Follicular dendritic cell sarcoma associated with Castelmans disease presenting in the oral cavity

Follicular dendritic cell sarcoma (FDCS) is a rare intermediate grade malignant neoplasm of reticular dendritic origin. Castleman’s disease (CD) represents a non-neoplastic lymphoproliferative disorder with various clinical and morphological features. FDCS has been reported to be associated with CD. In this article, we describe the first case of follicular dendritic cell sarcoma associated with Castleman’s disease presenting in the oral cavity. Copyright © 2005 Elsevier Ltd All rights reserved.

The key role of CD40 ligand in overcoming tumor-induced dendritic cell dysfunction

Overcoming dendritic cell (DC) dysfunction is a prerequisite for successful active immunotherapy against breast cancer. CD40 ligand (CD40L), a key molecule in the interface between T-lymphocytes and DCs, seems to be instrumental in achieving that goal. Commenting on our data that CD40L protects circulating DCs from apoptosis induced by breast tumor products, Lenahan and Avigan highlighted the potential of CD40L for immunotherapy. We expand on that argument by pointing to additional findings that CD40L not only rescues genuine DCs but also functionally improves populations of immature antigen-presenting cells that fill the DC compartment in patients with breast cancer.

A key role for PTP1B in dendritic cell maturation, migration and T cell activation

Acknowledgements We thank the Iain Fraser Flow Cytometry Centre and the Medical Research Facility of the University of Aberdeen. We are grateful to Drs West, Zaru, and Davidson (University of Dundee) for the scientific discussion and technical assistance. Wethank Derek Mitchell (University of Dundee) for aiding with the quantification of focal contacts. Funding This work was supported by Saving Sight in Grampian and the Development Trust of the UoA (both to J.V.F.). Work on this project was partly funded by project grants from British Heart Foundation and European Foundation for the Study of Diabetes/Lilly diabetes programme grant (to M.D.).

A key role for PTP1B in dendritic cell maturation, migration and T cell activation

Acknowledgements We thank the Iain Fraser Flow Cytometry Centre and the Medical Research Facility of the University of Aberdeen. We are grateful to Drs West, Zaru, and Davidson (University of Dundee) for the scientific discussion and technical assistance. Wethank Derek Mitchell (University of Dundee) for aiding with the quantification of focal contacts. Funding This work was supported by Saving Sight in Grampian and the Development Trust of the UoA (both to J.V.F.). Work on this project was partly funded by project grants from British Heart Foundation and European Foundation for the Study of Diabetes/Lilly diabetes programme grant (to M.D.).

FdDCA : A Novel Fuzzy Deterministic Dendritic Cell Algorithm

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Characterization of plasmacytoid dendritic cells in the CD4C/HIV transgenic mouse model

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Characterization of plasmacytoid dendritic cells in the CD4C/HIV transgenic mouse model

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system, providing the initial detection of pathogenic invaders. Research into this family of cells has revealed that they perform information fusion which directs immune responses. We have derived a Dendritic Cell Algorithm based on the functionality of these cells, by modelling the biological signals and differentiation pathways to build a control mechanism for an artificial immune system. We present algorithmic details in addition to experimental results, when the algorithm was applied to anomaly detection for the detection of port scans. The results show the Dendritic Cell Algorithm is successful at detecting port scans.

The deterministic Dendritic Cell Algorithm

The Dendritic Cell Algorithm is an immune-inspired algorithm originally based on the function of natural dendritic cells. The original instantiation of the algorithm is a highly stochastic algorithm. While the performance of the algorithm is good when applied to large real-time datasets, it is difficult to analyse due to the number of random-based elements. In this paper a deterministic version of the algorithm is proposed, implemented and tested using a port scan dataset to provide a controllable system. This version consists of a controllable amount of parameters, which are experimented with in this paper. In addition the effects are examined of the use of time windows and variation on the number of cells, both which are shown to influence the algorithm. Finally a novel metric for the assessment of the algorithms output is introduced and proves to be a more sensitive metric than the metric used with the original Dendritic Cell Algorithm.

Further exploration of the Dendritic Cell Algorithm: antigen multiplier and time windows

As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces promising performance in the field of anomaly detection. This paper presents the application of the DCA to a standard data set, the KDD 99 data set. The results of different implementation versions of the DCA, including antigen multiplier and moving time windows, are reported. The real-valued Negative Selection Algorithm (NSA) using constant-sized detectors and the C4.5 decision tree algorithm are used, to conduct a baseline comparison. The results suggest that the DCA is applicable to KDD 99 data set, and the antigen multiplier and moving time windows have the same effect on the DCA for this particular data set. The real-valued NSA with contant-sized detectors is not applicable to the data set. And the C4.5 decision tree algorithm provides a benchmark of the classification performance for this data set.

Exploration of the dendritic cell algorithm with the duration calculus

As one of the newest members in Articial Immune Systems (AIS), the Dendritic Cell Algorithm (DCA) has been applied to a range of problems. These applications mainly belong to the eld of anomaly detection. However, real-time detection, a new challenge to anomaly detection, requires improvement on the real-time capability of the DCA. To assess such capability, formal methods in the research of real-time systems can be employed. The ndings of the assessment can provide guideline for the future development of the algorithm. Therefore, in this paper we use an interval logic based method, named the Duration Calcu- lus (DC), to specify a simplied single-cell model of the DCA. Based on the DC specications with further induction, we nd that each individual cell in the DCA can perform its function as a detector in real-time. Since the DCA can be seen as many such cells operating in parallel, it is potentially capable of performing real-time detection. However, the analysis process of the standard DCA constricts its real-time capability. As a result, we conclude that the analysis process of the standard DCA should be replaced by a real-time analysis component, which can perform periodic analysis for the purpose of real-time detection.

PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm

As one of the newest members in the field of articial immune systems (AIS), the Dendritic Cell Algorithm (DCA) is based on behavioural models of natural dendritic cells (DCs). Unlike other AIS, the DCA does not rely on training data, instead domain or expert knowledge is required to predetermine the mapping between input signals from a particular instance to the three categories used by the DCA. This data preprocessing phase has received the criticism of having manually over-fitted the data to the algorithm, which is undesirable. Therefore, in this paper we have attempted to ascertain if it is possible to use principal component analysis (PCA) techniques to automatically categorise input data while still generating useful and accurate classication results. The integrated system is tested with a biometrics dataset for the stress recognition of automobile drivers. The experimental results have shown the application of PCA to the DCA for the purpose of automated data preprocessing is successful.