Unclear associations between small pelagic fish and jellyfish in several major marine ecosystems

During the last 20 years, a series of studies has suggested trends of increasing jellyfish (Cnidaria and Ctenophora) biomass in several major ecosystems worldwide. Some of these systems have been heavily fished, causing a decline among their historically dominant small pelagic fish stocks, or have experienced environmental shifts favouring jellyfish proliferation. Apparent reduction in fish abundance alongside increasing jellyfish abundance has led to hypotheses suggesting that jellyfish in these areas could be replacing small planktivorous fish through resource competition and/or through predation on early life stages of fish. In this study, we test these hypotheses using extended and published data of jellyfish, small pelagic fish and crustacean zooplankton biomass from four major ecosystems within the period of 1960 to 2014: the Southeastern Bering Sea, the Black Sea, the Northern California Current and the Northern Benguela. Except for a negative association between jellyfish and crustacean zooplankton in the Black Sea, we found no evidence of jellyfish biomass being related to the biomass of small pelagic fish nor to a common crustacean zooplankton resource. Calculations of the energy requirements of small pelagic fish and jellyfish stocks in the most recent years suggest that fish predation on crustacean zooplankton is 2–30 times higher than jellyfish predation, depending on ecosystem. However, compared with available historical data in the Southeastern Bering Sea and the Black Sea, it is evident that jellyfish have increased their share of the common resource, and that jellyfish can account for up to 30% of the combined fish-jellyfish energy consumption. We conclude that the best available time-series data do not suggest that jellyfish are outcompeting, or have replaced, small pelagic fish on a regional scale in any of the four investigated ecosystems. However, further clarification of the role of jellyfish requires higher-resolution spatial, temporal and taxonomic sampling of the pelagic community.publishedVersio

Development of a hybrid Bayesian network model for predicting acute fish toxicity using multiple lines of evidence

A hybrid Bayesian network (BN) was developed for predicting the acute toxicity of chemicals to fish, using data from fish embryo toxicity (FET) testing in combination with other information. This model can support the use of FET data in a Weight-of-Evidence (WOE) approach for replacing the use of ju-venile fish. The BN predicted correct toxicity intervals for 69%–80% of the tested substances. The model was most sensitive to components quantified by toxicity data, and least sensitive to compo-nents quantified by expert knowledge. The model is publicly available through a web interface. Fur-ther development of this model should include additional lines of evidence, refinement of the discre-tisation, and training with a larger dataset for weighting of the lines of evidence. A refined version of this model can be a useful tool for predicting acute fish toxicity, and a contribution to more quantitative WOE approaches for ecotoxicology and environmental assessment more generally.publishedVersio

Evaluation of a Bayesian Network for Strengthening the Weight of Evidence to Predict Acute Fish Toxicity from Fish Embryo Toxicity Data

The use of fish embryo toxicity (FET) data for hazard assessments of chemicals, in place of acute fish toxicity (AFT) data, has long been the goal for many environmental scientists. The FET test was first proposed as a replacement to the standardized AFT test nearly 15 y ago, but as of now, it has still not been accepted as a standalone replacement by regulatory authorities such as the European Chemicals Agency (ECHA). However, the ECHA has indicated that FET data can be used in a weight of evidence (WoE) approach, if enough information is available to support the conclusions related to the hazard assessment. To determine how such a WoE approach could be applied in practice has been challenging. To provide a conclusive WoE for FET data, we have developed a Bayesian network (BN) to incorporate multiple lines of evidence to predict AFT. There are 4 different lines of evidence in this BN model: 1) physicochemical properties, 2) AFT data from chemicals in a similar class or category, 3) ecotoxicity data from other trophic levels of organisms (e.g., daphnids and algae), and 4) measured FET data. The BN model was constructed from data obtained from a curated database and conditional probabilities assigned for the outcomes of each line of evidence. To evaluate the model, 20 data‐rich chemicals, containing a minimum of 3 AFT and FET test data points, were selected to ensure a suitable comparison could be performed. The results of the AFT predictions indicated that the BN model could accurately predict the toxicity interval for 80% of the chemicals evaluated. For the remaining chemicals (20%), either daphnids or algae were the most sensitive test species, and for those chemicals, the daphnid or algal hazard data would have driven the environmental classification. Integr Environ Assess Manag 2020;00:1–9. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC

“The people who are out of ‘right’ English”: Japanese university students' social evaluations of English language diversity and the internationalisation of Japanese higher education

Previous research indicates that evaluations of speech forms reflect stereotypes of, and attitudes towards, the perceived group(s) of speakers of the language/variety under consideration. This study, employing both implicit and explicit attitude measures, investigates 158 Japanese university students' perceptions of forms of UK, US, Japanese, Chinese, Thai and Indian English speech. The results show a general convergence between students' explicit and implicit attitudes, for instance, regarding US and UK English as the most correct, and solidarity with Japanese speakers of English. The findings are discussed in relation to intergroup relations between the traditional Japanese cohort and specific groups of overseas students, particularly in light of recent internationalisation policies adopted by many Japanese universities, and the resultant increase in international students from South and East Asia

Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Although 58 genomic regions have been associated with CAD thus far, most of the heritability is unexplained, indicating that additional susceptibility loci await identification. An efficient discovery strategy may be larger-scale evaluation of promising associations suggested by genome-wide association studies (GWAS). Hence, we genotyped 56,309 participants using a targeted gene array derived from earlier GWAS results and performed meta-analysis of results with 194,427 participants previously genotyped, totaling 88,192 CAD cases and 162,544 controls. We identified 25 new SNP-CAD associations (P < 5 × 10(-8), in fixed-effects meta-analysis) from 15 genomic regions, including SNPs in or near genes involved in cellular adhesion, leukocyte migration and atherosclerosis (PECAM1, rs1867624), coagulation and inflammation (PROCR, rs867186 (p.Ser219Gly)) and vascular smooth muscle cell differentiation (LMOD1, rs2820315). Correlation of these regions with cell-type-specific gene expression and plasma protein levels sheds light on potential disease mechanisms

Identification of known and novel recurrent viral sequences in data from multiple patients and multiple cancers

Virus discovery from high throughput sequencing data often follows a bottom-up approach where taxonomic annotation takes place prior to association to disease. Albeit effective in some cases, the approach fails to detect novel pathogens and remote variants not present in reference databases. We have developed a species independent pipeline that utilises sequence clustering for the identification of nucleotide sequences that co-occur across multiple sequencing data instances. We applied the workflow to 686 sequencing libraries from 252 cancer samples of different cancer and tissue types, 32 non-template controls, and 24 test samples. Recurrent sequences were statistically associated to biological, methodological or technical features with the aim to identify novel pathogens or plausible contaminants that may associate to a particular kit or method. We provide examples of identified inhabitants of the healthy tissue flora as well as experimental contaminants. Unmapped sequences that co-occur with high statistical significance potentially represent the unknown sequence space where novel pathogens can be identified

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.