The Experts below are selected from a list of 71841 Experts worldwide ranked by ideXlab platform

Simpson, André J. - One of the best experts on this subject based on the ideXlab platform.

  • 1D “Spikelet” Projections from Heteronuclear 2D NMR Data—Permitting 1D Chemometrics While Preserving 2D Dispersion
    2019
    Co-Authors: Tabatabaei Anaraki Maryam, Bermel Wolfgang, Dutta Majumdar Rudraksha, Soong Ronald, Simpson Myrna, Monnette Martine, Simpson, André J.
    Abstract:

    Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for the non-targeted metabolomics of intact biofluids and even living organisms. However, spectral overlap can limit the information that can be obtained from 1D 1H NMR. For example, magnetic susceptibility broadening in living organisms prevents any metabolic information being extracted from solution-state 1D 1H NMR. Conversely, the additional spectral dispersion afforded by 2D 1H-13C NMR allows a wide range of metabolites to be assigned in-vivo in 13C enriched organisms, as well as a greater depth of information for biofluids in general. As such, 2D 1H-13C NMR is becoming more and more popular for routine metabolic screening of very complex samples. Despite this, there are only a very limited number of statistical software packages that can handle 2D NMR datasets for chemometric analysis. In comparison, a wide range of commercial and free tools are available for analysis of 1D NMR datasets. Overtime, it is likely more software solutions will evolve that can handle 2D NMR directly. In the meantime, this Application Note offers a simple alternative solution that converts 2D 1H-13C Heteronuclear Single Quantum Correlation (HSQC) data into a 1D “spikelet” format that preserves not only the 2D spectral information, but also the 2D dispersion. The approach allows 2D NMR data to be converted into a standard 1D Bruker format that can be read by software packages that can only handle 1D NMR data. This Application Note uses data from Daphnia magna (water fleas) in-vivo to demonstrate how to generate and interpret the converted 1D spikelet data from 2D datasets, including the code to perform the conversion on Bruker spectrometers

Andre J Simpson - One of the best experts on this subject based on the ideXlab platform.

  • 1d spikelet projections from heteronuclear 2d nmr data permitting 1d chemometrics while preserving 2d dispersion
    Metabolites, 2019
    Co-Authors: Maryam Tabatabaei Anaraki, Wolfgang Bermel, Rudraksha Dutta Majumdar, Ronald Soong, Myrna J Simpson, Martine Monnette, Andre J Simpson
    Abstract:

    Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for the non-targeted metabolomics of intact biofluids and even living organisms. However, spectral overlap can limit the information that can be obtained from 1D 1H NMR. For example, magnetic susceptibility broadening in living organisms prevents any metabolic information being extracted from solution-state 1D 1H NMR. Conversely, the additional spectral dispersion afforded by 2D 1H-13C NMR allows a wide range of metabolites to be assigned in-vivo in 13C enriched organisms, as well as a greater depth of information for biofluids in general. As such, 2D 1H-13C NMR is becoming more and more popular for routine metabolic screening of very complex samples. Despite this, there are only a very limited number of statistical software packages that can handle 2D NMR datasets for chemometric analysis. In comparison, a wide range of commercial and free tools are available for analysis of 1D NMR datasets. Overtime, it is likely more software solutions will evolve that can handle 2D NMR directly. In the meantime, this Application Note offers a simple alternative solution that converts 2D 1H-13C Heteronuclear Single Quantum Correlation (HSQC) data into a 1D “spikelet” format that preserves not only the 2D spectral information, but also the 2D dispersion. The approach allows 2D NMR data to be converted into a standard 1D Bruker format that can be read by software packages that can only handle 1D NMR data. This Application Note uses data from Daphnia magna (water fleas) in-vivo to demonstrate how to generate and interpret the converted 1D spikelet data from 2D datasets, including the code to perform the conversion on Bruker spectrometers.

Frank Wheatley - One of the best experts on this subject based on the ideXlab platform.

  • Application Note 7534 a new pspice electro thermal subcircuit for power mosfets
    2004
    Co-Authors: Alain Laprade, Scott Pearson, Stan Benczkowski, Gary Dolny, Frank Wheatley
    Abstract:

    An empirical self-heating SPICE MOSFET model which accurately portrays the vertical DMOS power MOSFET electrical and thermal responses is presented. This macro-model implementation is the culmination of years of evolution in MOSFET modeling. This new version brings together the thermal and the electrical models of a VDMOS MOSFET. The existing electrical model [2,3] is highly accurate and is recognized in the industry. The sequence of the model calibration procedure using parametric data is described. Simulation response of the new self-heating MOSFET model track the dynamic thermal response and is independent of SPICE’s global temperature definition.

  • Application Note 7532 a new pspice electro thermal subcircuit for power mosfets
    2003
    Co-Authors: Alain Laprade, Scott Pearson, Stan Benczkowski, Gary Dolny, Frank Wheatley
    Abstract:

    An empirical self-heating SPICE MOSFET model which accurately portrays the vertical DMOS power MOSFET electrical and thermal responses is presented. This macro-model implementation is the culmination of years of evolution in MOSFET modeling. This new version brings together the thermal and the electrical models of a VDMOS MOSFET. The existing electrical model [2,3] is highly accurate and is recognized in the industry. The sequence of the model calibration procedure using parametric data is described. Simulation response of the new self-heating MOSFET model track the dynamic thermal response and is independent of SPICE’s global temperature definition. 1. Introduction Many power MOSFET models available today are based on an ideal lateral MOSFET device. They offer poor correlation between simulated and actual circuit performance in several areas. They have low and high current inaccuracies that could mislead power circuit designers. This situation is further complicated by the dynamic performance of the models. The ideal low power SPICE level-1 NMOS MOSFET model does not account for the nonlinear capacitive characteristics Ciss, Coss, Crss of a power MOSFET. Higher level SPICE MOSFET models may be used to implement the non-linear capacitance with mixed results. The need for this higher level modeling accuracy becomes apparent in high frequency Applications where gate charge losses as a proportion of overall losses become significant.

Hoffman, Michael M. - One of the best experts on this subject based on the ideXlab platform.

  • Segway 2.0 Application Note Datasets
    2018
    Co-Authors: Chan, Rachel C.w., Libbrecht, Maxwell W., Roberts, Eric G., Noble, William Stafford, Hoffman, Michael M.
    Abstract:

    Learned parameters and resulting segmentation corresponding to the analyses shown in the Segway 2.0 Application Note. Directory structure: GMM (datasets corresponding to the mixture of Gaussians analysis) 1-component traindir/ log/ (training log likelihood progression) params/ (learned parameters) identifydir/ segway.bed.gz (segmentation) 3-component traindir/ log/ (training log likelihood progression) params/ (learned parameters) identifydir/ segway.bed.gz (segmentation) minibatch-fixed (datasets corresponding to the minibatch learning analysis) fixed/ traindir/ log/ (training and validation log likelihood progression) params/ (learned parameters) minibatch/ traindir/ log/ (training and validation log likelihood progression) params/ (learned parameters)

  • Segway 2.0 Application Note Datasets
    2018
    Co-Authors: Chan, Rachel C.w., Libbrecht, Maxwell W., Roberts, Eric G., Noble, William Stafford, Bilmes, Jeffrey A., Hoffman, Michael M.
    Abstract:

    Learned parameters and resulting segmentation corresponding to the analyses shown in the Segway 2.0 Application Note. Directory structure: GMM (datasets corresponding to the mixture of Gaussians analysis) 1-component traindir/ log/ (training log likelihood progression) params/ (learned parameters) identifydir/ segway.bed.gz (segmentation) 3-component traindir/ log/ (training log likelihood progression) params/ (learned parameters) identifydir/ segway.bed.gz (segmentation) minibatch-fixed (datasets corresponding to the minibatch learning analysis) fixed/ traindir/ log/ (training and validation log likelihood progression) params/ (learned parameters) minibatch/ traindir/ log/ (training and validation log likelihood progression) params/ (learned parameters) TSS_prediction (datasets corresponding to the TSS prediction analysis) (where k=component number=1-5, n=random start number=1-10) outputs_[date]_k/ traindir/ log/ (training and validation log likelihood progression) params/ (learned parameters) identifydir_n/ segway.bed.gz (segmentation)

Tabatabaei Anaraki Maryam - One of the best experts on this subject based on the ideXlab platform.

  • 1D “Spikelet” Projections from Heteronuclear 2D NMR Data—Permitting 1D Chemometrics While Preserving 2D Dispersion
    2019
    Co-Authors: Tabatabaei Anaraki Maryam, Bermel Wolfgang, Dutta Majumdar Rudraksha, Soong Ronald, Simpson Myrna, Monnette Martine, Simpson, André J.
    Abstract:

    Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for the non-targeted metabolomics of intact biofluids and even living organisms. However, spectral overlap can limit the information that can be obtained from 1D 1H NMR. For example, magnetic susceptibility broadening in living organisms prevents any metabolic information being extracted from solution-state 1D 1H NMR. Conversely, the additional spectral dispersion afforded by 2D 1H-13C NMR allows a wide range of metabolites to be assigned in-vivo in 13C enriched organisms, as well as a greater depth of information for biofluids in general. As such, 2D 1H-13C NMR is becoming more and more popular for routine metabolic screening of very complex samples. Despite this, there are only a very limited number of statistical software packages that can handle 2D NMR datasets for chemometric analysis. In comparison, a wide range of commercial and free tools are available for analysis of 1D NMR datasets. Overtime, it is likely more software solutions will evolve that can handle 2D NMR directly. In the meantime, this Application Note offers a simple alternative solution that converts 2D 1H-13C Heteronuclear Single Quantum Correlation (HSQC) data into a 1D “spikelet” format that preserves not only the 2D spectral information, but also the 2D dispersion. The approach allows 2D NMR data to be converted into a standard 1D Bruker format that can be read by software packages that can only handle 1D NMR data. This Application Note uses data from Daphnia magna (water fleas) in-vivo to demonstrate how to generate and interpret the converted 1D spikelet data from 2D datasets, including the code to perform the conversion on Bruker spectrometers