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- Type:
- Article
- 摘抄:
- We have designed ROS-activated cytotoxic agents that are active against AML cancer cells. In this study the mechanism and synergistic effects against cells co-expressing the AML oncogenes MLL-AF9 fusion and FLT3-ITD was investigated. The agent had an IC50 value of 1.8±0.3 μM with a selectivity of 9-fold compared to untransformed cells. Treatment induced DNA strand breaks, apoptosis, and cell cycle arrest. Proteomics and transcriptomics revealed enhanced expression of the pentose phosphate pathway, DNA repair, and pathways common to cell stress. Western blotting confirmed repair by homologous recombination. Importantly, RAC1 treatment was synergistic in combination with multiple pathway targeting therapies in AML cells but less so in untransformed cells. Taken together, these results demonstrate that RAC1 can selectively target poor prognosis AML and do so by creating DNA double strand breaks that require homologous recombination.
- 作者:
- Wunderlich, Mark; Mulloy, James C.; Greis, Kenneth D.; Merino, Edward J.; Thowfeik, Fathima Shazna; Wyder, Michael; AbdulSalam, Safnas F., and Kadekaro, Ana L.
- 提交者:
- Kenneth Greis
- 上传日期:
- 03/03/2017
- 更改日期:
- 04/07/2017
- 创建:
- 2015-09
- 证书:
- All rights reserved
-
- Type:
- Article
- 摘抄:
- A genetic algorithm was used to optimize performance of a fuzzy inference system acting as a controller for a magnetically actuated CubeSat. A solely magnetically controlled satellite is a nonlinear, underactuated system for which the uncontrollable axis varies as a function of orbit position and attitude; variation is approximately periodic with orbit position. Therefore, controllability is not guaranteed, making solely magnetic control a less than ideal option for spacecraft requiring a high degree of pointing accuracy or spacecraft subject to relatively large disturbances. However, for small spacecraft, such as CubeSats, with modest pointing and disturbance rejection requirements, solely magnetic actuation is a good option. The genetic-algorithm-tuned fuzzy controller solution was compared to a similar linear quadratic regulator solution that was tuned to minimize the cost function used by the genetic algorithm. Both were optimized with respect to a single set of initial conditions. The genetic-algorithm-tuned fuzzy controller was found to be a lower-cost solution than the linear quadratic regulator for the optimized set of initial conditions. Additionally, a Monte Carlo analysis showed the genetic-algorithm-tuned fuzzy controller tended to settle faster than the linear quadratic regulator over a variety of initial conditions.
- 作者:
- Cohen, Kelly; Putman, Philip T., and Walker, Alex R.
- 提交者:
- Kelly Cohen
- 上传日期:
- 02/13/2017
- 更改日期:
- 04/05/2017
- 创建:
- 2015-09
- 证书:
- All rights reserved