Synthetic microplastic abundance and composition along a longitudinal gradient traversing the subtropical gyre in the North Atlantic Ocean.

Plastic pollution has been reported in the North Atlantic Ocean since the 1970s, yet limited data over subsequent decades pose challenges when assessing spatio-temporal trends in relation to global leakages and intervention strategies. This study quantified microplastics within the upper ocean along a longitudinal transect of the North Atlantic and its subtropical gyre. Microplastics were sampled from surface and subsurface (-25 m) water using a manta trawl and NIKSIN bottle respectively. The surface water polymer community varied significantly between geographic positions ('inshore', 'gyre', 'open ocean'), and was significantly influenced by fragment quantity. Compared to other positions, the North Atlantic gyre was associated with high concentrations of polyethylene, polypropylene, acrylic and polyamide fragments. Subsurface water was dominated by polyamide and polyester fibres. Backtracked 2-year Lagrangian simulations illustrated connectivity patterns. Continued monitoring of microplastics throughout the water column of the North Atlantic Ocean is required to address knowledge gaps and assess spatio-temporal trends

Does Reviewing Lead to Better Learning and Decision Making? Answers from a Randomized Stock Market Experiment

status: publishe

Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies.

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies' exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony's health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project's data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping

Analysing the opinions of UK veterinarians on practice-based research using corpus linguistic and mathematical methods

The use of corpus linguistic techniques and other related mathematical analyses have rarely, if ever, been applied to qualitative data collected from the veterinary field. The aim of this study was to explore the use of a combination of corpus linguistic analyses and mathematical methods to investigate a free-text questionnaire dataset collected from 3796 UK veterinarians on evidence-based veterinary medicine, specifically, attitudes towards practice-based research (PBR) and improving the veterinary knowledge base. The corpus methods of key word, concordance and collocate analyses were used to identify patterns of meanings within the free text responses. Key words were determined by comparing the questionnaire data with a wordlist from the British National Corpus (representing general English text) using cross-tabs and log-likelihood comparisons to identify words that occur significantly more frequently in the questionnaire data. Concordance and collocation analyses were used to account for the contextual patterns in which such key words occurred, involving qualitative analysis and Mutual Information Analysis (MI3). Additionally, a mathematical topic modelling approach was used as a comparative analysis; words within the free text responses were grouped into topics based on their weight or importance within each response to find starting points for analysis of textual patterns. Results generated from using both qualitative and quantitative techniques identified that the perceived advantages of taking part in PBR centred on the themes of improving knowledge of both individuals and of the veterinary profession as a whole (illustrated by patterns around the words learning, improving, contributing). Time constraints (lack of time, time issues, time commitments) were the main concern of respondents in relation to taking part in PBR. Opinions of what vets could do to improve the veterinary knowledge base focussed on the collecting and sharing of information (record, report), particularly recording and discussing clinical cases (interesting cases), and undertaking relevant continuing professional development activities. The approach employed here demonstrated how corpus linguistics and mathematical methods can help to both identify and contextualise relevant linguistic patterns in the questionnaire responses. The results of the study inform those seeking to coordinate PBR initiatives about the motivators of veterinarians to participate in such initiatives and what concerns need to be addressed. The approach used in this study demonstrates a novel way of analysing textual data in veterinary research

Clustering of classical swine fever virus isolates by codon pair bias

<p>Abstract</p> <p>Background</p> <p>The genetic code consists of non-random usage of synonymous codons for the same amino acids, termed codon bias or codon usage. Codon juxtaposition is also non-random, referred to as codon context bias or codon pair bias. The codon and codon pair bias vary among different organisms, as well as with viruses. Reasons for these differences are not completely understood. For classical swine fever virus (CSFV), it was suggested that the synonymous codon usage does not significantly influence virulence, but the relationship between variations in codon pair usage and CSFV virulence is unknown. Virulence can be related to the fitness of a virus: Differences in codon pair usage influence genome translation efficiency, which may in turn relate to the fitness of a virus. Accordingly, the potential of the codon pair bias for clustering CSFV isolates into classes of different virulence was investigated.</p> <p>Results</p> <p>The complete genomic sequences encoding the viral polyprotein of 52 different CSFV isolates were analyzed. This included 49 sequences from the GenBank database (NCBI) and three newly sequenced genomes. The codon usage did not differ among isolates of different virulence or genotype. In contrast, a clustering of isolates based on their codon pair bias was observed, clearly discriminating highly virulent isolates and vaccine strains on one side from moderately virulent strains on the other side. However, phylogenetic trees based on the codon pair bias and on the primary nucleotide sequence resulted in a very similar genotype distribution.</p> <p>Conclusion</p> <p>Clustering of CSFV genomes based on their codon pair bias correlate with the genotype rather than with the virulence of the isolates.</p

Activation and modulation of antiviral and apoptotic genes in pigs infected with classical swine fever viruses of high, moderate or low virulence

The immune response to CSFV and the strategies of this virus to evade and suppress the pigs’ immune system are still poorly understood. Therefore, we investigated the transcriptional response in the tonsils, median retropharyngeal lymph node (MRLN), and spleen of pigs infected with CSFV strains of similar origin with high, moderate, and low virulence. Using a porcine spleen/intestinal cDNA microarray, expression levels in RNA pools prepared from infected tissue at 3 dpi (three pigs per virus strain) were compared to levels in pools prepared from uninfected homologue tissues (nine pigs). A total of 44 genes were found to be differentially expressed. The genes were functionally clustered in six groups: innate and adaptive immune response, interferon-regulated genes, apoptosis, ubiquitin-mediated proteolysis, oxidative phosphorylation and cytoskeleton. Significant up-regulation of three IFN-γ-induced genes in the MRLNs of pigs infected with the low virulence strain was the only clear qualitative difference in gene expression observed between the strains with high, moderate and low virulence. Real-time PCR analysis of four response genes in all individual samples largely confirmed the microarray data at 3 dpi. Additional PCR analysis of infected tonsil, MRLN, and spleen samples collected at 7 and 10 dpi indicated that the strong induction of expression of the antiviral response genes chemokine CXCL10 and 2′–5′ oligoadenylate synthetase 2, and of the TNF-related apoptosis-inducing ligand (TRAIL) gene at 3 dpi, decreased to lower levels at 7 and 10 dpi. For the highly and moderately virulent strains, this decrease in antiviral and apoptotic gene expression coincided with higher levels of virus in these immune tissues

Rhamnolipids: diversity of structures, microbial origins and roles

Rhamnolipids are glycolipidic biosurfactants produced by various bacterial species. They were initially found as exoproducts of the opportunistic pathogen Pseudomonas aeruginosa and described as a mixture of four congeners: α-L-rhamnopyranosyl-α-L-rhamnopyranosyl-β-hydroxydecanoyl-β-hydroxydecanoate (Rha-Rha-C10-C10), α-L-rhamnopyranosyl-α-L-rhamnopyranosyl-β-hydroxydecanoate (Rha-Rha-C10), as well as their mono-rhamnolipid congeners Rha-C10-C10 and Rha-C10. The development of more sensitive analytical techniques has lead to the further discovery of a wide diversity of rhamnolipid congeners and homologues (about 60) that are produced at different concentrations by various Pseudomonas species and by bacteria belonging to other families, classes, or even phyla. For example, various Burkholderia species have been shown to produce rhamnolipids that have longer alkyl chains than those produced by P. aeruginosa. In P. aeruginosa, three genes, carried on two distinct operons, code for the enzymes responsible for the final steps of rhamnolipid synthesis: one operon carries the rhlAB genes and the other rhlC. Genes highly similar to rhlA, rhlB, and rhlC have also been found in various Burkholderia species but grouped within one putative operon, and they have been shown to be required for rhamnolipid production as well. The exact physiological function of these secondary metabolites is still unclear. Most identified activities are derived from the surface activity, wetting ability, detergency, and other amphipathic-related properties of these molecules. Indeed, rhamnolipids promote the uptake and biodegradation of poorly soluble substrates, act as immune modulators and virulence factors, have antimicrobial activities, and are involved in surface motility and in bacterial biofilm development