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Primary stability is crucial for implant osseointegration and the long-term stability of cementless total joint replacements. Biomechanical studies have shown the potential of femoral stems for total knee replacements to reduce micromotions at the bone-implant interface. However, approaches such as focusing on the structural elasticity of the femoral stems are rarely described. Three groups with different femoral stem designs were investigated: group 1: flexible surface stem, group 2: flexible surface stem with open-porous structured lamellas, and group 3: solid stem (reference). The stems were implanted into bone substitute material and dynamically loaded for 1000 cycles. Relative movement and subsidence were measured optically, and axial pull-out forces were determined after dynamic testing. Relative movements increased to 0.10 mm (groups 1 and 2) compared to 0.03 mm (group 3). Subsidence increased to 0.08 mm (group 1) and 0.11 mm (group 2) compared to 0.06 mm (group 3). For each group, subsidence mainly occurred during the first 500 cycles. A similar convergence was observed in the further course. Pull-out forces increased to 1815.0 N (group 1) and 1347.1 N (group 2) compared to 1306.4 N (group 3). The flexible surface stem design resulted in higher relative movements and subsidence, but also exhibited increased pull-out forces. The relative movements were below the critical limit of 0.15 mm and represent a superposition of the elastic deformations of the interacting implant components as well as the micromotion at the bone-implant interface. Therefore, the novel flexible surface stem design appears to offer promising primary implant fixation. Graphical abstract [ABSTRACT FROM AUTHOR]

High-resolution annotations of transcriptomes from all domains of life are essential for many sequencing-based RNA analyses, including Nanopore direct RNA sequencing (DRS), which would otherwise be hindered by misalignments and other analysis artefacts. DRS allows the capture and full-length sequencing of native RNAs, without recoding or amplification bias, and resulting data may be interrogated to define the identity and location of chemically modified ribonucleotides, as well as the length of poly(A) tails on individual RNA molecules. Existing software solutions for generating high-resolution transcriptome annotations are poorly suited to small gene dense organisms such as viruses due to the challenge of identifying distinct transcript isoforms where alternative splicing and overlapping RNAs are prevalent. To resolve this, we identified key characteristics of DRS datasets and developed a novel approach to transcriptome. We demonstrate, using a combination of synthetic and original datasets, that our novel approach yields a high level of precision and recall when reconstructing both gene sparse and gene dense transcriptomes from DRS datasets. We further apply this approach to generate a new high resolution transcriptome annotation of the neglected pathogen human adenovirus type F 41 for which we identify 77 distinct transcripts encoding at least 23 different proteins.