2,711 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Highly heterogenous humoral immune response in Lyme disease patients revealed by broad machine learning-assisted antibody binding profiling with random peptide arrays

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    IntroductionLyme disease (LD), a rapidly growing public health problem in the US, represents a formidable challenge due to the lack of detailed understanding about how the human immune system responds to its pathogen, the Borrelia burgdorferi bacterium. Despite significant advances in gaining deeper insight into mechanisms the pathogen uses to evade immune response, substantial gaps remain. As a result, molecular tools for the disease diagnosis are lacking with the currently available tests showing poor performance. High interpersonal variability in immune response combined with the ability of the pathogen to use a number of immune evasive tactics have been implicated as underlying factors for the limited test performance.MethodsThis study was designed to perform a broad profiling of the entire repertoire of circulating antibodies in human sera at the single-individual level using planar arrays of short linear peptides with random sequences. The peptides sample sparsely, but uniformly the entire combinatorial sequence space of the same length peptides for profiling the humoral immune response to a B.burg. infection and compare them with other diseases with etiology similar to LD and healthy controls.ResultsThe study revealed substantial variability in antibody binding profiles between individual LD patients even to the same antigen (VlsE protein) and strong similarity between individuals diagnosed with Lyme disease and healthy controls from the areas endemic to LD suggesting a high prevalence of seropositivity in endemic healthy control.DiscussionThis work demonstrates the utility of the approach as a valuable analytical tool for agnostic profiling of humoral immune response to a pathogen

    Climate Change and Critical Agrarian Studies

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    Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution — as well as industrialisation under state-led socialism — the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people — in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation — are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change – and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial

    Machine Learning Model for Repurposing Drugs to Target Viral Diseases

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    With recent events, such as the Covid-19 pandemic, it is increasingly important to develop strategies to combat viral diseases. Due to technological advancements, computer-aided drug design and machine learning (ML)-based hit identification strategies have gained popularity. Applying these techniques to identify novel scaffolds and/or repurpose existing therapeutics for viral diseases is a promising approach. As an avenue to improve existing classification models for antiviral applications, this thesis aimed to make improvements to non-binding data selection within these models. We created a classification model using molecular fingerprints to assess the performance of machine learning predictions when the model is trained using randomly selected and rationally selected non-binding datasets. Our analyses revealed that machine learning predictions can be improved using a rational selection approach. We further used this approach and trained three machine learning models based on XGBoost, Random Forest, and Support Vector Machine to predict potential inhibitors for the SARS-CoV2 main protease (Mpro) enzyme. Probability-ranked hits from the combined model were further analyzed using classical structure-based methods. The binding modes and affinities of the hits were identified using AutoDock Vina, and molecular dynamics simulations-enabled MM-GBSA calculations. The top hits identified from this multi-step screening approach revealed potential candidates that show improved affinity and stability than existing non-covalent Mpro inhibitors. Thus, our approach and the model could be useful for screening large ligand libraries

    Investigation of the metabolism of rare nucleotides in plants

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    Nucleotides are metabolites involved in primary metabolism, and specialized metabolism and have a regulatory role in various biochemical reactions in all forms of life. While in other organisms, the nucleotide metabolome was characterized extensively, comparatively little is known about the cellular concentrations of nucleotides in plants. The aim of this dissertation was to investigate the nucleotide metabolome and enzymes influencing the composition and quantities of nucleotides in plants. For this purpose, a method for the analysis of nucleotides and nucleosides in plants and algae was developed (Chapter 2.1), which comprises efficient quenching of enzymatic activity, liquid-liquid extraction and solid phase extraction employing a weak-anionexchange resin. This method allowed the analysis of the nucleotide metabolome of plants in great depth including the quantification of low abundant deoxyribonucleotides and deoxyribonucleosides. The details of the method were summarized in an article, serving as a laboratory protocol (Chapter 2.2). Furthermore, we contributed a review article (Chapter 2.3) that summarizes the literature about nucleotide analysis and recent technological advances with a focus on plants and factors influencing and hindering the analysis of nucleotides in plants, i.e., a complex metabolic matrix, highly stable phosphatases and physicochemical properties of nucleotides. To analyze the sub-cellular concentrations of metabolites, a protocol for the rapid isolation of highly pure mitochondria utilizing affinity chromatography was developed (Chapter 2.4). The method for the purification of nucleotides furthermore contributed to the comprehensive analysis of the nucleotide metabolome in germinating seeds and in establishing seedlings of A. thaliana, with a focus on genes involved in the synthesis of thymidilates (Chapter 2.5) and the characterization of a novel enzyme of purine nucleotide degradation, the XANTHOSINE MONOPHOSPHATE PHOSPHATASE (Chapter 2.6). Protein homology analysis comparing A. thaliana, S. cerevisiae, and H. sapiens led to the identification and characterization of an enzyme involved in the metabolite damage repair system of plants, the INOSINE TRIPHOSPHATE PYROPHOSPHATASE (Chapter 2.7). It was shown that this enzyme dephosphorylates deaminated purine nucleotide triphosphates and thus prevents their incorporation into nucleic acids. Lossof-function mutants senesce early and have a constitutively increased content of salicylic acid. Also, the source of deaminated purine nucleotides in plants was investigated and it was shown that abiotic factors contribute to nucleotide damage.Nukleotide sind Metaboliten, die am Primärstoffwechsel und an spezialisierten Stoffwechselvorgängen beteiligt sind und eine regulierende Rolle bei verschiedenen biochemischen Reaktionen in allen Lebensformen spielen. Während bei anderen Organismen das Nukleotidmetabolom umfassend charakterisiert wurde, ist in Pflanzen vergleichsweise wenig über die zellulären Konzentrationen von Nukleotiden bekannt. Ziel dieser Dissertation war es, das Nukleotidmetabolom und die Enzyme zu untersuchen, die die Zusammensetzung und Menge der Nukleotide in Pflanzen beeinflussen. Zu diesem Zweck wurde eine Methode zur Analyse von Nukleotiden und Nukleosiden in Pflanzen und Algen entwickelt (Kapitel 2.1), die ein effizientes Stoppen enzymatischer Aktivität, eine Flüssig-Flüssig-Extraktion und eine Festphasenextraktion unter Verwendung eines schwachen Ionenaustauschers umfasst. Mit dieser Methode konnte das Nukleotidmetabolom von Pflanzen eingehend analysiert werden, einschließlich der Quantifizierung von Desoxyribonukleotiden und Desoxyribonukleosiden mit geringer Abundanz. Die Einzelheiten der Methode wurden in einem Artikel zusammengefasst, der als Laborprotokoll dient (Kapitel 2.2). Darüber hinaus wurde ein Übersichtsartikel (Kapitel 2.3) verfasst, der die Literatur über die Analyse von Nukleotiden und die jüngsten technologischen Fortschritte zusammenfasst. Der Schwerpunkt lag hierbei auf Pflanzen und Faktoren, die die Analyse von Nukleotiden in Pflanzen beeinflussen oder behindern, d. h. eine komplexe Matrix, hochstabile Phosphatasen und physikalisch-chemische Eigenschaften von Nukleotiden. Um die subzellulären Konzentrationen von Metaboliten zu analysieren, wurde ein Protokoll für die schnelle Isolierung hochreiner Mitochondrien unter Verwendung einer Affinitätschromatographie entwickelt (Kapitel 2.4). Die Methode zur Analyse von Nukleotiden trug außerdem zu einer umfassenden Analyse des Nukleotidmetaboloms in keimenden Samen und in sich etablierenden Keimlingen von A. thaliana bei, wobei der Schwerpunkt auf Genen lag, die an der Synthese von Thymidilaten beteiligt sind (Kapitel 2.5), sowie zu der Charakterisierung eines neuen Enzyms des Purinnukleotidabbaus, der XANTHOSINE MONOPHOSPHATE PHOSPHATASE (Kapitel 2.6). Eine Proteinhomologieanalyse, die A. thaliana, S. cerevisiae und H. sapiens miteinander verglich führte zur Identifizierung und Charakterisierung eines Enzyms, das an der Reparatur von geschädigten Metaboliten in Pflanzen beteiligt ist, der INOSINE TRIPHOSPHATE PYROPHOSPHATASE (Kapitel 2.7). Es konnte gezeigt werden, dass dieses Enzym desaminierte Purinnukleotidtriphosphate dephosphoryliert und so deren Einbau in Nukleinsäuren verhindert. Funktionsverlustmutanten altern früh und weisen einen konstitutiv erhöhten Gehalt an Salicylsäure auf. Außerdem wurde die Quelle der desaminierten Purinnukleotide in Pflanzen untersucht, und es wurde gezeigt, dass abiotische Faktoren zur Nukleotidschädigung beitragen

    Using Machine Learning in Forestry

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    Advanced technology has increased demands and needs for innovative approaches to apply traditional methods more economically, effectively, fast and easily in forestry, as in other disciplines. Especially recently emerging terms such as forestry informatics, precision forestry, smart forestry, Forestry 4.0, climate-intelligent forestry, digital forestry and forestry big data have started to take place on the agenda of the forestry discipline. As a result, significant increases are observed in the number of academic studies in which modern approaches such as machine learning and recently emerged automatic machine learning (AutoML) are integrated into decision-making processes in forestry. This study aims to increase further the comprehensibility of machine learning algorithms in the Turkish language, to make them widespread, and be considered a resource for researchers interested in their use in forestry. Thus, it was aimed to bring a review article to the national literature that reveals both how machine learning has been used in various forestry activities from the past to the present and its potential for use in the future

    Data- og ekspertdreven variabelseleksjon for prediktive modeller i helsevesenet : mot økt tolkbarhet i underbestemte maskinlæringsproblemer

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    Modern data acquisition techniques in healthcare generate large collections of data from multiple sources, such as novel diagnosis and treatment methodologies. Some concrete examples are electronic healthcare record systems, genomics, and medical images. This leads to situations with often unstructured, high-dimensional heterogeneous patient cohort data where classical statistical methods may not be sufficient for optimal utilization of the data and informed decision-making. Instead, investigating such data structures with modern machine learning techniques promises to improve the understanding of patient health issues and may provide a better platform for informed decision-making by clinicians. Key requirements for this purpose include (a) sufficiently accurate predictions and (b) model interpretability. Achieving both aspects in parallel is difficult, particularly for datasets with few patients, which are common in the healthcare domain. In such cases, machine learning models encounter mathematically underdetermined systems and may overfit easily on the training data. An important approach to overcome this issue is feature selection, i.e., determining a subset of informative features from the original set of features with respect to the target variable. While potentially raising the predictive performance, feature selection fosters model interpretability by identifying a low number of relevant model parameters to better understand the underlying biological processes that lead to health issues. Interpretability requires that feature selection is stable, i.e., small changes in the dataset do not lead to changes in the selected feature set. A concept to address instability is ensemble feature selection, i.e. the process of repeating the feature selection multiple times on subsets of samples of the original dataset and aggregating results in a meta-model. This thesis presents two approaches for ensemble feature selection, which are tailored towards high-dimensional data in healthcare: the Repeated Elastic Net Technique for feature selection (RENT) and the User-Guided Bayesian Framework for feature selection (UBayFS). While RENT is purely data-driven and builds upon elastic net regularized models, UBayFS is a general framework for ensembles with the capabilities to include expert knowledge in the feature selection process via prior weights and side constraints. A case study modeling the overall survival of cancer patients compares these novel feature selectors and demonstrates their potential in clinical practice. Beyond the selection of single features, UBayFS also allows for selecting whole feature groups (feature blocks) that were acquired from multiple data sources, as those mentioned above. Importance quantification of such feature blocks plays a key role in tracing information about the target variable back to the acquisition modalities. Such information on feature block importance may lead to positive effects on the use of human, technical, and financial resources if systematically integrated into the planning of patient treatment by excluding the acquisition of non-informative features. Since a generalization of feature importance measures to block importance is not trivial, this thesis also investigates and compares approaches for feature block importance rankings. This thesis demonstrates that high-dimensional datasets from multiple data sources in the medical domain can be successfully tackled by the presented approaches for feature selection. Experimental evaluations demonstrate favorable properties of both predictive performance, stability, as well as interpretability of results, which carries a high potential for better data-driven decision support in clinical practice.Moderne datainnsamlingsteknikker i helsevesenet genererer store datamengder fra flere kilder, som for eksempel nye diagnose- og behandlingsmetoder. Noen konkrete eksempler er elektroniske helsejournalsystemer, genomikk og medisinske bilder. Slike pasientkohortdata er ofte ustrukturerte, høydimensjonale og heterogene og hvor klassiske statistiske metoder ikke er tilstrekkelige for optimal utnyttelse av dataene og god informasjonsbasert beslutningstaking. Derfor kan det være lovende å analysere slike datastrukturer ved bruk av moderne maskinlæringsteknikker for å øke forståelsen av pasientenes helseproblemer og for å gi klinikerne en bedre plattform for informasjonsbasert beslutningstaking. Sentrale krav til dette formålet inkluderer (a) tilstrekkelig nøyaktige prediksjoner og (b) modelltolkbarhet. Å oppnå begge aspektene samtidig er vanskelig, spesielt for datasett med få pasienter, noe som er vanlig for data i helsevesenet. I slike tilfeller må maskinlæringsmodeller håndtere matematisk underbestemte systemer og dette kan lett føre til at modellene overtilpasses treningsdataene. Variabelseleksjon er en viktig tilnærming for å håndtere dette ved å identifisere en undergruppe av informative variabler med hensyn til responsvariablen. Samtidig som variabelseleksjonsmetoder kan lede til økt prediktiv ytelse, fremmes modelltolkbarhet ved å identifisere et lavt antall relevante modellparametere. Dette kan gi bedre forståelse av de underliggende biologiske prosessene som fører til helseproblemer. Tolkbarhet krever at variabelseleksjonen er stabil, dvs. at små endringer i datasettet ikke fører til endringer i hvilke variabler som velges. Et konsept for å adressere ustabilitet er ensemblevariableseleksjon, dvs. prosessen med å gjenta variabelseleksjon flere ganger på en delmengde av prøvene i det originale datasett og aggregere resultater i en metamodell. Denne avhandlingen presenterer to tilnærminger for ensemblevariabelseleksjon, som er skreddersydd for høydimensjonale data i helsevesenet: "Repeated Elastic Net Technique for feature selection" (RENT) og "User-Guided Bayesian Framework for feature selection" (UBayFS). Mens RENT er datadrevet og bygger på elastic net-regulariserte modeller, er UBayFS et generelt rammeverk for ensembler som muliggjør inkludering av ekspertkunnskap i variabelseleksjonsprosessen gjennom forhåndsbestemte vekter og sidebegrensninger. En case-studie som modellerer overlevelsen av kreftpasienter sammenligner disse nye variabelseleksjonsmetodene og demonstrerer deres potensiale i klinisk praksis. Utover valg av enkelte variabler gjør UBayFS det også mulig å velge blokker eller grupper av variabler som representerer de ulike datakildene som ble nevnt over. Kvantifisering av viktigheten av variabelgrupper spiller en nøkkelrolle for forståelsen av hvorvidt datakildene er viktige for responsvariablen. Tilgang til slik informasjon kan føre til at bruken av menneskelige, tekniske og økonomiske ressurser kan forbedres dersom informasjonen integreres systematisk i planleggingen av pasientbehandlingen. Slik kan man redusere innsamling av ikke-informative variabler. Siden generaliseringen av viktighet av variabelgrupper ikke er triviell, undersøkes og sammenlignes også tilnærminger for rangering av viktigheten til disse variabelgruppene. Denne avhandlingen viser at høydimensjonale datasett fra flere datakilder fra det medisinske domenet effektivt kan håndteres ved bruk av variabelseleksjonmetodene som er presentert i avhandlingen. Eksperimentene viser at disse kan ha positiv en effekt på både prediktiv ytelse, stabilitet og tolkbarhet av resultatene. Bruken av disse variabelseleksjonsmetodene bærer et stort potensiale for bedre datadrevet beslutningsstøtte i klinisk praksis
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