From BookTok to Bookshelf: Algorithms and Book Recommendations on TikTok

TikTok, a social media platform focused on short-form videos, is gaining a reputation for renewing interest in books (Bateman 2022; Harris 2021). While reviewing and recommending books is not new, the ability to do so on a large scale used to be limited to a select group of critics. Social media allows readers to voice their opinions, and by gaining followings these readers can then influence at a similar scale as traditional reviewers. This raises various questions as to how culture is created and curated. Today, this curation is done largely by algorithms through recommending and promoting content. The rise of BookTok emphasizes this, combining recommendations with TikTok’s algorithm to boost the popularity of certain books. In particular, BookTok has made headlines by repeatedly raising backlist books back onto the bestseller lists. This increases the shift from traditional curators of culture to a community of fellow readers, which can in turn popularize specific genres. Thus, the main question this thesis aims to answer is: what distinguishes BookTok from other digital platforms, enabling it to have such a cultural impact going beyond the online book community? The BookTok phenomenon will be explained by using a mixed-method approach looking at how creators use platform affordances, aesthetic features, and their algorithmic imaginaries to appeal to both users and the TikTok algorithm. The data used in this thesis consists of 148 BookTok videos gathered over a two-week period from the “For You” page. A content analysis was conducted to find patterns in the construction of the videos, the use of specific aesthetic features, and the selection of recommended book titles. Based on this data, it was possible to detect and describe different genres of BookTok videos and to identify the use of relevant platform affordances. This was complemented by a thematic analysis of interviews with three video creators, selected from the authors of the material in the dataset. The interviews gave insight into the algorithmic imaginary of the creators and how the construction of the algorithm informs the creative process. The analysis showed that while the algorithm is what makes the recommendations popular by distributing them to a receptive audience, the TikTok format is what makes the recommendations memorable and has a positive impact on book sales. As the algorithm informs every aspect of the book recommendations, from the creator’s decisions of picking a certain book to the decisions on when to make the video and who the algorithm subsequently recommends the video to, the book recommendations on BookTok can be examined as examples of algorithmic curation. By taking up the topic of literature and literary readership from a digital culture perspective, this thesis aims to contribute to the greater discussions on algorithms, personalization, and its’ effect on cultural production and curation.Master's Thesis in Digital CultureDIKULT350MAHF-DIKU

Delivering rhFGF-18 via a bilayer collagen membrane to enhance microfracture treatment of chondral defects in a large animal model.

Augmented microfracture techniques use growth factors, cells, and/or scaffolds to enhance the healing of microfracture-treated cartilage defects. This study investigates the effect of delivering recombinant human fibroblastic growth factor 18 (rhFHF18, Sprifermin) via a collagen membrane on the healing of a chondral defect treated with microfracture in an ovine model. Eight millimeter diameter chondral defects were created in the medial femoral condyle of 40 sheep (n = 5/treatment group). Defects were treated with microfracture alone, microfracture + intra-articular rhFGF-18 or microfracture + rhFGF-18 delivered on a membrane. Outcome measures included mechanical testing, weight bearing, International Cartilage Repair Society repair score, modified O'Driscoll score, qualitative histology, and immunohistochemistry for types I and II collagen. In animals treated with 32 μg rhFGF-18 + membrane and intra-articularly, there was a statistically significant improvement in weight bearing at 2 and 4 weeks post surgery and in the modified O'Driscoll score compared to controls. In addition, repair tissue stained was more strongly stained for type II collagen than for type I collagen. rhFGF-18 delivered via a collagen membrane at the point of surgery potentiates the healing of a microfracture treated cartilage defect.This is the author accepted manuscript. The final version is available via Wiley at http://onlinelibrary.wiley.com/doi/10.1002/jor.22882/abstract

Tissue distribution of sprifermin (recombinant human fibroblast growth factor 18) in the rat following intravenous and intra-articular injection

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Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks

A Magnetic Resonance Imaging (MRI) exam typically consists of the acquisition of multiple MR pulse sequences, which are required for a reliable diagnosis. Each sequence can be parameterized through multiple acquisition parameters affecting MR image contrast, signal-to-noise ratio, resolution, or scan time. With the rise of generative deep learning models, approaches for the synthesis of MR images are developed to either synthesize additional MR contrasts, generate synthetic data, or augment existing data for AI training. However, current generative approaches for the synthesis of MR images are only trained on images with a specific set of acquisition parameter values, limiting the clinical value of these methods as various sets of acquisition parameter settings are used in clinical practice. Therefore, we trained a generative adversarial network (GAN) to generate synthetic MR knee images conditioned on various acquisition parameters (repetition time, echo time, image orientation). This approach enables us to synthesize MR images with adjustable image contrast. In a visual Turing test, two experts mislabeled 40.5% of real and synthetic MR images, demonstrating that the image quality of the generated synthetic and real MR images is comparable. This work can support radiologists and technologists during the parameterization of MR sequences by previewing the yielded MR contrast, can serve as a valuable tool for radiology training, and can be used for customized data generation to support AI training

Return to work after acromioclavicular joint stabilization: a retrospective case control study

Background: Considering the epidemiology of acromioclavicular (AC) dislocation related to young and active patients, the impact on working capacity is highly relevant. The purpose of this study was to determine the capacity of work and time to return to work (RTW) after AC joint stabilization. We hypothesized that manual working patients show more restrictions returning to work. Methods: In this retrospective case series, pre- and posttraumatic working capacity of 54 patients (FU-rate 80.1%, FU time 23, range 18–45 month) stabilized in single TightRope technique was analyzed. Clinical outcome (DASH, Constant-Murley score) and complications were evaluated in addition. Results: Fifty one of 54 patients (94.5%) were returned to work at final follow-up. The median time to return was 13 (5–143) weeks. Manual working patients showed lower RTW-rates (91.2% vs. 100%; p = .151), longer RTW-time (15.5 vs. 6 weeks; p = .008), and more often persistent shoulder symptoms at work (55.9% vs. 5%; p < .001). Conclusion: After stabilization of AC joint dislocation, the majority of patients returned to work, needing substantial time to return. Manual working patients required more time and often suffer under persistent symptoms at work