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Complex traits often exhibit complex underlying genetic architectures resulting from a combination of evolution from standing variation, hard and soft sweeps, and alleles of varying effect size. Increasingly, studies implicate both large‐effect loci and polygenic patterns underpinning adaptation, but the extent that common genetic architectures are utilized during repeated adaptation is not well understood. Sea age or age at maturation represents a significant life history trait in Atlantic Salmon (Salmo salar), the genetic basis of which has been studied extensively in European Atlantic populations, with repeated identification of large‐effect loci. However, the genetic basis of sea age within North American Atlantic Salmon populations remains unclear, as does the potential for a parallel trans‐Atlantic genomic basis to sea age. Here, we used a large single‐nucleotide polymorphism (SNP) array and low‐coverage whole‐genome resequencing to explore the genomic basis of sea age variation in North American Atlantic Salmon. We found significant associations at the gene and SNP level with a large‐effect locus (vgll3) previously identified in European populations, indicating genetic parallelism, but found that this pattern varied based on both sex and geographic region. We also identified nonrepeated sets of highly predictive loci associated with sea age among populations and sexes within North America, indicating polygenicity and low rates of genomic parallelism. Despite low genome‐wide parallelism, we uncovered a set of conserved molecular pathways associated with sea age that were consistently enriched among comparisons, including calcium signaling, MapK signaling, focal adhesion, and phosphatidylinositol signaling. Together, our results indicate parallelism of the molecular basis of sea age in North American Atlantic Salmon across large‐effect genes and molecular pathways despite population‐specific patterns of polygenicity. These findings reveal roles for both contingency and repeated adaptation at the molecular level in the evolution of life history variation. [ABSTRACT FROM AUTHOR]

Oceans will play a crucial role in our efforts to combat the growing climate emergency. Researchers have proposed several strategies to harness greener energy through oceans and use oceans as carbon sinks. However, the risks these strategies might pose to the ocean and marine ecosystem are not well understood. It is imperative that we quickly develop a range of tools to monitor ocean processes and marine ecosystems alongside the technology to deploy these solutions on a large scale into the oceans. Large arrays of inexpensive cameras placed deep underwater coupled with machine learning pipelines to automatically detect, classify, count, and estimate fish populations have the potential to continuously monitor marine ecosystems and help study the impacts of these solutions on the ocean. In this paper, we successfully demonstrate the application of YOLOv4 and YOLOv7 deep learning models to classify and detect six species of fish in a dark artificially lit underwater video dataset captured 500 m below the surface, with a mAP of 76.01% and 85.0%, respectively. We show that 2,000 images per species, for each of the six species of fish is sufficient to train a machine-learning species classification model for this low-light environment. This research is a first step toward systems to autonomously monitor fish deep underwater while causing as little disruption as possible. As such, we discuss the advances that will be needed to apply such systems on a large scale and propose several avenues of research toward this goal. [ABSTRACT FROM AUTHOR]

Acoustic tags are increasingly being used in research to track movements of marine animals, generating large, complex datasets which require automated analysis methods. In this work, we explore the use of modern machine learning methods to automate the analysis of data from acoustic tags with a novel encoding developed for the study of large numbers of fish in noisy environments. These tags emit pulses at a configurable interval but without any other encoded identification message. This reduces power usage and enables continuous tracking but the challenge is that tags must be identified based on the transmission rate and the position reconstructed by triangulating detections by at least three independent receivers. Utilizing a visual representation of the time series data generated by the tag, we adapt a deep neural network architecture known as U-Net to the task of identifying individual acoustic tag transmissions. Testing our model on unseen data, we achieve an accuracy of 97%, outperforming the solution currently in use, which achieves an accuracy of 41%. Our model shows significant promise and represents the first step towards enabling the large-scale application of this new acoustic tracking technology to fisheries operations. [ABSTRACT FROM AUTHOR]

The article discusses the book "Cryopaedia," by Xenophon. The book discusses the rule of Cyrus, an ancient Persian emperor who was able to unite numerous cultures under one empire. He did this by treating his subjects like animals. The book often characterizes Cyrus's soldiers as dogs, and his enemies as rabbits. His empire is often spoken of as a herd of livestock, and sometimes as a beehive.

The article examines the life of Cyrus the Great by Xenophon in his philosophical work the "Cyropaedia." The "Cyropaedia" tackles the knowledge of Cyrus to deceive other rulers that render nations to be obedient to him and build the Persian Empire. It is inferred that human beings resist being ruled by anyone they perceive trying to rule over them, and one way to avoid this inevitable resistance is to disguise his true intentions.