MISSEL: a method to identify a large number of small species-specific genomic subsequences and its application to viruses classification

Continuous improvements in next generation sequencing technologies led to ever-increasing collections of genomic sequences, which have not been easily characterized by biologists, and whose analysis requires huge computational effort. The classification of species emerged as one of the main applications of DNA analysis and has been addressed with several approaches, e.g., multiple alignments-, phylogenetic trees-, statistical- and character-based methods

Agent-based modeling for environmental management. Case study: virus dynamics affecting Norwegian fish farming in fjords

Background: Norwegian fish-farming industry is an important industry, rapidly growing, and facing significant challenges such as the spread of pathogens1, trade-off between locations, fish production and health. There is a need for research, i.e. the development of theories (models), methods, techniques and tools for analysis, prediction and management, i.e. strategy development, policy design and decision making, to facilitate a sustainable industry. Loss due to the disease outbreaks in the aquaculture systems pose a large risk to a sustainable fish industry system, and pose a risk to the coastal and fjord ecosystem systems as a whole. Norwegian marine aquaculture systems are located in open areas (i.e. fjords) where they overlap and interact with other systems (e.g. transport, wild life, tourist, etc.). For instance, shedding viruses from aquaculture sites affect the wild fish in the whole fjord system. Fish disease spread and pathogen transmission in such complex systems, is process that it is difficult to predict, analyze, and control. There are several time-variant factors such as fish density, environmental conditions and other biological factors that affect the spread process. In this thesis, we developed methods to examine these factors on fish disease spread in fish populations and on pathogen spread in the time-space domain. Then we develop methods to control and manage the aquaculture system by finding optimal system settings in order to have a minimum infection risk and a high production capacity. Aim: The overall objective of the thesis is to develop agent-based models, methods and tools to facilitate the management of aquaculture production in Norwegian fjords by predicting the pathogen dynamics, distribution, and transmission in marine aquaculture systems. Specifically, the objectives are to assess agent-based modeling as an approach to understanding fish disease spread processes, to develop agent-based models that help us predict, analyze and understand disease dynamics in the context of various scenarios, and to develop a framework to optimize the location and the load of the aquaculture systems so as to minimize the infection risk in a growing fish industry. Methods: We use agent-based method to build models to simulate disease dynamics in fish populations and to simulate pathogen transmission between several aquaculture sites in a Norwegian fjord. Also, we use particle swarm optimization algorithm to identify agent-based models’ parameters so as to optimize the dynamics of the system model. In this context, we present a framework for using a particle swarm optimization algorithm to identify the parameter values of the agent-based model of aquaculture system that are expected to yield the optimal fish densities and farm locations that avoid the risk of spreading disease. The use of particle swarm optimization algorithm helps in identifying optimal agent-based models’ input parameters depending on the feedback from the agentbased models’ outputs. Results: As the thesis is built on three main studies, the results of the thesis work can be divided into three components. In the first study, we developed many agent-based models to simulate fish disease spread in stand-alone fish populations. We test the models in different scenarios by varying the agents (i.e. fish and pathogens) parameters, environment parameters (i.e. seawater temperature and currents), and interactions (interaction between agents-agents, and agents-environment) parameters. We use sensitivity analysis method to test different key input parameters such as fish density, fish swimming behavior, seawater temperature, and sea currents to show their effects on the disease spread process. Exploring the sensitivity of fish disease dynamics to these key parameters helps in combatting fish disease spread. In the second study, we build infection risk maps in a space-time domain, by developing agent-based models to identify the pathogen transmission patterns. The agent-based method helps us advance our understanding of pathogen transmission and builds risk maps to help us reduce the spread of infectious fish diseases. By using this method, we may study the spatial and dynamic aspects of the spread of infections and address the stochastic nature of the infection process. In the third study, we developed a framework for the optimization of the aquaculture systems. The framework uses particle swarm optimization algorithm to optimize agent-based models’ parameters so as to optimize the objective function. The framework was tested by developing a model to find optimal fish densities and farm locations in marine aquaculture system in a Norwegian fjord. Results show so that the rapid convergence of the presented particle swarm optimization algorithm to the optimal solution, - the algorithm requires a maximum of 18 iterations to find the best solution which can increase the fish density to three times while keeping the risk of infection at an accepted level. Conclusion: There are many contributions of this research work. First, we assessed the agent-based modeling as a method to simulate and analyze fish disease spread dynamics as a foundation for managing aquaculture systems. Results from this study demonstrate how effective the use of agentbased method is in the simulation of infectious diseases. By using this method, we are able to study spatial aspects of the spread of fish diseases and address the stochastic nature of infections process. Agent-based models are flexible, and they can include many external factors that affect fish disease dynamics such as interactions with wild fish and ship traffic. Agent-based models successfully help us to overcome the problem associated with lack of data in fish disease transmission and contribute to our understanding of different cause-effects relationships in the dynamics of fish diseases. Secondly, we developed methods to build infection risk maps in a space-time domain conditioned upon the identification of the pathogen transmission patterns in such a space-time domain, so as to help prevent and, if needed, combat infectious fish diseases by informing the management of the fish industry in Norway. Finally, we developed a method by which we may optimize the fish densities and farm locations of aquaculture systems so as to ensure a sustainable fish industry with a minimum risk of infection and a high production capacity. This PhD study offers new research-based approaches, models and tools for analysis, predictions and management that can be used to facilitate a sustainable development of the marine aquaculture industry with a maximal economic outcome and a minimal environmental impact

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

Serum extracellular vesicles profiling is associated with COVID-19 progression and immune responses

Coronavirus disease 2019 (COVID-19) has transformed very quickly into a world pandemic with severe and unexpected consequences on human health. Concerted efforts to generate better diagnostic and prognostic tools have been ongoing. Research, thus far, has primarily focused on the virus itself or the direct immune response to it. Here, we propose extracellular vesicles (EVs) from serum liquid biopsies as a new and unique modality to unify diagnostic and prognostic tools for COVID-19 analyses. EVs are a novel player in intercellular signalling particularly influencing immune responses. We herein show that innate and adaptive immune EVs profiling, together with SARS-CoV-2 Spike S1$^{+}$ EVs provide a novel signature for SARS-CoV-2 infection. It also provides a unique ability to associate the co-existence of viral and host cell signatures to monitor affected tissues and severity of the disease progression. And provide a phenotypic insight into COVID-associated EVs

New Approaches for Isolation and Characterization of Extracellular Vesicles

Extracellular vesicles (EVs) are membrane vesicles secreted by cells and distributed widely in all biofluids. Extracellular vesicles can modulate the biological activities of the recipient cells. Due to their role in intercellular communication, they are receiving attention for therapeutic and diagnostic applications. The first step to better understand EVs and to utilize them as therapeutic and diagnostic tools is to purify them from a variety of biofluids. Membranes have been extensively used for purification of different biological species from biological fluids. As the first aim, a novel microfluidic system, termed as tangential flow for analyte capture (TFAC) was developed to isolate nanoparticles and EVs using ultrathin nanomembranes. Ultrathin nanomembranes were found well-suited for TFAC system when compared with conventional thickness membranes. TFAC also proved feasible for capturing of EVs from undiluted plasma. Fluorescent labeling of EVs has been employed for studying uptake and biodistribution of EVs. However, far too little attention has been paid to the effect of the fluorescent labeling on the size of EVs. In the second aim, the effect of PKH labeling, the most commonly used dye, on the size of EVs was systematically evaluated by nanoparticle tracking analysis (NTA). PKH labeling did not preserve the size of EVs and caused a size increase in all the PKH labeling conditions tested. The observed size shift may alter the uptake and biodistribution of EVs, suggesting that PKH labeling is not a reliable technique. Precise quantification and characterization of EVs is an important step towards utilizing them as therapeutic and diagnostic tools. EVs have been analyzed using bulk techniques such as western blot which is challenging due to the heterogeneity of EVs. Therefore, a robust and well-established technique for quantification and characterization of individual EVs is required. As the third aim, the efficacy of a virus detection technology for EVs was evaluated. Virus Counter 3100 (VC3100) is a fluorescence-based technique with similar principles as flow cytometry and was purpose-built for detection of small nanoparticles such as viruses. Due to the similarity in size and density of viruses and EVs in many biofluids, it was hypothesized that the VC3100 could detect EVs similarly to flow cytometry characterization of cells. Fluorescently labeled EVs from different sources were successfully quantified by the VC3100. Furthermore, VC3100 was also used to determine the expression level of target protein markers. Therefore, VC3100 is a powerful technique for precise quantification and protein profiling of EVs

A Proposed Framework to Improve Diagnosis of Covid-19 Based on Patient’s Symptoms using Feature Selection Optimization

Recently, an epidemic called COVID-19 appeared, and it was one of the largest epidemics that affected the world in all economic, educational, health, and other aspects due to its rapid spread worldwide. The surge in infection rates made traditional diagnostic methods ineffective. Systems for automatic diagnosis and detection are crucial for controlling the outbreak. Other than PCR-RT, further diagnostic and detection techniques are needed. Individuals who receive positive test results often experience a range of symptoms, ranging from mild to severe, including coughing, fever, sore throats, and body pains. In more extreme cases, infected individuals may exhibit severe symptoms that make breathing challenging, ultimately leading to catastrophic organ failure. A hybrid approach called SDO-NMR-Hill has been developed for diagnosing COVID-19 based on a patient’s initial symptoms. This approach incorporates traits from three models, including two distinct feature selection optimization methods and a local search. Supply-demand optimization and the naked mole rat were preferred among metaheuristic methods because they have fewer parameters and a lower computing overhead, which can help you find superfluous and uninformative characteristics. Hill climbing was preferred among local search methods to maximize a criterion among several candidate solutions. We used decision trees, random forests, and adaptive boosting machine-learning classifiers in various experiments on three COVID-19 datasets. We carried out a natural selection of the classifier’s hyper-parameters to optimize outcomes. The optimal performance was attained using the adaptive boosting classifier, with an accuracy of 88.88% and 98.98% for the first and third datasets, respectively. The optimal performance for the second dataset was attained using the random forest classifier, with an accuracy of 97.97%. The suggested SDO-NMR-Hill model is evaluated using nine benchmark UCI datasets, and 15 different optimization techniques are contrasted

Platelet Diagnostics:A novel liquid biomarker

The aim of this thesis is to find a novel liquid biomarker for the detection of cancer and to optimize treatment. The first chapter gives an introduction to the oncology biomarker field and focuses on platelets and their role in cancer. In part 1, we evaluate extracellular vesicles (EVs). EVs are small vesicles released by all types of cells, including tumor cells, into the circulation. They carry protein kinases and can be isolated from plasma. We demonstrate that AKT and ERK kinase protein levels in EVs reflect the cellular expression levels and treatment with kinase inhibitors alters their concentration, depending on the clinical response to the drug. Therefore, EVs may provide a promising biomarker biosource for monitoring of treatment responses. Part 2 starts with reviews describing the function and role of platelets in greater depth. Chapter 3 focusses on thrombocytogenesis and several biological processes in which platelets play a role. Furthermore, the RNA processing machineries harboured by platelets are discussed. Both chapter 3 and 4 evaluate the change platelets undergo after being exposed to tumor and its environment. The exchange of biomolecules with tumor cells results in educated platelets, so-called tumor educated platelets (TEPs). TEPs play a role in several hallmarks of cancer and have the ability to respond to systemic alterations making them an interesting biomarker. In chapter 5 the diagnostic potential of platelets is first discussed. We determine their potential by sequencing the RNA of 283 platelet samples, of which 228 are patients with cancer, and 55 are healthy controls. We reach an accuracy of 96%. Furthermore, we are able to pinpoint the location of the primary tumor with an accuracy of 71%. In part 3, our developed thromboSeq platform is taken to the next level. Several potential confounding factors are taken into account such as age and comorbidity. We show that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels. In a validation cohort we apply these algorithms to non-small-cell lung cancer and reach an accuracy of 88% in late stage (n=518) and early-stage 81% accuracy. Finally, in chapter 7 we describe our wet- and dry-lab protocols in detail. This includes platelet RNA isolation, mRNA amplification, and preparation for next-generation sequencing. The dry-lab protocol describes the automated FASTQ file pre-processing to quantified gene counts, quality controls, data normalization and correction, and swarm intelligence-enhanced support vector machine (SVM) algorithm development. Part 4 focuses on central nervous system (CNS) malignancies especially on glioblastoma. Chapter 8 gives an overview of the different liquid biomarkers for diffuse glioma, the most common primary CNS malignancy. In chapter 9 we assess the specificity of the platelet education due to glioblastoma by comparing the RNA profile of TEPs from glioblastoma patients with a neuroinflammatory disease and brain metastasis patients. This results in a detection accuracy of 80%. Secondly, analysis of patients with glioblastoma versus healthy controls in an independent validation series provide a detection accuracy of 95%. Furthermore, we describe the potential value of platelets as a monitoring biomarker for patients with glioma, distinguishing pseudoprogression from real tumor progression. In part 5 thromboSeq is applied to breast cancer diagnostics both as a screening tool in the general population and in a high risk population, BRCA mutated women. In chapter 11 we first apply our technique to an inflammatory condition, multiple sclerosis (MS). Platelet RNA is used as input for the development of a diagnostic MS classifier capable of detecting MS with 80% accuracy in the independent validation series. In the final part we conclude this thesis with a general discussion of the main findings and suggestions for future research

Toward the isolation of exosomes by flow cytometry

In the last two decades the Extracellular Vesicles (EVs) field has attracted a lot of attention from the scientific community, especially after the discovery that EVs can shuttle functional proteins and nucleic acids between cells. Some recent studies have shown an association between tumorigenesis and increased exosomes production. Exosomes and their influence has also been reported in the establishment of new metastatic niches. Besides that, the EV field remains confusing due to numerous and ambiguous definitions, specially caused by the huge heterogeneity of the vesicles, both in composition and function. Extracellular vesicles are divided into microvesicles which are originated from the plasma membrane and exosomes which have an endosomal origin. For now, it is technically challenging to obtain a pure exosome fraction, free from non-vesicular components, due to the fact the extracellular milieu is quite complex and can contain microvesicles or apoptotic bodies similar in size and structure to exosomes. The two most used methods, ultracentrifugation and commercial kits, don’t show a good efficiency when distinguishing the exosomes fraction specifically from the microvesicles fraction. Due to this sub-optimal efficiency demonstrated by these two methods, we have decided to use Flow Cytometry to see if we can achieve better exosome purification. We will use Fluorescence-activated cell sorting (FACS) to purify endogenous exosomes. This would be quite challenging especially due to the exosomes size and heterogeneity but on the other hand, if we have success with our approach, it would be possible to do downstream analysis in order to know their protein composition, functions and elaborate some more studies to try to find some “exosome-specific” marker. This would have a huge impact in the pharmaceutical industry, both for diagnosis and therapy.Durante as últimas duas décadas, a investigação desenvolvida sobre Vesículas extracelulares (VE), atraíu o bastante interesse por parte da comunidade científica, especialmente após ter sido descoberto que as VE podem transportar proteínas funcinais e ácidos nucleicos entre diferentes células. Estudos mais recentes mostraram uma relação entre tumorogenese e um aumento na produção de exosomas. Estes foram também associados ao estabelecimento de novas metástases. Apesar de todas estas descobertas, o domínio das VE continua significativamente confuso, nomeadamente devido às numerosas e ambíguas definições utilizadas, especialmente devido ao facto da imensa heterogeneidade entre as diversas VE, tanto a nível de composição como de função. Vesículas extracellulares estão divididas em microvesículas, que são originárias da membrana plasmática, e exosomas que têm uma oigem endossomal. No presente, é tecnicamente bastante complicado de obter uma fracção de pura exosomas que não apresente componentes não vesiculares, principalmente pelo facto do meio extracellular ser bastante complexo e poder conter microvesícula e corpos apoptóticos semelhantes em termos de tamanho e estrutura. Os dois métodos mais usados, a ultracentrifugação e kits comerciais, não apresentam uma boa eficiência na distinção de exosomas, especialmente das microvesículas. Devido a esta eficiência sub-óptima demonstrada por estes dois métodos, decídimos usar a separação celular por citometria de fluxo (FACS) para proceder ao isolamento de exosomas endógenos. Este objectivo será bastante desafiador especialmente pelo tamanho e heterogeneidade dos exosomas mas, por outro lado, se formos suficientemente bem sucedidos na nossa abordagem, será possível realizar análises posteriores, de modo a conhecer a sua composição proteica, funções e partir para novos estudos de modo a tentar identificar um marcador molecular específico para exosomas. Isto teria um impacto significativo na indústria farmacêutica, tanto a nível de diagnóstico como terapêutico

Hybrid optimization techniques based automatic artificial respiration system for corona patient

Artificial ventilation is widely used for various respiratory problems of human beings. The oxygen level of the corona patients has to be maintained for smooth breathing which is very difficult. For achieving this state, the air pressure should be controlled in the respiration system that has a piston mechanism driven by a motor. An Automatic respiration system model is designed and controller parameters are tuned using hybrid Optimization techniques. Hybrid Controllers like genetic algorithm based Fractional Order Proportional Integral Derivative controller (FOPID), Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller, and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) controllers were designed and verified. Integral Square Error is considered as the objective function of the optimization technique to find the controller parameters. The output responses of all three hybrid controllers are compared based on the error indices, time domain specifications, set-point tracking and Convergence speed graph. The genetic algorithm-based FOPID controller gives better results when compared with the Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) for the proposed artificial ventilation system