The microbial community of 21 full-scale biogas reactors was examined using 454 pyrosequencing of 16S rRNA gene sequences. These reactors included seven (six mesophilic and one thermophilic) digesting sewage sludge (SS) and 14 (ten mesophilic and four thermophilic) codigesting (CD) various combinations of wastes from slaughterhouses, restaurants, households, etc. The pyrosequencing generated more than 160,000 sequences representing 11 phyla, 23 classes, and 95 genera of Bacteria and Archaea. The bacterial community was always both more abundant and more diverse than the archaeal community. At the phylum level, the foremost populations in the SS reactors included Actinobacteria, Proteobacteria, Chloroflexi, Spirochetes, and Euryarchaeota, while Firmicutes was the most prevalent in the CD reactors. The main bacterial class in all reactors was Clostridia. Acetoclastic methanogens were detected in the SS, but not in the CD reactors. Their absence suggests that methane formation from acetate takes place mainly via syntrophic acetate oxidation in the CD reactors. A principal component analysis of the communities at genus level revealed three clusters: SS reactors, mesophilic CD reactors (including one thermophilic CD and one SS), and thermophilic CD reactors. Thus, the microbial composition was mainly governed by the substrate differences and the process temperature.

Investigator group: The members of the WHO European Region sequencing laboratories and GISAID EpiCoV group are listed at the end of the article

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American mink and ferret are highly susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but no information is available for other mustelid species. SARS-CoV-2 spreads very efficiently within mink farms once introduced, by direct and indirect contact, high within-farm animal density increases the chance for transmission. Between-farm spread is likely to occur once SARS-CoV-2 is introduced, short distance between SARS-CoV-2 positive farms is a risk factor. As of 29 January 2021, SARS-CoV-2 virus has been reported in 400 mink farms in eight countries in the European Union. In most cases, the likely introduction of SARS-CoV-2 infection into farms was infected humans. Human health can be at risk by mink-related variant viruses, which can establish circulation in the community, but so far these have not shown to be more transmissible or causing more severe impact compared with other circulating SARS-CoV-2. Concerning animal health risk posed by SARS-CoV-2 infection the animal species that may be included in monitoring plans are American mink, ferrets, cats, raccoon dogs, white-tailed deer and Rhinolophidae bats. All mink farms should be considered at risk of infection; therefore, the monitoring objective should be early detection. This includes passive monitoring (in place in the whole territory of all countries where animals susceptible to SARS-CoV-2 are bred) but also active monitoring by regular testing. First, frequent testing of farm personnel and all people in contact with the animals is recommended. Furthermore randomly selected animals (dead or sick animals should be included) should be tested using reverse transcriptase-polymerase chain reaction (RT-PCR), ideally at weekly intervals (i.e. design prevalence approximately 5% in each epidemiological unit, to be assessed case by case). Suspected animals (dead or with clinical signs and a minimum five animals) should be tested for confirmation of SARS-CoV-2 infection. Positive samples from each farm should be sequenced to monitor virus evolution and results publicly shared.

One-dimensional 1 H nuclear magnetic resonance (1D 1 H-NMR) has been used extensively as a metabolic profiling tool for investigating urine and other biological fluids. Under ideal conditions, 1 H-NMR peak intensities are directly proportional to metabolite concentrations and thus are useful for class prediction and biomarker discovery. However, many biological, experimental and instrumental variables can affect absolute NMR peak intensities. Normalizing or scaling data to minimize the influence of these variables is a critical step in producing robust, reproducible analyses. Traditionally, analyses of biological fluids have relied on the total spectral area [constant sum (CS)] to normalize individual intensities. This approach can introduce considerable intersample variance as changes in any individual metabolite will affect the scaling of all of the observed intensities. To reduce normalization-related variance, we have developed a histogram matching (HM) approach adapted from the field of image processing. We validate our approach using mixtures of synthetic compounds that mimic a biological extract and apply the method to an analysis of urine from rats treated with ethionine. We show that HM is a robust method for normalizing 1 H-NMR data and propose it as an alternative to the traditional CS method.