The transportation of fresh food by rail from and to the district of South Holland in Lincolnshire

Executive summary - The University of Lincoln has conducted a review of the logistics of fresh foods in the district of South Holland in Lincolnshire. This report lays out the findings of that review and identifies an opportunity for a partial change in the mode of transport from road to rail. The report includes a review of the issues that will have to be addressed if a Fresh Food Intermodal Hub were to be created and also indicates the potential benefits to the District, County and Region if a Rail Bridge could be created

Seal integrity and the impact on food waste

An investigation into the contribution that inadequate heat sealing of food packaging might make to the generation of food waste, in the supply chain and the househol

Polarised light stress analysis and laser scatter imaging for non-contact inspection of heat seals in food trays

This paper introduces novel non-contact methods for detecting faults in heat seals of food packages. Two alternative imaging technologies are investigated; laser scatter imaging and polarised light stress images. After segmenting the seal area from the rest of the respective image, a classifier is trained to detect faults in different regions of the seal area using features extracted from the pixels in the respective region. A very large set of candidate features, based on statistical information relating to the colour and texture of each region, is first extracted. Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating faults from non-faults. With this approach, different features can be selected and optimised for the different imaging methods. In experiments we compare the performance of classifiers trained using features extracted from laser scatter images only, polarised light stress images only, and a combination of both image types. The results show that the polarised light and laser scatter classifiers achieved accuracies of 96\% and 90\%, respectively, while the combination of both sensors achieved an accuracy of 95\%. These figures suggest that both systems have potential for commercial development

A report on the impact of automation in the food process industry

Research Objectives: To understand how the food industry in Europe is using automation To ascertain what the food processing industry requires from equipment suppliers Furthermore to identify variations by sector and by countr

Commentary: Tobacco consumption and body weight: Mendelian randomization across a range of exposure.

Tobacco consumption is consistently associated with reduced body weight, creating an incentive to initiate smoking and a disincentive to cease, although the health risks associated with the habit outweigh the benefits of reduced weight. Among smokers however, increasing con-sumption has been associated with increased body weight. To determine whether this contradiction reflects causal processes, Winsløw et al.1 have applied Mendelian ran-domization (MR) in testing the association of a genetic variant, rs1051730 in CHRNA3, with measures of body weight among 80 342 members of the Copenhagen General Population Study. Among smokers, each minor (T) allele carried was associated with an increase of about one cigarette per day, but with a decrease in several meas-ures of body weight, in contrast to the observational re

Empirical Bayes factors for common hypothesis tests

Bayes factors for composite hypotheses have difficulty in encoding vague prior knowledge, leading to conflicts between objectivity and sensitivity including the Jeffreys-Lindley paradox. To address these issues we revisit the posterior Bayes factor, in which the posterior distribution from the data at hand is re-used in the Bayes factor for the same data. We argue that this is biased when calibrated against proper Bayes factors, but propose bias adjustments to allow interpretation on the same scale. In the important case of a regular normal model, the bias in log scale is half the number of parameters. The resulting empirical Bayes factor is closely related to the widely applicable information criterion. We develop test-based empirical Bayes factors for several standard tests and propose an extension to multiple testing closely related to the optimal discovery procedure. When only a P-value is available, such as in non-parametric tests, we obtain a Bayes factor calibration of 10p. We propose interpreting the strength of Bayes factors on a logarithmic scale with base 3.73, reflecting the sharpest distinction between weaker and stronger belief. Empirical Bayes factors are a frequentist-Bayesian compromise expressing an evidential view of hypothesis testing

Estimation of significance thresholds for genomewide association scans

The question of what significance threshold is appropriate for genomewide association studies is somewhat unresolved. Previous theoretical suggestions have yet to be validated in practice, whereas permutation testing does not resolve a discrepancy between the genomewide multiplicity of the experiment and the subset of markers actually tested. We used genotypes from the Wellcome Trust Case-Control Consortium to estimate a genomewide significance threshold for the UK Caucasian population. We subsampled the genotypes at increasing densities, using permutation to estimate the nominal P-value for 5% family-wise error. By extrapolating to infinite density, we estimated the genomewide significance threshold to be about 7.2 × 10−8. To reduce the computation time, we considered Patterson's eigenvalue estimator of the effective number of tests, but found it to be an order of magnitude too low for multiplicity correction. However, by fitting a Beta distribution to the minimum P-value from permutation replicates, we showed that the effective number is a useful heuristic and suggest that its estimation in this context is an open problem. We conclude that permutation is still needed to obtain genomewide significance thresholds, but with subsampling, extrapolation and estimation of an effective number of tests, the threshold can be standardized for all studies of the same population

Candidate gene-environment interactions in breast cancer.

Gene-environment interactions have the potential to shed light on biological processes leading to disease, identify individuals for whom risk factors are most relevant, and improve the accuracy of epidemiological risk models. We review the progress that has been made in investigating gene-environment interactions in the field of breast cancer. Although several large-scale analyses have been carried out, only a few significant interactions have been reported. One of these, an interaction between CASP8-rs1045485 and alcohol consumption has been replicated, but others have not, including LSP1- rs3817198 and parity, and 1p11.2-rs11249433 and ever being parous. False positive interactions may arise if the gene and environment are correlated and the causal variant is less frequent than the tag SNP. We conclude that while much progress has been made in this area it is still too soon to tell whether gene-environment interactions will fulfil their promise. Before we can make this assessment we will need to replicate (or refute) the reported interactions, identify the causal variants that underlie tag-SNP associations and validate the next generation of epidemiological risk models

Accuracy of Gene Scores when Pruning Markers by Linkage Disequilibrium.

OBJECTIVE: Gene scores are often used to model the combined effects of genetic variants. When variants are in linkage disequilibrium, it is common to prune all variants except the most strongly associated. This avoids duplicating information but discards information when variants have independent effects. However, joint modelling of correlated variants increases the sampling error in the gene score. In recent applications, joint modelling has offered only small improvements in accuracy over pruning. We aimed to quantify the relationship between pruning and joint modelling in relation to sample size. METHODS: We derived the coefficient of determination R2 for a gene score constructed from pruned markers, and for one constructed from correlated markers with jointly estimated effects. RESULTS: Pruned scores tend to have slightly lower R2 than jointly modelled scores, but the differences are small at sample sizes up to 100,000. If the proportion of correlated variants is high, joint modelling can obtain modest improvements asymptotically. CONCLUSIONS: The small gains observed to date from joint modelling can be explained by sample size. As studies become larger, joint modelling will be useful for traits affected by many correlated variants, but the improvements may remain small. Pruning remains a useful heuristic for current studies