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Background Novartis and the University of Oxford’s Big Data Institute (BDI) have established a research alliance with the aim to improve health care and drug development by making it more efficient and targeted. Using a combination of the latest statistical machine learning technology with an innovative IT platform developed to manage large volumes of anonymised data from numerous data sources and types we plan to identify novel patterns with clinical relevance which cannot be detected by humans alone to identify phenotypes and early predictors of patient disease activity and progression. Method The collaboration focuses on highly complex autoimmune diseases and develops a computational framework to assemble a research-ready dataset across numerous modalities. For the Multiple Sclerosis (MS) project, the collaboration has anonymised and integrated phase II to phase IV clinical and imaging trial data from ≈35,000 patients across all clinical phenotypes and collected in more than 2200 centres worldwide. For the “IL-17” project, the collaboration has anonymised and integrated clinical and imaging data from over 30 phase II and III Cosentyx clinical trials including more than 15,000 patients, suffering from four autoimmune disorders (Psoriasis, Axial Spondyloarthritis, Psoriatic arthritis (PsA) and Rheumatoid arthritis (RA)). Results A fundamental component of successful data analysis and the collaborative development of novel machine learning methods on these rich data sets has been the construction of a research informatics framework that can capture the data at regular intervals where images could be anonymised and integrated with the de-identified clinical data, quality controlled and compiled into a research-ready relational database which would then be available to multi-disciplinary analysts. The collaborative development from a group of software developers, data wranglers, statisticians, clinicians, and domain scientists across both organisations has been key. This framework is innovative, as it facilitates collaborative data management and makes a complicated clinical trial data set from a pharmaceutical company available to academic researchers who become associated with the project. Conclusions An informatics framework has been developed to capture clinical trial data into a pipeline of anonymisation, quality control, data exploration, and subsequent integration into a database. Establishing this framework has been integral to the development of analytical tools.

The method of entrainment-limited kinetics enables atmospheric chemistry models that do not resolve clouds to simulate heterogeneous (surface and multiphase) cloud chemistry more accurately and efficiently than previous numerical methods. The method, which was previously described for reactions with first-order kinetics in clouds, incorporates cloud entrainment into the kinetic rate coefficient. This technical note shows how bimolecular reactions with second-order kinetics in clouds can also be treated with entrainment-limited kinetics, enabling efficient simulations of a wider range of cloud chemistry reactions. Accuracy is demonstrated using oxidation of SO2 to S(VI) – a key step in the formation of acid rain – as an example. Over a large range of reaction rates, cloud fractions, and initial reactant concentrations, the numerical errors in the entrainment-limited bimolecular reaction rates are typically ≪1 % and always %; thus, they are far smaller than the errors found in several commonly used methods of simulating cloud chemistry with fractional cloud cover.

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human–AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial., The CONSORT-AI and SPIRIT-AI extensions improve the transparency of clinical trial design and trial protocol reporting for artificial intelligence interventions.

Background Exploration and modelling of heterogeneous treatment effects as a function of baseline covariates is an important aspect of precision medicine in randomised controlled trials (RCTs). Randomisation generally guarantees the internal validity of an RCT, but heterogeneity in treatment effect can reduce external validity. Estimation of heterogeneous treatment effects is usually done via a predictive model for individual outcomes, where one searches for interactions between treatment allocation and important patient baseline covariates. However, such models are prone to overfitting and multiple testing and typically demand a transformation of the outcome measurement, for example, from the absolute risk in the original RCT to log-odds of risk in the predictive model. Methods We show how reference classes derived from baseline covariates can be used to explore heterogeneous treatment effects via a two-stage approach. We first estimate a risk score which captures on a single dimension some of the heterogeneity in outcomes of the trial population. Heterogeneity in the treatment effect can then be explored via reweighting schemes along this axis of variation. This two-stage approach bypasses the search for interactions with multiple covariates, thus protecting against multiple testing. It also allows for exploration of heterogeneous treatment effects on the original outcome scale of the RCT. This approach would typically be applied to multivariable models of baseline risk to assess the stability of average treatment effects with respect to the distribution of risk in the population studied. Case study We illustrate this approach using the single largest randomised treatment trial in severe falciparum malaria and demonstrate how the estimated treatment effect in terms of absolute mortality risk reduction increases considerably in higher risk strata. Conclusions ‘Local’ and ‘tilting’ reweighting schemes based on ranking patients by baseline risk can be used as a general approach for exploring, graphing and reporting heterogeneity of treatment effect in RCTs. Trial registration ISRCTN clinical trials registry: ISRCTN50258054. Prospectively registered on 22 July 2005.

Background Retrospective exploratory analyses of randomised controlled trials (RCTs) seeking to identify treatment effect heterogeneity (TEH) are prone to bias and false positives. Yet the desire to learn all we can from exhaustive data measurements on trial participants motivates the inclusion of such analyses within RCTs. Moreover, widespread advances in machine learning (ML) methods hold potential to utilise such data to identify subjects exhibiting heterogeneous treatment response. Methods We present a novel analysis strategy for detecting TEH in randomised data using ML methods, whilst ensuring proper control of the false positive discovery rate. Our approach uses random data partitioning with statistical or ML-based prediction on held-out data. This method can test for both crossover TEH (switch in optimal treatment) and non-crossover TEH (systematic variation in benefit across patients). The former is done via a two-sample hypothesis test measuring overall predictive performance. The latter is done via ‘stacking’ the ML predictors alongside a classical statistical model to formally test the added benefit of the ML algorithm. An adaptation of recent statistical theory allows for the construction of a valid aggregate p value. This testing strategy is independent of the choice of ML method. Results We demonstrate our approach with a re-analysis of the SEAQUAMAT trial, which compared quinine to artesunate for the treatment of severe malaria in Asian adults. We find no evidence for any subgroup who would benefit from a change in treatment from the current standard of care, artesunate, but strong evidence for significant TEH within the artesunate treatment group. In particular, we find that artesunate provides a differential benefit to patients with high numbers of circulating ring stage parasites. Conclusions ML analysis plans using computational notebooks (documents linked to a programming language that capture the model parameter settings, data processing choices, and evaluation criteria) along with version control can improve the robustness and transparency of RCT exploratory analyses. A data-partitioning algorithm allows researchers to apply the latest ML techniques safe in the knowledge that any declared associations are statistically significant at a user-defined level.

In this paper, the first demonstration of simultaneous UV written Bragg gratings and waveguides in germanium and boron‐doped planar silica is presented using a 5th harmonic solid‐state nanosecond laser operating at 213 nm wavelength. The fabrication of high‐quality gratings by using a high peak power density, yielding sufficient uniformity and without any surface damage is demonstrated. The photosensitivity of the doped silica layer is investigated by measuring the local effective refractive index of the optical modes. The written gratings are used to measure grating refractive index modulation, grating detuning bandwidth and the waveguide propagation loss with a minimum value of 0.28 ± 0.07 dB cm−1. This paper shows that this new generation of pulsed UV lasers is a promising alternative for conventional longer wavelength CW laser sources used in small spot direct grating writing in doped silica.

BackgroundEthnically diverse and socio-economically deprived communities have been differentially affected by the COVID-19 pandemic in the UK.MethodUsing a multilevel regression model we assess the time-varying association between SARS-CoV-2 infections and areal level deprivation and ethnicity. We separately consider weekly test positivity rate (number of positive tests over the total number of tests) and estimated unbiased prevalence (proportion of individuals in the population who would test positive) at the Lower Tier Local Authority (LTLA) level. The model also adjusts for age, urbanicity, vaccine uptake and spatio-temporal correlation structure.FindingsComparing the least deprived and predominantly White areas with most deprived and predominantly non-White areas over the whole study period, the weekly positivity rate increases by 13% from 2·97% to 3·35%. Similarly, prevalence increases by 10% from 0·37% to 0·41%. Deprivation has a stronger effect until October 2020, while the effect of ethnicity becomes slightly more pronounced at the peak of the second wave and then again in May-June 2021. Not all BAME groups were equally affected: in the second wave of the pandemic, LTLAs with large South Asian populations were the most affected, whereas areas with large Black populations did not show increased values for either outcome during the entire period under analysis.InterpretationAt the area level, IMD and BAME% are both associated with an increased COVID-19 burden in terms of prevalence (disease spread) and test positivity (disease monitoring), and the strength of association varies over the course of the pandemic. The consistency of results across the two outcome measures suggests that community level characteristics such as deprivation and ethnicity have a differential impact on disease exposure or susceptibility rather than testing access and habits.FundingsEPSRC, MRC, The Alan Turing Institute, NIH, UKHSA, DHSC, NIHR

No abstract available.

Avoiding emergency hospital admission (EA) is advantageous to individual health and the healthcare system. We develop a statistical model estimating risk of EA for most of the Scottish population (> 4.8Mindividuals) using electronic health records, such as hospital episodes and prescribing activity. We demonstrate good predictive accuracy (AUROC 0.80), calibration and temporal stability. We find strong prediction of respiratory and metabolic EA, show a substantial risk contribution from socioeconomic decile, and highlight an important problem in model updating. Our work constitutes a rare example of a population-scale machine learning score to be deployed in a healthcare setting.

Two methods to inscribe co‐located fibre Bragg gratings using the small spot direct UV writing technique are described and compared, namely: superimposed gratings, which are written one over the other at the same spatial location, and superstructured gratings, where a refractive index modulation is digitally designed and all spectral signatures are written in a single step writing process. In this letter, we report a record number of 45 superimposed and 39 superstructured gratings being inscribed with an interference spot of only 14 μm in diameter.

Severe falciparum malaria has substantially affected human evolution. Genetic association studies of patients with clinically defined severe malaria and matched population controls have helped characterise human genetic susceptibility to severe malaria, but phenotypic imprecision compromises discovered associations. In areas of high malaria transmission, the diagnosis of severe malaria in young children and, in particular, the distinction from bacterial sepsis are imprecise. We developed a probabilistic diagnostic model of severe malaria using platelet and white count data. Under this model, we re-analysed clinical and genetic data from 2220 Kenyan children with clinically defined severe malaria and 3940 population controls, adjusting for phenotype mis-labelling. Our model, validated by the distribution of sickle trait, estimated that approximately one-third of cases did not have severe malaria. We propose a data-tilting approach for case-control studies with phenotype mis-labelling and show that this reduces false discovery rates and improves statistical power in genome-wide association studies., eLife digest In areas of sub-Saharan Africa where malaria is common, most people are frequently exposed to the bites of mosquitoes carrying malaria parasites, so they often have malaria parasites in their blood. Young children, who have not yet built up strong immunity against malaria, often fall ill with severe malaria, a life-threatening disease. It is unclear why some children develop severe malaria and die, while other children with high numbers of parasites in their blood do not develop any apparent symptoms. Genetic susceptibility studies are designed to uncover why such differences exist by comparing individuals with severe malaria (referred to as ‘cases’) with individuals drawn from the general population (known as ‘controls’). But severe malaria can be a challenge to diagnose. Since high numbers of malaria parasites can be found in healthy children, it is sometimes difficult to determine whether the parasites are making a child ill, or whether they are a coincidental finding. Consequently, some of the ‘cases’ recruited into these studies may actually have a different disease, such as bacterial sepsis. This ultimately affects how the studies are interpreted, and introduces error and inaccuracy into the data. Watson, Ndila et al. investigated whether measuring blood biomarkers in patients (derived from the complete blood count, including platelet counts and white blood cell counts) could improve the accuracy with which malaria is diagnosed. They developed a new mathematical model that incorporates platelet and white blood cell counts. This model estimates that in a large cohort of 2,220 Kenyan children diagnosed with severe malaria, around one third of enrolled children did not actually have this disease. Further analysis suggests that patients with severe malaria are highly unlikely to have platelet counts higher than 200,000 per microlitre. This defines a cut-off that researchers can use to avoid recruiting patients who do not have severe malaria in future studies. Additionally, the ability to diagnose severe malaria more accurately can make it easier to detect and treat other diseases with similar symptoms in children with high numbers of malaria parasites in their blood. Watson, Ndila et al.’s findings support the recommendation that all children with suspected malaria be given broad spectrum antibiotics, as many misdiagnosed children will likely have bacterial sepsis. It also suggests that using complete blood counts, which are cheap to obtain and increasingly available in low-resource settings, could improve diagnostic accuracy in future clinical studies of severe malaria. This could ultimately improve the ability of these studies to find new treatments for this life-threatening disease.

Flexible and stretchable photonics is a rapidly evolving area, with various materials, design, processing, and device technologies being explored [1] . As with flexible electronics, flexible photonics particularly lends itself to integration upon engineered and natural structures, such as interfacing with the soft, curvilinear, and dynamic surfaces of living organisms. Here we explore a potential new growth opportunity interfacing with engineered structures, namely embedding into advanced composite materials (such as Carbon Fibre Reinforced Polymer and Glass Fibre Reinforced Polymer). The motivation being ultimately new optical functionality and intelligence for engineered structures (e.g. wind turbines, aircraft, civil infrastructure), going beyond what has been developed with embedded optical fibre through realising the full geometric diversity offered by flexible photonics. This work embeds ultra-thin glass [3] , utilising its flexibility in order to be conformal to the shape of composite structures which flex and in parts are non-planar. It is noted that cyclic olefin copolymer planar chips have recently shown feasibility in composite material [2] , whilst these demonstrations use rigid planar optics they do show feasibility for other flexible photonic materials to exploit this application.

Purpose The purpose of this paper is to research how corporate communication regarding a specific corporate event (i.e. Tesla’s tweets about a new product) as well as the framing of both the event itself and the market reactions therewith in the news media influence the formation of the share price of the respective company over time. In so doing, the study provides insights into the nature of market-moving information and the role of financial news flows in shaping market reactions in today’s high-frequency news and information environment. Design/methodology/approach Using a multi-method case study approach, combining quantitative intraday event studies with a qualitative text analysis of financial online news and tweets by Elon Musk and Twitter, the authors shed light on the complex interaction between market events, financial information and stock market reactions. The analysis covers a period of four days, encompassing the announcement and introduction of the new battery pack for Model S and X by Tesla as well as the accompanying and follow-up reporting by the financial news media. Findings Findings show that market reactions are driven by business events and expectations among the market rather than the follow-up reporting by financial news media. Financial online news instead seems to heavily rely on Elon Musk’s attention-triggering news to sustain its 24-h airtime with a variety of reporting tools, keeping the highly demanded audience engaged. Eventually, Twitter accounts of media visible companies and personalities, such as Tesla and its CEO Elon Musk, have been found to be useful market information sources for day traders and shareholders to trade at a profit. Originality/value The study is a response to recent discussions about the legitimacy of Twitter communication by CEOs or representatives of listed companies. The findings show that Twitter communication needs to be well considered in light of strict market regulations (e.g. SEC in the USA) regarding insider-trading and the publication of market-relevant information. In addition, corporate financial communication should avoid impetuous communication via social media channels as this could have deterrent effects on the market valuation of a listed company.

This work quantifies water contamination in jet fuel (Jet A-1), using silica-based Bragg gratings. The optical sensor geometry exposes the evanescent optical field of a guided mode to enable refractometery. Quantitative analysis is made in addition to the observation of spectral features consistent with the emulsification of water droplets and Stokes’ settling. The measurements are observed for cooling and heating cycles between ranges of 22 °C and −60 °C. The maximum spectral sensitivity for water contamination was 2.4 pm/ppm-v, with a resolution of

Showcasing cutting edge physics research to school children and the general public humanizes ‘the scientist’. This can help towards removing stereotypes and enabling greater diversity in physics. Here we share some of the lessons learnt and activities conducted at the University of Southampton to achieve this goal.

Background Interest in the usefulness of machine learning (ML) methods for outcomes prediction has continued to increase in recent years. However, the advantage of advanced ML model over traditional logistic regression (LR) remains controversial. We performed a systematic review and meta-analysis of studies comparing the discrimination accuracy between ML models versus LR in predicting operative mortality following cardiac surgery. Methods The present systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement. Discrimination ability was assessed using the C-statistic. Pooled C-statistics and its 95% credibility interval for ML models and LR were obtained were obtained using a Bayesian framework. Pooled estimates for ML models and LR were compared to inform on difference between the 2 approaches. Results We identified 459 published citations of which 15 studies met inclusion criteria and were used for the quantitative and qualitative analysis. When the best ML model from individual study was used, meta-analytic estimates showed that ML were associated with a significantly higher C-statistic (ML, 0.88; 95% credibility interval, 0.83-0.93 vs LR, 0.81; 95% credibility interval, 0.77-0.85; P = .03). When individual ML algorithms were instead selected, we found a nonsignificant trend toward better prediction with each of ML algorithms. We found no evidence of publication bias (P = .70). Conclusions The present findings suggest that when compared with LR, ML models provide better discrimination in mortality prediction after cardiac surgery. However, the magnitude and clinical influence of such an improvement remains uncertain.

Background: The SARS-CoV-2 variant B.1.1.7 was first identified in December, 2020, in England. We aimed to investigate whether increases in the proportion of infections with this variant are associated with differences in symptoms or disease course, reinfection rates, or transmissibility. Methods: We did an ecological study to examine the association between the regional proportion of infections with the SARS-CoV-2 B.1.1.7 variant and reported symptoms, disease course, rates of reinfection, and transmissibility. Data on types and duration of symptoms were obtained from longitudinal reports from users of the COVID Symptom Study app who reported a positive test for COVID-19 between Sept 28 and Dec 27, 2020 (during which the prevalence of B.1.1.7 increased most notably in parts of the UK). From this dataset, we also estimated the frequency of possible reinfection, defined as the presence of two reported positive tests separated by more than 90 days with a period of reporting no symptoms for more than 7 days before the second positive test. The proportion of SARS-CoV-2 infections with the B.1.1.7 variant across the UK was estimated with use of genomic data from the COVID-19 Genomics UK Consortium and data from Public Health England on spike-gene target failure (a non-specific indicator of the B.1.1.7 variant) in community cases in England. We used linear regression to examine the association between reported symptoms and proportion of B.1.1.7. We assessed the Spearman correlation between the proportion of B.1.1.7 cases and number of reinfections over time, and between the number of positive tests and reinfections. We estimated incidence for B.1.1.7 and previous variants, and compared the effective reproduction number, R t, for the two incidence estimates. Findings: From Sept 28 to Dec 27, 2020, positive COVID-19 tests were reported by 36 920 COVID Symptom Study app users whose region was known and who reported as healthy on app sign-up. We found no changes in reported symptoms or disease duration associated with B.1.1.7. For the same period, possible reinfections were identified in 249 (0·7% [95% CI 0·6–0·8]) of 36 509 app users who reported a positive swab test before Oct 1, 2020, but there was no evidence that the frequency of reinfections was higher for the B.1.1.7 variant than for pre-existing variants. Reinfection occurrences were more positively correlated with the overall regional rise in cases (Spearman correlation 0·56–0·69 for South East, London, and East of England) than with the regional increase in the proportion of infections with the B.1.1.7 variant (Spearman correlation 0·38–0·56 in the same regions), suggesting B.1.1.7 does not substantially alter the risk of reinfection. We found a multiplicative increase in the R t of B.1.1.7 by a factor of 1·35 (95% CI 1·02–1·69) relative to pre-existing variants. However, R t fell below 1 during regional and national lockdowns, even in regions with high proportions of infections with the B.1.1.7 variant. Interpretation: The lack of change in symptoms identified in this study indicates that existing testing and surveillance infrastructure do not need to change specifically for the B.1.1.7 variant. In addition, given that there was no apparent increase in the reinfection rate, vaccines are likely to remain effective against the B.1.1.7 variant. Funding: Zoe Global, Department of Health (UK), Wellcome Trust, Engineering and Physical Sciences Research Council (UK), National Institute for Health Research (UK), Medical Research Council (UK), Alzheimer's Society.

This work was supported by Health Data Research UK (HDR-9006; CFC0110) and the Medical Research Council (MR/S027750/1). Health Data Research UK is funded by: UK Medical Research Council; Engineering and Physical Sciences Research Council; Economic and Social Research Council; National Institute for Health Research (England); Chief Scientist Office of the Scottish Government Health and Social Care Directorates; Health and Social Care Research and Development Division (Welsh Government); Public Health Agency (Northern Ireland); British Heart Foundation and Wellcome Trust. Introduction Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence. We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity. Methods and analysis The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity. Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation. Ethics and dissemination The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. Publisher PDF

In this paper we present the first example of waveguides fabricated by UV writing in non-hydrogen loaded Ge-doped planar silica with 213 nm light. Single mode waveguides were fabricated and the numerical apertures and mode field diameters were measured for a range of writing fluences. A peak index change of 5.3 x 10 −3 was inferred for the waveguide written with 70 kJ cm −2. The refractive index change is sufficient to match the index structure of standard optical fiber. Uniformity of the written structures was measured and a propagation loss of 0.39 ± 0.03 dB cm −1 was determined through cutback measurements.

Exploration and modelling of heterogeneous treatment effects as a function of baseline covariates is an important aspect of precision medicine in randomised controlled trials (RCTs). Randomisation generally guarantees the internal validity of an RCT, but heterogeneity in treatment effect can reduce external validity. Estimation of heterogeneous treatment effects is usually done via a predictive model for individual outcomes, where one searches for interactions between treatment allocation and important patient baseline covariates. However, such models are prone to overfitting and multiple testing and typically demand a transformation of the outcome measurement, for example, from the absolute risk in the original RCT to log-odds of risk in the predictive model.We show how reference classes derived from baseline covariates can be used to explore heterogeneous treatment effects via a two-stage approach. We first estimate a risk score which captures on a single dimension some of the heterogeneity in outcomes of the trial population. Heterogeneity in the treatment effect can then be explored via reweighting schemes along this axis of variation. This two-stage approach bypasses the search for interactions with multiple covariates, thus protecting against multiple testing. It also allows for exploration of heterogeneous treatment effects on the original outcome scale of the RCT. This approach would typically be applied to multivariable models of baseline risk to assess the stability of average treatment effects with respect to the distribution of risk in the population studied.We illustrate this approach using the single largest randomised treatment trial in severe falciparum malaria and demonstrate how the estimated treatment effect in terms of absolute mortality risk reduction increases considerably in higher risk strata.'Local' and 'tilting' reweighting schemes based on ranking patients by baseline risk can be used as a general approach for exploring, graphing and reporting heterogeneity of treatment effect in RCTs.ISRCTN clinical trials registry: ISRCTN50258054. Prospectively registered on 22 July 2005.

Genetic surveillance of malaria parasites supports malaria control programmes, treatment guidelines and elimination strategies. Surveillance studies often pose questions about malaria parasite ancestry (e.g. how antimalarial resistance has spread) and employ statistical methods that characterise parasite population structure. Many of the methods used to characterise structure are unsupervised machine learning algorithms which depend on a genetic distance matrix, notably principal coordinates analysis (PCoA) and hierarchical agglomerative clustering (HAC). PCoA and HAC are sensitive to both the definition of genetic distance and algorithmic specification. Importantly, neither algorithm infers malaria parasite ancestry. As such, PCoA and HAC can inform (e.g. via exploratory data visualisation and hypothesis generation), but not answer comprehensively, key questions about malaria parasite ancestry. We illustrate the sensitivity of PCoA and HAC using 393 Plasmodium falciparum whole genome sequences collected from Cambodia and neighbouring regions (where antimalarial resistance has emerged and spread recently) and we provide tentative guidance for the use and interpretation of PCoA and HAC in malaria parasite genetic epidemiology. This guidance includes a call for fully transparent and reproducible analysis pipelines that feature (i) a clearly outlined scientific question; (ii) a clear justification of analytical methods used to answer the scientific question along with discussion of any inferential limitations; (iii) publicly available genetic distance matrices when downstream analyses depend on them; and (iv) sensitivity analyses. To bridge the inferential disconnect between the output of non-inferential unsupervised learning algorithms and the scientific questions of interest, tailor-made statistical models are needed to infer malaria parasite ancestry. In the absence of such models speculative reasoning should feature only as discussion but not as results., Author summary Genetic epidemiology studies of malaria attempt to characterise what is happening in malaria parasite populations. In particular, they are an important tool to track the spread of drug resistance and to validate genetic markers of drug resistance. To make sense of parasite genetic data, researchers usually characterise the population structure using statistical methods. This is most often done as a two step process. The first is a data reduction step, whereby the data are summarised into a distance matrix (each entry represents the genetic distance between two isolates). The distance matrix is then input into an unsupervised machine learning algorithm. Principal coordinates analysis and hierarchical agglomerative clustering are the two most popular unsupervised machine learning algorithms used for this purpose in malaria genetic epidemiology. We highlight that this procedure is sensitive to the choice of genetic distance and to the specification of the algorithms. These unsupervised methods are useful for exploratory data analysis but cannot be used to infer historical events. We provide some guidance on how to make genetic epidemiology analyses more transparent and reproducible.

For machine learning developers, the use of prediction tools in real-world clinical settings can be a distant goal. Recently published guidelines for reporting clinical research that involves machine learning will help connect clinical and computer science communities, and realize the full potential of machine learning tools.

Summary: The CONSORT 2010 statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders), and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human–AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret, and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

We demonstrated two different methods for fabricating a record number of over 40 co-located fiber Bragg gratings: consecutively written superimposed and digitally synthesized superstructure gratings fabricated through small spot direct UV writing. The development of ’AC’ refractive index for superimposed gratings has been analyzed.

We report the first demonstration of integrated waveguides and Bragg gratings direct UV written into planar silica using a fifth harmonic 213 nm laser without hydrogen loading. We observe 0.44 ±0.26 dB cm − 1 propagation loss.

In this paper, a comprehensive model is developed in order to take into account the effect of stress birefringence, longitudinal strain and temperature change on a sensor based on a Fiber Bragg Grating (FBG) written in a Polarization-Maintaining (PM) Panda Fiber. The electromagnetic and mechanical investigation is performed via the Finite Element Method (FEM) to determine stress-induced birefringence and its effect on the light propagation in single mode polarization maintaining optical fibers. The sensing feature of a FBG, subjected to longitudinal strain and temperature variation, is investigated with reference to the resonant wavelength shift. The reflection spectra, obtained by using transfer matrix method, show high sensitivity optical responses to thermal and mechanical stresses.

Summary Background The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease. Methods In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK–CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1·5, 2·0, and 3·0 at differing infection rate scenarios, including full suppression (0·001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation. Findings We included 3 862 012 individuals (1 957 935 [50·7%] women and 1 904 077 [49·3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13·7% were older than 70 years and 6·3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4·46% (95% CI 4·41–4·51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1·5, four with an RR of 2·0, and seven with an RR of 3·0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1·5, 36 749 with an RR of 2·0, and 73 498 with an RR of 3·0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1·5, 293 991 with an RR of 2·0, and 587 982 with an RR of 3·0. Interpretation We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indirect) effects of the pandemic on excess mortality. Funding National Institute for Health Research University College London Hospitals Biomedical Research Centre, Health Data Research UK.

Martin White and coauthors consider that the commercial food system has the potential to show leadership and support for dietary public health, but systemic change is needed first and this is likely to require governmental action

We demonstrated the fabrication of a record number of over 40 overlaid fiber Bragg gratings. We compared two different fabrication techniques: consecutively superim- posed and digitally superstructured gratings and analyzed the evolving AC refractive index change.

In this paper, we demonstrated the use of the k-Nearest Neighbor, a machine learning algorithm, to identify mechanical vibration modes of a cantilever beam in a frequency range between 40-300 Hz at an accelerations of $1.1 \pm 0.1\mathrm{g}$. We attached fiber Bragg gratings to the cantilever structure and analyzed the spectral response during vibration. We observe small increases in spectral bandwidth of three Bragg gratings to perform a 3-dimensional classification environment and evaluated the accuracy of the algorithm with independent testing data. © 2020 The Author(s)

We demonstrated the possibility of trimming the operating wavelength of nitrogen-rich silicon nitride racetrack resonators by exposing the devices to an ultraviolet laser. The results revealed that in a few seconds only, the resonant wavelength of fabricated devices could be tuned to any point within a free spectral range. This technique can also be applied to trim the response of any other silicon photonic device based on nitrogen-rich silicon nitride.

Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanced random forests with Hidden Markov Models, to reliably detect a number of activity modes. We show that physical activity and sleep behaviours can be classified with 87% accuracy in 159,504 minutes of recorded free-living behaviours from 132 adults. These trained models can be used to infer fine resolution activity patterns at the population scale in 96,220 participants. For example, we find that men spend more time in both low- and high-intensity behaviours, while women spend more time in mixed behaviours. Walking time is highest in spring and sleep time lowest during the summer. This work opens the possibility of future public health guidelines informed by the health consequences associated with specific, objectively measured, physical activity and sleep behaviours.

Organizations are often called upon to restructure their work processes—and themselves—in nearly real-time to deal with unplanned-for contingencies. However, researchers are seldom present at the right place and time to deploy specialized instrumentation and thus capture appropriate data on these improvisations. Somewhat paradoxically, now more than ever, organizations are producing vast quantities of data on work process and structure. The goal of this work is to draw upon one such data set—on post-disaster recovery operations—to explore the interplay between improvised organizational structure and processes. The result of this work shows the pattern of organizational improvising behavior and elicits the relationship between ideation and decision making - two elemental processes of organizational improvisation. Future work directions are also discussed.

As more reclaimed asphalt pavement (RAP) is utilised in asphalt mixtures, there are increasing concerns about the potential negative effect of the aged RAP binder on the field performance, especially cracking resistance, of the high RAP mixtures. To address the concerns, there have been increasing interests in utilising rejuvenators to improve the cracking performance of high RAP mixtures. The objective of this study was to determine the benefits of using a new rejuvenator made from renewable sources in asphalt mixtures with high RAP contents. The study was conducted by determining and comparing the laboratory performance properties of three mixtures and the binders extracted from the mixes. The three mixtures evaluated in this study included two 50% RAP mixtures (RAP binder ratio = 0.55) with and without the rejuvenator and a comparable virgin mix. Results of this study suggested that the new rejuvenator was effective in improving both the intermediate and low-temperature cracking performance characteris...

Consolidation of flame hydrolysis deposited silica soot with a 9.3 μm CO2 laser has been demonstrated. A range of laser parameters were investigated and the surface roughness of the resulting silica layers were characterised with a stylus profiler and white light interferometer. The surface roughness parameters were Ra = 68.9 nm, Rq = 83.8 nm perpendicular to the trajectory of the translated laser beam for a speed of 300 mm s-1 and an average laser power of 42.5 W, and Ra = 29.5 nm, Rq = 36.18 nm along the trajectory of the translated laser beam for a speed of 250 mm s-1 and an average laser power of 35.8 W.

This work reports the fabrication of an integrated axicon lens within a monolithic fiber-upon-planar format. The lens is self-assembled around a tapered optical fiber during flame hydrolysis planarization. The formed lens approximates an oblate axicon that upon launch generates a quasi-Bessel beam, guided in the planar optical layer of the substrate. Experimental observations are theoretically concurred using Fourier-based beam propagation.

ObjectiveNrf2 overexpression confers poor prognosis in some cancers but its role in colorectal cancer (CRC) is unknown. Due to its role as a transcription factor we hypothesise a metagene of NRF2 regulated genes could act as a prognostic biomarker in CRC.DesignUsing known NRF2 regulated genes, we defined an NRF2 metagene to represent the pathway expression using principal component analysis and Cox proportional hazard models. The NRF2 metagene was validated in four independent datasets, including the recently profiled MRC FOCUS randomised controlled trial.Results36 genes comprised the final prognostic metagene in the training set. 1,360 patients were included in the validation analyses. High NRF2 metagene expression is associated with worse disease free survival (DFS) and overall survival (OS) outcomes in stage I/II/III disease and worse OS in stage IV disease. In multivariate analyses, NRF2 expression remained significant when adjusted for known prognostic factors of adjuvant chemotherapy and stage in stage I/II/III disease, as well as BRAF V600E mutation and sidedness in stage IV disease. NRF2 metagene expression exhibits variation within each of the Consensus Molecular Subtypes (CMS) but high expression is particularly enriched in CMS 4.ConclusionWe demonstrate in a large scale analysis that NRF2 expression is a novel biomarker of poor prognosis across all stages of colorectal cancer. Higher expression observed in CMS 4 further refines the molecular taxonomy of colorectal cancer.

Tailored laser sources for UV/blue wavelengths are in ever-increasing demand due to the advancing fields of quantum ion-trapping, metrology and computation. In particular, compact sources are required for the field deployment of quantum technologies. In recent work, we demonstrated the ability to fabricate ridge waveguides in Periodically Poled Lithium Niobate (PPLN) for Rb atom trapping based on 1560 nm Second Harmonic Generation (SHG) and mode matched to standard telecommunications optical fibre [1]. Here, we present our advancement of this work toward UV generation at 369 nm for Yb+ Doppler cooling, with an emphasis on manufacturing scalability.Our first example is a zinc-indiffused PPLN ridge waveguide for conversion of ~780 nm to 390 nm light. Electric field poling was used to achieve periodic gratings in 5% MgO-doped lithium niobate with a width of 200 μm and a phase-matching period of 2.2 μm through the entire 500 μm thickness of the crystal. Metallic zinc was deposited and indiffused to create a planar waveguide in the MgO:PPLN crystal, followed by ridge and facet definition via ultra-precision ductile dicing.Fig. 1. (a) SHG spectra generated from a 5.6 μm wide PPLN ridge waveguide with an overlaid model of a theoretical sinc2 with the corresponding Fabry-Perot interference fringes. (b) Pump mode profile, demonstrating single-mode operation. (c) Photograph of the device and the input/output coupling optics for characterisation. (d) Colour photograph of prism separated fundamental and second harmonic output.Fig. 1(a) shows a SHG spectra from a 5.6 μm wide diced ridge waveguide. Detailed spectra in low power operation is taken using lock-in amplification. As the sample is non-AR coated, the associated Fabry-Perot interference fringes for a 14 mm cavity can modelled to explain the SHG spectrum, also shown in Fig. 1(a).Fig. 1(b), the corresponding pump 2nd moment MFD is calculated as 4.7 x 6.1 μm in the x and y axes, respectively.Fig. 1(c) shows the device and the characterisation optics for simple input coupling using a 15 mm focal length aspheric lens from a collimated, fibre coupled source; the 390nm signal can be seen in Fig. 1(d). A conversion efficiency of ~10 %/W/cm2 has been attained with a 14 mm-long waveguide. This device offers a higher efficiency than typical UV-transmissive, nonlinear crystals in low-power operation, such as LBO, which typically require cavity enhancement for application [2]. This approach provides a simpler fabrication process to other ridge-style PPLN waveguides achieved through thinning, resin bonding and dicing to 1.5 μm depth ridge waveguides [3].We will present the optimisation of parameters for both periodic poling and waveguide fabrication. Current results on device efficiency, spectra and advancements to longer devices will also be presented. The effects of ridge width variation to understand distortion from the expected sinc2 SHG spectra will also be discussed to further understand the fabrication limitations in these indiffused, ridge waveguides.References:1. L. G. , S. A. Berry, R. H. Bannerman, A. C. Gray, J. W. Field, C. Holmes, J. C. Gates, P. G. R. Smith and C. B. E. Gawith, “Developing PPLN Waveguides for Quantum Rubidium Atom Traps in Space” Nonlinear Photonics Optical Society of America, (pp. NpTh1C-6). (2018).2. M. Pizzocaro, D. Calonico, P. C. Pastor, J. Catani, G. A. Costanzo, F. Levi, and L. Lorini. "Efficient frequency doubling at 399nm" Applied optics, 53, 3388-3392 (2014).3. K. Mizuuchi, T. Sugita, K. Yamamoto, T. Kawaguchi, T. Yoshino, and M. Imaeda "Efficient 340-nm light generation by a ridge-type waveguide in a first-order periodically poled MgO:LiNbO3" Optics letters 28, 1344-1346 (2003).

Micromechanical devices are typically fabricated in expensive cleanrooms using techniques that are not conducive towards rapid and varied prototyping. This is typically because photolithography remains the main method for patterning of layers and should a small change be desired in the design, a new mask would have to be made, which is both a costly and slow process. This work reports a laser based approach for micromechanical diaphragm fabrication. The technique uses rapid thermal heating and subsequent quenching to a pattern a hard thermal oxide layer on a silicon substrate. This method used a computer controlled 9.3 micrometre wavelength CO 2 laser beam to spot mark areas that were subsequently wet etched. This approach was found to be extremely repeatable and gave good consistency. It does not require cleanroom processing and is significantly more cost and time effective. Diaphragm feature size was observed to have a variability of

We demonstrate thermal classification of sequentially written fiber Bragg gratings. This Letter presents a process to determine the type of fiber Bragg grating written in SMF28 and GF4A by introducing the gratings to thermal treatment. This technique can be applied to several approaches based on sequential writing, including the small spot direct ultraviolet writing technique. Four different types of gratings have been identified, which are dependent on the fiber type and fluence used during the writing process.

In semi-supervised learning for classification, it is assumed that every ground truth class of data is present in the small labelled dataset. Many real-world sparsely-labelled datasets are plausibly not of this type. It could easily be the case that some classes of data are found only in the unlabelled dataset -- perhaps the labelling process was biased -- so we do not have any labelled examples to train on for some classes. We call this learning regime $\textit{semi-unsupervised learning}$, an extreme case of semi-supervised learning, where some classes have no labelled exemplars in the training set. First, we outline the pitfalls associated with trying to apply deep generative model (DGM)-based semi-supervised learning algorithms to datasets of this type. We then show how a combination of clustering and semi-supervised learning, using DGMs, can be brought to bear on this problem. We study several different datasets, showing how one can still learn effectively when half of the ground truth classes are entirely unlabelled and the other half are sparsely labelled., 8 pages, plus appendix

Rapid thermal consolidation of doped silica soot in a silicon carbide lined kiln subjected to microwave radiation is reported. The silica soot is fabricated through flame hydrolysis deposition and peak temperatures within the kiln reach >1350 °C after 18 minutes of irradiation. The glass underwent Newtonian cooling with an e−1 of approximately 5 minutes. Optical characteristics of the planar glass layer are compared against traditional furnace consolidation approaches. Single-mode waveguides and Bragg gratings were direct UV written into the consolidated doped silica, and a propagation loss of 0.34 ± 0.15 dB cm−1 was measured.

Purpose: To report a novel management technique using pericardial patch graft for severe corneal wound burn following phacoemulsification of dense cataract with shallow anterior chamber (AC) and overfilled AC with viscoelastic. Methods: Case report. Results: A 46-year-old patient with a shallow AC and dense cataract, who underwent phacoemulsification using “soft shell” technique had severe wound burn which was refractory to conventional management with corneal suturing and placing bandage contact lens. He underwent Tutoplast® (Innovative Ophthalmic Products, Inc., Costa Mesa, CA, USA) pericardium patch graft, which fully resorbed and resolved the wound leak over 6 weeks, leaving a well-healed corneal wound. Conclusion: Pericardium patch graft is a safe and effective technique to manage extensive phaco wound burn which is refractory to conventional management options.

This essay examines recent work on world literature theory, with a particular focus on those theorists who treat individual texts as in dialogue with the circulations of linguistic and cultural translation. I treat world literature as the theory without an object and make the counterintuitive claim that its objectlessness makes it well suited for leaving behind antiquated modes of categorizing and canonizing so-called world texts, in order to make room for new kinds of structured thinking about the world. The essay begins with an introduction to the manifold interconnections of the new world literature, including its broad overlap with fields of globalization, translation studies, comparative literature, and postcolonialism. I position four key theorists of 21st century world literature – Emily Apter, Rebecca Walkowitz, Berthold Schoene, and David Palumbo-Liu – in oppositional pairs in order to show the underlying commonality in their thinking about the value of a literature of and for the world. My entry point into the “thinking machine” of the world literature text is the concept of the limit. Far from the commonplace understanding of limit as a limitation or boundary, I argue that understanding world literature at the limit allows literary texts and theory to be read as an event of thinking that is in-process, in-common, and incomplete, an analogue to the necessary impossibility of knowing the world.

We have demonstrated the inscription of Bragg gratings into five individual cores of a seven core fiber using small spot direct UV writing. With this technique, we defined spectrally multiplexed Bragg gratings consecutively in separate cores as well as spectrally multiplexed gratings at the same longitudinal location in different cores. The effect of bending on the optical spectrum was evaluated to allow the differentiation between cross-exposure and cross-talk, and an alignment process to reduce cross-exposure by 13 dB was found.

A planarized optical fibre designed for two-dimensional bend monitoring and external refractive index sensing is presented. The approach uses two single-mode waveguides each containing a set of spectrally multiplexed Bragg gratings. To achieve sensitivity to bending and external refractive index, the cladding material of the fibre is partially removed through physical machining. This acts to offset the neutral axis, increasing bend sensitivity, and exposes the evanescent field of the guided mode, permitting external refractive index monitoring. Through collective monitoring of the Bragg grating array, real time, multiparameter sensing is attainable, allowing new capability for intelligent monitoring.

We demonstrate the possibility of post-fabrication trimming of the response of nitrogen-rich silicon nitride racetrack resonators by using an ultraviolet laser. The results revealed the possibility to efficiently tune the operating wavelength of fabricated racetrack resonators to any point within the full free spectral range. This process is much faster than similar, previously presented methods (in the order of seconds, compared to hours). This technique can also be applied to accurately trim the optical performance of any other silicon photonic device based on nitrogen-rich silicon nitride.

Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit.

The high intensity and continuous nature of small-spot direct UV writing makes characterisation of gratings types difficult by conventional approaches. Investigating the thermal response of gratings up to 850 °C allows elucidation of grating type.