Cell-free DNA (cfDNA) are DNA fragments originating from dying cells that enter the plasma. Uncontrolled cell death, for example caused by cancer, induces an elevated concentration of cfDNA. As a result, determining the cell type origins of cfDNA can provide information about an individual's health. This research looks into how to increase the sensitivity of a methylation-based cell type deconvolution method. We do this by adapting an existing method, CelFiE, which uses the methylation values of individual CpG sites to estimate cell type proportions. Our new method, named CelFEER, instead differentiates cell types by the average methylation values of individual reads. We additionally improved the originally reported performance of CelFiE by using a new approach for finding marker regions in the genome that are differentially methylated between cell types. This approach compares the methylation values over 500 bp regions instead of at single CpG sites and solely takes hypomethylated regions into account. We show that CelFEER estimates cell type proportions with a higher correlation than CelFiE on simulated mixtures of cfDNA. Moreover, we found that it can find a significant difference between skeletal muscle cfDNA in ALS patients (n=4) and a control group (n=4).

In recent years many new text generation models have been developed while evaluation of text generation remains a considerable challenge.  Currently, the only metric that is able to fully capture the quality of a generated text is human evaluation, which is expensive and time consuming. One of the most used intrinsic evaluation metrics is perplexity. This paper researched the correspondence between perplexity scores and human evaluation of scripts for the TV-show \textit{Friends} generated using OpenAI's GPT-2 model.  This was done by conducting a survey taken by 226 participants that evaluated selected scripts on creativity, realism and coherence.  The survey results revealed that generations with a perplexity value close to that of an actual Friends script perform best on creativity, but score low on realism and coherence.  The most realistic and coherent generations were those with a lower perplexity value, while the worst in all fields were the generations with the highest perplexity value.  The research shows that perplexity is not an adequate measure for the quality of generated TV-show scripts.