A Study to Explore the Impact of Endometriosis in the United Kingdom: A Qualitative Content Analysis

Objective To gain insight into the areas that impact women with endometriosis. Design A qualitative content analysis of an online survey. Setting Online questionnaire via Endometriosis UK. Population Women diagnosed with endometriosis of any age range. Methods Free-text online questionnaire through Endometriosis UK completed by women. Results were analysed using NVivo version 9, qualitative analysis software. The software creates links between common words (codes), and these links allow data to be placed in nodes (called themes) which are then developed into categories. Content analysis was used to understand this data.  Main outcome measures  Impact of endometriosis on women’s lives. Results In total, 1872 questionnaires were returned but not everyone was able to identify ten separate features that affected them. As such, 1872 women provided at least one area that affected them, 1800 provided two areas, 1770 provided three areas and 1600 provided four areas. The results show that the main areas of concern for these women were pain (53%), heavy menstrual bleeding (11%), low mood (8%) and the perceived lack of understanding displayed by other people (7%). Other important factors were fertility concerns, impact on employment, problems with the medical team and uncertainty. These then impacted on their daily life whereby some women felt “guilty” for not ‘being a normal mother’. A key term that resonated was that endometriosis is an “invisible disease”. Conclusion This analysis provides us with insight into the complex psycho-social factors that interact with bio-physical symptoms. Further research is required in sub-population groups such as teenagers and ethnic minority women to explore any differences in impact and how care can be guided accordingly

ADAPTIVE SAMPLING METHODS FOR TESTING AUTONOMOUS SYSTEMS

In this dissertation, I propose a software-in-the-loop testing architecture that uses adaptive sampling to generate test suites for intelligent systems based upon identifying transitions in high-level mission criteria. Simulation-based testing depends on the ability to intelligently create test-cases that reveal the greatest information about the performance of the system in the fewest number of runs. To this end, I focus on the discovery and analysis of performance boundaries. Locations in the testing space where a small change in the test configuration leads to large changes in the vehicle's behavior. These boundaries can be used to characterize the regions of stable performance and identify the critical factors that affect autonomous decision making software. By creating meta-models which predict the locations of these boundaries we can efficiently query the system and find informative test scenarios. These algorithms form the backbone of the Range Adversarial Planning Tool (RAPT): a software system used at naval testing facilities to identify the environmental triggers that will cause faults in the safety behavior of unmanned underwater vehicles (UUVs). This system was used to develop UUV field tests which were validated on a hardware platform at the Keyport Naval Testing Facility. The development of test cases from simulation to deployment in the field required new analytical tools. Tools that were capable of handling uncertainty in the vehicle's performance, and the ability to handle large datasets with high-dimensional outputs. This approach has also been applied to the generation of self-righting plans for unmanned ground vehicles (UGVs) using topological transition graphs. In order to create these graphs, I had to develop a set of manifold sampling and clustering algorithms which could identify paths through stable regions of the configuration space. Finally, I introduce an imitation learning approach for generating surrogate models of the target system's control policy. These surrogate agents can be used in place of the true autonomy to enable faster than real-time simulations. These novel tools for experimental design and behavioral modeling provide a new way of analyzing the performance of robotic and intelligent systems, and provide a designer with actionable feedback

Using Cognitive Science to Think about the Twelfth Century: Revisiting the Individual through Latin Texts

This study has several key purposes. First, it tests the potential applicability of the modern discourses of neuro- and cognitive science to the study of medieval texts and languages: more specifically, it does this by using two core methodological tools, namely the embodied view of the mind and a theory of metaphor developed collaboratively by the linguist, George Lakoff, and the philosopher, Mark Johnson, to explore the range of significances which may be drawn from the ways in which human life and existence are represented in a sample of twelfth-century Latin texts. Second, it challenges the view, held by some modern scholars, that by the medieval period Latin was an intrinsically inadequate language for the purposes of self-expression. And finally, it problematises the existing discourses in medieval studies on the individual, self, and subjectivity, first, by developing a new mode of analysing the mental lives of medieval people, and second, by challenging the view that advanced forms of self-awareness were “discovered” during the twelfth century. By following this course, this study offers a number of fresh insights into twelfth-century texts and the phenomena of the individual, self, and subjectivity. Most importantly, it shows that the ways in which human life and existence are represented in medieval texts are best understood in terms of complex interactions between the biological mind and body and their effects in the world (especially their “socio-cultural” effects). From this conclusion, it is argued that the basis of the individual, self, or subject must be found, not just in socio-cultural development, but also the biological realities of human existence. Furthermore, this study contributes to existing literature on the twelfth century by exploring the range of influences, ancient and contemporary, which affected how medieval people thought about themselves and other people, while affirming their basis in the interaction between the mind, body, and culture.Department of History, University of Exete

E-Scooter Rider Detection and Classification in Dense Urban Environments

Accurate detection and classification of vulnerable road users is a safety critical requirement for the deployment of autonomous vehicles in heterogeneous traffic. Although similar in physical appearance to pedestrians, e-scooter riders follow distinctly different characteristics of movement and can reach speeds of up to 45kmph. The challenge of detecting e-scooter riders is exacerbated in urban environments where the frequency of partial occlusion is increased as riders navigate between vehicles, traffic infrastructure and other road users. This can lead to the non-detection or mis-classification of e-scooter riders as pedestrians, providing inaccurate information for accident mitigation and path planning in autonomous vehicle applications. This research introduces a novel benchmark for partially occluded e-scooter rider detection to facilitate the objective characterization of detection models. A novel, occlusion-aware method of e-scooter rider detection is presented that achieves a 15.93% improvement in detection performance over the current state of the art

The Impact of Partial Occlusion on Pedestrian Detectability

Robust detection of vulnerable road users is a safety critical requirement for the deployment of autonomous vehicles in heterogeneous traffic. One of the most complex outstanding challenges is that of partial occlusion where a target object is only partially available to the sensor due to obstruction by another foreground object. A number of leading pedestrian detection benchmarks provide annotation for partial occlusion, however each benchmark varies greatly in their definition of the occurrence and severity of occlusion. Recent research demonstrates that a high degree of subjectivity is used to classify occlusion level in these cases and occlusion is typically categorized into 2 to 3 broad categories such as partially and heavily occluded. This can lead to inaccurate or inconsistent reporting of pedestrian detection model performance depending on which benchmark is used. This research introduces a novel, objective benchmark for partially occluded pedestrian detection to facilitate the objective characterization of pedestrian detection models. Characterization is carried out on seven popular pedestrian detection models for a range of occlusion levels from 0-99%, in order to demonstrate the efficacy and increased analysis capabilities of the proposed characterization method. Results demonstrate that pedestrian detection performance degrades, and the number of false negative detections increase as pedestrian occlusion level increases. Of the seven popular pedestrian detection routines characterized, CenterNet has the greatest overall performance, followed by SSDlite. RetinaNet has the lowest overall detection performance across the range of occlusion levels

Seshat: The Global History Databank

The vast amount of knowledge about past human societies has not been systematically organized and, therefore, remains inaccessible for empirically testing theories about cultural evolution and historical dynamics. For example, what evolutionary mechanisms were involved in the transition from the small-scale, uncentralized societies, in which humans lived 10,000 years ago, to the large-scale societies with an extensive division of labor, great differentials in wealth and power, and elaborate governance structures of today? Why do modern states sometimes fail to meet the basic needs of their populations? Why do economies decline, or fail to grow? In this article, we describe the structure and uses of a massive databank of historical and archaeological information, Seshat: The Global History Databank. The data that we are currently entering in Seshat will allow us and others to test theories explaining how modern societies evolved from ancestral ones, and why modern societies vary so much in their capacity to satisfy their members’ basic human needsPeer reviewedFinal Published versio

Culturable diversity of bacterial endophytes associated with medicinal plants of the Western Ghats, India

Bacterial endophytes are found in the internal tissues of plants and have intimate associations with their host. However, little is known about the diversity of medicinal plant endophytes (ME) or their capability to produce specialised metabolites that may contribute to therapeutic properties. We isolated 75 bacterial ME from 24 plant species of the Western Ghats, India. Molecular identification by 16S rRNA gene sequencing grouped MEs into 13 bacterial genera, with members of Gammaproteobacteria and Firmicutes being the most abundant. To improve taxonomic identification, 26 selected MEs were genome sequenced and average nucleotide identity (ANI) used to identify them to the species-level. This identified multiple species in the most common genus as Bacillus. Similarly, identity of the Enterobacterales was also distinguished within Enterobacter and Serratia by ANI and core-gene analysis. AntiSMASH identified non-ribosomal peptide synthase, lantipeptide and bacteriocin biosynthetic gene clusters (BGC) as the most common BGCs found in the ME genomes. A total of five of the ME isolates belonging to Bacillus, Serratia and Enterobacter showed antimicrobial activity against the plant pathogen Pectobacterium carotovorum. Using molecular and genomic approaches we have characterised a unique collection of endophytic bacteria from medicinal plants. Their genomes encode multiple specialised metabolite gene clusters and the collection can now be screened for novel bioactive and medicinal metabolites

The Genome Sequences of Three Paraburkholderia sp. Strains Isolated from Wood-Decay Fungi Reveal Them as Novel Species with Antimicrobial Biosynthetic Potential.

Three strains of fungus-associated Burkholderiales bacteria with antagonistic activity against Gram-negative plant pathogens were genome sequenced to investigate their taxonomic placement and potential for antimicrobial specialized metabolite production. The selected strains were identified as novel taxa belonging to the genus Paraburkholderia and carry multiple biosynthetic gene clusters