlled trial and the estimates of minimal clinically important difference were not sufficiently precise to provide specific clinical guidance for individuals. We had low power in testing whether or not initial severity and duration of depressive symptoms are related to treatment response. Conclusions The results of the trial support the use of sertraline and probably other selective serotonin reuptake inhibitors because of their action in reducing anxiety symptoms and the likelihood of longer-term benefit on depressive symptoms. Sertraline could be prescribed for anxiety symptoms that commonly occur with depression and many patients will experience a clinical benefit. The Patient Health Questionnaire-9 items and similar self-administered scales should not be used on their own to assess clinical outcome, but should be supplemented with further clinical assessment. Future work We need to examine the longer-term effects of antidepressant treatment. We need more precise estimates of the treatment effects and minimal clinically important difference at different severities to provide more specific guidance for individuals. However, the methods we have developed provide an approach towards providing such detailed guidance. Trial registration Current Controlled Trials ISRCTN84544741 and EudraCT number 2013-003440-22. Funding This project was funded by the National Institute for Health Research (NIHR) Programme Grants for Applied Research programme and will be published in full in Programme Grants for Applied Research; Vol. 7, No. 10. See the NIHR Journals Library website for further project information
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