Discovering new kinds of patient safety incidents

Every year, large numbers of patients in National Health Service (NHS) care suffer because of a patient safety incident. The National Patient Safety Agency (NPSA) collects large amounts of data describing individual incidents. As well as being described by categorical and numerical variables, each incident is described using free text. The aim of the work was to find quite small groups of similar incidents, which were of types that were previously unknown to the NPSA. A model of the text was produced, such that the position of each incident reflected its meaning to the greatest extent possible. The basic model was the vector space model. Dimensionality reduction was carried out in two stages: unsupervised dimensionality reduction was carried out using principal component analysis, and supervised dimensionality reduction using linear discriminant analysis. It was then possible to look for groups of incidents that were more tightly packed than would be expected given the overall distribution of the incidents. The process for assessing these groups had three stages. Firstly, a quantitative measure was used, allowing a large number of parameter combinations to be examined. The groups found for an ‘optimum’ parameter combination were then divided into categories using a qualitative filtering method. Finally, clinical experts assessed the groups qualitatively. The transition probabilities model was also examined: this model was based on the empirical probabilities that two word sequences were seen in the text. An alternative method for dimensionality reduction was to use information about the subjective meaning of a small sample of incidents elicited from experts, producing a mapping between high and low dimensional models of the text. The analysis also included the direct use of the categorical variables to model the incidents, and empirical analysis of the behaviour of high dimensional spaces

A multi-agent crop production decision support system for technology transfer

The purpose of this research was to study agricultural crop production 'decision support systems' as a means of transferring agricultural technology from research labs and plots to producers, extension specialists, agriculture service agencies, and scientists, on the Western Canadian Prairies. A 'decision support system' is a computer program that analyses problems spanning several knowledge or problem areas producing results that aid the management decision-making process. The primary objective was to develop a computer application program that would fulfill the farm manager's decision support needs and be "open" to future enhancements. This interdisciplinary study has a strong agricultural presence in the application context of the resultant computerized agricultural decision support system, with agronomics being the foundation on which the system was built, and computer science being the toolbox used to build it. Farm Smart 2000 is the resultant decision support system, providing "single-window" access to three different tiers of decision support utilizing the Internet, ' expert systems' and integrated multiple heterogeneous 'reusable agents' in a cooperative problem-solving environment. An ' expert system' is a computer program that solves complicated problems, within a specific knowledge or problem area, that would otherwise require human expertise. Expert systems integrated with each other within a decision support system are called 'agents. Reusable agents' are modular computer programs (e.g. expert systems) which can be used in more than one computer application with little or no modification. Farm Smart 2000 provides support for most management aspects of crop production including variety selection, crop rotations, weed management, disease management, residue management, harvesting, soil conservation, and economics, for the crops of wheat, canola, barley, peas, and flax. Tier-3, the most sophisticated level of Farm Smart 2000, is the focus of this dissertation and utilizes multiple reusable agents, integrating them such that they cooperate together to solve complex interrelated crop production problems. A Global Control Expert achieves the required communication and coordination among the agents resulting in an "open system", enabling Farm Smart 2000 to extend its problem-solving capabilities by integrating additional agents and knowledge, without system re-engineering, thereby remaining an ongoing technology transfer vehicle

Conjugate Transfer Processes in a Pilot-Scale Unbaffled Agitated Vessel with a Plain Jacket

Conjugate flow and heat transfer has been investigated in an unbaffled pilot-scale stirred tank reactor with a plain jacket. The vessel volume was 25 litres with a nominal capacity of 20 litres. Experiments and three-dimensional CFD simulations have been conducted on this vessel. The experiments involved heating, boiling, and cooling of methanol as well as water. The heat transfer medium in the jacket was an oil mixture called ‘DW-Therm’. The CFD simulations of some aspects of these experiments have been broken down into jacket-only and process-only simulations, followed by a fully conjugate simulation. The link between flow patterns, pressure drop and heat transfer in conventional jackets of stirred tank reactors has been analysed. The experiments and CFD simulations have been performed using a range of DW-Therm inlet temperatures. The CFD results were compared with experimental data of temperature measurements and with the use of engineering correlations found in the literature to predict heat transfer coefficients from the experimental data. The simulations produced values of total heat transferred by the jacket within 10% of the experimental results. The simulations of boiling inside the vessel approximated a constant process temperature which was used to investigate the jacket-only phenomena. The process-only and the conjugate simulations simulated heating of water inside the vessel. Mathematical analysis as well as and industrially and academically used correlations from the literature were used to estimate heat transfer coefficients for boiling and external heat loss. These correlations for overall heat transfer coefficients overlook maldistribution of heat transfer coefficients in jackets that use a liquid heat transfer medium. This is industrially important because it provides new information to consider when maintaining highly temperature-dependent processes, in which adequate heat transfer to or from the process is required. This could be for a variety of reasons, from maintenance of product quality to preventing runaway reactions

Scaling priors for intrinsic Gaussian Markov random fields applied to blood pressure data

An Intrinsic Gaussian Markov Random Field (IGMRF) can be used to induce conditional dependence in Bayesian hierarchical models. IGMRFs have both a precision matrix, which defines the neighborhood structure of the model, and a precision, or scaling, parameter. Previous studies have shown the importance of selecting the prior for this scaling parameter appropriately for different types of IGMRF, as it can have a substantial impact on posterior estimates. Here, we focus on cases in one and two dimensions, where tuning of the prior is achieved by mapping it to the marginal SD of an IGMRF of corresponding dimensionality. We compare the effects of scaling various IGMRFs, including an application to real two‐dimensional blood pressure data using MCMC methods

Growing health : global linkages between patterns of food supply, sustainability, and vulnerability to climate change

Funding Information: This study was supported by the Wellcome Trust (grant number 210794/Z/18/Z), and forms part of the Sustainable and Healthy Food Systems programme, supported by the Wellcome Trust's Our Planet, Our Health programme (grant number 205200/Z/16/Z). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Publisher Copyright: © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licensePeer reviewedPublisher PD

Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants

Background Underweight and severe and morbid obesity are associated with highly elevated risks of adverse health outcomes. We estimated trends in mean body-mass index (BMI), which characterises its population distribution, and in the prevalences of a complete set of BMI categories for adults in all countries. Methods We analysed, with use of a consistent protocol, population-based studies that had measured height and weight in adults aged 18 years and older. We applied a Bayesian hierarchical model to these data to estimate trends from 1975 to 2014 in mean BMI and in the prevalences of BMI categories (<18.5 kg/m2[underweight], 18.5 kg/m2to <20 kg/m2, 20 kg/m2to <25 kg/m2, 25 kg/m2to <30 kg/m2, 30 kg/m2to <35 kg/m2, 35 kg/m2to <40 kg/m2, ≥40 kg/m2[morbid obesity]), by sex in 200 countries and territories, organised in 21 regions. We calculated the posterior probability of meeting the target of halting by 2025 the rise in obesity at its 2010 levels, if post-2000 trends continue. Findings We used 1698 population-based data sources, with more than 19.2 million adult participants (9.9 million men and 9.3 million women) in 186 of 200 countries for which estimates were made. Global age-standardised mean BMI increased from 21.7 kg/m2(95% credible interval 21.3-22.1) in 1975 to 24.2 kg/m2(24.0-24.4) in 2014 in men, and from 22.1 kg/m2(21.7-22.5) in 1975 to 24.4 kg/m2(24.2-24.6) in 2014 in women. Regional mean BMIs in 2014 for men ranged from 21.4 kg/m2in central Africa and south Asia to 29.2 kg/m2(28.6-29.8) in Polynesia and Micronesia; for women the range was from 21.8 kg/m2(21.4-22.3) in south Asia to 32.2 kg/m2(31.5-32.8) in Polynesia and Micronesia. Over these four decades, age-standardised global prevalence of underweight decreased from 13.8% (10.5-17.4) to 8.8% (7.4-10.3) in men and from 14.6% (11.6-17.9) to 9.7% (8.3-11.1) in women. South Asia had the highest prevalence of underweight in 2014, 23.4% (17.8-29.2) in men and 24.0% (18.9-29.3) in women. Age-standardised prevalence of obesity increased from 3.2% (2.4-4.1) in 1975 to 10.8% (9.7-12.0) in 2014 in men, and from 6.4% (5.1-7.8) to 14.9% (13.6-16.1) in women. 2.3% (2.0-2.7) of the world's men and 5.0% (4.4-5.6) of women were severely obese (ie, have BMI ≥35 kg/m2). Globally, prevalence of morbid obesity was 0.64% (0.46-0.86) in men and 1.6% (1.3-1.9) in women. Interpretation If post-2000 trends continue, the probability of meeting the global obesity target is virtually zero. Rather, if these trends continue, by 2025, global obesity prevalence will reach 18% in men and surpass 21% in women; severe obesity will surpass 6% in men and 9% in women. Nonetheless, underweight remains prevalent in the world's poorest regions, especially in south Asia

The use of in vitro unbound drug fraction and permeability in predicting central nervous system drug penetration

The permeation of drugs across the blood-brain barrier (BBB) is a prerequisite for central nervous system (CNS) drug penetration. The BBB, possessing efflux transporters and tight junctions, limits drug penetration to the brain. Consequently, the discovery of novel drugs to treat CNS diseases remains problematic and is lagging behind other therapeutic areas. In vitro assays have progressed understanding of the factors that govern brain penetration. Central nervous system drug penetration is now thought to be modulated by three main processes, namely BBB permeability, active transport at the BBB and drug binding in blood and brain tissue. A more integrated approach to CNS drug discovery programmes is emerging which encompasses these processes in order to examine the rate and extent of drug brain penetration across species and improve predictions in human.A primary porcine in vitro BBB model was developed and characterised for the prediction of CNS drug permeability in vivo. Characterisation confirmed that the model exhibited physiologically realistic cell architecture, the formation of tight junction protein complexes, transcellular electrical resistance consistently >2000 Ω.cm2, functional expression the P-gp efflux transporter and ?-glutamyl transpeptidase and alkaline phosphatase activities.Transport of 12 centrally acting test drugs was investigated across four in vitro BBB models in order make comparisons between models and to generate in vitro permeability and efflux measurements. Blood-brain barrier permeability and active efflux processes are two major influences on the rate of drug penetration across the BBB. Species differences in fublood and fubrain, two prime influences on the extent of drug penetration, were investigated using equilibrium dialysis. Fraction unbound in brain was shown to be comparable across species suggesting that species differences in brain penetration could be due to variation in fublood for drugs that cross the BBB by passive diffusion, and/or species differences in transporter characteristics for drugs that are subject to active transport processes at the BBB. An in-house hybrid-PBPK rat CNS model was used to predict calculated rat Kp,uu using in vitro permeability, efflux, fublood and fubrain parameters generated during this work. The predicted Kp,uu generated using the rat CNS hybrid-PBPK model were within 3-fold of calculated Kp,uu. The rat CNS hybrid-PBPK model has potential use, as a tool for drug discovery scientists to aid the prediction of the extent of drug penetration in the early stages of drug discovery.This work has demonstrated that in vitro permeability and unbound drug fraction can be used to predict CNS drug penetration.EThOS - Electronic Theses Online ServiceGlaxoSmithKlineBBSRCGBUnited Kingdo