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  • Forced Oscillations, All Data.xlsx... Oscillation... Frequency
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  • Spreadsheets and Tables in .csv, .mat, .xls used for the statistical analyses in Waldman et al., Pathological high-frequency oscillations disrupt verbal memory encoding. The statistical analysis can be reproduced using the code available on https://github.com/shennanw/waldman_RAM/. Please contact shennan.weiss@jefferson.edu for additional data requests. The intracranial EEG recordings used for this study can be obtained at http://memory.psych.upenn.edu/RAM_Public_Data.
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  • Spreadsheets and Tables in .csv, .mat, .xls used for the statistical analyses in Waldman et al., Pathological high-frequency oscillations disrupt verbal memory encoding. The statistical analysis can be reproduced using the code available on https://github.com/shennanw/waldman_RAM/. Please contact shennan.weiss@jefferson.edu for additional data requests. The intracranial EEG recordings used for this study can be obtained at http://memory.psych.upenn.edu/RAM_Public_Data.
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    • Software/Code
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  • We present the code for an extended heterogeneous oscillator model of cardiac conduction system for generation of realistic 12 lead ECG waveforms. We incorporate an artificial RR-tachogram with the specific statistics of a heart rate, the frequency-domain characteristics of heart rate variability produced by Mayer and respiratory sinus arrhythmia waves, normally distributed additive noise and a baseline wander that couple the respiratory frequency. The standard 12 lead ECG is calculated by means of a weighted linear combination of atria and ventricle signals and thus can be fitted to clinical ECG of real subject. The model is capable to simulate accurately realistic ECG characteristics including local pathological phenomena accounting for biophysical properties of the human heart.
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  • Comparison of 3 oscillating elevations in cytosolic Ca2+ (created using electrical stimulation and measured using aequorin luminescence) in Arabidopsis seedlings. Treatment 1; high frequency high amplitude osc., Treatment 2; high frequency low amplitude osc., Treatment 3; low frequency low amplitude osc. One biological sample per experiment processed as technical dye swaps against intreated control.
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  • Oscillation detection in a single electrode with weak alpha. The electrode was selected from the same subject as in Figs. 2 and 4. (A) The 256-electrode array with the selected electrode highlighted in yellow. (B) Background wavelet power spectrum mean and standard deviation (blue), and the linear regression fit to the background (green). (C) Oscillations detected across all frequencies by the oscillatory episode detection method. Red vertical lines indicate when participants were instructed to close their eyes and black vertical lines indicate when participants were instructed to open their eyes. (D) The proportion of time (Pepisode) during the eyes -closed condition (red) and eyes-open condition (black) that oscillations were detected at each frequency. (E) The raw signal from the chosen electrode, with detected oscillations at the peak alpha frequency (9.5Hz) highlighted in red. Vertical lines are the same as above. (F) An expansion of the highlighted section in E, to show the spindle-like appearance of the alpha oscillation. ... Temporal independence of two alpha components. (A) An 8-s epoch from the alpha component shown in Fig. 2, with detected alpha-frequency oscillations highlighted in red. (B) The same time segment as in A, from the alpha component in Fig. 6. Note the alpha oscillation is maximal in B when the oscillation is at a minimum in A, demonstrating why these were extracted as temporally independent components. ... Lateralized alpha component. From the same subject as Figs. 2 and 4–5. (A) The spline-interpolated scalp distribution of an alpha component extracted by ICA. Color scale denotes electrode weight (unitless). (B) Background wavelet power spectrum mean and standard deviation (blue), and the linear regression fit to the background (green). (C) Oscillations detected across all frequencies by the oscillatory episode detection method. Red vertical lines indicate when participants were instructed to close their eyes and black vertical lines indicate when participants were instructed to open their eyes. (D) The proportion of time (Pepisode) during the eyes-closed condition (red) and eyes-open condition (black) that oscillations were detected at each frequency. (E) The time-domain representation of the chosen component, with detected oscillations at the peak alpha frequency (9.5Hz) highlighted in red. Vertical lines are the same as above. (F) An expansion of the highlighted section in E. ... Oscillation detection in an ICA alpha component. (A) The spline-interpolated scalp distribution of an alpha component extracted by ICA. Color scale denotes electrode weight (unitless). (B) Background wavelet power spectrum mean and standard deviation (blue) and the linear regression fit to the background (green). (C) Oscillations detected across all frequencies by the oscillatory episode detection method. Red vertical lines indicate when participants were instructed to close their eyes and black vertical lines indicate when participants were instructed to open their eyes. (D) The proportion of time (Pepisode) during the eyes-closed condition (red) and eyes-open condition (black) that oscillations were detected at each frequency. (E) The time-domain representation of the chosen component, with detected oscillations at the peak alpha frequency (9.5Hz) highlighted in red. Vertical lines are the same as above. (F) An expansion of the highlighted section in E, to show the spindle-like appearance of the alpha oscillation. ... Oscillation... Oscillation detection in a single electrode with strong alpha. The electrode was selected from the same subject as in Fig. 2. (A) The 256-electrode array with the selected electrode highlighted in yellow. (B) Background wavelet power spectrum mean and standard deviation (blue), and the linear regression fit to the background (green). (C) Oscillations detected across all frequencies by the oscillatory episode detection method. Red vertical lines indicate when participants were instructed to close their eyes and black vertical lines indicate when participants were instructed to open their eyes. (D) The proportion of time (Pepisode) during the eyes-closed condition (red) and eyes-open condition (black) that oscillations were detected at each frequency. (E) The raw signal from the chosen electrode, with detected oscillations at the peak alpha frequency (9.5Hz) highlighted in red. Vertical lines are the same as above. (F) An expansion of the highlighted section in E to show the spindle-like appearance of the alpha oscillation.
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  • Effective oscillator strength distribution... Schematic of the dipole, quadrupole and octupole transitions to pseudo-states showing only the core excitations of Na. The 1s22s22p6 core electrons are assumed to only excite to the pseudo-states np˜, nd˜ and nf˜ with energies Δ(1), Δ(2) and Δ(3) via dipole, quadrupole and octupole transitions respectively. The oscillator strengths are fc(1), fc(2) and fc(3) respectively. ... The dipole, quadrupole and octupole effective oscillator strength distributions. See explanation of tables. ... Oscillator strength sum- rule... Convergence of the Cn dispersion parameters (in a.u.) for lithium dimer. The parameters are calculated using effective oscillator strength distributions with different sizes. Ne gives the number of effective oscillator strengths that were adopted. The ‘exact’ results were calculated using Eq. (9). We thus adopt the Ne=3 set of effective oscillator strengths, i.e. for each multipole (fe1(ℓ),ϵe1(ℓ),fe2(ℓ),ϵe2(ℓ), fe3(ℓ),ϵe3(ℓ)), which is given later in the paper. ... The effective oscillator strength distributions for all atoms or ions. k is the order of multipole. E is the transition energy. F is the oscillator strength.
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  • Plasma Vortex Theory is the engineered application of known sciences to create efficient velocity during spaceflight using electricity and propellant gas. Oscillation of granulate and liquid reagents using simple harmonic motion has been shown to excite particles to form geometric patterns when using calibrated frequencies discovered by the late Dr. Hans Jenny. Calibration methods will be used to attain vortex formations in the reagents Lycopodium, Sulfur Hexafluoride, CO2 and Xenon. Frequencies which form vortex patterns in Lycopodium powder using known methods will be used to excite Sulfur Hexafluoride (density 6.17 kg/m3), at incremental partial pressures. Air-filled mass objects will be used to observe acceleration, force and velocity data for a dense gas during oscillation and vortex formation. Xenon gas (density 5.761 kg/m3) will be ionized by external electrode field before, during and after vortex formations are created using acoustic measures.
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