Research, published.

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.

Medical action recognition is an increasingly necessary task as healthcare has shifted towards digital and more remote methods of patient monitoring. To remotely assess a patient with computer-aided diagnosis it is first necessary to identify different actions. In tandem with action recognition, this remote monitoring also requires biomarker identification. This allows meaningful features to be extracted from the patient's actions, however, each action often requires a different set of features to be monitored. Finally, the mode of data collection requires both portability and accuracy to be used in the healthcare industry. To combine each of these, one of the best solutions for condition-related action recognition is using videos in combination with human pose estimation to create spatio-temporal skeleton data for the patients which allows further analysis to be fast and accurate. By first using manual feature extraction on the skeleton sequences, it is possible to utilise machine learning to both classify different actions and identify latent features as the biomarkers that distinguish each of the action classes. Thus, this new method proposes a new zero-dimensional feature extraction method to classify skeleton sequences and extract medically meaningful, explainable, and objective biomarkers that can be used for the diagnosis and monitoring of patients in a remote setting.

In recent years the NHS has been having increased difficulty seeing all low-risk patients, this includes 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 for diagnosis of knee disorders and the monitoring of treatment progression. The proposed method allows for the measurement and analysis of biomechanics and analyse their clinical significance, in both a cheap and sensitive alternative to the currently available commercial alternatives. These methods and results validate the capabilities of standard RGB cameras in clinical environments to capture motion and show that when compared to alternatives such as depth cameras there is a comparable accuracy in the clinical environment. Biomarker identification using Principal Component Analysis (PCA) allows the reduction of the dimensionality to produce the most representative features from motion data, these new biomarkers can then be used to assess the success of treatment and track the progress of rehabilitation. This was validated by applying these techniques on a case study utilising the exploratory use of local anaesthetic applied on knee pain, this allows these new representative biomarkers to be validated as statistically significant (p-value < 0.05).

Marker-less motion capture is a rapidly advancing field that can take simple RGB image sequences, or more advanced RGB-D image sequences obtained using depth sensors, and output an estimated human pose. This method of human pose estimation allows for the extraction of biomechanical features which can then be analysed by clinicians to give more insights into a patient's movement capabilities. When compared to other, more clinically proven technologies such as the Knee Kinesiography (KneeKG), biomechanics presented have the advantage of being more representative of natural movement without the obstructive markers placed on the body. This significant difference of up to 10 degrees in range of motion for the knee could be the key to better identifying a person's gait or tracking their natural walking pattern over time, while also being more robust and better suited to a smaller clinical environment.