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The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.
In dieser Bachelorarbeit werden Modelle, mit einer hohen Anzahl an Vertices, mittels CPU und GPU geclustered und die Performance der hierzu verwendeten Algorithmen miteinander verglichen. Die Nutzung der GPU findet hierbei unter Verwendung von OpenGL statt. Zunächst werden Grundlagen von Clustering, die für die später implementierten Algorithmen wichtig sind, geklärt. Zusätzlich werden Prozesse erkärt mit denen die Ergebnisse der, auf der GPU ausgeführten, Algorithmen, auf die CPU zurückgeführt werden können. Anschließend erfolgt eine Beschreibung der implementierten Algorithmen sowie eine Erklärung ihrer Funktionsweise. Abschließend wurde ein Benchmarking der Algorithmen vorgenommen, um ihre Laufzeiten miteinander zu vergleichen.
Students of computer science studies enter university education with very different competencies, experience and knowledge. 145 datasets collected of freshmen computer science students by learning management systems in relation to exam outcomes and learning dispositions data (e. g. student dispositions, previous experiences and attitudes measured through self-reported surveys) has been exploited to identify indicators as predictors of academic success and hence make effective interventions to deal with an extremely heterogeneous group of students.
We propose and create a new data model for learning specific environments and learning analytics applications. This is motivated from the experience in the Fiber Bundle Data Model used for large - time and space dependent - data. Our proposed data model integrates file or stream-based data structures from capturing devices more easily. Learning analytics algorithms are added directly to the data, and formulation of queries and analytics is done in Python. It is designed to improve collaboration in the field of learning analytics. We leverage a hierarchical data structure, where varying data is located near the leaves. Abstract data types are identified in four distinct pathways, which allow storing most diverse data sources. We compare different implementations regarding its memory footprint and performance. Our tests indicate that LeAn Bundles can be smaller than a naïve xAPI export. The benchmarks show that the performance is comparable to a MongoDB, while having the benefit of being portable and extensible.
Purpose: Discordance between pre-operative biopsy and final pathology for Upper Tract Urothelial Carcinoma (UTUC) is high and optimal management remains controversial. The aim of this study is to evaluate the accuracy of pre-operative biopsy, to identify prognostic factors and to evaluate the effect of adjuvant chemotherapy on survival and oncologic outcome in UTUC.
Methods: We analyzed records of patients receiving surgical treatment for UTUC. Pathology of pre-operative biopsy was compared to surgical specimen. We used Kaplan-Meier method to estimate survival probabilities and Cox's proportional hazards models to estimate the association between covariates and event times. Primary endpoint was overall survival (OS). A matched-pair analysis was performed to evaluate the effect of adjuvant chemotherapy.
Results: 151 patients underwent surgical treatment (28% open, 36% laparoscopic, 17% robotic radical nephroureterectomy; 14% segmental ureteral resections and 5% palliative nephrectomy) for UTUC and were included in the analysis. Upstaging from <pT1 in endoscopic biopsy to ≥pT1 in final pathology occurred in 61% of patients and upgrading from low-grade to high-grade occurred in 30% of patients. Five-year OS was 59.5%. In the univariate Cox-regression model pathological stage, grade, lymphovascular invasion and positive surgical margins were associated with OS. Matched pair analysis for stage (<pT3; ≥pT3; pN+) and age revealed a significant survival benefit for adjuvant chemotherapy (HR 0.40, 0.14–0.77, p < 0.018) in this cohort.
Conclusion: UTUC is often underestimated in pre-operative biopsy, and it is associated with significant mortality. Pathological stage and grade, lymphovascular invasion and lymph node metastases are predictors of oncologic outcome and survival.