A generalized model for fuel channel bore estimation in AGR cores

One of the major life-limiting factors of an Advanced Gas-cooled Reactor (AGR) nuclear power station is the graphite core as it cannot be repaired or replaced and therefore detailed information about the health of the core is vital for continued safe operation. The graphite bricks that comprise the core experience gradual degradation during operation as a result of irradiation. Routine physical inspection of the graphite core fuel channels is performed by specialist inspection equipment during outages every 12 months to 3 years. It has also been shown to be advantageous to supplement this periodic inspection information with analysis of operational data which can provide additional insights into the core health. One such approach is through the use of online monitoring data called the Fuel Grab Load Trace (FGLT). An FGLT is a measure of the perceived load of the fuel assembly with contributions from aerodynamic forces and frictional forces, which is related to bore diameter. This paper describes enhancements to existing analysis of FGLT data which, to date, has focussed solely on using data from a single reactor at a time to build bore estimation models, by considering data from multiple reactors to produce a generalised model of bore estimation. This paper initially describes the process of producing a bore estimation from an FGLT by isolating the contribution that relates to the fuel channel bore and then discusses the limitations with the existing bore estimation model. Improvements are then proposed for the bore estimation model and a detailed assessment is undertaken to understand the effect of each of these proposed improvements. In addition, the effect of introducing non-linear regression models to further enhance the bore estimation is explored. The existing model is trained on data from one reactor in the UK and therefore the results produced from it are only applicable to this reactor. However, out of the remaining 13 nuclear reactors currently in operation, 3 also have a similar construction to the reactor the model is trained on, and these should all produce similar FGLT data. Therefore, a generalised model is proposed that produces bore estimations for four AGRs station’s reactors, compared with one previously. It is shown that this approach offers an improved overall bore estimation model

Machine learning model for event-based prognostics in gas circulator condition monitoring

Gas circulator (GC) units are an important rotating asset used in the Advanced Gas-cooled Reactor (AGR) design, facilitating the flow of CO2 gas through the reactor core. The ongoing maintenance and examination of these machines is important for operators in order to maintain safe and economic generation. GCs experience a dynamic duty cycle with periods of non-steady state behavior at regular refuelling intervals, posing a unique analysis problem for reliability engineers. In line with the increased data volumes and sophistication of available the technologies, the investigation of predictive and prognostic measurements has become a central interest in rotating asset condition monitoring. However, many of the state-of-the-art approaches finding success deal with the extrapolation of stationary time series feeds, with little to no consideration of more-complex but expected events in the data. In this paper we demonstrate a novel modelling approach for examining refuelling behaviors in GCs, with a focus on estimating their health state from vibration data. A machine learning model was constructed using the operational history of a unit experiencing an eventual inspection-based failure. This new approach to examining GC condition is shown to correspond well with explicit remaining useful life (RUL) measurements of the case study, improving on the existing rudimentary extrapolation methods often employed in rotating machinery health monitoring

Improved explicability for pump diagnostics in nuclear power plants

To ensure the continued safe operation of many of the UK's fleet of advanced gas-cooled reactors (AGRs) effective and reliable monitoring of several of the key plant items is essential. Out of these key items, a significant portion of these are rotating plant assets, one asset in particular that is crucial to the operation of the station are the boiler feed pumps (BFPs). The BFPs in an AGR station move water from a condenser into a boiler, the water is then heated which produces steam and this steam turns the electricity-generating turbines. Currently, the operator of the AGR stations employs a time-based maintenance strategy for BFP assets: after a defined amount of time each asset is removed, replaced with a rotated spare, and a complete overhaul is then performed on the removed asset. This procedure can result in the removal of an asset before any significant wear has occurred, therefore increasing maintenance and generation costs. Conversely, this could result in an unplanned outage due to a component failure which leads to both a decrease in power output of the station and hence a decrease in revenue for the operator. Because these pumps are essential for the generation of electricity there are several pressure, temperature, vibration and speed parameters constantly monitored during the operation of this asset. Currently, data analysts have to manually analyse all this data by following a set diagnosis process, the consequential time burden on the analyst is therefore extremely high. Data-driven approaches to solve this problem, and other similar problems, have the capability to produce accurate results similar to what the analysts can achieve in a fraction of the time. However, the majority of these techniques are black box techniques and lack explicability which is often a requirement for problems involving critical assets in the nuclear industry. The main outcomes of this work are to address the time burden placed on the analysts by automating elements of the existing diagnosis process, through the implementation of an intelligent rule-based expert system, that provides adequate explicability to the user to satisfy requirements. Additionally, a recurring problem in the design of expert systems for industry is the cost involved with the knowledge elicitation process. Here we propose a questionnaire style approach, similar to what the domain experts currently use, to extract this knowledge without the need for a structured interview. By using this information a signal-to-symbol transformation algorithm is designed to assign time periods symbols that relate to the various rules defined by the domain experts. The final system combines the data-driven signal-to-symbol transformation algorithm and the rule-based expert system to produce a hybrid system that can be used to classify defects based on a set of rules and also explain to the user the reasoning behind this solution

Integrative GWAS and co-localisation analysis suggests novel genes associated with age-related multimorbidity

Abstract Advancing age is the greatest risk factor for developing multiple age-related diseases. Therapeutic approaches targeting the underlying pathways of ageing, rather than individual diseases, may be an effective way to treat and prevent age-related morbidity while reducing the burden of polypharmacy. We harness the Open Targets Genetics Portal to perform a systematic analysis of nearly 1,400 genome-wide association studies (GWAS) mapped to 34 age-related diseases and traits, identifying genetic signals that are shared between two or more of these traits. Using locus-to-gene (L2G) mapping, we identify 995 targets with shared genetic links to age-related diseases and traits, which are enriched in mechanisms of ageing and include known ageing and longevity-related genes. Of these 995 genes, 128 are the target of an approved or investigational drug, 526 have experimental evidence of binding pockets or are predicted to be tractable, and 341 have no existing tractability evidence, representing underexplored genes which may reveal novel biological insights and therapeutic opportunities. We present these candidate targets for exploration and prioritisation in a web application

Do cravings predict smoking cessation in smokers calling a national quit line: secondary analyses from a randomised trial for the utility of ‘urges to smoke’ measures

BACKGROUND: Single-item urges to smoke measures have been contemplated as important measures of nicotine dependence This study aimed to prospectively determine the relationships between measures of craving to smoke and smoking cessation, and compare their ability to predict cessation with the Heaviness of Smoking Index, an established measure of nicotine dependence. METHODS: We conducted a secondary analysis of data from the randomised controlled PORTSSS trial. Measures of nicotine dependence, ascertained before making a quit attempt, were the HSI, frequency of urges to smoke (FUTS) and strength of urges to smoke (SUTS). Self-reported abstinence at six months after quitting was the primary outcome measure. Multivariate logistic regression and Receiver Operating Characteristic (ROC) analysis were used to assess associations and abilities of the nicotine dependence measures to predict smoking cessation. RESULTS: Of 2,535 participants, 53.5% were female; the median (Interquartile range) age was 38 (28–50) years. Both FUTS and HSI were inversely associated with abstinence six months after quitting; for each point increase in HSI score, participants were 16% less likely to have stopped smoking (OR 0.84, 95% C.I 0.78-0.89, p < 0.0001). Compared to participants with the lowest possible FUTS scores, those with greater scores had generally lower odds of cessation (p across frequency of urges categories=0.0026). SUTS was not associated with smoking cessation. ROC analysis suggested the HSI and FUTS had similar predictive validity for cessation. CONCLUSIONS: Higher FUTS and HSI scores were inversely associated with successful smoking cessation six months after quit attempts began and both had similar validity for predicting cessation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13011-015-0011-8) contains supplementary material, which is available to authorized users

Angiotensin-(1-7) and angiotensin-(1-9): function in cardiac and vascular remodeling

The renin angiotensin system (RAS) is integral to cardiovascular physiology, however, dysregulation of this system largely contributes to the pathophysiology of cardiovascular disease (CVD). It is well established that angiotensin II (Ang II), the main effector of the RAS, engages the angiotensin type 1 receptor and promotes cell growth, proliferation, migration and oxidative stress, all processes which contribute to remodeling of the heart and vasculature, ultimately leading to the development and progression of various CVDs including heart failure and atherosclerosis. The counter-regulatory axis of the RAS, which is centered on the actions of angiotensin converting enzyme 2 (ACE2) and the resultant production of angiotensin-(1-7) (Ang-(1-7) from Ang II, antagonizes the actions of Ang II via the receptor Mas, thereby providing a protective role in CVD. More recently, another ACE2 metabolite, Ang-(1-9), has been reported to be a biologically active peptide within the counter-regulatory axis of the RAS. This review will discuss the role of the counter-regulatory RAS peptides, Ang-(1-7) and Ang-(1-9) in the cardiovascular system, with a focus on their effects in remodeling of the heart and vasculature

Lentivirus-mediated gene therapy for Fabry disease

Enzyme and chaperone therapies are used to treat Fabry disease. Such treatments are expensive and require intrusive biweekly infusions; they are also not particularly efficacious. In this pilot, single-arm study (NCT02800070), five adult males with Type 1 (classical) phenotype Fabry disease were infused with autologous lentivirus-transduced, CD34+-selected, hematopoietic stem/progenitor cells engineered to express alpha-galactosidase A (α-gal A). Safety and toxicity are the primary endpoints. The non-myeloablative preparative regimen consisted of intravenous melphalan. No serious adverse events (AEs) are attributable to the investigational product. All patients produced α-gal A to near normal levels within one week. Vector is detected in peripheral blood and bone marrow cells, plasma and leukocytes demonstrate α-gal A activity within or above the reference range, and reductions in plasma and urine globotriaosylceramide (Gb3) and globotriaosylsphingosine (lyso-Gb3) are seen. While the study and evaluations are still ongoing, the first patient is nearly three years post-infusion. Three patients have elected to discontinue enzyme therapy

Clinical and Molecular Features of Renal and Pheochromocytoma/Paraganglioma Tumor Association Syndrome (RAPTAS): Case Series and Literature Review.

CONTEXT: The co-occurrence of pheochromocytoma (PC) and renal tumors was linked to the inherited familial cancer syndrome von Hippel-Lindau (VHL) disease more than six decades ago. Subsequently, other shared genetic causes of predisposition to renal tumors and to PC, paraganglioma (PGL), or head and neck paraganglioma (HNPGL) have been described, but case series of non-VHL-related cases of renal tumor and pheochromocytoma/paraganglioma tumor association syndrome (RAPTAS) are rare. OBJECTIVE: To determine the clinical and molecular features of non-VHL RAPTAS by literature review and characterization of a case series. DESIGN: A review of the literature was performed and a retrospective study of referrals for investigation of genetic causes of RAPTAS. RESULTS: Literature review revealed evidence of an association, in addition to VHL disease, between germline mutations in SDHB, SDHC, SDHD, TMEM127, and MAX genes and RAPTAS [defined here as the co-occurrence of tumors from both classes (PC/PGL/HNPGL and renal tumors) in the same individual or in first-degree relatives]. In both the literature review and our case series of 22 probands with non-VHL RAPTAS, SDHB mutations were the most frequent cause of non-VHL RAPTAS. A genetic cause was identified in 36.3% (8/22) of kindreds. CONCLUSION: Renal tumors and PC/PGL/HNPGL tumors share common molecular features and their co-occurrence in an individual or family should prompt genetic investigations. We report a case of MAX-associated renal cell carcinoma and confirm the role of TMEM127 mutations with renal cell carcinoma predisposition

Enhancing carbon sequestration in soil with coal combustion products: a technology for minimising carbon footprints in coal-power generation and agriculture

Coal-fired power generation and agriculture account for more than half of global greenhouse gas emissions, but the coal fly ash (CFA) produced in the former can be a resource for reducing emissions from agriculture to minimise environmental footprints in both industries. Our aim in this study was to test how acidic and alkaline CFA addition could minimise loss of C and N from acidic soil, with or without added manure. We determined composition and structural characteristics of acidic and alkaline CFA for their capacity to adsorb organic carbon, but observed poor adsorption because of low concentrations of cenospheres and unburnt carbon as the primary absorbents in the ash. Addition of CFA had no impact on the loss of carbon or nitrogen from unmanured soil in which concentrations of these nutrients were low. Loss of carbon from manured soil was reduced by 36% with alkaline ashes and by 3-fold with acidic ashes; while loss of N was 30–50% lower with acidic ashes, but 28% higher with alkaline ashes, compared with no ash treatment. The increases in C sparing with CFA addition were achieved not by direct C absorption but by restraining microbial population and respiration, and potentially emissions. Alkaline CFA increased soil pH and if used to substitute just 10% of lime for ameliorating soil acidity would reduce CO2 emission associated with the mining of the lime and its eventual dissolution in soil by ~ 2.66 Tg or 2.8% of Australia’s annual agricultural emissions. High concentrations of oxides of phosphorus, silicon, titanium and clay particles in acidic ashes, and oxides of cations in alkaline ashes, were associated with potential for promoting C storage and acidity amelioration in soil

Red hot frogs:Identifying the Australian frogs most at risk of extinction

More than a third of the world’s amphibian species are listed as Threatened or Extinct, with a recent assessment identifying 45 Australian frogs (18.4% of the currently recognised species) as ‘Threatened’ based on IUCN criteria. We applied structured expert elicitation to 26 frogs assessed as Critically Endangered and Endangered to estimate their probability of extinction by 2040. We also investigated whether participant experience (measured as a self-assigned categorical score, i.e. ‘expert’ or ‘non-expert’) influenced the estimates. Collation and analysis of participant opinion indicated that eight species are at high risk (>50% chance) of becoming extinct by 2040, with the disease chytridiomycosis identified as the primary threat. A further five species are at moderate–high risk (30–50% chance), primarily due to climate change. Fourteen of the 26 frog species are endemic to Queensland, with many species restricted to small geographic ranges that are susceptible to stochastic events (e.g. a severe heatwave or a large bushfire). Experts were more likely to rate extinction probability higher for poorly known species (those with <10 experts), while non-experts were more likely to rate extinction probability higher for better-known species. However, scores converged following discussion, indicating that there was greater consensus in the estimates of extinction probability. Increased resourcing and management intervention are urgently needed to avert future extinctions of Australia’s frogs. Key priorities include developing and supporting captive management and establishing or extending in-situ population refuges to alleviate the impacts of disease and climate change