2,568 research outputs found

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Feminisms in Movement: Theories and Practices from the Americas

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    Feminist movements from the Americas provide some of the most innovative, visible, and all-encompassing forms of organizing and resistance. With their diverse backgrounds, these movements address sexism, sexualized violence, misogyny, racism, homo- and transphobia, coloniality, extractivism, climate crisis, and neoliberal capitalist exploitation as well as the interrelations of these systems. Fighting interlocking axes of oppression, feminists from the Americas represent, practice, and theorize a truly "intersectional" politics. Feminisms in Movement: Theories and Practices from the Americas brings together a wide variety of perspectives and formats, spanning from the realms of arts and activism to academia. Black and decolonial feminist voices and queer/cuir perspectives, ecofeminist approaches and indigenous women's mobilizations inspire future feminist practices and inform social and cohabitation projects. With contributions from Rita Laura Segato, Mara Viveros Vigoya, Yuderkys Espinosa-Miñoso, and interviews with Anielle Franco (Brazilian activist and minister) and with the Chilean feminist collective LASTESIS

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

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    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 grĂ¶ĂŸten 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 PorengrĂ¶ĂŸe von etwa 400 Å und einer dĂŒnnen, porösen Schicht die beste Performance insbesondere fĂŒr grĂ¶ĂŸere 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 anschließender 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 Peptidpolyens‘ 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 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

    An Evaluation of the risks to food safety and shellfish farming in Great Britain,posed by marine biotoxins from, current and future emerging, marine microalgal species

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    Harmful marine microalgae are a global concern, impacting human and ecosystem health as well as having socioeconomic impacts for coastal communities. The changing world climate has an impact on marine organisms including the harmful algal species. These changes will have impacts on species already present in a nations waters whilst also influencing the emergence of novel species. This is assessed here, in part, with regards to Great Britain (GB). This thesis explores the current extent of a harmful species, Alexandrium minutum, globally and in the South of GB. This shows that A. minutum occurs widely across the globe with different populations possessing varying toxin profiles. Populations from GB geographically neighbouring areas share similar toxin profiles. Within the South of GB, the current extent of A. minutum appears patchy, with evidence gathered by toxin profile analysis but successful germinations of vegetative cells from field samples proving unsuccessful. Experimental work determined a mechanism for the use of chemotaxonomy to differentiate the source of shellfish intoxications, allowing for separation of two key GB saxitoxin producers, A. minutum and Alexandrium catenella. This technique could enhance routine monitoring data with little additional cost. Assessment of harmful microalgal taxa considered as non-native species (NNS) to GB suggested that several species could pose a risk of future successful invasion of GB coastal waters, within the next 30 years. This was principally based on the environmental tolerances of NNS. If established the impacts which NNS could impose on GB include similar impacts to native harmful species as well as a higher risk of environmental damage. Experimental work with a high-risk potential invasive species, Ostreopsis cf. ovata, indicated that this impact could be acute, with rapid mortalities observed in exposed naĂŻve GB mussels. Taken together this body of work shows the validity of chemotaxonomic assessment of toxin profiles as an additional tool for the tracking of harmful microalgal species as well as proactively assessing the risk and impacts which climate change might have for the future impacts of harmful marine microalgal species around GB

    Predicting Paid Certification in Massive Open Online Courses

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    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 “the Year of the MOOCs”, 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 4.5–13% and 1–3%, 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 learner’s 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 learners’ 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 iii iv 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 learners’ discussion forums to predict paid certification after annotating learners’ 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 – specifically that allow multi-input architecture - to early (i.e., weekly) investigate if data obtained from MOOC learners’ interaction in discussion forums can predict learners’ 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 learners’ 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

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Exploring the Predictors of Indonesian Reading Literacy based on PISA Data

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    Reading achievement in Indonesia has remained low since 2000 when it first participated in PISA. In Indonesia, reading is not a specific subject, but rather an essential skill integrated with other subjects like Bahasa Indonesia, English, Social Sciences, Natural Sciences, and Mathematics, and as such, it is assessed in the PISA test. Apart from the cognitive tests, PISA also collects additional information related to schools’, teachers’, parents’, and students’ characteristics and perceptions that are related to students’ cognitive ability. Thus, the main research topics in this field are reading literacy and the factors associated with reading ability. The research study examines student and school factors and their relationship impact o student reading literacy in Indonesia, considering paper-based (PISA 2000, 2009, and follow up 2020) and computer-based reading performance (PISA 2018). A quantitative research design is used based on the research problems addressed in general that fell within the factors of reading achievement based on PISA data. This approach is used to confirm the validity and reliability of the constructs included in this study and to examine the relationships that exist among those constructs. Data collection consists of primary and secondary data collection. The study uses secondary data from PISA, as well as primary data collected in 2020 concerning the reading questionnaire and cognitive test. Secondary data from PISA 2000, 2009, and 2018 student and school questionnaires are used to examine how schools and students interrelate, which affects student achievement. The study also uses primary data collected in 2020 in a follow-up study with questionnaires adopted from PISA 2018 as the latest test with additional variables from parents and teachers. In addition to taking account of school and student factors, the results of 2020 are compared with those taken in 2000, 2009, and 2018. Thus, the longitudinal study of reading literacy based on PISA data is attempted. All constructs except the demographic items are validated using the confirmatory factor analysis (CFA) and Rasch Analysis. An analysis of all constructs that have already been anchored to the weighted likelihood estimates is conducted using structural equation modelling (SEM) and hierarchical linear modelling (HLM). To examine the factors that significantly influence students’ reading literacy in Indonesia over the four cycles, the structural equation model (with single and path analysis) and hierarchical linear model are applied. The study hypothesises that school-level factors affect the reading literacy of students. The structural equation model is used to impose a theoretical model on student variables and school variables measured by observed variables. With this model, the study explains the interrelationships between construct and observed variables. Meanwhile, a hierarchical linear model is used since the data had students who are nested in schools or students who were nested in classrooms, and classrooms are nested in schools. With this model, the study examines the effects of group variables (school- and teacher-level) and individual variables (student-level) and seeks the interaction across levels. In the analysis of the hierarchical approach, it is determined that there are consistency and nonconsistency factors towards reading literacy throughout the four cycles of analysis. There is evidence of consistent predictors at the student level in the factors of gender, reading engagement, and time spent reading. At school-level, the significant factors are: school sector in the 2000, 2018 and 2020 cycles; school location in the 2018 and 2020 cycles; ICT in the 2020 cycle; resources and technology in the 2018 cycle; assessment in the 2000 and 2018 cycles; leadership in the 2018 cycle; and school climate in the 2000 cycle. It is surprising to find that no factor was significant at the teacher-level in the 2020 cycle but a direct effect is found between teacher professional and teacher lesson activities. At student-level, the significant factors are: gender in the 2000, 2009, and 2018 cycles; the number of books in the 2000 cycle; home and educational resources in the 2018 cycle; reading engagement in the 2000, 2009, and 2018 cycles; reading diversity in the 2000, 2009, and 2018 cycles; reading online in the 2018 cycle; reading strategies in the 2009 cycle, reading confidence in the 2018 cycle, and reading time in the 2000, 2009, and 2018 cycles. The predictors are consistently available in the factor of gender, reading engagement, and reading time. In addition, the results indicate that computer-based tests (2018 cycle) provided more predictors than text-based tests (2000, 2009, and 2020 cycles). This research is particularly valuable in terms of its contributions to the theoretical, practical, and methodological aspects of reading literacy in Indonesia. This study suggests that, in general, private schools and schools located in rural or village areas require more attention regarding ICT, technology, assessment, leadership, and school climate. This likewise suggest that males should receive greater attention to reading activities, such as reading engagement and reading diversity, as well as reading states, such as reading strategies, reading confidence, and reading time. Meanwhile, females should receive more attention when it comes to online reading. Teacher professional activities plays an important role in supporting the delivery of better lessons in the classroom. In addition, it is important not to underestimate parental support in terms of the income and education of the parents. It would be beneficial for the Indonesian government in the future to maintain a curriculum based on autonomy to increase student reading achievement. Likewise, the government should include teacher and parent survey in future PISA Tests so that a more comprehensive analysis of the factors influencing reading ability can be conducted.Thesis (Ph.D.) -- University of Adelaide, School of Education, 202

    Identification and interpretation of pathogenic variants following Next Generation Sequencing (NGS) analysis in human Mendelian disorders

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    Durante il programma di dottorato, l'attenzione Ăš stata rivolta al supporto del laboratorio di diagnostica nell'implementazione della convalida o nella scoperta di varianti insolite. Questo Ăš di massima importanza per comprendere i meccanismi eziopatogenetici molecolari, ma anche per offrire la migliore consulenza alle famiglie. Di conseguenza, sono stati portati a termine diversi progetti come segue: I) un caso enigmatico di una femmina con un disturbo granulomatoso cronico legato all'X (CGD) con una presunta variante di splicing: (NM_000397:ex9:c.1151+2T>C) nel gene CYBB. II) una nuova presunta variante di splicing emizigote nel gene MAGT1 (NM_032121:c.627+2T>C) situato sul cromosoma X. III) Analisi delle variazioni del numero di copie (CNV) per aumentare il tasso di diagnosi di un pannello NGS per gli errori congeniti dell'immunitĂ , poichĂ© Ăš ben noto che le CNV (inserzioni o eliminazioni di dimensioni comprese tra 2 e 50 megabasi) rappresentano circa il 12% delle anomalie genetiche. Identificare questa ampia variazione Ăš ancora problematico, specialmente con le piattaforme Ion Torrent che utilizziamo per la diagnostica, pertanto abbiamo eseguito un'approfondita analisi in silico utilizzando diversi nuovi software. IV) Otto famiglie con una storia personale o familiare di cancro sono state testate per un pannello di geni multipli osono state sottoposte al sequenziamento completo dell’esoma. Sono state trovate otto varianti patogeniche e verificate tramite sequenziamento di Sanger o MLPA e PCR in Real-time. L'uso di NGS e la rilevazione di CNV hanno migliorato la diagnosi nei pazienti affetti da cancro. Alcune delle famiglie iraniane che soddisfacevano i criteri di Amsterdam sono state incluse in programmi di sorveglianza indipendentemente dal loro stato di portatori di mutazioni prima dei test genetici, mentre dopo la rivelazione del portatore solo i portatori sono stati inclusi, migliorando la conformitĂ  e riducendo i costi di gestione.During the PhD program the focus was to support diagnostic lab implementing validation or discover of unusual variants. This is of utmost importance to understand molecular etiopathogenic mechanisms, but also in order to offer the best counselling to families. Thus, different projects were accomplished as follows: I) a puzzling patient of a female with X-linked chronic granulomatous disorder (CGD) with a putative splicing variant: (NM_000397:ex9:c.1151+2T>C) in the CYBB gene. II) a novel hemizygous putative splicing mutation in the MAGT1 gene (NM_032121:c.627+2T>C) located on the X-chromosome. III) Analysis of Copy Number Variations (CNVs) to increase the diagnostic rate of a NGS panel for Inborn errors of immunity, as is well known that CNVs (indels between 2 and 50 megabases), account for roughly 12% of genetic abnormalities. Identifying this large variation is still problematic, especially with the Ion Torrent platforms we use for diagnostic, thus we performed an extensive in silico analysis using multiple new softwares. IV) Eight families possessing a familial or personal history of cancer underwent multigene panel testing or whole exome sequencing. Eight pathogenic variants were found and verified through Sanger sequencing or MLPA and real-time PCR. The use of NGS and CNV detection improved the diagnostic yields in cancer patients. Some of Iranian families who met Amsterdam criteria were enrolled in surveillance programs irrespective of their mutation carrier status before genetic testing, while after carrier detection disclosures only carriers were enrolled improving compliance and decreasing the managing cost

    An Empirical Analysis of Optimal Nonlinear Pricing

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    In continuous-choice settings, consumers decide not only on whether to purchase a product, but also on how much to purchase. Thus, firms optimize a full price schedule rather than a single price point. This paper provides a methodology to empirically estimate the optimal schedule under multi-dimensional consumer heterogeneity. We apply our method to novel data from an educational-services firm that contains purchase-size information not only for deals that materialized, but also for potential deals that eventually failed. We show that this data, combined with identifying assumptions, helps infer how price sensitivity varies with "customer size". Using our estimated model, we show that the optimal second-degree price discrimination (i.e., optimal nonlinear tariff) improves the firm's profit upon linear pricing by at least 5.5%. That said, this second-degree price discrimination scheme only recovers 5.1% of the gap between the profitability of linear pricing and that of infeasible first degree price discrimination. We also conduct several further counterfactual analyses (i) empirically quantifying the magnitude by which incentive-compatibility constraints impact the optimal pricing and profits, (ii) comparing the role of demand- v.s. cost-side factors in shaping the optimal price schedule, and (iii) studying the implications of fixed fees for the optimal contract and profitability
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