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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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  • The resonant frequencies (fs and fp) of the FBAR before and after the immobilization of artificial antigens on the sensing Au surface. ... The dependence of parameter kobs on the MAb concentrations. The experimental points (mean values from five measurements) represent results of fitting of the time-dependent frequency profiles. ... The frequency spectrum of the FBAR oscillator after the PBS was injected into the testing channel. ... (a) The circuit diagrams and (b) an assembled circuitry of the FBAR oscillator. ... The typical time-dependent frequency profiles when the pure parathion MAb solution and the mixed solution of parathion and MAb were respectively injected into the testing channel. (a) working in PBS; (b) injection of the solution; (c) injection of glycine–HCl buffer; (d) another injection of PBS.
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  • Fig. S2. Power dissipation of 19-stage ring oscillators as a function of frequency. Frequency was measured at 10V operating voltage for 36 ring oscillators having different WN:WP channel width and different [PFBT]/[PT] ratio’s. ... (a) Micrograph of inkjet printed ring oscillators. (b) Stage delays of 19-stage complementary ring oscillators as a function of supply voltage VDD for different ratio’s of transistor widths of p-type (WP) and n-type (WN) transistors. The gold electrodes were modified using a 40/60 [PFBT]/[PT] SAM. Inset: oscillatory signal of 1:4, measured at a supply voltage of 20V with frequency of 2.6kHz.
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  • Effect of parameters on robust signal transfer to p-AKT in the presence of noise. For each plot, three values of the parameter are chosen based on the nominal condition. The input insulin oscillation is same as Fig. 7. (A) Effect of PTEN. PTEN is kept at 0.5, 1.0 and 1.5. (B) Effect of negative feedback parameter, Kd. Increasing strength of negative feedback leads to attenuation of output amplitude. (C) Effect of receptor activation rates. Increasing active receptor levels can lead to suppression of the signal due to saturation of the receptors. Note that all the profiles are normalized to the mean levels during the oscillations. ... Response of pathway outputs to different cycle periods in insulin stimulus. Four outputs are plotted here. (A) Surface p-IR, (B) p-IRS1 (Y), (C) p-IRS1 (S) and (D) p-AKT. Insulin levels are subjected to a cycle, modeled as a single square waveform. The duration of the waveform is varied and the resulting cycle in the output is measured. DT stands for duration of half of the cycle. For most cycle durations, the cycle is faithfully transmitted down the pathway, but for a duration of less than 20min, the cycle is no longer transmitted. This is seen as a flat curve where the level of the output remains stable at the pre-cycle steady state. Therefore, any oscillations with a frequency greater than 0.05min−1 (or period less than 20min) will be cut-off by the pathway (for the current waveform). All the results in this section are generated by numerical integration of the ODEs. (For interpretation of the references to color in the text, the reader is referred to the web version of the article.) ... Signal propagation in the pathway under noisy stimulus. (A) Insulin stimulus oscillations with time. Random normal noise is added to the main signal. The pathway is first allowed to reach steady state for insulin concentration of 10−9M. At 300min, the levels of insulin are subjected to oscillations with ω=0.01 and amplitude=0.9. (B) p-IR oscillations with time. (C) p-IRS1 (Y) and (S) oscillations with time (D) p-AKT oscillations with time. The output values are normalized to the mean value during the oscillations. The signal is propagated with high fidelity even in the presence of noise. The effect of noise is dominant during the down half of the cycle when the levels of molecules are low. ... Signal propagation in the pathway without noise. (A) Insulin stimulus oscillations with time. The pathway is first allowed to reach steady state for insulin concentration of 10−9M. At 300min, the levels of insulin are subjected to oscillations with ω=0.01 and amplitude of 0.9. (B) p-IR oscillations with time. (C) p-IRS1 (Y) and (S) oscillations with time (D) p-AKT oscillations with time. In general, the signal is transmitted with attenuation down the pathway for molecules directly upstream of p-AKT. The amplitude is not damped significantly for p-IRS1 (S). For this figure, each output is normalized to the mean value of the oscillations. ... Parametric dependence of signal transfer efficiency to p-AKT. (A) Influence of input amplitude on output oscillations. The amplitude of the input is kept at three levels 0.9, 0.7 and 0.5 for the nominal conditions. For low frequencies, we see a flat response, with a fixed but lower amplitude in the p-AKT output. For frequency higher than 0.1, the amplitude falls down linearly to negligible values. All parameters and initial concentrations are kept at the nominal values and PTP is kept at 1. The numbers indicated in the flat region represent the ratio of output amplitude to input amplitude. (B) Influence of input frequency on the output amplitude of p-AKT in the entire parameter space. The input amplitude is kept constant at 0.9. The value of σ of 0.5 is selected for the analysis. PTP levels are kept at 1. In general, frequency values greater that 0.5 (log scale) show very low output amplitude. GSA is performed in the region highlighted by the rectangle. Parameter variations have the most effect at lower frequencies. Inset: Histogram of output distribution. The Y-axis denotes the number of samples out of 105. (C) First order Sobol’ indices showing influence of different parameters on the output amplitude when input amplitude is kept constant at 0.9 and a frequency of 0.01min−1. The value of σ of 0.5 is selected for the analysis. (D) Second order Sobol’ indices.
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  • Effect of MK-801 (0.05 and 0.2mg/kg i.p.) and memantine (2.5 and 10mg/kg s.c.) on the peak frequency of pedunculopontine-induced hippocampal theta in anesthetized mice. Changes in theta peak frequency following vehicle (0.9% NaCl), MK-801 (a) and memantine (b) were compared to the frequency before drug administration. Data are represented as mean±SEM, and were analyzed by a two-way ANOVA followed by Bonferoni post-hoc analysis. * p<0.05. ... Effect of vehicle, MK-801 (0.05 and 0.2mg/kg i.p.) and memantine (2.5 and 10mg/kg s.c.) on spontaneous oscillatory activity in anesthetized mice. Power spectra showing the effect of vehicle (0.9% NaCl) (a), MK-801 (0.05 and 0.2mg/kg i.p., b and c, respectively), and memantine (2.5 and 10mg/kg s.c., d and e, respectively) on frequencies between 0 and 20 (Hz) (left panel) and on frequencies between 20 and 60Hz (right panel). The power spectrum between 0 and 80Hz was analyzed on a 5s period immediately preceding the onset of stimulation. Power spectral analysis was performed for each animal before (average over a 15min period preceding the injection) and after drug administration (average over a 15min period starting 45min after injection). For each animal, the power within each frequency band (resolution of 0.24Hz) was further normalized to the total power between 0 and 80Hz. Average (mean±SEM) power spectra for all animals in each group are represented before (gray lines) and following drug treatment (black lines). ... Effect of vehicle, MK-801 (0.05 and 0.2mg/kg i.p.) and memantine (2.5 and 10mg/kg s.c.) on hippocampal oscillatory activity during stimulation of the pedunculopontine nucleus in anesthetized mice. Power spectra showing the effect of vehicle (0.9% NaCl) (a), MK-801 (0.05 and 0.2mg/kg i.p., b and c, respectively), and memantine (2.5 and 10mg/kg s.c., d and e, respectively) on frequencies between 0 and 20 (Hz) (left panel) and on frequencies between 20 and 60Hz (right panel). The power spectrum between 0 and 80Hz was analyzed for each animal before (average over a 15min period preceding the injection) and after drug administration (average over a 15min period starting 45min after injection). For each animal, the power within each frequency band (resolution of 0.24Hz) was further normalized to the total power between 0 and 80Hz. Average (mean±SEM) power spectra for all animals in each group are represented before (gray lines) and following drug treatment (black lines).
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  • Dynamic stall phenomena bring risk for negative damping and instability in wind turbine blades. It is crucial to model these phenomena accurately to reduce inaccuracies in predicting design driving (fatigue) loads. Inaccuracies in current dynamic stall models may be due to the facts that they are not properly designed for high angles of attack, and that they do not specifically describe vortex shedding behaviour. The Snel second order dynamic stall model attempts to explicitly model unsteady vortex shedding. This model could therefore be a valuable addition to DNV GL’s turbine design software Bladed. In this thesis the model has been validated with oscillating airfoil experiments and improvements have been proposed for reducing inaccuracies. The proposed changes led to an overall reduction in error between the model and experimental data. Furthermore the vibration frequency prediction improved significantly. The improved model has been implemented in Bladed and tested against small scale turbine experiments at parked conditions. At high angles of attack the model looks promising for reducing mismatches between predicated and measured (fatigue) loading. Leading to possible lower safety factors for design and more cost efficient designs for future wind turbines.
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  • frequency ratio sampling/signal:... However, it is interesting to understand why there is such a large incidence of noise in figures 14 and 15 (when using the laptop computer) whereas when using the diapason (figure 11) there is a crisp peak at 440Hz and even the second harmonic is clearly noticeable at 880Hz, with the noise signal being dwarfed by an impressively distinct FFT peak. When using the signal generator, there is a very crisp peak at 2000Hz, and when looking at figure 15, this is not the case. It can be concluded that in terms of quality of disturbance, the diapason ranks first, followed by the signal generator and by the laptop computer, responsible for the most noise. From this, the idea of subtracting the noise spectrum from the other graphs, even when possessing matching folding frequencies, is invalid, as the noise is dependent on the source causing the disturbance.... Distance travelled by the mirror From figure (6) it can be seen that the reciprocal of the distance between the peaks gives the time it takes for one bright fringe to turn into another bright fringe, and this is directly proportional to the distance travelled by the mirror; to be able to achieve a clear peak to peak frequency, the mirror must have moved a full λ before coming back to its original position, or it must be moving with constant velocity for a distance Nλ before moving backwards. The occurrence of double peaks proves that the mirror effectively does move by λ as opposed to a distance Nλ because the regularity of the disturbance must be caused by a one off event, periodic with the oscillation of the mirror. The scenario of the perturbation occurring in regular steps of Nλ, thus dependent upon the mirror’s horizontal displacement is very unlikely. Hence, by analysing the disturbance it is possible to conclude that the mirror does indeed move a full λ in distance; the mirror moves 633nm per oscillation. The double peaks were removed by tweaking the mirrors making the central maxima form in the centre of the detector.... frequency 800Hz.txt... Proceeding When the tuning fork is hit, a sound wave will propagate through the air. This will cause compressions and expansions in the air, resulting in higher and lower density regions respectively. The density of particles is proportional to the refractive index, hence when shining a laser beam through this perturbed region, it will be affected by these fluctuations in refractive index. By looking at how such fluctuations affect the interference pattern produced on the screen one can extract important information such as the frequency of the sound wave.... FFT frequency The folding frequency[footnoteRef:4] is the step for which the FFT components are calculated, it is found by adding the folding frequency to each data point, starting from 0. Hence, the first FFT data point would be plotted to an x coordinate of 0, the second would be the folding frequency, the third would be two times the folding frequency and so on. According to the Nyquist theorem of sampling; the maximum frequency component that can be determined using a given dataset of points equally spaced t seconds apart is equal to 1/(2t). The folding frequency is therefore:... In a Michelson interferometer, light from a monochromatic source (S) is divided by a beam splitter (BS), oriented at an angle of 45° to the beam, producing two beams of equal intensity. The transmitted beam (T) travels to mirror M2 where it is reflected back to BS. 50% of the returning beam is then deflected by 90° at the beam splitter and is made to strike the detector (D). The reflected beam travels to mirror M1, where it is reflected. Again, 50% of the beam passes straight through the BS and reaches the detector. The Laser is a He-Ne laser, having a polarized wavelength of 633nm (red). The wave is coherent and monochromatic; since the beam is coherent, light from other sources will not interfere with the interference pattern. Mirrors provide a way for the beam to change its direction of travel, if M1 and M2 are misaligned, the recombination of the beams occurs at a different location in the BS, resulting in the formation of two signals on D which do not form an interference pattern. When working with laser light, a cube beamsplitter (CB) possesses the best combination of optical performance and power handling ,CBs avoid displacing the beam by being perpendicular to the incident beam. To achieve the best possible performance, CBs should be operated with collimated light as convergent or divergent beams will contribute unwanted spherical aberrations to the setup. A piezoelectric was connected to a signal generator and attached to M2. This acted as a test for the apparatus and allowed the mirror to oscillate at various frequencies. The distance travelled by M2 due to excitation of the piezoelectric was a secondary investigation inherent in the project. The detector used allowed the intensity of light hitting it to be recorded. When two or more waves interact with one another an interference pattern is produced. This pattern is a result of the phase difference between the waves. When the waves are in phase constructive interference occurs and the resulting amplitude of the two superimposed waves is a maximum, on a screen, this is seen as a light fringe. When the waves are π out of phase, destructive interference occurs and the resulting amplitude is 0, on a screen this is seen as a dark fringe.
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