Project description:Long rough dab (Hippoglossoides platessoides) is an important flatfish fish species in the north Atlantic arctic and sub-arctic marine foodweb that could be vulnerable to contaminant exposure from offshore petroleum related activities. The study was conducted to map transcriptome responses in long rough dab precision cut liver slice (PCLS) culture exposed to benzo[a]pyrene (BaP). BaP is a polyaromatic hydrocarbon (PAH) which is among the most toxic compounds found in crude oil. PCLS culture was performed under 10 µM BaP exposure for 72 h and transcriptome analysis (RNA-seq) analysis was performed to characterize de novo transcriptome of the liver and identify genes responding to BaP exposure.
Project description:Otoliths (ear-stones) in the inner ears of vertebrates containing visible year zones are used extensively to determine fish age. Analysis of otoliths is a time-consuming and difficult task that requires the education of human experts. Human age estimates are inconsistent, as several readings by the same human expert might result in different ages assigned to the same otolith, in addition to an inherent bias between readers. To improve efficiency and resolve inconsistent results in the age reading from otolith images by human experts, an automated procedure based on convolutional neural networks (CNNs), a class of deep learning models suitable for image processing, is investigated. We applied CNNs that perform image regression to estimate the age of Greenland halibut (Reinhardtius hippoglossoides) with good results for individual ages as well as the overall age distribution, with an average CV of about 10% relative to the read ages by experts. In addition, the density distribution of predicted ages resembles the density distribution of the ground truth. By using k*l-fold cross-validation, we test all available samples, and we show that the results are rather sensitive to the choice of test set. Finally, we apply explanation techniques to analyze the decision process of deep learning models. In particular, we produce heatmaps indicating which input features that are the most important in the computation of predicted age.
