The Experts below are selected from a list of 315324 Experts worldwide ranked by ideXlab platform
Vincent C J De Boer - One of the best experts on this subject based on the ideXlab platform.
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ocrbayes a bayesian hierarchical Modeling Framework for seahorse extracellular flux oxygen consumption rate data analysis
PLOS ONE, 2021Co-Authors: Xiang Zhang, Taolin Yuan, Jaap Keijer, Vincent C J De BoerAbstract:BACKGROUND Mitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference. RESULTS To rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical Modeling Framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets. CONCLUSIONS We showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.
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ocrbayes a bayesian hierarchical Modeling Framework for seahorse extracellular flux oxygen consumption rate data analysis
bioRxiv, 2021Co-Authors: Xiang Zhang, Taolin Yuan, Jaap Keijer, Vincent C J De BoerAbstract:Mitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference. To rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical Modeling Framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets. We showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.
Xiang Zhang - One of the best experts on this subject based on the ideXlab platform.
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ocrbayes a bayesian hierarchical Modeling Framework for seahorse extracellular flux oxygen consumption rate data analysis
PLOS ONE, 2021Co-Authors: Xiang Zhang, Taolin Yuan, Jaap Keijer, Vincent C J De BoerAbstract:BACKGROUND Mitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference. RESULTS To rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical Modeling Framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets. CONCLUSIONS We showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.
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ocrbayes a bayesian hierarchical Modeling Framework for seahorse extracellular flux oxygen consumption rate data analysis
bioRxiv, 2021Co-Authors: Xiang Zhang, Taolin Yuan, Jaap Keijer, Vincent C J De BoerAbstract:Mitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference. To rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical Modeling Framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets. We showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.
Walid Saad - One of the best experts on this subject based on the ideXlab platform.
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an open source Modeling Framework for interdependent energy transportation communication infrastructure in smart and connected communities
IEEE Access, 2019Co-Authors: Kathryn Hinkelman, Jing Wang, Wangda Zuo, Qianqian Zhang, Walid SaadAbstract:Infrastructure in future smart and connected communities is envisioned as an aggregate of public services, including energy, transportation, and communication systems, all intertwined with each other. The intrinsic interdependency among these systems may exert the underlying influence on both design and operation of the heterogeneous infrastructures. However, few prior studies have tapped into the interdependency among these systems in order to quantify their potential impacts during standard operation. In response to this, this paper proposes an open-source, flexible, integrated Modeling Framework suitable for designing coupled energy, transportation, and communication systems and for assessing the impact of their interdependencies. First, a novel multi-level, multi-layer, multi-agent approach is proposed to enable flexible Modeling of the interconnected systems. Then, for the Framework’s proof of concept, preliminary component and system-level models for different systems are designed and implemented using Modelica, an equation-based object-oriented Modeling language. Finally, three case studies of gradually increasing complexity are presented (energy, energy + transportation, and energy + transportation + communication) to evaluate the interdependencies among the three systems. Quantitative analyses show that the deviation of the average velocity on the road can be 10.5% and the deviation of the power drawn from the grid can be 7% with or without considering the transportation and communication system at the peak commute time, indicating the presence of notable interdependencies. The proposed Modeling Framework also has the potential to be further extended for various Modeling purposes and use cases, such as dynamic Modeling and optimization, resilience analysis, and integrated decision making in future connected communities.
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an open source Modeling Framework for interdependent energy transportation communication infrastructure in smart and connected communities
arXiv: Systems and Control, 2019Co-Authors: Kathryn Hinkelman, Jing Wang, Wangda Zuo, Qianqian Zhang, Walid SaadAbstract:Infrastructure in future smart and connected communities is envisioned as an aggregate of public services, including the energy, transportation and communication systems, all intertwined with each other. The intrinsic interdependency among these systems may exert underlying influence on both design and operation of the heterogeneous infrastructures. However, few prior studies have tapped into the interdependency among the three systems in order to quantify their potential impacts during standard operation. In response to this, this paper proposes an open source, flexible, integrated Modeling Framework suitable for designing coupled energy, transportation, and communication systems and for assessing the impact of their interdependencies. First, a novel multi-level, multi-layer, multi-agent approach is proposed to enable flexible Modeling of the interconnected energy, transportation, and communication systems. Then, for the Framework's proof-of-concept, preliminary component and system-level models for different systems are designed and implemented using Modelica, an equation-based object-oriented Modeling language. Finally, three case studies of gradually increasing complexity are presented (energy, energy + transportation, energy + transportation + communication) to evaluate the interdependencies among the three systems. Quantitative analyses show that the deviation of the average velocity on the road can be 10.5\% and the deviation of the power draw from the grid can be 7\% with or without considering the transportation and communication system at the peak commute time, indicating the presence of notable interdependencies. The proposed Modeling Framework also has the potential to be further extended for various Modeling purposes and use cases, such as dynamic Modeling and optimization, resilience analysis, and integrated decision making in future connected communities.
Jaap Keijer - One of the best experts on this subject based on the ideXlab platform.
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ocrbayes a bayesian hierarchical Modeling Framework for seahorse extracellular flux oxygen consumption rate data analysis
PLOS ONE, 2021Co-Authors: Xiang Zhang, Taolin Yuan, Jaap Keijer, Vincent C J De BoerAbstract:BACKGROUND Mitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference. RESULTS To rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical Modeling Framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets. CONCLUSIONS We showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.
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ocrbayes a bayesian hierarchical Modeling Framework for seahorse extracellular flux oxygen consumption rate data analysis
bioRxiv, 2021Co-Authors: Xiang Zhang, Taolin Yuan, Jaap Keijer, Vincent C J De BoerAbstract:Mitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference. To rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical Modeling Framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets. We showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.
Taolin Yuan - One of the best experts on this subject based on the ideXlab platform.
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ocrbayes a bayesian hierarchical Modeling Framework for seahorse extracellular flux oxygen consumption rate data analysis
PLOS ONE, 2021Co-Authors: Xiang Zhang, Taolin Yuan, Jaap Keijer, Vincent C J De BoerAbstract:BACKGROUND Mitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference. RESULTS To rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical Modeling Framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets. CONCLUSIONS We showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.
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ocrbayes a bayesian hierarchical Modeling Framework for seahorse extracellular flux oxygen consumption rate data analysis
bioRxiv, 2021Co-Authors: Xiang Zhang, Taolin Yuan, Jaap Keijer, Vincent C J De BoerAbstract:Mitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference. To rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical Modeling Framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets. We showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.
