Machine learning techniques for implicit interaction using mobile sensors

Interactions in mobile devices normally happen in an explicit manner, which means that they are initiated by the users. Yet, users are typically unaware that they also interact implicitly with their devices. For instance, our hand pose changes naturally when we type text messages. Whilst the touchscreen captures finger touches, hand movements during this interaction however are unused. If this implicit hand movement is observed, it can be used as additional information to support or to enhance the users’ text entry experience. This thesis investigates how implicit sensing can be used to improve existing, standard interaction technique qualities. In particular, this thesis looks into enhancing front-of-device interaction through back-of-device and hand movement implicit sensing. We propose the investigation through machine learning techniques. We look into problems on how sensor data via implicit sensing can be used to predict a certain aspect of an interaction. For instance, one of the questions that this thesis attempts to answer is whether hand movement during a touch targeting task correlates with the touch position. This is a complex relationship to understand but can be best explained through machine learning. Using machine learning as a tool, such correlation can be measured, quantified, understood and used to make predictions on future touch position. Furthermore, this thesis also evaluates the predictive power of the sensor data. We show this through a number of studies. In Chapter 5 we show that probabilistic modelling of sensor inputs and recorded touch locations can be used to predict the general area of future touches on touchscreen. In Chapter 7, using SVM classifiers, we show that data from implicit sensing from general mobile interactions is user-specific. This can be used to identify users implicitly. In Chapter 6, we also show that touch interaction errors can be detected from sensor data. In our experiment, we show that there are sufficient distinguishable patterns between normal interaction signals and signals that are strongly correlated with interaction error. In all studies, we show that performance gain can be achieved by combining sensor inputs.

Molecular probes for monitoring mitochondrial movement and function

This thesis explores two distinct parts of mitochondrial physiology: the role of mitochondria in generation of reactive oxygen species (ROS) and mitochondrial morphology and dynamics within cells. The first area of research is covered in Chapters 1-8. Mitochondrial biofunctionality and ROS production are discussed in Chapter 1, followed by the strategy of targeting bioactive compounds to mitochondria by linking them to lipophilic triphenylphosphonium cations (TPP) (Chapter 2). ROS sensors relevant to the research are reviewed in Chapter 3. Chapter 4 presents design and synthesis of novel probes for superoxide detection in mitochondria (MitoNeo-D), cytosol (Neo-D) and extracellular environment (ExCellNeo-D). The results of biological validation of MitoNeo-D and Neo-D performed in the MRC MBU in Cambridge are presented in Chapter 5. A dicationic hydrogen peroxide sensor that utilizes in situ click chemistry is discussed in Chapter 6. Preliminary work on the synthesis of mitochondria-targeted superoxide generators, which led to the development of mitochondria-targeted analogue of paraquat, MitoPQ, is presented in Chapter 7. A set of bifunctional probes (BCN-Mal, BCN-E-BCN and Mito-iTag) for assessing the redox states of protein thiols is discussed in Chapter 8 along with their biological validation. The second part of the thesis is aimed at the study of mitochondrial morphology and dynamics and is presented in Chapters 9-11. Chapter 9 provides background on the classes of fluorophores relevant to the research, the phenomenon of fluorescence quenching and the principle of photoactivation with examples of photoactivatable fluorophores. Next, the background on mitochondrial morphology and heterogeneity is presented in Chapter 10, followed by the ways of imaging and tracking mitochondria within cells by conventional fluorophores and by photoactivatable fluorophores exploiting super-resolution microscopy. Chapter 11 presents the design and synthesis of four photoactivatable fluorophores for mitochondrial tracking, MitoPhotoRhod110, MitoPhotoNIR, Photo-E+, MitoPhoto-E+, along with results of biological validation of MitoPhotoNIR. The results and discussion concludes with Chapter 12, which is a summary and suggestions for future work, followed by the chemistry experimental procedures (Chapter 13), materials and methods for biological experiments (Chapter 14) and references.