The influence of alcohol content variation in UK packaged beers on the uncertainty of calculations using the Widmark equation

It is common for forensic practitioners to calculate an individual's likely blood alcohol concentration following the consumption of alcoholic beverage(s) for legal purposes, such as in driving under the influence (DUI) cases. It is important in these cases to be able to give the uncertainty of measurement on any calculated result, for this reason uncertainty data for the variables used for any calculation are required. In order to determine the uncertainty associated with the alcohol concentration of beer in the UK the alcohol concentration (%v/v) of 218 packaged beers (112 with an alcohol concentration of ≤5.5%v/v and 106 with an alcohol concentration of >5.5%v/v) were tested using an industry standard near infra-red (NIR) analyser. The range of labelled beer alcohol by volume (ABV's) tested was 3.4%v/v – 14%v/v. The beers were obtained from a range of outlets throughout the UK over a period of 12 months. The root mean square error (RMSE) was found to be ±0.43%v/v (beers with declared %ABV of ≤5.5%v/v) and ±0.53%v/v (beers with declared %ABV of >5.5%v/v) the RMSE for all beers was ±0.48%v/v. The standard deviation from the declared %ABV is larger than those previously utilised for uncertainty calculations and illustrates the importance of appropriate experimental data for use in the determination of uncertainty in forensic calculations

MapReduce Particle Filtering with Exact Resampling and Deterministic Runtime

Particle filtering is a numerical Bayesian technique that has great potential for solving sequential estimation problems involving non-linear and non-Gaussian models. Since the estimation accuracy achieved by particle filters improves as the number of particles increases, it is natural to consider as many particles as possible. MapReduce is a generic programming model that makes it possible to scale a wide variety of algorithms to Big data. However, despite the application of particle filters across many domains, little attention has been devoted to implementing particle filters using MapReduce. In this paper, we describe an implementation of a particle filter using MapReduce. We focus on a component that what would otherwise be a bottleneck to parallel execution, the resampling component. We devise a new implementation of this component, which requires no approximations, has $O\left(N\right)$ spatial complexity and deterministic $O\left(\left(\log N\right)^2\right)$ time complexity. Results demonstrate the utility of this new component and culminate in consideration of a particle filter with $2^{24}$ particles being distributed across $512$ processor cores

MapReduce particle filtering with exact resampling and deterministic runtime

Particle filtering is a numerical Bayesian technique that has great potential for solving sequential estimation problems involving non-linear and non-Gaussian models. Since the estimation accuracy achieved by particle filters improves as the number of particles increases, it is natural to consider as many particles as possible. MapReduce is a generic programming model that makes it possible to scale a wide variety of algorithms to Big data. However, despite the application of particle filters across many domains, little attention has been devoted to implementing particle filters using MapReduce. In this paper, we describe an implementation of a particle filter using MapReduce. We focus on a component that what would otherwise be a bottleneck to parallel execution, the resampling component. We devise a new implementation of this component, which requires no approximations, has O(N) spatial complexity and deterministic O((logN)2) time complexity. Results demonstrate the utility of this new component and culminate in consideration of a particle filter with 224 particles being distributed across 512 processor cores

Postmortem tissue distribution of morphine and its metabolites in a series of heroin related deaths

The abuse of heroin (diamorphine) and heroin deaths are growing around the world. The interpretation of the toxicological results from suspected heroin deaths is notoriously difficult especially in cases where there may be limited samples. In order to help forensic practitioners with heroin interpretation we determined the concentration of morphine (M), morphine‐3‐glucuronide (M3G) and morphine‐6‐glucuronide (M6G) in blood (femoral and cardiac), brain (thalamus), liver (deep right lobe), bone marrow (sternum), skeletal muscle (psoas) and vitreous humor in 44 heroin related deaths. The presence of 6‐monoacetylmorphine (6‐MAM) in any of the postmortem samples was used as confirmation of heroin use. Quantitation was carried out using a validated LC‐MS/MS method with solid phase extraction. We also determined the presence of papaverine, noscapine and codeine in the samples, substances often found in illicit heroin and that may help determine illicit heroin use. The results of this study show that vitreous is the best sample to detect 6‐MAM (100% of cases), and thus heroin use. The results of the M, M3G and M6G quantitation in this study allow a degree of interpretation when samples are limited. However in some cases it may not be possible to determine heroin/morphine use as in 4 cases in muscle (3 cases in bone marrow) no morphine, morphine‐3‐glucuronide or morphine‐6‐glucuronide was detected, even though they were detected in other case samples. As always postmortem cases of suspected morphine/heroin intoxication should be interpreted with care and with as much case knowledge as possible

Interventions for the management of malignant pleural effusions:An updated network meta-analysis

Talc slurry and poudrage are effective pleurodesis agents. IPCs have lower pleurodesis rates but comparable breathlessness control and reduced risk of repeat invasive procedures. It is essential that patients have access to a range of treatment strategies. https://bit.ly/38v30y

EPICOG-SCH: A brief battery to screen cognitive impact of schizophrenia in stable outpatients

Brief batteries in schizophrenia, are needed to screen for the cognitive impact of schizophrenia. We aimed to validate and co-norm the Epidemiological Study of Cognitive Impairment in Schizophrenia (EPICOG-SCH) derived brief cognitive battery. A cross-sectional outpatient evaluation was conducted of six-hundred-seventy-two patients recruited from 234 centers. The brief battery included well-known subtests available worldwide that cover cognitive domains related to functional outcomes: WAIS-III-Letter-Number-Sequencing-LNS, Category Fluency Test-CFT, Logical-Memory Immediate Recall-LM, and Digit-Symbol-Coding-DSC. CGI-SCH Severity and WHO-DAS-S were used to assess clinical severity and functional impairment, respectively. Unit Composite Score (UCS) and functional regression-weighted Composite Scores (FWCS) were obtained; discriminant properties of FWCS to identify patients with different levels of functional disability were analyzed using receiver-operating characteristic (ROC) technique. The battery showed good internal consistency, Cronbach's alpha = 0.78. The differences between cognitive performance across CGI-SCH severity level subscales ranged from 0.5 to 1 SD. Discriminant capacity of the battery in identifying patients with up to moderate disability levels showed fair discriminant accuracy with areas under the curve (AUC) > 0.70, p < 0.0001. An FWCS mean cut-off score ≥ 100 showed likelihood ratios (LR) up to 4.7, with an LR+ of 2.3 and a LR− of 0.5. An FWCS cut-off ≥ 96 provided the best balance between sensitivity (0.74) and specificity (0.62). The EPICOG-SCH proved to be a useful brief tool to screen for the cognitive impact of schizophrenia, and its regression-weighted Composite Score was an efficient complement to clinical interviews for confirming patients' potential functional outcomes and can be useful for monitoring cognition during routine outpatient follow-up visits

Environment and Rural Affairs Monitoring & Modelling Programme - ERAMMP Year 1 Report 22: A Review of the contribution of species records held by Local Environmental Record Centres in Wales to ERAMMP Evidence Needs

I. Better use of Local Environmental Record Centre (LERC) data in delivering biodiversity objectives is stated explicitly in the Nature Recovery Action Plan for Wales. Consistent with this aspiration we carried out two quantitative assessments of LERC data to determine the availability of species records at the resolution required for ERAMMP and WFG (Indicator 44) evidence needs; <=1km. II. A comparison of the availability of 1km square records for section 7 reptiles, amphibians and mammals between LERC and NBN Atlas showed that LERC data were more numerous in every case and sometimes markedly so (on average 17 times as many 1km square records in LERC data). For these species the NBN Atlas tends to have a greater number of records available at 10 rather than 1km square resolution. III. An assessment of the contribution of LERC 1km square records to national trends modelling demonstrated that substantial benefits in increased species coverage and precision of modelled trends are likely to arise by including additional LERC data alongside surveillance scheme data already used for trends modelling. By combining datasets the number of species that could be modelled increased by 267% on average across all the taxonomic groups previously modelled. IV. The design of the new Wales-only Indicator 44 “status of biological diversity” is currently under consultation. Our results show that species coverage for this indicator will benefit from combining multiple datasets with the current analytical state-of-the-art for trends modelling. While results are always dependent on sufficient data, there would seem to be scope for exploring how an ecologically more comprehensive Indicator 44 could be developed in partnership with Wales LERC and others. V. Our assessment also suggests that exploiting the more numerous 1km square records for section 7 species will increase the chances of detecting legacy and future effects of management scheme interventions for biodiversity and resilience objectives. A strategy for extracting the most biodiversity understanding for time spent would most likely involve applying state-of-the-art spatio-temporal modelling in collaboration with the Wales LERC and surveillance schemes. VI. A key benefit of working more closely with LERC is their ability to identify recording gaps and to mobilise new recording effort among the interested public as well as scholarly recording societies. This kind of reactive engagement activity could also contribute to efficient risk-based surveillance but with the proviso that voluntary effort typically exhibits strong spatial bias and variation in recording quality. VII. Further evidence needs driven by recent legislation and policy in Wales are likely to become clearer as indicators for SoNaRR, in particular the resilience objective of SMNR evolve in the near future