3,364 research outputs found

    Characterization and Bioanalysis of Protein-Based Biopharmaceuticals, Peptides and Amino Acids by Liquid Chromatography and Mass Spectrometry

    Get PDF
    Biopharmazeutika sind zu einer essenziellen Klasse von Therapeutika geworden und werden für verschiedene medizinische Indikationen wie Diabetes, Krebs, entzündliche Erkrankungen und Infektionskrankheiten eingesetzt. Monoklonale Antikörper (mAbs) haben innerhalb der Biopharmazeutika den größten Anteil bezogen auf die Zulassungszahlen. Den Vorteilen bezüglich hoher Spezifität und Effektivität stehen jedoch Nachteile durch hohe Kosten und erhöhter Komplexität gegenüber. Die Komplexität ergibt sich einerseits aufgrund des hohen Molekulargewichts und anderseits aufgrund der strukturellen Heterogenität, wodurch die analytische Charakterisierung und Qualitätskontrolle von mAbs und anderer Biopharmazeutika zu einer Herausforderung wird. Neben diesen protein-basierten Biopharmazeutika ist auch die Aufklärung der absoluten Konfiguration von therapeutischen und natürlichen (Lipo)peptiden von besonderem Interesse für die Wirkstoffforschung. Zur Bewältigung dieser Herausforderungen wurden in der hier präsentierten Arbeit flüssigchromatographische (LC) und massenspektrometrische (MS) Methoden für die umfassende Analyse eingesetzt. Die erste Publikation dieser Dissertation bezog sich auf die Analyse von Ladungsvarianten von mAbs, welche wichtige Qualitätsmerkmale darstellen und die Sicherheit und Wirksamkeit des Arzneimittels beeinflussen können. Zur Charakterisierung der Ladungsvarianten wurden die mAbs auf Ebene des intakten Proteins als auch auf Fragmentebene nach begrenztem Verdau und Reduzierung der Disulfidbrücken mittels starker Kationenaustauschflüssigkeitschromatographie (SCX) analysiert. Die SCX-Methode wurde systematisch mittels statistischer Versuchsplanung (DoE) dahingehend optimiert, die höchstmögliche Anzahl an Ladungsvarianten zu trennen. Die mobile Phase der optimierten SCX-Methode enthielt jedoch eine hohe Konzentration an nicht-flüchtigen Salzen, wodurch sie nicht mit MS Detektion kompatibel ist, welche wiederum entscheidend für die Identifikation der Ladungsvarianten ist. Um dieser Herausforderung zu begegnen, wurde erfolgreich eine online zweidimensionale flüssigchromatographische (2D-LC) Methode entwickelt, bei der SCX in der ersten Trenndimension und Umkehrphasenflüssigchromatographie (RP-LC) in der zweiten Trenndimension zur Entsalzung vor der MS Detektion verwendet wurde. Die Entwicklung einer extrem kurzen (≤ 1 min) RP-LC Methode war unabdingbar zur Etablierung einer umfassenden 2D-LC Methode. Dazu wurde eine Säulenvergleichsstudie mit monolithischen und oberflächlich porösen Partikelsäulen (SPP-Säulen) durchgeführt und die Trenneffizienz sowie die Analysengeschwindigkeit untersucht. Eine noch umfassendere Säulenvergleichsstudie mit Fokus auf das kinetische Leistungsvermögen wurde in der zweiten Arbeit dieser Dissertation durchgeführt. Eine Auswahl von 13 RP-Proteintrennsäulen inklusive monolithischer, SPP und vollporöser Partikelsäulen (FPP-Säulen) wurde hinsichtlich ihrer Fähigkeit, Peaks in der kürzest möglichen Zeit zu trennen, untersucht. Es konnte gezeigt werden, dass SPP-Säulen mit einer Porengröße von etwa 400 Å und einer dünnen, porösen Schicht die beste Performance insbesondere für größere Proteinen besitzen. Proteine selbst können auch potenzielle Ziele für Arzneistoffe sein, wie z.B. das Tumorsuppressorprotein p53, welches in der dritten Publikation dieser Arbeit untersucht wurde. Intakte Protein LC-MS wurde erfolgreich verwendet, um die Bindungseffizienz und -spezifität des kovalenten Inhibitors an p53 nachzuweisen. Aminosäuren sind die Bausteine von Proteinen und Peptiden und die Mehrheit dieser Aminosäuren sind chiral. Die biologische Aktivität ist in der Regel abhängig von der absoluten Konfiguration der Aminosäuren, wodurch die enantiomerenselektive Analyse von höchster Wichtigkeit für die Strukturaufklärung und zur Qualitätskontrolle ist. Daher war die Entwicklung schneller und umfassender Trennmethoden zur Analyse von Aminosäuren, deren Enantiomeren, Diastereomeren und konstitutionellen Isomeren ein Ziel dieser Arbeit. Dieses konnte durch Derivatisierung mittels 6-Aminochinolyl-N-hydroxysuccinimidylcarbamat (AQC) und anschließender Analyse durch enantioselektiver flüssigchromatographischer Ionenmobilitäts-Massenspektrometrie (LC-IM-MS) erreicht werden. Eine sehr schnelle dreiminütige Analysenmethode konnte entwickelt und zur Strukturaufklärung von therapeutischen Peptiden und eines natürlichen Lipopeptides eingesetzt werden. Die absolute Konfiguration eines Tetrapeptides als Bestandteil des natürlichen, antimikrobiellen Peptidpolyens‘ Epifadin konnte mittels chiraler LC-MS bestimmt werden, was wiederum entscheidend für die Strukturaufklärung war. In dieser Arbeit konnten alle acht Enantiomerenpaare erfolgreich getrennt werden und die Diastereomerentrennung wurde optimiert.Biopharmaceuticals have become an essential class of therapeutics and are used for different medical indications such as diabetes, cancer, inflammatory diseases, and infectious diseases. Monoclonal antibodies (mAbs) have the biggest share within the biopharmaceuticals regarding the drug approval numbers. However, the benefits in terms of high specificity and efficacy come with the drawback of higher cost and higher complexity. This complexity arises from the high molecular weight on the one hand and high structural heterogeneity on the other hand, making the analytical characterization and quality control of mAbs and other biopharmaceuticals a significant challenge. In addition to these protein-based biopharmaceuticals, the elucidation of the absolute configuration of therapeutic peptides and natural (lipo)peptides is also of particular interest for drug discovery. To address these challenges, different liquid chromatography (LC) and mass spectrometric (MS) methods were used for the more comprehensive analysis in the presented work. The first publication of this dissertation was dedicated to the analysis of charge variants of mAbs, which is an important quality attribute that might affect safety and efficacy of the drug product. To characterize the charge variants, the mAbs were analysed at the intact protein level and the subunit level after limited digestion and disulphide reduction using strong cation-exchange chromatography (SCX). The SCX method was systematically optimized to enable the separation of the maximum number of charge variants using a design of experiments (DoE) approach. The optimized SCX mobile phase, however, contains high concentrations of non-volatile salt in the mobile phase, which is incompatible with MS detection. On the other hand, MS analysis is essential for the identification of the charge variants. To overcome this limitation, an online two-dimensional liquid chromatographic (2D-LC) method was successfully developed, which uses SCX in the first separation dimension and reversed-phase (RP) LC in the second separation dimension, which can be used for de-salting prior MS analysis. An ultra-short analysis time (≤ 1 min) of the second dimension RP method was essential to establish a full comprehensive 2D-LC analysis. For this purpose, a column comparison study was performed using a set of monolithic and superficially porous particle (SPP) columns, and the separation efficiency and analysis speed were investigated. An even more comprehensive column comparison study focusing on the kinetic performance was done for the second work presented in this dissertation. A set of 13 RP protein separation columns including monolithic, SPP, and fully porous particle (FPP) columns was investigated regarding their capability to separate peaks in the shortest possible time. It could be demonstrated that SPP columns with a pore size of 400 Å and a thin, porous shell provided the best performance especially for large proteins such as mAbs. Proteins themselves can also be the potential targets of drug products such as the tumour suppressor protein p53 studied in publication III. Intact protein LC-MS was successfully used to investigate the binding efficiency and specificity of covalent inhibitors. Amino acids are the building blocks of proteins and peptides and most of these amino acids are chiral. As the biological activity is usually dependent on the absolute configuration of the amino acids, the enantioselective analysis is of utmost importance for structural elucidation and quality control. Therefore, one goal of the presented work was to develop a fast and comprehensive method to separate amino acids, their enantiomers, diastereomers, and constitutional isomers. This was achieved by derivatization using 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) and subsequent analysis by enantioselective liquid chromatography ion mobility-mass spectrometry (LC-IM-MS). A very fast three minutes short analysis method could be developed and was applied for the successful structure elucidation of a therapeutic peptide and a natural lipopeptide. The absolute configuration of a tetrapeptide originating from the natural antimicrobial peptide-polyene epifadin could be determined using chiral LC-MS, which was crucial for the structure elucidation. In this work, all eight enantiomer peak pairs could be successfully separated and the separation of the diastereomers was optimized

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments

    Get PDF
    In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident. In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion. This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture. Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data. As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis

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

    Get PDF
    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

    Simulation and validation studies of a large drift tube Muon Tracker

    Get PDF
    Cosmic ray muons are massive, charged particles created from high energy cosmic rays colliding with atomic nuclei in Earth’s atmosphere. Because of their high momenta and weak interaction, these muons can penetrate through large thicknesses of dense material before being absorbed, making them ideal for nondestructive imaging of objects composed of high-Z elements. A Giant Muon Tracker with two horizontal 8 × 6 in.2 and two vertical 6 × 6 in.2 modules of drift tubes was used to measure muon tracks passing through samples placed inside the detector volume. The experimental results were used to validate a Monte Carlo simulation of the Giant Muon Tracker. The imaging results of simulated samples were reconstructed and compared with those from the experiment, which showed excellent agreement

    Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging

    Get PDF
    No abstract available

    Numerical simulation of surfactant flooding with relative permeability estimation using inversion method

    Get PDF
    Surfactant flooding attracts significant interest in the hydrocarbon industry, with a definite promise to improve oil recovery from depleting oil reserves. In this thesis, surfactant flooding is the primary area of focus as it has significant potential for integration with other chemical enhanced oil recovery techniques, including polymer, nanofluid, alkali, and foam. This combined approach has the potential to reduce interfacial tension to ultralow levels, decrease adsorption, and offer other benefits. However, due to the various mechanism, surfactant flooding poses a more complex model for simulators by encountering numerical issues (e.g., the appearance of spurious oscillations, erratic pulses, and numerical instabilities), rendering the methods ineffective. To address these challenges, the analytical modelling technique of surfactant flooding was studied, leading to the development of a novel inversion method in the MATLAB programming environment. Numerical accuracy issues were discovered in 1D models that used typical cell sizes found in well-scale models, leading to pulses in the oil bank and a dip in water saturation, particularly for low levels of adsorption, highlighting the need for more refined models. Based on these findings, we examined the surfactant flooding technique in 2D models to recover viscous oil in short reservoir aspect ratios. Instabilities such as viscous fingering and gravity tongue were observed on the flood fronts, and the magnitude of the viscous fingers was influenced by vertical dispersion, resulting in errors in computed mobility values at the fronts. Interestingly, introducing heterogeneity only minimally affected the spreading of the front and did not significantly impact viscous fingering or numerical artifacts. To optimize the nonlinearity of flow behaviour and degree of mobility control at the fronts, a homogenous model was considered to develop the inversion method. In summary, the developed inversion method accurately estimated the two-phase relative permeability curves, which were validated using fractional flow theory. The precision of the inverted curves was further improved using the optimization algorithm, demonstrating the method's ability to predict outcomes closer to the observed values for 2D models with instabilities. The obtained results are of significant value for core flood analysis, interpretation, matching, and upscaling, providing insights into the potential of surfactant flooding for enhanced oil recovery. Additionally, the use of the developed MATLAB Scripts promotes open innovation and reproducibility, contributing to the benchmarking, analytical, and numerical method development exercises for tutorials aimed at improving the overall understanding of surfactant flooding

    Undergraduate Catalog of Studies, 2022-2023

    Get PDF

    20th SC@RUG 2023 proceedings 2022-2023

    Get PDF
    corecore