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  • Forced Oscillations, All Data.xlsx... Oscillation... Frequency
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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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  • The relation between seizure frequency per month and number of channels with (A) ripples (>1/min), (B) fast ripples (>1/min), and (C) more than 20 fast ripples per minute. There were no patients with 0 channels with ripples (>1/min; A), but there were patients with 0 channels with fast ripples (>1 or >20/min; B and C). The seizure frequency was shown on a logarithmic scale, because of the distribution. As indicated in the text, there was no correlation between seizure frequency per month and the number of channels with more than 1 ripple or fast ripple per minute, but there was a positive correlation between seizure frequency and more than 20 fast ripples per minute. ... This table shows the correlation coefficients Rho for different alternative comparisons: seizure frequency (seizures/month) compared to the number and percentage of channels with ripples, fast ripples, spikes and ripples and fast ripples without spikes (first two lines), seizure frequency compared to number of channels with higher rates of ripples and fast ripples (>5, >10 and >20, lines 3–5) and number of seizure-days/month compared to channels with ripples and fast ripples. All comparisons were done for all patients, all patients with temporal lobe epilepsy and all patients with unilateral mesiotemporal seizure onset.
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  • Channels with potential muscle artefacts were excluded from the analysis. This was done by reviewing the SEEG at normal time scale with a filter of 80Hz together with the available epidural, ECG and EMG channels. Muscle artifact can be recognized as a simultaneous high frequency artifact over channels that are potentially outside of the brain, like channels LS6-7 and above, LC5-6 and above and RS5-6 and above in this example. Another clue could be obtained by filtering at lower frequencies as well. If still in doubt, the signal was reviewed at a timescale showing all samples. Muscle artifact shows a less sinusoid shape than HFOs and the frequency spectrum shows relatively more frequencies (Otsubo et al., 2008). Whenever there was doubt, the channel was excluded. ... High frequency oscillations
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  • RANKL-induces Ca2+ oscillations and transient cation currents in RAW 264.7 cells. RAW 264.7 cells were cultured with or without RANKL (30ng/ml) or GST-RANKL (20ng/ml) for 18h and intracellular Ca2+ concentration ([Ca2+]i) and membrane currents were recorded. (A) Spontaneous Ca2+ oscillations in six cells treated with RANKL for 18h were reversibly inhibited by an application of ruthenium red (5μM RR), an inhibitor of TRPV channels. Traces shown in the panel were obtained from six independent cells. (B) Average frequency of [Ca2+]i oscillations (times per 10min) before (control; n=10) and after application of ruthenium red (RR, 5μM; n=10). Each column indicates mean±SEM. Number of cells studied is indicated in parentheses. **PFrequency (times per 1min) of transient inward currents (only counting those with an amplitudes of more than 2pA/pF) before and after application of ruthenium red (RR, 10μM). Each column indicates mean±SEM from number of cells (n) studied. **P<0.01. ... Inhibition of RANKL-induced Ca2+ oscillations by tetracycline-inducible shRNA silencing targeted to store-operated Ca2+ entry associated proteins in RAW264.7/teton/shStim1 or/shOrai1 cells. (A) and (B) RAW264.7/teton/shStim1 (A) or/shOrai1 (B) cells were incubated for 24h in the absence (a) or presence (b) of tetracycline (1μg/ml) and then treated with RANKL (30ng/ml) for 18h, and [Ca2+]i was measured. The changes in [Ca2+]i shown in each graph were simultaneously recorded from four or five cells. Expression of Stim1 (A) or Orai1 (B) protein was reduced in tetracycline treated (tetracycline +) cells as compared to untreated (tetracycline −) cells. ... Effects of PLC inhibitor, U73122, on RANKL-induced [Ca2+]i oscillations and transiently activated cation currents in RAW 264.7/teton/shRNA/TRPV2 cells. Cells treated with RANKL for 18h in the absence of tetracycline were exposed to a phosphlipase C inhibitor, U73343 and its inactive analogue (U73122). (A) Effects of U73343 (10μM) and U73122 (10μM) on Ca2+ oscillations. Recordings were obtained from four cells. (B) Frequency of [Ca2+]i oscillations (times per 10min) before (control; n=12) and 10min after treatment with U73343 (n=5) or U73122 (n=10). Each column indicates mean±SEM. Number of cells studied is indicated in parentheses. **PFrequency (times per 1min) of the transiently activated inward currents (only counting those with amplitude of more than 2pA/pF), before and 15min after treatment with U73343 or U73122. Each column indicates mean±SEM from number of cells (n) studied. **P<0.01. ... Calcium oscillations... Inhibition of RANKL-induced transient activation of cation currents by TRPV2 silencing in RAW 264.7/teton/shTRPV2 cells. RAW 264.7/teton/shTRPV2 cells were cultured with RANKL (30ng/ml) for 18h in the absence (A) or presence (B) of tetracycline (1μg/ml) or doxycycline (10ng/ml) and whole-cell currents (at a membrane potential of −60mV) were recorded using the whole-cell configuration of patch-clamp technique. (C) Frequency (times per 1min) of the transiently activated inward currents (only counting those with amplitudes of more than 2pA/pF) was determined in cells treated with (tetracycline +, closed columns) or without tetracycline (tetracycline −, open columns). Each column indicates mean±SEM. Number of cells studied is indicated in parentheses. **P<0.01. ... Inhibition of RANKL-induced Ca2+ oscillations by tetracycline-inducible shRNA silencing targeted to TRPV2 in RAW264.7/teton/shTRPV2 cells. RAW264.7/teton/shTRPV2 cells were incubated for 24h in the absence (A) or presence (B) of tetracycline (1μg/ml) or doxycycline (10ng/ml) and then treated with RANKL (30ng/ml) for 18h, and their [Ca2+]i was measured. Changes in [Ca2+]i shown in each graph were simultaneously recorded from four cells. (C) Expression of TRPV2 protein was reduced in tetracycline treated (tetracycline +) cells as compared to untreated (tetracycline −) cells. (D) Mean frequency of [Ca2+]i oscillations in cells before (0h) and 18h or 48h after RANKL-treatment in the presence (tetracycline +, closed columns) or absence (tetracycline −, open columns) of tetracycline. Each column indicates mean±SEM. Number of cells studied is indicated in parentheses. **P<0.01.
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  • Critical frequency for the one-stage gene circuit. (A) The amplitude of output oscillations decreased with fin. fc was calculated as the intersection between the “average noise level” curve and the “oscillation amplitude” curve. (B) Calculations of fout for varying fin using stochastic simulations. (C) Fraction of stochastic simulations that generated correct fout (i.e., where fout=fin). ... Analysis of frequency signals with noise. (A) A one-stage gene circuit where the output protein P is controlled by a transcription activator, A. (B) An oscillatory input signal can generate an output signal with oscillations compounded with noise. The mean and standard deviation of the output signal of the linearized model can be analytically computed. Here, we define the mean value as the oscillatory component and the standard deviation as the noise component. Alternatively, the stochastic simulations of the output signal for the nonlinear system can be analyzed by the FFT method to obtain its dominant frequency (see Methods for more details). ... Transmission of a multiplexed signal. (A) A multiplexed input signal. (B) The corresponding output signal computed by stochastic simulation. (C) Power spectra of the input signal. (D) Power spectra of the output signal. Power spectra of the output signal indicate that all three frequencies were transmitted with complete fidelity. Even though power spectra decreased when the input frequency increased, they were still at least 10-fold higher than the power spectra of background noise. Three frequencies (0.005/min, 0.0067/min, and 0.01/min) were multiplexed in a composite signal with an amplitude of five molecules for each input frequency.
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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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  • Frequency domain spectra of the C2 samples with and without degassing O2: (1) 0–2.048μs, (2) 2.048–4.092μs, (3) 4.092–6.136μs, (4) 5.952–8.000μs (decay at 470nm). ... The relation curves between frequency spectrum peak height and the timing course from the decays at 520nm (open square) and at 530nm (solid circle). The unit in ordinate was regulated and in abscissa 512ns for convenience and simplicity (left); the relation curves between frequency change of the top peak and timing course from the decays at 520nm (open square) and at 530nm (solid circle). The unit in ordinate was regulated and in abscissa 512ns for convenience and simplicity (right). ... (a) The transient absorption kinetic curve with the abnormal signals of the C1 compound at 500nm (measured in 2008) and corresponding to the frequency-domain spectra at each time period: (1) 0–1.024μs, (2) 1.024–2.046μs, (3) 16.932–17.954μs, (4) 17.954–18.976μs, (5) 18.976–19.998μs. (b) The transient absorption kinetic curve with the abnormal signals of the C1 compound at 470nm (measured in July 2012) and corresponding to the frequency-domain spectra at each time period: (1) 0–2.048μs, (2) 2.048–4.092μs, (3) 3.956–6.000μs, (4) 23.132–25.176μs, (5) 31.308–33.352μs, (6) 35.396–37.440μs.
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  • As current gravitational wave (GW) detectors increase in sensitivity, and particularly as new instruments are being planned, there is the possibility that ground-based GW detectors will observe GWs from highly eccentric neutron star binaries. We present the first detailed study of highly eccentric BNS systems with full (3+1)D numerical relativity simulations using consistent initial conditions, i.e., setups which are in agreement with the Einstein equations and with the equations of general relativistic hydrodynamics in equilibrium. Overall, our simulations cover two different equations of state (EOSs), two different spin configurations, and three to four different initial eccentricities for each pairing of EOS and spin. We extract from the simulated waveforms the frequency of the f-mode oscillations induced during close encounters before the merger of the two stars. The extracted frequency is in good agreement with f-mode oscillations of individual stars for the irrotational cases, which allows an independent measure of the supranuclear equation of state not accessible for binaries on quasicircular orbits. The energy stored in these f-mode oscillations can be as large as 10−3  M⊙∼1051  erg, even with a soft EOS. In order to estimate the stored energy, we also examine the effects of mode mixing due to the stars’ offset from the origin on the f-mode contribution to the GW signal. While in general (eccentric) neutron star mergers produce bright electromagnetic counterparts, we find that for the considered cases with fixed initial separation the luminosity decreases when the eccentricity becomes too large, due to a decrease of the ejecta mass. Finally, the use of consistent initial configurations also allows us to produce high-quality waveforms for different eccentricities which can be used as a test bed for waveform model development of highly eccentric binary neutron star systems.
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