Kai Armstrong

DNA origami enables the programmable self-assembly of nucleic acids into precisely defined nanostructures, yet the influence of primary base sequence on folding reliability remains incompletely understood. In particular, off-target interactions between scaffold and staple strands may introduce kinetic traps and reduce assembly yield, even when the intended Watson-Crick complementarity is preserved. Here we show that scaffold sequence strongly affects DNA origami assembly through the prevalence of off-target binding reactions implicit in the chosen base sequence. We developed a multi-objective computational framework that scores candidate scaffold sequences according to four classes of off-target interactions and selects variants predicted to minimise these effects for a given origami design. Using this approach, we identified both favourable and unfavourable scaffold regions from biological and synthetic sequences and tested them experimentally across 2D and 3D DNA origami structures. Atomic force microscopy showed that scaffolds predicted to have fewer off-target interactions consistently folded with higher yield, whereas off-target-prone scaffolds largely failed despite having fully complementary staple sets. Single-molecule optical tweezers further revealed that scaffold variants with fewer predicted off-target interactions assemble into more mechanically uniform origami structures. These results establish off-target sequence effects as a major determinant of origami folding and we provide a software tool to select scaffold sequences that minimise off-target reactions for any DNA origami design.

This work aims to discuss the current landscape of kinematic analysis tools, ranging from the state-of-the-art in sports biomechanics such as inertial measurement units (IMUs) and retroreflective marker-based optical motion capture (MoCap) to more novel approaches from the field of computing such as human pose estimation and human mesh recovery. Primarily, this comparative analysis aims to validate the use of marker-less MoCap techniques in a clinical setting by showing that these marker-less techniques are within a reasonable range for kinematics analysis compared to the more cumbersome and less portable state-of-the-art tools. Not only does marker-less motion capture using human pose estimation produce results in-line with the results of both the IMU and MoCap kinematics but also benefits from a reduced set-up time and reduced practical knowledge and expertise to set up. Overall, while there is still room for improvement when it comes to the quality of the data produced, we believe that this compromise is within the room of error that these low-speed actions that are used in small clinical tests.

In recent years the healthcare industry has had increased difficulty seeing all low-risk patients, including but not limited to suspected osteoarthritis (OA) patients. To help address the increased waiting lists and shortages of staff, we propose a novel method of automated biomarker identification and quantification for the monitoring of treatment or disease progression through the analysis of clinical motion data captured from a standard RGB video camera. The proposed method allows for the measurement of biomechanics information and analysis of their clinical significance, in both a cheap and sensitive alternative to the traditional motion capture techniques. These methods and results validate the capabilities of standard RGB cameras in clinical environments to capture clinically relevant motion data. Our method focuses on generating 3D human shape and pose from 2D video data via adversarial training in a deep neural network with a self-attention mechanism to encode both spatial and temporal information. Biomarker identification using Principal Component Analysis (PCA) allows the production of representative features from motion data and uses these to generate a clinical report automatically. These new biomarkers can then be used to assess the success of treatment and track the progress of rehabilitation or to monitor the progression of the disease. These methods have been validated with a small clinical study, by administering a local anaesthetic to a small population with knee pain, this allows these new representative biomarkers to be validated as statistically significant (p-value < 0.05). These significant biomarkers include the cumulative acceleration of elbow flexion/extension in a sit-to-stand, as well as the smoothness of the knee and elbow flexion/extension in both a squat and sit-to-stand.