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- Trautmann, M., et al.
(författare)
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FUS-DDIT3 Fusion Protein-Driven IGF-IR Signaling is a Therapeutic Target in Myxoid Liposarcoma
- 2017
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Ingår i: Clinical Cancer Research. - : American Association for Cancer Research (AACR). - 1078-0432 .- 1557-3265. ; 23:20, s. 6227-6238
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Myxoid liposarcoma is an aggressive disease with particular propensity to develop hematogenic metastases. Over 90% of myxoid liposarcoma are characterized by a reciprocal t(12;16)(q13;p11) translocation. The resulting chimeric FUS-DDIT3 fusion protein plays a crucial role in myxoid liposarcoma pathogenesis; however, its specific impact on oncogenic signaling pathways remains to be substantiated. We here investigate the functional role of FUS-DDIT3 in IGF-IR/PI3K/Akt signaling driving myxoid liposarcoma pathogenesis. Experimental Design: Immunohistochemical evaluation of key effectors of the IGF-IR/PI3K/Akt signaling axis was performed in a comprehensive cohort of myxoid liposarcoma specimens. FUS-DDIT3 dependency and biological function of the IGF-IR/PI3K/Akt signaling cascade were analyzed using a HT1080 fibrosarcoma-based myxoid liposarcoma tumor model and multiple tumor-derived myxoid liposarcoma cell lines. An established myxoid liposarcoma avian chorioallantoic membrane model was used for in vivo confirmation of the preclinical in vitro results. Results: A comprehensive subset of myxoid liposarcoma specimens showed elevated expression and phosphorylation levels of various IGF-IR/PI3K/Akt signaling effectors. In HT1080 fibrosarcoma cells, overexpression of FUS-DDIT3 induced aberrant IGF-IR/PI3K/Akt pathway activity, which was dependent on transcriptional induction of the IGF2 gene. Conversely, RNAi-mediated FUS-DDIT3 knockdown in myxoid liposarcoma cells led to an inactivation of IGF-IR/PI3K/Akt signaling associated with diminished IGF2 mRNA expression. Treatment of myxoid liposarcoma cell lines with several IGF-IR inhibitors resulted in significant growth inhibition in vitro and in vivo. Conclusions: Our preclinical study substantiates the fundamental role of the IGF-IR/PI3K/Akt signaling pathway in myxoid liposarcoma pathogenesis and provides a mechanism-based rationale for molecular-targeted approaches in myxoid liposarcoma cancer therapy. (C)2017 AACR.
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| 3. |
- Cui, Yue
(författare)
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A Fault Detection Framework Using Recurrent Neural Networks for Condition Monitoring of Wind Turbines
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The global energy system is experiencing a transition to a sustainable system with ambitious targets for increased use of renewable energy. One key trend for this transition has been the large introduction of wind power and integration into the electricity grid. In order to succeed in this transition, there is a need to develop efficient tools to support the handling of the assets. Asset management is a coordinated activity for the organization to get value from an asset. As the main part of asset management, maintenance includes all the technical and corresponding administrative actions to keep or restore the asset to the desired state in which it can perform its required functions. Traditional maintenance is usually based on scheduled monitoring and physical inspections. However, with new access to data and information about condition-based maintenance shows to be an efficient solution for asset management. This thesis explores data-driven solutions for electrical equipment to generate alerts towards potential operation risks, which targets digital, efficient, and cost-effective asset management. Specifically, the thesis investigates wind turbines.This thesis proposes a fault detection framework for cost-effective preventive maintenance of wind turbines by using condition monitoring systems. The thesis utilizes the data from supervisory control and data acquisition systems as the main input. For log events, each event is mapped to corresponding components based on the Reliawind taxonomy. For operation data, recurrent neural networks are applied to model normal behaviors, which can learn the long-time temporal dependencies between various time series. Based on the estimation results, a two-stage threshold method is proposed as post-processing to determine operation conditions. The method evaluates the shift values deviating from the estimated behaviors and their duration time to attenuate minor fluctuations. A two-level condition monitoring system is constructed to apply the proposed fault detection framework, which targets to detect possible faults of components and conduct performance analysis of turbines. The fault detection framework is tested with the experience data from onshore wind farms. The results demonstrate that the framework can detect operational risks and reduce false alarms.
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