2,875 research outputs found

    Exploring missing heritability in neurodevelopmental disorders:Learning from regulatory elements

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    In this thesis, I aimed to solve part of the missing heritability in neurodevelopmental disorders, using computational approaches. Next to the investigations of a novel epilepsy syndrome and investigations aiming to elucidate the regulation of the gene involved, I investigated and prioritized genomic sequences that have implications in gene regulation during the developmental stages of human brain, with the goal to create an atlas of high confidence non-coding regulatory elements that future studies can assess for genetic variants in genetically unexplained individuals suffering from neurodevelopmental disorders that are of suspected genetic origin

    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

    Image Filtering Techniques for Object Recognition in Autonomous Vehicles

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    The deployment of autonomous vehicles has the potential to significantly lessen the variety of current harmful externalities, (such as accidents, traffic congestion, security, and environmental degradation), making autonomous vehicles an emerging topic of research. In this paper, a literature review of autonomous vehicle development has been conducted with a notable finding that autonomous vehicles will inevitably become an indispensable future greener solution. Subsequently, 5 different deep learning models, YOLOv5s, EfficientNet-B7, Xception, MobilenetV3, and InceptionV4, have been built and analyzed for 2-D object recognition in the navigation system. While testing on the BDD100K dataset, YOLOv5s and EfficientNet-B7 appear to be the two best models. Finally, this study has proposed Hessian, Laplacian, and Hessian-based Ridge Detection filtering techniques to optimize the performance and sustainability of those 2 models. The results demonstrate that these filters could increase the mean average precision by up to 11.81%, reduce detection time by up to 43.98%, and significantly reduce energy consumption by up to 50.69% when applied to YOLOv5s and EfficientNet-B7 models. Overall, all the experiment results are promising and could be extended to other domains for semantic understanding of the environment. Additionally, various filtering algorithms for multiple object detection and classification could be applied to other areas. Different recommendations and future work have been clearly defined in this study

    Applications of Machine Learning to the Monopole & Exotics Detector at the Large Hadron Collider

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    MoEDAL is the Monopole and Exotics Detector at the Large Hadron Collider. The Moedal Experiment uses Passive Nuclear Track Detector foils (NTDs) to look for magnetic monopoles, and other heavily ionising exotic particles at the Large Hadron Collider (LHC). Heavy particle radiation backgrounds at the Large Hadron Collider make image analysis of these NTD foils non-trivial compared to NTD image analysis under lower background conditions such as medical ion beam calibration or nuclear dosimetry. This thesis looks at multichannel and multidimensional Convolutional Neural Network (CNN) and Fully Convolutional Neural Network (FCN) based image recognition for identifying anomalous heavily ionising particle (HIP) etch pits within calibration NTD foils that have been exposed to both a calibration signal (heavy ion beam), and real LHC background exposure, serving as detector research and development for future MoEDAL NTD analyses. Image data was collected with Directed-Bright/Dark-Field illumination, parametrised at multiple off-axis illumination angles. Angular control of the light intensity distri- bution was achieved via a paired Fresnel lens and LED array. Information about the 3D structure of the etch pits is contained in these parametrised images which may as- sist in their identification and classification beyond what is possible in a simple 2D image. Convolutional Neural Network etch pit classifiers were trained using Xe, and Pb ion data with differing levels of LHC background exposure. An ensemble approach of combining classifiers trained on different objects, and data-channels is shown to improve classification performance. Transfer learning was used to generate Fully Convolutional Neural Networks for identifying HIP etch-pit candidates from wide area foil scan images. The performance of the FCN algorithm is evaluated using a novel MoEDAL R&D foil stack, in order to obtain blinded estimates of the signal acceptance and false prediction rate of an ML based NTD analysis. Additionally a method for pixel to pixel alignment of NTD foil scans is demonstrated that can be used for the training of U-Net FCN architectures

    Applications of Deep Learning Models in Financial Forecasting

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    In financial markets, deep learning techniques sparked a revolution, reshaping conventional approaches and amplifying predictive capabilities. This thesis explored the applications of deep learning models to unravel insights and methodologies aimed at advancing financial forecasting. The crux of the research problem lies in the applications of predictive models within financial domains, characterised by high volatility and uncertainty. This thesis investigated the application of advanced deep-learning methodologies in the context of financial forecasting, addressing the challenges posed by the dynamic nature of financial markets. These challenges were tackled by exploring a range of techniques, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), autoencoders (AEs), and variational autoencoders (VAEs), along with approaches such as encoding financial time series into images. Through analysis, methodologies such as transfer learning, convolutional neural networks, long short-term memory networks, generative modelling, and image encoding of time series data were examined. These methodologies collectively offered a comprehensive toolkit for extracting meaningful insights from financial data. The present work investigated the practicality of a deep learning CNN-LSTM model within the Directional Change framework to predict significant DC events—a task crucial for timely decisionmaking in financial markets. Furthermore, the potential of autoencoders and variational autoencoders to enhance financial forecasting accuracy and remove noise from financial time series data was explored. Leveraging their capacity within financial time series, these models offered promising avenues for improved data representation and subsequent forecasting. To further contribute to financial prediction capabilities, a deep multi-model was developed that harnessed the power of pre-trained computer vision models. This innovative approach aimed to predict the VVIX, utilising the cross-disciplinary synergy between computer vision and financial forecasting. By integrating knowledge from these domains, novel insights into the prediction of market volatility were provided

    Converging organoids and extracellular matrix::New insights into liver cancer biology

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    Converging organoids and extracellular matrix::New insights into liver cancer biology

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    Primary liver cancer, consisting primarily of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), is a heterogeneous malignancy with a dismal prognosis, resulting in the third leading cause of cancer mortality worldwide [1, 2]. It is characterized by unique histological features, late-stage diagnosis, a highly variable mutational landscape, and high levels of heterogeneity in biology and etiology [3-5]. Treatment options are limited, with surgical intervention the main curative option, although not available for the majority of patients which are diagnosed in an advanced stage. Major contributing factors to the complexity and limited treatment options are the interactions between primary tumor cells, non-neoplastic stromal and immune cells, and the extracellular matrix (ECM). ECM dysregulation plays a prominent role in multiple facets of liver cancer, including initiation and progression [6, 7]. HCC often develops in already damaged environments containing large areas of inflammation and fibrosis, while CCA is commonly characterized by significant desmoplasia, extensive formation of connective tissue surrounding the tumor [8, 9]. Thus, to gain a better understanding of liver cancer biology, sophisticated in vitro tumor models need to incorporate comprehensively the various aspects that together dictate liver cancer progression. Therefore, the aim of this thesis is to create in vitro liver cancer models through organoid technology approaches, allowing for novel insights into liver cancer biology and, in turn, providing potential avenues for therapeutic testing. To model primary epithelial liver cancer cells, organoid technology is employed in part I. To study and characterize the role of ECM in liver cancer, decellularization of tumor tissue, adjacent liver tissue, and distant metastatic organs (i.e. lung and lymph node) is described, characterized, and combined with organoid technology to create improved tissue engineered models for liver cancer in part II of this thesis. Chapter 1 provides a brief introduction into the concepts of liver cancer, cellular heterogeneity, decellularization and organoid technology. It also explains the rationale behind the work presented in this thesis. In-depth analysis of organoid technology and contrasting it to different in vitro cell culture systems employed for liver cancer modeling is done in chapter 2. Reliable establishment of liver cancer organoids is crucial for advancing translational applications of organoids, such as personalized medicine. Therefore, as described in chapter 3, a multi-center analysis was performed on establishment of liver cancer organoids. This revealed a global establishment efficiency rate of 28.2% (19.3% for hepatocellular carcinoma organoids (HCCO) and 36% for cholangiocarcinoma organoids (CCAO)). Additionally, potential solutions and future perspectives for increasing establishment are provided. Liver cancer organoids consist of solely primary epithelial tumor cells. To engineer an in vitro tumor model with the possibility of immunotherapy testing, CCAO were combined with immune cells in chapter 4. Co-culture of CCAO with peripheral blood mononuclear cells and/or allogenic T cells revealed an effective anti-tumor immune response, with distinct interpatient heterogeneity. These cytotoxic effects were mediated by cell-cell contact and release of soluble factors, albeit indirect killing through soluble factors was only observed in one organoid line. Thus, this model provided a first step towards developing immunotherapy for CCA on an individual patient level. Personalized medicine success is dependent on an organoids ability to recapitulate patient tissue faithfully. Therefore, in chapter 5 a novel organoid system was created in which branching morphogenesis was induced in cholangiocyte and CCA organoids. Branching cholangiocyte organoids self-organized into tubular structures, with high similarity to primary cholangiocytes, based on single-cell sequencing and functionality. Similarly, branching CCAO obtain a different morphology in vitro more similar to primary tumors. Moreover, these branching CCAO have a higher correlation to the transcriptomic profile of patient-paired tumor tissue and an increased drug resistance to gemcitabine and cisplatin, the standard chemotherapy regimen for CCA patients in the clinic. As discussed, CCAO represent the epithelial compartment of CCA. Proliferation, invasion, and metastasis of epithelial tumor cells is highly influenced by the interaction with their cellular and extracellular environment. The remodeling of various properties of the extracellular matrix (ECM), including stiffness, composition, alignment, and integrity, influences tumor progression. In chapter 6 the alterations of the ECM in solid tumors and the translational impact of our increased understanding of these alterations is discussed. The success of ECM-related cancer therapy development requires an intimate understanding of the malignancy-induced changes to the ECM. This principle was applied to liver cancer in chapter 7, whereby through a integrative molecular and mechanical approach the dysregulation of liver cancer ECM was characterized. An optimized agitation-based decellularization protocol was established for primary liver cancer (HCC and CCA) and paired adjacent tissue (HCC-ADJ and CCA-ADJ). Novel malignancy-related ECM protein signatures were found, which were previously overlooked in liver cancer transcriptomic data. Additionally, the mechanical characteristics were probed, which revealed divergent macro- and micro-scale mechanical properties and a higher alignment of collagen in CCA. This study provided a better understanding of ECM alterations during liver cancer as well as a potential scaffold for culture of organoids. This was applied to CCA in chapter 8 by combining decellularized CCA tumor ECM and tumor-free liver ECM with CCAO to study cell-matrix interactions. Culture of CCAO in tumor ECM resulted in a transcriptome closely resembling in vivo patient tumor tissue, and was accompanied by an increase in chemo resistance. In tumor-free liver ECM, devoid of desmoplasia, CCAO initiated a desmoplastic reaction through increased collagen production. If desmoplasia was already present, distinct ECM proteins were produced by the organoids. These were tumor-related proteins associated with poor patient survival. To extend this method of studying cell-matrix interactions to a metastatic setting, lung and lymph node tissue was decellularized and recellularized with CCAO in chapter 9, as these are common locations of metastasis in CCA. Decellularization resulted in removal of cells while preserving ECM structure and protein composition, linked to tissue-specific functioning hallmarks. Recellularization revealed that lung and lymph node ECM induced different gene expression profiles in the organoids, related to cancer stem cell phenotype, cell-ECM integrin binding, and epithelial-to-mesenchymal transition. Furthermore, the metabolic activity of CCAO in lung and lymph node was significantly influenced by the metastatic location, the original characteristics of the patient tumor, and the donor of the target organ. The previously described in vitro tumor models utilized decellularized scaffolds with native structure. Decellularized ECM can also be used for creation of tissue-specific hydrogels through digestion and gelation procedures. These hydrogels were created from both porcine and human livers in chapter 10. The liver ECM-based hydrogels were used to initiate and culture healthy cholangiocyte organoids, which maintained cholangiocyte marker expression, thus providing an alternative for initiation of organoids in BME. Building upon this, in chapter 11 human liver ECM-based extracts were used in combination with a one-step microfluidic encapsulation method to produce size standardized CCAO. The established system can facilitate the reduction of size variability conventionally seen in organoid culture by providing uniform scaffolding. Encapsulated CCAO retained their stem cell phenotype and were amendable to drug screening, showing the feasibility of scalable production of CCAO for throughput drug screening approaches. Lastly, Chapter 12 provides a global discussion and future outlook on tumor tissue engineering strategies for liver cancer, using organoid technology and decellularization. Combining multiple aspects of liver cancer, both cellular and extracellular, with tissue engineering strategies provides advanced tumor models that can delineate fundamental mechanistic insights as well as provide a platform for drug screening approaches.<br/

    Robust Decoding of Rich Dynamical Visual Scenes With Retinal Spikes

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    Sensory information transmitted to the brain activates neurons to create a series of coping behaviors. Understanding the mechanisms of neural computation and reverse engineering the brain to build intelligent machines requires establishing a robust relationship between stimuli and neural responses. Neural decoding aims to reconstruct the original stimuli that trigger neural responses. With the recent upsurge of artificial intelligence, neural decoding provides an insightful perspective for designing novel algorithms of brain-machine interface. For humans, vision is the dominant contributor to the interaction between the external environment and the brain. In this study, utilizing the retinal neural spike data collected over multi trials with visual stimuli of two movies with different levels of scene complexity, we used a neural network decoder to quantify the decoded visual stimuli with six different metrics for image quality assessment establishing comprehensive inspection of decoding. With the detailed and systematical study of the effect and single and multiple trials of data, different noise in spikes, and blurred images, our results provide an in-depth investigation of decoding dynamical visual scenes using retinal spikes. These results provide insights into the neural coding of visual scenes and services as a guideline for designing next-generation decoding algorithms of neuroprosthesis and other devices of brain-machine interface.</p

    Facing the storm:Assessing global storm tide hazards in a changing climate

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    Coastal flooding is one of the most frequent natural hazards around the globe and can have devastating societal impacts. It is caused by extreme storm tides, which are composed of storm surges and tides, on top of mean sea levels. Due to socio-economic developments in the world’s coastal zones, the impacts of coastal floods have increased in recent decades. In addition, projected changes in the frequency and intensity of storms, as well as sea level rise due to climate change are expected to increase the coastal flood hazard. These trends show that it is crucial to further improve coastal flood hazard assessments to support coastal flood management. A lack of understanding of the influence of tropical cyclones (TCs) on storm tide level return periods (RPs) currently prevails. Available meteorological data does not adequately capture the structure of TCs, and the temporal length of this data is too short to accurately compute RPs because TCs are low-probability events. Existing large scale coastal flood hazard assessments assume an infinite flood duration and do not capture the physical hydrodynamic processes that drive coastal flooding. Furthermore, future changes in the frequency and intensity of TCs and extratropical cyclones (ETCs) are often neglected in coastal flood hazard assessments. As such, the goal of this thesis is to improve global storm tide modelling through the better representation of TC-related extremes and enable dynamic flood mapping in both current and future climates. The research in this thesis contributes to ongoing efforts in the coastal risk community to better understand coastal flood hazards and risks on a global scale. The COAST-RP dataset can help identify hotspot regions most prone to coastal flooding. Such information can then be used to determine where more detailed local-scale coastal flood hazard assessments are most needed. Combining data from COAST-RP with the HGRAPHER method allows us to move away from planar towards more advanced dynamic inundation methods. This will improve the accuracy of the coastal flood hazard maps. Lastly, the developed TC intensity Δ method that is applicable to different kinds of future climate TC datasets opens the door to studying the future intensity of TCs and corresponding storm surges by placing them in a future climate
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