Baseball Stadium in Baltimore, Maryland

Baseball Stadium in Baltimore, Maryland. Britton award winner, thesis board

Thesis Board by Jon Eric Moss

Thesis Board, Britton Award Winner. Photographs of Site Model and Model

Mapping organizational members' sense of fit

Despite its importance in the organizational behavior literature, person–organization (P–O) fit remains an elusive construct. One reason for this is the lack of research about organizational members’ own sense of their P–O fit. In this paper we report an empirical study that explored organizational members’ own sense of fit using storytelling and causal mapping techniques. The results suggest that organizational members categorize their perceptions of their fit into five discrete domains (job, people, employment, values, and extrawork) comprising thirteen subdomains: nature of work, profession or vocation, skills and knowledge, emotions, relationships with colleagues, relationship with line manager, physical environment, conditions of employment, opportunities for growth and development, organizational values, mission, family and personal life. Reviews of respondents’ causal maps and interview transcripts gave some insight into the consequences of organizational members’ perceptions of fit and provided further insights into the nature of fit. These insights included the fragility of fit, how the degree of seniority changed the emphasis in organizational members’ fit, and the role and nature of trigger events that change people’s sense of fit from good fit to misfit

The brain makes the fit: on the materialist hypothesis to consciousness, neuropsychology and person-organisation fit

This theory paper presents an analysis of the materialist hypothesis to consciousness and its implications for person–organization (P–O) fit. Some implications from neuropsychology are also considered. Three implications for P–O fit are discussed: (1) how it affects the underpinning theory; (2) how it changes our definition of the term; and, (3) how P–O fit is captured. The materialist hypothesis reinforces the underpinning theory, but causes a redefinition of P–O fit. It suggests that P–O fit should be defined in terms of the individual employee's unconscious physical interaction of internal features and environmental stimuli. The review also suggests that researchers need to consider using data gathering techniques that capture unconscious dimensions of P–O fit

GPCRTree: online hierarchical classification of GPCR function

Background: G protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence. Findings: Using techniques drawn from data mining and proteochemometrics, an alignment-free approach to GPCR classification has been devised. It uses a simple representation of a protein's physical properties. GPCRTree, a publicly-available internet server, implements an algorithm that classifies GPCRs at the class, sub-family and sub-subfamily level. Conclusion: A selective top-down classifier was developed which assigns sequences within a GPCR hierarchy. Compared to other publicly available GPCR prediction servers, GPCRTree is considerably more accurate at every level of classification. The server has been available online since March 2008 at URL: http://igrid-ext.cryst.bbk.ac.uk/gpcrtree

Engineering a machine learning pipeline for automating metadata extraction from longitudinal survey questionnaires

Data Documentation Initiative-Lifecycle (DDI-L) introduced a robust metadata model to support the capture of questionnaire content and flow, and encouraged through support for versioning and provenancing, objects such as BasedOn for the reuse of existing question items. However, the dearth of questionnaire banks including both question text and response domains has meant that an ecosystem to support the development of DDI ready Computer Assisted Interviewing (CAI) tools has been limited. Archives hold the information in PDFs associated with surveys but extracting that in an efficient manner into DDI-Lifecycle is a significant challenge.
 While CLOSER Discovery has been championing the provision of high-quality questionnaire metadata in DDI-Lifecycle, this has primarily been done manually. More automated methods need to be explored to ensure scalable metadata annotation and uplift.
 This paper presents initial results in engineering a machine learning (ML) pipeline to automate the extraction of questions from survey questionnaires as PDFs. Using CLOSER Discovery as a ‘training and test dataset’, a number of machine learning approaches have been explored to classify parsed text from questionnaires to be output as valid DDI items for inclusion in a DDI-L compliant repository.
 The developed ML pipeline adopts a continuous build and integrate approach, with processes in place to keep track of various combinations of the structured DDI-L input metadata, ML models and model parameters against the defined evaluation metrics, thus enabling reproducibility and comparative analysis of the experiments.  Tangible outputs include a map of the various metadata and model parameters with the corresponding evaluation metrics’ values, which enable model tuning as well as transparent management of data and experiments

Engineering a machine learning pipeline for automating metadata extraction from longitudinal survey questionnaires

Data Documentation Initiative-Lifecycle (DDI-L) introduced a robust metadata model to support the capture of questionnaire content and flow, and encouraged through support for versioning and provenancing, objects such as BasedOn for the reuse of existing question items. However, the dearth of questionnaire banks including both question text and response domains has meant that an ecosystem to support the development of DDI ready Computer Assisted Interviewing (CAI) tools has been limited. Archives hold the information in PDFs associated with surveys but extracting that in an efficient manner into DDI-Lifecycle is a significant challenge.
 While CLOSER Discovery has been championing the provision of high-quality questionnaire metadata in DDI-Lifecycle, this has primarily been done manually. More automated methods need to be explored to ensure scalable metadata annotation and uplift.
 This paper presents initial results in engineering a machine learning (ML) pipeline to automate the extraction of questions from survey questionnaires as PDFs. Using CLOSER Discovery as a ‘training and test dataset’, a number of machine learning approaches have been explored to classify parsed text from questionnaires to be output as valid DDI items for inclusion in a DDI-L compliant repository.
 The developed ML pipeline adopts a continuous build and integrate approach, with processes in place to keep track of various combinations of the structured DDI-L input metadata, ML models and model parameters against the defined evaluation metrics, thus enabling reproducibility and comparative analysis of the experiments.  Tangible outputs include a map of the various metadata and model parameters with the corresponding evaluation metrics’ values, which enable model tuning as well as transparent management of data and experiments

Analysing Longitudinal Social Science Questionnaires: Topic modelling with BERT-based Embeddings

Unsupervised topic modelling is a useful unbiased mechanism for topic labelling of complex longitudinal questionnaires covering multiple domains such as social science and medicine. Manual tagging of such complex datasets increases the propensity of incorrect or inconsistent labels and is a barrier to scaling the processing of longitudinal questionnaires for provision of question banks for data collection agencies. Towards this effort, we propose a tailored BERTopic framework that takes advantage of its novel sentence embedding for creating interpretable topics, and extend it with an enhanced visualisation for comparing the topic model labels with the tags manually assigned to the question literals. The resulting topic clusters uncover instances of mislabelled question tags, while also enabling showcasing the semantic shifts and evolution of the topics across the time span of the longitudinal questionnaires. The tailored BERTopic framework outperforms existing topic modelling baselines for the quantitative evaluation metrics of topic coherence and diversity, while also being 18 times faster than the next best-performing baseline

Autophagy coordinates chondrocyte development and early joint formation in zebrafish

Autophagy is a catabolic process responsible for the removal of waste and damaged cellular components by lysosomal degradation. It plays a key role in fundamental cell processes, including ER stress mitigation, control of cell metabolism, and cell differentiation and proliferation, all of which are essential for cartilage cell (chondrocyte) development and survival, and for the formation of cartilage. Correspondingly, autophagy dysregulation has been implicated in several skeletal disorders such as osteoarthritis and osteoporosis. To test the requirement for autophagy during skeletal development in zebrafish, we generated an atg13 CRISPR knockout zebrafish line. This line showed a complete loss of atg13 expression, and restricted autophagic activity in vivo. In the absence of autophagy, chondrocyte maturation was accelerated, with chondrocytes exhibiting signs of premature hypertrophy. Focussing on the jaw element, autophagy disruption affected joint articulation causing restricted mouth opening. This gross behavioural phenotype corresponded with a failure to thrive, and death in homozygote atg13 nulls within 17 days. Taken together, our results are consistent with autophagy contributing to the timely regulation of chondrocyte maturation and for extracellular matrix formation