Communications: Mechanical Deformation of Dendrites by Fluid Flow

It is generally accepted that liquid agitation during alloy solidification assists in crystal multiplication, as in dendrite fragmentation and the detachment of side arms in the mushy region of a casting. Even without deliberate stirring by electromagnetic or mechanical means, there is often vigorous interdendritic fluid flow promoted by natural thermosolutal convection. In this analysis, we shall estimate the stress at the root of a secondary dendrite arm of aluminum arising from the action of a flow of molten metal past the dendrite arm

Hereditary haemochromatosis: associations with morbidity and iron supplement use in 451,243 UK Biobank participants

This is the author accepted manuscript. The final version is available from BMJ Publishing Group via the DOI in this recordSociety for Social Medicine and Population Health and International Epidemiology Association European Congress Annual Scientific Meeting 2019, Hosted by the Society for Social Medicine & Population Health and International Epidemiology Association (IEA), School of Public Health, University College Cork, Cork, Ireland, 4–6 September 2019Medical Research Council (MRC

In vitro activities of MCB3681 and 8 comparators against Clostridium difficile isolates with known ribotypes and diverse geographical spread

Treatments for Clostridium difficile infection remain limited, despite the introduction of fidaxomicin, and development of new agents is necessary. We determined the in vitro susceptibilities of 199 prevalent or emerging Clostridium difficile PCR ribotypes to MCB3681, a novel investigational quinolonyl-oxazolidinone, and 8 comparators (metronidazole, vancomycin, fidaxomicin, moxifloxacin, ciprofloxacin, clindamycin, tigecycline and linezolid). MCB3681 showed good activity against C. difficile with no evidence of MCB3681 resistance in isolates showing either or both moxifloxacin and linezolid resistance

Predicting treatment outcome in psychological treatment services by identifying latent profiles of patients

BACKGROUND: The outcomes of psychological therapies for anxiety and depression vary across individuals and symptom domains. Being able to predict treatment response from readily available patient data at presentation has potentially important benefits in aiding decisions about the most suitable interventions for a patient. This paper presents a method of identifying subgroups of patients using latent profile analysis, and comparing response to psychological treatments between these profiles. METHODS: All outpatients taken into treatment at two psychological treatment services in London, UK and who provided basic demographic information and standardized symptom measures were included in the analysis (n=16636). RESULTS: Latent Profile Analysis was performed on intake data to identify statistically different groups of patients, which were then examined in longitudinal analyses to determine their capacity to predict treatment outcomes. Comparison between profiles showed considerable variation in recovery (74-15%), deterioration rates (5-20%), and levels of attrition (17-40%). Further variation in outcomes was found within the profiles when different intensities of psychological intervention were delivered. LIMITATIONS: Latent profiles were identified using data from two services, so generalisability to other services should be considered. Routinely collected patient data was included, additional patient information may further enhance utility of the profiles. CONCLUSIONS: These results suggest that intake data can be used to reliably classify patients into profiles that are predictive of outcome to different intensities of psychological treatment in routine care. Algorithms based on these kinds of data could be used to optimize decision-making and aid the appropriate matching of patients to treatment

Trends in depression & anxiety symptom severity among mental health service attendees during the COVID-19 pandemic

Background: General population surveys have shown that some groups, particularly young women, experienced increased distress during nationally mandated restrictions to control the spread of COVID-19. However, there has been limited research on such trends among people with pre-existing mental health conditions, leaving mental health services ill equipped to plan for current and future lockdowns. / Methods: Mean weekly scores on the GAD-7 and PHQ-9 between 01/01/2020-22/06/2020 (n=9,538 individuals) for all patients of two psychological treatment services (Improving Access to Psychological Therapies) in London, were compared to mean weekly scores from the same time periods in 2017-2019 (n=37,849). The proportion of scores which were above the clinical thresholds for ‘caseness’ each week were compared, and scores between groups based on gender, age group, and ethnicity, were also compared. / Results: Confirmed community transmission in the UK (26/02/2020-03/03/2020) and the announcement of the national ‘lockdown’ (23/03/2020) were associated with significant increases in anxiety symptom scores. ‘Lockdown’ was associated with a decrease in depression scores. These changes were not maintained during lockdown. Significant increases in depression and anxiety were observed at week 23, as restrictions were eased. / Limitations: This was an exploratory analysis in two services only. Residual confounding and selection biases cannot be ruled out. / Conclusions: Differences in the weekly average symptom scores were short-term; they did not continue throughout ‘lockdown’ as might have been expected, except among older people. Replication of this study in other settings and investigating the potential benefits of more regular reviews or more intensive treatments for at-risk groups, are warranted

Making a smart city legible

This chapter discusses Lancaster City Council's AI for Lancaster Programme. The programme has been a collaboration between the City Council, the International Organization for Artificial Intelligence Legibility, PETRAS and Imagination Lancaster. The chapter describes the design concepts implemented in the city in order to communicate the use of artificial intelligence (AI) to citizens, juxtaposing the designs themselves with extracts from interviews and research conducted to evaluate them. Any innovation supporting the implementation of responsible AI systems in urban contexts should be welcomed, and tools like Design Fiction should be employed to smooth the way for this process. In conclusion, the chapter also discusses how the insights derived from the AI for Lancaster Programme can help inform smart city initiatives while supporting the emergence of hybrid sociologies to describe the urban, social, and technological world we live in

Systematic reduction of complex tropospheric chemical mechanisms using sensitivity and time-scale analyses

International audienceExplicit mechanisms describing the complex degradation pathways of atmospheric volatile organic compounds (VOCs) are important, since they allow the study of the contribution of individual VOCS to secondary pollutant formation. They are computationally expensive to solve however, since they contain large numbers of species and a wide range of time-scales causing stiffness in the resulting equation systems. This paper and the following companion paper describe the application of systematic and automated methods for reducing such complex mechanisms, whilst maintaining the accuracy of the model with respect to important species and features. The methods are demonstrated via application to version 2 of the Leeds Master Chemical Mechanism. The methods of local concentration sensitivity analysis and overall rate sensitivity analysis proved to be efficient and capable of removing the majority of redundant reactions and species in the scheme across a wide range of conditions relevant to the polluted troposphere. The application of principal component analysis of the rate sensitivity matrix was computationally expensive due to its use of the decomposition of very large matrices, and did not produce significant reduction over and above the other sensitivity methods. The use of the quasi-steady state approximation (QSSA) proved to be an extremely successful method of removing the fast time-scales within the system, as demonstrated by a local perturbation analysis at each stage of reduction. QSSA species were automatically selected via the calculation of instantaneous QSSA errors based on user-selected tolerances. The application of the QSSA led to the removal of a large number of alkoxy radicals and excited Criegee bi-radicals via reaction lumping. The resulting reduced mechanism was shown to reproduce the concentration profiles of the important species selected from the full mechanism over a wide range of conditions, including those outside of which the reduced mechanism was generated. As a result of a reduction in the number of species in the scheme of a factor of 2, and a reduction in stiffness, the computational time required for simulations was reduced by a factor of 4 when compared to the full scheme

Systematic reduction of complex tropospheric chemical mechanisms, Part II: Lumping using a time-scale based approach

This paper presents a formal method of species lumping that can be applied automatically to intermediate compounds within detailed and complex tropospheric chemical reaction schemes. The method is based on grouping species with reference to their chemical lifetimes and reactivity structures. A method for determining the forward and reverse transformations between individual and lumped compounds is developed. Preliminary application to the Leeds Master Chemical Mechanism (MCMv2.0) has led to the removal of 734 species and 1777 reactions from the scheme, with minimal degradation of accuracy across a wide range of test trajectories relevant to polluted tropospheric conditions. The lumped groups are seen to relate to groups of peroxy acyl nitrates, nitrates, carbonates, oxepins, substituted phenols, oxeacids and peracids with similar lifetimes and reaction rates with OH. In combination with other reduction techniques, such as sensitivity analysis and the application of the quasi-steady state approximation (QSSA), a reduced mechanism has been developed that contains 35% of the number of species and 40% of the number of reactions compared to the full mechanism. This has led to a speed up of a factor of 8 in terms of computer calculation time within box model simulations