Food safety vulnerability: Neighbourhood determinants of non-compliant establishments in England and Wales

This paper utilises logistic regression to identify ecological determinants of non-compliant food outlets in England and Wales. We consider socio-demographic, urbanness and business type features to better define vulnerable populations based on the characteristics of the area within which they live. We find a clear gradient of association between deprivation and non-compliance, with outlets in the most deprived areas 25% less likely (OR = 0.75) to meet hygiene standards than those in the least deprived areas. Similarly, we find outlets located in conurbation areas have a lower probability of compliance (OR = 0.678) than establishments located in rural and affluent areas. Therefore, individuals living in these neighbourhoods can be considered more situationally vulnerable than those living in rural and non-deprived areas. Whilst comparing compliance across business types, we find that takeaways and sandwich shops (OR = 0.504) and convenience retailers (OR = 0.905) are significantly less likely to meet hygiene standards compared to restaurants. This is particularly problematic for populations who may be unable to shop outside their immediate locality. Where traditional food safety interventions have failed to consider the prospect of increased risk based on proximity to unsafe and unhygienic food outlets, we re-assess the meaning of vulnerability by considering the type of neighbourhoods within which non-compliant establishments are located. In-lieu of accurate foodborne illness data, we recommend prioritised inspections for outlets in urban and deprived areas. Particularly takeaways, sandwich shops and small convenience retailers

Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach

Consumer food environments have transformed dramatically in the last decade. Food outlet prevalence has increased, and people are eating food outside the home more than ever before. Despite these developments, national spending on food control has reduced. The National Audit Office report that only 14% of local authorities are up to date with food business inspections, exposing consumers to unknown levels of risk. Given the scarcity of local authority resources, this paper presents a data-driven approach to predict compliance for newly opened businesses and those awaiting repeat inspections. This work capitalizes on the theory that food outlet compliance is a function of its geographic context, namely the characteristics of the neighborhood within which it sits. We explore the utility of three machine learning approaches to predict non-compliant food outlets in England and Wales using openly accessible socio-demographic, business type, and urbanness features at the output area level. We find that the synthetic minority oversampling technique alongside a random forest algorithm with a 1:1 sampling strategy provides the best predictive power. Our final model retrieves and identifies 84% of total non-compliant outlets in a test set of 92,595 (sensitivity = 0.843, specificity = 0.745, precision = 0.274). The originality of this work lies in its unique and methodological approach which combines the use of machine learning with fine-grained neighborhood data to make robust predictions of compliance

A Survey of Honey Bee Colony Losses in the U.S., Fall 2007 to Spring 2008

Honey bees are an essential component of modern agriculture. A recently recognized ailment, Colony Collapse Disorder (CCD), devastates colonies, leaving hives with a complete lack of bees, dead or alive. Up to now, estimates of honey bee population decline have not included losses occurring during the wintering period, thus underestimating actual colony mortality. Our survey quantifies the extent of colony losses in the United States over the winter of 2007–2008.Surveys were conducted to quantify and identify management factors (e.g. operation size, hive migration) that contribute to high colony losses in general and CCD symptoms in particular. Over 19% of the country's estimated 2.44 million colonies were surveyed. A total loss of 35.8% of colonies was recorded; an increase of 11.4% compared to last year. Operations that pollinated almonds lost, on average, the same number of colonies as those that did not. The 37.9% of operations that reported having at least some of their colonies die with a complete lack of bees had a total loss of 40.8% of colonies compared to the 17.1% loss reported by beekeepers without this symptom. Large operations were more likely to have this symptom suggesting that a contagious condition may be a causal factor. Sixty percent of all colonies that were reported dead in this survey died without dead bees, and thus possibly suffered from CCD. In PA, losses varied with region, indicating that ambient temperature over winter may be an important factor.Of utmost importance to understanding the recent losses and CCD is keeping track of losses over time and on a large geographic scale. Given that our surveys are representative of the losses across all beekeeping operations, between 0.75 and 1.00 million honey bee colonies are estimated to have died in the United States over the winter of 2007–2008. This article is an extensive survey of U.S. beekeepers across the continent, serving as a reference for comparison with future losses as well as providing guidance to future hypothesis-driven research on the causes of colony mortality

A Systematic Review and Meta-Analysis to Inform Cancer Screening Guidelines in Idiopathic Inflammatory Myopathies

OBJECTIVES: To identify clinical factors associated with cancer risk in the idiopathic inflammatory myopathies (IIMs) and to systematically review the existing evidence related to cancer screening. METHODS: A systematic literature search was carried out on Medline, Embase and Scopus. Cancer risk within the IIM population (i.e. not compared to the general population) was expressed as risk ratios (RR) for binary variables and weighted mean differences (WMD) for continuous variables. Evidence relating to cancer screening practices in the IIMs were synthesised via narrative review. RESULTS: Sixty nine studies were included in the meta-analysis. Dermatomyositis subtype (RR 2.21), older age (WMD 11.19), male gender (RR 1.53), dysphagia (RR 2.09), cutaneous ulceration (RR 2.73), and anti-transcriptional intermediary factor-1 gamma positivity (RR 4.66) were identified as being associated with significantly increased risk of cancer. Polymyositis (RR 0.49) and clinically amyopathic dermatomyositis (RR 0.44) subtypes, Raynaud's phenomenon (RR 0.61), interstitial lung disease (RR 0.49), very high serum creatine kinase (WMD -1189.96) or lactate dehydrogenase (WMD -336.52) levels, and anti-Jo1 (RR 0.45) or anti-EJ (RR 0.17) positivity were identified as being associated with significantly reduced risk of cancer. Nine studies relating to IIM-specific cancer screening were included. Computed tomography (CT) scanning of the thorax, abdomen and pelvis appeared to be effective in identifying underlying asymptomatic cancers. DISCUSSION: Cancer risk factors should be evaluated in patients with IIM for risk stratification. Screening evidence is limited but CT scanning could be useful. Prospective studies and consensus guidelines are needed to establish cancer screening strategies in IIM patients

Social Waves in Giant Honeybees Repel Hornets

Giant honeybees (Apis dorsata) nest in the open and have evolved a plethora of defence behaviors. Against predatory wasps, including hornets, they display highly coordinated Mexican wave-like cascades termed ‘shimmering’. Shimmering starts at distinct spots on the nest surface and then spreads across the nest within a split second whereby hundreds of individual bees flip their abdomens upwards. However, so far it is not known whether prey and predator interact and if shimmering has anti-predatory significance. This article reports on the complex spatial and temporal patterns of interaction between Giant honeybee and hornet exemplified in 450 filmed episodes of two A. dorsata colonies and hornets (Vespa sp.). Detailed frame-by-frame analysis showed that shimmering elicits an avoidance response from the hornets showing a strong temporal correlation with the time course of shimmering. In turn, the strength and the rate of the bees' shimmering are modulated by the hornets' flight speed and proximity. The findings suggest that shimmering creates a ‘shelter zone’ of around 50 cm that prevents predatory wasps from foraging bees directly from the nest surface. Thus shimmering appears to be a key defence strategy that supports the Giant honeybees' open-nesting life-style

Progress Towards Using Linked Population-Based Data For Geohealth Research: Comparisons Of Aotearoa New Zealand And The United Kingdom

Globally, geospatial concepts are becoming increasingly important in epidemiological and public health research. Individual level linked population-based data afford researchers with opportunities to undertake complex analyses unrivalled by other sources. However, there are significant challenges associated with using such data for impactful geohealth research. Issues range from extracting, linking and anonymising data, to the translation of findings into policy whilst working to often conflicting agendas of government and academia. Innovative organisational partnerships are therefore central to effective data use. To extend and develop existing collaborations between the institutions, in June 2019, authors from the Leeds Institute for Data Analytics and the Alan Turing Institute, London, visited the Geohealth Laboratory based at the University of Canterbury, New Zealand. This paper provides an overview of insight shared during a two-day workshop considering aspects of linked population-based data for impactful geohealth research. Specifically, we discuss both the collaborative partnership between New Zealand’s Ministry of Health (MoH) and the University of Canterbury’s GeoHealth Lab and novel infrastructure, and commercial partnerships enabled through the Leeds Institute for Data Analytics and the Alan Turing Institute in the UK. We consider the New Zealand Integrated Data Infrastructure as a case study approach to population-based linked health data and compare similar approaches taken by the UK towards integrated data infrastructures, including the ESRC Big Data Network centres, the UK Biobank, and longitudinal cohorts. We reflect on and compare the geohealth landscapes in New Zealand and the UK to set out recommendations and considerations for this rapidly evolving discipline

Valuing Insect Pollination Services with Cost of Replacement

Value estimates of ecosystem goods and services are useful to justify the allocation of resources towards conservation, but inconclusive estimates risk unsustainable resource allocations. Here we present replacement costs as a more accurate value estimate of insect pollination as an ecosystem service, although this method could also be applied to other services. The importance of insect pollination to agriculture is unequivocal. However, whether this service is largely provided by wild pollinators (genuine ecosystem service) or managed pollinators (commercial service), and which of these requires immediate action amidst reports of pollinator decline, remains contested. If crop pollination is used to argue for biodiversity conservation, clear distinction should be made between values of managed- and wild pollination services. Current methods either under-estimate or over-estimate the pollination service value, and make use of criticised general insect and managed pollinator dependence factors. We apply the theoretical concept of ascribing a value to a service by calculating the cost to replace it, as a novel way of valuing wild and managed pollination services. Adjusted insect and managed pollinator dependence factors were used to estimate the cost of replacing insect- and managed pollination services for the Western Cape deciduous fruit industry of South Africa. Using pollen dusting and hand pollination as suitable replacements, we value pollination services significantly higher than current market prices for commercial pollination, although lower than traditional proportional estimates. The complexity associated with inclusive value estimation of pollination services required several defendable assumptions, but made estimates more inclusive than previous attempts. Consequently this study provides the basis for continued improvement in context specific pollination service value estimates

‘Special agents’ trigger social waves in giant honeybees (Apis dorsata)

Giant honeybees (Apis dorsata) nest in the open and have therefore evolved a variety of defence strategies. Against predatory wasps, they produce highly coordinated Mexican wavelike cascades termed ‘shimmering’, whereby hundreds of bees flip their abdomens upwards. Although it is well known that shimmering commences at distinct spots on the nest surface, it is still unclear how shimmering is generated. In this study, colonies were exposed to living tethered wasps that were moved in front of the experimental nest. Temporal and spatial patterns of shimmering were investigated in and after the presence of the wasp. The numbers and locations of bees that participated in the shimmering were assessed, and those bees that triggered the waves were identified. The findings reveal that the position of identified trigger cohorts did not reflect the experimental path of the tethered wasp. Instead, the trigger centres were primarily arranged in the close periphery of the mouth zone of the nest, around those parts where the main locomotory activity occurs. This favours the ‘special-agents’ hypothesis that suggest that groups of specialized bees initiate the shimmering

Exploring the symbiotic pangenome of the nitrogen-fixing bacterium Sinorhizobium meliloti

<p>Abstract</p> <p>Background</p> <p><it>Sinorhizobium meliloti </it>is a model system for the studies of symbiotic nitrogen fixation. An extensive polymorphism at the genetic and phenotypic level is present in natural populations of this species, especially in relation with symbiotic promotion of plant growth. AK83 and BL225C are two nodule-isolated strains with diverse symbiotic phenotypes; BL225C is more efficient in promoting growth of the <it>Medicago sativa </it>plants than strain AK83. In order to investigate the genetic determinants of the phenotypic diversification of <it>S. meliloti </it>strains AK83 and BL225C, we sequenced the complete genomes for these two strains.</p> <p>Results</p> <p>With sizes of 7.14 Mbp and 6.97 Mbp, respectively, the genomes of AK83 and BL225C are larger than the laboratory strain Rm1021. The core genome of Rm1021, AK83, BL225C strains included 5124 orthologous groups, while the accessory genome was composed by 2700 orthologous groups. While Rm1021 and BL225C have only three replicons (Chromosome, pSymA and pSymB), AK83 has also two plasmids, 260 and 70 Kbp long. We found 65 interesting orthologous groups of genes that were present only in the accessory genome, consequently responsible for phenotypic diversity and putatively involved in plant-bacterium interaction. Notably, the symbiosis inefficient AK83 lacked several genes required for microaerophilic growth inside nodules, while several genes for accessory functions related to competition, plant invasion and bacteroid tropism were identified only in AK83 and BL225C strains. Presence and extent of polymorphism in regulons of transcription factors involved in symbiotic interaction were also analyzed. Our results indicate that regulons are flexible, with a large number of accessory genes, suggesting that regulons polymorphism could also be a key determinant in the variability of symbiotic performances among the analyzed strains.</p> <p>Conclusions</p> <p>In conclusions, the extended comparative genomics approach revealed a variable subset of genes and regulons that may contribute to the symbiotic diversity.</p