X-ray diffraction from bone employing annular and semi-annular beams

This is the final version of the article. Available from the publisher via the DOI in this record.There is a compelling need for accurate, low cost diagnostics to identify osteo-tissues that are associated with a high risk of fracture within an individual. To satisfy this requirement the quantification of bone characteristics such as 'bone quality' need to exceed that provided currently by densitometry. Bone mineral chemistry and microstructure can be determined from coherent x-ray scatter signatures of bone specimens. Therefore, if these signatures can be measured, in vivo, to an appropriate accuracy it should be possible by extending terms within a fracture risk model to improve fracture risk prediction.In this preliminary study we present an examination of a new x-ray diffraction technique that employs hollow annular and semi-annular beams to measure aspects of 'bone quality'. We present diffractograms obtained with our approach from ex vivo bone specimens at Mo Kα and W Kα energies. Primary data is parameterized to provide estimates of bone characteristics and to indicate the precision with which these can be determined.We acknowledge gratefully the funding provided by the UK Engineering and Physical Sciences Research Council (EPSRC) grant number EP/K020196/

Trust and transparency in times of crisis: Results from an online survey during the first wave (April 2020) of the COVID-19 epidemic in the UK

BACKGROUND: The success of a government's COVID-19 control strategy relies on public trust and broad acceptance of response measures. We investigated public perceptions of the UK government's COVID-19 response, focusing on the relationship between trust and perceived transparency, during the first wave (April 2020) of the COVID-19 pandemic in the United Kingdom. METHODS: Anonymous survey data were collected (2020-04-06 to 2020-04-22) from 9,322 respondents, aged 20+ using an online questionnaire shared primarily through Facebook. We took an embedded-mixed-methods approach to data analysis. Missing data were imputed via multiple imputation. Binomial & multinomial logistic regression were used to detect associations between demographic characteristics and perceptions or opinions of the UK government's response to COVID-19. Structural topic modelling (STM), qualitative thematic coding of sub-sets of responses were then used to perform a thematic analysis of topics that were of interest to key demographic groups. RESULTS: Most respondents (95.1%) supported government enforcement of behaviour change. While 52.1% of respondents thought the government was making good decisions, differences were apparent across demographic groups, for example respondents from Scotland had lower odds of responding positively than respondents in London. Higher educational levels saw decreasing odds of having a positive opinion of the government response and decreasing household income associated with decreasing positive opinion. Of respondents who thought the government was not making good decisions 60% believed the economy was being prioritised over people and their health. Positive views on government decision-making were associated with positive views on government transparency about the COVID-19 response. Qualitative analysis about perceptions of government transparency highlighted five key themes: (1) the justification of opacity due to the condition of crisis, (2) generalised mistrust of politics, (3) concerns about the role of scientific evidence, (4) quality of government communication and (5) questions about political decision-making processes. CONCLUSION: Our study suggests that trust is not homogenous across communities, and that generalised mistrust, concerns about the transparent use and communication of evidence and insights into decision-making processes can affect perceptions of the government's pandemic response. We recommend targeted community engagement, tailored to the experiences of different groups and a new focus on accountability and openness around how decisions are made in the response to the UK COVID-19 pandemic

Rough paths in idealized financial markets

This paper considers possible price paths of a financial security in an idealized market. Its main result is that the variation index of typical price paths is at most 2, in this sense, typical price paths are not rougher than typical paths of Brownian motion. We do not make any stochastic assumptions and only assume that the price path is positive and right-continuous. The qualification "typical" means that there is a trading strategy (constructed explicitly in the proof) that risks only one monetary unit but brings infinite capital when the variation index of the realized price path exceeds 2. The paper also reviews some known results for continuous price paths and lists several open problems.Comment: 21 pages, this version adds (in Appendix C) a reference to new results in the foundations of game-theoretic probability based on Hardin and Taylor's work on hat puzzle

Disease prevention versus data privacy : using landcover maps to inform spatial epidemic models

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock

Energy-dispersive X-ray diffraction using an annular beam

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.We demonstrate material phase identification by measuring polychromatic diffraction spots from samples at least 20 mm in diameter and up to 10 mm thick with an energy resolving point detector. Within our method an annular X-ray beam in the form of a conical shell is incident with its symmetry axis normal to an extended polycrystalline sample. The detector is configured to receive diffracted flux transmitted through the sample and is positioned on the symmetry axis of the annular beam. We present the experiment data from a range of different materials and demonstrate the acquisition of useful data with sub-second collection times of 0.5 s; equating to 0.15 mAs. Our technique should be highly relevant in fields that demand rapid analytical methods such as medicine, security screening and non-destructive testing.We acknowledge gratefully the funding provided by the UK Engineering and Physical Sciences Research Council (EPSRC) grant number EP/K020196/1

Use of Temporally Validated Machine Learning Models To Predict Outcomes of Percutaneous Nephrolithotomy Using Data from the British Association of Urological Surgeons Percutaneous Nephrolithotomy Audit

\ua9 2024 European Association of Urology. Background and objective: Machine learning (ML) is a subset of artificial intelligence that uses data to build algorithms to predict specific outcomes. Few ML studies have examined percutaneous nephrolithotomy (PCNL) outcomes. Our objective was to build, streamline, temporally validate, and use ML models for prediction of PCNL outcomes (intensive care admission, postoperative infection, transfusion, adjuvant treatment, postoperative complications, visceral injury, and stone-free status at follow-up) using a comprehensive national database (British Association of Urological Surgeons PCNL). Methods: This was an ML study using data from a prospective national database. Extreme gradient boosting (XGB), deep neural network (DNN), and logistic regression (LR) models were built for each outcome of interest using complete cases only, imputed, and oversampled and imputed/oversampled data sets. All validation was performed with complete cases only. Temporal validation was performed with 2019 data only. A second round used a composite of the most important 11 variables in each model to build the final model for inclusion in the shiny application. We report statistics for prognostic accuracy. Key findings and limitations: The database contains 12 810 patients. The final variables included were age, Charlson comorbidity index, preoperative haemoglobin, Guy\u27s stone score, stone location, size of outer sheath, preoperative midstream urine result, primary puncture site, preoperative dimercapto-succinic acid scan, stone size, and image guidance (https://endourology.shinyapps.io/PCNL_Demographics/). The areas under the receiver operating characteristic curve was >0.6 in all cases. Conclusions and clinical implications: This is the largest ML study on PCNL outcomes to date. The models are temporally valid and therefore can be implemented in clinical practice for patient-specific risk profiling. Further work will be conducted to externally validate the models. Patient summary: We applied artificial intelligence to data for patients who underwent a keyhole surgery to remove kidney stones and developed a model to predict outcomes for this procedure. Doctors could use this tool to advise patients about their risk of complications and the outcomes they can expect after this surgery

Interpolated sequences and critical LL-values of modular forms

Recently, Zagier expressed an interpolated version of the Ap\'ery numbers for $\zeta(3)$ in terms of a critical $L$-value of a modular form of weight 4. We extend this evaluation in two directions. We first prove that interpolations of Zagier's six sporadic sequences are essentially critical $L$-values of modular forms of weight 3. We then establish an infinite family of evaluations between interpolations of leading coefficients of Brown's cellular integrals and critical $L$-values of modular forms of odd weight.Comment: 23 pages, to appear in Proceedings for the KMPB conference: Elliptic Integrals, Elliptic Functions and Modular Forms in Quantum Field Theor