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    Gifted, black and under scrutiny: radicalism of Black women writers and their counter literary struggle with the FBI

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    According to the civil rights historian Kenneth O’Reilly, “there is no doubt that Hoover was a racist, an anticommunist of the highest order, and a determined foe of the civil rights movement […]” (17). What J. Edgar Hoover’s personal racist and anti-Communist opinions led to was to create one of the most powerful law enforcement agencies in history, the duties of which went far beyond investigating criminals and keeping the United States safe from foreign agitation. Hoover had a special penchant for literary agitation; especially the kind that he feared could incite rebellion within the nation against the white heteropatriarchal supremacism that he was decided upon upholding. Beyond monitoring and summarizing literatures that Hoover and his Federal Bureau of Investigation (FBI) agents deemed potentially subversive, Hoover began specifically investigating authors and their affiliations. Literature, as all media, had the power to educate and change minds, to incite and inform, to disrupt and disturb. Therefore, Hoover’s FBI developed its own counterliterary tactics to investigate, interfere with, intimidate the writers, and infiltrate the lives and career, organisations, and appearances of these authors. This counterliterary obsession together with Hoover’s other proclivity – his racism – led to him disproportionately directing his agents to investigate African American authors, their works and their respective affiliations and organisations. From 1940s onward there was a sudden uptick on especially the FBI files opened on Black women writers, as the Black woman of the twentieth century was, as Anne Spencer called it, “so involved and interesting. We are the PROBLEM – the great national game of taboo.” The focus will be on specific events and literatures where there was an increase in literary and, thus, counterliterary relationship between some of the most empowered Black women writers of the twentieth century and the intimidation, infiltration, and intensity of scrutiny by the FBI under the directorship of J. Edgar Hoover. At the core of this research is to contribute to a gap in academic research that includes the empowered counterliterary and revolutionary activism of Black women writers. The fact that the FBI had a deep interest in literature and journalism is no secret. Over the past half-century, a lot has been published in terms of literary criticism and research of and by the FBI. There is a growing need for research and criticism on the counterliterary relationship between Black writers and the FBI as it was under Hoover’s directorship. This thesis aims is to begin an interdisciplinary and intersectional discussion that will become essential component when discussing Black American literary activism

    Detection of developmental deficits in epileptic children using multimodal tensor decomposition techniques

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    Early childhood epilepsy can affect the child’s development and lead to developmental deficits. Early detection and intervention are key to enabling the child to develop normally. Resting state electroencephalogram (EEG) and Magnetic resonance imaging (MRI) are the main tools clinicians use to diagnose children with epilepsy. This motivates us to take advantage of these available data and jointly analyse them to explore the features related to developmental deficits and predict the developmental scores of newly-onset patients. In particular, our work considers EEG information, sMRI volumetric data, and psychometric evaluation scores. We use matrix-tensor decompositions to analyse the shared features between each modality all at once. This allows us to investigate the occurrence of shared profiles in EEG and sMRI related to developmental impairment. Hence, this thesis develops data fusion methods based on well-established tensor decomposition methods (canonical polyadic decomposition, CPD; block term decomposition, BTD; and Tucker decomposition, TD). The methods are validated in a publicly available dataset with healthy children (Child Mind Institute: CMI) and, more importantly, in a local dataset of preschool children with epilepsy (NEUROPROFILE: Neu). First, the thesis focuses on a CPD data fusion model, which decomposes the multi-way data into a sum of rank-one factor matrices with the subject factor shared across three modalities. The model is optimised via grid search. The CPD model reveals distinct features associated with developmental deficits that agree with prior clinical knowledge. Then, we expand the model through direct projection to predict the developmental scores from EEG and sMRI data. A support vector machine (SVM) is used as a benchmark to compare the predicted score performance. The result reveals CPD model is better at estimating the developmental scores than the SVM. The CPD shows the feasibility of score prediction but still lacks the ability to correctly identify the deficits, which highlights the need for a more flexible data fusion model. Next, the thesis adopts block term decomposition (BTD) to bring in additional flexibility in the modelling of the EEG tensor data. In BTD (Lᵣ,Lᵣ, 1), one mode of interest is fixed to rank one while the others vary together to rank L. Subjects with missing scores and more sMRI regions and sub-scores are included in this analysis. Bayesian optimisation is applied to reduce the hyperparameter optimisation time. The results show that BTD (Lᵣ,Lᵣ, 1) can extract additional features related to the deficits that the CPD model does not pick up. Then, we built a model to predict the developmental scores. Overall, the prediction from BTD is generally better than the CPD. However, the result shows both models may not be fully compatible with EEG tensors and suggests the need for a better-fit model. Therefore, we adopt TD as a flexible model for the EEG data. TD can decompose tensors into factor matrices with different ranks interacting through a core tensor. However, TD without constraints is not unique. Thus, we promote the sparseness in the TD core tensor in our joint decomposition. In addition, we use structural connectivity information in the form of diffusion tensor imaging (DTI) as a graph regularisation to the data fusion model to promote interpretability. The effects of each constraint are investigated, and the most stable result is extended to predict the scores. Since not all the patients have DTI data, the score prediction is executed for both patients with and without DTI. Implementing the DTI graph regularisation is found to result in predicted scores in a more plausible range. The sparse core TD with graph regularisation performs best with the Neu dataset. However, some deficit patients are estimated to score within the normal range, which does not fulfil the aim of identifying deficits accurately. In addition, and given that the BTD (Lᵣ,Lᵣ, 1) tensor decomposition is closely related to CPD, we investigate and expand the existing principle of CPD core consistency diagnosis (CORCONDIA) to BTD (Lᵣ,Lᵣ, 1). BTDCORCONDIA is built to assist in determining the number of components and the data compatibility to the model. The model is tested with simulated and real EEG tensor data. We show that data generated with a unique core compatible with BTD (Lᵣ,Lᵣ, 1) results in BTDCORCONDIA values of ∼ 100%. In contrast, incompatible data will lead to low values. The result confirms that it is possible to perform a core consistency diagnosis to check the compatibility between the model and data in BTD. In summary, multimodal data fusion of paediatric brain data through matrix-tensor decomposition offers a new approach to studying the shared underlying profiles and developmental status of children with neurological diseases such as epilepsy. This could be a stepping stone for future research seeking to integrate and adopt data fusion approaches as additional tools for clinicians to prioritise children for an exhaustive assessment of their development

    Recalibrating machine learning for social biases: demonstrating a new methodology through a case study classifying gender biases in archival documentation

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    This thesis proposes a recalibration of Machine Learning for social biases to minimize harms from existing approaches and practices in the field. Prioritizing quality over quantity, accuracy over efficiency, representativeness over convenience, and situated thinking over universal thinking, the thesis demonstrates an alternative approach to creating Machine Learning models. Drawing on GLAM, the Humanities, the Social Sciences, and Design, the thesis focuses on understanding and communicating biases in a specific use case. 11,888 metadata descriptions from the University of Edinburgh Heritage Collections' Archives catalog were manually annotated for gender biases and text classification models were then trained on the resulting dataset of 55,260 annotations. Evaluations of the models' performance demonstrates that annotating gender biases can be automated; however, the subjectivity of bias as a concept complicates the generalizability of any one approach. The contributions are: (1) an interdisciplinary and participatory Bias-Aware Methodology, (2) a Taxonomy of Gendered and Gender Biased Language, (3) data annotated for gender biased language, (4) gender biased text classification models, and (5) a human-centered approach to model evaluation. The contributions have implications for Machine Learning, demonstrating how bias is inherent to all data and models; more specifically for Natural Language Processing, providing an annotation taxonomy, annotated datasets and classification models for analyzing gender biased language at scale; for the Gallery, Library, Archives, and Museum sector, offering guidance to institutions seeking to reconcile with histories of marginalizing communities through their documentation practices; and for historians, who utilize cultural heritage documentation to study and interpret the past. Through a real-world application of the Bias-Aware Methodology in a case study, the thesis illustrates the need to shift away from removing social biases and towards acknowledging them, creating data and models that surface the uncertainty and multiplicity characteristic of human societies

    Fanconi anaemia and LINE-1 retrotransposition in the mammalian genome

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    Transposable elements (TEs) are discrete, repetitive sequences of DNA that mobilise within genomes. For decades, TEs were dismissed as “junk DNA”, however, it is now clear that these elements have the potential to trigger genome instability, cause disease and shape the course of genome evolution. L1 elements constitute the only autonomous elements which remain active in the human genome and comprises approximately 17% of human DNA. As a retrotransposon, L1 canonically mobilises through a “cut and paste” mechanism called target primed reverse transcription (TPRT). Due to the deleterious impacts of L1 activity, mammalian cells have evolved a range of mechanisms to supress the mobilisation of these elements. The interactions between L1 elements and the host factors which regulate them are therefore an area of active research. Several DNA repair genes have shown potential as regulators of L1 activity. Moreover, in cell lines deficient in non-homologous end-joining, L1 has shown the potential to retrotranspose without its ORF2p endonuclease, which is usually a requirement for canonical TPRT. This retrotransposition has been termed endonuclease independent (ENi) retrotransposition, and takes place at unrepaired double stranded breaks in the DNA. Interestingly, several DNA repair factors have also been identified as potential regulators of L1 retrotransposition (both positive and negative), including a number of proteins from the Fanconi Anaemia pathway. The relationship between these factors and L1 has yet to be fully characterised, and it remains to be seen whether L1 can exploit other DNA lesions in the way that it utilises DSBs in ENi retrotransposition. This thesis aims to further investigate the relationship between L1 retrotransposition in the mammalian genome and DNA repair factors, particularly those comprising the Fanconi Anaemia pathway. Using cultured cell retrotransposition assays, I systematically tested a battery of mutant element in cells deficient in different proteins of the FANC pathway. In this way, I establish that ENi retrotransposition can be observed in a FANC background. I also demonstrate that FANC A deficient cells support retrotransposition of several L1 mutants which are immobile in parental cell lines. This includes elements with severe ORF1p mutations, mutations in the ORF2p endonuclease domain and mutations in the ORF2p PIP box. Despite testing a range of cell lines deficient in different DNA repair factors, including cells deficient in a range of FANC proteins, the retrotransposition of ORF1p, PIP and mutants appears to be unique to FANC A. My results are potentially indicative of a unique mechanism of retrotransposition in FANC A cells, a phenomena which has precedence in the ENi pathway of retrotransposition. Mass spectrometry of immunoprecipitated T7-tagged ORF1p, both in FANC A and parental cells, demonstrated that a different selection of host factors interact with ORF1p in the two cell lines. Several of these have not been previously identified as L1 interactors, including YTHDF2, a protein which binds and destabilises m6A-containing RNA. Previous reports suggest that YTHDF2 regulates the stability of RNA:DNA hybrids in vivo, and associates with R loop containing loci. Through co-immunoprecipitation of YTHDF2 with ORF1p, I confirm that the protein interacts with L1 elements in vitro

    Injuries and illnesses in golfers and returning to play following orthopaedic surgery

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    This thesis represents a discussion and critical appraisal of a selected number of research articles published in international peer-reviewed journals in the field of sports medicine and orthopaedic surgery. This work reflects over 5 years of dedication and passion in the field of golf medicine. The research is novel in a number of domains including assessing rates of COVID-19 in elite golfers, rates of returning to golf following orthopaedic surgery, and introducing new methods to this field. This body of work was initially planned to be focussed on the injuries of elite golfers however, two unique scenarios arose early in the process. The first was the COVID-19 pandemic and the need for evidence-based protocols for conducting professional golf events. The second was the opportunity for international collaboration around returning to golf following orthopaedic surgery which developed during the 2nd International Congress for Golf and Health. Therefore, this thesis is divided into two themes; the first on injuries and illnesses in golfers and the second on returning to golf following orthopaedic surgery. The thesis contains; two retrospective studies, four prospective studies, one prospective study protocol, one systematic review, one systematic review/meta-analysis, one narrative review, one consensus statement, one letter to the editor and one infographic. It flows in chronological order from theme one into theme two which represents the strategy in place moving from one project to another and the interconnected nature of each study. There was a small amount of overlap as theme two began and theme 1 concluded and this represents the opportunity to begin collaborative work following the golf and health congress. Each study is critically appraised in turn covering the aims and objectives, methodology, results and conclusions as well the contribution of the study to the literature and my own contribution. This thesis builds on existing research, identifies knowledge gaps, and presents reviews and original research that contribute to and enhance knowledge in the area of golf injury and illness.

    The effect of autologous macrophage therapy in cirrhosis in response to individual immune reparative pathways: developing a novel therapy

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    BACKGROUND: Liver cirrhosis is the end stage of any injury process to the liver. Once established it inevitably progresses to complications such as portal hypertension, cancer and death. There is not cure for liver cirrhosis besides liver transplant. We face an unmet demand for treatment of this condition. The role of macrophages in fibrosis development and resolution in the liver has been extensively investigated. Prof Forbes group invested in the development of autologous macrophage product to promote fibrosis resolution hence cirrhosis regression. This has demonstrated its efficacy and safety in animal models. From these encouraging pre-clinic data a phase 1 first in human clinical trial of autologous activated macrophage product for cirrhotic patients was developed. METHODS: Using an established 3+3 dose escalation model we enrolled a total of 9 subject in the phase 1 trial reaching a maximum achieved and safe dose of 1x10^9 macrophages. In addition to adverse events, dose toxicity and macrophage activation syndrome (MAS) parameter, we evaluated a varied range of circulating cytokines and chemokine pre and post treatment using a commercial kit. Moreover we developed a protocol for P13- magnetic resonance spectrometry (MRS) for the analysis of the metabolically active liver parenchyma. Data from the phase 1 trial were used to improve the autologous cellular produce and phase 2 randomised controlled trial. RESULTS: The autologous activated macrophage produce is demonstrated not to cause any toxicity in this first in human study of cirrhotic population of different aetiology. Cytokine and chemokine analysis supports these findings and specifically demonstrates low levels of IL-8, which represent cardinal feature of MAS. Other interesting cytokine signals may support extra cellular matrix remodelling effect of the autologous macrophage product infusion. In addition we demonstrated a reproducible protocol for MRS in liver disease. DISCUSSION: Autologous activated macrophage infusion did not result in any toxicity in cirrhotic subjects taking part in this study and shows preliminary signs of efficacy in fibrosis resolution both clinically and biochemically. This work places the basis of development of cellular products for treatment of cirrhosis and fibrosis and provides invaluable insight in immune response to cellular treatment

    Linking language and emotion: how emotion is understood in language comprehension, production and prediction using psycholinguistic methods

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    Emotions are an integral part of why and how we use language in everyday life. We communicate our concerns, express our woes, and share our joy through the use of non-verbal and verbal language. Yet there is a limited understanding of when and how emotional language is processed differently to neutral language, or of how emotional information facilitates or inhibits language processing. Indeed, various efforts have been made to bring back emotions into the discipline of psycholinguistics in the last decade. This can be seen in many interdisciplinary models focusing on the role played by emotion in each aspect of linguistic experience. In this thesis, I answer this call and pursue questions that remain unanswered in psycholinguistics regarding its interaction with emotion. The general trend that I am using to bring emotion into psycholinguistic research is straightforward. Where applicable and relevant, I use well-established tasks or paradigms to investigate the effects of emotional content in language processing. Hence, I focused on three main areas of language processing: comprehension, production and prediction. The first experimental chapter includes a series of experiments utilising the Modality Switching Paradigm to investigate whether sentences describing emotional states are processed differently from sentences describing cognitive states. No switching effects were found consistently in my 3 experiments. My results suggest that these distinct classes of interoceptive concepts, such as ‘thinking’ or ‘being happy’, are not processed differently from each other, suggesting that people do not switch attention between different interoceptive systems when comprehending emotional or cognitive sentences. I discuss the implications for grounded cognition theory in the embodiment literature. In my second experimental chapter, I used the Cumulative Semantic Interference Paradigm to investigate these two questions: (1) whether emotion concepts interfere with one another when repeatedly retrieved (emotion label objects), and (2) whether similar interference occurs for concrete objects that share similar valence association (emotion-laden objects). This could indicate that people use information such as valence and arousal to group objects in semantic memory. I found that interference occurs when people retrieve direct emotion labels repeatedly (e.g., “happy” and “sad”) but not when they retrieve the names of concrete objects that have similar emotion connotations (e.g., “puppy” and “rainbow”). I discuss my findings in terms of the different types of information that support representation of abstract vs. concrete concepts. In my final experimental chapter, I used the Visual World Paradigm to investigate whether the emotional state of an agent is used to inform predictions during sentence processing. I found that people do use the description of emotional state of an agent (e.g., “The boy is happy”) to predict the cause of that affective state during sentence processing (e.g., “because he was given an ice-cream”). A key result here is that people were more likely to fixate on the emotionally congruent objects (e.g., ice-cream) compared to incongruent objects (e.g., broccoli). This suggests that people rapidly and automatically inform predictions about upcoming sentence information based on the emotional state of the agent. I discuss our findings as a novel contribution to the Visual World literature. I conducted a diverse set of experiments using a range of established psycholinguistic methods to investigate the roles of emotional information in language processing. I found clear results in the eye-tracking study but inconsistent effects in both switching and interference studies. I interpret these mixed findings in the following way: emotional content does not always have effects in language processing and that effect are most likely in tasks that explicitly require participants to simulate emotion states in some way. Regardless, not only was I successful in finding some novel results by extending previous tasks, but I was also able to show that this is an avenue that can be explored more to advance the affective psycholinguistic field

    Genomic solutions for selective breeding towards increased disease resistance in sheep

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    Gastrointestinal parasitism is a global problem for grazing ruminants which can be addressed sustainably by breeding animals to be more resistant to disease caused by gastrointestinal parasites. This thesis sets out to estimate the genetic parameters of parasitic infection associated with natural nematode and coccidian infections, productivity and immunological phenotypes associated with immune responses including various cytokines and immunoglobulin A (IgA). The thesis sheds light on the genetic architecture of these traits and uses animal genomic and phenotypic data to identify candidate genes associated with resistance to disease. Individual animal phenotypic data on faecal egg counts from different nematode species (Strongyles (FECS), Nematodirus (FECN) and faecal oocyst counts (FOC) from coccidian parasites were collected on Scottish Blackface lambs together with a faecal soiling score in the breach area ‘dag’ score (DAG) and live weight (LWT). Data from 3,731 Scottish Blackface sheep lambs reared on one experimental farm at SRUC (Castlelaw) were used from 2011 to 2017. Parasitic infection traits (FEC, FOC and DAG) were shown to be heritable (0.09±0.02 to 0.17±0.03) exhibiting significant genetic variation among individuals to underpin a selective breeding programme with the goal of enhancing animal resistance. FECS was shown to be positively (genetically) correlated with FECN (0.74±0.09) and FOC (0.39±0.15). Additionally, DAG was negatively (genetically) correlated with LWT (-0.33±0.15). Significant and positive associations between FECS and FECN, and FECS and FOC at around 3 months of age show that co-selection for increased resistance to different parasites is feasible. Furthermore, selection for increased resistance is unlikely to adversely affect LWT, as no significant antagonistic relationship was found between faecal counts and LWT. Significant antagonistic phenotypic correlations between LWT and DAG, and LWT and FECS/FECN indicate that the expression of manifestation of disease in lambs via the DAG score may be a meaningful indicator of the impact of parasitic burden on productivity. Additionally, whole blood stimulation assays were used to characterise the adaptive immune response of 1,040 lambs measured in 2016-2017, with either pokeweed mitogen (PWM, a lectin that non-specifically activates lymphocytes irrespectively of their antigen specificity), and Teladorsagia circumcincta (T-ci) larval antigen to activate parasite-specific T lymphocytes. The type of adaptive immune response was determined by quantifying the cytokines interferon-gamma (IFN-γ), interleukin (IL)-4, and IL-10, which relate to T-helper type 1 (Th1), Th2 and regulatory T cell (Treg) responses, respectively. T-ci specific IgA within serum was also quantified. Heritability estimates for each immune trait, and genetic and phenotypic correlations with parasitic infection and productivity phenotypes were estimated. Heritabilities of cytokine production varied from low to high (0.14±0.06 to 0.77±0.09), while IgA heritability was found to be moderate (0.41±0.09). A positive genetic correlation was found between FOC and PWM-induced IFN-γ (IFN-γ(PWM)) production (0.67±0.30) while a negative correlation was found between FOC and T-ci induced IL-10 (IL-10(T-ci)) (-0.84±0.31). Live weight was negatively, genetically correlated with IFN-γ responses (-0.54±0.18 and -0.51±0.20). Overall, IFN-γ and IL-4 responses were positively correlated (from 0.50±0.15 to 0.74±0.21), providing little evidence of cross-regulation of Th1 and Th2 immunity within individual sheep. The results show a negative correlation between IL-10(PWM) and IL-4(T.ci)¬, which might indicative of a regulatory function of IL-10 over IL-4. Furthermore, Immunoglobulin A was shown to be genetically correlated with IL-10(PWM) and IL-4(T-ci) (0.85±0.17 and 0.32±0.17, respectively). The results suggest that while selection for high IFN-γ responsiveness may be beneficial for coccidian parasite control, selection for this trait may negatively affect productivity, which will need to be considered in genetic improvement programmes. DNA samples from a subset of 1,766 animals in the study were collected and whole genome sequenced. The genotypic effects on each one of the traits described above were quantified, including the additive and dominance effects as well as the proportion of additive genetic variance due to each SNP locus. A total of 15 SNPs were associated at least at a suggestive level with FECS, FECN, DAG, IgA, PWM-induced IFN-γ and IL-4, and T-ci-induced IL-10. A total of 52 genes closely related to immune function were found to be in close proximity to these SNPs. While most of the SNPs were not significant beyond a suggestive level, this study was able to confirm the polygenic nature of both parasitic infection and immunological traits such as FEC and IgA. The results highlight several C-type lectins and killer cell lectin-like family members close to a SNP associated with FECN, and several genes encoding IL-1 cytokine family members associated with a SNP associated with IgA. There were also several potential candidate genes belonging to, or in close proximity to, the Major Histocompatibility Complex (MHC) which, due to its importance in the control of immune responses, could play important roles in resistance to such parasitic infections. These include HFE and butyrophilin coding genes, associated with IFN-γ(PWM), and IL-17 coding genes associated with IgA. The results reveal a largely complex and polygenic genetic control on resistance to parasitic infection and immunological traits in this Scottish Blackface sheep population. Lastly, these results also suggested that the studied animal traits are amenable to improvement with genomic selection

    Deep generative models for network data synthesis and monitoring

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    Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network. Although networks inherently have abundant amounts of monitoring data, its access and effective measurement is another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset without leaking commercial sensitive information. Second, it could be very expensive to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources in the network element that can be applied to support the measurement function are too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex structure. Various emerging optimization-based solutions (e.g., compressive sensing) or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet meet the current network requirements. The contributions made in this thesis significantly advance the state of the art in the domain of network measurement and monitoring techniques. Overall, we leverage cutting-edge machine learning technology, deep generative modeling, throughout the entire thesis. First, we design and realize APPSHOT , an efficient city-scale network traffic sharing with a conditional generative model, which only requires open-source contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time network telemetry system with latent GANs and spectral-temporal networks. Finally, we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through this research are summarized, and interesting topics are discussed for future work in this domain. All proposed solutions have been evaluated with real-world datasets and applied to support different applications in real systems

    Genome editing of candidate genes related to disease resistance to Piscirickettsia salmonis in Atlantic salmon (Salmo salar)

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    Salmon Rickettsial Syndrome (SRS), caused by the bacterium Piscirickettsia salmonis, is one of the most severe infectious diseases threatening the Chilean Atlantic salmon industry. Among the leading causes of mortality and morbidity, SRS significantly affect the seawater production stage, where biomass losses account for a major economic impact. One potential avenue to tackle SRS is the improvement of host resistance using selective breeding. To accomplish this, insight into the genetic basis of host response, identifying specific genes and pathways involved in this response, and comprehending the potential function these genes have in infection overcome, is valuable. Consequently, this study aims to identify functional genes and pathways that contribute to genetic host resistance to SRS and investigate the effect of CRISPR/Cas9 knockout on these genes during P.salmonis infection. Candidate genes were identified from a previous in vivo large-scale infection study of 2,265 Atlantic salmon smolts injected with P.salmonis and genotyped. These data were used to estimate SRS resistance breeding values. Head-kidney and liver samples for RNA-Seq were obtained from 48 individuals at pre-infection, 3 and 9 days post-infection, and tests of differential expression between pre- and post-infection, and between high and low resistance breeding values were performed. From the thousands of differentially expressed genes, enrichment of several KEGG pathways related to immune response such as bacterial internalisation, intracellular trafficking, apoptosis, and inflammasome was observed in both tissues in fish relatively more resistant to infection. A literature review of the biological function of genes in these pathways highlighted the most suitable candidates for functional studies. Subsequently, five genes related to SRS resistance were successfully edited using a CRISPR/Cas9 Ribonucleoprotein (RNP) transfection to knockout these genes in an Atlantic salmon cell line (SHK-1). An in vitro infection challenge model of the knockout and control cell lines with P.salmonis was performed to elucidate the impact on cytopathic damage, cell viability and bacterial load during infection. These findings suggest a promising avenue of research into the genetic architecture of host resistance to SRS

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