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    Object Counting via Convolutional Neural Network

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    Counting is a crucial information acquisition capability with significant impact across various fields such as congestion management and cell counting. While various counting methods exist, visually-based counting is the most intuitive and easiest for humans to understand. However, manually counting a large number of objects in images is not user-friendly, labour-intensive, and prone to errors. Therefore, the computer vision approach has been employed for object counting. The current state-of-the-art in computer vision based counting methods is dominated by convolutional neural network (CNN) based deep learning techniques. These CNN-based counting methods can provide more accurate, efficient, and adaptable solutions for object counting.However, due to the vast differences in countable object features and the varying scenarios in which counting models can be used, a single CNN model is difficult to solve all counting tasks. This necessitates the use of numerous datasets in current research, resulting in increasingly bulky models that require extensive computational resources for training. Existing datasets designed for counting tasks primarily focus on human targets, lacking specialized datasets for other objects. Addressing the diverse counting needs under such data conditions is a practical challenge for CNN-based counting research. Driven by data-centric approaches, this research aims to achieve counting for different complex scenarios through CNN-based counting architectures to address these research gaps.In this research, three counting frameworks have been developed for different situations: density map-based counting, point-based counting, and non-point-based counting. First, the Multi-Fusion Convolutional Neural Network (MFCNN) is used in hospital monitoring. This density map-based method implements crowd counting in an indoor scenario. The method combines multidimensional data to enable comprehensive assessment and evidence-based decision-making on the allocation of healthcare resources within a hospital. Second, recognising the systematic errors inherent in the data transformation of density map-based counting, a Point-Detection-based Counting Network (PDCNet) that avoids these errors by utilising direct point labelling is developed. The PDCNet combines three sophisticated point-matching algorithms with a dynamically optimised trained adaptive loss function, reducing computational requirements when compared to traditional methods. Last, to alleviate the requirement of high-accuracy labelling for traditional counting frameworks, this research also proposes a non-point-based counting framework, an Efficient Lightweight Multi-scale-feature-fusion Multi-task GAN (ELMGAN) model. To address the challenges of multi-task training, the framework makes innovative use of the generative adversarial network (GAN) to improve the training performance of generative multi-task models. The effectiveness of the model in generating high-quality segmented images and improving computational efficiency is demonstrated in various public datasets of medical cells.</p

    Serial Monitoring of Circulating Tumour DNA for Early Detection of Recurrence in Colorectal Cancer

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    Colorectal cancer (CRC) 5-year survival remains poor. Advances in next-generation sequencing (NGS) technology may help introduce circulating tumour DNA (ctDNA) use into clinical practice. This thesis investigated the use of ctDNA surveillance following curative CRC resection. Methods were chosen for easy implementation in current clinical settings, such as automated DNA extraction and commercial panel-based single nucleotide variant analysis. Twenty-one patients with stages II-IV CRC were surveilled to 12 months. Circulating tumour DNA was detected in at least one sample per patient. Following primary tumour resection, 68 % saw a fall in ctDNA while 42 % saw a fall in carcinoembryonic antigen (CEA), for patients with stage II or III disease. Six patients experienced disease recurrence. Pre-operative ctDNA levels were comparable in both groups, with BRAF and KRAS mutations most frequent in those developing recurrence, but PIK3CA and TP53 mutations most frequent in those without recurrence. Three patients died of recurrence, all with stage III CRC and complete resection margins. None of these three patients received adjuvant chemotherapy and postoperative ctDNA increased despite falls in post-operative CEA, with ctDNA detection preceding recurrence diagnosis by a wider window than CEA detection (33-161 versus 0-28 days, respectively). In three patients alive with recurrence, post-operative ctDNA preceded recurrence diagnosis by 1-6 months, with CEA preceding recurrence diagnosis by 1 month in two patients and undetectable at all times in one patient. This thesis shows the potential utility of ctDNA in CRC surveillance for prognostication, decision making for adjuvant therapy, to highlight residual disease and as a potential substitute for patients with undetectable CEA.</p

    Searching for Brown Dwarf Companions to White Dwarfs

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    To date, thousands of exoplanets have been discovered and characterised but the high contrast between planets and their host stars makes atmospheric studies difficult. White dwarf–brown dwarf binaries offer unique opportunities to study brown dwarfs and their atmospheres with minimal contamination from the host star when observing in the infrared. Although up to 0.5% of white dwarfs are predicted to have brown dwarf companions, these systems are rare, with only ∼10 known close white dwarf–brown dwarf binaries, and ∼7 such binaries with a wide separation. In this thesis, I present the discovery and characterisation of multiple white dwarf–brown dwarf binary systems. With near-infrared spectroscopy, I find a new wide white dwarf–brown dwarf binary SDSS J2225+0016, which has the third smallest separation for a spatially resolved white dwarf–brown dwarf binary after GD 165AB and PHL 5038AB. I characterise the orbit as well as the brown dwarf and compare this binary to other brown dwarf and exoplanetary systems. I analyse new infrared spectroscopic data of the close, eclipsing white dwarf–brown dwarf binary WD1032+011, extracting the phase-dependent spectra of the brown dwarf. I show the effects of constant irradiation on the atmosphere of the brown dwarf, causing a dayside-nightside temperature contrast and slowing the contraction of the brown dwarf such that its radius is inflated. I examine the candidate white dwarf–brown dwarf binary WD0950+0115 using near-infrared and optical spectroscopy to determine the presence of a companion that causes a radial velocity variation in the Hα and Hβ lines in the atmosphere of the white dwarf. I also present near-infrared spectroscopy of 10 candidate brown dwarf companions to white dwarfs identified via the Backyard Worlds citizen science project. I identify 7 new brown dwarf companions amongst this sample, determining their spectral types, thus adding to the small sample of these rare binaries.</p

    Be Kinder to Yourself: Awe Promotes Self-Compassion via Self-Transcendence

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    Deficits in self-compassion heighten the vulnerability of mental disorders and jeopardize well-being, emphasizing the necessity of fostering self-compassion during unexpected suffering. In this research, we investigate awe as an antecedent for promptly promoting self-compassion. Across five studies (three preregistered), employing various self-compassion metrics (self-report scale and behaviors) and testing in both controlled and natural settings, we found that awe was positively associated with, or promoted, self-reported self-compassion (Studies 1, 2, 4, and 5) and self-compassionate behaviors in real life (Study 5). These effects were distinct from general positive emotions (Studies 1 and 4) or nature exposure (Study 4). We further found that self-transcendence mediated this effect (Studies 2, 4, and 5) beyond self-diminishment (Study 4) and had a causal effect on promoting self-compassion (Study 3). These findings imply that awe enhances self-compassion via self-transcendence. </p

    Weakly supervised pathological differentiation of PCNSL and GBM on multi-site whole slide images

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    PurposeDifferentiating primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is crucial because their prognosis and treatment differ substantially. Manual examination of their histological characteristics is considered the golden standard in clinical diagnosis. However, this process is tedious and time-consuming and might lead to misdiagnosis caused by morphological similarity between their histology and tumor heterogeneity. Existing research focuses on radiological differentiation, which mostly uses multi-parametric magnetic resonance imaging. By contrast, we investigate the pathological differentiation between the two types of tumors using whole slide images (WSIs) of postoperative formalin-fixed paraffin-embedded samples.ApproachTo learn the specific and intrinsic histological feature representations from the WSI patches, a self-supervised feature extractor is trained. Then, the patch representations are fused by feeding into a weakly supervised multiple-instance learning model for the WSI classification. We validate our approach on 134 PCNSL and 526 GBM cases collected from three hospitals. We also investigate the effect of feature extraction on the final prediction by comparing the performance of applying the feature extractors trained on the PCNSL/GBM slides from specific institutions, multi-site PCNSL/GBM slides, and large-scale histopathological images.ResultsDifferent feature extractors perform comparably with the overall area under the receiver operating characteristic curve value exceeding 85% for each dataset and close to 95% for the combined multi-site dataset. Using the institution-specific feature extractors generally obtains the best overall prediction with both of the PCNSL and GBM classification accuracies reaching 80% for each dataset.ConclusionsThe excellent classification performance suggests that our approach can be used as an assistant tool to reduce the pathologists’ workload by providing an accurate and objective second diagnosis. Moreover, the discriminant regions indicated by the generated attention heatmap improve the model interpretability and provide additional diagnostic information.</p

    Data for psychometric comparison of control group, victims of Ponzi scams and agents who collected money.

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    An empirical research was conducted in West Bengal, India (2023), to check if Ponzi victims are psychologically more biased towards decisional vulnerabilities. Three psychometric tests were run and scored on Likert scale - general trust (6 questions), deferment of gratification (12 questions) and self regulation (21 questions). Tests were also run on a control group (n=58) and a group of agents (n=202) who collected money for the scams, apart from the depositors/victims (n = 91). The scores for each test were aggregated and have been presented in descending order. </p

    An ancient observation of T Coronae Borealis?

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    The ‘new star’ observed by Hipparchos, as reported by Pliny the Elder, may have been T Coronae Borealis. Recent calculations of its periodicity are consistent with the likely dates of Hipparchos’s career in the 2nd century BC.</p

    Use Of Molecularly Imprinted Polymers To Improve Efficiency Of Adeno Associated Virus Mediated Gene Therapy

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    Gene therapy has the possibility to change the lives of patients around the world suffering from genetic diseases. By addressing the underlying genetic cause, it may be able to provide lifelong curative treatments to currently incurable conditions.Adeno associated virus has emerged as a leading vector in this field to deliver corrective genes to patients for the treatment of monogenic disorders. Interest in the field is reflected in the rapidly growing number of therapies in clinical trials. There are, however, some limitations in their practical applications due to challenges both in their delivery and manufacture which must be overcome if these therapies are to reach their full potential of prolonging and improving the lives of as many patients as possible.Developing techniques for addressing these limitations will improve the overall efficiency of gene therapy. This thesis describes the first steps taken to address these issues by implementing molecularly imprinted polymers (MIPs) to identify potential peptide targets for separation and as novel biomimetic affinity chromatography solutions.The work described seeks to overcome one clinical and one manufacturing challenge. The clinical challenge to address is neutralising of viruses by neutralising antibodies (NAbs). These are present in certain patient populations with pre-existing humoral immunity to AAVs and prevent gene transfer. The key industrial challenge is achieving separation of the therapeutically efficacious genome containing capsids from empty capsids in a manner that is robust, efficient, and scalable. Empty and Full particles are similar in size, capsid morphology and physicochemical properties and empty particles in the drug product increase the immunogenic load to the patient while providing no therapeutic benefit.Based upon the challenges posed by NAbs and Empty/Full particle separation this thesis successfully demonstrates use of a novel molecular imprinting technique to identify novel peptide sequences on NAbs and AAV capsids as potential targets for affinity purification. The peptides were used to generate molecularly imprinted polymers which were then integrated with chromatography resin as biomimetic affinity ligands. Generating novel prototype nanoMIP columns for technically challenging biological separations and looking towards what manufacturing challenges and scales are required to achieve this.Providing affinity solutions to these clinical and industrial challenges is critical to the successful widespread proliferation of AAV based gene therapies and thus bringing life changing curative treatments to those with monogenic disorders.</p

    Language Teacher Emotional Experiences: A Systematic Review

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    This handbook synthesizes accumulated research evidence about the main areas of language teacher education. It systematically applies research synthesis to the field, providing coherent, systematic insights into various aspects of language teaching. Each chapter compares research conducted between 2010–2020 within a specialized area of teacher education. The chapters discuss the theoretical and research underpinnings of each area, describing the purposes, methods and findings of the research, including impacts of teacher education on teacher gains and teaching effectiveness. Areas addressed in this handbook include: teacher identity, motivation, demotivation and burnout, reflective practice, action research, Content and Language Integrated Learning (CLIL) teacher education, English Medium Instruction (EMI) teacher education, self-efficacy, assessment literacy, language awareness, Technological Pedagogical Content Knowledge (TPACK), supervision and mentoring, and nativeness/non-nativeness. This handbook is an invaluable resource for teacher educators, student/Preservice teachers, inservice teachers, graduate students of Teaching English to speakers of other languages (TESOL) and Applied Linguistics, and teacher education researchers.</p

    Acute and chronic effects of an intervention aiming to reduce prolonged sitting on glucose regulation in individuals with dysglycaemia

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    Acute studies have consistently demonstrated small-to-medium glycaemic responses to breaking prolonged sitting, yet it’s not known whether acute effects are maintained following a period of intervention or whether behavioural interventions lead to sustained benefits. A single arm, 4-week intervention with pre and post ‘two-arm’ randomised cross-over conditions, study was conducted to investigate whether reducing prolonged sitting in free-living affects acute and chronic glucose and insulin responses. Adults aged 40-75 years living with overweight or obesity with an elevated HbA1c (5.7-7.5%) underwent four experimental conditions (two prolonged sitting [CON], two sitting with self-paced light upright movement breaks [LUMB]) in a randomised order. One of each condition was conducted before and after the intervention. A total of 33 participants completed the study. There was no change in sitting or glucose/insulin levels over the 4-week intervention. However, glucose and insulin were reduced acutely in the LUMB conditions compared with CON (glucose [mmol/L]: CON: 5.77 [5.51; 6.02], LUMB: 5.55 [5.30; 5.81], p = 0.006, insulin [mIU/L]: (CON: 77.70 [61.58; 93.83], LUMB: 61.28 [51.19; 71.38], p = <0.001); these responses did not change over time. In conclusion, the intervention did not lead to reduced sitting time or chronic changes to postprandial metabolism.</p

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