Phylogenetic analyses of ray-finned fishes (Actinopterygii) using collagen type I protein sequences

Ray-finned fishes (Actinopterygii) are the largest and most diverse group of vertebrates, comprising over half of all living vertebrate species. Phylogenetic relationships between ray-finned fishes have historically pivoted on the study of morphology, which has notoriously failed to resolve higher order relationships, such as within the percomorphs. More recently, comprehensive genomic analyses have provided further resolution of actinopterygian phylogeny, including higher order relationships. Such analyses are rightfully regarded as the ‘gold standard’ for phylogenetics. However, DNA retrieval requires modern or well-preserved tissue and is less likely to be preserved in archaeological or fossil specimens. By contrast, some proteins, such as collagen, are phylogenetically informative and can survive into deep time. Here, we test the utility of collagen type I amino acid sequences for phylogenetic estimation of ray-finned fishes. We estimate topology using Bayesian approaches and compare the congruence of our estimated trees with published genomic phylogenies. Furthermore, we apply a Bayesian molecular clock approach and compare estimated divergence dates with previously published genomic clock analyses. Our collagen-derived trees exhibit 77% of node positions as congruent with recent genomic-derived trees, with the majority of discrepancies occurring in higher order node positions, almost exclusively within the Percomorpha. Our molecular clock trees present divergence times that are fairly comparable with genomic-based phylogenetic analyses. We estimate the mean node age of Actinopteri at ∼293 million years (Ma), the base of Teleostei at ∼211 Ma and the radiation of percomorphs beginning at ∼141 Ma (∼350 Ma, ∼250–283 Ma and ∼120–133 Ma in genomic trees, respectively). Finally, we show that the average rate of collagen (I) sequence evolution is 0.9 amino acid substitutions for every million years of divergence, with the α3 (I) sequence evolving the fastest, followed by the α2 (I) chain. This is the quickest rate known for any vertebrate group. We demonstrate that phylogenetic analyses using collagen type I amino acid sequences generate tangible signals for actinopterygians that are highly congruent with recent genomic-level studies. However, there is limited congruence within percomorphs, perhaps due to clade-specific functional constraints acting upon collagen sequences. Our results provide important insights for future phylogenetic analyses incorporating extinct actinopterygian species via collagen (I) sequencing

Approximations of Algorithmic and Structural Complexity Validate Cognitive-behavioural Experimental Results

We apply methods for estimating the algorithmic complexity of sequences to behavioural sequences of three landmark studies of animal behavior each of increasing sophistication, including foraging communication by ants, flight patterns of fruit flies, and tactical deception and competition strategies in rodents. In each case, we demonstrate that approximations of Logical Depth and Kolmogorv-Chaitin complexity capture and validate previously reported results, in contrast to other measures such as Shannon Entropy, compression or ad hoc. Our method is practically useful when dealing with short sequences, such as those often encountered in cognitive-behavioural research. Our analysis supports and reveals non-random behavior (LD and K complexity) in flies even in the absence of external stimuli, and confirms the "stochastic" behaviour of transgenic rats when faced that they cannot defeat by counter prediction. The method constitutes a formal approach for testing hypotheses about the mechanisms underlying animal behaviour.Comment: 28 pages, 7 figures and 2 table

Privacy and security in cyber-physical systems

Data privacy has attracted increasing attention in the past decade due to the emerging technologies that require our data to provide utility. Service providers (SPs) encourage users to share their personal data in return for a better user experience. However, users' raw data usually contains implicit sensitive information that can be inferred by a third party. This raises great concern about users' privacy. In this dissertation, we develop novel techniques to achieve a better privacy-utility trade-off (PUT) in various applications. We first consider smart meter (SM) privacy and employ physical resources to minimize the information leakage to the SP through SM readings. We measure privacy using information-theoretic metrics and find private data release policies (PDRPs) by formulating the problem as a Markov decision process (MDP). We also propose noise injection techniques for time-series data privacy. We characterize optimal PDRPs measuring privacy via mutual information (MI) and utility loss via added distortion. Reformulating the problem as an MDP, we solve it using deep reinforcement learning (DRL) for real location trace data. We also consider a scenario for hiding an underlying ``sensitive'' variable and revealing a ``useful'' variable for utility by periodically selecting from among sensors to share the measurements with an SP. We formulate this as an optimal stopping problem and solve using DRL. We then consider privacy-aware communication over a wiretap channel. We maximize the information delivered to the legitimate receiver, while minimizing the information leakage from the sensitive attribute to the eavesdropper. We propose using a variational-autoencoder (VAE) and validate our approach with colored and annotated MNIST dataset. Finally, we consider defenses against active adversaries in the context of security-critical applications. We propose an adversarial example (AE) generation method exploiting the data distribution. We perform adversarial training using the proposed AEs and evaluate the performance against real-world adversarial attacks.Open Acces