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1.
Theodoros Eleftheriadis Georgios Pissas Marta Crespo Evdokia Nikolaou Vassilios Liakopoulos Ioannis Stefanidis 《International journal of molecular sciences》2021,22(4)
Direct allorecognition is the earliest and most potent immune response against a kidney allograft. Currently, it is thought that passenger donor professional antigen-presenting cells (APCs) are responsible. Further, many studies support that graft ischemia-reperfusion injury increases the probability of acute rejection. We evaluated the possible role of primary human proximal renal tubular epithelial cells (RPTECs) in direct allorecognition by CD4+ T-cells and the effect of anoxia-reoxygenation. In cell culture, we detected that RPTECs express all the required molecules for CD4+ T-cell activation (HLA-DR, CD80, and ICAM-1). Anoxia-reoxygenation decreased HLA-DR and CD80 but increased ICAM-1. Following this, RPTECs were co-cultured with alloreactive CD4+ T-cells. In T-cells, zeta chain phosphorylation and c-Myc increased, indicating activation of T-cell receptor and co-stimulation signal transduction pathways, respectively. T-cell proliferation assessed with bromodeoxyuridine assay and with the marker Ki-67 increased. Previous culture of RPTECs under anoxia raised all the above parameters in T-cells. FOXP3 remained unaffected in all cases, signifying that proliferating T-cells were not differentiated towards a regulatory phenotype. Our results support that direct allorecognition may be mediated by RPTECs even in the absence of donor-derived professional APCs. Also, ischemia-reperfusion injury of the graft may enhance the above capacity of RPTECs, increasing the possibility of acute rejection. 相似文献
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Georgios D. Styliaras Georgios K. Tsolis Chris M. Papaterpos 《International Journal on Digital Libraries》2007,8(1):61-78
In this paper, AssetCollector is presented, which is a system for managing collections of cultural assets. AssetCollector
covers the needs of collection curators towards defining, populating and searching a collection in a flexible way, while supporting
them in generating reports based on the collection’s assets and reusing them in order to build web sites and CD-ROMs. In order
to support the above functionality, the system provides the content structuring subsystem, the content input subsystem, the
search subsystem and the report subsystem. The use of the subsystems is straightforward and requires no technical skills from
the curators. AssetCollector has been successfully applied for organizing various collections of cultural assets in Greece,
such as archaeological sites, museums and published books. In the future, an evaluation procedure is planned in order to further
refine the use of the system according to the targeted users’ needs. Furthermore, more import and export facilities will be
provided, which will make the system compliant with widely accepted standards. 相似文献
4.
Performance analysis of IEEE 802.11 ad hoc networks in the presence of exposed terminals 总被引:1,自引:0,他引:1
The paper evaluates the performance effects of exposed terminals in IEEE 802.11 ad hoc networks in finite load conditions. It derives analytical models for the estimation of channel utilization and media access delay for IEEE 802.11 ad hoc networks in finite load conditions with and without exposed terminals. The simulation results show that the analytical estimated channel utilization and media access delay metrics are fairly accurate. 相似文献
5.
A new approach to the combination of exergetic and economic analysis (exergoeconomic analysis) for the investigation of energy-conversion processes is presented. This approach allows the monetary evaluation of costs caused by irreversibilities (exergy losses), as well as comparisons between these costs and the investment and operating costs for each component of a plant. The present analysis permits identification and evaluation of inefficiencies in an energy-conversion plant and of opportunities for improvement. 相似文献
6.
Coalition formation is a central problem in multiagent systems research, but most models assume common knowledge of agent
types. In practice, however, agents are often unsure of the types or capabilities of their potential partners, but gain information about these capabilities through repeated interaction. In this paper, we
propose a novel Bayesian, model-based reinforcement learning framework for this problem, assuming that coalitions are formed
(and tasks undertaken) repeatedly. Our model allows agents to refine their beliefs about the types of others as they interact
within a coalition. The model also allows agents to make explicit tradeoffs between exploration (forming “new” coalitions
to learn more about the types of new potential partners) and exploitation (relying on partners about which more is known),
using value of information to define optimal exploration policies. Our framework effectively integrates decision making during
repeated coalition formation under type uncertainty with Bayesian reinforcement learning techniques. Specifically, we present
several learning algorithms to approximate the optimal Bayesian solution to the repeated coalition formation and type-learning
problem, providing tractable means to ensure good sequential performance. We evaluate our algorithms in a variety of settings,
showing that one method in particular exhibits consistently good performance in practice. We also demonstrate the ability
of our model to facilitate knowledge transfer across different dynamic tasks. 相似文献
7.
Georgios Kontaxis Michalis Polychronakis Evangelos P. Markatos 《International Journal of Information Security》2012,11(5):321-332
Over the past few years, a large and ever increasing number of Web sites have incorporated one or more social login platforms and have encouraged users to log in with their Facebook, Twitter, Google, or other social networking identities. Research results suggest that more than two million Web sites have already adopted Facebook’s social login platform, and the number is increasing sharply. Although one might theoretically refrain from such social login features and cross-site interactions, usage statistics show that more than 250 million people might not fully realize the privacy implications of opting-in. To make matters worse, certain Web sites do not offer even the minimum of their functionality unless users meet their demands for information and social interaction. At the same time, in a large number of cases, it is unclear why these sites require all that personal information for their purposes. In this paper, we mitigate this problem by designing and developing a framework for minimum information disclosure in social login interactions with third-party sites. Our example case is Facebook, which combines a very popular single sign-on platform with information-rich social networking profiles. Whenever users want to browse to a Web site that requires authentication or social interaction using a Facebook identity, our system employs, by default, a Facebook session that reveals the minimum amount of information necessary. Users have the option to explicitly elevate that Facebook session in a manner that reveals more or all of the information tied to their social identity. This enables users to disclose the minimum possible amount of personal information during their browsing experience on third-party Web sites. 相似文献
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10.
Ioannis Marras Georgios Tzimiropoulos Stefanos Zafeiriou Maja Pantic 《Image and vision computing》2014
We introduce a robust framework for learning and fusing of orientation appearance models based on both texture and depth information for rigid object tracking. Our framework fuses data obtained from a standard visual camera and dense depth maps obtained by low-cost consumer depth cameras such as the Kinect. To combine these two completely different modalities, we propose to use features that do not depend on the data representation: angles. More specifically, our framework combines image gradient orientations as extracted from intensity images with the directions of surface normals computed from dense depth fields. We propose to capture the correlations between the obtained orientation appearance models using a fusion approach motivated by the original Active Appearance Models (AAMs). To incorporate these features in a learning framework, we use a robust kernel based on the Euler representation of angles which does not require off-line training, and can be efficiently implemented online. The robustness of learning from orientation appearance models is presented both theoretically and experimentally in this work. This kernel enables us to cope with gross measurement errors, missing data as well as other typical problems such as illumination changes and occlusions. By combining the proposed models with a particle filter, the proposed framework was used for performing 2D plus 3D rigid object tracking, achieving robust performance in very difficult tracking scenarios including extreme pose variations. 相似文献