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2013 1

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ACSL5, a prognostic factor in acute myeloid leukemia, modulates the activity of Wnt/-catenin signaling by palmitoylation modification

《医学前沿（英文）》 2023年第17卷第4期页码 685-698 doi: 10.1007/s11684-022-0942-1

摘要： Acyl-CoA synthetase long chain family member 5 (ACSL5), is a member of the acyl-CoA synthetases (ACSs) family that activates long chain fatty acids by catalyzing the synthesis of fatty acyl-CoAs. The dysregulation of ACSL5 has been reported in some cancers, such as glioma and colon cancers. However, little is known about the role of ACSL5 in acute myeloid leukemia (AML). We found that the expression of ACSL5 was higher in bone marrow cells from AML patients compared with that from healthy donors. ACSL5 level could serve as an independent prognostic predictor of the overall survival of AML patients. In AML cells, the ACSL5 knockdown inhibited cell growth both in vitro and in vivo. Mechanistically, the knockdown of ACSL5 suppressed the activation of the Wnt/β-catenin pathway by suppressing the palmitoylation modification of Wnt3a. Additionally, triacsin c, a pan-ACS family inhibitor, inhibited cell growth and robustly induced cell apoptosis when combined with ABT-199, the FDA approved BCL-2 inhibitor for AML therapy. Our results indicate that ACSL5 is a potential prognosis marker for AML and a promising pharmacological target for the treatment of molecularly stratified AML.

关键词： acute myeloid leukemia acyl-CoA synthetase long chain family member 5 Wnt3a palmitoylation ABT-199

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Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment

Simarjit KAUR, Yajvender Pal VERMA, Sunil AGRAWAL

《能源前沿（英文）》 2013年第7卷第4期页码 468-478 doi: 10.1007/s11708-013-0282-6

摘要： In a competitive and deregulated power scenario, the utilities try to maintain their real electric power generation in balance with the load demand, which creates a need for the precise real time generation scheduling (GS). In this paper, the GS problem is solved to perform the unit commitment (UC) based on frequency prediction by using artificial neural network (ANN) with the objective to minimize the overall system cost of the state utility. The introduction of availability-based tariff (ABT) signifies the importance of frequency in GS. Under-prediction or over-prediction will result in an unnecessary commitment of generating units or buying power from central generating units at a higher cost. Therefore, an accurate frequency prediction is the first step toward optimal GS. The dependency of frequency on various parameters such as actual generation, load demand, wind power and power deficit has been considered in this paper. The proposed technique provides a reliable solution for the input parameter different from the one presented in the training data. The performance of the frequency predictor model has been evaluated based on the absolute percentage error (APE) and the mean absolute percentage error (MAPE). The proposed predicted frequency sensitive GS model is applied to the system of Indian state of Tamilnadu, which reduces the overall system cost of the state utility by keeping off the dearer units selected based on the predicted frequency.

关键词： artificial neural network (ANN) frequency prediction availability-based tariff (ABT) generation scheduling (GS)