Use of cumulative incidence of novel influenza A/H1N1 in foreign travelers to estimate lower bounds on cumulative incidence in Mexico

Background: An accurate estimate of the total number of cases and severity of illness of an emerging infectious disease is required both to define the burden of the epidemic and to determine the severity of disease. When a novel pathogen first appears, affected individuals with severe symptoms are more likely to be diagnosed. Accordingly, the total number of cases will be underestimated and disease severity overestimated. This problem is manifest in the current epidemic of novel influenza A/H1N1. Methods and Results: We used a simple approach to leverage measures of incident influenza A/H1N1 among a relatively small and well observed group of US, UK, Spanish and Canadian travelers who had visited Mexico to estimate the incidence among a much larger and less well surveyed population of Mexican residents. We estimate that a minimum of 113,000 to 375,000 cases of novel influenza A/H1N1 have occurred in Mexicans during the month of April, 2009. Such an estimate serves as a lower bound because it does not account for underreporting of cases in travelers or for nonrandom mixing between Mexican residents and visitors, which together could increase the estimates by more than an order of magnitude. Conclusions: We find that the number of cases in Mexican residents may exceed the number of confirmed cases by two to three orders of magnitude. While the extent of disease spread is greater than previously appreciated, our estimate suggests that severe disease is uncommon since the total number of cases is likely to be much larger than those of confirmed cases

Evaluation of elicitation methods to quantify Bayes linear models

The Bayes linear methodology allows decision makers to express their subjective beliefs and adjust these beliefs as observations are made. It is similar in spirit to probabilistic Bayesian approaches, but differs as it uses expectation as its primitive. While substantial work has been carried out in Bayes linear analysis, both in terms of theory development and application, there is little published material on the elicitation of structured expert judgement to quantify models. This paper investigates different methods that could be used by analysts when creating an elicitation process. The theoretical underpinnings of the elicitation methods developed are explored and an evaluation of their use is presented. This work was motivated by, and is a precursor to, an industrial application of Bayes linear modelling of the reliability of defence systems. An illustrative example demonstrates how the methods can be used in practice

Consistent Application of Maximum Entropy to Quantum-Monte-Carlo Data

Bayesian statistics in the frame of the maximum entropy concept has widely been used for inferential problems, particularly, to infer dynamic properties of strongly correlated fermion systems from Quantum-Monte-Carlo (QMC) imaginary time data. In current applications, however, a consistent treatment of the error-covariance of the QMC data is missing. Here we present a closed Bayesian approach to account consistently for the QMC-data.Comment: 13 pages, RevTeX, 2 uuencoded PostScript figure

Pilot-scale production and physicochemical characterisation of spray-dried nanoparticulated whey protein powders

Spray-dried whey protein isolate (WPI) powders were prepared at pilot-scale from solutions without heat (WPIUH), heated (WPIH) or heated with calcium (WPIHCa), which were analysed and compared with a control sample (WPIC). WPIC, WPIUH, WPIH and WPIHCa solutions had whey protein denaturation levels of 0.0, 3.2, 64.4 and 74.4%, respectively. Computerised tomography scanning showed that 52.6, 84.0, 74.5 and 41.9% of WPIC, WPIUH, WPIH and WPIHCa powder particles had diameters of ≤30 µm. WPIHCa and WPIH powders were cohesive, while WPIC and WPIUH powders were easy flowing. Marked differences in microstructure were observed between WPIH and WPIHCa. There were no measured differences in wall friction, bulk density or colour

Supporting User-Defined Functions on Uncertain Data

Uncertain data management has become crucial in many sensing and scientific applications. As user-defined functions (UDFs) become widely used in these applications, an important task is to capture result uncertainty for queries that evaluate UDFs on uncertain data. In this work, we provide a general framework for supporting UDFs on uncertain data. Specifically, we propose a learning approach based on Gaussian processes (GPs) to compute approximate output distributions of a UDF when evaluated on uncertain input, with guaranteed error bounds. We also devise an online algorithm to compute such output distributions, which employs a suite of optimizations to improve accuracy and performance. Our evaluation using both real-world and synthetic functions shows that our proposed GP approach can outperform the state-of-the-art sampling approach with up to two orders of magnitude improvement for a variety of UDFs. 1

Bayesian Inference in Processing Experimental Data: Principles and Basic Applications

This report introduces general ideas and some basic methods of the Bayesian probability theory applied to physics measurements. Our aim is to make the reader familiar, through examples rather than rigorous formalism, with concepts such as: model comparison (including the automatic Ockham's Razor filter provided by the Bayesian approach); parametric inference; quantification of the uncertainty about the value of physical quantities, also taking into account systematic effects; role of marginalization; posterior characterization; predictive distributions; hierarchical modelling and hyperparameters; Gaussian approximation of the posterior and recovery of conventional methods, especially maximum likelihood and chi-square fits under well defined conditions; conjugate priors, transformation invariance and maximum entropy motivated priors; Monte Carlo estimates of expectation, including a short introduction to Markov Chain Monte Carlo methods.Comment: 40 pages, 2 figures, invited paper for Reports on Progress in Physic

Present and Future CP Measurements

We review theoretical and experimental results on CP violation summarizing the discussions in the working group on CP violation at the UK phenomenology workshop 2000 in Durham.Comment: 104 pages, Latex, to appear in Journal of Physics

Northern Ireland Farm Level Management Factors for Recurrent Bovine Tuberculosis Herd Breakdowns

Bovine tuberculosis (bTB) is a chronic, infectious and zoonotic disease of domestic and wild animals caused mainly by Mycobacterium bovis. This study investigated farm management factors associated to recurrent bTB herd breakdowns (n=2935) disclosed in the period 23/05/2016 to 21/05/2018 and is a follow up to our 2020 paper which looked at long duration bTB herd breakdowns. A case control study design was used to construct an explanatory set of farm level management factors associated to recurrent bTB herd breakdowns. In Northern Ireland, a Department of Agriculture Environment and Rural Affairs (DAERA) Veterinarian investigates bTB herd breakdowns using standardised guidelines to allocate a disease source. In this study, source was strongly linked to carryover of infection, suggesting that the diagnostic tests had failed to clear herd infection during the breakdown period. Other results from this study associated to recurrent bTB herd breakdowns were herd size and type (dairy herds 43% of cases), with both these variables intrinsically linked. Other associated risk factors were time of application of slurry, badger access to silage clamps, badger setts in the locality, cattle grazing silage fields immediately post-harvest, number of parcels of land the farmer associated with bTB, number of land parcels used for grazing and region of the country

Determining Frequent Patterns of Copy Number Alterations in Cancer

Cancer progression is often driven by an accumulation of genetic changes but also accompanied by increasing genomic instability. These processes lead to a complicated landscape of copy number alterations (CNAs) within individual tumors and great diversity across tumor samples. High resolution array-based comparative genomic hybridization (aCGH) is being used to profile CNAs of ever larger tumor collections, and better computational methods for processing these data sets and identifying potential driver CNAs are needed. Typical studies of aCGH data sets take a pipeline approach, starting with segmentation of profiles, calls of gains and losses, and finally determination of frequent CNAs across samples. A drawback of pipelines is that choices at each step may produce different results, and biases are propagated forward. We present a mathematically robust new method that exploits probe-level correlations in aCGH data to discover subsets of samples that display common CNAs. Our algorithm is related to recent work on maximum-margin clustering. It does not require pre-segmentation of the data and also provides grouping of recurrent CNAs into clusters. We tested our approach on a large cohort of glioblastoma aCGH samples from The Cancer Genome Atlas and recovered almost all CNAs reported in the initial study. We also found additional significant CNAs missed by the original analysis but supported by earlier studies, and we identified significant correlations between CNAs

Endothelin receptor antagonist and airway dysfunction in pulmonary arterial hypertension

<p>Abstract</p> <p>Background</p> <p>In idiopathic pulmonary arterial hypertension (IPAH), peripheral airway obstruction is frequent. This is partially attributed to the mediator dysbalance, particularly an excess of endothelin-1 (ET-1), to increased pulmonary vascular and airway tonus and to local inflammation. Bosentan (ET-1 receptor antagonist) improves pulmonary hemodynamics, exercise limitation, and disease severity in IPAH. We hypothesized that bosentan might affect airway obstruction.</p> <p>Methods</p> <p>In 32 IPAH-patients (19 female, WHO functional class II (n = 10), III (n = 22); (data presented as mean ± standard deviation) pulmonary vascular resistance (11 ± 5 Wood units), lung function, 6 minute walk test (6-MWT; 364 ± 363.7 (range 179.0-627.0) m), systolic pulmonary artery pressure, sPAP, 79 ± 19 mmHg), and NT-proBNP serum levels (1427 ± 2162.7 (range 59.3-10342.0) ng/L) were measured at baseline, after 3 and 12 months of oral bosentan (125 mg twice per day).</p> <p>Results and Discussion</p> <p>At baseline, maximal expiratory flow at 50 and 25% vital capacity were reduced to 65 ± 25 and 45 ± 24% predicted. Total lung capacity was 95.6 ± 12.5% predicted and residual volume was 109 ± 21.4% predicted. During 3 and 12 months of treatment, 6-MWT increased by 32 ± 19 and 53 ± 69 m, respectively; p < 0.01; whereas sPAP decreased by 7 ± 14 and 10 ± 19 mmHg, respectively; p < 0.05. NT-proBNP serum levels tended to be reduced by 123 ± 327 and by 529 ± 1942 ng/L; p = 0.11). There was no difference in expiratory flows or lung volumes during 3 and 12 months.</p> <p>Conclusion</p> <p>This study gives first evidence in IPAH, that during long-term bosentan, improvement of hemodynamics, functional parameters or serum biomarker occur independently from persisting peripheral airway obstruction.</p