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BackgroundThe diverse microbial communities in agricultural biogas fermenters are assumed to be well adapted for the anaerobic transformation of plant biomass to methane. Compared to natural systems, biogas reactors are limited in their hydrolytic potential. The reasons for this are not understood.ResultsIn this paper, we show that a typical industrial biogas reactor fed with maize silage, cow manure, and chicken manure has relatively lower hydrolysis rates compared to feces samples from herbivores. We provide evidence that on average, 2.5 genes encoding cellulolytic GHs/Mbp were identified in the biogas fermenter compared to 3.8 in the elephant feces and 3.2 in the cow rumen data sets. The ratio of genes coding for cellulolytic GH enzymes affiliated with the Firmicutes versus the Bacteroidetes was 2.8:1 in the biogas fermenter compared to 1:1 in the elephant feces and 1.4:1 in the cow rumen sample. Furthermore, RNA-Seq data indicated that highly transcribed cellulases in the biogas fermenter were four times more often affiliated with the Firmicutes compared to the Bacteroidetes, while an equal distribution of these enzymes was observed in the elephant feces sample.ConclusionsOur data indicate that a relatively lower abundance of bacteria affiliated with the phylum of Bacteroidetes and, to some extent, Fibrobacteres is associated with a decreased richness of predicted lignocellulolytic enzymes in biogas fermenters. This difference can be attributed to a partial lack of genes coding for cellulolytic GH enzymes derived from bacteria which are affiliated with the Fibrobacteres and, especially, the Bacteroidetes. The partial deficiency of these genes implies a potentially important limitation in the biogas fermenter with regard to the initial hydrolysis of biomass. Based on these findings, we speculate that increasing the members of Bacteroidetes and Fibrobacteres in biogas fermenters will most likely result in an increased hydrolytic performance.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-016-0534-x) contains supplementary material, which is available to authorized users.

L isteriosis is a severe, mainly foodborne, human infection associated with higher casefatality and hospitalization rates than other bacterial gastrointestinal pathogens (1). The causative agent, Listeria monocytogenes, occurs ubiquitously in the environment and disseminates into the food production chain. Patients develop either self-limiting noninvasive gastroenteritis or invasive listeriosis (2,3). Listeriosis adversely affects older and immunocompromised persons, as well as pregnant women, causing a severe invasive form of the disease that leads to sepsis, meningitis, and encephalitis, as well as neonatal infections and miscarriage (4). Case-fatality rates of invasive listeriosis are ≈30% for neurolisteriosis and even higher in septic patients (5). In Europe and North America, invasive listeriosis affects 0.3-0.6 persons/100,000 population/year (6,7). L. monocytogenes forms hard-to-remove biofilms in food-processing plants, can acquire tolerance to sanitizers, and multiplies even at temperatures used for refrigeration (8). These properties complicate efficient prevention of L. monocytogenes contaminations in different types of ready-to-eat products, including dairy, meat, and fish, and in fruits and vegetables, all of which have been vehicles for listeriosis outbreaks in the past (9-12).

Abstract. Eastern boundary upwelling systems (EBUS) are among the most productive marine ecosystems on Earth. The production of organic material is fueled by upwelling of nutrient-rich deep waters and high incident light at the sea surface. However, biotic and abiotic factors can modify surface production and related biogeochemical processes. Determining these factors is important because EBUS are considered hotspots of climate change, and reliable predictions of their future functioning requires understanding of the mechanisms driving the biogeochemical cycles therein. In this field experiment, we used in situ mesocosms as tools to improve our mechanistic understanding of processes controlling organic matter cycling in the coastal Peruvian upwelling system. Eight mesocosms, each with a volume of ∼55 m3, were deployed for 50 d ∼6 km off Callao (12∘ S) during austral summer 2017, coinciding with a coastal El Niño phase. After mesocosm deployment, we collected subsurface waters at two different locations in the regional oxygen minimum zone (OMZ) and injected these into four mesocosms (mixing ratio ≈1.5 : 1 mesocosm: OMZ water). The focus of this paper is on temporal developments of organic matter production, export, and stoichiometry in the individual mesocosms. The mesocosm phytoplankton communities were initially dominated by diatoms but shifted towards a pronounced dominance of the mixotrophic dinoflagellate (Akashiwo sanguinea) when inorganic nitrogen was exhausted in surface layers. The community shift coincided with a short-term increase in production during the A. sanguinea bloom, which left a pronounced imprint on organic matter C : N : P stoichiometry. However, C, N, and P export fluxes did not increase because A. sanguinea persisted in the water column and did not sink out during the experiment. Accordingly, export fluxes during the study were decoupled from surface production and sustained by the remaining plankton community. Overall, biogeochemical pools and fluxes were surprisingly constant for most of the experiment. We explain this constancy by light limitation through self-shading by phytoplankton and by inorganic nitrogen limitation which constrained phytoplankton growth. Thus, gain and loss processes remained balanced and there were few opportunities for blooms, which represents an event where the system becomes unbalanced. Overall, our mesocosm study revealed some key links between ecological and biogeochemical processes for one of the most economically important regions in the oceans.

Marine multicellular organisms in composition with their associated microbiota—representing metaorganisms—are confronted with constantly changing environmental conditions. In 2110, the seawater temperature is predicted to be increased by ~5°C, and the atmospheric carbon dioxide partial pressure (pCO2) is expected to reach approximately 1000 ppm. In order to assess the response of marine metaorganisms to global changes, e.g., by effects on host-microbe interactions, we evaluated the response of epibacterial communities associated with Fucus vesiculosus forma mytili (F. mytili) to future climate conditions. During an 11-week lasting mesocosm experiment on the island of Sylt (Germany) in spring 2014, North Sea F. mytili individuals were exposed to elevated pCO2 (1000 ppm) and increased temperature levels (Δ+5°C). Both abiotic factors were tested for single and combined effects on the epibacterial community composition over time, with three replicates per treatment. The respective community structures of bacterial consortia associated to the surface of F. mytili were analyzed by Illumina MiSeq 16S rDNA amplicon sequencing after 0, 4, 8, and 11 weeks of treatment (in total 96 samples). The results demonstrated that the epibacterial community structure was strongly affected by temperature, but only weakly by elevated pCO2. No interaction effect of both factors was observed in the combined treatment. We identified several indicator operational taxonomic units (iOTUs) that were strongly influenced by the respective experimental factors. An OTU association network analysis revealed that relationships between OTUs were mainly governed by habitat. Overall, this study contributes to a better understanding of how epibacterial communities associated with F. mytili may adapt to future changes in seawater acidity and temperature, ultimately with potential consequences for host-microbe interactions.

The application of next-generation sequencing technology in microbial community analysis increased our knowledge and understanding of the complexity and diversity of a variety of ecosystems. In contrast to Bacteria, the archaeal domain was often not particularly addressed in the analysis of microbial communities. Consequently, established primers specifically amplifying the archaeal 16S ribosomal gene region are scarce compared to the variety of primers targeting bacterial sequences. In this study, we aimed to validate archaeal primers suitable for high throughput next generation sequencing. Three archaeal 16S primer pairs as well as two bacterial and one general microbial 16S primer pairs were comprehensively tested by in-silico evaluation and performing an experimental analysis of a complex microbial community of a biogas reactor. The results obtained clearly demonstrate that comparability of community profiles established using different primer pairs is difficult. 16S rRNA gene data derived from a shotgun metagenome of the same reactor sample added an additional perspective on the community structure. Furthermore, in-silico evaluation of primers, especially those for amplification of archaeal 16S rRNA gene regions, does not necessarily reflect the results obtained in experimental approaches. In the latter, archaeal primer pair ArchV34 showed the highest similarity to the archaeal community structure compared to observed by the metagenomic approach and thus appears to be the appropriate for analyzing archaeal communities in biogas reactors. However, a disadvantage of this primer pair was its low specificity for the archaeal domain in the experimental application leading to high amounts of bacterial sequences within the dataset. Overall our results indicate a rather limited comparability between community structures investigated and determined using different primer pairs as well as between metagenome and 16S rRNA gene amplicon based community structure analysis. This finding, previously shown for Bacteria, was as well observed for the archaeal domain.

The eastern tropical North Atlantic (ETNA) is characterized by a highly productive coastal upwelling system and a moderate oxygen minimum zone with lowest open-ocean oxygen (O2) concentrations of approximately 40 μmol kg−1. The recent discovery of re-occurring mesoscale eddies with close to anoxic O2 concentrations (< 1 μmol kg−1) located just below the mixed layer has challenged our understanding of O2 distribution and biogeochemical processes in this area. Here, we present the first microbial community study from a deoxygenated anticyclonic modewater eddy in the open waters of the ETNA. In the eddy, we observed significantly lower bacterial diversity compared to surrounding waters, along with a significant community shift. We detected enhanced primary productivity in the surface layer of the eddy indicated by elevated chlorophyll concentrations and carbon uptake rates of up to three times as high as in surrounding waters. Carbon uptake rates below the euphotic zone correlated to the presence of a specific high-light ecotype of Prochlorococcus, which is usually underrepresented in the ETNA. Our data indicate that high primary production in the eddy fuels export production and supports enhanced respiration in a specific microbial community at shallow depths, below the mixed-layer base. The transcription of the key functional marker gene for dentrification, nirS, further indicated a potential for nitrogen loss processes in O2-depleted core waters of the eddy. Dentrification is usually absent from the open ETNA waters. In light of future projected ocean deoxygenation, our results show that even distinct events of anoxia have the potential to alter microbial community structure with critical impacts on primary productivity and biogeochemical processes of oceanic water bodies

Automated medical image analysis has a significant value in diagnosis and treatment of lesions. Brain tumors segmentation has a special importance and difficulty due to the difference in appearances and shapes of the different tumor regions in magnetic resonance images. Additionally the data sets are heterogeneous and usually limited in size in comparison with the computer vision problems. The recently proposed adversarial training has shown promising results in generative image modeling. In this paper we propose a novel end-to-end trainable architecture for brain tumor semantic segmentation through conditional adversarial training. We exploit conditional Generative Adversarial Network (cGAN) and train a semantic segmentation Convolution Neural Network (CNN) along with an adversarial network that discriminates segmentation maps coming from the ground truth or from the segmentation network for BraTS 2017 segmentation task [15,4,2,3]. We also propose an end-to-end trainable CNN for survival day prediction based on deep learning techniques for BraTS 2017 prediction task [15,4,2,3]. The experimental results demonstrate the superior ability of the proposed approach for both tasks. The proposed model achieves on validation data a DICE score, Sensitivity and Specificity respectively 0.68, 0.99 and 0.98 for the whole tumor, regarding online judgment system.