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Tahvili, Sahar

Publications and source records attributed to Tahvili, Sahar.

Induction of S100A9 homodimer formation in vivo

Highlights: • Formation of pro-inflammatory S100A9 homodimers is induced in vivo under inflammatory challenge or tumor burden. • CD11b{sup +}GR1{sup +} myeloid cells are the major source of S100A9 homodimers. • S100A9 homodimers are secreted extracellularly. We show here that increased S100A8 and S100A9 protein expression is induced in spleen of animals with active inflammation or with inoculated tumors. In tumor bearing animals an increased expression was also detected in the lung. To further analyze the induced proteins, we performed chemical cross-linking followed by Western blotting. We observed in protein extracts from spleen that both S100A8/S100A9 heterodimers as well as S100A9 homodimers were formed, both after tumor and inflammatory challenge. The cellular source for S100A9 homodimers were CD11b{sup +}GR1{sup +} cells. S100A9 homodimers were also secreted into the extracellular space. Lastly, in the spleen from normal and tumor bearing animals cells expressing relatively higher levels of S100A9 compared to S100A8 could be observed by immunohistochemistry. Taken together, these data show that the biologically potent dimeric form of S100A9 is induced in vivo in situations of tumor burden or inflammatory challenge.

60 APPLIED LIFE SCIENCES↗

Solving complex maintenance planning optimization problems using stochastic simulation and multi-criteria fuzzy decision making

One of the most important factors in the operations of many cooperations today is to maximize profit and one important tool to that effect is the optimization of maintenance activities. Maintenance activities is at the largest level divided into two major areas, corrective maintenance (CM) and preventive maintenance (PM). When optimizing maintenance activities, by a maintenance plan or policy, we seek to find the best activities to perform at each point in time, be it PM or CM. We explore the use of stochastic simulation, genetic algorithms and other tools for solving complex maintenance planning optimization problems in terms of a suggested framework model based on discrete event simulation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗