Gerätesteuerung durch ein holografisches Userinterface

Gegenstand dieser Bachelorarbeit ist die Konzeption und Realisierung einer adaptiven Gerätesteuerung, die sich in der Luft durch eine virtuelle Hand bedienen lässt. Das virtuelle Handmodell imitiert die Bewegungen der realen Hand des Benutzers und ermöglicht so die Interaktion mit einer im Raum dargestellten Benutzeroberfläche. Das Bedienkonzept wird anhand des Prototyps evaluiert.Subject of this thesis is the design and implementation of an adaptive device control that can be operated in mid-air by a virtual hand. The virtual hand model imitates the motion of the real hand of the user and enables interaction with a user interface shown in mid-air. The interaction concept is evaluated on the basis of the prototype

Untersuchung von Active Learning Methoden für BERT-Modelle

Automatisierte Textklassifikation ist für viele praktische Anwendungen ein aussichtsreiches Analyse- und Moderationsinstrument. In der Praxis steht Textklassifikation jedoch oft teuren Annotationskosten und einem Ungleichgewicht der Klassen in den Trainingsdaten gegenüber. Active Learning beschreibt ein Paradigma, dass die Kosten für die Annotation signifikant senken kann, indem über mehrere Iterationen geeignete Daten gezielt annotiert werden. In Verbindung mit vortrainierten Sprachmodellen, wie BERT und seinen Variationen, ist Active Learning bisher wenig untersucht. Diese Arbeit untersucht verschiedene Active Learning Methoden für BERT-Modelle unter dem Problem der Mehr-Klassen-Textklassifikation. Der Fokus liegt auf Szenarien mit praxisnahen Startmengen und ihre Auswirkung für vielfältige Datensätze. Die Ergebnisse zeigen, dass Discriminate Active Learning im Umfeld der Untersuchung als einzige Methode über die Modelle und Daten hinweg signifikant besser ist als der Zufall. Andere Methoden sind in der Regel nicht besser als der Zufall, außer in einigen praxisnahen Situationen. Die Arbeit gewährt ebenfalls einen Einblick in den Einsatz des Menschen als Orakel. Durch eine Benutzerstudie wird beobachtet, dass für ein kleines Annotationsbudget Menschen eine konstante Leistung zeigen und Domänenwissen auch bei einfachen Kategorien hilfreich ist.Automated text classification is a promising analysis and moderation tool for many practical applications. In practice, text classification often faces expensive annotation costs and class imbalance in the training data. Active Learning describes a paradigm that can significantly reduce annotation costs by selectively annotating the most suitable data over multiple iterations. In conjunction with pre-trained language models, such as BERT and its variations, Active Learning has been little studied. This work investigates Active Learning for BERT models under the problem of multi-class text classification. The focus is on scenarios with practical warmstart sets and their impact for diverse datasets. The results show that Discriminate Active Learning is the only method that significantly outperforms the random baseline across models and datasets in the setting of the study. Other methods are outperfoming the baseline only for some real-world scenarios. The work also provides insight into the use of humans as an oracle. A user study concludes that humans show consistent performance over a small annotation budget and that domain knowledge is helpful even for simple categories

Functionally distinct patterns of nucleosome remodeling at enhancers in glucocorticoid-treated acute lymphoblastic leukemia

Background: Precise nucleosome positioning is an increasingly recognized feature of promoters and enhancers, reflecting complex contributions of DNA sequence, nucleosome positioning, histone modification and transcription factor binding to enhancer activity and regulation of gene expression. Changes in nucleosome position and occupancy, histone variants and modifications, and chromatin remodeling are also critical elements of dynamic transcriptional regulation, but poorly understood at enhancers. We investigated glucocorticoid receptor-associated (GR) nucleosome dynamics at enhancers in acute lymphoblastic leukemia. Results: For the first time, we demonstrate functionally distinct modes of nucleosome remodeling upon chromatin binding by GR, which we term central, non-central, phased, and minimal. Central and non-central remodeling reflect nucleosome eviction by GR and cofactors, respectively. Phased remodeling involves nucleosome repositioning and is associated with rapidly activated enhancers and induction of gene expression. Minimal remodeling sites initially have low levels of enhancer-associated histone modification, but the majority of these regions gain H3K4me2 or H3K27Ac to become de novo enhancers. Minimal remodeling regions are associated with gene ontologies specific to decreased B cell number and mTOR inhibition and may make unique contributions to glucocorticoid-induced leukemia cell death. Conclusions: Our findings form a novel framework for understanding the dynamic interplay between transcription factor binding, nucleosome remodeling, enhancer function, and gene expression in the leukemia response to glucocorticoids. Electronic supplementary material The online version of this article (doi:10.1186/s13072-015-0046-0) contains supplementary material, which is available to authorized users

Time to SARS‐CoV‐2 clearance among patients with cancer and COVID‐19

Abstract Background For cancer patients, coronavirus disease 19 (COVID‐19) infection can lead to delays in cancer therapy both due to the infection itself and due to the need to minimize exposure to other patients and to staff. Clearance guidelines have been proposed, but expected time to clearance has not been established. Methods We identified all patients at a tertiary care hospital cancer center between 25 March 2020 and 6 June 2020 with a positive nasopharyngeal reverse transcriptase polymerase chain reaction (RT‐PCR) test for the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), a cancer‐related visit within 3 years, and at least one follow‐up assay. We determined the time to clearance using American Society of Clinical Oncology (ASCO), the UK National Institute for Health and Care Excellence (UK‐NICE), and Centers for Disease Control and Prevention (CDC) criteria. A matched non‐cancer comparison cohort was also identified. Results Thirty‐two cancer patients were identified. Nineteen were cleared by ASCO criteria, with estimated median time to clearance of 50 days. Fourteen patients resumed chemotherapy prior to clearance. Using UK‐NICE criteria, median time to clearance would have been 31 days, and using CDC criteria, it would have been 13 days. The matched non‐cancer cohort had similar clearance time, but with less frequent testing. Conclusion SARS‐CoV‐2 clearance times differ substantially depending on the criteria used and may be prolonged in cancer patients. This could lead to a delay in cancer care, increased use of clearance testing, and extension of infection control precautions

MOESM1 of Functionally distinct patterns of nucleosome remodeling at enhancers in glucocorticoid-treated acute lymphoblastic leukemia

Additional file 1. GREAT output tables for the Gene Ontology (GO) and Mouse Phenotype ontologies (Excel format). GREAT analysis using all GR ChIP-Seq peaks (a), as well as subsets of GR peaks with central remodeling (b), minimal remodeling (c), non-central remodeling (d), or phased remodeling (e)

Neurodegeneration by α-synuclein-specific T cells in AAV-A53T-α-synuclein Parkinson’s disease mice

Background Antigen-specific neuroinflammation and neurodegeneration are characteristic for neuroimmunological diseases. In Parkinson’s disease (PD) pathogenesis, α-synuclein is a known culprit. Evidence for α-synuclein-specific T cell responses was recently obtained in PD. Still, a causative link between these α-synuclein responses and dopaminergic neurodegeneration had been lacking. We thus addressed the functional relevance of α-synuclein-specific immune responses in PD in a mouse model. Methods We utilized a mouse model of PD in which an Adeno-associated Vector 1/2 serotype (AAV1/2) expressing human mutated A53T-α-Synuclein was stereotactically injected into the substantia nigra (SN) of either wildtype C57BL/6 or Recombination-activating gene 1 (RAG1)$^{-/-}$ mice. Brain, spleen, and lymph node tissues from different time points following injection were then analyzed via FACS, cytokine bead assay, immunohistochemistry and RNA-sequencing to determine the role of T cells and inflammation in this model. Bone marrow transfer from either CD4$^{+}$/CD8$^{-}$, CD4$^{-}$/CD8$^{+}$, or CD4$^{+}$/CD8$^{+}$ (JHD$^{-/-}$) mice into the RAG-1$^{-/-}$ mice was also employed. In addition to the in vivo studies, a newly developed A53T-α-synuclein-expressing neuronal cell culture/immune cell assay was utilized. Results AAV-based overexpression of pathogenic human A53T-α-synuclein in dopaminergic neurons of the SN stimulated T cell infiltration. RNA-sequencing of immune cells from PD mouse brains confirmed a pro-inflammatory gene profile. T cell responses were directed against A53T-α-synuclein-peptides in the vicinity of position 53 (68–78) and surrounding the pathogenically relevant S129 (120–134). T cells were required for α-synuclein-induced neurodegeneration in vivo and in vitro, while B cell deficiency did not protect from dopaminergic neurodegeneration. Conclusions Using T cell and/or B cell deficient mice and a newly developed A53T-α-synuclein-expressing neuronal cell culture/immune cell assay, we confirmed in vivo and in vitro that pathogenic α-synuclein peptide-specific T cell responses can cause dopaminergic neurodegeneration and thereby contribute to PD-like pathology