Experimental Aspects of Synthesis

We discuss the problem of experimentally evaluating linear-time temporal logic (LTL) synthesis tools for reactive systems. We first survey previous such work for the currently publicly available synthesis tools, and then draw conclusions by deriving useful schemes for future such evaluations. In particular, we explain why previous tools have incompatible scopes and semantics and provide a framework that reduces the impact of this problem for future experimental comparisons of such tools. Furthermore, we discuss which difficulties the complex workflows that begin to appear in modern synthesis tools induce on experimental evaluations and give answers to the question how convincing such evaluations can still be performed in such a setting.Comment: In Proceedings iWIGP 2011, arXiv:1102.374

Towards PACE-CAD Systems

Despite phenomenal advancements in the availability of medical image datasets and the development of modern classification algorithms, Computer-Aided Diagnosis (CAD) has had limited practical exposure in the real-world clinical workflow. This is primarily because of the inherently demanding and sensitive nature of medical diagnosis that can have far-reaching and serious repercussions in case of misdiagnosis. In this work, a paradigm called PACE (Pragmatic, Accurate, Confident, & Explainable) is presented as a set of some of must-have features for any CAD. Diagnosis of glaucoma using Retinal Fundus Images (RFIs) is taken as the primary use case for development of various methods that may enrich an ordinary CAD system with PACE. However, depending on specific requirements for different methods, other application areas in ophthalmology and dermatology have also been explored. Pragmatic CAD systems refer to a solution that can perform reliably in day-to-day clinical setup. In this research two, of possibly many, aspects of a pragmatic CAD are addressed. Firstly, observing that the existing medical image datasets are small and not representative of images taken in the real-world, a large RFI dataset for glaucoma detection is curated and published. Secondly, realising that a salient attribute of a reliable and pragmatic CAD is its ability to perform in a range of clinically relevant scenarios, classification of 622 unique cutaneous diseases in one of the largest publicly available datasets of skin lesions is successfully performed. Accuracy is one of the most essential metrics of any CAD system's performance. Domain knowledge relevant to three types of diseases, namely glaucoma, Diabetic Retinopathy (DR), and skin lesions, is industriously utilised in an attempt to improve the accuracy. For glaucoma, a two-stage framework for automatic Optic Disc (OD) localisation and glaucoma detection is developed, which marked new state-of-the-art for glaucoma detection and OD localisation. To identify DR, a model is proposed that combines coarse-grained classifiers with fine-grained classifiers and grades the disease in four stages with respect to severity. Lastly, different methods of modelling and incorporating metadata are also examined and their effect on a model's classification performance is studied. Confidence in diagnosing a disease is equally important as the diagnosis itself. One of the biggest reasons hampering the successful deployment of CAD in the real-world is that medical diagnosis cannot be readily decided based on an algorithm's output. Therefore, a hybrid CNN architecture is proposed with the convolutional feature extractor trained using point estimates and a dense classifier trained using Bayesian estimates. Evaluation on 13 publicly available datasets shows the superiority of this method in terms of classification accuracy and also provides an estimate of uncertainty for every prediction. Explainability of AI-driven algorithms has become a legal requirement after Europe’s General Data Protection Regulations came into effect. This research presents a framework for easy-to-understand textual explanations of skin lesion diagnosis. The framework is called ExAID (Explainable AI for Dermatology) and relies upon two fundamental modules. The first module uses any deep skin lesion classifier and performs detailed analysis on its latent space to map human-understandable disease-related concepts to the latent representation learnt by the deep model. The second module proposes Concept Localisation Maps, which extend Concept Activation Vectors by locating significant regions corresponding to a learned concept in the latent space of a trained image classifier. This thesis probes many viable solutions to equip a CAD system with PACE. However, it is noted that some of these methods require specific attributes in datasets and, therefore, not all methods may be applied on a single dataset. Regardless, this work anticipates that consolidating PACE into a CAD system can not only increase the confidence of medical practitioners in such tools but also serve as a stepping stone for the further development of AI-driven technologies in healthcare

Transcription factor expression levels and environmental signals constrain transcription factor innovation

Evolutionary innovation of transcription factors frequently drives phenotypic diversification and adaptation to environmental change. Transcription factors can gain or lose connections to target genes, resulting in novel regulatory responses and phenotypes. However the frequency of functional adaptation varies between different regulators, even when they are closely related. To identify factors influencing propensity for innovation, we utilise a Pseudomonas fluorescens SBW25 strain rendered incapable of flagellar mediated motility in soft-agar plates via deletion of the flagellar master regulator (fleQ). This bacterium can evolve to rescue flagellar motility via gene regulatory network rewiring of an alternative transcription factor to rescue activity of FleQ. Previously, we have identified two members (out of 22) of the RpoN-dependent enhancer binding protein (RpoN-EBP) family of transcription factors (NtrC and PFLU1132) that are capable of innovating in this way. These two transcription factors rescue motility repeatably and reliably in a strict hierarchy – with NtrC the only route in a ∆fleQ background, and PFLU1132 the only route in a ∆fleQ∆ntrC background. However, why other members in the same transcription factor family have not been observed to rescue flagellar activity is unclear. Previous work shows that protein homology cannot explain this pattern within the protein family (RpoN-EBPs), and mutations in strains that rescued motility suggested high levels of transcription factor expression and activation drive innovation. We predict that mutations that increase expression of the transcription factor are vital to unlock evolutionary potential for innovation. Here, we construct titratable expression mutant lines for 11 of the RpoN-EBPs in P. fluorescens. We show that in five additional RpoN-EBPs (FleR, HbcR, GcsR, DctD, AauR and PFLU2209), high expression levels result in different mutations conferring motility rescue, suggesting alternative rewiring pathways. Our results indicate that expression levels (and not protein homology) of RpoN-EBPs are a key constraining factor in determining evolutionary potential for innovation. This suggests that transcription factors that can achieve high expression through few mutational changes, or transcription factors that are active in the selective environment, are more likely to innovate and contribute to adaptive gene regulatory network evolution

Optical imaging methods for the study of disease models from the nano to the mesoscale

The visualisation of disease phenotypes allows scientists to study fundamental mechanisms of disease. Optical imaging methods are useful not only to observe anatomical features of biological samples, but also to infer interactions between molecular species using fluorescence labelling. This thesis presents the development of imaging and analysis tools to study biological questions in three models of disease, with samples ranging from the sub-cellular to the organ scale. First, the role of the alpha-synuclein (a-syn) protein, whose dysfunction is a hallmark of Parkinson’s Disease, was studied with respect to vesicle trafficking at the synapse. Synaptic vesicles are ∼40 nm in diameter; imaging vesicles therefore requires methods with resolution below the diffraction limit. Single-molecule localisation microscopy (SMLM), which circumvents the diffraction limit by separating fluorophore emission in time to localise individual molecules in space with ∼20 nm precision, was thus implemented to study a-syn in purified synaptic boutons. A software package was developed to analyse the colocalisation of a-syn with internalised vesicles, and the clustering of a-syn under differing synaptic calcium levels. The colocalisation of a-syn and internalised vesicles was found to be temperature independent, suggesting that a-syn is involved in non-canonical trafficking mechanisms. Ground truth simulations from a synaptosome model were used to benchmark two cluster analysis methods. Both methods applied on the experimental data showed that a-syn becomes less clustered at low synaptic calcium levels. Second, the spatiotemporal association of ESCRT-II, a protein complex whose role in the budding of the human immunodeficiency virus (HIV) was previously considered dispensable, and the HIV polyprotein Gag was studied during viral egress using novel image analysis tools. A nearest-neighbour analysis showed the ESCRT-II protein EAP45 colocalises with Gag similarly to ALIX, a protein well known to be involved in HIV budding. However, upon deletion of EAP45’s N-terminus, its colocalisation with Gag was significantly impaired, highlighting the importance of this EAP45 domain in linking to Gag. Single particle tracking was used to trace the trajectories of EAP45 and Gag in live cells, and an algorithm was developed to visualise the simultaneous motion of two particles; these analyses revealed three types of potential dynamic interaction between EAP45 and Gag. Finally, an open-source instrument to visualise phenotypes from large organs in 3D was developed for the study of chronic obstructive pulmonary disease (COPD) models. The instrument implements Optical Projection Tomography, a technique which can reconstruct cross-sectional slices of a transparent object from its orthographic projections, using off-the- shelf components and novel ImageJ plugins for artefact correction and volume reconstructions. Excised and cleared mouse lungs were imaged in which high order airways can be discerned with 50 μm resolution. The raw lung data, instructions for building the instrument, the free ImageJ plugins, and a detailed software manual are available in an online repository to encourage the widespread use of OPT for imaging large samples.Gates Cambridg

Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall

A number of studies have investigated the large-scale drivers and upstream-precursors of extreme weather events, making it clear that the earliest warning signs of extreme events can be remote in both time and space from the impacted region. Integrating and leveraging our understanding of dynamical precursors provides a new perspective on ensemble forecasting for extreme events, focused on building story-lines of possible event evolution. This then acts as a tool for raising awareness of the conditions conducive to high-impact weather, and providing early warning of their possible development. However, operational applications of this developing knowledge-base is limited so far, perhaps for want of a clear framework for doing so. Here, we present such a framework, supported by open software tools, designed for identifying large-scale precursors of categorical weather events in an automated fashion, and for reducing them to scalar indices suitable for statistical prediction, forecast interpretation, and model validation. We demonstrate this framework by systematically analysing the precursor circulations of daily rainfall extremes across 18 regional- to national-scale European domains. We discuss the precursor rainfall dynamics for three disparate regions, and show our findings are consistent with, and extend, previous work. We provide an estimate of the predictive utility of these precursors across Europe based on logistic regression, and show that large-scale precursors can usefully predict heavy rainfall between two and six days ahead, depending on region and season. We further show how for more continental-scale applications the regionally-specific precursors can be synthesised into a minimal set of indices that drive heavy precipitation. We then provide comments and guidance for generalisation and application of our demonstrated approach to new variables, timescales and regions.Comment: 3 figure SI, 22 manuscript pages, 10 figures, submitted to QJRM

Interictal Network Dynamics in Paediatric Epilepsy Surgery

Epilepsy is an archetypal brain network disorder. Despite two decades of research elucidating network mechanisms of disease and correlating these with outcomes, the clinical management of children with epilepsy does not readily integrate network concepts. For example, network measures are not used in presurgical evaluation to guide decision making or surgical management plans. The aim of this thesis was to investigate novel network frameworks from the perspective of a clinician, with the explicit aim of finding measures that may be clinically useful and translatable to directly benefit patient care. We examined networks at three different scales, namely macro (whole brain diffusion MRI), meso (subnetworks from SEEG recordings) and micro (single unit networks) scales, consistently finding network abnormalities in children being evaluated for or undergoing epilepsy surgery. This work also provides a path to clinical translation, using frameworks such as IDEAL to robustly assess the impact of these new technologies on management and outcomes. The thesis sets up a platform from which promising computational technology, that utilises brain network analyses, can be readily translated to benefit patient care

A Creative Data Ontology for the Moving Image Industry

The moving image industry produces an extremely large amount of data and associated metadata for each media creation project, often in the range of terabytes. The current methods used to organise, track, and retrieve the metadata are inadequate, with metadata often being hard to find. The aim of this thesis is to explore whether there is a practical use case for using ontologies to manage metadata in the moving image industry and to determine whether an ontology can be designed for such a purpose and can be used to manage metadata more efficiently to improve workflows. It presents a domain ontology, hereby referred to as the Creative Data Ontology, engineered around a set of metadata fields provided by Evolutions, Double Negative (DNEG), and Pinewood Studios, and four use cases. The Creative Data Ontology is then evaluated using both quantitative methods and qualitative methods (via interviews) with domain and ontology experts.Our findings suggest that there is a practical use case for an ontology-based metadata management solution in the moving image industry. However, it would need to be presented carefully to non-technical users, such as domain experts, as they are likely to experience a steep learning curve. The Creative Data Ontology itself meets the criteria for a high-quality ontology for the sub-sectors of the moving image industry domain that it provides coverage for (i.e. scripted film and television, visual effects, and unscripted television) and it provides a good foundation for expanding into other sub-sectors of the industry, although it cannot yet be considered a ``standard'' ontology. Finally, the thesis presents the methodological process taken to develop the Creative Data Ontology and the lessons learned during the ontology engineering process which can be valuable guidance for designers and developers of future metadata ontologies. We believe such guidance could be transferable across many domains where an ontology of metadata is required, which are unrelated to the moving image industry. Future research may focus on assisting non-technical users to overcome the learning curve, which may also also applicable to other domains that may choose to use ontologies in the future

Enhancing Geospatial Data: Collecting and Visualising User-Generated Content Through Custom Toolkits and Cloud Computing Workflows

Through this thesis we set the hypothesis that, via the creation of a set of custom toolkits, using cloud computing, online user-generated content, can be extracted from emerging large-scale data sets, allowing the collection, analysis and visualisation of geospatial data by social scientists. By the use of a custom-built suite of software, known as the ‘BigDataToolkit’, we examine the need and use of cloud computing and custom workflows to open up access to existing online data as well as setting up processes to enable the collection of new data. We examine the use of the toolkit to collect large amounts of data from various online sources, such as Social Media Application Programming Interfaces (APIs) and data stores, to visualise the data collected in real-time. Through the execution of these workflows, this thesis presents an implementation of a smart collector framework to automate the collection process to significantly increase the amount of data that can be obtained from the standard API endpoints. By the use of these interconnected methods and distributed collection workflows, the final system is able to collect and visualise a larger amount of data in real time than single system data collection processes used within traditional social media analysis. Aimed at allowing researchers without a core understanding of the intricacies of computer science, this thesis provides a methodology to open up new data sources to not only academics but also wider participants, allowing the collection of user-generated geographic and textual content, en masse. A series of case studies are provided, covering applications from the single researcher collecting data through to collection via the use of televised media. These are examined in terms of the tools created and the opportunities opened, allowing real-time analysis of data, collected via the use of the developed toolkit