Evolutionary systems biology of virus-host interactions

The evolution of virus-host interactions occurs at multiple levels of biological complexity, such as organismal, genetic, and molecular levels. In the first part of this study, the evolution of associations between herpesviruses (HVs) and theirhosts are examined across more than 400 million years. Recent studies have been demonstrating that cospeciations are not always the main event driving HV evolution, asinterhost speciations and host switches also play important roles. The present study shows that more than topological incongruences, mismatches on divergence times are the main source of disagreements between host and viral phylogenies, which reveals host switches, intrahost speciations and viral losses along the evolution of HVs. Herpesviruses have large genomes encoding dozens of proteins. Apart from amino acid substitutions, these viruses also evolve by acquiring, duplicating and losing protein domains. Although the domain repertoires of HVs differ across species, a core set of domains is shared among all of them. This second part of this study reveals that 28 out 41 core domains encoded by HV ancestors are still found in present-day repertoires, which over time were expanded by domain gains and duplications. Distinct evolutionary strategies led HVs to developed very specific domain repertoires, which may explain their host range and tissue tropism, and provide hints on the origins of herpesviruses. Despite the fact that most mutations in proteins are deleterious, few of them end up improving viral fitness and defining how viruses interact with their hosts. By using an integrative approach, the third part of this study investigates the evolution of protein-protein interactions (PPIs) involving the membrane proteins Nectins, and the herpesviral envelope glycoproteins D/G. By means of ancestral sequence reconstruction and homology modelling, ancestral structures of these protein complexes were generated, and analysis of their interaction energies revealed important differences of binding affinity along their evolution.Open Acces

Strategic Network Formation with Attack and Immunization

Strategic network formation arises where agents receive benefit from connections to other agents, but also incur costs for forming links. We consider a new network formation game that incorporates an adversarial attack, as well as immunization against attack. An agent's benefit is the expected size of her connected component post-attack, and agents may also choose to immunize themselves from attack at some additional cost. Our framework is a stylized model of settings where reachability rather than centrality is the primary concern and vertices vulnerable to attacks may reduce risk via costly measures. In the reachability benefit model without attack or immunization, the set of equilibria is the empty graph and any tree. The introduction of attack and immunization changes the game dramatically; new equilibrium topologies emerge, some more sparse and some more dense than trees. We show that, under a mild assumption on the adversary, every equilibrium network with $n$ agents contains at most $2n-4$ edges for $n\geq 4$. So despite permitting topologies denser than trees, the amount of overbuilding is limited. We also show that attack and immunization don't significantly erode social welfare: every non-trivial equilibrium with respect to several adversaries has welfare at least as that of any equilibrium in the attack-free model. We complement our theory with simulations demonstrating fast convergence of a new bounded rationality dynamic which generalizes linkstable best response but is considerably more powerful in our game. The simulations further elucidate the wide variety of asymmetric equilibria and demonstrate topological consequences of the dynamics e.g. heavy-tailed degree distributions. Finally, we report on a behavioral experiment on our game with over 100 participants, where despite the complexity of the game, the resulting network was surprisingly close to equilibrium.Comment: The short version of this paper appears in the proceedings of WINE-1

Low-complexity algorithms for automatic detection of sleep stages and events for use in wearable EEG systems

Objective: Diagnosis of sleep disorders is an expensive procedure that requires performing a sleep study, known as polysomnography (PSG), in a controlled environment. This study monitors the neural, eye and muscle activity of a patient using electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) signals which are then scored in to different sleep stages. Home PSG is often cited as an alternative of clinical PSG to make it more accessible, however it still requires patients to use a cumbersome system with multiple recording channels that need to be precisely placed. This thesis proposes a wearable sleep staging system using a single channel of EEG. For realisation of such a system, this thesis presents novel features for REM sleep detection from EEG (normally detected using EMG/EOG), a low-complexity automatic sleep staging algorithm using a single EEG channel and its complete integrated circuit implementation. Methods: The difference between Spectral Edge Frequencies (SEF) at 95% and 50% in the 8-16 Hz frequency band is shown to have high discriminatory ability for detecting REM sleep stages. This feature, together with other spectral features from single-channel EEG are used with a set of decision trees controlled by a state machine for classification. The hardware for the complete algorithm is designed using low-power techniques and implemented on chip using 0.18μm process node technology. Results: The use of SEF features from one channel of EEG resulted in 83% of REM sleep epochs being correctly detected. The automatic sleep staging algorithm, based on contextually aware decision trees, resulted in an accuracy of up to 79% on a large dataset. Its hardware implementation, which is also the very first complete circuit level implementation of any sleep staging algorithm, resulted in an accuracy of 98.7% with great potential for use in fully wearable sleep systems.Open Acces

Optimized Artificial Intelligence Model for DDoS Detection in SDN Environment

© Copyright 2023 The Authors. Distributed denial of service (DDoS) attacks continue to be a major security concern, threatening the availability and reliability of network services. Software-defined networking (SDN) has emerged as a promising solution to address this issue, enabling centralized network control and management. However, conventional SDN-based DDoS mitigation techniques often struggle to detect and mitigate sophisticated attacks due to their limited ability to analyze complex traffic patterns. This paper proposes an innovative and optimized approach that effectively combines mininet, Ryu controller, and one dimensional-convolutional neural network (1D-CNN) to detect and mitigate DDoS attacks in SDN environments. The proposed approach involves training the 1D-CNN model with labeled network traffic data to effectively identify abnormal patterns associated with DDoS attacks. Furthermore, seven hyperparameters of the trained 1D-CNN model were tuned using non-dominated sorting genetic algorithm II (NSGA-II) to achieve the best accuracy with minimum training time. Once the optimized 1D-CNN model detects an attack, the Ryu controller dynamically adapts the network policies and employs appropriate mitigation techniques to protect the network infrastructure. To evaluate the effectiveness of the optimized 1D-CNN model, extensive experiments were conducted using a simulated SDN environment with a realistic DDoS attack dataset. The experimental results demonstrate that the developed approach achieves significantly improved detection accuracy of 99.99% compared to other machine learning (ML) models. The NSGA-II enhances the optimized model accuracy with an improvement rate of 9.5%, 8%, 5.4%, and 2.6% when it is compared to logistic regression (LR), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) optimized models respectively. This research paves the way for future developments in leveraging deep learning (DL) driven techniques and SDN architectures to address evolving cybersecurity challenges

A comprehensive transcriptome and immune-gene repertoire of the lepidopteran model host Galleria mellonella

<p>Abstract</p> <p>Background</p> <p>The larvae of the greater wax moth <it>Galleria mellonella </it>are increasingly used (i) as mini-hosts to study pathogenesis and virulence factors of prominent bacterial and fungal human pathogens, (ii) as a whole-animal high throughput infection system for testing pathogen mutant libraries, and (iii) as a reliable host model to evaluate the efficacy of antibiotics against human pathogens. In order to compensate for the lack of genomic information in <it>Galleria</it>, we subjected the transcriptome of different developmental stages and immune-challenged larvae to next generation sequencing.</p> <p>Results</p> <p>We performed a <it>Galleria </it>transcriptome characterization on the Roche 454-FLX platform combined with traditional Sanger sequencing to obtain a comprehensive transcriptome. To maximize sequence diversity, we pooled RNA extracted from different developmental stages, larval tissues including hemocytes, and from immune-challenged larvae and normalized the cDNA pool. We generated a total of 789,105 pyrosequencing and 12,032 high-quality Sanger EST sequences which clustered into 18,690 contigs with an average length of 1,132 bases. Approximately 40% of the ESTs were significantly similar (<it>E </it>≤ e^-03) to proteins of other insects, of which 45% have a reported function. We identified a large number of genes encoding proteins with established functions in immunity related sensing of microbial signatures and signaling, as well as effector molecules such as antimicrobial peptides and inhibitors of microbial proteinases. In addition, we found genes known as mediators of melanization or contributing to stress responses. Using the transcriptomic data, we identified hemolymph peptides and proteins induced upon immune challenge by 2D-gelelectrophoresis combined with mass spectrometric analysis.</p> <p>Conclusion</p> <p>Here, we have developed extensive transcriptomic resources for <it>Galleria</it>. The data obtained is rich in gene transcripts related to immunity, expanding remarkably our knowledge about immune and stress-inducible genes in <it>Galleria </it>and providing the complete sequences of genes whose primary structure have only partially been characterized using proteomic methods. The generated data provide for the first time access to the genetic architecture of immunity in this model host, allowing us to elucidate the molecular mechanisms underlying pathogen and parasite response and detailed analyses of both its immune responses against human pathogens, and its coevolution with entomopathogens.</p

Implementation of Collaborative RF Localization Using a Software-Defined Radio Network

This thesis investigates the use of collaboration between sensor nodes that were tasked with localizing a radio frequency emitter. Localization is a necessary component for dynamic spectrum access. Using a set of software-defined radios as our sensors and a received signal strength-based maximum likelihood localization algorithm, we successfully localized transmitting nodes based on their received signal strength. Our experiment was conducted outdoors using a flexible topology that could be shaped into 21 sub-topologies that varied in size, and orientation with respect to the transmitters. This was made possible through application of a time shift concept and a post-processing technique. We were able to compare our real world results with the simulated results of the same topologies. Although our simulation results did not fully comply with our real world results, we observed some common trends regarding effective topology design

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Design and performance optimization of asynchronous networks-on-chip

As digital systems continue to grow in complexity, the design of conventional synchronous systems is facing unprecedented challenges. The number of transistors on individual chips is already in the multi-billion range, and a greatly increasing number of components are being integrated onto a single chip. As a consequence, modern digital designs are under strong time-to-market pressure, and there is a critical need for composable design approaches for large complex systems. In the past two decades, networks-on-chip (NoC’s) have been a highly active research area. In a NoC-based system, functional blocks are first designed individually and may run at different clock rates. These modules are then connected through a structured network for on-chip global communication. However, due to the rigidity of centrally-clocked NoC’s, there have been bottlenecks of system scalability, energy and performance, which cannot be easily solved with synchronous approaches. As a result, there has been significant recent interest in combing the notion of asynchrony with NoC designs. Since the NoC approach inherently separates the communication infrastructure, and its timing, from computational elements, it is a natural match for an asynchronous paradigm. Asynchronous NoC’s, therefore, enable a modular and extensible system composition for an ‘object-orient’ design style. The thesis aims to significantly advance the state-of-art and viability of asynchronous and globally-asynchronous locally-synchronous (GALS) networks-on-chip, to enable high-performance and low-energy systems. The proposed asynchronous NoC’s are nearly entirely based on standard cells, which eases their integration into industrial design flows. The contributions are instantiated in three different directions. First, practical acceleration techniques are proposed for optimizing the system latency, in order to break through the latency bottleneck in the memory interfaces of many on-chip parallel processors. Novel asynchronous network protocols are proposed, along with concrete NoC designs. A new concept, called ‘monitoring network’, is introduced. Monitoring networks are lightweight shadow networks used for fast-forwarding anticipated traffic information, ahead of the actual packet traffic. The routers are therefore allowed to initiate and perform arbitration and channel allocation in advance. The technique is successfully applied to two topologies which belong to two different categories – a variant mesh-of-trees (MoT) structure and a 2D-mesh topology. Considerable and stable latency improvements are observed across a wide range of traffic patterns, along with moderate throughput gains. Second, for the first time, a high-performance and low-power asynchronous NoC router is compared directly to a leading commercial synchronous counterpart in an advanced industrial technology. The asynchronous router design shows significant performance improvements, as well as area and power savings. The proposed asynchronous router integrates several advanced techniques, including a low-latency circular FIFO for buffer design, and a novel end-to-end credit-based virtual channel (VC) flow control. In addition, a semi-automated design flow is created, which uses portions of a standard synchronous tool flow. Finally, a high-performance multi-resource asynchronous arbiter design is developed. This small but important component can be directly used in existing asynchronous NoC’s for performance optimization. In addition, this standalone design promises use in opening up new NoC directions, as well as for general use in parallel systems. In the proposed arbiter design, the allocation of a resource to a client is divided into several steps. Multiple successive client-resource pairs can be selected rapidly in pipelined sequence, and the completion of the assignments can overlap in parallel. In sum, the thesis provides a set of advanced design solutions for performance optimization of asynchronous and GALS networks-on-chip. These solutions are at different levels, from network protocols, down to router- and component-level optimizations, which can be directly applied to existing basic asynchronous NoC designs to provide a leap in performance improvement

Patterns of positive selection on the transcriptome of western Iberian Squalius fish: a new approach accounting for alternative splicing

Tese de mestrado, Biologia Evolutiva e do Desenvolvimento, Universidade de Lisboa, Faculdade de Ciências, 2019One of the main goals of evolutionary biology is to understand the molecular mechanisms of adaptation. Advances on next generation sequencing (NGS) have allowed to improve our knowledge on the mechanisms of adaptation, including in non-model organisms. One example is the use of RNA-seq data to test at the transcriptome level for the presence of signatures of positive selection using the ratio of non-synonymous to synonymous mutations (dN/dS ratio). However, the identification of orthologous sequences between the transcriptomes of different species is challenging because of the possibility of mixing different splicing isoforms on the ortholog alignments. Even so, by providing tens of thousands of sequences for protein coding genes, RNA-seq can be a powerful tool for understanding the time and mode of the adaptative process. In Portugal, the western Iberian freshwater cyprinids of the Squalius genus are a good system to study adaptation. The reason is that there are four species (S. carolitertii, S. pyrenaicus, S. torgalensis and S. aradensis) distributed across a north-south temperature cline, encompassing two distinct climate types – Atlantic and Mediterranean. Recent studies found evidences of adaptation to temperature in one of the southern species (S. torgalensis). In this study, we compared the transcriptomes of these four species to look for genes with signatures of positive selection, infer branches of their phylogeny with evidence for positive selection, and identify biological functions that were enriched in genes under positive selection. We also characterized the relationship between these species at the transcriptome level. Since our RNA-seq data for the different species came from different organs our study was especially vulnerable to the effect of alternative splicing. We have thus developed a new approach to deal with alternative splicing in comparative studies using transcriptomic data. Our approach was based on identifying ortholog alignments with different splicing isoforms and remove the regions on the alignments with exons that were not common between isoforms. Our results suggest that our approach manages to reduce the quantity of false positives related to alternative splicing in comparison with a more conventional approach. Regarding the phylogenetic relationship between species, we found support for the paraphyly between S. pyrenaicus and S. carolitertii, which has been also suggested by recent studies. Regarding the patterns of positive selection on these species, we found positive selection in 1.4% to 2.0% of the identified ortholog gene groups, which is comparable to what has been estimated for bony fish species in other studies. Interestingly, we found a relatively higher number of genes under positive selection on the branches of the southern species under the Mediterranean climate type than on the northern species under the Atlantic climate type. This could suggest that the southern Squalius species might be under stronger selective pressures due to the characteristics of the Mediterranean climate type, like high summer temperatures. We also found that the genes with signatures of selection were enriched on several biological functions, including blood coagulation, immunity, proteolysis, development and metabolism. Rather than having particular functions associated with specific branches of the phylogeny, most of the biological functions were generic and distributed similarly across species. This suggests that these biological functions have been consistently selected on the phylogeny. In conclusion, in this study we present a new approach to deal with alternative splicing on comparative studies using transcriptomic data, which can be useful for comparative studies on other species. We also present new transcriptomic data for two species of western Iberian Squalius – S. aradensis and the Tagus population of S. pyrenaicus. These results can be used as a resource for further studies on adaptation using the western Iberian Squalius as a model