abstract: With the fast pace of globalization and the rise of encounters in digital spaces, CALL scholars have become increasingly interested in how digital tools mediate intercultural encounters. However, despite their evident success in connecting students from around the world, current online intercultural exchanges continue to present problems such a promotion of positive experiences over deep intercultural learning and lack of real-life value (O’ Dowd, 2018). In addition, digitally-mediated intercultural learning research is based on the same theoretical approaches to learning that guide CALL research (Firth & Wagner, 1997; Lafford, 2017). Although such frameworks are successful in allowing researchers to conceive of digital tools as mediators for human interaction, they have yet to embrace the potential of digital artifacts themselves as intercultural interlocutors. Aiming to address this gap in the research, this investigation used Atkinson’s (2010, 2014) sociocognitive approach to language learning to understand the role that digital tools have in intercultural learning. Also integrating Dervin’s (2011) liquid approach to interculturality—which focuses on understanding intercultural learning as a co-constructed process—the research questions that guided this investigation asked: (a) does film annotation mediate intercultural learning? and, (b) in what ways does film annotation mediate intercultural learning? In answering these questions, the study looked at the intercultural learning process of five advanced learners of Spanish, as they interacted with annotated film clips, and engaged in peer discussion around the themes of colonialism and coloniality presented in the film clips. Data were collected through pre and post-tests, video recordings of peer discussions, and screen recordings of participants’ interaction with the annotated film clips. Findings showed that film annotation allowed participants to notice, retrieve and take notes on important cultural information, which they later incorporated in discussion with peers. Based on this evidence, and aligned with the aforementioned theoretical frameworks, this investigation poses that intercultural learning is a fluid, iterative process. The study also suggests that digital artifacts—as well as human interlocutors—play an important role in enabling learning processes, therefore, the role of such artifacts should be studied more in depth. / Dissertation/Thesis / Doctoral Dissertation Spanish 2019

No description available.

During the last decades, mitochondria and their DNA have become a hot topic of research due to their essential roles which are necessary for cells survival and pathology. In this study, multiple methods have been developed to help with the understanding of mitochondrial DNA and its evolution. These methods tackle two essential problems in this area: the accurate annotation of protein-coding genes and mitochondrial genome rearrangements. Mitochondrial genome sequences are published nowadays with increasing pace, which creates the need for accurate and fast annotation tools that do not require manual intervention. In this work, an automated pipeline for fast de-novo annotation of mitochondrial protein-coding genes is implemented. The pipeline includes methods for enhancing multiple sequence alignment, detecting frameshifts and building protein profiles guided by phylogeny. The methods are tested on animal mitogenomes available in RefSeq, the comparison with reference annotations highlights the high quality of the produced annotations. Furthermore, the frameshift method predicted a large number of frameshifts, many of which were unknown. Additionally, an efficient partially-local alignment method to investigate genomic rearrangements in mitochondrial genomes is presented in this study. The method is novel and introduces a partially-local dynamic programming algorithm on three sequences around the breakpoint region. Unlike the existing methods which study the rearrangement at the genes order level, this method allows to investigate the rearrangement on the molecular level with nucleotides precision. The algorithm is tested on both artificial data and real mitochondrial genomic sequences. Surprisingly, a large fraction of rearrangements involve the duplication of local sequences. Since the implemented approach only requires relatively short parts of genomic sequence around a breakpoint, it should be applicable to non-mitochondrial studies as well.

The Internet provides a powerful medium for communication and collaboration. The ability one has to connect and interact with web-based tools from anywhere in the world makes the Internet ideal for such tasks. However, the lack of native tools can be a hindrance when deploying collaborative initiatives, as many current projects require specialized software in order to operate. This thesis demonstrates, with the comparably recent advances in browser technology and Document Object Model (DOM) implementation, a web-based collaborative annotation system can be developed that can be accessed by a user through a standards-compliant web browser. Such a system, demonstrated to work on the commonly-used web browsers constituting the vast majority of web traffic, was implemented using open-source tools and industry-recognized standards. Additionally, it accepts static copies of most standard document formats for both handwritten and typed annotations, while maintaining an archived copy of the original. The system developed for this thesis lends itself to use in a number of different process domains, as most collaborative annotation approaches can be described by a single process model. While a number of possible usage scenarios are discussed, this thesis approaches system usage only in an academic setting, focusing on applicability of the system to electronic grading and document exchange. From here, additional system usage can be easily extrapolated.

By observing human driving with a “digital head" (combined video camera and accelerometers) and taking a few hand annotations, we can automatically annotate regions in a robot's field of view that should be interpreted as obstacles to be avoided. This is accomplished by detecting the movement for a given frame in a video. Some hand annotations of video frames are necessary and they are used to create Probability Grids. Using the movement data and the Probability Grids, it is possible to annotate large amounts of video data quickly in an automated system.

Single-cell RNA-sequencing (scRNA-seq) technology enables researchers to investigate a genome at the cellular level with unprecedented resolution. An organism consists of a heterogeneous collection of cell types, each of which plays a distinct role in various biological processes. Hence, the first step of scRNA-seq data analysis often is to distinguish cell types so that they can be investigated separately. Researchers have recently developed several automated cell type annotation tools based on supervised machine learning algorithms, requiring neither biological knowledge nor subjective human decisions. Dropout is a crucial characteristic of scRNA-seq data which is widely utilized in differential expression analysis but not by existing cell annotation methods. We present scAnnotate, a cell annotation tool that fully utilizes dropout information. We model every gene’s marginal distribution using a mixture model, which describes both the dropout proportion and the distribution of the non-dropout expression levels. Then, using an ensemble machine learning approach, we combine the mixture models of all genes into a single model for cell-type annotation. This combining approach can avoid estimating numerous parameters in the high-dimensional joint distribution of all genes. Using fourteen real scRNA-seq datasets, we demonstrate that scAnnotate is competitive against nine existing annotation methods, and that it accurately annotates cells when training and test data are (1) similar, (2) cross-platform, and (3) cross-species. Of the cells that are incorrectly annotated by scAnnotate, we find that a majority are different from those of other methods. / Graduate / 2023-07-27

With the proliferation of social media, gathering data has became cheaper and easier than before. However, this data can not be used for supervised machine learning without labels. Asking experts to annotate sufficient data for training is both expensive and time-consuming. Current techniques provide two solutions to reducing the cost and providing sufficient labels: crowdsourcing and active learning. Crowdsourcing, which outsources tasks to a distributed group of people, can be used to provide a large quantity of labels but controlling the quality of labels is hard. Active learning, which requires experts to annotate a subset of the most informative or uncertain data, is very sensitive to the annotation errors. Though these two techniques can be used independently of one another, by using them in combination they can complement each other’s weakness. In this thesis, I investigate the development of active learning Support Vector Machines (SVMs) and expand this model to sequential data. Then I discuss the weakness of combining active learning and crowdsourcing, since the active learning is very sensitive to low quality annotations which are unavoidable for labels collected from crowdsourcing. In this thesis, I propose three possible strategies, incremental relabeling, importance-weighted label prediction and active Bayesian Networks. The incremental relabeling strategy requires workers to devote more annotations to uncertain samples, compared to majority voting which allocates different samples the same number of labels. Importance-weighted label prediction employs an ensemble of classifiers to guide the label requests from a pool of unlabeled training data. An active learning version of Bayesian Networks is used to model the difficulty of samples and the expertise of workers simultaneously to evaluate the relative weight of workers’ labels during the active learning process. All three strategies apply different techniques with the same expectation – identifying the optimal solution for applying an active learning model with mixed label quality to iii crowdsourced data. However, the active Bayesian Networks model, which is the core element of this thesis, provides additional benefits by estimating the expertise of workers during the training phase. As an example application, I also demonstrate the utility of crowdsourcing for human activity recognition problems.

No description available.

No description available.

The human cytomegalovirus (HCMV) is a ubiquitous pathogen that establishes a life- long infection upon primary infection. While primary infection is mostly asymptomatic, HCMV is responsible for significant morbidity and mortality in immunocompromised patients and neonates. There is currently no vaccine. The strict species specificity of this large double-stranded DNA virus poses a major challenge in understanding cytomegalovirus (CMV) pathogenesis. Murine cytomegalovirus (MCMV) exhibits significant similarity to HCMV and represents a widely used model to study CMV pathogenesis. The genomes of both human cytomegalovirus (HCMV) and murine cytomegalovirus (MCMV) were first sequenced over 20 years ago. Similar to HCMV, the MCMV genome had initially been proposed to harbour ~ 170 open reading frames (ORFs). More recently, RNA-seq based omics approaches revealed HCMV gene expression to be substantially more complex, comprising several hundred viral ORFs, similarly observed for other herpesviruses including KSHV, EBV and our own work on HSV-1, where an integrative analysis was further extended to provide a unified nomenclature for all gene products. The aim of my PhD project was to generate and utilize available multi-omics datasets to provide a state-of-the-art reannotation of lytic MCMV gene expression based on integrative analysis of a large set of omics data. By developing a novel nomenclature strategy, I annotated 365 viral transcription start sites (TiSS) giving rise to 380 and 454 viral transcripts and ORFs, respectively. The latter included >200 small ORFs, some of which represented the most highly expressed MCMV gene products. I further developed a novel time-resolved TiSS profiling approach (dSLAM-seq) to accurately annotate TiSS and determine the temporal kinetics of viral TiSS usage by metabolic labelling of newly-synthesized RNA. This not only resulted in the identification of a novel MCMV immediate early transcript encoding the m166.5 ORF, which we termed ie4, but also revealed three classes of viral early genes that differ in the onset of transcriptional activity. Furthermore, we identified a class of well-expressed viral transcripts (termed: ‘delayed late genes’) that are induced later than canonical true late genes and contain an initiator element (Inr) but no TATA- or TATT-box in their core promoters. For interesting candidate viral uORFs, I demonstrated their regulatory effects on their downstream ORFs (in cis) through elaborate reporter assays. As a proof-of-concept, I validated two novel ORFs in MCMV infection, including the truncated isoforms of the viral NK-cell immune evasin m145 expressed from a viral TiSS downstream of the canonical m145 mRNA. Despite being ≈5-fold more abundantly expressed than the canonical m145 protein it was not required for downregulating the NK cell ligand, MULT-I, but nevertheless provided evidence for additional unknown glycosylated protein isoforms expressed from the same genomic locus. Furthermore, I validated a novel MCMV protein overlapping the viral polymerase (M54), m54.5. Funded by a seed grant from the DFG research unit FOR2830, I characterized the interactome of the m54.5 protein revealing interactions with three exciting protein complexes for future functional studies. Finally, I tested the hypothesis that viral small ORFs represent a novel class of CD8+ T-cell antigens that are subject to direct but not cross-presentation due to their inherent instability of their encoded microproteins. By expressing model epitopes at the C-terminus of either the M35 uORF or ORF, I could demonstrate that sORF-derived epitopes can be efficiently presented to naïve CD8+ T-cells by MHC-I, and mediate CD8+ T-cell control of infection, but are poor in priming naïve CD8+ T cells in mice. In summary, my work unravel exciting new aspects of an important animal infection model and will pave the way for future mechanistic studies on previously unknown cytomegalovirus gene. / Das humane Cytomegalovirus (HCMV) ist ein ubiquitäres Pathogen, das nach einer Primärinfektion eine lebenslange Infektion verursacht. Während die Primärinfektion meist asymptomatisch verläuft, ist das HCMV für eine erhebliche Morbidität und Mortalität bei immungeschwächten Patienten und Neugeborenen verantwortlich. Derzeit gibt es keinen Impfstoff. Die strenge Speziesspezifität dieses großen Doppelstrang-DNA-Virus stellt eine große Herausforderung für das Verständnis der Pathogenese des Cytomegalovirus (CMV) dar. Das murine Cytomegalovirus (MCMV) weist eine erhebliche Ähnlichkeit mit dem HCMV auf und ist ein weit verbreitetes Modell zur Untersuchung der CMV-Pathogenese. Die Genome sowohl des humanen Cytomegalovirus (HCMV) als auch des murinen Cytomegalovirus (MCMV) wurden erstmals vor über 20 Jahren sequenziert. Ähnlich wie beim HCMV wurde zunächst angenommen, dass das MCMV-Genom etwa 170 offene Leserahmen (ORFs) enthält. In jüngerer Zeit haben RNA-seq-basierte Omics-Ansätze gezeigt, dass die HCMV- Genexpression wesentlich komplexer ist und mehrere hundert virale ORFs umfasst. Ähnliches wurde auch bei anderen Herpesviren beobachtet, darunter KSHV, EBV und unsere eigene Arbeit zu HSV-1, bei der eine integrative Analyse weiter ausgebaut wurde, um eine einheitliche Nomenklatur für alle Genprodukte zu erstellen. Ziel meines Promotionsprojekts war es, verfügbare Multi-omics-Datensätze zu generieren und zu nutzen, um eine hochmoderne Reannotation der lytischen MCMV- Genexpression auf der Grundlage einer integrativen Analyse eines großen Satzes von Omics-Daten zu erstellen. Durch die Entwicklung einer neuen Nomenklaturstrategie habe ich 365 virale Transkriptionsstartstellen (TiSS) annotiert, die zu 380 bzw. 454 viralen Transkripten und ORFs führen. Zu letzteren gehörten >200 kleine ORFs, von denen einige die am stärksten exprimierten MCMV-Genprodukte darstellten. Darüber hinaus entwickelte ich ein neuartiges zeitaufgelöstes TiSS-Profiling-Verfahren (dSLAM-seq), um TiSS genau zu annotieren und die zeitliche Kinetik der viralen TiSS- Nutzung durch metabolische Markierung von neu synthetisierter RNA zu bestimmen. Dies führte nicht nur zur Identifizierung eines neuartigen frühen MCMV-Transkripts, das für den m166.5 ORF kodiert und das wir als ie4 bezeichnet haben, sondern zeigte auch drei Klassen von frühen viralen Genen, die sich im Beginn der Transkriptionsaktivität unterscheiden. Darüber hinaus identifizierten wir eine Klasse von gut exprimierten viralen Transkripten (als "verzögerte späte Gene" bezeichnet), die später als die kanonischen echten späten Gene induziert werden und ein Initiatorelement (Inr), aber keine TATA- oder TATT-Box in ihren Kernpromotoren enthalten. Für interessante virale uORF-Kandidaten habe ich ihre regulatorischen Auswirkungen auf ihre nachgeschalteten ORFs (in cis) durch aufwendige Reporter- Assays nachgewiesen. Zum Nachweis des Konzepts habe ich zwei neue ORFs bei MCMV-Infektionen validiert, darunter die verkürzten Isoformen des viralen NK-Zell- Immunevasins m145, die von einem viralen TiSS stromabwärts der kanonischen m145 mRNA exprimiert werden. Obwohl es ≈5-mal häufiger exprimiert wird als das kanonische m145-Protein, war es für die Herabregulierung des NK-Zell-Liganden MULT-I nicht erforderlich, lieferte aber dennoch Beweise für weitere unbekannte glykosylierte Protein-Isoformen, die von demselben genomischen Locus exprimiert werden. Darüber hinaus habe ich ein neues MCMV-Protein validiert, das die virale Polymerase (M54) überlagert, m54.5. Finanziert durch eine Anschubfinanzierung der DFG-Forschergruppe FOR2830 habe ich das Interaktom des m54.5-Proteins charakterisiert und dabei Interaktionen mit drei interessanten Proteinkomplexen für zukünftige Funktionsstudien aufgedeckt. Schließlich testete ich die Hypothese, dass kleine virale ORFs eine neue Klasse von CD8+-T-Zell-Antigenen darstellen, die aufgrund der inhärenten Instabilität ihrer kodierten Mikroproteine zwar direkt, aber nicht kreuzweise präsentiert werden können. Durch die Expression von Modellepitopen am C-Terminus des M35 uORF oder ORF konnte ich zeigen, dass sORF-abgeleitete Epitope naiven CD8+ T-Zellen durch MHC-I effizient präsentiert werden können und die Kontrolle der Infektion durch CD8+ T-Zellen vermitteln, aber beim Priming von naiven CD8+ T-Zellen in Mäusen schlecht funktionieren. Zusammenfassend lässt sich sagen, dass meine Arbeit aufregende neue Aspekte eines wichtigen Tierinfektionsmodells aufdeckt und den Weg für künftige mechanistische Studien über bisher unbekannte Cytomegalovirus-Gene ebnen wird.