3,906 research outputs found
Gabriel Harvey and the History of Reading: Essays by Lisa Jardine and others
Few articles in the humanities have had the impact of Lisa Jardine and Anthony Graftonâs seminal âStudied for Actionâ (1990), a study of the reading practices of Elizabethan polymath and prolific annotator Gabriel Harvey. Their excavation of the setting, methods and ambitions of Harveyâs encounters with his books ignited the History of Reading, an interdisciplinary field which quickly became one of the most exciting corners of the scholarly cosmos. A generation inspired by the model of Harvey fanned out across the worldâs libraries and archives, seeking to reveal the many creative, unexpected and curious ways that individuals throughout history responded to texts, and how these interpretations in turn illuminate past worlds.
Three decades on, Harveyâs example and Jardineâs work remain central to cutting-edge scholarship in the History of Reading. By uniting âStudied for Actionâ with published and unpublished studies on Harvey by Jardine, Grafton and the scholars they have influenced, this collection provides a unique lens on the place of marginalia in textual, intellectual and cultural history. The chapters capture subsequent work on Harvey and map the fields opened by Jardine and Graftonâs original article, collectively offering a posthumous tribute to Lisa Jardine and an authoritative overview of the History of Reading
Language Design for Reactive Systems: On Modal Models, Time, and Object Orientation in Lingua Franca and SCCharts
Reactive systems play a crucial role in the embedded domain. They continuously interact with their environment, handle concurrent operations, and are commonly expected to provide deterministic behavior to enable application in safety-critical systems. In this context, language design is a key aspect, since carefully tailored language constructs can aid in addressing the challenges faced in this domain, as illustrated by the various concurrency models that prevent the known pitfalls of regular threads. Today, many languages exist in this domain and often provide unique characteristics that make them specifically fit for certain use cases. This thesis evolves around two distinctive languages: the actor-oriented polyglot coordination language Lingua Franca and the synchronous statecharts dialect SCCharts. While they take different approaches in providing reactive modeling capabilities, they share clear similarities in their semantics and complement each other in design principles. This thesis analyzes and compares key design aspects in the context of these two languages. For three particularly relevant concepts, it provides and evaluates lean and seamless language extensions that are carefully aligned with the fundamental principles of the underlying language. Specifically, Lingua Franca is extended toward coordinating modal behavior, while SCCharts receives a timed automaton notation with an efficient execution model using dynamic ticks and an extension toward the object-oriented modeling paradigm
Characterization and Bioanalysis of Protein-Based Biopharmaceuticals, Peptides and Amino Acids by Liquid Chromatography and Mass Spectrometry
Biopharmazeutika sind zu einer essenziellen Klasse von Therapeutika geworden und werden fĂŒr verschiedene medizinische Indikationen wie Diabetes, Krebs, entzĂŒndliche Erkrankungen und Infektionskrankheiten eingesetzt. Monoklonale Antikörper (mAbs) haben innerhalb der Biopharmazeutika den gröĂten Anteil bezogen auf die Zulassungszahlen. Den Vorteilen bezĂŒglich hoher SpezifitĂ€t und EffektivitĂ€t stehen jedoch Nachteile durch hohe Kosten und erhöhter KomplexitĂ€t gegenĂŒber. Die KomplexitĂ€t ergibt sich einerseits aufgrund des hohen Molekulargewichts und anderseits aufgrund der strukturellen HeterogenitĂ€t, wodurch die analytische Charakterisierung und QualitĂ€tskontrolle von mAbs und anderer Biopharmazeutika zu einer Herausforderung wird. Neben diesen protein-basierten Biopharmazeutika ist auch die AufklĂ€rung der absoluten Konfiguration von therapeutischen und natĂŒrlichen (Lipo)peptiden von besonderem Interesse fĂŒr die Wirkstoffforschung.
Zur BewĂ€ltigung dieser Herausforderungen wurden in der hier prĂ€sentierten Arbeit flĂŒssigchromatographische (LC) und massenspektrometrische (MS) Methoden fĂŒr die umfassende Analyse eingesetzt. Die erste Publikation dieser Dissertation bezog sich auf die Analyse von Ladungsvarianten von mAbs, welche wichtige QualitĂ€tsmerkmale 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 DisulfidbrĂŒcken mittels starker KationenaustauschflĂŒssigkeitschromatographie (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-flĂŒchtigen Salzen, wodurch sie nicht mit MS Detektion kompatibel ist, welche wiederum entscheidend fĂŒr die Identifikation der Ladungsvarianten ist. Um dieser Herausforderung zu begegnen, wurde erfolgreich eine online zweidimensionale flĂŒssigchromatographische (2D-LC) Methode entwickelt, bei der SCX in der ersten Trenndimension und UmkehrphasenflĂŒssigchromatographie (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 SĂ€ulenvergleichsstudie mit monolithischen und oberflĂ€chlich porösen PartikelsĂ€ulen (SPP-SĂ€ulen) durchgefĂŒhrt und die Trenneffizienz sowie die Analysengeschwindigkeit untersucht.
Eine noch umfassendere SĂ€ulenvergleichsstudie mit Fokus auf das kinetische Leistungsvermögen wurde in der zweiten Arbeit dieser Dissertation durchgefĂŒhrt. Eine Auswahl von 13 RP-ProteintrennsĂ€ulen inklusive monolithischer, SPP und vollporöser PartikelsĂ€ulen (FPP-SĂ€ulen) wurde hinsichtlich ihrer FĂ€higkeit, Peaks in der kĂŒrzest möglichen Zeit zu trennen, untersucht. Es konnte gezeigt werden, dass SPP-SĂ€ulen mit einer PorengröĂe von etwa 400 Ă
und einer dĂŒnnen, porösen Schicht die beste Performance insbesondere fĂŒr gröĂere Proteinen besitzen.
Proteine selbst können auch potenzielle Ziele fĂŒr 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 -spezifitĂ€t des kovalenten Inhibitors an p53 nachzuweisen.
AminosĂ€uren sind die Bausteine von Proteinen und Peptiden und die Mehrheit dieser AminosĂ€uren sind chiral. Die biologische AktivitĂ€t ist in der Regel abhĂ€ngig von der absoluten Konfiguration der AminosĂ€uren, wodurch die enantiomerenselektive Analyse von höchster Wichtigkeit fĂŒr die StrukturaufklĂ€rung und zur QualitĂ€tskontrolle ist. Daher war die Entwicklung schneller und umfassender Trennmethoden zur Analyse von AminosĂ€uren, deren Enantiomeren, Diastereomeren und konstitutionellen Isomeren ein Ziel dieser Arbeit. Dieses konnte durch Derivatisierung mittels 6-Aminochinolyl-N-hydroxysuccinimidylcarbamat (AQC) und anschlieĂender Analyse durch enantioselektiver flĂŒssigchromatographischer IonenmobilitĂ€ts-Massenspektrometrie (LC-IM-MS) erreicht werden. Eine sehr schnelle dreiminĂŒtige Analysenmethode konnte entwickelt und zur StrukturaufklĂ€rung von therapeutischen Peptiden und eines natĂŒrlichen Lipopeptides eingesetzt werden.
Die absolute Konfiguration eines Tetrapeptides als Bestandteil des natĂŒrlichen, antimikrobiellen Peptidpolyensâ Epifadin konnte mittels chiraler LC-MS bestimmt werden, was wiederum entscheidend fĂŒr die StrukturaufklĂ€rung 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 Ă
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
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
Predicting Paid Certification in Massive Open Online Courses
Massive open online courses (MOOCs) have been proliferating because of the free or low-cost offering of content for learners, attracting the attention of many stakeholders across the entire educational landscape. Since 2012, coined as âthe Year of the MOOCsâ, several platforms have gathered millions of learners in just a decade. Nevertheless, the certification rate of both free and paid courses has been low, and only about 4.5â13% and 1â3%, respectively, of the total number of enrolled learners obtain a certificate at the end of their courses. Still, most research concentrates on completion, ignoring the certification problem, and especially its financial aspects. Thus, the research described in the present thesis aimed to investigate paid certification in MOOCs, for the first time, in a comprehensive way, and as early as the first week of the course, by exploring its various levels. First, the latent correlation between learner activities and their paid certification decisions was examined by (1) statistically comparing the activities of non-paying learners with course purchasers and (2) predicting paid certification using different machine learning (ML) techniques. Our temporal (weekly) analysis showed statistical significance at various levels when comparing the activities of non-paying learners with those of the certificate purchasers across the five courses analysed. Furthermore, we used the learnerâs activities (number of step accesses, attempts, correct and wrong answers, and time spent on learning steps) to build our paid certification predictor, which achieved promising balanced accuracies (BAs), ranging from 0.77 to 0.95. Having employed simple predictions based on a few clickstream variables, we then analysed more in-depth what other information can be extracted from MOOC interaction (namely discussion forums) for paid certification prediction. However, to better explore the learnersâ discussion forums, we built, as an original contribution, MOOCSent, a cross- platform review-based sentiment classifier, using over 1.2 million MOOC sentiment-labelled reviews. MOOCSent addresses various limitations of the current sentiment classifiers including (1) using one single source of data (previous literature on sentiment classification in MOOCs was based on single platforms only, and hence less generalisable, with relatively low number of instances compared to our obtained dataset;) (2) lower model outputs, where most of the current models are based on 2-polar
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classifier (positive or negative only); (3) disregarding important sentiment indicators, such as emojis and emoticons, during text embedding; and (4) reporting average performance metrics only, preventing the evaluation of model performance at the level of class (sentiment). Finally, and with the help of MOOCSent, we used the learnersâ discussion forums to predict paid certification after annotating learnersâ comments and replies with the sentiment using MOOCSent. This multi-input model contains raw data (learner textual inputs), sentiment classification generated by MOOCSent, computed features (number of likes received for each textual input), and several features extracted from the texts (character counts, word counts, and part of speech (POS) tags for each textual instance). This experiment adopted various deep predictive approaches â specifically that allow multi-input architecture - to early (i.e., weekly) investigate if data obtained from MOOC learnersâ interaction in discussion forums can predict learnersâ purchase decisions (certification). Considering the staggeringly low rate of paid certification in MOOCs, this present thesis contributes to the knowledge and field of MOOC learner analytics with predicting paid certification, for the first time, at such a comprehensive (with data from over 200 thousand learners from 5 different discipline courses), actionable (analysing learners decision from the first week of the course) and longitudinal (with 23 runs from 2013 to 2017) scale. The present thesis contributes with (1) investigating various conventional and deep ML approaches for predicting paid certification in MOOCs using learner clickstreams (Chapter 5) and course discussion forums (Chapter 7), (2) building the largest MOOC sentiment classifier (MOOCSent) based on learnersâ reviews of the courses from the leading MOOC platforms, namely Coursera, FutureLearn and Udemy, and handles emojis and emoticons using dedicated lexicons that contain over three thousand corresponding explanatory words/phrases, (3) proposing and developing, for the first time, multi-input model for predicting certification based on the data from discussion forums which synchronously processes the textual (comments and replies) and numerical (number of likes posted and received, sentiments) data from the forums, adapting the suitable classifier for each type of data as explained in detail in Chapter 7
R-Pyocin Regulation, Release, and Susceptibility in Pseudomonas aeruginosa
Pseudomonas aeruginosa is a Gram-negative opportunistic pathogen and a major determinant of declining lung function in individuals with cystic fibrosis (CF). P. aeruginosa possesses many intrinsic antibiotic resistance mechanisms and isolates from chronic CF lung infections develop increasing resistance to multiple antibiotics over time. Chronic infection with P. aeruginosa remains one of the main causes of mortality and morbidity in CF patients, thus new therapeutic interventions are necessary.
R-type pyocins are narrow spectrum, phage tail-like bacteriocins, specifically produced by P. aeruginosa to kill other strains of P. aeruginosa. Due to their specific anti-pseudomonal activity and similarity to bacteriophage, R-pyocins have potential as additional therapeutics for P. aeruginosa, either in isolation, in combination with antibiotics, or as an alternative to phage therapy. There are five subtypes of R-pyocin (types R1-R5), and it is thought that each P. aeruginosa strain uniquely produces only one of these, suggesting a degree of strain-specificity. P. aeruginosa from CF lung infections develop increasing resistance to antibiotics, making new treatment approaches essential. It is known P. aeruginosa populations in CF chronic lung infection become phenotypically and genotypically diverse over time, however, little is known of the efficacy of R-pyocins against heterogeneous populations. Even less is known regarding the timing and regulation of R-pyocins in CF lung infections, or if P. aeruginosa utilizes R-pyocin production during infection for competition or otherwise â which may influence pressure towards R-pyocin resistance.
In this work, I evaluated R-pyocin type and susceptibility among P. aeruginosa isolates sourced from CF infections and found that (i) R1-pyocins are the most prevalent R-type among respiratory infection and CF strains; (ii) a large proportion of P. aeruginosa strains lack R-pyocin genes entirely; (iii) isolates from P. aeruginosa populations collected from the same patient at a single time point have the same R-pyocin type; (iv) there is heterogeneity in susceptibility to R-pyocins within P. aeruginosa populations and (v) susceptibility is likely driven by diversity of LPS phenotypes within clinical populations. These findings suggest that there is likely heterogeneity in response to other types of LPS-binding antimicrobials, including phage, which is important for consideration of antimicrobials as therapeutics.
To investigate the prevalence of R2-pyocin susceptible strains in CF, I then utilized 110 isolates of P. aeruginosa collected from five individuals with CF to test for R2-pyocin susceptibility and identify LPS phenotypes. From our collection we i) estimated that approximately 83% of sputum samples contain heterogenous P. aeruginosa populations without R2-pyocin resistant isolates and all sputum samples contained susceptible isolates; ii) we found that there is no correlation between R2-pyocin susceptibility and LPS phenotypes, and iii) we estimate that approximately 76% of isolates sampled from sputum lack O-specific antigen, 42% lack common antigen, and 27% exhibit altered LPS cores. This finding highlights that perhaps LPS packing density may play a more influential role in mediating R-pyocin susceptibility in infection. Finding the majority of our sampled P. aeruginosa populations to be R2-pyocin susceptible further supports the potential of these narrow-spectrum antimicrobials despite facing heterogenous susceptibility among diverse populations.
In order to evaluate how R-pyocins may influence strain competition and growth in CF lung infection, I assessed R-pyocin activity in an infection-relevant environment (Synthetic Cystic Fibrosis Sputum Medium; SCFM2) and found that (i) R-pyocins genes are transcribed more in the CF nutrient environment than in rich laboratory medium and (ii) in a structured, CF-like environment, R-pyocin induction is costly to producing strains in competition rather than beneficial. Our work suggests that R-pyocins may not be essential in CF lung infection and can be costly to producing cells in the presence of stress response-inducing stimuli, such as those commonly found in infection.
In this thesis I have studied R-pyocin susceptibility, regulation and release utilizing a biobank of whole populations of P. aeruginosa collected from 11 individuals with CF, as well as the CF infection model (SCFM) to understand the mechanisms of R-pyocin activity in an infection-relevant context and the role R-pyocins play in shaping P. aeruginosa populations during infection. The findings of this work have illuminated the impact of P. aeruginosa heterogeneity on R-pyocin susceptibility, furthered our understanding of R-pyocins as potential therapeutics, and built upon our knowledge of bacteriocin-mediated interactions.Ph.D
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
Software Design Change Artifacts Generation through Software Architectural Change Detection and Categorisation
Software is solely designed, implemented, tested, and inspected by expert people, unlike other engineering projects where they are mostly implemented by workers (non-experts) after designing by engineers. Researchers and practitioners have linked software bugs, security holes, problematic integration of changes, complex-to-understand codebase, unwarranted mental pressure, and so on in software development and maintenance to inconsistent and complex design and a lack of ways to easily understand what is going on and what to plan in a software system. The unavailability of proper information and insights needed by the development teams to make good decisions makes these challenges worse. Therefore, software design documents and other insightful information extraction are essential to reduce the above mentioned anomalies. Moreover, architectural design artifacts extraction is required to create the developerâs profile to be available to the market for many crucial scenarios. To that end, architectural change detection, categorization, and change description generation are crucial because they are the primary artifacts to trace other software artifacts.
However, it is not feasible for humans to analyze all the changes for a single release for detecting change and impact because it is time-consuming, laborious, costly, and inconsistent. In this thesis, we conduct six studies considering the mentioned challenges to automate the architectural change information extraction and document generation that could potentially assist the development and maintenance teams. In particular, (1) we detect architectural changes using lightweight techniques leveraging textual and codebase properties, (2) categorize them considering intelligent perspectives, and (3) generate design change documents by exploiting precise contexts of componentsâ relations and change purposes which were previously unexplored. Our experiment using 4000+ architectural change samples and 200+ design change documents suggests that our proposed approaches are promising in accuracy and scalability to deploy frequently. Our proposed change detection approach can detect up to 100% of the architectural change instances (and is very scalable). On the other hand, our proposed change classifierâs F1 score is 70%, which is promising given the challenges. Finally, our proposed system can produce descriptive design change artifacts with 75% significance. Since most of our studies are foundational, our approaches and prepared datasets can be used as baselines for advancing research in design change information extraction and documentation
A BIM - GIS Integrated Information Model Using Semantic Web and RDF Graph Databases
In recent years, 3D virtual indoor and outdoor urban modelling has become an essential geospatial information framework for civil and engineering applications such as emergency response, evacuation planning, and facility management. Building multi-sourced and multi-scale 3D urban models are in high demand among architects, engineers, and construction professionals to achieve these tasks and provide relevant information to decision support systems. Spatial modelling technologies such as Building Information Modelling (BIM) and Geographical Information Systems (GIS) are frequently used to meet such high demands. However, sharing data and information between these two domains is still challenging. At the same time, the semantic or syntactic strategies for inter-communication between BIM and GIS do not fully provide rich semantic and geometric information exchange of BIM into GIS or vice-versa. This research study proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graph databases. The suggested solution's originality and novelty come from combining the advantages of integrating BIM and GIS models into a semantically unified data model using a semantic framework and ontology engineering approaches. The new model will be named Integrated Geospatial Information Model (IGIM). It is constructed through three stages. The first stage requires BIMRDF and GISRDF graphs generation from BIM and GIS datasets. Then graph integration from BIM and GIS semantic models creates IGIMRDF. Lastly, the information from IGIMRDF unified graph is filtered using a graph query language and graph data analytics tools. The linkage between BIMRDF and GISRDF is completed through SPARQL endpoints defined by queries using elements and entity classes with similar or complementary information from properties, relationships, and geometries from an ontology-matching process during model construction. The resulting model (or sub-model) can be managed in a graph database system and used in the backend as a data-tier serving web services feeding a front-tier domain-oriented application. A case study was designed, developed, and tested using the semantic integrated information model for validating the newly proposed solution, architecture, and performance
Reviving Static Charts into Live Charts
Data charts are prevalent across various fields due to their efficacy in
conveying complex data relationships. However, static charts may sometimes
struggle to engage readers and efficiently present intricate information,
potentially resulting in limited understanding. We introduce "Live Charts," a
new format of presentation that decomposes complex information within a chart
and explains the information pieces sequentially through rich animations and
accompanying audio narration. We propose an automated approach to revive static
charts into Live Charts. Our method integrates GNN-based techniques to analyze
the chart components and extract data from charts. Then we adopt large natural
language models to generate appropriate animated visuals along with a
voice-over to produce Live Charts from static ones. We conducted a thorough
evaluation of our approach, which involved the model performance, use cases, a
crowd-sourced user study, and expert interviews. The results demonstrate Live
Charts offer a multi-sensory experience where readers can follow the
information and understand the data insights better. We analyze the benefits
and drawbacks of Live Charts over static charts as a new information
consumption experience
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