4,727 research outputs found

    The interplay between Natural Killer cells and Pancreatic Stellate cells in Pancreatic Ductal Adenocarcinoma

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    Pancreatic ductal adenocarcinoma (PDAC) is a disease with dismal prognosis. With five-year survival rates of less than 11%, PDAC is set to become the second leading cause of cancer related deaths by 2040. The role of pancreatic stellate cells in pancreatic ductal adenocarcinoma has been well established. However, to date, little remains know about the interaction between these crucial stromal cells and the innate lymphocytes, natural killer (NK) cells, in PDAC. Herein we demonstrate that naïve NK cells possess the functional efficacy to target and kill both quiescent (qPSC) and activated (aPSC) pancreatic stellate cells. Furthermore, qPSC, but not aPSC education of NK cells resulted in decreased NK cell-mediated cancer cell cytotoxicity. NK-PSC direct co-culture was found to modulate both PSC and NK phenotype, as well as functional changes within NK cells, an effect not observed with TranswellTM separation. Multiplex Luminex ELISA further revealed upregulation of IFN-γ and related chemokines in NK cells co-cultured with PSC (activated/quiescent), suggesting that this pathway may be involved in phenotypic modulation. Through global proteomic analysis we demonstrate NK cell-induced differential protein changes in aPSC versus qPSC. Furthermore, we demonstrate changes in intracellular NK pathways as a result of direct contact with PSCs, indicating a dynamic, bidirectional interaction between these two key players. Using multiplex immunohistochemical analysis, we demonstrate that NK cell proximity to CAFs, and not total NK cell infiltrate is correlated with overall survival in PDAC. Consequently, we suggest that the spatial biology of NK/CAFs may play a prognostic role in PDAC and may potentially be used as a tool for patient stratification Taken together, our results demonstrate a significant bidirectional relationship between NK cells and PSC/CAFs in the context of PDAC, providing novel insight into this crucial cell-cell interaction

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Differential spectrum modeling and sensitivity for keV sterile neutrino search at KATRIN

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    Starting in 2026, the KATRIN experiment will conduct a high-statistics measurement of the differential tritium β\beta-spectrum to energies deep below the kinematic endpoint. This enables the search for keV sterile neutrinos with masses less than the kinematic endpoint energy m4E0=18.6keVm_\mathrm{4} \leq E_0 = 18.6\,\mathrm{keV}, aiming for a statistical sensitivity of Ue42=sin2θ106|U_\mathrm{e4}|^2=\sin^2\theta\sim 10^{-6} for the mixing amplitude. The differential spectrum is obtained by decreasing the retarding potential of KATRIN\u27s main spectrometer, and by determining the β\beta-electron energies by their energy deposition in the new TRISTAN SDD array. In this mode of operation, the existing integral model of the tritium spectrum is insufficient, and a novel differential model is developed in this work. The new model (TRModel) convolves the differential tritium spectrum using responese matrices to predict the energy spectrum of registered events after data acquisition. Each response matrix encodes the spectral spectral distrortion from individual experimental effects, which depend on adjustable systematic parameters. This approach allows to efficiently assess the sensitivity impact of each systematics individually or in combination with others. The response matrices are obtained from monte carlo simulations, numerical convolution, and analytical computation. In this work, the sensitivity impact of 20 systematic parameters is assessed for the TRISTAN Phase-1 measurement for which nine TRISTAN SDD modules are integrated into the KATRIN beamline. Furthermore, it is demonstrated that the sensitivity impact is significantly mitigated with several beamline field adjustments and minimal hardware modifications

    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

    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/

    Modeling and MR-thermometry for adaptive hyperthermia in cervical Cancer

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    Self-supervised learning for transferable representations

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    Machine learning has undeniably achieved remarkable advances thanks to large labelled datasets and supervised learning. However, this progress is constrained by the labour-intensive annotation process. It is not feasible to generate extensive labelled datasets for every problem we aim to address. Consequently, there has been a notable shift in recent times toward approaches that solely leverage raw data. Among these, self-supervised learning has emerged as a particularly powerful approach, offering scalability to massive datasets and showcasing considerable potential for effective knowledge transfer. This thesis investigates self-supervised representation learning with a strong focus on computer vision applications. We provide a comprehensive survey of self-supervised methods across various modalities, introducing a taxonomy that categorises them into four distinct families while also highlighting practical considerations for real-world implementation. Our focus thenceforth is on the computer vision modality, where we perform a comprehensive benchmark evaluation of state-of-the-art self supervised models against many diverse downstream transfer tasks. Our findings reveal that self-supervised models often outperform supervised learning across a spectrum of tasks, albeit with correlations weakening as tasks transition beyond classification, particularly for datasets with distribution shifts. Digging deeper, we investigate the influence of data augmentation on the transferability of contrastive learners, uncovering a trade-off between spatial and appearance-based invariances that generalise to real-world transformations. This begins to explain the differing empirical performances achieved by self-supervised learners on different downstream tasks, and it showcases the advantages of specialised representations produced with tailored augmentation. Finally, we introduce a novel self-supervised pre-training algorithm for object detection, aligning pre-training with downstream architecture and objectives, leading to reduced localisation errors and improved label efficiency. In conclusion, this thesis contributes a comprehensive understanding of self-supervised representation learning and its role in enabling effective transfer across computer vision tasks

    A novel segmentation approach for crop modeling using a plenoptic light-field camera : going from 2D to 3D

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    OMICASCrop phenotyping is a desirable task in crop characterization since it allows the farmer to make early decisions, and therefore be more productive. This research is motivated by the generation of tools for rice crop phenotyping within the OMICAS research ecosystem framework. It proposes implementing the image process- ing technologies and artificial intelligence technics through a multisensory approach with multispectral information. Three main stages are covered: (i) A segmentation approach that allows identifying the biological material associated with plants, and the main contri- bution is the GFKuts segmentation approach; (ii) a strategy that allows the development of sensory fusion between three different cameras, a 3D camera, an infrared multispectral camera, and a thermal multispectral camera, this stage is developed through a complex object detection approach; and (iii) the characterization of a 4D model that generates topological relationships with the information of the point cloud, the main contribution of this strategy is the improvement of the point cloud captured by the 3D sensor, in this sense, this stage improves the acquisition of any 3D sensor. This research presents a development that receives information from multiple sensors, especially infrared 2D, and generates a single 4D model in geometric space [X, Y, Z]. This model integrates the color information of 5 channels and topological information, relating the points in space. Overall, the research allows the integration of the 3D information from any sensor\technology and the multispectral channels from any multispectral camera, to generate direct non-invasive measurements on the plant.Magíster en Ingeniería ElectrónicaMagíster en Inteligencia ArtificialMaestríahttps://orcid.org/ 0000-0002-1477-6825https://scholar.google.com/citations?user=cpuxcwgAAAAJ&amp;hl=eshttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=000155691
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