The Experts below are selected from a list of 20418 Experts worldwide ranked by ideXlab platform

Martin Fussenegger - One of the best experts on this subject based on the ideXlab platform.

  • synthetic gene network restoring endogenous pituitary thyroid Feedback control in experimental graves disease
    Proceedings of the National Academy of Sciences of the United States of America, 2016
    Co-Authors: Pratik Saxena, Ghislaine Charpinel Hamri, Marc Folcher, Henryk Zulewski, Martin Fussenegger
    Abstract:

    Graves’ disease is an autoimmune disorder that causes hyperthyroidism because of autoantibodies that bind to the thyroid-stimulating hormone receptor (TSHR) on the thyroid gland, triggering thyroid hormone release. The Physiological control of thyroid hormone homeostasis by the Feedback loops involving the hypothalamus–pituitary–thyroid axis is disrupted by these stimulating autoantibodies. To reset the endogenous thyrotrophic Feedback control, we designed a synthetic mammalian gene circuit that maintains thyroid hormone homeostasis by monitoring thyroid hormone levels and coordinating the expression of a thyroid-stimulating hormone receptor antagonist (TSHAntag), which competitively inhibits the binding of thyroid-stimulating hormone or the human autoantibody to TSHR. This synthetic control device consists of a synthetic thyroid-sensing receptor (TSR), a yeast Gal4 protein/human thyroid receptor-α fusion, which reversibly triggers expression of the TSHAntag gene from TSR-dependent promoters. In hyperthyroid mice, this synthetic circuit sensed pathological thyroid hormone levels and restored the thyrotrophic Feedback control of the hypothalamus–pituitary–thyroid axis to euthyroid hormone levels. Therapeutic plug and play gene circuits that restore Physiological Feedback control in metabolic disorders foster advanced gene- and cell-based therapies.

  • Synthetic gene network restoring endogenous pituitary–thyroid Feedback control in experimental Graves’ disease
    Proceedings of the National Academy of Sciences, 2016
    Co-Authors: Pratik Saxena, Ghislaine Charpinel Hamri, Marc Folcher, Henryk Zulewski, Martin Fussenegger
    Abstract:

    Graves’ disease is an autoimmune disorder that causes hyperthyroidism because of autoantibodies that bind to the thyroid-stimulating hormone receptor (TSHR) on the thyroid gland, triggering thyroid hormone release. The Physiological control of thyroid hormone homeostasis by the Feedback loops involving the hypothalamus–pituitary–thyroid axis is disrupted by these stimulating autoantibodies. To reset the endogenous thyrotrophic Feedback control, we designed a synthetic mammalian gene circuit that maintains thyroid hormone homeostasis by monitoring thyroid hormone levels and coordinating the expression of a thyroid-stimulating hormone receptor antagonist (TSHAntag), which competitively inhibits the binding of thyroid-stimulating hormone or the human autoantibody to TSHR. This synthetic control device consists of a synthetic thyroid-sensing receptor (TSR), a yeast Gal4 protein/human thyroid receptor-α fusion, which reversibly triggers expression of the TSHAntag gene from TSR-dependent promoters. In hyperthyroid mice, this synthetic circuit sensed pathological thyroid hormone levels and restored the thyrotrophic Feedback control of the hypothalamus–pituitary–thyroid axis to euthyroid hormone levels. Therapeutic plug and play gene circuits that restore Physiological Feedback control in metabolic disorders foster advanced gene- and cell-based therapies.

Dale W Esliger - One of the best experts on this subject based on the ideXlab platform.

  • Metadata Correction: Brain Activation in Response to Personalized Behavioral and Physiological Feedback From Self-Monitoring Technology: Pilot Study.
    Journal of Medical Internet Research, 2017
    Co-Authors: Maxine E Whelan, Paul S Morgan, Lauren B Sherar, Andrew P Kingsnorth, Daniele Magistro, Dale W Esliger
    Abstract:

    This is an Open Access Article. It is published by JMIR publications under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

  • erratum metadata correction brain activation in response to personalized behavioral and Physiological Feedback from self monitoring technology pilot study journal of medical internet research 2017 19 11 e384
    Journal of Medical Internet Research, 2017
    Co-Authors: Maxine E Whelan, Paul S Morgan, Lauren B Sherar, Andrew P Kingsnorth, Daniele Magistro, Dale W Esliger
    Abstract:

    ©Maxine E Whelan, Paul S Morgan, Lauren B Sherar, Andrew P Kingsnorth, Daniele Magistro, Dale W Esliger. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 21.12.2017. [This corrects the article DOI: 10.2196/jmir.8890.].

  • brain activation in response to personalized behavioral and Physiological Feedback from self monitoring technology pilot study
    Journal of Medical Internet Research, 2017
    Co-Authors: Maxine E Whelan, Paul S Morgan, Lauren B Sherar, Andrew P Kingsnorth, Daniele Magistro, Dale W Esliger
    Abstract:

    Background: The recent surge in commercially available wearable technology has allowed real-time self-monitoring of behavior (eg, physical activity) and physiology (eg, glucose levels). However, there is limited neuroimaging work (ie, functional magnetic resonance imaging [fMRI]) to identify how people’s brains respond to receiving this personalized health Feedback and how this impacts subsequent behavior. Objective: Identify regions of the brain activated and examine associations between activation and behavior. Methods: This was a pilot study to assess physical activity, sedentary time, and glucose levels over 14 days in 33 adults (aged 30 to 60 years). Extracted accelerometry, inclinometry, and interstitial glucose data informed the construction of personalized Feedback messages (eg, average number of steps per day). These messages were subsequently presented visually to participants during fMRI. Participant physical activity levels and sedentary time were assessed again for 8 days following exposure to this personalized Feedback. Results: Independent tests identified significant activations within the prefrontal cortex in response to glucose Feedback compared with behavioral Feedback (P<.001). Reductions in mean sedentary time (589.0 vs 560.0 minutes per day, P=.014) were observed. Activation in the subgyral area had a moderate correlation with minutes of moderate-to-vigorous physical activity (r=0.392, P=.043). Conclusion: Presenting personalized glucose Feedback resulted in significantly more brain activation when compared with behavior. Participants reduced time spent sedentary at follow-up. Research on deploying behavioral and Physiological Feedback warrants further investigation. [J Med Internet Res 2017;19(11):e384]

  • Brain Activation in Response to Personalized Behavioral and Physiological Feedback From Self-Monitoring Technology: Pilot Study
    Journal of Medical Internet Research, 2017
    Co-Authors: Maxine E Whelan, Paul S Morgan, Lauren B Sherar, Andrew P Kingsnorth, Daniele Magistro, Dale W Esliger
    Abstract:

    Background: The recent surge in commercially available wearable technology has allowed real-time self-monitoring of behavior (eg, physical activity) and physiology (eg, glucose levels). However, there is limited neuroimaging work (ie, functional magnetic resonance imaging [fMRI]) to identify how people’s brains respond to receiving this personalized health Feedback and how this impacts subsequent behavior. Objective: Identify regions of the brain activated and examine associations between activation and behavior. Methods: This was a pilot study to assess physical activity, sedentary time, and glucose levels over 14 days in 33 adults (aged 30 to 60 years). Extracted accelerometry, inclinometry, and interstitial glucose data informed the construction of personalized Feedback messages (eg, average number of steps per day). These messages were subsequently presented visually to participants during fMRI. Participant physical activity levels and sedentary time were assessed again for 8 days following exposure to this personalized Feedback. Results: Independent tests identified significant activations within the prefrontal cortex in response to glucose Feedback compared with behavioral Feedback (P

Pratik Saxena - One of the best experts on this subject based on the ideXlab platform.

  • synthetic gene network restoring endogenous pituitary thyroid Feedback control in experimental graves disease
    Proceedings of the National Academy of Sciences of the United States of America, 2016
    Co-Authors: Pratik Saxena, Ghislaine Charpinel Hamri, Marc Folcher, Henryk Zulewski, Martin Fussenegger
    Abstract:

    Graves’ disease is an autoimmune disorder that causes hyperthyroidism because of autoantibodies that bind to the thyroid-stimulating hormone receptor (TSHR) on the thyroid gland, triggering thyroid hormone release. The Physiological control of thyroid hormone homeostasis by the Feedback loops involving the hypothalamus–pituitary–thyroid axis is disrupted by these stimulating autoantibodies. To reset the endogenous thyrotrophic Feedback control, we designed a synthetic mammalian gene circuit that maintains thyroid hormone homeostasis by monitoring thyroid hormone levels and coordinating the expression of a thyroid-stimulating hormone receptor antagonist (TSHAntag), which competitively inhibits the binding of thyroid-stimulating hormone or the human autoantibody to TSHR. This synthetic control device consists of a synthetic thyroid-sensing receptor (TSR), a yeast Gal4 protein/human thyroid receptor-α fusion, which reversibly triggers expression of the TSHAntag gene from TSR-dependent promoters. In hyperthyroid mice, this synthetic circuit sensed pathological thyroid hormone levels and restored the thyrotrophic Feedback control of the hypothalamus–pituitary–thyroid axis to euthyroid hormone levels. Therapeutic plug and play gene circuits that restore Physiological Feedback control in metabolic disorders foster advanced gene- and cell-based therapies.

  • Synthetic gene network restoring endogenous pituitary–thyroid Feedback control in experimental Graves’ disease
    Proceedings of the National Academy of Sciences, 2016
    Co-Authors: Pratik Saxena, Ghislaine Charpinel Hamri, Marc Folcher, Henryk Zulewski, Martin Fussenegger
    Abstract:

    Graves’ disease is an autoimmune disorder that causes hyperthyroidism because of autoantibodies that bind to the thyroid-stimulating hormone receptor (TSHR) on the thyroid gland, triggering thyroid hormone release. The Physiological control of thyroid hormone homeostasis by the Feedback loops involving the hypothalamus–pituitary–thyroid axis is disrupted by these stimulating autoantibodies. To reset the endogenous thyrotrophic Feedback control, we designed a synthetic mammalian gene circuit that maintains thyroid hormone homeostasis by monitoring thyroid hormone levels and coordinating the expression of a thyroid-stimulating hormone receptor antagonist (TSHAntag), which competitively inhibits the binding of thyroid-stimulating hormone or the human autoantibody to TSHR. This synthetic control device consists of a synthetic thyroid-sensing receptor (TSR), a yeast Gal4 protein/human thyroid receptor-α fusion, which reversibly triggers expression of the TSHAntag gene from TSR-dependent promoters. In hyperthyroid mice, this synthetic circuit sensed pathological thyroid hormone levels and restored the thyrotrophic Feedback control of the hypothalamus–pituitary–thyroid axis to euthyroid hormone levels. Therapeutic plug and play gene circuits that restore Physiological Feedback control in metabolic disorders foster advanced gene- and cell-based therapies.

Maxine E Whelan - One of the best experts on this subject based on the ideXlab platform.

  • Metadata Correction: Brain Activation in Response to Personalized Behavioral and Physiological Feedback From Self-Monitoring Technology: Pilot Study.
    Journal of Medical Internet Research, 2017
    Co-Authors: Maxine E Whelan, Paul S Morgan, Lauren B Sherar, Andrew P Kingsnorth, Daniele Magistro, Dale W Esliger
    Abstract:

    This is an Open Access Article. It is published by JMIR publications under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

  • erratum metadata correction brain activation in response to personalized behavioral and Physiological Feedback from self monitoring technology pilot study journal of medical internet research 2017 19 11 e384
    Journal of Medical Internet Research, 2017
    Co-Authors: Maxine E Whelan, Paul S Morgan, Lauren B Sherar, Andrew P Kingsnorth, Daniele Magistro, Dale W Esliger
    Abstract:

    ©Maxine E Whelan, Paul S Morgan, Lauren B Sherar, Andrew P Kingsnorth, Daniele Magistro, Dale W Esliger. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 21.12.2017. [This corrects the article DOI: 10.2196/jmir.8890.].

  • brain activation in response to personalized behavioral and Physiological Feedback from self monitoring technology pilot study
    Journal of Medical Internet Research, 2017
    Co-Authors: Maxine E Whelan, Paul S Morgan, Lauren B Sherar, Andrew P Kingsnorth, Daniele Magistro, Dale W Esliger
    Abstract:

    Background: The recent surge in commercially available wearable technology has allowed real-time self-monitoring of behavior (eg, physical activity) and physiology (eg, glucose levels). However, there is limited neuroimaging work (ie, functional magnetic resonance imaging [fMRI]) to identify how people’s brains respond to receiving this personalized health Feedback and how this impacts subsequent behavior. Objective: Identify regions of the brain activated and examine associations between activation and behavior. Methods: This was a pilot study to assess physical activity, sedentary time, and glucose levels over 14 days in 33 adults (aged 30 to 60 years). Extracted accelerometry, inclinometry, and interstitial glucose data informed the construction of personalized Feedback messages (eg, average number of steps per day). These messages were subsequently presented visually to participants during fMRI. Participant physical activity levels and sedentary time were assessed again for 8 days following exposure to this personalized Feedback. Results: Independent tests identified significant activations within the prefrontal cortex in response to glucose Feedback compared with behavioral Feedback (P<.001). Reductions in mean sedentary time (589.0 vs 560.0 minutes per day, P=.014) were observed. Activation in the subgyral area had a moderate correlation with minutes of moderate-to-vigorous physical activity (r=0.392, P=.043). Conclusion: Presenting personalized glucose Feedback resulted in significantly more brain activation when compared with behavior. Participants reduced time spent sedentary at follow-up. Research on deploying behavioral and Physiological Feedback warrants further investigation. [J Med Internet Res 2017;19(11):e384]

  • Brain Activation in Response to Personalized Behavioral and Physiological Feedback From Self-Monitoring Technology: Pilot Study
    Journal of Medical Internet Research, 2017
    Co-Authors: Maxine E Whelan, Paul S Morgan, Lauren B Sherar, Andrew P Kingsnorth, Daniele Magistro, Dale W Esliger
    Abstract:

    Background: The recent surge in commercially available wearable technology has allowed real-time self-monitoring of behavior (eg, physical activity) and physiology (eg, glucose levels). However, there is limited neuroimaging work (ie, functional magnetic resonance imaging [fMRI]) to identify how people’s brains respond to receiving this personalized health Feedback and how this impacts subsequent behavior. Objective: Identify regions of the brain activated and examine associations between activation and behavior. Methods: This was a pilot study to assess physical activity, sedentary time, and glucose levels over 14 days in 33 adults (aged 30 to 60 years). Extracted accelerometry, inclinometry, and interstitial glucose data informed the construction of personalized Feedback messages (eg, average number of steps per day). These messages were subsequently presented visually to participants during fMRI. Participant physical activity levels and sedentary time were assessed again for 8 days following exposure to this personalized Feedback. Results: Independent tests identified significant activations within the prefrontal cortex in response to glucose Feedback compared with behavioral Feedback (P

Ferat Sahin - One of the best experts on this subject based on the ideXlab platform.

  • SoSE - HRC-SoS: Human Robot Collaboration Experimentation Platform as System of Systems
    2019 14th Annual Conference System of Systems Engineering (SoSE), 2019
    Co-Authors: Celal Savur, Shitij Kumar, Sarthak Arora, Tuly Hazbar, Ferat Sahin
    Abstract:

    This paper presents an experimentation platform for human robot collaboration as a system of systems as well as proposes a conceptual framework describing the aspects of Human Robot Collaboration. These aspects are Awareness, Intelligence and Compliance of the system. Based on this framework case studies describing experiment setups performed using this platform are discussed. Each experiment highlights the use of the subsystems such as the digital twin, motion capture system, human-Physiological monitoring system, data collection system and robot control and interface systems. A highlight of this paper showcases a subsystem with the ability to monitor human Physiological Feedback during a human robot collaboration task.

  • SoSE - HRC-SoS: Human Robot Collaboration Experimentation Platform as System of Systems
    2019 14th Annual Conference System of Systems Engineering (SoSE), 2019
    Co-Authors: Celal Savur, Shitij Kumar, Sarthak Arora, Tuly Hazbar, Ferat Sahin
    Abstract:

    This paper presents an experimentation platform for human robot collaboration as a system of systems as well as proposes a conceptual framework describing the aspects of Human Robot Collaboration. These aspects are Awareness, Intelligence and Compliance of the system. Based on this framework case studies describing experiment setups performed using this platform are discussed. Each experiment highlights the use of the subsystems such as the digital twin, motion capture system, human-Physiological monitoring system, data collection system and robot control and interface systems. A highlight of this paper showcases a subsystem with the ability to monitor human Physiological Feedback during a human robot collaboration task.