The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform

J P Mann - One of the best experts on this subject based on the ideXlab platform.

  • short communication a gis tool for modeling anthropogenic Noise Propagation in natural ecosystems
    Environmental Modelling and Software, 2012
    Co-Authors: S E Reed, John L. Boggs, J P Mann
    Abstract:

    SPreAD-GIS is a tool for modeling spatial patterns of anthropogenic Noise Propagation in natural ecosystems. SPreAD-GIS incorporates commonly available datasets on land cover, topography, and weather conditions to calculate Noise Propagation patterns and excess Noise above ambient conditions for one-third octave frequency bands around one or multiple sound sources. User-specified Noise source characteristics, ambient sound conditions, and frequency-weighting make SPreAD-GIS flexible to incorporate field measurements and model Noise Propagation for any type of source, environment, or species. SPreAD-GIS is a free, open-source application written in Python and implemented as a toolbox in ArcGIS software.

  • A GIS tool for modeling anthropogenic Noise Propagation in natural ecosystems
    Environmental Modelling and Software, 2012
    Co-Authors: S E Reed, John L. Boggs, J P Mann
    Abstract:

    SPreAD-GIS is a tool for modeling spatial patterns of anthropogenic Noise Propagation in natural ecosystems. SPreAD-GIS incorporates commonly available datasets on land cover, topography, and weather conditions to calculate Noise Propagation patterns and excess Noise above ambient conditions for one-third octave frequency bands around one or multiple sound sources. User-specified Noise source characteristics, ambient sound conditions, and frequency-weighting make SPreAD-GIS flexible to incorporate field measurements and model Noise Propagation for any type of source, environment, or species. SPreAD-GIS is a free, open-source application written in Python and implemented as a toolbox in ArcGIS software. © 2012 Elsevier Ltd.

Tetsu Tanaka - One of the best experts on this subject based on the ideXlab platform.

  • Noise Propagation through TSV in Mixed-Signal 3D-IC and Investigation of Liner Interface with Multi-Well Structured TSV
    2019 Electron Devices Technology and Manufacturing Conference (EDTM), 2019
    Co-Authors: Hisashi Kino, Takafumi Fukushima, Tetsu Tanaka
    Abstract:

    The effect of Noise Propagation from a digital circuit on an analog circuit was evaluated using an actual mixed-signal 3D-IC, which has a stacked structure of digital and analog IC chips. The Noise Propagation through the TSV was measured with a ring-oscillator as a Noise source. To investigate in detail, TSV-liner interface states were evaluated along depth direction using unique multi-well-structured TSVs and charge-pumping method. It was considered that the interface traps and non-conformal thickness of TSV liner increased the Noise Propagation among stacked chips.

  • Noise Propagation through TSV in Mixed-Signal 3D-IC and Investigation of Liner Interface with Multi-Well Structured TSV
    2019 Electron Devices Technology and Manufacturing Conference (EDTM), 2019
    Co-Authors: Hisashi Kino, Takafumi Fukushima, Tetsu Tanaka
    Abstract:

    The effect of Noise Propagation from a digital circuit on an analog circuit was evaluated using an actual mixed-signal 3D-IC, which has a stacked structure of digital and analog IC chips. The Noise Propagation through the TSV was measured with a ring-oscillator as a Noise source. To investigate in detail, TSV-liner interface states were evaluated along depth direction using unique multi-well-structured TSVs and charge-pumping method. It was considered that the interface traps and non-conformal thickness of TSV liner increased the Noise Propagation among stacked chips. (Keywords: 3D-IC, TSV, Noise, Mixed signal)

  • Investigation of TSV Liner Interface With Multiwell Structured TSV to Suppress Noise Propagation in Mixed-Signal 3D-IC
    IEEE Journal of the Electron Devices Society, 2019
    Co-Authors: Hisashi Kino, Takafumi Fukusima, Tetsu Tanaka
    Abstract:

    Mixed-signal 3D-ICs have a stacked structure of digital and analog circuit chips. In this study, the effect of Noise Propagation from a digital circuit on an analog circuit was evaluated using an actual mixed-signal 3D-IC. The Noise Propagation via through-silicon vias (TSVs) was measured, with a ring-oscillator as a Noise source. For a comprehensive investigation, TSV-liner interface states were evaluated along the depth direction using unique multiwell-structured TSVs and a charge-pumping method. It was considered that the interface traps and nonconformal thickness of the TSV liner increased the Noise Propagation among stacked chips.

S. Hooshangi - One of the best experts on this subject based on the ideXlab platform.

  • Sensitivity and Noise Propagation in complex synthetic gene networks
    2020
    Co-Authors: Ron Weiss, S. Hooshangi
    Abstract:

    The precise nature of information flow through a biological network, which is governed by factors such as response sensitivities and Noise Propagation, greatly affects the operation of biological systems. Quantitative analysis of these properties is often difficult in naturally occurring systems, but can be greatly facilitated by studying simple synthetic networks. In this thesis, I report the construction of a library of synthetic gene networks and analyze response sensitivity and Noise Propagation as a function of network complexity. First, I study a series of transcriptional cascades. I demonstrate experimentally steady state switching behavior that becomes sharper with longer cascades. The regulatory mechanisms that confer this ultrasensitive response both attenuate and amplify phenotypical variations depending on the system's input conditions. While Noise attenuation allows the cascade to act as a low-pass filter by rejecting short-lived perturbations in input conditions, Noise amplification results in loss of synchrony among a cell population. The experimental results correlate well with the simulations of a mathematical model of the system. To further investigate the effect of network topology on system behavior, I develop stochastic models to analyze how the strength and delay of negative feedback affect Noise Propagation and synchrony within a cell population. This analysis indicates that incorporating negative autoregulation to multi-stage transcriptional cascades does not attenuate Noise when compared to the original unregulated networks. On the other hand, delayed negative feedback can give rise to oscillatory behavior, a desirable trait for certain biological processes. The effect of autoregulation on response and Noise behavior of one stage and two stage cascades are then experimentally tested. I observe that the role of negative autoregulation in controlling Noise behavior is a complex matter. While a highly regulated system attenuates Noise, an increase in Noise levels is seen in intermediate autoregulatory strengths. These findings reinforce the notion that Noise Propagation within transcriptional networks is dependent on network topology in a complex fashion and should therefore always be studied within the context of the overall network architecture.

  • the effect of negative feedback on Noise Propagation in transcriptional gene networks
    Chaos, 2006
    Co-Authors: S. Hooshangi, Ron Weiss
    Abstract:

    This paper analyzes how the delay and repression strength of negative feedback in single-gene and multigene transcriptional networks influences intrinsic Noise Propagation and oscillatory behavior. We simulate a variety of transcriptional networks using a stochastic model and report two main findings. First, intrinsic Noise is not attenuated by the addition of negative or positive feedback to transcriptional cascades. Second, for multigene negative feedback networks, synchrony in oscillations among a cell population can be improved by increasing network depth and tightening the regulation at one of the repression stages. Our long term goal is to understand how the Noise characteristics of complex networks can be derived from the properties of modules that are used to compose these networks.

  • CSB Workshops - Noise Propagation in transcriptional cascades
    2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05), 2005
    Co-Authors: S. Hooshangi, S. Thiberge, R. Weis
    Abstract:

    The flow of information through a biological network can greatly influence the operation and behavior of the system. The synthetic transcriptional cascades of various lengths and study of their dynamic and steady state behavior both experimentally and through a stochastic model are presented. These systems are used to analyze sensitivity and Noise Propagation as a function of synthetic network complexity. The steady state switching behavior that becomes sharper with longer cascades is demonstrated experimentally. The experimental results demonstrating the network properties correlate well with the simulated model.

  • ultrasensitivity and Noise Propagation in a synthetic transcriptional cascade
    Proceedings of the National Academy of Sciences of the United States of America, 2005
    Co-Authors: S. Hooshangi, S. Thiberge, Ron Weiss
    Abstract:

    The precise nature of information flow through a biological network, which is governed by factors such as response sensitivities and Noise Propagation, greatly affects the operation of biological systems. Quantitative analysis of these properties is often difficult in naturally occurring systems but can be greatly facilitated by studying simple synthetic networks. Here, we report the construction of synthetic transcriptional cascades comprising one, two, and three repression stages. These model systems enable us to analyze sensitivity and Noise Propagation as a function of network complexity. We demonstrate experimentally steady-state switching behavior that becomes sharper with longer cascades. The regulatory mechanisms that confer this ultrasensitive response both attenuate and amplify phenotypical variations depending on the system's input conditions. Although Noise attenuation allows the cascade to act as a low-pass filter by rejecting short-lived perturbations in input conditions, Noise amplification results in loss of synchrony among a cell population. The experimental results demonstrating the above network properties correlate well with simulations of a simple mathematical model of the system.

  • Noise Propagation in transcriptional cascades
    2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05), 2005
    Co-Authors: S. Hooshangi, S. Thiberge, R. Weis
    Abstract:

    The flow of information through a biological network can greatly influence the operation and behavior of the system. The synthetic transcriptional cascades of various lengths and study of their dynamic and steady state behavior both experimentally and through a stochastic model are presented. These systems are used to analyze sensitivity and Noise Propagation as a function of synthetic network complexity. The steady state switching behavior that becomes sharper with longer cascades is demonstrated experimentally. The experimental results demonstrating the network properties correlate well with the simulated model.

S E Reed - One of the best experts on this subject based on the ideXlab platform.

  • short communication a gis tool for modeling anthropogenic Noise Propagation in natural ecosystems
    Environmental Modelling and Software, 2012
    Co-Authors: S E Reed, John L. Boggs, J P Mann
    Abstract:

    SPreAD-GIS is a tool for modeling spatial patterns of anthropogenic Noise Propagation in natural ecosystems. SPreAD-GIS incorporates commonly available datasets on land cover, topography, and weather conditions to calculate Noise Propagation patterns and excess Noise above ambient conditions for one-third octave frequency bands around one or multiple sound sources. User-specified Noise source characteristics, ambient sound conditions, and frequency-weighting make SPreAD-GIS flexible to incorporate field measurements and model Noise Propagation for any type of source, environment, or species. SPreAD-GIS is a free, open-source application written in Python and implemented as a toolbox in ArcGIS software.

  • A GIS tool for modeling anthropogenic Noise Propagation in natural ecosystems
    Environmental Modelling and Software, 2012
    Co-Authors: S E Reed, John L. Boggs, J P Mann
    Abstract:

    SPreAD-GIS is a tool for modeling spatial patterns of anthropogenic Noise Propagation in natural ecosystems. SPreAD-GIS incorporates commonly available datasets on land cover, topography, and weather conditions to calculate Noise Propagation patterns and excess Noise above ambient conditions for one-third octave frequency bands around one or multiple sound sources. User-specified Noise source characteristics, ambient sound conditions, and frequency-weighting make SPreAD-GIS flexible to incorporate field measurements and model Noise Propagation for any type of source, environment, or species. SPreAD-GIS is a free, open-source application written in Python and implemented as a toolbox in ArcGIS software. © 2012 Elsevier Ltd.

Ron Weiss - One of the best experts on this subject based on the ideXlab platform.

  • Sensitivity and Noise Propagation in complex synthetic gene networks
    2020
    Co-Authors: Ron Weiss, S. Hooshangi
    Abstract:

    The precise nature of information flow through a biological network, which is governed by factors such as response sensitivities and Noise Propagation, greatly affects the operation of biological systems. Quantitative analysis of these properties is often difficult in naturally occurring systems, but can be greatly facilitated by studying simple synthetic networks. In this thesis, I report the construction of a library of synthetic gene networks and analyze response sensitivity and Noise Propagation as a function of network complexity. First, I study a series of transcriptional cascades. I demonstrate experimentally steady state switching behavior that becomes sharper with longer cascades. The regulatory mechanisms that confer this ultrasensitive response both attenuate and amplify phenotypical variations depending on the system's input conditions. While Noise attenuation allows the cascade to act as a low-pass filter by rejecting short-lived perturbations in input conditions, Noise amplification results in loss of synchrony among a cell population. The experimental results correlate well with the simulations of a mathematical model of the system. To further investigate the effect of network topology on system behavior, I develop stochastic models to analyze how the strength and delay of negative feedback affect Noise Propagation and synchrony within a cell population. This analysis indicates that incorporating negative autoregulation to multi-stage transcriptional cascades does not attenuate Noise when compared to the original unregulated networks. On the other hand, delayed negative feedback can give rise to oscillatory behavior, a desirable trait for certain biological processes. The effect of autoregulation on response and Noise behavior of one stage and two stage cascades are then experimentally tested. I observe that the role of negative autoregulation in controlling Noise behavior is a complex matter. While a highly regulated system attenuates Noise, an increase in Noise levels is seen in intermediate autoregulatory strengths. These findings reinforce the notion that Noise Propagation within transcriptional networks is dependent on network topology in a complex fashion and should therefore always be studied within the context of the overall network architecture.

  • the effect of negative feedback on Noise Propagation in transcriptional gene networks
    Chaos, 2006
    Co-Authors: S. Hooshangi, Ron Weiss
    Abstract:

    This paper analyzes how the delay and repression strength of negative feedback in single-gene and multigene transcriptional networks influences intrinsic Noise Propagation and oscillatory behavior. We simulate a variety of transcriptional networks using a stochastic model and report two main findings. First, intrinsic Noise is not attenuated by the addition of negative or positive feedback to transcriptional cascades. Second, for multigene negative feedback networks, synchrony in oscillations among a cell population can be improved by increasing network depth and tightening the regulation at one of the repression stages. Our long term goal is to understand how the Noise characteristics of complex networks can be derived from the properties of modules that are used to compose these networks.

  • ultrasensitivity and Noise Propagation in a synthetic transcriptional cascade
    Proceedings of the National Academy of Sciences of the United States of America, 2005
    Co-Authors: S. Hooshangi, S. Thiberge, Ron Weiss
    Abstract:

    The precise nature of information flow through a biological network, which is governed by factors such as response sensitivities and Noise Propagation, greatly affects the operation of biological systems. Quantitative analysis of these properties is often difficult in naturally occurring systems but can be greatly facilitated by studying simple synthetic networks. Here, we report the construction of synthetic transcriptional cascades comprising one, two, and three repression stages. These model systems enable us to analyze sensitivity and Noise Propagation as a function of network complexity. We demonstrate experimentally steady-state switching behavior that becomes sharper with longer cascades. The regulatory mechanisms that confer this ultrasensitive response both attenuate and amplify phenotypical variations depending on the system's input conditions. Although Noise attenuation allows the cascade to act as a low-pass filter by rejecting short-lived perturbations in input conditions, Noise amplification results in loss of synchrony among a cell population. The experimental results demonstrating the above network properties correlate well with simulations of a simple mathematical model of the system.