40 research outputs found

    Defining the temporal evolution of gut dysbiosis and inflammatory responses leading to hepatocellular carcinoma in Mdr2 -/- mouse model.

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    BACKGROUND: Emerging evidence implicates the gut microbiome in liver inflammation and hepatocellular carcinoma (HCC) development. We aimed to characterize the temporal evolution of gut dysbiosis, in relation to the phenotype of systemic and hepatic inflammatory responses leading to HCC development. In the present study, Mdr2 -/- mice were used as a model of inflammation-based HCC. Gut microbiome composition and function, in addition to serum LPS, serum cytokines/chemokines and intrahepatic inflammatory genes were measured throughout the course of liver injury until HCC development. RESULTS: Early stages of liver injury, inflammation and cirrhosis, were characterized by dysbiosis. Microbiome functional pathways pertaining to gut barrier dysfunction were enriched during the initial phase of liver inflammation and cirrhosis, whilst those supporting lipopolysaccharide (LPS) biosynthesis increased as cirrhosis and HCC ensued. In parallel, serum LPS progressively increased during the course of liver injury, corresponding to a shift towards a systemic Th1/Th17 proinflammatory phenotype. Alongside, the intrahepatic inflammatory gene profile transitioned from a proinflammatory phenotype in the initial phases of liver injury to an immunosuppressed one in HCC. In established HCC, a switch in microbiome function from carbohydrate to amino acid metabolism occurred. CONCLUSION: In Mdr2 -/- mice, dysbiosis precedes HCC development, with temporal evolution of microbiome function to support gut barrier dysfunction, LPS biosynthesis, and redirection of energy source utilization. A corresponding shift in systemic and intrahepatic inflammatory responses occurred supporting HCC development. These findings support the notion that gut based therapeutic interventions could be beneficial early in the course of liver disease to halt HCC development

    Determine activity based on the classified identity of users by using Wi-Fi monitoring

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    The Wi-Fi technologies are used in everyday life on numerous applications that detect the crowd information for commercial, security and other reasons. The Wi-Fi monitoring can be used for tracking people when they are moving along different access points. The results from the Wi-Fi monitoring can provide the location of the users in an area and therefore, useful information can be extracted. The goal of this project is to recognize the activity of different users for different sessions of a Wi-Fi network. The Wi- Fi dataset that is used, is acquired from the Wi-Fi network of the Delft University of Technology (TU Delft). Initially, the estimation of the users’ occupation is determined with the use of a Markov model with the information that is derived from the Wi-Fi dataset. Their possible identity is used, in order to estimate the activity that a user is probably doing at a specific location of the research area. The results on the use of the research area, are calculated and visualised in different spatial levels, campus, building and floor level. The use of the building complex of the TU Delft Campus, is examined during irregular hours, to allow efficient real estate management and provide security solutions.Architecture and The Built EnvironmentGeo-information TechnologyGeomatics for the Built EnvironmentTRACK-i

    Extended Infrared Photoresponse in Te

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    Track-id: Activity Determination based on Wi-Fi Monitoring

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    The distribution of people in buildings, the occupancy of lecture-, work- and study places and the accessibility of facilities are essential information at university campuses who have to cope with limited and even shrinking budgets and huge, rising real estate costs. Only little insight is gained in both occupancy and movement patterns with traditional counting techniques and user-based questionnaires. Management teams state that rooms and facilities are hardly used, though staff and students complain about overcrowded facilities and limited flexibility. Actual and accurate data on a 24/7 scale with high-granularity is missing.In general Facility- and Asset Management lacks efficient methods for realtime, comprehensive and high-granularity information of location, capacity and use of tangible and intangible assets. Asset management could benefit from more detailed, more accurate and longitudinal data on assets, providing more insight into efficiency and effectiveness on different levels of scale through time.Existing technologies could provide a platform delivering those required insights. Navigation- and communication technologies such as GNSS, Wi-Fi, Bluetooth, RFID can be used to ‘locate’ users, estimate intensities and reveal patterns of movement and patterns of use. For Asset management indoorlocalisation is essential

    Coagent Networks: Generalized and Scaled

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    Coagent networks for reinforcement learning (RL) [Thomas and Barto, 2011] provide a powerful and flexible framework for deriving principled learning rules for arbitrary stochastic neural networks. The coagent framework offers an alternative to backpropagation-based deep learning (BDL) that overcomes some of backpropagation's main limitations. For example, coagent networks can compute different parts of the network \emph{asynchronously} (at different rates or at different times), can incorporate non-differentiable components that cannot be used with backpropagation, and can explore at levels higher than their action spaces (that is, they can be designed as hierarchical networks for exploration and/or temporal abstraction). However, the coagent framework is not just an alternative to BDL; the two approaches can be blended: BDL can be combined with coagent learning rules to create architectures with the advantages of both approaches. This work generalizes the coagent theory and learning rules provided by previous works; this generalization provides more flexibility for network architecture design within the coagent framework. This work also studies one of the chief disadvantages of coagent networks: high variance updates for networks that have many coagents and do not use backpropagation. We show that a coagent algorithm with a policy network that does not use backpropagation can scale to a challenging RL domain with a high-dimensional state and action space (the MuJoCo Ant environment), learning reasonable (although not state-of-the-art) policies. These contributions motivate and provide a more general theoretical foundation for future work that studies coagent networks
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