Organic research and development 1996-2010 - effects on industry and society

This publication contains the most important conclusions from the analysis and focuses on how the results from the research programmes DARCOF I-III and CORE Organic have been implemented in industry and society

Estimating the burden of multiple endemic diseases and health conditions using Bayes’ Theorem: A conditional probability model applied to UK dairy cattle

The Global Burden of Animal Diseases (GBADs) is an international collaboration aiming, in part, to measure and improve societal outcomes from livestock. One GBADs objective is to estimate the economic impact of endemic diseases in livestock. However, if individual disease impact estimates are linearly aggregated without consideration for associations among diseases, there is the potential to double count impacts, overestimating the total burden. Accordingly, the authors propose a method to adjust an array of individual disease impact estimates so that they may be aggregated without overlap. Using Bayes’ Theorem, conditional probabilities were derived from inter-disease odds ratios in the literature. These conditional probabilities were used to calculate the excess probability of disease among animals with associated conditions, or the probability of disease overlap given the odds of coinfection, which were then used to adjust disease impact estimates so that they may be aggregated. The aggregate impacts, or the yield, fertility, and mortality gaps due to disease, were then attributed and valued, generating disease-specific losses. The approach was illustrated using an example dairy cattle system with input values and supporting parameters from the UK, with 13 diseases and health conditions endemic to UK dairy cattle: cystic ovary, disease caused by gastrointestinal nematodes, displaced abomasum, dystocia, fasciolosis, lameness, mastitis, metritis, milk fever, neosporosis, paratuberculosis, retained placenta, and subclinical ketosis. The diseases and conditions modelled resulted in total adjusted losses of £ 404/cow/year, equivalent to herd-level losses of £ 60,000/year. Unadjusted aggregation methods suggested losses 14–61% greater. Although lameness was identified as the costliest condition (28% of total losses), variations in the prevalence of fasciolosis, neosporosis, and paratuberculosis (only a combined 22% of total losses) were nearly as impactful individually as variations in the prevalence of lameness. The results suggest that from a disease control policy perspective, the costliness of a disease may not always be the best indicator of the investment its control warrants; the costliness rankings varied across approaches and total losses were found to be surprisingly sensitive to variations in the prevalence of relatively uncostly diseases. This approach allows for disease impact estimates to be aggregated without double counting. It can be applied to any livestock system in any region with any set of endemic diseases, and can be updated as new prevalence, impact, and disease association data become available. This approach also provides researchers and policymakers an alternative tool to rank prevention priorities

Økologisk forskning og udvikling gennem 15 år - effekter i erhverv og samfund

ICROFS har lavet en samlet analyse af, hvilke effekter den danske økologiforskning i perioden 19996-2010 har haft på den økologiske sektor og samfundet i øvrigt. Denne publikation indeholder således analysens vigtigste konklusioner og sætter fokus på, hvordan resultaterne fra forskningsprogrammerne FØJO I-III samt CORE Organic er blevet brugt i erhverv og samfund

The microbiome of the Melitaea cinxia butterfly shows marked variation but is only little explained by the traits of the butterfly or its host plant

Understanding of the ecological factors that shape intraspecific variation of insect microbiota in natural populations is relatively poor. In Lepidopteran caterpillars, microbiota is assumed to be mainly composed of transient bacterial symbionts acquired from the host plant. We sampled Glanville fritillary (Melitaea cinxia) caterpillars from natural populations to describe their gut microbiome and to identify potential ecological factors that determine its structure. Our results demonstrate high variability of microbiota composition even among caterpillars that shared the same host plant individual and most likely the same genetic background. We observed that the caterpillars harboured microbial classes that varied among individuals and alternated between two distinct communities (one composed of mainly Enterobacteriaceae and another with more variable microbiota community). Even though the general structure of the microbiota was not attributed to the measured ecological factors, we found that phylogenetically similar microbiota showed corresponding responses to the sex and the parasitoid infection of the caterpillar and to those of the host plant's microbial and chemical composition. Our results indicate high among-individual variability in the microbiota of the M. cinxia caterpillar and contradict previous findings that the host plant is the major driver of the microbiota communities of insect herbivores.Peer reviewe

On the nature of the z=0 X-ray absorbers: I. Clues from an external group

Absorption lines of OVII at redshift zero are observed in high quality Chandra spectra of extragalactic sightlines. The location of the absorber producing these lines, whether from the corona of the Galaxy or from the Local Group or even larger scale structure, has been a matter of debate. Here we study another poor group like our Local Group to understand the distribution of column density from galaxy to group scales. We show that we cannot yet rule out the group origin of z=0 systems. We further argue that the debate over Galactic vs. extragalactic origin of z=0 systems is premature as they likely contain both components and predict that future higher resolution observations will resolve the z=0 systems into multiple components.Comment: Submitted to ApJ

Animal models for COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the aetiological agent of coronavirus disease 2019 (COVID-19), an emerging respiratory infection caused by the introduction of a novel coronavirus into humans late in 2019 (first detected in Hubei province, China). As of 18 September 2020, SARS-CoV-2 has spread to 215 countries, has infected more than 30 million people and has caused more than 950,000 deaths. As humans do not have pre-existing immunity to SARS-CoV-2, there is an urgent need to develop therapeutic agents and vaccines to mitigate the current pandemic and to prevent the re-emergence of COVID-19. In February 2020, the World Health Organization (WHO) assembled an international panel to develop animal models for COVID-19 to accelerate the testing of vaccines and therapeutic agents. Here we summarize the findings to date and provides relevant information for preclinical testing of vaccine candidates and therapeutic agents for COVID-19

Testing the Effect of Relative Pollen Productivity on the REVEALS Model : A Validated Reconstruction of Europe-Wide Holocene Vegetation

Reliable quantitative vegetation reconstructions for Europe during the Holocene are crucial to improving our understanding of landscape dynamics, making it possible to assess the past effects of environmental variables and land-use change on ecosystems and biodiversity, and mitigating their effects in the future. We present here the most spatially extensive and temporally continuous pollen-based reconstructions of plant cover in Europe (at a spatial resolution of 1° × 1°) over the Holocene (last 11.7 ka BP) using the 'Regional Estimates of VEgetation Abundance from Large Sites' (REVEALS) model. This study has three main aims. First, to present the most accurate and reliable generation of REVEALS reconstructions across Europe so far. This has been achieved by including a larger number of pollen records compared to former analyses, in particular from the Mediterranean area. Second, to discuss methodological issues in the quantification of past land cover by using alternative datasets of relative pollen productivities (RPPs), one of the key input parameters of REVEALS, to test model sensitivity. Finally, to validate our reconstructions with the global forest change dataset. The results suggest that the RPPs.st1 (31 taxa) dataset is best suited to producing regional vegetation cover estimates for Europe. These reconstructions offer a long-term perspective providing unique possibilities to explore spatial-temporal changes in past land cover and biodiversity