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MicroRNAs-mediated regulation of muscle wasting in cancer-associated cachexia
No full textCancer-associated cachexia (CAC) affects approximately 70% of cancer patients and is responsible for up to 22% of cancer deaths. CAC is a severe syndrome characterised by significant muscle wasting, systemic inflammation, and metabolic disturbances, which greatly contribute to the increased mortality among cancer patients. MicroRNAs (miRs) are small, non-coding RNAs that regulate gene expression. Changes in the expression of specific miRs during cachexia, including those with known roles in muscle function, have been observed. However, there is limited understanding of sex-specific molecular changes during cachexia, early alterations in muscle during tumour development, or the potential of miRs to regulate key signalling pathways involved in cachexia. I hypothesised that modulating miRs levels during cancer cachexia could prevent or slow the progression of muscle wasting associated with cancer.
In vivo and in vitro models of cachexia, combined with miR overexpression and inhibition, followed by global and molecular phenotype characterisation, were performed to determine the role of miRs in cancer cachexia.
The work in Chapter 3 used global proteomic and transcriptomic approaches to demonstrate sex-specific changes in muscle in an in vivo model of early stages of cachexia at the proteome and transcriptome levels. In males, the main pathways affected were mitochondrial respiration and oxidative phosphorylation, whereas in females, pro-inflammatory pathways were more prominently affected. Mitochondrial function and protein folding pathways were affected in both males and females during early stages of cachexia. These findings are consistent with observations previously demonstrated in the late stages of cachexia in mice and humans.
In Chapter 4, the systemic delivery of miR-379-3p during initiation of cachexia, resulted in a partial rescue of muscle mass loss resulting from tumour presence, with a more pronounced effect observed in females. miR-379-3p improved mitochondrial function, particularly in males, and exhibited potential anti-inflammatory effects in females, where it also improved neuromuscular homeostasis markers, for example genes associated with myelination.
In Chapter 5 the delivery of miR-26a-5p in vitro preserved myotube size by enhancing mitochondrial biogenesis, reducing apoptosis, and modulating inflammatory pathways. These findings suggested the potential of miR-26a-5p in maintaining muscle function in cachectic conditions and warrant further in vivo experiments.
In Chapter 6, I explored a combinatorial miR therapeutic approach through concomitant overexpression of miRs downregulated in muscle of cachectic mice and humans: miR-379-3p, miR-26a-5p, miR-181a-5p, and inhibition of miR overexpressed in muscle of cachectic mice and humans: miR-24. The results from this chapter indicated only modest improvements in muscle preservation and did not necessarily yield synergistic benefits, suggesting that further work is needed to determine optimal combination of miRs and their potential benefit over single miR manipulation.
Overall, this research advances our understanding of the molecular drivers of the early stages of cancer cachexia, as well as sex-specific characteristics, providing potential therapeutic candidates with mechanistic involvement in muscle wasting during cancer. Moreover, it demonstrates the potential of miR-379-3p as a therapeutic agent to tackle muscle wasting during cachexia. These findings suggest that early intervention with miR therapies targeting mitochondrial function, apoptosis, and inflammation could mitigate muscle wasting and enhance the quality of life for cancer patients suffering from cachexia. Future research should focus on optimising these therapeutic strategies, considering patient-specific factors such as sex and the stage of disease, and exploring the integration of miR therapies with existing cancer treatments
Machine learning and genomics based LCA to optimize crop productivity and environmental sustainability
No full textThe simultaneous selection of crop genotypes that deliver high yield, resilience to (a)biotic stress, and reduced environmental impact represents one of the most pressing challenges in both tropical and temperate agrifood systems. As climate change alters growing environments, natural resources become increasingly constrained, and global food demand continues to rise, crop improvement strategies must evolve beyond the pursuit of individual traits. Instead, there is a critical need for integrated, multi-trait breeding frameworks that can effectively address this complex and dynamic landscape. In response, this study presents a comprehensive, cross-disciplinary framework that unifies genome-wide association studies (GWAS), quantitative trait loci (QTL) mapping, joint linkage association mapping (JLAM), genomic prediction (GP), life cycle assessment (LCA), and machine learning (ML) to quantify and optimize both the agronomic performance and ecological footprint of improved cereal, legume, and forage crops under a range of biotic and abiotic stress conditions. The framework is designed to support data-driven decision-making in breeding programs by linking genetic architecture with environmental sustainability metrics. Three core objectives underpin this approach: (i) to dissect the genomic architecture underlying yield, nutritional quality, agronomic traits, and tolerance to critical stressors in cereals; (ii) to evaluate the environmental impacts of stress-resilient cereal genotypes using a machine learning-enhanced LCA methodology; and (iii) to assess the scalability and cross-crop applicability of the framework, demonstrated through its implementation in legume and forage systems under real-world stress scenarios. The framework was applied to a set of target crops - tropical maize (Zea mays L.), soybean (Glycine max (L.) Merr.), and perennial ryegrass (Lolium perenne L.) - cultivated under diverse environmental stressors, including drought, low soil nitrogen, Striga spp. infestation, and northern corn leaf blight (NCLB) pressure. Multi-environment trials were conducted across contrasting agroecological zones in Kenya, Ireland, Mexico, South Africa, Thailand, Zambia, and Zimbabwe. This integrative approach provides a scalable pathway toward breeding climate-smart crops that align high productivity with enhanced nutritional value and environmental sustainability, thus supporting resilient agrifood systems across varied geographies.
To address the first objective - dissecting the genomic architecture underlying grain yield and related traits under a spectrum of (a)biotic stress conditions - multi-environment field trials and molecular analyses were conducted on over 3,000 tropical maize genotypes. These included evaluations under low soil nitrogen, drought, Striga spp. infestation and NCLB disease pressure. In Kenya and South Africa, a panel of 410 inbred lines and four bi-parental populations was phenotyped under both optimum and nitrogen-limited soil environments. Broad-sense heritability (H²) estimates for key grain quality traits (i.e., protein, starch, and oil content) under low nitrogen stress ranged from 0.18 to 0.86, indicating substantial genetic variation. GWAS identified 42 significant single nucleotide polymorphisms (SNPs) linked to grain quality traits, corresponding to 51 putative candidate genes. Of these, 80.4% (41 genes) had functional annotations, while 19.6% (10 genes) encoded proteins of unknown function. Several annotated genes were associated with nitrogen-responsive metabolic pathways. For instance, GRMZM2G159307 and GRMZM2G104325, encoding ATP-binding proteins with serine/threonine kinase activity, were linked to grain yield and starch content under optimal conditions. Under nitrogen-limited conditions, genes such as GRMZM2G10816 (yield), GRMZM2G070523, and GRMZM2G080516 (oil content) were involved in DNA biosynthesis, while GRMZM2G033694 - a histone-lysine N-methyltransferase associated with shoot apex development – was responsive across both soil nitrogen regimes. Complementary linkage mapping revealed multiple quantitative trait loci (QTLs) for grain yield and quality traits across nitrogen conditions. Notably, Chr. 1 harboured multi-trait QTLs in regions spanning 209-214 Mb and 268-280 Mb. On Chr. 2, bins 2.03 and 2.06 contained QTL clusters associated with grain yield, starch, and oil content, while chromosome 3 bin 3.06 exhibited co-localization of QTLs for protein, starch, and oil content. Additional trait-linked QTLs were identified on chromosomes 4, 5, 6, and 10. Two GWAS-significant SNPs - S1_269023923 (oil content) and S5_11883140 (grain yield) - co-localized with QTL qOC_01_269 and qGY_05_15, respectively. Genomic prediction analyses under low nitrogen conditions demonstrated high accuracy for oil content (r = 0.78) and lower accuracy for grain yield (r = 0.08), underscoring the yield trait’s sensitivity to soil nitrogen limitation. The CML550/CML504 test cross yielded the highest prediction accuracies for protein (0.66), oil (0.73), and starch (0.7) content under low N stress.
Parallel drought-stress trials conducted in Kenya and Zimbabwe using three F3 populations (753 families) revealed grain yield reductions of 31-59%. Under well-watered conditions, the four parental lines - CML543, CML444, LapostasequiaC7-F71, and CKL5009 - achieved grain yields of 6.97, 6.30, 6.31, and 5.87 t ha-1, respectively, while under drought stress, yields declined to 2.32, 2.68, 5.08, and 3.69 t ha-1. QTL analyses identified 93 and 41 QTLs loci associated with grain yield, anthesis-to-silking interval, plant height, and ear height under well-watered and drought-stressed conditions, respectively. Eight major-effect QTLs (explaining >10% phenotypic variance) were detected under optimal conditions, compared to only two under drought stress. Joint linkage association mapping (JLAM) identified 25 QTLs for grain yield under well-watered and 4 under water-limited conditions, with phenotypic variance explained (PVE) ranging from 0.80-3.9% (well-watered) and 1.4-1.8% (drought), primarily located on chromosomes 4 and 6. Five-fold cross-validation supported moderate to high genomic prediction accuracies (r = -0.15 to 0.90), reflecting the polygenic nature of drought tolerance.
In Kenya, one association panel, three doubled haploid (DH), and three F3 populations were evaluated for northern corn leaf blight (NCLB) resistance in disease hotspots. Disease severity scores on a 1.0-9.0 scale indicated high susceptibility in DH populations, with means of 5.17 (CML494×CML550) and 4.69 (CML511×CML550). In contrast, F3 populations CZL0723×CZL0719 (mean = 3.06) and CZL0009×CML505 (mean = 2.21) showed superior resistance. Across six populations, 23 QTLs conferring resistance were identified: three, six, and four in DH populations 1, 2, and 3, respectively, explaining 34.3%, 51.4%, and 41.1% of phenotypic variation; and two to four per F3 population, with individual QTLs explaining 2.8-15.8% of the variance. Through JLAM, 37 NCLB resistance QTLs were mapped across all 10 chromosomes, accounting for 49.4% of total phenotypic variation. GWAS using 337,110 high-quality SNPs identified 15 significant marker–trait associations. Several SNPs were located within genes containing functional domains related to stress response and developmental regulation. For example, SNP S2_213818302 was associated with peroxidase activity and oxidative stress tolerance, while S6_100083188 corresponded to a gene encoding phosphoglycerate kinase (PGK), a key enzyme in plant defence. Genomic prediction models yielded moderate accuracies for NCLB resistance (r = 0.42-0.55), supporting the trait’s quantitative architecture shaped by a combination of major-effect loci and numerous minor-effect QTLs.
To evaluate Striga hermonthica resistance, 328 maize testcrosses and six commercial hybrids were phenotyped under artificial infestation in Kenya. Broad-sense heritability ranged from 0.28 to 0.76. The donor line TZSTR167 was among the top performers, yielding 5.03 t ha-1 with a Striga damage rating scores (SDR) of 2.3 (on a 1 to 5 scale). GWAS using five multi-locus models (FastmrMLM, FASTmrEMMA, pLARmEB, pKWmEB and ISIS EM-BLASSO) identified 81 quantitative trait nucleotides (QTNs) distributed across all 10 chromosomes. Key candidate genes included those encoding FAD-dependent oxidoreductase, RPM1-interacting protein 4 (RIN4), and Expansin-B4, associated with grain yield under infestation. Genomic prediction models achieved high accuracy for Striga counts at 10 weeks (r = 0.70) and SDR (0.60, but lower accuracy for grain yield and silking date (r = 0.40). These results confirm the complex, polygenic nature of (a)biotic stress resistance in tropical maize.
In pursuit of the second objective - evaluating the environmental performance of stress-tolerant genotypes - a machine learning-supported life cycle assessment (LCA) framework was applied to maize grown under low nitrogen and drought stress. In Kenya, average yield dropped by 47% under low nitrogen (3.78 t ha-1) versus optimum conditions (7.14 t ha-1). Protein and oil content decreased by 2.98% and 12.75%, respectively, while starch content increased by 0.86%. Genotypes such as CML505/LaPostaSeqC7-F64-2-6-2-2-B-B5017-B, CML505/LaPostaSeqC7-F64-2-6-2-2-B-B5023-B and CML505/LaPostaSeqC7-F64-2-6-2-2-B-B5350-B were identified as tolerant based on indices like stress tolerance (TOL), stress susceptibility index (SSI), yield stability index (YSI), and percent yield reduction (PYR. LCA results for these genotypes under low N stress showed average per-kilogram grain impacts of 0.39 kg CO2-eq (global warming), 8.03×10-5 kg P-eq (eutrophication), 0.005 kg SO2-eq (acidification), 0.0012 kg NOₓ-eq (oxidant formation), and 2.19 kg CFC-11-eq (ozone depletion). DH lines (CML550/CML511)-DH111 and DH26 showed the most environmentally efficient profiles, while others, including CML505/LaPostaSeqC7-F64-2-6-2-2-B-B5017-B and CML505/LaPostaSeqC7-F64-2-6-2-2-B-B5350-B, exhibited higher global warming potential (GWP100) and nutrient loss potentials. XGBoost classifiers differentiated high-yield/high-impact genotypes from low-impact, stress-resilient ones, achieving a mean accuracy of 0.84 (±0.10). SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations) and Principal component analysis (PCA) analyses identified clusters of genotypes: (1) high-yield, high-impact; (2) moderate-yield, nitrogen-efficient; and (3) low-yield, low-impact stress-resilient.
An expanded evaluation across Kenya, Mexico, and Thailand included 1,053 maize genotypes under drought and optimum conditions. Yield reductions ranged from 58% ((CML543/LapostaSequiaF71)F3_Pop_2_241) to 64.6% ((CML543/CML444)F3Pop_1_180) in Kenya, 87% (CML-326-B-B/CML-312 SR) to 92.6% (CML311/MBR C3 Bc F95-2-2-1-B-B-B-B-B-B-B/CML-312 SR) in Mexico, and 69.8% (CLA149-B-B/CML-312 SR) to 94.7% (DTPWC9-F67-2-2-1-B-B-B-B-B/CML-312 SR) in Thailand. In Kenya, GWP100 ranged from 0.26 to 0.38 kg CO2-eq per kg grain under optimal conditions (mean = 0.31 kg CO2-eq) and increased to 0.43 - 0.84 kg CO2-eq (mean = 0.58 kg CO2-eq) under drought. Mexico recorded GWP100 of 0.26-0.62 kg CO2-eq (mean = 0.36 kg CO2-eq) under optimal conditions, increasing markedly under drought to 0.30 - 1.39 kg CO2-eq (mean = 1.08 kg CO2-eq). Thailand recorded the lowest GWP100 values under optimal conditions (0.32 kg CO2-eq; range: 0.20 - 0.51), but drought stress drove GWP100 values significantly higher (0.52 -1.29 kg CO2-eq; mean = 0.8 kg CO2-eq). The lowest GWP100 values under optimum conditions were recorded for La Posta Seq C7-F78-2-1-1-1-B-B-B-B/CML-312 SR (0.2 kg CO2-eq in Thailand), (CML543/LapostaSequiaF71)F3Pop 2_22 (0.26 kg CO2-eq in Kenya) and CIMCALI8843/S9243-BB-#-B-5-1-BB-4-1-3/CML-312 SR (0.26 kg CO2-eq in Mexico). Under drought, CLQ-RCWQ39=(CML159*CML144)-B-27-1-2-B*3-B-B/CML-312 SR (Mexico) recorded the lowest GWP100 score: 0.3 kg CO2-eq. To model the relationship between stress tolerance and environmental impacts, linear regression, random forest, and XGBoost algorithms were applied across eight Recipe 2016 (H) midpoint LCA categories. For drought-induced GWP100, linear regression performed best (R2 = 0.87, RMSE = 0.11, MAE = 0.065), outperforming random forest (R2 = 0.68) and XGBoost (R2 = 0.72). SHAP analyses highlighted strong associations between stress indices – particularly relative stress index (RSI), geometric mean productivity (GMP), mean relative performance (MRP), and stress susceptibility index (SSI) - and GWP100. Yield index (YI), mean productivity (MP), and MRP consistently showed negative correlations with environmental impacts, supporting their utility for sustainability-focused genotype selection.
To achieve the third objective - testing the cross-crop scalability of a genomic-LCA-machine learning (ML) framework - methodologies were extended to forage- and legume-based agri-food systems in temperate and tropical environments. In Ireland, a perennial ryegrass-based pasture dairy model was developed using the Farm Level Module of the GOBLIN (General Overview for a Back-casting approach of Livestock Intensification) model. Baseline emissions per kg fat-and-protein-corrected milk (FPCM) were 1.08 kg CO2-eq (GWP100), 0.0066 kg PO4-eq (eutrophication), and 0.013 kg SO2-eq (acidification). XGBoost regressors trained on 10,000 simulated scenarios achieved high predictive accuracy (R² = 0.99), dry matter digestibility, crude protein, and nitrogen fertiliser input as primary emission drivers. Optimizing these traits revealed an ‘LCA-designed’ ideotype capable of reducing GWP100 by 36.7%, acidification by 31%, and eutrophication by 29% without compromising system productivity. These results highlight the critical but often overlooked contribution of forage ideotypes to environmental performance in livestock systems, reinforcing the need for integrated crop-livestock breeding strategies.
In Zambia, yield-scaled life cycle assessments of 70 soybean genotypes across three agro-ecological zones revealed significant variability in both yield (μ = 2.3 t/ha, p<2×10⁻¹⁶) and environmental impacts. Mean GWP100 was 921.6 kg CO2-eq per tonne of grain, with top performing genotypes achieving values as low as 654 kg CO2-eq. Genotypes exhibiting moderate yield stability (CV = 0.37-0.51) showed optimal trade-offs across global warming, eutrophication (0.24 kg P-eq), acidification (1.98 kg SO2-eq), and particulate matter formation (1.16 kg PM2.5-eq) indicators. XGBoost consistently outperformed other ML algorithms across environmental categories, confirming its utility in genotype-environment-impact prediction pipelines. Principal component and correlation analyses further revealed key trade-offs between stability and footprint, underscoring the complexity of multi-objective selection. Together, these results demonstrate the flexibility and robustness of the genomic-LCA-ML framework for guiding the development of climate-smart ideotypes across distinct crop systems and production ecologies
Multi-modality imaging in structural interventions
No full textSignificant advancements are being made in the field of interventions for structural heart diseases, driven by the expansion of guideline-directed indications and an increasing patient population requiring intervention or re-intervention for valvular heart disease. Precise diagnosis, optimum timing of intervention, and optimal device selection are essential for successful management. This thesis emphasizes the importance of adopting multimodality imaging, including including echocardiography, angiography, magnetic resonance imaging (MRI), and cardiac computed tomography (CT), in planning, guiding, and evaluating structural heart interventions. These imaging approaches are crucial for patient selection, procedural planning, and post-procedural outcomes assessment
Marine mammals and tidal energy potential: Ecological assessments and spatial analyses in the Irish exclusive economic zone
No full textMarine renewable energy, specifically tidal energy, is central to Ireland's efforts to address climate change and enhance sustainable energy production. However, tidal energy projects can have ecological impacts on marine wildlife, particularly marine mammals such as cetaceans and pinnipeds, necessitating comprehensive assessments to inform sustainable development strategies.
Cetaceans and pinnipeds face several risks from tidal energy installations, including collision with turbine blades, disturbance from underwater noise, displacement from key habitats, and habitat fragmentation. Understanding these interactions is crucial for mitigating negative impacts and supporting the coexistence of marine renewable energy and marine biodiversity.
A comprehensive review of existing literature has highlighted significant ecological concerns, emphasizing collision risks, acoustic disturbances, behavioural changes, and habitat alterations associated with tidal energy devices. Key knowledge gaps identified include the long-term cumulative impacts on marine mammal populations, effectiveness of mitigation strategies, and responses of different species to tidal energy devices.
Spatial analyses conducted using Geographic Information Systems (GIS) and Kernel Density Estimation (KDE) evaluated the extent of spatial overlap between marine mammal habitats and zones designated for tidal energy potential within the Irish Exclusive Economic Zone (EEZ). Significant overlaps were found for bottlenose dolphins, common dolphins, and harbour porpoises, particularly in ecologically critical coastal areas such as the Shannon Estuary, Dingle Bay, and waters off Cork and West Cork. Baleen whales exhibited relatively limited overlap due to their offshore distribution patterns. Data limitations, particularly for pinnipeds, underscored the necessity for improved monitoring and standardized data collection practices.
The integration of robust ecological assessments into tidal energy planning is essential to mitigate risks to marine mammals. Recommendations include targeted monitoring efforts, standardized ecological data collection, and adaptive management strategies to ensure tidal energy development aligns with marine conservation objectives
Navigating the complex interplay of intimate partner violence, employment, and wages: A case study in Pakistan
No full textThis thesis provides a critical and systematic examination of the economic dimensions of intimate partner violence (IPV) in Pakistan. Grounded in a conceptual framework informed by the nested ecological model, the study investigates the socioeconomic factors associated with IPV victimization (IPV-V) and perpetration (IPV-P) and assesses their impact on labour market outcomes for married individuals. The analysis employs a rigorous empirical strategy, including Instrumental Variable techniques to address endogeneity and advanced decomposition methods (Oaxaca-Blinder and CDECO) to estimate the microeconomic costs of IPV.
The findings reveal a complex and nuanced relationship between IPV and women's economic lives. For women who experience IPV, the analysis uncovers a paradoxical role of employment: while it may function as a means of empowerment, it can concurrently exacerbate the risk and intensity of IPV victimisation. Contrary to conventional assumptions, IPV-V emerges as a coercive “push factor” that increases women’s likelihood of employment but also imposes significant penalties on their net earnings and wages – ranging from 26 to 31 per cent at the mean – particularly at the lower end of the wage distribution.
For men, the thesis provides novel evidence that IPV-P is associated with a reduced likelihood of stable, full-time wage employment. It imposes substantial penalties on perpetrators’ earnings and wages – between 17 and 24 per cent at the mean – challenging the notion that IPV is a purely private matter. These penalties reflect the hidden labour market costs of IPV and the behavioural dysfunctions that undermine men’s economic inclusion.
By quantifying the microeconomic costs and disentangling the complex interplay of violence and economic agency for both victims and perpetrators, this thesis makes a significant contribution to the literature. It offers compelling evidence that addressing IPV requires integrated policy solutions that extend beyond promoting employment to tackle structural inequalities and transform entrenched gender norm
Physics-informed machine learning for nonlinear deformations in soft solids
No full textMild traumatic brain injury (mTBI), often referred to as concussion, is a significant public health concern, particularly in contact sports, vehicular collisions and similar scenarios. The key mechanisms behind mTBI involve the transmission of shear shock waves through the brain tissue following an impact. These shock waves induce rapid, nonlinear deformations within the soft, heterogeneous structure of the brain, disrupting the integrity of the brain. The complexity of these injuries necessitates highly accurate biomechanical models that can capture the intricate response of brain tissue under loading. Traditionally, hyperelastic models such as the Neo-Hookean (NH) and Mooney-Rivlin (MR) have been employed to simulate brain tissue mechanics. However, these models fall short of accurately describing the highly nonlinear and rate-dependent behaviour observed in mTBI scenarios. A fourth-order Landau (LA) hyperelastic model offers a more suitable alternative, as it can capture the material stiffening effects and large strain behaviours intrinsic to brain tissue, making it particularly well-suited for modelling shear shock wave propagation.
Finite Element Methods (FEM) have been the standard for solving the partial differential equations (PDEs) that govern such biomechanical systems. These solvers provide highly accurate predictions of tissue deformation and stress distributions. However, their computational intensity, especially when incorporating spatial resolution and nonlinear material behaviour, makes them impractical for real-time applications such as injury prediction in sports helmets or real-time diagnostics in clinical settings. To overcome this limitation, Physics-Informed Neural Networks (PINNs) have emerged as a compelling alternative. PINNs offer the potential to simulate real-time brain deformation under impact conditions with far lower computational costs than traditional FEM based solvers. PINNs are a class of deep learning models that embed the governing PDEs of physical systems directly into the training process of a neural network. Instead of relying on labelled data, PINNs minimise the residuals of the PDEs and enforce initial and boundary conditions during training. This allows the neural network to learn solutions that are consistent with physical laws, even with sparse or noisy data. PINNs are being used to solve partial differential equations in various physical systems.
PINNs have garnered significant interest due to their applications in solving partial differential equations that govern various physical phenomena. In 2019, Raissi et al. proposed the concept of PINN, which has since led to hundreds of publications with applications in various disciplines. However, baseline PINN face several challenges, leading to inaccurate solutions in many scenarios. This work proposes a mesh-free Causal Marching Physics-Informed Neural Networks (CMPINN) model for various hyperelastic models to capture their nonlinear mechanical response of higher-order hyperelastic materials. It marks the first attempt to develop physics-informed neural networks to capture complex fourth-order deformation behaviours in soft biological tissues such as the brain. The developed CMPINN framework introduces a ”multinet” architecture, which is designed to handle multimaterial domains effectively. This structure enables the simultaneous modelling of different materials within a single computational domain, which is crucial for realistic simulations involving heterogeneous tissues. Enforcing material incompressibility can be numerically challenging. Another key advancement of CMPINN is the tailored enforcement of incompressibility to address floating-point errors, ensuring more stable and accurate training. Additionally, CMPINN introduces an automated model selection strategy across temporal or load-stepping iterations. The CMPINN framework autonomously selects the most accurate model for each test case by continuously monitoring training loss during these steps. To further refine performance, the study introduces a hyperparameter tuning strategy designed to efficiently identify the optimal set of training parameters.
The proposed CMPINN framework is developed for a cube undergoing homogeneous isotropic, incompressible and canonical deformations: uniaxial tension/compression, simple shear, biaxial tension/compression, and pure shear. Three other tests for scenarios involving spatially varying material properties and inhomogeneous deformations are performed and benchmarked with numerical solutions. The approach systematically identifies optimal values for network depth, learning rate, loss weighting, and optimiser training epochs. The developed CMPINN model is mesh-free and accurately captures the mechanical deformation without labelled data. Once trained, the model can rapidly respond to any spatial coordinate within the physical domain. We develop the CMPINN framework to describe the propagation of nonlinear shear waves in soft solids, inspired by Tripathi et al.. The training of the CMPINN was performed without any labelled data to incorporate the causality of the wave propagation. Different tests involving linear and nonlinear shear propagation were performed, and the results were benchmarked against the numerical solution. In summary, this work advances the state of the art in PINN-based modelling by creating a stable and adaptive framework capable of handling the fourth-order hyperelastic deformation in soft solids. It opens new avenues for efficient and accurate simulation in biomechanics, particularly for applications such as real-time brain injury prediction
CALM: Foundations for causality-aware language models for causal question-answering
No full textCausality is a crucial aspect of human rationality, and causal reasoning is a critical element in the development of our mental models of reality. Theories of causality drive scientific inquiry and pervade all dimensions of our daily lives, from the minutiae of deciding what to wear based on weather predictions to medical diagnoses for experienced ailments. Recent advances in language models and AI technologies have demonstrated remarkable performance across a wide range of reasoning and QA tasks, resulting in promises of human-level general intelligence. However, the causal reasoning capabilities of such models remain underexplored, as prior research has been disparate and limited to narrow investigations of causal question-answering (causal QA) tasks. In this thesis, we aim to unify causal QA research, measure the baseline causal reasoning capabilities of language models, and propose foundational resources for the development of Causality-Aware Language Models (CALM) which are effective across diverse causal QA tasks. Inspired by cognitive theories and philosophical accounts of reasoning, we posit a unified definition of causal QA aligned with human causality. Specifically, we introduce CALM-Bench, a multi-task benchmark that consists of diverse causal QA tasks, ranging from causal abduction to effect quantification, and conduct extensive transfer learning experiments. We then introduce CALM-Schema, a semantic schema for the organization of causal knowledge as causal systems, and CALM-KB, the first synthetically generated knowledge base consisting of approximately 5.4K causal systems. CALM-KB is extensively validated in the knowledge injection and RAG settings, and we further detail the benefits and limitations of causal knowledge across common causal reasoning categories. Finally, we investigate the causal explanation capabilities of LLMs and propose IBE-Eval, an interpretable framework for the automatic evaluation of LLM-generated causal explanations. Our research lays the groundwork for future studies on language model causal reasoning and introduces foundational resources for the advancement of CALM models
Navigating urban environments: Red Squirrel Sciurus vulgaris ecology in Irish cities
No full textWith increasing global urbanisation, urban ecology has become an essential aspect of native species conservation. The Eurasian red squirrel (Sciurus vulgaris), typically found in rural woodlands, can also thrive in urban parks and green spaces if there is sufficient tree cover., Maintaining connectivity between suitable habitats is crucial to prevent population isolation and link established populations. This project investigated the suitability of urban habitats for red squirrels. The initial study was conducted in Galway City, an urban area in western Ireland where invasive grey squirrels (Sciurus carolinensis) are absent. Using non-invasive monitoring techniques, including hair tube surveys, trail camera surveys, and a citizen science survey, we determined the distribution of red squirrels across Galway's urban centre. Red squirrels were found to be established in three woodlands in Galway's urban centre, with transient red squirrels from these populations observed in surrounding green urban areas. Notably, they were absent from other seemingly suitable woodlands in the city. Red squirrels preferred broadleaf habitats in summer due to greater food availability but were detected less frequently with hair tubes in that habitat during spring. Squirrels observed in green spaces surrounding woodlands exploited anthropogenic food sources, including bird feeders, a behaviour linked to higher population densities in other urban areas. The population demographics of one of the identified red squirrel populations in a fragmented woodland in Galway were examined through live trapping. Radio tracking techniques were employed to examine the habitat usage of individuals in Menlo. Mean population densities were estimated at 0.63 squirrels per hectare using the Lincoln Index in a 23.8-hectare broadleaf woodland. Mean body weight of the squirrels was 268.6g, which is lower than typical values from Britain and Ireland. The weight of male squirrels exhibited greater stability over the course of the study compared to females, which demonstrated more pronounced fluctuations. Low fecundity was attributed to lower body weight, resulting in fewer second litters
Monocyte-mediated infiltration and iron oxide nanoparticle uptake in adrenocortical carcinoma: Mechanisms, interactions, and therapeutic implications
No full textAdrenocortical carcinoma (ACC) is a rare, aggressive malignancy characterized by limited treatment options and poor survival rates. This thesis explores the role of monocyte/macrophage infiltration and iron oxide nanoparticle (IONP) uptake in ACC, examining their potential therapeutic implications.
Using immunohistochemistry and transcriptomic analysis, this study demonstrated substantial macrophage infiltration in ACC tissues, predominantly exhibiting an immunosuppressive M2 phenotype. High levels of M0 macrophages correlated with poorer patient outcomes, whereas elevated M2 macrophages showed an unexpected positive association with survival, suggesting complex roles in tumour progression and immune modulation. Further investigations revealed differential recruitment and functional polarization of peripheral monocytes by ACC cells via chemokine-driven mechanisms, particularly involving the CCL2-CCR2 axis. Monocytes/Macrophages interactions significantly modulated ACC cell proliferation and responsiveness to mitotane treatment, highlighting their potential role in therapeutic resistance.
Additionally, this research assessed the potential of IONPs as theranostic agents. ACC cells exhibited concentration and time-dependent nanoparticle uptake, primarily through macropinocytosis, without substantial impairment of cellular steroidogenesis or viability at therapeutically relevant concentrations. Notably, nanoparticle uptake dynamics were significantly influenced by interactions with endothelial cells and primary monocytes, suggesting competitive internalization pathways.
Collectively, these findings underscore the intricate interplay between monocytes/macrophages and ACC cells, emphasizing the importance of the tumour microenvironment in disease progression and treatment response. This work advances understanding of ACC pathophysiology and provides a foundation for developing XIII targeted therapies and nanoparticle-based theranostic.SFI Research Center for Medical Devices, National Institutes of Health (NIH
Abrupt climate change in Ireland: Assessing the role of 'Seasonality' in the last glacial termination
No full textPeriods of abrupt climate change in the North Atlantic during glaciations have attracted considerable research over the last 40 years. Going by several names, including North Atlantic stadials, Greenland stadials, and Heinrich Stadials, they are characterised by cooling signatures in the North Atlantic basin; slowdown, displacement, or cessation of North Atlantic oceanic currents; and an anomalous southern spread of sea ice. In contrast, the Southern Hemisphere continues to warm during these stadials, leading to the concept of a ‘bipolar seesaw’. Despite this apparent antiphasing, similar records of deglaciation during these periods cover both the Northern and Southern Hemispheres, including areas adjacent to or downwind of the deeply cold North Atlantic. The ‘seasonality’ hypothesis was developed as a response, where winters are deeply cold but summers become warm enough to melt glaciers in the northern regions displaying this deeply cold climatic signature, while resolving the need for a global mechanism. As such, questions remain about stadial initiation, presentation, maintenance, and broader connection to global phenomena, including the size and response of local ice sheets to these stadials. The Connemara region on the central west coast of Ireland is an ideal location to investigate these questions: local ice sheet size and configuration has historically involved two competing hypotheses, and its plentiful relict glacial features and varied quartz-rich lithologies adjacent to large limestone formations make it an ideal location to test ice flow direction and perform cosmogenic nuclide surface-exposure dating to ascertain timing and pace of ice dissolution.
The three studies presented here attempt to address these questions of cryospheric response, timing, and morphology. The first investigation looks at glacial erratic distributions throughout the Connemara region, presents 15 new 10Be cosmogenic nuclide dates, documents glacial meltwater features and striae throughout the region, establishes a radial flow pattern from central Connemara, and discusses potential mechanisms for the rapid deglaciation found at approximately 17 ka. The second manuscript presents a previously undocumented suite of moraines in a Connemara mountain valley, analyses the moraine till for depositional conditions, traces lithologies within the till to the bedrock locations, establishes a period of northward ice flow into the Connemara uplands during deglaciation, and finds rapid deglaciation within the statistical error of the sites from the first study, with an added vertical dimension. The third study investigates a previously documented, but largely unstudied, moraine suite in eastern Connemara finding evidence of the same brief readvance within rapid deglaciation suggested by the moraines in the second manuscript from ice flowing radially from Connemara, supported by glacio-geomorphological evidence, lithology tracing, and new 10Be dates analysed together with previously published 10Be dates and discusses the global and climatic connections this rapid deglaciation and brief readvance sequence represents. The project finds rapid deglaciation in Ireland during stadial conditions, rapid enough that the cause must include summer melt-season activity, supported by the tight statistical distributions of 10Be data, abundant meltwater features in the region, till characteristics, and European meltwater indices. The project also finds incontrovertible geomorphological evidence indicating an ice centre in Connemara that could not have supported an ice shelf post-Last Glacial Maximum, with ice retreat moving toward central Connemara rather than west to east toward the midlands