7,183 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Using machine learning to predict pathogenicity of genomic variants throughout the human genome
GeschĂ€tzt mehr als 6.000 Erkrankungen werden durch VerĂ€nderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das SpleiĂen der mRNA in eine andere Isoform begĂŒnstigen. All diese Prozesse mĂŒssen ĂŒberprĂŒft werden, um die zum beschriebenen PhĂ€notyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer PathogenitĂ€t.
Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier prĂ€sentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst auĂerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores.
Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells fĂŒr das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. AuĂerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-SpleiĂing verbessern. AuĂerdem werden Varianteneffektmodelle aufgrund eines neuen, auf AllelhĂ€ufigkeit basierten, Trainingsdatensatz entwickelt.
Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfĂŒgbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity.
Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants.
The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency.
In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org
Multi-dimensional omics approaches to dissect natural immune control mechanisms associated with RNA virus infections
In recent decades, global health has been challenged by emerging and re-emerging
viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), human
immunodeficiency viruses (HIV-1), and CrimeanâCongo hemorrhagic fever virus (CCHFV).
Studies have shown dysregulations in the host metabolic processes against SARS-CoV2
and HIV-1 infections, and the research on CCHFV infection is still in the infant stage. Hence,
understanding the host metabolic re-programming on the reaction level in infectious
disease has therapeutic importance. The thesis uses systems biology methods to
investigate the host metabolic alterations in response to SARS-CoV2, HIV-1, and CCHFV
infections.
The three distinct viruses induce distinct effects on human metabolism that,
nevertheless, show some commonalities. We have identified alterations in various
immune cell types in patients during the infections of the three viruses. Further,
differential expression analysis identified that COVID-19 causes disruptions in pathways
related to antiviral response and metabolism (fructose mannose metabolism, oxidative
phosphorylation (OXPHOS), and pentose phosphate pathway). Up-regulation of OXPHOS
and ROS pathways with most changes in OXPHOS complexes I, III, and IV were identified
in people living with HIV on treatment (PLWHART). The acute phase of CCHFV infection is
found to be linked with OXPHOS, glycolysis, N-glycan biosynthesis, and NOD-like receptor
signaling pathways. The dynamic nature of the metabolic process and adaptive immune
response in CCHFV-pathogenesis are also observed.
Further, we have identified different metabolic flux in reactions transporting TCA cycle
intermediates from the cytosol to mitochondria in COVID-19 patients. Genes such as
monocarboxylate transporter (SLC16A6) and nucleoside transporter (SLC29A1) and
metabolites such as α-ketoglutarate, succinate, and malate were found to be linked with
COVID-19 disease response. Metabolic reactions associated with amino acid,
carbohydrate, and energy metabolism pathways and various transporter reactions were
observed to be uniquely disrupted in PLWHART along with increased production of αketoglutarate (αKG) and ATP molecules. Changes in essential (leucine and threonine) and
non-essential (arginine, alanine, and glutamine) amino acid transport were found to be
caused by acute CCHFV infection. The altered flux of reactions involving TCA cycle
compounds such as pyruvate, isocitrate, and alpha-ketoglutarate was also observed in
CCHFV infection.
The research described in the thesis displayed dysregulations in similar metabolic
processes against the three viral Infections. But further downstream analysis unveiled
unique alterations in several metabolic reactions specific to each virus in the same
metabolic pathways showing the importance of increasing the resolution of knowledge
about host metabolism in infectious diseases
Ciguatoxins
Ciguatoxins (CTXs), which are responsible for Ciguatera fish poisoning (CFP), are liposoluble toxins produced by microalgae of the genera Gambierdiscus and Fukuyoa. This book presents 18 scientific papers that offer new information and scientific evidence on: (i) CTX occurrence in aquatic environments, with an emphasis on edible aquatic organisms; (ii) analysis methods for the determination of CTXs; (iii) advances in research on CTX-producing organisms; (iv) environmental factors involved in the presence of CTXs; and (v) the assessment of public health risks related to the presence of CTXs, as well as risk management and mitigation strategies
Bio-inspired optimization in integrated river basin management
Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the riverâs ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM.
In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin.
Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices.
It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms
The influence of the Paris Agreement on mitigation actions toward the reduction of greenhouse gas emissions post 2015: A comparative study of Nordic, Asian and African regions
The Intergovernmental Panel on Climate Change (IPCC) has stepped up its warning on climate tipping points as scientists warn of the impending irrevocable disaster that will occur with continued emissions. Since the signing of the Paris Agreement in 2015, countries are encouraged to substantially reduce their greenhouse gas (GHG) emissions to limit the global temperature increase to 2oC and pursue efforts to limit global temperatures to 1.5oC. So, have countries adhered to the IPCC warnings by reducing emissions and does the international environmental regime (IER) have anything to do with their emissions-reductions efforts since 2015? To answer these questions, this thesis tracks the emissions reductions efforts of eight countries to determine whether the IER vis-Ă -vis the Paris Agreement and the United Nations Framework for Climate Change (UNFCCC) have influenced the emissions reduction effort in these countries. The eight countries are China, Denmark, Finland, India, Morocco, Nigeria, Norway and Sweden selected based on their emissions contributions, emissions reductions ambition and efforts since 2015.
Further, the significance of the IER has been interrogated for several decades in relation to major environmental concerns such as ozone layer depletion, biodiversity loss and climate change. The thesis responds to the current gap in the literature that has not addressed the influence of the Paris Agreement on emissions reductions efforts across four continents. Previous literature has examined other international environmental agreements (IEA) such as the Montreal Protocol and the Kyoto Protocol and have utilised parameters to measure the IERâs effectiveness. The thesis distinguishes by examining the influence of the Paris Agreement utilising the existing parameters proffered by various scholars such as compliance, enforcement, monitoring, problem structure and institutional design. The thesis also introduces new parameters that have not been used in the existing literature to analyse international environmental regime influence, such as political will subsumed under behavioural changes, equipping of environmental judges and climate litigation under the enforcement parameter, and NDC target review under the implementation parameter.
The thesis builds a conceptual framework using the green political theory and the regime theory as its pillars. These theories are best suited to the thesis as they support state and non-state engagement in environmental issues concerning the global commons. The thesis also relies on the Paris Agreementâs preamble that recognises the importance of all levels of government and various actors (corporate and non-state actors) to aid its analysis of the selected countriesâ engagement with emissions reduction.
The analysis of the selected countries reveals that their climate action benefited from cross-influences from the IER, regional environmental organisations (REOs) and non-state actors. The thesis found that there was significant IER influence in Morocco, India and Nigeria. The regime also moderately influenced Sweden, Norway, Denmark, Finland and China. In addition, the thesis found that REOs such as the European Union (EU), Economic Community of West African States (ECOWAS) and Association of Southeast Asian Nations (ASEAN) played a commendable role in encouraging emissions reductions efforts. Non-state actors also played a crucial role to pressure governments to act through climate litigation and protests.
The thesisâ significance lies in its ability to present an up-to-date view of the interplay among the IER, the REOs and other non-state actors in emissions reductions post-Paris 2015. In addition, new parameters as mentioned above, have been introduced that could be relevant in assessing the influence of future environmental regimes
Inferring ecological interactions from dynamics in phage-bacteria communities
Characterizing how viruses interact with microbial hosts is critical to understanding microbial community structure and function. However, existing methods for quantifying bacteria-phage interactions are not widely applicable to natural communities. First, many bacteria are not culturable, preventing direct experimental testing. Second, â-omicsâ based methods, while high in accuracy and specificity, have been shown to be extremely low in power. Third, inference methods based on time-series or co-occurrence data, while promising, have for the most part not been rigorously tested. This thesis work focuses on this final category of quantification strategies: inference methods.
In this thesis, we further our understanding of both the potential and limitations of several inference methods, focusing primarily on time-series data with high time resolution. We emphasize the quantification of efficacy by using time-series data from multi-strain bacteria-phage communities with known infection networks. We employ both in silico simulated bacteria-phage communities as well as an in vitro community experiment. We review existing correlation-based inference methods, extend theory and characterize tradeoffs for model-based inference which uses convex optimization, characterize pairwise interactions in a 5x5 virus-microbe community experiment using Markov chain Monte Carlo, and present analytic tools for microbiome time-series analysis when a dynamical model is unknown. Together, these chapters bridge gaps in existing literature in inference of ecological interactions from time-series data.Ph.D
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