1,704 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Therapeutic and prognostic strategies in neuroblastoma : exploring nuclear hormone receptors, MYC targets, and DIAPH3

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    Neuroblastoma (NB) is a pediatric cancer derived from the cells of neural crest origin that form the sympathoadrenal system. Typically, the tumor cells migrate along the spinal cord and spread to the chest, neck, and/or abdomen. Different clinical behaviors are observed in this disease: some tumors spontaneously regress without treatment, while others are highly aggressive and resistant to current therapies. Approximately 40% of high-risk NB patients have MYCN amplification while 10% have MYC (i.e. encoding c-MYC) overexpression. These patients have undifferentiated tumors with a poor prognosis. Our group previously found that the expression and activation of nuclear hormone receptors (NHRs) estrogen receptor alpha (ERα) by 17-β-estradiol (E2), and the glucocorticoid receptor (GR) by dexamethasone (DEX), could trigger differentiation by disrupting the regulation of the miR-17 ~ 92 microRNA cluster by MYCN. In paper I, we sought to investigate whether the simultaneous activation of both ERα and GR has a more beneficial effect compared to the activation of either ERα or GR alone. We examined cell survival, alterations in cell shape as indicated by neurite extension, variations in metabolic pathways, accumulation of lipid droplets, and performed xenograft experiments. Our findings revealed that the simultaneous activation of GR and ERα, compared to their single activation, led to reduced viability and a more robust differentiation. This dual activation also caused changes in glycolysis and oxidative phosphorylation, increased lipid droplet accumulation, and decreased aggressiveness in mouse models. The triple activation with an additional activation of the retinoic acid receptor using all trans-retinoic acid (ATRA), amplified the differentiation phenotype. Bulk-sequencing analysis showed that patients with high levels of NHRs are related to favorable survival and clinical outcome. In summary, our data suggest that combination activation of these NHRs could be a potential differentiation induction treatment. Paper II investigates target genes of c-MYC and MYCN to explore if it is possible to obtain a better prognosis prediction using the expression of this group of genes, instead of the expression of MYC and/or MYCN alone. In addition, we analyzed if there are different prediction power capabilities between c-MYC and MYCN target genes, and their different role during sympathoadrenal development. We screened lists of target genes by using comprehensive approaches, including differential expression analysis between clinical risk groups, INSS stages, MYCN amplification status, progression status; Univariate Cox regression analysis to select the target genes related to prognosis prediction power, and protein interaction network analysis to select genes that share a meaningful biology function. Following the training and validation of (LASSO) regression prediction models in three different patient cohorts (SEQC, Kocak, and Versteeg), we found that a risk score computed on c-MYC/MYCN target genes with prognostic value, could effectively classify patients in groups with different survival probabilities. The high-risk group of patients exhibited unfavorable clinical outcomes and low survival rates. Further, single cell RNA sequencing analysis revealed that c-MYC and MYCN targets have different expression patterns during sympathoadrenal development. Notably, genes linked to adverse outcomes were predominantly expressed in sympathoblasts in comparison to chromaffin cells. In summary, our research provides new insights into the importance of c-MYC/MYCN target genes during sympathoadrenal development and their value in predicting patient outcome. In paper III we studied the function of one member of the formin protein family involved in cytoskeleton modulation: Diaphanous Related Formin 3 (DIAPH3). We found that high DIAPH3 expression in NB tumors are associated with MYCN amplification, higher stage, risk, progression and negative clinical outcome. Elevated DIAPH3 expression was also found in specific cells during mouse sympathoadrenal development and in progenitor cells of the post- natal human adrenal gland. Furthermore, the knockdown of DIAPH3 resulted in a slight decrease in cell growth and cell cycle arrest. Our study suggests that DIAPH3 could be a promising target for new therapeutic strategies

    Graduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2022-2023

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    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Defining The Role of a Novel Gene “MyoD Family Inhibitor Domain Containing (MDFIC)” Important in Cardiovascular Development

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    Central conducting lymphatic anomaly (CCLA), characterised by dysfunction of core collecting lymphatic vessels including the thoracic duct and cisterna chyli, often manifests in utero as non-immune hydrops fetalis (NIHF) (also known as foetal hydrops). Clinical presentation of CCLA may also include chylothorax, pleural effusions, chylous ascites or lymphoedema, and is a severe disease for which few effective treatments are available. The genetic aetiology of CCLA remains uncharacterised in the majority of cases. Here, by exploring the genetics underlying lymphatic vascular disorders, we identified seven affected individuals in six independent families with CCLA in whom biallelic variants in MDFIC, encoding the MyoD family inhibitor domain containing protein, were identified. Generation of a mouse model of a recurrent human MDFIC truncating variant (Met131Asnfs*3) revealed that MdficM131fs*/M131fs* homozygous mutant mice died perinatally exhibiting chylothorax with accumulation of lipid rich chyle in the thoracic cavity. The lymphatic vasculature of these mice was profoundly mis-patterned, particularly in the diaphragm and thoracic wall, and exhibited defects in lymphatic vessel valve development. This work is the first to identify pathogenic MDFIC variants underlying human lymphatic vascular disease and reveals that MDFIC plays a pivotal role in the development of lymphatic vessel valves. Mechanistically, we demonstrate that the cysteine-rich C-terminus of MDFIC, which is absent in the MDFIC p.Met131fs* truncated protein, is essential for interaction with GATA2, a transcription factor with an essential role in lymphatic vessel valve development. Alteration in GATA2 subcellular localisation and transcriptional activity within cells in a setting of MDFIC deficiency was detected. Our preliminary data also suggest that biallelic truncating MDFIC variants in patients exhibiting CCLA increases MAPK/ERK signalling activity, raising the question as to whether the dampening activity of this pathway might provide a therapeutic opportunity for the treatment of CCLA caused by MDFIC variants. Future work aims to characterise the mechanisms by which MDFIC controls the activity of GATA2 and RAS/MAPK signalling in the lymphatic vasculature and to investigate the efficacy of small molecule inhibitors of GATA2 and RAS/MAPK signalling in rescuing the symptoms and lethality of CCLA in our novel genetic mouse model of this disease.Thesis (Ph.D.) -- University of Adelaide, Adelaide Medical School, 202
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