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  • 1
    ISSN: 1432-0584
    Keywords: Key words G-CSF ; Myelodysplastic syndrome ; Thrombopoiesis ; CFU-Meg ; BFU-E
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract  We report a patient with refractory anemia with excess blasts who showed a lineage-unrestricted hematologic response to granulocyte colony-stimulating factor (G-CSF). After 17 months of a stable disease state, the patient developed pneumonia, progression of cytopenia, and reduced cellularity and blast mass in the bone marrow. He was given G-CSF to overcome the pneumonia. Not only the neutrophil count, but also the platelet count increased soon after initiation of the G-CSF therapy; both counts became normal on the fifth day of the G-CSF therapy. Additionally, the anemia improved gradually. The neutrophil and platelet counts were maintained in the normal range for 3 months after cessation of the G-CSF. In vitro studies showed that G-CSF alone stimulated megakaryocyte colony formation from bone marrow mononuclear cells (BMMNC), and accessory cells in the BMMNC were necessary for expression of this G-CSF-induced in vitro megakaryocytopoiesis. These results suggest that, in coordination with accessory cells, G-CSF stimulated megakaryocytopoiesis in the patient. This case provides valuable information for understanding the mechanisms of a lineage-unrestricted hematologic response to G-CSF, which is very rarely observed in MDS.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    User modeling and user adapted interaction 8 (1998), S. 5-47 
    ISSN: 1573-1391
    Keywords: Plan recognition ; Bayesian Belief Networks ; language learning ; abstraction ; performance evaluation.
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present an approach to keyhole plan recognition which uses a dynamic belief (Bayesian) network to represent features of the domain that are needed to identify users' plans and goals. The application domain is a Multi-User Dungeon adventure game with thousands of possible actions and locations. We propose several network structures which represent the relations in the domain to varying extents, and compare their predictive power for predicting a user's current goal, next action and next location. The conditional probability distributions for each network are learned during a training phase, which dynamically builds these probabilities from observations of user behaviour. This approach allows the use of incomplete, sparse and noisy data during both training and testing. We then apply simple abstraction and learning techniques in order to speed up the performance of the most promising dynamic belief networks without a significant change in the accuracy of goal predictions. Our experimental results in the application domain show a high degree of predictive accuracy. This indicates that dynamic belief networks in general show promise for predicting a variety of behaviours in domains which have similar features to those of our domain, while reduced models, obtained by means of learning and abstraction, show promise for efficient goal prediction in such domains.
    Type of Medium: Electronic Resource
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