Now showing items 41-60 of 1533

    • Application of Hansen Solubility Parameters and Thermomechanical Techniques to the Prediction of Miscibility of Amorphous Solid Dispersion. Investigating the role of cohesive energy and free volume to predict phase separation kinetics in hot-melt extruded amorphous solid dispersion using dynamic mechanical analyser, shear rheometer and solubility parameters data

      Isreb, Mohammad; Gough, Timothy D.; Timmins, Peter; Mousa, Mohamad A.M.R. (University of BradfordSchool of Pharmacy. Faculty of Life Sciences, 2022)
      Hot-melt extruded solid dispersion technique is increasingly employed to improve the solubility of poorly water-soluble drugs. The technique relies on the enhanced solubility of the amorphous form of the drug compared to its crystalline counterpart. These systems however are thermodynamically unstable. This means that the drug crystallises with time. Therefore, efforts to measure the stability of these systems over the life span of the product are crucial. This study focused on investigating the use of Hansen Solubility Parameters to quantify polymer-drug interaction and to predict the stability of solid dispersions. This was achieved through a systematic review of hot-melt extruded solid dispersion literature. The study also investigated the use of a combined mechanical and rheological model to characterise the physicochemical and release behaviour of three solid dispersion immediately after preparation and after storage for one month at 40oC or three months at room temperature. Results revealed that the total solubility parameter |ΔбT| was able to predict the stability of the systems for more than 4 months using a cut-off point of 3 MPa-1 with a negative predictive value of 0.9. This was followed by ΔбD with a cut-off point of 1.5 MPa- 1. Moreover, Dynamic Mechanical Analyser and shear rheometry data were shown to be more sensitive than Differential Scanning Calorimetry, Powder X-Ray Diffraction, Scanning Electron Microscope and Fourier Transform Infrared in detecting crystallisation and the interaction between the drug and the polymer. The Dynamic Mechanical Analyser data were consistent with the dissolution behaviour of the samples when comparing the freshly prepared samples with those after storage. The results highlight the need for a unified characterisation approach and the necessity of verifying the homogeneity of mixing during the extrusion process.
    • Study of the Continuous Intention to use Artificial Intelligence Based Internet of Medical Things (IoMT) During Concurrent Diffusion. The Influence Diffusion of Innovation Factors Has as Determinants of Continuous Intention to Use Ai-Based IoMT

      Weerakkody, Vishanth J.P.; Sivarajah, Uthayasankar; Aldhaen, Fatema S.F.A. (University of BradfordFaculty of Management, Law and Social Sciences, 2022)
      This research was about the continuous intention of healthcare professionals to use internet of medical things (IoMT) embedded with artificial intelligence (AI). IoMT and AI are evolving innovations and diffusing at the same time. It was not known in what way the two complex technologies diffusing concurrently could influence continuous intention to use IoMT. In addition, behavioural aspects namely motivation and training to use IoMT have been argued to intervene in the relationship between an AI based IoMT and continuous intention to use IoMT. Diffusion of Innovation theory was applied to explain the relationship between diffusion factors that aid the diffusion of AI based IoMT and continuous intention to use IoMT. The five factors relative advantage, compatibility, complexity, observability and trialability were chosen as determinants of continuous intention to use IoMT using DoI theory. Self-determination theory and theory of planned behaviour were used to introduce the interventions in the relationship between diffusion factors and continuous intention to use IoMT. UTAUT was used to explain the influence of the moderators artificial intelligence awareness, novelty seeking behaviour and age of healthcare professionals. The central issue investigated was the determinants of continuous intention of healthcare professionals to use IoMT with behavioural attributes of motivation and training conceived as mediators of the relationship between diffusion factors and continuous intention to use IoMT in the presence of moderators. Quantitative research methodology was used to test the research model developed to understand the relationship between the five diffusion of innovation theory factors and continuous intention to use IoMT when AI based IoMT is still diffusing. The concurrent diffusion of two new technologies was investigated using a research model that was developed for studying the healthcare professionals and their intention. The research was conducted in Bahrain in the healthcare sector. A sample of 354 healthcare professionals participated in the research. Structural equation modelling was used to analyse the data and test the hypothesis. The research showed that healthcare professionals will continue to use concurrently diffusing technologies depending on the relative advantage, complexity and compatibility of the innovations that diffuse. In addition, the results show that healthcare professionals will be motivated by the compatibility of AI-based IoMT if they have to continuously use IoMT. Furthermore, training enables both the organization and the healthcare professionals to overcome dilemma in case they have to continue to use an innovation during its diffusion or when new innovation surface in the market. Finally, artificial intelligence awareness is able to moderate the relationship between relative advantage, complexity and training to use IoMT. Thus, this research contributes to the discipline of behavioural intention of healthcare professionals in determining the influence of an artificial intelligence based IoMT on continuous intention to use IoMT when artificial intelligence embedded in IoMT diffuses concurrently with IoMT. Where IoMT diffusion factors can be used as a determine of continuous intention to use IoMT, artificial intelligence could be understood as a moderator of the relationship between diffusion factors and training to use IoMT, thus demonstrating the combined diffusion of the two technologies diffusing concurrently.
    • Self-Congruity Theory: An Investigation of the Pro-Environmental Tourist Behaviours. An Application and Extension of Self-Congruity Theory of the Eco-Tourism Destinations in Pakistan and UK

      Trivedi, Rohit; Waseem, Donia; Amin, Obaidullah (University of BradfordSchool of Management. Faculty of Management, Law and Social Sciences, 2023)
    • Investigation to Identify the Influence of the Surface Energetics of the Dry Powder Formulations of Budesonide and Theophylline on Their Aerodynamic Dose Emission Characteristics.

      Assi, Khaled H.; Vangala, Venu R.; Jamal, Abdullateef J.A.M.A. (University of BradfordSchool of Pharmacy. Faculty of Life Sciences, 2022)
      Surface energetics play a key role in the delivery of a dry powder inhaler formulation into the lungs, as there must be a sufficient balance of adhesive and cohesive forces to allow optimal lung delivery. In this study, measuring the surface energies of a set of single drug and carrier (budesonide or theophylline with either mannitol or lactose) with different levels of surfactant using Inverse Gas Chromatography, and comparing them to their lung deposition performance using a Next Generation Impactor established a relationship between the two. A 1:10 mixing ratio of budesonide with either carrier was found to have the highest FPF. Coating the carriers with 0.05% sodium lauryl sulphate resulted in a further increase in the FPF when using either budesonide or theophylline as the API, and the same results were seen when a sonocrystallised version of the API was substituted for the micronised form. The calculated IGC values then showed that the highest performing formulations had the lowest dispersive energy and total free surface energy. Furthermore, a trend was observed in the work of adhesion (Wa) and work of cohesion (Wc) for each set of formulations depending on which API was chosen, where for the less polar drug (budesonide) a higher Wa/Wc ratio was associated with the highest formulation performance, and for the more polar drug (theophylline) a smaller Wa/Wc ratio was associated with the highest formulation performance, enabling the estimation of lung performance for a set of single drug and carrier using their surface energy data.
    • An Evaluation of Technological, Organizational and Environmental Determinants of Emerging Technologies Adoption Driving SMEs’ Competitive Advantage

      Sivarajah, Uthayasankar; Rana, Nripendra P.; Vincent, Charles; Dobre, Marius (University of BradfordSchool of Management. Faculty of Management, Law, and Social Sciences, 2022)
      This research evaluates the technological, organizational, and environmental determinants of emerging technologies adoption represented by Artificial Intelligence (AI) and Internet of Things (IoT) driving SMEs’ competitive advantage within a resource-based view (RBV) theoretical approach supported by the technological-organizational-environmental (TOE)-framework setting. Current literature on SMEs competitive advantage as outcome of emerging technologies in the technological, organisational, and environmental contexts presents models focused on these contexts individual components. There are no models in the literature to represent the TOE framework as an integrated structure with gradual levels of complexity, allowing for incremental evaluation of the business context in support of decision making towards emerging technologies adoption supporting the firm competitive advantage. This research gap is addressed with the introduction of a new concept, the IT resource-based renewal, underpinned by the RBV, and supported by the TOE framework for providing a holistic understanding of the SMEs strategic renewal decision through information technology. This is achieved through a complex measurement model with four level constructs, leading into a parsimonious structural model that evaluates the relationships between IT resource-based renewal, and emerging technologies adoption driving SMEs competitive advantage. The model confirms the positive association between the IT resource-based renewal and emerging technologies adoption, and between the IT resource-based renewal and SME competitive advantage for the SMEs managers model, with the SME owners model outcomes are found not being supportive towards emerging technologies adoption driving SME competitive advantage. As methodology, PLS-SEM is used for its capabilities of assessing complex paths among model variables. Analysis is done on three models, one for the full sample, with two subsequent ones for owners and managers, respectively, as SME decision makers, with data collected using a web-based survey in Canada, the UK, and the US, that has provided 510 usable answers. This research has a theoretical contribution represented by the introduction of the IT resource-based renewal concept, that integrates the RBV perspective and the TOE framework for supporting organization’s decision on emerging technologies adoption driving SMEs competitive advantage. As practical implications, this thesis provides SMEs with a reference framework on adopting emerging technologies, offering SME managers and owners a comprehensive model of hierarchical factors contributing to SMEs competitive advantage acquired as outcome of AI and IoT adoption. This research makes an original contribution to the enterprise management, information systems adoption, and SME competitive advantage literature, with an empirical approach that verifies a model of emerging technologies adoption determinants driving SMEs competitive advantage.
    • Historic settlement on Unst, Shetland. An holistic study of abandoned settlements on Unst, Shetland utilising historical archaeology and prospection approaches

      Bond, Julie; Gaffney, Christopher F.; Heron, Carl P.; Legg, Robert M. (University of BradfordSchool of Archaeological and Forensic Sciences. Faculty of Life Sciences, 2018)
      A holistic study of abandoned house sites on the island of Unst was conducted to address the extent to which perceptions of historic settlement on Shetland are supportable. These perceptions cast long lived nucleated settlement as the normative traditional form of historic settlement, and dispersed settlements as short-lived exceptions to this norm. Historic settlement, in these perceptions are argued to be static, which is not borne out in archaeological evidence. Issues associated with historic Shetland settlement models were identified to parallel traditional views of Scottish highland rural settlement, which cast the highland society as ahistoric and unchanging. Historical, archaeological and geographic evidence for settlement on Unst were used to assess the geographical distribution of historic settlement on the island. Two detailed case studies integrated archaeological prospection techniques with the historical, archaeological and landscape contexts to form new narratives for the field remains around two abandoned house sites. Assessment of the historical settlement of Unst highlighted a much greater degree of variation between the different evidence strands for the perceptions to truly represent the island’s historical settlement. Similarly, findings from the case studies highlighted a much greater degree of alterations to the field systems and enclosures associated with the settlements than would be anticipated. Alternative narratives with several phases were hypothesised for field remains of each case study.
    • The therapeutic/anti-carcinogenic effect of cord blood stem cells-derived exosomes in malignant melanoma

      Najafzadeh, Mojgan; Anderson, Diana; Isreb, Mohammad; Baumgartner, Adolf; Wright, Andrew; Naeem, Parisa (University of BradfordFaculty of Life Sciences, 2022)
      Malignant melanoma is an invasive type of skin cancer with high mortality rates, if not detected promptly. The mortality trends are generally linked to multiple dysplastic nevi, positive family history, genetic susceptibility and phenotypic features including fair skin, freckles, numerous atypical nevi, light coloured hair and eyes, inability to tan and prolonged exposure to ultraviolet radiation B (UVB). To date, the major anti-cancer therapeutics for melanoma include surgery, chemotherapy, radiotherapy, and immunotherapy. Recently, extracellular vesicles, especially exosomes, have been highlighted for their therapeutic benefits in numerous chronic diseases such as cancer. Exosomes display multifunctional properties, including inhibition of cancer cell proliferation and initiation of apoptosis. Hence, this study aimed to evaluate the genotoxicity and cytotoxicity of cord blood stem cell-derived (CBSC) exosomes on 6 samples of peripheral blood lymphocytes taken from healthy individuals and melanoma patients and on 3 samples of melanoma (CHL-1) cells. The limited number of samples was due to the time limitations and restrictions that were in place due to the COVID-19 pandemic. In this in vitro study, the optimal concentration of CBSC-derived exosomes (0, 100, 200, 300, 400 μg/ml protein at 24, 48 and 72h treatments) was confirmed by the CCK-8 assay. CBSC exosomes (300 μg/ml) were used to treat lymphocytes and CHL-1 cells in the Comet assay and evaluated using the real-time polymerase chain reaction (qPCR) and Western blotting (WB). The data of the CCK-8 and Comet assays illustrated that exosomes exerted genotoxic effects on CHL-1 cells (CCK-8 assay, ****p < 0.0001), (Comet assay, *p <0.05, **p < 0.01). However, the data portraying a reduction in the viability of lymphocytes needs further investigation as the number of samples was limited, therefore, further clarification is required. Importantly, no significant adverse effect was observed in healthy lymphocytes when treated with the same exosomes (p = ns). When further challenged with UVA+B radiation, the exosomes did not induce any genoprotective effect on ROS-induced CHL-1 cells, compared to the positive control (p = ns). Our data insinuates that the damage might be caused by inducing apoptosis. The anti-tumourigenic potential of exosomes was observed by activating the p53-mediated apoptotic pathway in CHL-1 cells, up-regulating p53, p21 and caspase 3 and down-regulating BCL-2 at mRNA (**p < 0.01, ***p <0.001, ****p <0.0001) and protein levels (*p < 0.05, **p <0.01). The potency of CBSC exosomes in inhibiting cancer progression in CHL-1 cells whilst causing no harm to the healthy lymphocytes makes it an ideal potential candidate for anti-cancer therapy. More samples are required to evaluate the therapeutic effect of exosomes on lymphocytes from cancer patients to fully understand their mechanism of action.
    • Fabrication Characterisation and Optimisation of Electrospun Scaffolds for Ligament Tissue Reconstruction. The Development of an Anterior Cruciate Ligament (ACL) Analogue using Electrospun PCL, PVA Hydrogel and Polyester Sutures

      Sefat, Farshid; Twigg, Peter C.; Agbabiaka, Oluwadamilola A. (University of BradfordSchool of Biomedical and Electronics Engineering. Faculty of Engineering and Informatics, 2022)
      Year 2019, football, rugby, netball and skiing had most occurring ACL injuries, listed by United Kingdom National Ligament Report (NLR). The standard procedure treatment of complete laceration of the ACL, is performed by tissue autograft implantation designed from a patellar tendon, for replacement of damaged tissue using orthopaedic surgery. The aim of this thesis is to design and fabricate an ACL graft, attempting to mimic the natural ACL, for the purpose of tissue reconstruction. The desired graft analogues exhibited properties imitating native connective tissue, reducing pain through drug delivery with great biocompatibility and enhance suture mechanical strength. Various biomaterials were implemented into this study, utilising strategies; polymer solution fabrication, electrospinning, hydrogel synthesis, mechanical braiding and graft assembly to fabricate an ACL graft. The polymeric material poly (E- caprolactone) (PCL) was researched, utilising its ability to fabricate scaffolds. Results showed, three analogue ACL grafts (Braided PCL-BP, Braided PCL + Hydrogel-BPH & Braided PCL + Sutures-BPS) created utilising the properties of braiding, hydrogels and sutures, ultimately improving the versatility of electrospinning for tissue engineering and reconstruction. Graft analogues were tested and compared against patellar tendons producing similar tensile properties. Poly vinyl alcohol (PVA) hydrogels successfully held ibuprofen, revealing drug delivery characteristics, polyester threads improved mechanical properties of electrospun grafts and dry degradation showed that PCL did not lose significant mass over two months. Conclusion, tensile strength of patella tendon was 395x, 790x & 56x of analogue grafts (BP, BPH & BPS) respectively, having potential for improvement of tensile parameters for ligament reconstruction.
    • Sparse Representation and its Application to Multivariate Time Series Classification

      Lei, Ci; Neagu, Daniel; Sani, Habiba M. (University of BradfordDepartment of Computer Science. Faculty of Engineering and Informatics, 2022)
      In signal processing field, there are various measures that can be employed to analyse and represent the signal in order to obtain meaningful outcome. Sparse representation (SR) has continued to receive great attention as one of the well-known tools in statistical theory which among others, is used to extract specific latent temporal features that can reveal salient primitive and sparsely represented features of complex data signals, including temporal data analysis. Under reasonable conditions, many signals are assumed to be sparse within a domain, such as spatial, time, or timefrequency domain, and this sparse characteristics of such signals can be obtained through the SR. The ECG signal, for instance, is typically a temporal sparse signal, comprises of various periodic activities such as time delay and frequency amplitudes, plus additive noise and possible interference. Particularly challenging in signal processing, especially time series signals is how to reconstruct and extract the various features that characterized the signal. Many problems (e.g., signal components analysis, feature extraction/selection in signals, signal reconstruction, and classification) can be formulated as linear models and solved using the SR technique The reconstruction of signals through SR can offer a rich representation of the sparsified temporal structure of the original signal. Due to its numerous advantages, such as noise tolerance and widespread use in various signal processing tasks, this has motivated many researchers to adopt the use of this technique for various signal representation analysis for a better and richer representation of the original input signal. In line with this, therefore, the goal of this study is to propose a SR-based mathematical framework and a coherence function for reconstruction and feature extraction from signals for subsequent analysis. The time embedding principle was first applied to restructure the signal into tine delay vectors and then the proposed approach, referred to as temporal subsequence SR approach was used to reconstruct the noisy signals and provides a sparsified time dependent input signal representation, and then the coherence function is further used to compute and extract the correlational coefficient quantities between the temporal subsequence signals to form the final feature vectors representing the discriminative features for each of the signal. These final feature vectors representing the signal are further used as inputs to machine learning classifiers. Experiments are carried out to illustrate the usefulness of the proposed methods and to assess their impact on the classification performance of the SVM and MLP classifiers using the popular and widely used ECG time series benchmark dataset. This research study supports the general hypothesis that, signal reconstruction methods (datadriven approach) can be valuable in learning compact features from the original signals for classifications.
    • Towards the development of an integrated case-finding tool to facilitate the review of anticholinergic prescribing for frail older people

      Petty, Duncan R.; Faisal, Muhammad; Johnson, O.A.; Gardner, Peter; Mehdizadeh, David (University of BradfordSchool of Pharmacy and Medical Sciences. Faculty of Life Sciences, 2022)
      Background: The cumulative effect of taking anticholinergic medicines (anticholinergic burden) is associated with adverse outcomes for older people. Prevalence of anticholinergic prescribing is increasing, and there is a need for tools to proactively identify at-risk patients for medication reviews. Aim: To explore the need for, and feasibility of, an integrated case-finding tool that predicts risks using electronic health records (EHRs), facilitating the review of anticholinergic medicines for frail older people. Methods: Mixed methods, adopting a pragmatic approach. A systematic review, prediction modelling of cohort study data, and qualitative interviews were undertaken. Results: The systematic review found anticholinergic exposure was associated with adverse outcomes for the frail; poorer physical function, falls, and mortality, indicating a need for a risk reducing intervention. In the prediction modelling study, predicting risks using composite measures of anticholinergic burden and frailty indicated limited feasibility. Neither enhanced the performance of best subset models using cohort study data. Their predictive utility needs to be investigated using EHR data, to determine their feasibility within primary care. The qualitative study found healthcare professionals needed a proactive tool, supporting risk prediction as a feasible approach. Factors influencing future implementation were; upskilling requirements, deprescribing confidence, patient reluctance, motivation, holistic care, interoperability, trust in risk prediction, remuneration, among other barriers and facilitators. Conclusions: Through identifying a need, and potential feasibility, foundations towards the future developments of a case-finding tool have been provided, informing an early tool prototype (AC-FRAIL). Recommendations for further work suggest a roadmap ahead, to maximise the potential for integrated solutions to proactively reduce anticholinergic risks.
    • Proteomic Investigation of Endocrine Therapy Resistance in Breast Cancer Investigating the Molecular Mechanisms for SERM Resistant Cell Lines Using SILAC-Based Proteomic Approach

      Sutton, Chris W.; Shnyder, Steven; Al-Kabariti, Aya Y. (University of BradfordSchool of Pharmacy. Faculty of Life Sciences, 2022)
      Introduction: Breast cancer is the second highest cause of cancer mortality in women worldwide. Hormonal therapy is considered one of the most effective therapies and is used against luminal-type malignancies. However, 40-50% of tumour cells can develop resistance, thereby limiting the success in breast cancer treatment. In this study, mechanisms of resistance were investigated using a novel multi-stable isotope labelled amino acids (SILAC) proteomics approach in phenotype-specific breast cancer cell lines resistant to endocrine treatment. Method: In vitro chemo-sensitivity (IC50) was determined for MCF7, T47D, MDA-MB-231, MDA-MB-468, MDA-MB-453, BT-20 and MCF-10A breast cell lines using four endocrine-based therapeutic agents (Tamoxifen, 4-Hydroxytamoxifen OHT, Raloxifene, Anastrozole) to select viable strains for resistance studies. MCF7 (luminal-type A) and MDA-MB-231 (triple negative breast cancer, TNBC) were selected and initially subject to OHT or raloxifene exposure with gradual increments for 10 months. WT cells were grown in the absence of drug in parallel as passage controls. Resistant cell lines were assessed by MTT and IF for comparison with parental cell lines. Resistant cell lines, along with the passage control and a SILAC control, were grown in “light” SILAC medium together with WT strains cultured in “heavy” SILAC medium. Proteins were extracted, concentrations determined and analysed by SDS PAGE for quality control. An aliquot of each “light” cell line (resistant, passage control or SILAC control) was combined with an equal amount of “heavy” WT, trypsin digested and analysed by nano-HPLC Orbitrap Fusion mass spectrometry (2D-LC MS/MS). Proteins were identified by database searching using MascotTM. Relative changes (resistant/WT ratio) in protein levels were determined and bioinformatics tools (STRING and UniProt) used to explore significantly changed pathways associated with resistance. Western blotting was used to verify selected target proteins. Results: Four consistently resistant sublines were generated MCF7 OHT Res (2.00-fold more resistant), MCF7 Ralx Res (2.00-fold), MDA-MB-231 OHT Res (1.90-fold change) and MDA-MB-231 Ralx Res (2.00-fold), in addition to two high passage controls. ER expression by IF was decreased in MCF7 OHT Res compared to the WT and MCF7 Ralx Res, whereas CD44 was increased. Proteomic analysis revealed 2247 and 2880 total proteins in MCF7 OHT Res and MCF7 Ralx Res whilst 3471 and 3495 total proteins were identified in MDA-MB-231 OHT Res and MDA-MB-231 Ralx Res, respectively. Bioinformatics tools identified significantly changed pathways included apoptosis, cytoskeleton, cell motility and redox cell homeostasis. Components of the MAPK-signalling cascade were consistently found to be upregulated in resistant cell lines. MAPK1 (ERK2), previously associated with tamoxifen resistance was increased in MDA-MB-231 Ralx Res cell lines by 4.45-fold and confirmed by Western blotting. Sorcin, which contributes to calcium homeostasis and is also linked to multidrug resistance was increased 4.11- and 2.35-fold in MCF7 OHT Res and Ralx Res sub cell lines, respectively. Some results, such as those for c-Jun, were inconsistent between proteomic analysis and Western blotting and require further investigation. Conclusion: The unique resistant cell lines generated here, as well the MCF7 OHT resistant line, provided novel data that give insights into the biological pathways involved in mechanisms of endocrine drug resistance in breast cancer. Proteomics analysis provided extensive data on common functionality and pathways across the resistant cell lines independent of phenotype or SERM. Overall, the results provided interesting targets for re-sensitising resistant breast cancer and the potential to investigate novel combination therapies in the future.
    • Proteomic profiling of matched normal and tumour tongue biopsies from smokers and non-smokers. Oncoproteomic applications for oral tongue squamous cell carcinoma biomarker discovery

      Sutton, Chris W.; Pors, Klaus; Saeed, Sidra (University of BradfordFaculty of Life Sciences. School of Pharmacy, 2021)
      Despite considerable development in the therapeutic repertoire for managing cancer-related malignancies, head and neck cancer mortality has not significantly improved. The burden of HNSCC fluctuates across countries and has been associated with exposure to tobacco-derived carcinogens, excessive alcohol consumption or combinations. Due to late detection, patients often present with oral pre-malignant lesions which have progressed to an advanced stage of HNSCC. In this study, the samples were from a male cohort as generally, men are at two to four-fold higher risk than women with over 90% of HNSCCs arising in the upper aerodigestive tract. Therefore, the purpose of this thesis was to identify HNSCC biomarkers in males associated within defined anatomical region (tongue) and causative agents, specific to smoking. An iTRAQ proteomic approach was used to profile protein changes in matched normal and tumour samples from male non-smoking (n=6) and smoking patients (n=6) with tongue carcinomas revealing identification of potential targets specific to cancer. Samples were subjected to liquid nitrogen cryo-pulverisation and protein determination. Protein extracts from the same category were pooled, trypsin digested and iTRAQ 4-plex labelled. Data was generated by 2D-LC/MS on an Orbitrap Fusion and significantly changed proteins (median ± SD) were subject to bioinformatics appraisal. A total of 3426 proteins were identified and quantified by proteomic analysis. Comparison of non-smoker tumour (NS:T) with smoker tumour (S:T) distinguished 64 proteins that were upregulated and 62 downregulated, S:T vs S:N categorised 349 proteins up- and 395 down-regulated respectively and NS:T vs NS:N identified 469 proteins up- and 431 down-regulated, respectively. Arginase-1 (ARG1), Keratin Type-2 Cytoskeletal 8 (KRT8), Lipocalin-1 (LCN1) and DNA replication licensing factor MCM2 (MCM2) were identified as biologically associated with smoking compared to non-smoking, providing viable targets for verification by immunochemical methods which further supported the proteomic data. Overall, the project demonstrated the importance of using matched biopsies with good clinicopathological data for experimental design and provided a set of unique targets for a more expanded verification study.
    • Time will tell: Material surface cues for the visual perception of material ageing Insights from psychophysics, online experiments, image processing and a science festival

      Bloj, Marina; Denniss, Jonathan; Logan, Andrew J.; De Korte, Elisabeth M. (University of BradfordSchool of Optometry and Vision Science. Faculty of Life Sciences, 2022)
      This thesis explores the visual perception of material change over time, a novel topic that has received little attention so far. We aimed to understand the material surface features and mental representations associated with material change over time by the human visual system, and possibly wider cognitive systems. To this end, we performed a series of experiments with varying methodologies. These included a psychophysics experiment, online experiments, and data collection during a science festival. The latter showed that the general public mentioned “Faded (colour)” most often to describe material change over time and that specific material surface change features clustered around specific materials. In another experiment, material type, but not colour or the geometrical distribution, had a significant effect on perceived material change. Other experiments partially contradicted this finding. It was found that perceived material type showed a significant, non-linear association with perceived material change, replicating earlier findings on the effect of material type. In contrast, material surface lightness, a constituent of colour, was associated with perceived material change. The same held for components of the geometrical distribution. They showed a minor contribution to the perception of material change, but a major one to perceived material type. Together, our findings suggest that the human visual system seems to use constituents of material surface colour as a cue to material change over time. The geometrical distribution seems to play a minor role. Although these contributions may vary with material type, as our findings showed that material type affected the perception of material change over time.
    • Online Anomaly Detection for Time Series. Towards Incorporating Feature Extraction, Model Uncertainty and Concept Drift Adaptation for Improving Anomaly Detection

      Neagu, Daniel; Gheorghe, Marian; Tambuwal, Ahmad I. (University of BradfordDepartment of Computer Science. Faculty of Engineering and Informatics, 2021)
      Time series anomaly detection receives increasing research interest given the growing number of data-rich application domains. Recent additions to anomaly detection methods in research literature include deep learning algorithms. The nature and performance of these algorithms in sequence analysis enable them to learn hierarchical discriminating features and time-series temporal nature. However, their performance is affected by the speed at which the time series arrives, the use of a fixed threshold, and the assumption of Gaussian distribution on the prediction error to identify anomalous values. An exact parametric distribution is often not directly relevant in many applications and it’s often difficult to select an appropriate threshold that will differentiate anomalies with noise. Thus, implementations need the Prediction Interval (PI) that quantifies the level of uncertainty associated with the Deep Neural Network (DNN) point forecasts, which helps in making a better-informed decision and mitigates against false anomaly alerts. To achieve this, a new anomaly detection method is proposed that computes the uncertainty in estimates using quantile regression and used the quantile interval to identify anomalies. Similarly, to handle the speed at which the data arrives, an online anomaly detection method is proposed where a model is trained incrementally to adapt to the concept drift that improves prediction. This is implemented using a window-based strategy, in which a time series is broken into sliding windows of sub-sequences as input to the model. To adapt to concept drift, the model is updated when changes occur in the new arrival instances. This is achieved by using anomaly likelihood which is computed using the Q-function to define the abnormal degree of the current data point based on the previous data points. Specifically, when concept drift occurs, the proposed method will mark the current data point as anomalous. However, when the abnormal behavior continues for a longer period of time, the abnormal degree of the current data point will be low compared to the previous data points using the likelihood. As such, the current data point is added to the previous data to retrain the model which will allow the model to learn the new characteristics of the data and hence adapt to the concept changes thereby redefining the abnormal behavior. The proposed method also incorporates feature extraction to capture structural patterns in the time series. This is especially significant for multivariate time-series data, for which there is a need to capture the complex temporal dependencies that may exist between the variables. In summary, this thesis contributes to the theory, design, and development of algorithms and models for the detection of anomalies in both static and evolving time series data. Several experiments were conducted, and the results obtained indicate the significance of this research on offline and online anomaly detection in both static and evolving time-series data. In chapter 3, the newly proposed method (Deep Quantile Regression Anomaly Detection Method) is evaluated and compared with six other prediction-based anomaly detection methods that assume a normal distribution of prediction or reconstruction error for the identification of anomalies. Results in the first part of the experiment indicate that DQR-AD obtained relatively better precision than all other methods which demonstrates the capability of the method in detecting a higher number of anomalous points with low false positive rates. Also, the results show that DQR-AD is approximately 2 – 3 times better than the DeepAnT which performs better than all the remaining methods on all domains in the NAB dataset. In the second part of the experiment, sMAP dataset is used with 4-dimensional features to demonstrate the method on multivariate time-series data. Experimental result shows DQR-AD have 10% better performance than AE on three datasets (SMAP1, SMAP3, and SMAP5) and equal performance on the remaining two datasets. In chapter 5, two levels of experiments were conducted basis of false-positive rate and concept drift adaptation. In the first level of the experiment, the result shows that online DQR-AD is 18% better than both DQR-AD and VAE-LSTM on five NAB datasets. Similarly, results in the second level of the experiment show that the online DQR-AD method has better performance than five counterpart methods with a relatively 10% margin on six out of the seven NAB datasets. This result demonstrates how concept drift adaptation strategies adopted in the proposed online DQR-AD improve the performance of anomaly detection in time series.
    • Physicochemical and biopharmaceutical characterization of novel derivatives of gallic acid

      Paluch, Krzysztof J.; Sheldrake, Helen M.; Kantamneni, Sriharsha; Alhyari, Dania H. (University of BradfordSchool of Pharmacy and Medical Sciences. Faculty of Life Sciences, 2022)
      Gallic acid is a known antioxidant and has anti-inflammatory activity in addition to other biological activities, but GA efficiency is restricted due to low permeability and low oral bioavailability. This study was designed to investigate the solubility, permeability, oral bioavailability, enzymatic stability with cytochrome CYP2D6, antioxidant and anti-inflammatory activity of novel gallic acid sulfonamide derivatives; TMBS, and THBS. In addition, a novel in silico permeability model was designed to predict the permeability and bioavailability of eighty derivatives of GA. In sillico prediction of intestinal permeability of GA derivative indicated an increase in permeability with increased lipophilicity and decreased aqueous solubility, replacing the carboxylic group with sulfonamide group has increased intestinal permeability. A significant (P <0.01) increase was observed in the permeability of TMBS and THBS over GA, in both gastric fluids and HIEC cells. TMBS was O-demethylated by CYP2D6. TMBS had greater ROS scavenging activity than GA in HIEC-6 cells. There was a significant (P< 0.05) increase in anti-inflammatory activity of THBS, and TMBS compared to ibuprofen. TMBS, and THBS had better oral bioavailability than GA. This data suggests that the in silico permeability model can be used in the future to study new candidate of gallic acid, and further in vivo and clinical investigations are required to introduce TMBS and THBS as a new antioxidant and anti-inflammatory drugs.
    • Measuring The Information Literacy of School Stakeholders in Implementing Blended Learning in High Schools in The State of Kuwait to Propose a School Management System. Examining the level of information literacy of stakeholders in High schools and to propose a new school management system to achieve the best implementation of information literacy and blended learning practices

      Abd-Alhameed, Raed; Kamala, Mumtaz A.; Carruthers, Andrew; AlQaoud, Fatima N.H. (University of BradfordFaculty of Engineering and Informatics, 2022)
      Information Literacy (IL) has been implemented among workers in schools in recent years in order to enhance workers’ skills and competencies through the utilisation of technology in education. To implement a sustainable IL system, the building of an IT infrastructure is required including the provision of PCs and network access, enabling students to connect to the internet during class time in order to access educational web-based content as well as to share discussions about subjects with their peers in and out of the class. Assessing the level of information literacy (IL) of stakeholders will assist in creating a new proposed school management system (SMS) to be used among them in the future. This will encourage teachers to use blended learning approach for teaching their students. Therefore, it is important to examine the IL of stakeholders (principal, head of division, teacher) in Kuwait to increase their skills and competencies which will lead to greater innovation in future developments. This work aims at assessing information literacy level of stakeholders, their skills and competencies in utilising technology in a workplace environment, which will assist the researcher to create a new school system. In addition, this work presents a new proposed school management system (SMS) for stakeholders in the current educational system of high schools in Kuwait, therefore, applying the BL approach in order to achieve a high quality of students' learning outcomes.
    • Collision and Avoidance Modelling of Autonomous Vehicles using Genetic Algorithm and Neural Network

      Not named; Gadinaik, Yogesh Y. (University of BradfordFaculty of Engineering and Informatics, 2022)
      This thesis is to study the optimisation problems in autonomous vehicles, especially the modelling and optimisation of collision avoidance, and to develop some optimisation algorithms based on genetic algorithms and neural networks to operate autonomous vehicles without any collision. Autonomous vehicles, also called self-driving vehicles or driverless vehicles are completely robotised driving frameworks to allow the vehicle to react to outside conditions within a bunch of calculations to play out the undertakings. This thesis summarised artificial intelligence and optimisation techniques for autonomous driving systems in the literature. The optimisation problems related to autonomous vehicles are categorised into four groups: lane change, motion planner, collision avoidance, and artificial intelligence. A chart had been developed to summarise those research and related optimisation methods to help future researchers in the selection of optimisation methods Collision Avoidance is one of streamlining issues in autonomous vehicles. Several sensors had been used to identify position and dangers and collision avoidance algorithms had been developed to analyse the dangers and to use vehicles to avoid a collision. In this thesis, the current research on collision avoidance has been reviewed and some challenges and future works were presented to select the research direction of this thesis, the aim of this research will be the development of optimisation methods to avoid collisions in a predefined environment. The contributions of this thesis are that (1) a simulation model had been developed using Matlab for collision avoidance and serval scenarios were proposed and experimented with. The sensors are used as the inputs to determine collision in the learning preparation of the algorithm; (2) a neural network was used for collision avoidance of autonomous vehicles; (3) a new method was proposed with the combination of genetic algorithm and neural network. In the proposed frame, the neural network is used for decision making and a genetic algorithm is used for the training of the neural network. The results and experimentation show that the proposed strategies are well in the designed environment.
    • Pregnancy, Transition to Motherhood, Infant Feeding Attitudes and Health Locus of Control in Nigeria

      Johnson, Sally E.; Stewart-Knox, Barbara; Lesk, Valerie E.; Adegbayi, Adenike (University of BradfordDivision of Psychology Faculty of Management, Law and Social Sciences, 2022)
      Exclusive breastfeeding and holistic maternity care are strategic to improving maternal and infant health outcomes in Nigeria. This thesis aimed at informing policies and interventions to promote breastfeeding and to improve Nigerian mother’s experiences in antenatal and intrapartum care. The study in this research focused upon psychological dynamics underlying societal culture around maternity and breastfeeding. Using quantitative method, attitudes toward breastfeeding and health orientation were surveyed in 400 Nigerian men and women using the Iowa Infant Feeding Attitude Scale (IIFAS) and the Multidimensional Health Locus of Control Scale (MHLoC). There were more positive attitudes toward breastfeeding in males, participants in the 20-29-year-old age category, and in those who identified as single. Higher internal HLoC was associated with more positive attitudes to breastfeeding and higher EHLoC scores were associated with more negative attitudes to formula feeding. The second study explored the experience of pregnancy and childbirth in Nigerian women. Qualitative interviews with 12 women implied that Nigerian women perceive pregnancy and childbirth as a multidimensional experience comprising physiological and psychological elements and also as risky. Control mechanisms that reflected internal HLoC included choosing multiple antenatal care sources to obtain holistic care, adopting new technology in bridging perceived communication gaps with health care providers and adopting physical and mental strategies in controlling the somatic and sensory changes that accompany pregnancy. Pregnancy and childbirth were viewed through an external HLoC lens as spiritual, and reflected in an entrenched belief in the intervention of deity to mitigate pain and risk associated with childbirth. These results have implications for practice, intervention and policy to promote breastfeeding at the societal level and improve maternity services for the current and next child-bearing generation.
    • Intersections between culture, sociodemographic change and caring: a qualitative study of current and prospective family caregivers in mainland China.

      Oyebode, Jan; Quinn, Catherine; Breen, Liz; Bifarin, Oladayo O. (University of BradfordFaculty of Health Studies, 2022)
      Aim: As the ageing population in China increases, support required from family caregivers for older relatives living with long-term health conditions also increases. This being so, this thesis explored the experiences and perceptions of current and prospective family caregivers, under the culture of Xiao (孝; filial piety). Design and Methods: Phase 1 was conducted with 19 Chinese students using 3 focus groups to gain greater familiarity with the culture and inform the main study (Phase 2). Adopting a social constructivist philosophical position, data for Phase 2 were obtained from three generational sub-samples: only-children affected by the One-Child Policy (OCP), parents affected by OCP, and family caregivers in the workforce, totaling 23 participants through virtual in-depth interviews with participants in mainland China. Interviews were translated, transcribed, and analysed using reflective thematic analysis. Findings and Conclusion: Phase 1 confirmed the centrality of the concept of Xiao to attitudes and beliefs around future caregiving for parents. Phase 2 findings’ overarching theme was ‘Competing pressures’, which comprised of three inextricably linked themes: (i) Caregiving beliefs, (ii) Contextual factors, and (iii) Caregiving conditions. Participants expressed meaningful desires to fulfil obligations, reflecting value-based convictions, stemming from their socio-cultural environment. Stressors experienced reveals structural and personal barriers to seeking support. Ultimately, extensive demands and limited coping strategies could diminish meaning in caregiving. This thesis makes a novel contribution on perceptions and experiences of family caregivers of older relatives within China as a collectivist society. Findings have implications for research, policy, and practice, highlighting the need for culturally attuned services to build resilience.
    • Strategic Decision-making Process in the Qatari Public Sector. Relationship between the Decision-Making Process, Implementation, and Outcome

      Weerakkody, Vishanth J.P.; Sivarajah, Uthayasankar; Al-Hashimi, Khalid M.I.A. (University of BradfordSchool of Management. Faculty of Management, Law, and Social Science, 2022)
      Although several multi-dimensional models of strategic decision-making processes (SDMPs) have been examined in the literature, these studies have paid insufficient attention to the public sector context and Gulf Cooperation Council (GCC) region. SDMP in the public sector and the State of Qatar can vary to SDMP in the private sector due to institutional and socio-cultural differences respectively. Therefore, more research is urgently needed to better understand SDPM within this context. To contribute to filling this void, this study develops and tests a multi-dimensional SDMP model including SDMP dimensions, implementation, and outcome. The study model examines (𝑖) the impact of four SDMP dimensions—procedural rationality, intuition, constructive politics, and participation—on the implementation success of the strategic decision; (𝑖𝑖) the impact of the successful implementation of SD over the SD quality; (𝑖𝑖𝑖) the mediation role of the implementation success of SD; (𝑖𝑣) the moderation effect of stakeholder uncertainty. The model was analysed using Partial Least Square Structural Equation Modelling (PLS-SEM) and tested using data from multiple informants on 170 strategic decisions in 38 Qatari public organisations. The study finds that procedural rationality, constructive politics, participations, and the implementation Success of SD plays a significant and positive role on SDMP and its overall outcome. Finally, the study provides substantial and original contributions to the knowledge of SDMP in the public sector; implications for decision-makers and directions for future research.