Universal closed-tube barcoding for monitoring the shark and ray trade in megadiverse conservation hotspots

Trade restrictions for many endangered elasmobranch species exist to disincentivise their exploitation and curb their declines. However, the variety of products and the complexity of import/export routes make trade monitoring challenging. We investigate the use of a portable, universal, DNA-based tool which would greatly facilitate in-situ monitoring. We collected shark and ray samples across the Island of Java, Indonesia, and selected 28 species (including 22 CITES-listed species) commonly encountered in landing sites and export hubs to test a recently developed real-time PCR single-assay originally developed for screening bony fish. We employed a deep learning algorithm to recognize species based on DNA melt-curve signatures. By combining visual and machine learning assignment methods, we distinguished 25 out of 28 species, 20 of which were CITES-listed. With further refinement, this method can provide a practical tool for monitoring elasmobranch trade worldwide, without the need for a lab or the bespoke design of species-specific assays

Genome sequencing reveals diversification of virulence factor content and possible host adaptation in distinct subpopulations of Salmonella enterica

<p>Abstract</p> <p>Background</p> <p>Divergence of bacterial populations into distinct subpopulations is often the result of ecological isolation. While some studies have suggested the existence of <it>Salmonella enterica </it>subsp. <it>enterica </it>subclades, evidence for these subdivisions has been ambiguous. Here we used a comparative genomics approach to define the population structure of <it>Salmonella enterica </it>subsp. <it>enterica</it>, and identify clade-specific genes that may be the result of ecological specialization.</p> <p>Results</p> <p>Multi-locus sequence analysis (MLSA) and single nucleotide polymorphisms (SNPs) data for 16 newly sequenced and 30 publicly available genomes showed an unambiguous subdivision of <it>S. enterica </it>subsp. <it>enterica </it>into at least two subpopulations, which we refer to as clade A and clade B. Clade B strains contain several clade-specific genes or operons, including a β-glucuronidase operon, a S-fimbrial operon, and cell surface related genes, which strongly suggests niche specialization of this subpopulation. An additional set of 123 isolates was assigned to clades A and B by using qPCR assays targeting subpopulation-specific SNPs and genes of interest. Among 98 serovars examined, approximately 20% belonged to clade B. All clade B isolates contained two pathogenicity related genomic islands, SPI-18 and a cytolethal distending toxin islet; a combination of these two islands was previously thought to be exclusive to serovars Typhi and Paratyphi A. Presence of β-glucuronidase in clade B isolates specifically suggests an adaptation of this clade to the vertebrate gastrointestinal environment.</p> <p>Conclusions</p> <p><it>S. enterica </it>subsp. <it>enterica </it>consists of at least two subpopulations that differ specifically in genes involved in host and tissue tropism, utilization of host specific carbon and nitrogen sources and are therefore likely to differ in ecology and transmission characteristics.</p

The CanOE Strategy: Integrating Genomic and Metabolic Contexts across Multiple Prokaryote Genomes to Find Candidate Genes for Orphan Enzymes

Of all biochemically characterized metabolic reactions formalized by the IUBMB, over one out of four have yet to be associated with a nucleic or protein sequence, i.e. are sequence-orphan enzymatic activities. Few bioinformatics annotation tools are able to propose candidate genes for such activities by exploiting context-dependent rather than sequence-dependent data, and none are readily accessible and propose result integration across multiple genomes. Here, we present CanOE (Candidate genes for Orphan Enzymes), a four-step bioinformatics strategy that proposes ranked candidate genes for sequence-orphan enzymatic activities (or orphan enzymes for short). The first step locates “genomic metabolons”, i.e. groups of co-localized genes coding proteins catalyzing reactions linked by shared metabolites, in one genome at a time. These metabolons can be particularly helpful for aiding bioanalysts to visualize relevant metabolic data. In the second step, they are used to generate candidate associations between un-annotated genes and gene-less reactions. The third step integrates these gene-reaction associations over several genomes using gene families, and summarizes the strength of family-reaction associations by several scores. In the final step, these scores are used to rank members of gene families which are proposed for metabolic reactions. These associations are of particular interest when the metabolic reaction is a sequence-orphan enzymatic activity. Our strategy found over 60,000 genomic metabolons in more than 1,000 prokaryote organisms from the MicroScope platform, generating candidate genes for many metabolic reactions, of which more than 70 distinct orphan reactions. A computational validation of the approach is discussed. Finally, we present a case study on the anaerobic allantoin degradation pathway in Escherichia coli K-12

Among-individual diet variation within a lake trout ecotype: lack of stability of niche use

In a polyphenic species, differences in resource use are expected among ecotypes, and homogeneity in resource use is expected within an ecotype. Yet, using a broad resource spectrum has been identified as a strategy for fishes living in unproductive northern environments, where food is patchily distributed and ephemeral. We investigated whether specialization of trophic resources by individuals occurred within the generalist piscivore ecotype of lake trout from Great Bear Lake, Canada, reflective of a form of diversity. Four distinct dietary patterns of resource use within this lake trout ecotype were detected from fatty acid composition, with some variation linked to spatial patterns within Great Bear Lake. Feeding habits of different groups within the ecotype were not associated with detectable morphological or genetic differentiation, suggesting that behavioral plasticity caused the trophic differences. A low level of genetic differentiation was detected between exceptionally large‐sized individuals and other piscivore individuals. We demonstrated that individual trophic specialization can occur within an ecotype inhabiting a geologically young system (8,000–10,000 yr BP), a lake that sustains high levels of phenotypic diversity of lake trout overall. The characterization of niche use among individuals, as done in this study, is necessary to understand the role that individual variation can play at the beginning of differentiation processes

Enhanced Convolutional Neural Network for solar radiation nowcasting: All-Sky camera infrared images embedded with exogeneous parameters

Electrical power production by renewable energy sources is unpredictable in nature and this may cause imbalance between power generation and demand. Therefore, an accurate prediction of solar radiation is crucial for the stability and efficient management of electric grid. This study focuses on very short-term forecasts of solar radiation with a horizon in the range of 5–15 min. In this paper, a Convolutional Neural Network is proposed that uses sequences of infrared images captured by an All-Sky Imager to forecast the Global Horizontal Irradiance on different time horizon. A real case study, exploiting six months of high-resolution data, is analyzed. Additionally, an innovative technique, the Enhanced Convolutional Neural Network (ECNN), is proposed in which exogenous data, as the solar radiation measurement, is encoded in terms of colored pixels in the upper corner of the images. Considering the naïve persistence method as a baseline, a clear improvement across the key metrics has been noted with the proposed methodology. A deeper analysis of the results reveals that the proposed models are more accurate than persistence when high fluctuations of solar radiation are experienced. In that case, the ECNN achieves a forecast skill exceeding 19% for all the tested forecast horizons