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Parent, E.

Publications and source records attributed to Parent, E..

3 recordsLinked to original sources

Genetic tools for the study of the mangrove killifish, Kryptolebias marmoratus, an emerging vertebrate model for phenotypic plasticity

Kryptolebias marmoratus (Kmar), a teleost fish of the order Cyprinodontiformes, has a suite of unique phenotypes and behaviors not observed in other fishes. Many of these phenotypes are discrete and highly plastic -varying over time within an individual, and in some cases reversible. Kmar and its interfertile sister species, K. hermaphroditus, are the only known self-fertile vertebrates. This unusual sexual mode has the potential to provide unique insights into the regulation of vertebrate sexual development, and also lends itself to genetics. Kmar is easily adapted to the lab and requires little maintenance. However, its internal fertilization and small clutch size limits its experimental use. To support Kmar as a genetic model, we compared alternative husbandry techniques to maximize recovery of early cleavage-stage embryos. We find that frequent egg collection enhances yield, and that protease treatment promotes the greatest hatching successes from diapause. We completed a forward mutagenesis screen and recovered several mutant lines that serve as important tools for genetics in this model. Several will serve as useful viable recessive markers for marking crosses. Importantly, the mutant kissylips lays embryos at twice the rate of wild-type. Combining frequent egg collection with the kissylips mutant background allows for a substantial enhancement of early embryo yield. These improvements were sufficient to allow experimental analysis of early development and the successful mono- and bi-allelic targeted knockout of an endogenous tyrosinase gene with CRISPR/Cas9 nucleases. Collectively, these tools will facilitate modern developmental genetics in this fascinating fish, leading to future insights into the regulation of plasticity.

developmental biology↗

Maximizing the reliability and the number of species assignments in metabarcoding studies

The use of environmental DNA (eDNA) for biodiversity assessments has increased rapidly over the last decade. However, the reliability of taxonomic assignments in metabarcoding studies is variable, and affected by the reference databases and the assignment methods used. Species level assignments are usually considered as reliable using regional libraries but unreliable using public repositories. In this study, we aimed to test this assumption for metazoan species detected in the Gulf of St. Lawrence, in the Northwest Atlantic. We first created a regional library with COI barcode sequences including a reliability ranking system for species assignments. We then estimated the accuracy of the public repository NCBI-nt for species assignments using sequences from the regional library, and contrasted assigned species and their reliability using NCBI-nt or the regional library with a metabarcoding dataset and popular assignment methods. With NCBI-nt and sequences from the regional library, Blast-LCA was the most accurate method for species assignments but the proportions of accurate species assignments were higher with Blast-TopHit (>80 % overall taxa, between 70 and 90 % amongst taxonomic groups). With the metabarcoding dataset, the reliability of species assignments was greater using the GSL-rl compared to NCBI-nt. However, we also observed that the total number of reliable species assignments could be maximized using both GSL-rl and NCBI-nt, and their optimal assignment methods, which differed. The use of a two-step approach in species assignments, using a regional library and a public repository, could improve the reliability and the number of detected species in metabarcoding studies.

genetics↗

Machine learning prediction of particle-size distribution from infrared spectra, methodologies and soil features

Accuracy of infrared (IR) models to measure soil particle-size distribution (PSD) depends on soil preparation, methodology (sedimentation, laser), settling times and relevant soil features. Compositional soil data may require log ratio (ilr) transformation to avoid numerical biases. Machine learning can relate numerous independent variables that may impact on NIR spectra to assess particle-size distribution. Our objective was to reach high IRS prediction accuracy across a large range of PSD methods and soil properties. A total of 1298 soil samples from eastern Canada were IR-scanned. Spectra were processed by Stochastic Gradient Boosting (SGB) to predict sand, silt, clay and carbon. Slope and intercept of the log-log relationships between settling time and suspension density function (SDF) (R2 = 0.84-0.92) performed similarly to NIR spectra using either ilr-transformed (R2 = 0.81-0.93) or raw percentages (R2 = 0.76-0.94). Settling times of 0.67-min and 2-h were the most accurate for NIR predictions (R2 = 0.49-0.79). The NIR prediction of sand sieving method (R2 = 0.66) was more accurate than Bouyoucos (R2 = 0.53). The NIR 2X gain was less accurate (R2 = 0.69-0.92) than 4X (R2 = 0.87-0.95). The MIR (R2 = 0.45-0.80) performed better than NIR (R2 = 0.40-0.71) spectra. Adding soil carbon, reconstituted bulk density, pH, red-green-blue color, oxalate and Mehlich3 extracts returned R2 value of 0.86-0.91 for texture prediction. In addition to slope and intercept of the SDF, 4X gain, method and pre-treatment classes, soil carbon and color appeared to be promising features for routine SGB-processed NIR particle-size analysis. Soil Classification (Soil Taxonomy): Inceptisols, Spodosols

ecology↗