Culture and behaviour in the English National Health Service:overview of lessons from a large multimethod study

Background - Problems of quality and safety persist in health systems worldwide. We conducted a large research programme to examine culture and behaviour in the English National Health Service (NHS). Methods - Mixed-methods study involving collection and triangulation of data from multiple sources, including interviews, surveys, ethnographic case studies, board minutes and publicly available datasets. We narratively synthesised data across the studies to produce a holistic picture and in this paper present a highlevel summary. Results - We found an almost universal desire to provide the best quality of care. We identified many 'bright spots' of excellent caring and practice and high-quality innovation across the NHS, but also considerable inconsistency. Consistent achievement of high-quality care was challenged by unclear goals, overlapping priorities that distracted attention, and compliance-oriented bureaucratised management. The institutional and regulatory environment was populated by multiple external bodies serving different but overlapping functions. Some organisations found it difficult to obtain valid insights into the quality of the care they provided. Poor organisational and information systems sometimes left staff struggling to deliver care effectively and disempowered them from initiating improvement. Good staff support and management were also highly variable, though they were fundamental to culture and were directly related to patient experience, safety and quality of care. Conclusions - Our results highlight the importance of clear, challenging goals for high-quality care. Organisations need to put the patient at the centre of all they do, get smart intelligence, focus on improving organisational systems, and nurture caring cultures by ensuring that staff feel valued, respected, engaged and supported.

Retinal vascular dysfunction relates to cognitive impairment in Alzheimer's disease

Cerebral vascular dysregulation has been increasingly implicated as a risk factor in the development of Alzheimer disease (AD)1; however, because of the difficulties associated with assessing and visualizing the cerebral vasculature directly, the ability to detect such dysregulation, noninvasively, is currently limited.2 Consequently, one concept that is being increasingly explored is the possibility of using the eye as a "window to the brain"; this approach has reasonable scientific validity as the retinal and brain vessels share a large number of embryological, anatomic, and functional similarities.2 Indeed, previous research has demonstrated a correlation between cognition and the geometry of the retinal vessels in elderly people.3 The aim of this pilot study, therefore, was to explore whether microvascular functional anomalies are evident at the retinal level in mild AD patients and to determine whether these anomalies relate to the degree of concurrent cognitive deficit..

Visualisation of bioinformatics datasets

Analysing the molecular polymorphism and interactions of DNA, RNA and proteins is of fundamental importance in biology. Predicting functions of polymorphic molecules is important in order to design more effective medicines. Analysing major histocompatibility complex (MHC) polymorphism is important for mate choice, epitope-based vaccine design and transplantation rejection etc. Most of the existing exploratory approaches cannot analyse these datasets because of the large number of molecules with a high number of descriptors per molecule. This thesis develops novel methods for data projection in order to explore high dimensional biological dataset by visualising them in a low-dimensional space. With increasing dimensionality, some existing data visualisation methods such as generative topographic mapping (GTM) become computationally intractable. We propose variants of these methods, where we use log-transformations at certain steps of expectation maximisation (EM) based parameter learning process, to make them tractable for high-dimensional datasets. We demonstrate these proposed variants both for synthetic and electrostatic potential dataset of MHC class-I. We also propose to extend a latent trait model (LTM), suitable for visualising high dimensional discrete data, to simultaneously estimate feature saliency as an integrated part of the parameter learning process of a visualisation model. This LTM variant not only gives better visualisation by modifying the project map based on feature relevance, but also helps users to assess the significance of each feature. Another problem which is not addressed much in the literature is the visualisation of mixed-type data. We propose to combine GTM and LTM in a principled way where appropriate noise models are used for each type of data in order to visualise mixed-type data in a single plot. We call this model a generalised GTM (GGTM). We also propose to extend GGTM model to estimate feature saliencies while training a visualisation model and this is called GGTM with feature saliency (GGTM-FS). We demonstrate effectiveness of these proposed models both for synthetic and real datasets. We evaluate visualisation quality using quality metrics such as distance distortion measure and rank based measures: trustworthiness, continuity, mean relative rank errors with respect to data space and latent space. In cases where the labels are known we also use quality metrics of KL divergence and nearest neighbour classifications error in order to determine the separation between classes. We demonstrate the efficacy of these proposed models both for synthetic and real biological datasets with a main focus on the MHC class-I dataset.

Spectrum of morphological and visual changes due to vitreomacular interface disorders encountered in a large consecutive cohort of patients

Aim: Identify the incidence of vitreomacular traction (VMT) and frequency of reduced vision in the absence of other coexisting macular pathology using a pragmatic classification system for VMT in a population of patients referred to the hospital eye service. Methods: A detailed survey of consecutive optical coherence tomography (OCT) scans was done in a high-throughput ocular imaging service to ascertain cases of vitreomacular adhesion (VMA) and VMT using a departmental classification system. Analysis was done on the stages of traction, visual acuity, and association with other macular conditions. Results: In total, 4384 OCT scan episodes of 2223 patients were performed. Two hundred and fourteen eyes had VMA/VMT, with 112 eyes having coexisting macular pathology. Of 102 patients without coexisting pathology, 57 patients had VMT grade between 2 and 8, with a negative correlation between VMT grade and number of Snellen lines (r= -0.61717). There was a distinct cutoff in visual function when VMT grade was higher than 4 with the presence of cysts and sub retinal separation and breaks in the retinal layers. Conclusions: VMT is a common encounter often associated with other coexisting macular pathology. We estimated an incidence rate of 0.01% of VMT cases with reduced vision and without coexisting macular pathology that may potentially benefit from intervention. Grading of VMT to select eyes with cyst formation as well as hole formation may be useful for targeting patients who are at higher risk of visual loss from VMT.

Large scale multi-view stereopsis evaluation

The seminal multiple view stereo benchmark evaluations from Middlebury and by Strecha et al. have played a major role in propelling the development of multi-view stereopsis methodology. Although seminal, these benchmark datasets are limited in scope with few reference scenes. Here, we try to take these works a step further by proposing a new multi-view stereo dataset, which is an order of magnitude larger in number of scenes and with a significant increase in diversity. Specifically, we propose a dataset containing 80 scenes of large variability. Each scene consists of 49 or 64 accurate camera positions and reference structured light scans, all acquired by a 6-axis industrial robot. To apply this dataset we propose an extension of the evaluation protocol from the Middlebury evaluation, reflecting the more complex geometry of some of our scenes. The proposed dataset is used to evaluate the state of the art multiview stereo algorithms of Tola et al., Campbell et al. and Furukawa et al. Hereby we demonstrate the usability of the dataset as well as gain insight into the workings and challenges of multi-view stereopsis. Through these experiments we empirically validate some of the central hypotheses of multi-view stereopsis, as well as determining and reaffirming some of the central challenges.

One-year outcome of bevacizumab therapy for chronic macular edema in central and branch retinal vein occlusions in real-world clinical practice in the UK

Background: The purpose of this study was to investigate the 12-month outcome of macular edema secondary to both chronic and new central and branch retinal vein occlusions treated with intravitreal bevacizumab in the real-life clinical setting in the UK. Methods: Retrospective case notes analysis of consecutive patients with retinal vein occlusions treated with bevacizumab in 2010 to 2012. Outcome measures were visual acuity (measured with Snellen, converted into logMAR [logarithm of the minimum angle of resolution] for statistical calculation) and central retinal thickness at baseline, 4 weeks post-loading phase, and at 1 year. Results: There were 56 and 100 patients with central and branch retinal vein occlusions, respectively, of whom 62% had chronic edema and received prior therapies and another 32% required additional laser treatments post-baseline bevacizumab. Baseline median visual acuity was 0.78 (interquartile range [IQR] 0.48–1.22) in the central group and 0.6 (IQR 0.3–0.78) in the branch group. In both groups, visual improvement was statistically significant from baseline compared to post-loading (P,0.001 and P=0.03, respectively), but was not significant by month 12 (P=0.058 and P=0.166, respectively); 30% improved by at least three lines and 44% improved by at least one line by month 12. Baseline median central retinal thickness was 449 μm (IQR 388–553) in the central group and 441 µm (IQR 357–501) in the branch group. However, the mean reduction in thickness was statistically significant at post-loading (P,0.001) and at the 12-month time point (P,0.001) for both groups. The average number of injections in 1 year was 4.2 in the central group and 3.3 in the branch group. Conclusion: Our large real-world cohort results indicate that bevacizumab introduced to patients with either new or chronic edema due to retinal vein occlusion can result in resolution of edema and stabilization of vision in the first year.

Pregnancy complications in hyperemesis-affected pregnancy:a large hospital database study

Objective: Vomiting in pregnancy is a common condition affecting 80% of pregnant women. Hyperemesis is at one end of the spectrum, seen in 0.5–2% of the pregnant population. Known factors such as nulliparity, younger age and high body mass indexare associated with an increased risk of this condition in the first trimester. Late pregnancy complications attributable to hyperemesis, the pathogenesis of which is poorly understood, have not been studied in large population-based studies in the United Kingdom. The objective of this study was to determine a plausible association between hyperemesis and pregnancy complications,such as pregnancy-related hypertension, gestational diabetes and liver problems in pregnancy, and the rates of elective (ElCS) and emergency caesarean section (EmCS). Methods: Using a database based on ICD-10 classification, anonymised data of admissions to a large multi-ethnic hospital in Manchester, UK between 2000 and 2012 were examined.Notwithstanding the obvious limitations with hospital database-based research, this large volume of datasets allows powerful studies of disease trends and complications.Results Between 2000 and 2012, 156 507 women aged 45 or under were admitted to hospital. Of these, 1111 women were coded for hyperemesis (0.4%). A greater proportion of women with hyperemesis than without hyperemesis were coded forhypertensive disorders in pregnancy such as pregnancy-induced hypertension, pre-eclampsia and eclampsia (2.7% vs 1.5%;P=0.001). The proportion of gestational diabetes and liver disorders in pregnancy was similar for both groups (diabetes:0.5% vs. 0.4%; P=0.945, liver disorders: 0.2% vs. 0.1%;P=0.662). Hyperemesis patients had a higher proportion of elective and emergency caesarean sections compared with the non-hyperemesis group (ElCS: 3.3% vs. 2%; P=0.002, EmCS: 5% vs.3%; P=0.00). Conclusions: There was a higher rate of emergency and elective caesarean section in women with hyperemesis, which could reflect the higher prevalence of pregnancy-related hypertensive disorders(but not diabetes or liver disorders) in this group. The factors contributing to the higher prevalence of hypertensive disorders arenot known, but these findings lead us to question whether there is a similar pathogenesis in the development of both the conditions and hence whether further study in this area is warranted.

A large-scale dynamic vector and raster data visualization geographic information system based on parallel map tiling

With the exponential increasing demands and uses of GIS data visualization system, such as urban planning, environment and climate change monitoring, weather simulation, hydrographic gauge and so forth, the geospatial vector and raster data visualization research, application and technology has become prevalent. However, we observe that current web GIS techniques are merely suitable for static vector and raster data where no dynamic overlaying layers. While it is desirable to enable visual explorations of large-scale dynamic vector and raster geospatial data in a web environment, improving the performance between backend datasets and the vector and raster applications remains a challenging technical issue. This dissertation is to implement these challenging and unimplemented areas: how to provide a large-scale dynamic vector and raster data visualization service with dynamic overlaying layers accessible from various client devices through a standard web browser, and how to make the large-scale dynamic vector and raster data visualization service as rapid as the static one. To accomplish these, a large-scale dynamic vector and raster data visualization geographic information system based on parallel map tiling and a comprehensive performance improvement solution are proposed, designed and implemented. They include: the quadtree-based indexing and parallel map tiling, the Legend String, the vector data visualization with dynamic layers overlaying, the vector data time series visualization, the algorithm of vector data rendering, the algorithm of raster data re-projection, the algorithm for elimination of superfluous level of detail, the algorithm for vector data gridding and re-grouping and the cluster servers side vector and raster data caching.

Quantitative Comparison of Plant Community Hydrology Using Large-Extent, Long-Term Data

Large-extent vegetation datasets that co-occur with long-term hydrology data provide new ways to develop biologically meaningful hydrologic variables and to determine plant community responses to hydrology. We analyzed the suitability of different hydrological variables to predict vegetation in two water conservation areas (WCAs) in the Florida Everglades, USA, and developed metrics to define realized hydrologic optima and tolerances. Using vegetation data spatially co-located with long-term hydrological records, we evaluated seven variables describing water depth, hydroperiod length, and number of wet/dry events; each variable was tested for 2-, 4- and 10-year intervals for Julian annual averages and environmentally-defined hydrologic intervals. Maximum length and maximum water depth during the wet period calculated for environmentally-defined hydrologic intervals over a 4-year period were the best predictors of vegetation type. Proportional abundance of vegetation types along hydrological gradients indicated that communities had different realized optima and tolerances across WCAs. Although in both WCAs, the trees/shrubs class was on the drier/shallower end of hydrological gradients, while slough communities occupied the wetter/deeper end, the distribution ofCladium, Typha, wet prairie and Salix communities, which were intermediate for most hydrological variables, varied in proportional abundance along hydrologic gradients between WCAs, indicating that realized optima and tolerances are context-dependent.

Labeling large scale social media data using budget-driven One-class SVM classification

The social media classification problems draw more and more attention in the past few years. With the rapid development of Internet and the popularity of computers, there is astronomical amount of information in the social network (social media platforms). The datasets are generally large scale and are often corrupted by noise. The presence of noise in training set has strong impact on the performance of supervised learning (classification) techniques. A budget-driven One-class SVM approach is presented in this thesis that is suitable for large scale social media data classification. Our approach is based on an existing online One-class SVM learning algorithm, referred as STOCS (Self-Tuning One-Class SVM) algorithm. To justify our choice, we first analyze the noise-resilient ability of STOCS using synthetic data. The experiments suggest that STOCS is more robust against label noise than several other existing approaches. Next, to handle big data classification problem for social media data, we introduce several budget driven features, which allow the algorithm to be trained within limited time and under limited memory requirement. Besides, the resulting algorithm can be easily adapted to changes in dynamic data with minimal computational cost. Compared with two state-of-the-art approaches, Lib-Linear and kNN, our approach is shown to be competitive with lower requirements of memory and time.

Distributed Feature Selection in Large n and Large p Regression Problems

Fitting statistical models is computationally challenging when the sample size or the dimension of the dataset is huge. An attractive approach for down-scaling the problem size is to first partition the dataset into subsets and then fit using distributed algorithms. The dataset can be partitioned either horizontally (in the sample space) or vertically (in the feature space), and the challenge arise in defining an algorithm with low communication, theoretical guarantees and excellent practical performance in general settings. For sample space partitioning, I propose a MEdian Selection Subset AGgregation Estimator ({\em message}) algorithm for solving these issues. The algorithm applies feature selection in parallel for each subset using regularized regression or Bayesian variable selection method, calculates the `median' feature inclusion index, estimates coefficients for the selected features in parallel for each subset, and then averages these estimates. The algorithm is simple, involves very minimal communication, scales efficiently in sample size, and has theoretical guarantees. I provide extensive experiments to show excellent performance in feature selection, estimation, prediction, and computation time relative to usual competitors.

While sample space partitioning is useful in handling datasets with large sample size, feature space partitioning is more effective when the data dimension is high. Existing methods for partitioning features, however, are either vulnerable to high correlations or inefficient in reducing the model dimension. In the thesis, I propose a new embarrassingly parallel framework named {\em DECO} for distributed variable selection and parameter estimation. In {\em DECO}, variables are first partitioned and allocated to m distributed workers. The decorrelated subset data within each worker are then fitted via any algorithm designed for high-dimensional problems. We show that by incorporating the decorrelation step, DECO can achieve consistent variable selection and parameter estimation on each subset with (almost) no assumptions. In addition, the convergence rate is nearly minimax optimal for both sparse and weakly sparse models and does NOT depend on the partition number m. Extensive numerical experiments are provided to illustrate the performance of the new framework.

For datasets with both large sample sizes and high dimensionality, I propose a new "divided-and-conquer" framework {\em DEME} (DECO-message) by leveraging both the {\em DECO} and the {\em message} algorithm. The new framework first partitions the dataset in the sample space into row cubes using {\em message} and then partition the feature space of the cubes using {\em DECO}. This procedure is equivalent to partitioning the original data matrix into multiple small blocks, each with a feasible size that can be stored and fitted in a computer in parallel. The results are then synthezied via the {\em DECO} and {\em message} algorithm in a reverse order to produce the final output. The whole framework is extremely scalable.

Global distributions of diazotrophs abundance and biomass - Depth integrated values computed from a collection of source datasets - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types

The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. The present data set presents depth integrated values of diazotrophs abundance and biomass, computed from a collection of source data sets.

Global distributions of diazotrophs nitrogen fixation rates - Depth integrated values computed from a collection of source datasets - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types

The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. The present data set presents depth integrated values of diazotrophs nitrogen fixation rates, computed from a collection of source data sets.

Geochemistry of basalts from the Caribbean Large Igneous Province

Oceanic flood basalts are poorly understood, short-term expressions of highly increased heat flux and mass flow within the convecting mantle. The uniqueness of the Caribbean Large Igneous Province (CLIP, 92-74 Ma) with respect to other Cretaceous oceanic plateaus is its extensive sub-aerial exposures, providing an excellent basis to investigate the temporal and compositional relationships within a starting plume head. We present major element, trace element and initial Sr-Nd-Pb isotope composition of 40 extrusive rocks from the Caribbean Plateau, including onland sections in Costa Rica, Colombia and Curaçao as well as DSDP Sites in the Central Caribbean. Even though the lavas were erupted over an area of ~3*10**6 km**2, the majority have strikingly uniform incompatible element patterns (La/Yb=0.96+/-0.16, n=64 out of 79 samples, 2sigma) and initial Nd-Pb isotopic compositions (e.g. 143Nd/144Ndin=0.51291+/-3, epsilon-Nd i=7.3+/-0.6, 206Pb/204Pbin=18.86+/-0.12, n=54 out of 66, 2sigma). Lavas with endmember compositions have only been sampled at the DSDP Sites, Gorgona Island (Colombia) and the 65-60 Ma accreted Quepos and Osa igneous complexes (Costa Rica) of the subsequent hotspot track. Despite the relatively uniform composition of most lavas, linear correlations exist between isotope ratios and between isotope and highly incompatible trace element ratios. The Sr-Nd-Pb isotope and trace element signatures of the chemically enriched lavas are compatible with derivation from recycled oceanic crust, while the depleted lavas are derived from a highly residual source. This source could represent either oceanic lithospheric mantle left after ocean crust formation or gabbros with interlayered ultramafic cumulates of the lower oceanic crust. High 3He/4He in olivines of enriched picrites at Quepos are ~12 times higher than the atmospheric ratio suggesting that the enriched component may have once resided in the lower mantle. Evaluation of the Sm-Nd and U-Pb isotope systematics on isochron diagrams suggests that the age of separation of enriched and depleted components from the depleted MORB source mantle could have been <=500 Ma before CLIP formation and interpreted to reflect the recycling time of the CLIP source. Mantle plume heads may provide a mechanism for transporting large volumes of possibly young recycled oceanic lithosphere residing in the lower mantle back into the shallow MORB source mantle.

Feasibility of MR-Based Body Composition Analysis in Large Scale Population Studies

Introduction Quantitative and accurate measurements of fat and muscle in the body are important for prevention and diagnosis of diseases related to obesity and muscle degeneration. Manually segmenting muscle and fat compartments in MR body-images is laborious and time-consuming, hindering implementation in large cohorts. In the present study, the feasibility and success-rate of a Dixon-based MR scan followed by an intensity-normalised, non-rigid, multi-atlas based segmentation was investigated in a cohort of 3,000 subjects. Materials and Methods 3,000 participants in the in-depth phenotyping arm of the UK Biobank imaging study underwent a comprehensive MR examination. All subjects were scanned using a 1.5 T MR-scanner with the dual-echo Dixon Vibe protocol, covering neck to knees. Subjects were scanned with six slabs in supine position, without localizer. Automated body composition analysis was performed using the AMRA Profiler™ system, to segment and quantify visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT) and thigh muscles. Technical quality assurance was performed and a standard set of acceptance/rejection criteria was established. Descriptive statistics were calculated for all volume measurements and quality assurance metrics. Results Of the 3,000 subjects, 2,995 (99.83%) were analysable for body fat, 2,828 (94.27%) were analysable when body fat and one thigh was included, and 2,775 (92.50%) were fully analysable for body fat and both thigh muscles. Reasons for not being able to analyse datasets were mainly due to missing slabs in the acquisition, or patient positioned so that large parts of the volume was outside of the field-of-view. Discussion and Conclusions In conclusion, this study showed that the rapid UK Biobank MR-protocol was well tolerated by most subjects and sufficiently robust to achieve very high success-rate for body composition analysis. This research has been conducted using the UK Biobank Resource.