Representativity error for temperature and humidity using the Met Office high-resolution model

The observation-error covariance matrix used in data assimilation contains contributions from instrument errors, representativity errors and errors introduced by the approximated observation operator. Forward model errors arise when the observation operator does not correctly model the observations or when observations can resolve spatial scales that the model cannot. Previous work to estimate the observation-error covariance matrix for particular observing instruments has shown that it contains signifcant correlations. In particular, correlations for humidity data are more significant than those for temperature. However it is not known what proportion of these correlations can be attributed to the representativity errors. In this article we apply an existing method for calculating representativity error, previously applied to an idealised system, to NWP data. We calculate horizontal errors of representativity for temperature and humidity using data from the Met Office high-resolution UK variable resolution model. Our results show that errors of representativity are correlated and more significant for specific humidity than temperature. We also find that representativity error varies with height. This suggests that the assimilation scheme may be improved if these errors are explicitly included in a data assimilation scheme. This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland

Fast Ensemble Smoothing

Smoothing is essential to many oceanographic, meteorological and hydrological applications. The interval smoothing problem updates all desired states within a time interval using all available observations. The fixed-lag smoothing problem updates only a fixed number of states prior to the observation at current time. The fixed-lag smoothing problem is, in general, thought to be computationally faster than a fixed-interval smoother, and can be an appropriate approximation for long interval-smoothing problems. In this paper, we use an ensemble-based approach to fixed-interval and fixed-lag smoothing, and synthesize two algorithms. The first algorithm produces a linear time solution to the interval smoothing problem with a fixed factor, and the second one produces a fixed-lag solution that is independent of the lag length. Identical-twin experiments conducted with the Lorenz-95 model show that for lag lengths approximately equal to the error doubling time, or for long intervals the proposed methods can provide significant computational savings. These results suggest that ensemble methods yield both fixed-interval and fixed-lag smoothing solutions that cost little additional effort over filtering and model propagation, in the sense that in practical ensemble application the additional increment is a small fraction of either filtering or model propagation costs. We also show that fixed-interval smoothing can perform as fast as fixed-lag smoothing and may be advantageous when memory is not an issue

Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling

Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular diseases often rely on subjective human assessment as well as manual analysis of medical images. Both factors limit the sensitivity in quantifying complex structural and functional phenotypes. Deep learning approaches have recently achieved success for tasks such as classification or segmentation of medical images, but lack interpretability in the feature extraction and decision processes, limiting their value in clinical diagnosis. In this work, we propose a 3D convolutional generative model for automatic classification of images from patients with cardiac diseases associated with structural remodeling. The model leverages interpretable task-specific anatomic patterns learned from 3D segmentations. It further allows to visualise and quantify the learned pathology-specific remodeling patterns in the original input space of the images. This approach yields high accuracy in the categorization of healthy and hypertrophic cardiomyopathy subjects when tested on unseen MR images from our own multi-centre dataset (100%) as well on the ACDC MICCAI 2017 dataset (90%). We believe that the proposed deep learning approach is a promising step towards the development of interpretable classifiers for the medical imaging domain, which may help clinicians to improve diagnostic accuracy and enhance patient risk-stratification

HIV Prevention in Care and Treatment Settings: Baseline Risk Behaviors among HIV Patients in Kenya, Namibia, and Tanzania.

HIV care and treatment settings provide an opportunity to reach people living with HIV/AIDS (PLHIV) with prevention messages and services. Population-based surveys in sub-Saharan Africa have identified HIV risk behaviors among PLHIV, yet data are limited regarding HIV risk behaviors of PLHIV in clinical care. This paper describes the baseline sociodemographic, HIV transmission risk behaviors, and clinical data of a study evaluating an HIV prevention intervention package for HIV care and treatment clinics in Africa. The study was a longitudinal group-randomized trial in 9 intervention clinics and 9 comparison clinics in Kenya, Namibia, and Tanzania (N = 3538). Baseline participants were mostly female, married, had less than a primary education, and were relatively recently diagnosed with HIV. Fifty-two percent of participants had a partner of negative or unknown status, 24% were not using condoms consistently, and 11% reported STI symptoms in the last 6 months. There were differences in demographic and HIV transmission risk variables by country, indicating the need to consider local context in designing studies and using caution when generalizing findings across African countries. Baseline data from this study indicate that participants were often engaging in HIV transmission risk behaviors, which supports the need for prevention with PLHIV (PwP). TRIAL REGISTRATION: ClinicalTrials.gov NCT01256463

Star Clusters

This review concentrates almost entirely on globular star clusters. It emphasises the increasing realisation that few of the traditional problems of star cluster astronomy can be studied in isolation: the influence of the Galaxy affects dynamical evolution deep in the core, and the spectrum of stellar masses; in turn the evolution of the core determines the highest stellar densities, and the rate of encounters. In this way external tidal effects indirectly influence the formation and evolution of blue stragglers, binary pulsars, X-ray sources, etc. More controversially, the stellar density appears to influence the relative distribution of normal stars. In the opposite sense, the evolution of individual stars governs much of the early dynamics of a globular cluster, and the existence of large numbers of primordial binary stars has changed important details of our picture of the dynamical evolution. New computational tools which will become available in the next few years will help dynamical theorists to address these questions.Comment: 10 pages, 3 figures, Te

Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

Chedoke Arm and Hand Activity Inventory-9 (CAHAI-9): Perceived clinical utility within 14 days of stroke

Purpose: The Chedoke Arm and Hand Activity Inventory-9 (CAHAI-9) is an activity-based assessment developed to include relevant functional tasks and to be sensitive to clinically important changes in upper limb function. The aim of this study was to explore both therapists' and clients' views on the clinical utility of CAHAI-9 within 14 days of stroke. Method: Twenty-one occupational therapists actively working in stroke settings were recruited by convenience sampling from 8 hospitals and participated in semistructured focus groups. Five clients within 14 days of stroke were recruited by consecutive sampling from 1 metropolitan hospital and participated in structured individual interviews. The transcripts were analyzed thematically. Results: Six themes emerged from the focus groups and interviews: collecting information, decisions regarding client suitability, administration and scoring, organizational demands, raising awareness, and clients' perceptions of CAHAI-9 utility. All therapists agreed CAHAI-9 was suited for the stroke population and assisted identification of client abilities or difficulties within functional contexts. Opinions varied as to whether CAHAI-9 should be routinely administered with clients who had mild and severe upper limb deficits, but therapists agreed it was appropriate for clients with moderate deficits. Therapists made suggestions regarding refinement of the scoring and training to increase utility. All clients with stroke felt that the assessment provided reassurance regarding their recovery. Conclusion: The findings indicate that CAHAI-9 shows promise as an upper limb ability assessment for clients within 14 days of stroke

Public understandings of addiction: where do neurobiological explanations fit?

Developments in the field of neuroscience, according to its proponents, offer the prospect of an enhanced understanding and treatment of addicted persons. Consequently, its advocates consider that improving public understanding of addiction neuroscience is a desirable aim. Those critical of neuroscientific approaches, however, charge that it is a totalising, reductive perspective–one that ignores other known causes in favour of neurobiological explanations. Sociologist Nikolas Rose has argued that neuroscience, and its associated technologies, are coming to dominate cultural models to the extent that 'we' increasingly understand ourselves as 'neurochemical selves'. Drawing on 55 qualitative interviews conducted with members of the Australian public residing in the Greater Brisbane area, we challenge both the 'expectational discourses' of neuroscientists and the criticisms of its detractors. Members of the public accepted multiple perspectives on the causes of addiction, including some elements of neurobiological explanations. Their discussions of addiction drew upon a broad range of philosophical, sociological, anthropological, psychological and neurobiological vocabularies, suggesting that they synthesised newer technical understandings, such as that offered by neuroscience, with older ones. Holding conceptual models that acknowledge the complexity of addiction aetiology into which new information is incorporated suggests that the impact of neuroscientific discourse in directing the public's beliefs about addiction is likely to be more limited than proponents or opponents of neuroscience expect

Stationary cocycles and Busemann functions for the corner growth model

We study the directed last-passage percolation model on the planar square lattice with nearest-neighbor steps and general i.i.d. weights on the vertices, out- side of the class of exactly solvable models. Stationary cocycles are constructed for this percolation model from queueing fixed points. These cocycles serve as bound- ary conditions for stationary last-passage percolation, solve variational formulas that characterize limit shapes, and yield existence of Busemann functions in directions where the shape has some regularity. In a sequel to this paper the cocycles are used to prove results about semi-infinite geodesics and the competition interface

Phenotypic Variation and Bistable Switching in Bacteria

Microbial research generally focuses on clonal populations. However, bacterial cells with identical genotypes frequently display different phenotypes under identical conditions. This microbial cell individuality is receiving increasing attention in the literature because of its impact on cellular differentiation, survival under selective conditions, and the interaction of pathogens with their hosts. It is becoming clear that stochasticity in gene expression in conjunction with the architecture of the gene network that underlies the cellular processes can generate phenotypic variation. An important regulatory mechanism is the so-called positive feedback, in which a system reinforces its own response, for instance by stimulating the production of an activator. Bistability is an interesting and relevant phenomenon, in which two distinct subpopulations of cells showing discrete levels of gene expression coexist in a single culture. In this chapter, we address techniques and approaches used to establish phenotypic variation, and relate three well-characterized examples of bistability to the molecular mechanisms that govern these processes, with a focus on positive feedback.