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Deciphering the morphological features underlying breast cancer behaviour using computational pathology and Artificial intelligence (with focus on the role of the tumour microenvironment)
Full text linkBackground:
Breast cancer (BC) is a heterogeneous disease with variable presentations, morphologies, and behaviours. The tumour microenvironment (TME) plays a key role in tumour progression. Molecular and phenotypic alterations in the neoplastic cells and their behaviour usually attracts more attention. However, there is evidence that the stroma of BC plays an important role in tumour behaviour. The basement membrane (BM), which comprises a physical barrier that separates the proliferating epithelial cells from the surrounding stroma is used to distinguish malignant in situ and invasive lesions. However, exceptions exist. The stroma of BC also varies widely with a variable degree of fibrosis, collagenisation, and elastosis in addition to other microenvironment components such as immune cells. The stromal cells are relatively genetically stable compared to the tumour cells, therefore they may be targets for therapeutic agents for cancer. The revolution of using digitalised whole slide images (WSI) in pathology developed a way of investigating BC cases. In addition, using artificial intelligence (AI) enables the integration of knowledge beyond human limits. However, this is a recently introduced technology, so further assessment of its uses and applications in the clinical setting is of paramount importance. Therefore, this study integrates digital pathology (DP) and AI to decipher the morphological features of TME in BC and identify novel biomarkers for improved BC prognosis.
Patient and Methods:
This study included multiple large cohorts of primary BCs and ductal carcinoma in situ (DCIS) in either tissue microarray (TMA) or full-face sections. The Nottingham cohort and the online cohort the Cancer Genome Atlas (TCGA) BC dataset. The detailed clinicopathological and outcome data were collected for the Nottingham cohort. For the DP part of the study, the haematoxylin and eosin (H&E)-stained slides were histologically reviewed and scanned into WSIs, and the same sections were picrosirius (PSR) stained to be examined under polarised microscopy or kept unstained for differential interference contrast microscopy (DIC). Image analysis assessment of the geometric characterisation of collagen was performed using AI applications.
For the immunohistochemical (IHC) part of the study, TMAs were constructed, and full-face sections were also utilised. Mining for stromal-related genes and collagens-related genes as novel biomarkers that may have potential prognostic and predictive values in BC was performed. This included hypoxia-inducible factors alpha 1 (HIF-1α), Lysyl oxidase (LOX), collagen XV and XIX.
Differential gene expression (DGE) analysis was performed to identify a set of genes associated with stroma in the main molecular BC subtypes and their prognostic roles in each molecular subtype. Stromal features and types were scored in 822 cases and the association to outcome was evaluated.
Results:
This study aimed to decipher the morphological features of TME in BC. Therefore, we started with BM as a part of TME and hypothesised that differentiation of a native BM from a reactive BM surrounding an invasive lesion is clinically important for early diagnosis. The study included 150 cases divided into six groups, each containing 25 patients per group. The six groups included normal breast tissue as a control group, DCIS, encapsulated papillary carcinoma (EPC), invasive carcinoma (INV), special types of invasive carcinoma (S.INV) and metastatic lymph node (LN). Full-face sections were stained with PSR stain and examined using polarized microscopy. Image analysis software was used to assess collagen fibre parameters. DCIS’ BMs resembled normal tissue BMs except the collagen fibres were higher in density, straighter, disorganised and less aligned. However, EPC, invasive and special invasive groups had a higher density of collagen with thicker fibres, and more collagen I content which were wider, shorter, straighter, less aligned and disorganised. The EPC group resembled invasive groups regarding alignment but had longer fibres than special BC but less wide fibres, and less collagen I (thick fibre) content, than invasive BC.
Therefore, the EPC group was further analysed to assess the differences between the inner and outer parts of the thickened capsule and to compare the capsule parts with the other groups with an additional encapsulated papillary thyroid carcinoma (EPTC) control group. The collagen fibre parameters of EPC capsules differed from the BMs of normal breast tissue and DCIS, and EPC was surrounded with BM-like material resembling those surrounding some invasive tumour types. This provides further evidence that EPC is a reactive process rather than a thickened native BM. In addition, most papillary tumours in different organs, such as EPTC which are surrounded with BM-like material showed indolent behaviour with better prognosis.
To study collagen fibre parameters of the stroma, a TMA was generated from 200 cases, comprising ductal carcinoma in situ (DCIS; n=100) and invasive tumours (n=100), with an extra 50 (25 invasive BC and 25 DCIS) cases for validation was unstained for examination using DIC microscopy. The collagen fibres had higher density, and were thinner, straighter more disorganized and less aligned in the invasive BC compared to DCIS. A model considering these features was developed that could distinguish between DCIS and invasive tumours with 94% accuracy. There were strong correlations between fibre characteristics and clinicopathological parameters in both groups. A statistically significant association between fibre characteristics and patients’ outcomes was observed in the invasive group but not in DCIS.
Stromal-related genes and collagen-related genes were selected for immunohistochemistry assessment to explore novel biomarkers. The assessment of the selected biomarker panel revealed a significant association between their protein expression and patient outcome. For the assessment of collagen XV and XIX, 100 cases of full-face sections were used which included both normal and neoplastic breast tissue. The cases were divided into four groups, each containing 25 patients. The four groups included normal breast tissue, DCIS, EPC, and invasive carcinoma. Collagen XV and XIX were significantly involved in the structural and biological changes associated with BC and may act as potential biomarkers. Collagen XIX could be used as a diagnostic tool to differentiate between invasive and non-invasive lesions in challenging cases. For the assessment of HIF-1α and LOX protein expression, TMA sections of a large well-characterised cohort of BC (n=876) were used. HIF-1α and LOX could be new biomarkers for BC prognosis and therapy response.HIF-1α levels in BC may be used to prospectively stratify patients who received neoadjuvant therapy. LOX could be a good therapeutic target specifically for metastasis prevention.
The mechanisms of stroma formation and composition in different molecular subtypes, which could explain the different prognostic values, were evaluated. Two large well-characterized BC cohorts were used, an in-house BC cohort (n=822) and the public domain dataset TCGA, n=978) as a validation cohort and for differential gene expression (DGE) analysis. DGE was performed to identify a set of genes associated with high stroma tumour ratio (STR) in the three main molecular subtypes. In each subtype, stromal assessment was carried out and tumours. were assigned to two groups: high and low STR, and further correlations with tumour characteristics and patient outcomes were investigated. The contribution of tumour-infiltrating lymphocytes (TILs) to the stroma was also studied. High STR was associated with favourable patient outcomes in the whole cohort and the luminal subtype, whereas high STR showed an association with poor outcomes in triple-negative (TNBC). No association with outcome was found in the HER2-enriched BC. DGE analysis identified various pathways in luminal and TNBC subtypes, with immune upregulation and hypoxia pathways enriched in TNBC, and pathways related to fibrosis and stromal remodelling enriched in the luminal group instead. Low STR accompanied by high TILs was shown to carry the most favourable prognosis in TNBC. In line with the DGE results, TILs played a major prognostic role in the stroma of TNBC, but not in the luminal or HER2-enriched subtypes. The stroma type could be a predictor factor for predicting outcomes. Desmoplastic sclerotic stroma is the most common type and showed a worse prognosis compared to other stromal types in all histological types except lobular carcinoma.
Conclusion
This comprehensive study emphasises the importance of considering all morphological features of TME in tumour behaviour and prognosis. Collagen parameters of BM and stroma could be used as a diagnostic and prognostic tool to differentiate between invasive and non-invasive lesions. Most papillary tumours in different organs which are surrounded with BM-like material showed indolent behaviour with better prognosis, therefore mechanisms which explain these phenomena should be further investigated. Stromal and collagen-related genes could be used as prognostic biomarkers for BC. The underlying molecular mechanisms and composition of the stroma in BC are variable in the molecular subtypes which explain the differences in its prognostic significance in each molecular subtype. Evaluation of tumour stroma types and features may be easily performed in routine clinic practice as a guide in stratifying patient risk
The Role of Chaperones in Outer Membrane Biogenesis and Host Cell Interactions in Neisseria gonorrhoeae
No full textNeisseria gonorrhoeae, the gonococcus, is the aetiologic agent of the sexually transmitted disease gonorrhoea, which poses a global public health concern. Owing to the emergence of multidrug-resistant strains and the lack of an effective vaccine against this pathogen, novel interventions are required. The assembly of outer membrane proteins (OMPs) is essential for the survival and virulence of Gram-negative pathogens, such as N. gonorrhoeae. The periplasmic translocation and outer membrane (OM) assembly mechanism of OMPs is not completely understood, but it is dependent on the assistance of periplasmic chaperones. This study investigates the roles of three periplasmic chaperones -NGO1656, SurA and Skp - in the translocation and assembly of OMPs of N. gonorrhoeae, shedding light on their contributions to OM biogenesis and pathogenesis. To this end, the three chaperones were characterised by a bioinformatics approach to determine their degree of conservation across gonococcal strains. Subsequently, mutants of strain FA1090 with single or double knockouts of the periplasmic chaperones were generated using standard molecular biology techniques. Complemented derivatives of mutants were also constructed to validate the resulting phenotypes. The in vitro growth kinetics of the generated mutant and complemented strains were determined. The impact of individual or double deficiency of periplasmic chaperones on the overall OMP profiles was studied using SDS-PAGE. Moreover, the effect of the mutations on the assembly of individual OMPs, particularly those involved in iron acquisition, was tested using immunoblotting assays. The capacity of tested strains to colonise and invade host cells was examined by in vitro adhesion and invasion assays using human cervical carcinoma cells. Furthermore, biofilm formation was studied using a crystal violet assay. The results showed that NGO1656, SurA and Skp are highly conserved among gonococcal strains but are not essential for FA1090 viability under the conditions tested. The results of the 1D SDS-PAGE did not reveal any observable difference in OMP profiles between the mutant strains and the wild-type FA1090. However, immunoblotting analyses demonstrated that NGO1656 contributes to the translocation and assembly of transferrin-binding protein A (TbpA) and lactoferrin-binding protein A (LbpA), while Skp is the primary chaperone that contributes to the assembly and translocation of TbpB into the outer membrane. These results were confirmed by the reduced capacity of the mutant strains to use iron from human transferrin and lactoferrin in feeding assays. The three chaperones, especially NGO1656 were also shown to contribute to adhesion and possibly invasion. In addition, NGO1656 redundantly promotes the ability of gonococci to form biofilms together with SurA or Skp. Unlike E. coli SurA, N. gonorrhoeae FA1090 SurA does not appear to play a major role in translocation of OMPs to the outer membrane. These findings support the suggestion that the examined chaperones are viable options for therapeutic or preventive interventions against gonococcal infections, particularly when considered in the light of complementary data from other studies that demonstrated the chaperones are immunogenic and accessible to antibodies. This study reports for the first time on the contribution of NGO1656 in the acquisition of host-specific nutrients, adherence to host cells and biofilm formation in N. gonorrhoeae. Further investigations are needed to identify the exact role played by this periplasmic chaperone in these pathogenicity-related processes
Navigating institutional logics: an ethnography of mental health peer support worker implementation
Full text linkPeer support worker (PSW) roles are increasingly implemented in mental health services in the United Kingdom. While existing studies suggest that organisational culture (OC) plays a pivotal role in shaping PSW implementation success, there is a lack of theoretical clarity regarding what constitutes OC in this context. To address this gap, this thesis applies the theory of institutional logics as one approach to exploring aspects of OC and its impact on PSW role implementation within a community mental health team. A qualitative ethnographic approach was employed, collecting data over a five-month period through four hundred and thirty-two hours of non-participant observation, thirteen semi-structured interviews, informal interviews, and sixty-eight documents and artefacts. To clarify what constitutes OC in relation to PSW implementation, this thesis highlights three intersecting and sometimes competing institutional logics: managerial, therapeutic, and professional. Time and risk were identified as key mechanisms that intersected with each logic, bringing their nuances into sharp focus and exposing areas of tension and alignment, influencing the implementation of the PSW. Time emerged as a critical mechanism affecting role boundaries and interactions, with the PSW facilitating swift, meaningful connections with service users, alleviating time pressures on clinicians, and enabling them to focus on tasks aligned with their expertise. Similarly, risk was reconceptualised, with the lived experience of PSW providing an alternative perspective on risk, enhancing overall risk management, and allowing clinicians to focus on their roles while relying on the PSW’s additional capacity for managing certain risk aspects. Three processes were used to navigate competing institutional logics, including (1) eliminating, (2) reframing, and (3) reassigning. Despite challenges relating to the limited number of PSWs, the findings suggest that PSWs can help mitigate broader systemic constraints, particularly in austerity-impacted environments. This thesis is the first to apply institutional logics to PSW implementation, offering an in-depth theoretical understanding of how aspects of OC influence this integration. Two key recommendations are proposed: (1) organisations should develop clear guidelines that outline how PSWs may operate differently to other team members, in order to maintain the integrity of the PSW role and ensure their unique contributions - especially in relation to risk and time - are recognised and effectively utilised, and (2) managers should be trained to understand when and how to adapt team processes to support PSWs, fostering a collaborative environment that enhances their cross-team impact. This thesis contributes new theoretical insights into PSW role implementation and provides guidance for policymakers, clinicians, and organisational leaders to enhance the impact, sustainability, and contributions of PSWs in mental health services
A framework for knowledge representation learning-based building control
Full text linkCurrent Building Automation Systems (BASs) have crucial context-awareness limitations that must be addressed before they can reach human-like levels and better adapt to the dynamic needs of modern buildings. Among other limitations, our buildings still lack sensors, actuators, and control agents that can learn reliable models of the environment and plan complex action sequences. Moreover, modern Machine Learning (ML)-backed BASs, though trained on massive datasets, are usually overly specialised (trained for one task) and brittle (prone to errors). In contrast, human learning is very efficient, and with only a few examples, we can find intuitive ways to complete a task while generalising our knowledge to other tasks. To address the above limitations, this thesis proposes a foundational framework that aims to advance the context-awareness capabilities of BASs using knowledge graphs and Knowledge Representation Learning (KRL). At the framework’s core is the notion of using Semantic Web Technologies (SWT) to model the semantic relationships between different building components. These relationships are then packaged inside a network-like data structure called a Building Information Modeling (BIM)-based Knowledge Graph (BIM-KG), and KRL is applied to learn the hidden patterns within the BIM-KG. During the learning phase, KRL utilises message-passing to propagate the learnt information throughout all nodes/entities in the BIM-KG. This research hypothesises that building automation agents can leverage this notion of message-passing to aggregate contextual information from all entities in the graph and use it to continuously update their understanding of a building’s systems and components. The perception is that imbuing building automation agents with holistic information about the buildings they control can presumably support context-aware decision-making during downstream automation tasks.
To test the research hypothesis, a three-phase investigation was carried out: literature review, framework development, and framework applicability. Phase one focused on situating the research within the scholarly discourse of BIM, BIM-KGs, building automation, and KRL. The results show that since 2010, SWTs have been a driving force advancing BIM research in the Architecture, Engineering, Construction and Facility Management (AEC/FM) fields by providing the mechanics to represent complex relationships within the built environment. Concurrently, KRL has seen significant development in domains such as bioinformatics, where it has been used to understand complex biological relationships and processes. However, despite the apparent suitability of applying KRL to the BIM field, such integration has not materialised and remains largely unexplored. To get around these research shortcomings, the next phase of this thesis was to develop a framework for applying KRL to BIM-KGs using performance analysis experiments. Five baseline KRL models were chosen for this. The chosen models are well-regarded techniques from existing studies, cover a wide range of methodologies, and have been extensively investigated in the context of drug discovery, whose data structures closely mirror those of BIM-KGs. Two publicly available BIM-KGs datasets were used in these experiments. The overall goal was not to identify the best KRL model configurations. Instead, the study examined more closely how model performance can be affected by modifications to the training step, selection of hyperparameters and their optimisation. The experimental results were used to define the prerequisites for integrating KRL with BIM-KGs in a domain-independent framework. This means that although a building automation use case is used to formulate the framework, it can assumingly be applied to other AEC/FM domains such as heritage, quantity-takeoff and energy analysis. The experimental findings show that RotatE and TransE consistently outperform other models across both datasets, establishing themselves as robust baselines when integrating KRL with BIM-KGs. It is also interesting to see that older models like TransE can still be competitive with optimised training and Hyper-parameter Optimization (HPO) configurations. Adam and NSSA emerged as favourable training setup choices, suggesting their potential as initial benchmarks for future evaluations. Despite extensive hyperparameter searches, there was considerable variance among top-performing model configurations, indicating the need for nuanced parameter combinations. This complexity suggests that manual tuning may not yield optimal results, advocating for the adoption of HPO strategies. Furthermore, the disparity in hyperparameters between the two datasets underscores the influence of dataset-specific parameters. Finally, random search methods, when repeated sufficiently, yield configurations closely comparable to more systematic approaches, albeit in less time.
To illustrate the applicability of the framework, phase three lays out a high-level system architecture consisting of a BIM model, Internet of Things (IoT) devices, and a prototype program of the framework wrapped inside an Application Programming Interface (API). The API consists of a server-side module and a client-side module. The server-side module demonstrates how a building automation system can communicate with KRL configurators, external services such as BIM-KG databases, sensor data stores, and Message Queuing Telemetry Transport (MQTT) brokers. The client-side module consists of a Graphical User Interface (GUI) with a Construction Operations Building Information Exchange (COBie) handler service that facilitates the curation of BIM-KGs from COBie files and an interrogation service that facilitates declarative interrogation of the server-side module using SPARQL Protocol and RDF Query Language (SPARQL) and Graph Query Language (GraphQL).
In conclusion, for KRL to impact the AEC/FM domain, this work emphasizes the critical importance of comprehensively reporting model architectures, training setups, and hyperparameters to enhance trust, reproducibility, and understanding of KRL-based methods among AEC/FM stakeholders and researchers. This insight highlights a prevalent issue in the AEC/FM field where results are often difficult to replicate due to incomplete documentation
The application of machine learning to predict disease, production and reproduction outcomes from the transition period of dairy cattle
Full text linkData collected under a transition period monitoring service, from 133 herds over
the course of 2 years, were utilised in order to build predictive models for
disease, production and reproductive outcomes. Both cow level and pen level
variables were used as potential predictor variables, while a variety of methods
including linear regression, decision tree, random forest, multiple adaptive
regression splines (MARS) and artificial neural networks (ANNs) for continuous
outcomes; and logistic regression, decision tree, random forest, ANNs, support
vector machines (SVM) and naïve Bayes for binary outcomes. Models
generating predictions on both the individual and the herd/quarter-year group
level were produced.
Various health outcomes (occurrence or not of milk fever, LDA, RFM and
metritis, as well as a collective disease status outcome) were explored. On the
individual lactation level all models lacked predictive value; the best performing
model was that for collective disease outcome, with a kappa value (measuring
agreement between predicted and observed data) of 0.16, although accuracy
was relatively high at 0.86. When building models on the herd/quarter-year
level, the best performing model was for the milk fever outcome; predicted
group prevalence of milk fever explained around 44% of variation in observed
prevalence, suggesting relatively low predictiveness. Better prediction
performance was revealed when individual lactation level model predictions
were aggregated at herd-quarter-year level and compared with observed
aggregated disease prevalences; just over two thirds (67%) of the variation in4
observed outcome was explained by the aggregated predictions for occurrence
of metritis.
Moving to the reproductive outcomes, probability of insemination success, as
well as time from calving to successful insemination, were investigated. Kappa
values for the former ranged from 0.04 to 0.17, while the R2 value describing
the relationship between aggregated predictions and actual aggregated values
on the herd-quarter-year level was found to be 0.37. When building models on
the aggregated level instead, the maximum R2 value was found to be at 0.24
for the MARS model. Regarding the time to insemination outcome, the
maximum R2 value calculated was found just at 0.024 for the linear regression,
indicating very low predictive value. Interestingly, while no strong predictive
value was found in these models, inferential models were built for those same
outcomes and found strong associations between insemination success and
lactation number, calving month, as well as calf mortality; and between time to
insemination and metritis, corrected protein percentage in milk, calving month
and lactation number.
For the production outcomes, models for both the 305-day predicted milk yield
and the daily residual milk yield (difference between observed yield for a given
cow on a given day, and expected daily yield based on lactation curve shape
for the appropriate parity in the cow’s herd) were built. For the individual
lactation level of the 305-day milk yield models, R2 values were again relatively
low, at around 0.1, with the exception of the random forest that had a value of
0.34. Similarly, when comparing aggregated predictions using the individual
lactation models and actual aggregated values, the R2 was as low as 0.024.
Building models on a herd/quarter-year level yielded similar results with R25
ranging from 0.12 to 0.39 for the linear regression and the random forest
models respectively. For the daily residual milk yield outcome, the R2 values of
individual lactation models had a maximum value of 0.21 for the random forest
model, while regarding the aggregated models the maximum value was at
0.134. When using the individual lactation level models to compare aggregated
predictions with actual aggregated values the R2 was found to be at 0.34. Not
unlike our results on the reproductive outcomes, various strong inferential
associations were identified for these outcomes, regardless of the predictive
models’ performance.
Since transition management is key to successful dairy farming, machine
learning would be useful both in terms of predicting which individuals may get
a negative outcome and possibly require enhanced observation or other
preventive interventions, and also in providing a potential monitoring metric.
The latter would mean that even if individual predictions are not good, knowing
the predicted disease prevalence, insemination success or yield ineach group’s
cows could be used as a measure of overall transition “success”. Overall, very
few of our models were predictive enough to be useful in either context most
likely, but that could perhaps improve if we had other data available such as
sensor data or history from previous lactations. The project as a whole provides
a good example of why it is important to be cautious with choice of prediction
performance metrics and avoid accuracy as the main measure in unbalanced
data, and of how in many areas inferential models can find strongly significant
associations but still generate very poor predictions when applied to new data
Exploring the potential role of endothelial ZEB1 in developmental and pathological angiogenesis
Full text linkThe impact of frailty and multimorbidity on postoperative outcomes in older patients
Full text linkOlder adults represent a growing proportion of the surgical population, with surgical interventions offering potential benefits including increased longevity and improved symptom management. However, older patients, particularly those living with frailty and multimorbidity, are at heightened risk of poor postoperative outcomes. Whilst evidence suggests that perioperative medicine services, especially those incorporating comprehensive geriatric assessment and optimisation, can improve these outcomes, the prevalence of frailty and multimorbidity amongst older surgical patients and their specific impact on postoperative outcomes have not been comprehensively described in a large, generalisable UK population.
This thesis, based on the 3rd Sprint National Anaesthesia Project (SNAP-3) body of work, explores the impact of frailty and multimorbidity on older surgical patients and examines the provision of perioperative medicine services.
First, a narrative review outlines evidence-based interventions to reduce postoperative delirium. Second, the methodologies of a large, inclusive observational cohort study and two national surveys are detailed and critically appraised. Third, the availability of perioperative medicine services across UK and Irish hospitals is described, revealing inconsistencies in service provision, particularly regarding the identification of frailty. Fourth, a survey of on-call medical registrar referrals for older surgical patients highlights the effect of perioperative medicine services on both patient care quality and medical team workload.
Fifth, the cohort of older surgical patients is characterised, revealing that 19% were living with frailty and 63% with multimorbidity. Sixth, analysis of specific surgical subgroups provides further insight into key issues, including day surgery; non-elective pathways; shared decision-making; and the organisation of perioperative medicine services. Seventh, the impact of frailty and multimorbidity on postoperative outcomes is modelled, demonstrating that increasing frailty is associated with longer hospital stays, higher odds of delirium, morbidity, and mortality up to one year. Notably, an increase in odds is observed amongst those classified as ‘living with very mild frailty’ or ‘prefrail’.
In conclusion, this thesis highlights the substantial impact of frailty on postoperative outcomes and the variation in perioperative medicine services designed to identify and manage frailty and multimorbidity in older surgical patients. These findings reinforce the national drive to expand perioperative medicine services, particularly with the expertise of geriatricians. Chapters 3, 4, 5, 6, 7, 8 and 10 contain published works. Further outputs from SNAP-3 will include a detailed analysis of postoperative delirium and its consequences, the influence of frailty, multimorbidity, and delirium on quality of life, readmission rates, and long-term mortality, and a refined study of multimorbidity using alternative definitions beyond simple comorbidity counts
Conversational alignment in early school-aged children during adult-child interactions
No full textConversation is a fundamental part of children’s everyday lives. From a very early age, they begin to interact with others. To become competent conversational partners, children must not only acquire speech, language, and pragmatic skills but also learn to coordinate their communicative behaviours with those of their conversational partner. One such coordination ability is conversational alignment, which refers to similarities in communicative behaviour (e.g., verbal and non-verbal). While alignment has been extensively examined in adults, there remains a gap in our understanding of this phenomenon within the paediatric population, particularly in non-WEIRD (Western, Educated, Industrialised, Rich, and Democratic) communities. To bridge this gap, this thesis investigated the alignment of early school-aged children and their adult interlocutors from Malaysia, a non-WEIRD country using a cross-sectional study design with a semi-structured conversational paradigm. Given the limited use of such paradigms in past literature, we first developed a novel methodological framework. Then, across three studies, we (a) determine whether early school-aged neurotypical children align their lexical choices during interactions, (b) examine whether speech rate alignment follows a similar developmental trajectory as lexical alignment in this age group, (c) investigate lexical alignment of autistic children and their neurotypical peers, and (d) assess the influence of conversational contexts, specifically task and partner familiarity, on the degree of alignment.
Chapter 2 presents the development of a semi-structured conversational paradigm, in which an adult interacted with a child virtually through task-oriented conversations. Refined through a series of pilot studies; first with the neurotypical children, then with the autistic children, the paradigm was designed to elicit natural yet structured interactions that reveal how children coordinate their speech and language behaviour in real-time communication. The final experimental design for the children consisted of two virtual sessions, during which each child engaged in task-oriented conversations with two different conversational partners. In one session, the child interacted with their parent (i.e., a familiar partner). In the other session, the child interacted with a university student (i.e., an unfamiliar partner). The goal was to examine the influence of task and partner type on conversational alignment. All sessions were conducted through Zoom and were audio-video recorded. These conversational files were subsequently annotated and transcribed by trained research assistants, using the PRAAT TextGrid function.
Study 1 (presented in Chapter 3) examined lexical alignment in early school-aged neurotypical children and the role of conversational context in their alignment. The lexical alignment score was operationalised as similarity in word choices (encompassing all word types) between interlocutors across consecutive conversational turns. This study recruited 45 English-dominant children aged 5 to 8 years to engage in two experimental sessions. During each session, an adult and a child engaged in two task-oriented conversations: a play-based task and a problem-solving task. Lexical alignment scores were extracted using automated ALIGN software. Our findings revealed that both early school-aged children and adults aligned their lexical choices, with adults demonstrating a greater degree of alignment than children. Additionally, the degree of alignment for both adults and children were moderated by the conversational contexts. Collectively, these findings suggest that lexical alignment is a robust phenomenon that serves as an important coordination strategy for early school-aged children and adults.
Study 2 (presented in Chapter 4) built on the findings of Study 1 by examining the development of alignment across multiple levels of communication. Using the same conversational corpus in Study 1, which demonstrated evidence of lexical alignment, this study investigated whether children aligned their speech rates with the adults. Speech rate alignment score was operationalised as similarity in speech rate values between two interlocutors across consecutive turns, without accounting for changes over time. The findings showed that only adults, not children, aligned their speech rates. Additionally, children's speech rate alignment was not influenced by task type, partner type, or age group. Furthermore, no correlation was observed between children’s lexical and speech rate alignments. These results indicate that alignment development is not a unitary phenomenon, which means that alignment at one level does not cascade into alignment at another level. Instead, the findings suggest that alignment at different levels of communication may be driven by distinct mechanisms that require different underlying skills and degrees of automaticity.
Study 3 (presented in Chapter 5) was designed to compare lexical alignment between neurotypical and autistic children within a semi-structured conversational paradigm. An additional conversational corpus, comprising recordings and conversational transcripts of interactions between autistic children and adult partners (n = 26), was collected. Each autistic child participated in two experimental sessions: one with their parent and one with the university student. In each session, the adult and the child engaged in a play-based task. Data from this corpus were compared with data from the play-based task in a previously collected corpus (i.e., Study 1) of neurotypical child-adult dyads (n = 45). The findings demonstrated that both autistic children and adults interacting with them exhibited a greater degree of alignment than neurotypical children and adults who interacted with them. Additional analyses explored whether the alignment between autistic children and their adult partners was influenced by partner type (i.e., parent or university student). However, these analyses found no compelling evidence that partner type influenced alignment. Taken together, the findings suggest that lexical alignment is neurotype-dependent. The variation in the degree of lexical alignment may reflect differences in the language, cognitive, and social communication abilities of autistic children compared to neurotypical children, potentially influencing how they dynamically re-use the adult conversational partners’ lexical choices.
In summary, this thesis offers insights into the conversational alignment of early school-aged children from a non-WEIRD community. The results show that alignment is a robust phenomenon in early school-aged children. While it is evident in children, the development and mechanisms of alignment are more complex than previously hypothesised. First, the findings suggest that lexical alignment is context-dependent. Next, the development of alignment across different levels of communication is modality-dependent. Finally, autistic children align to a different degree than neurotypical children, indicating that alignment is neurotype-dependent. Taken together, these results suggest that alignment in early school-aged children is shaped by context, modality, and neurotype
Human dimensions of coexistence with elephants in agricultural landscapes in Malaysia
Full text linkThis thesis explores human-elephant coexistence in Malaysia's agricultural sector, focusing on the palm oil industry. This study employs an interdisciplinary approach that combines the psychological, social and ecological factors of conflicts to investigate the human dimensions of coexistence. The research was conducted across four states in Malaysia and involved 223 questionnaire respondents, 12 focus group discussions, and 75 participants in stakeholder mapping exercises. The questionnaire utilised constructs from The Theory of Planned Behaviour (TPB) and the Norm Activation Model (NAM) to examine the drivers of conflict mitigation intentions and ideas of coexistence. The respondents included executives from the private sector as well as organised and independent smallholders. The findings from Partial Least Squares-Structural Equation modelling (PLS-SEM) reveal that norms towards the government (β=.407, p<.001), negative attitudes (β=-.204, p=.001), and self-efficacy (β=.151, p=.015) significantly explained behavioural intentions (R2= 0.277). Notably, norms towards the government emerged as the strongest predictor, contrary to the findings of previous studies. The extended model incorporating Norm Activation Model constructs improved explanatory power by 12%, with moral obligation, awareness of consequences, and norms towards the government as significant predictors of behavioural intentions (R2= 0.398). The model also investigated the ideas of coexistence through these theories. In the first model, Behavioural intentions (β=.253 p=.004) significantly explained Coexistence ideas (R2=0.065). However, the extended model revealed that awareness of the consequences (β=.337, p<.001) is the strongest predictor variable for coexistence ideas (R2= 0.154). Next, results from reflexive thematic analysis of the focus group discussions with organised smallholders under the Federal Land Development Authority (FELDA) scheme provide a nuanced understanding of the perceived barriers and opportunities for coexistence. Failed mitigation strategies and financial instability owing to crop damage have resulted in prolonged stress and multigenerational debts for settlers. Responses to these conflicts were categorised as "fight”, "flight”, or "freeze”, reflecting accumulated stress from unresolved conflicts. Finally, stakeholder analysis maps key actors within the conflict landscape, revealing their influence, support for coexistence, and potential for collaboration. Non-governmental organizations, village heads, religious leaders, and individuals directly affected by conflict were identified as influential and supportive stakeholders across all four states. The analysis also revealed a trend towards higher influence and support for local-level actors compared to state and federal entities, suggesting the potential benefits of a decentralised approach to conflict management. This thesis proposes reframing human-elephant conflict to coexistence to help shift the focus towards increasing safety for people and elephants, and the exploration of other types of mitigation methods. This emphasises the need for collaboration among intergovernmental agencies, the inclusivity of local stakeholders, and the potential role of sustainability certifications in promoting coexistence strategies. The study's findings suggest that personal moral obligations have a greater influence than governmental pressure on conflict mitigation intentions. This insight, combined with the significant supportive roles of local stakeholders, presents an opportunity to leverage cultural and religious values to promote elephant conservation in agricultural communities. In conclusion, the thesis provides a comprehensive understanding of human-elephant conflicts in Malaysia's agricultural settings, offering insights into the psychological, social, and governance aspects of the issue. It highlights the potential for coexistence based on moral, cultural and religious values while emphasising the need for increased collaboration among various stakeholders to address this complex challenge effectively
Machine Learning & Software Development for Sustainable Chemistry
Full text linkSustainability represents one of the most pressing challenges for chemical synthesis in the 21st century. Traditional methods often rely on non-sustainable practices, including the use of chemicals that are harmful to human health and the environment. Significant efforts have been made to improve the sustainability of chemical synthesis and finding greener synthetic routes is a common aim for researchers.
Digitalisation presents an opportunity to embed intelligent tools into the workflows of chemists. Many academic researchers continue to use paper lab notebooks, highlighting the need for accessible electronic laboratory notebooks (ELNs) tailored to their needs. Machine learning can be used to create predictive models from high-quality data, offering a powerful approach to enhancing these tools.
In this thesis, software tools for sustainable chemistry are explored, and machine learning theory and its application to chemistry is introduced and exemplified. The development of the AI4Green ELN and the integration of machine learning models with an accompanying sustainability assessment is described. Integrating software and machine learning tools for sustainable chemistry directly into the ELN can help chemists measure and improve their sustainability without requiring duplicated data entry. The ELN captures reaction data in a structured, machine-readable format, facilitating the development of additional tools and modernising research data management
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