### 253 results for qubit oscillator frequency

Contributors: Gong-yi Tu, Wei-feng Li, Wen-wei Qian, Zhe-hang Shi, Hai-feng Liu, Fu-chen Wang

Date: 2015-09-09

The relationship between frequency and Reynolds number in Reactor II. () self-sustained flapping oscillation **frequencies** with excitation, and () the excitation **frequencies** corresponding to excited deflecting oscillation.
...The oscillation behaviors in T-jets reactors with excitation are experimentally studied by a flow visualization technique. The images of the smoke-seeded flow are captured by a particle imaging velocimetry (PIV) system and a high-speed camera. The effects of the Reynolds number, the excitation **frequency** and the excitation amplitude on the oscillation behaviors in T-jets reactors have been investigated. The impingement plane flaps periodically caused by the pulsed inflow, and the excited flapping **frequencies** of the impingement planes are equal to the excitation **frequencies**. Different oscillation behaviors in T-jets reactors with excitation are identified, and the interaction between the self-sustained oscillations and the excited flapping oscillations is investigated and discussed. Results show that the excitation as well as the geometry parameters of T-jets reactors has significant effects on oscillation behaviors. The excited oscillation amplitudes of impingement planes increase with Reynolds numbers and excitation amplitudes, but non-monotonically decrease with excitation **frequencies**....The relationship between **frequency** and Reynolds number in Reactor II. () self-sustained flapping **oscillation** **frequencies** with excitation, and () the excitation **frequencies** corresponding to excited deflecting **oscillation**.
...The **oscillation** behaviors in T-jets reactors with excitation are experimentally studied by a flow visualization technique. The images of the smoke-seeded flow are captured by a particle imaging velocimetry (PIV) system and a high-speed camera. The effects of the Reynolds number, the excitation **frequency** and the excitation amplitude on the **oscillation** behaviors in T-jets reactors have been investigated. The impingement plane flaps periodically caused by the pulsed inflow, and the excited flapping **frequencies** of the impingement planes are equal to the excitation **frequencies**. Different **oscillation** behaviors in T-jets reactors with excitation are identified, and the interaction between the self-sustained **oscillations** and the excited flapping **oscillations** is investigated and discussed. Results show that the excitation as well as the geometry parameters of T-jets reactors has significant effects on **oscillation** behaviors. The excited **oscillation** amplitudes of impingement planes increase with Reynolds numbers and excitation amplitudes, but non-monotonically decrease with excitation **frequencies**....Map of flow regimes in Reactor II with excitation at various Reynolds numbers and excitation **frequencies**.
...**Oscillation** behavior...Animation in Reactor I at Re=35, fe=1Hz and ae=40%.
...The **oscillation** amplitude of impingement plane at z=−L in Reactor II at various (a) excitation amplitudes and (b) excitation **frequencies**.
...Oscillation amplitude of impingement plane at z=−L in Reactor I at various (a) excitation amplitudes and (b) excitation **frequencies**.
...Summary of **oscillation** behaviors in T-jets reactors with and without excitation.
...**Oscillation** amplitude of impingement plane at z=−L in Reactor I at various (a) excitation amplitudes and (b) excitation **frequencies**.
...(a) Time series of impingement plane locations at z=−L in Reactor I for fe=2.5Hz and ae=40%, and (b) the corresponding power-frequency spectrum at Re=210.
...Flapping **oscillation** **frequencies** of the impingement plane at z=−L in Reactor I.
... The **oscillation** behaviors in T-jets reactors with excitation are experimentally studied by a flow visualization technique. The images of the smoke-seeded flow are captured by a particle imaging velocimetry (PIV) system and a high-speed camera. The effects of the Reynolds number, the excitation **frequency** and the excitation amplitude on the **oscillation** behaviors in T-jets reactors have been investigated. The impingement plane flaps periodically caused by the pulsed inflow, and the excited flapping **frequencies** of the impingement planes are equal to the excitation **frequencies**. Different **oscillation** behaviors in T-jets reactors with excitation are identified, and the interaction between the self-sustained **oscillations** and the excited flapping **oscillations** is investigated and discussed. Results show that the excitation as well as the geometry parameters of T-jets reactors has significant effects on **oscillation** behaviors. The excited **oscillation** amplitudes of impingement planes increase with Reynolds numbers and excitation amplitudes, but non-monotonically decrease with excitation **frequencies**.

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Contributors: Seine A. Shintani, Kotaro Oyama, Norio Fukuda, Shin’ichi Ishiwata

Date: 2015-02-06

HSOs in cardiomyocytes with blockade of the SR functions. (A) Top, left: epi-illumination image of a myocyte expressed with AcGFP in Z-disks. Fluo-8 was loaded into the myocyte, and 200μM ryanodine and 4μM thapsigargin were present. Laser center is indicated (closed red circle). The yellow arrow indicates the sarcomere used for the analysis. Top, right: time-dependent changes in temperature induced by IR laser irradiation for 10s (2nd irradiation; see Fig. S1B for data of 1st, 2nd and 3rd irradiations). Given the distance from the laser center, the temperature in the sarcomeres indicated by the yellow arrow in Top, left was estimated to be 40.8°C. Bottom, left: enlarged view of the graph (2–6s) showing the occurrence of HSOs. Note that HSOs were induced in a delayed fashion (∼1.5s after the onset of heating). Bottom, right: enlarged view of the graph showing HSOs at the steady-state. Note no periodic F.I. (i.e., [Ca2+]i) changes in any of the graphs, due to the presence of ryanodine and thapsigargin. (B) Effect of heating on peak SLs during HSOs. IR laser irradiation (for 10s each) was applied three times consecutively. Minimal and maximal SLs, obtained at the peak of shortening and lengthening, respectively. (C) Effect of heating on the amplitude of HSOs. Amplitude was defined as maximal SL (at the peak of lengthening) minus minimal SL (at the peak of shortening) [cf. (B)]. (D) Effect of heating on the **frequency** of HSOs. (E) Effect of heating on the sarcomere shortening velocity during HSOs. Velocity was calculated based on our previous study employing SL nanometry [13]. In (B), (C), (D) and (E), data obtained 1.5, 5 and 8.5s after the onset of heating (i.e., beginning, middle and end of heating) were analyzed for 2nd and 3rd heating. Due to time delay of the appearance of HSOs [see (A) and Fig. S1B], data obtained 4, 6.5 and 9s after the onset of heating (i.e., beginning, middle and end of heating) were analyzed for 1st heating. ∗P<0.05 compared with the corresponding value obtained in the beginning of heating. #P<0.05 compared with the corresponding value obtained in the middle of heating. †P<0.05 compared with the corresponding value during the 1st heating. ‡P<0.05 compared with the corresponding value during the 2nd heating.
...Temperature dependence of the occurrence of HSOs. (A) Top: relationship between the distance from the laser center and ΔT in spontaneously beating myocytes, showing whether or not HSOs were induced by heat pulses. Myocytes were set on the microscopic system at the initial temperature of 25°C, and the heat pulse was given for 10s. The values of ΔT that induced HSOs are shown in orange, and those at which HSOs were not observed are shown in gray. The lowest and highest end of the temperature range for HSOs are shown in blue and red triangles, respectively. Bottom: temperature dependence of the state of sarcomeres in neonatal myocytes, i.e., spontaneous beating without HSOs (green) or spontaneous beating with HSOs (yellow), or contraction [with no **oscillations** (red)]. Cell numbers are indicated on left. Of the total 16 myocytes tested, four myocytes (#13–16) did not exhibit HSOs (one even at 41.5°C). Note that the lowest temperatures at which HSOs occur in various myocytes are likely lower than those shown in this graph (Table 2). (B) Top: same as in (A) top in non-beating myocytes, showing whether or not HSOs were induced by heat pulses. As in (A), myocytes were set on the microscopic system at the initial temperature of 25°C, and the heat pulse was given for 10s. The values of ΔT that induced HSOs are shown in orange, and those at which HSOs were not observed are shown in gray. The lowest and highest end of the temperature range for HSOs are shown in blue and red triangles, respectively. Bottom: temperature dependence of the state of sarcomeres in neonatal myocytes, either relaxation (green), HSOs (yellow) or contraction [with no **oscillations** (red)]. Cell numbers are indicated on left. Of the total 8 myocytes tested, one myocyte (#5) did not exhibit relaxation, presumably because the lowest temperature at which observation started was relatively high (i.e., 40.6°C).
...High-**frequency** sarcomeric auto-oscillations induced by heating in living neonatal cardiomyocytes of the rat...Summary of maximal and minimal temperatures for HSOs in beating and non-beating myocytes. Tmax (Tmin), maximal (minimal) temperature at which HSOs were observed in the experiments of Fig. 3. Note that the real Tmin value may be even lower than the value (∼38.6°C) in this table and hence closer to the body temperature of rat neonates [22] in beating myocytes, because in all myocytes showing HSOs, the **oscillations** were already induced at the lowest temperatures (37–40°C; see Fig. 3A) given. ΔT, Tmax minus Tmin. n, 12 and 8 for beating and non-beating myocytes, respectively.
...In the present study, we investigated the effects of infra-red laser irradiation on sarcomere dynamics in living neonatal cardiomyocytes of the rat. A rapid increase in temperature to >∼38°C induced [Ca2+]i-independent high-**frequency** (∼5–10Hz) sarcomeric auto-oscillations (Hyperthermal Sarcomeric Oscillations; HSOs). In myocytes with the intact sarcoplasmic reticular functions, HSOs coexisted with [Ca2+]i-dependent spontaneous beating in the same sarcomeres, with markedly varying **frequencies** (∼10 and ∼1Hz for the former and latter, respectively). HSOs likewise occurred following blockade of the sarcoplasmic reticular functions, with the amplitude becoming larger and the **frequency** lower in a time-dependent manner. The present findings suggest that in the mammalian heart, sarcomeres spontaneously oscillate at higher **frequencies** than the sinus rhythm at temperatures slightly above the physiologically relevant levels....Summary of the effects of heating on the cross-bridge attachment rate and the properties of HSOs. Our mathematical model predicts that heating increases amplitude and decreases frequency via an increase in the attachment rate constant of cross-bridges, α. Arrows, directions of change. See [19,20] for details of our model.
...HSOs in spontaneously beating cardiomyocytes. (A) Epi-illumination image of a myocyte expressed with AcGFP in Z-disks. Fluo-8 loaded into the myocyte. Laser center is indicated (closed red circle). A yellow arrow indicates the sarcomere for the analysis in (C). (B) Top: changes in temperature induced by IR laser irradiation for 10s. Bottom: relationship of the distance from laser center and ΔT. Given the distance from the laser center, the temperature in the sarcomeres, indicated by the yellow arrow in (A), was estimated to be 39.7°C. (C) Left, top: time-dependent changes in SL and F.I. in the myocyte in (A) upon IR laser irradiation (2nd irradiation; see Fig. S1A for data of 1st and 2nd irradiations). F.I. was obtained from the whole myocyte in the view window. Heat pulse was given for 10s, i.e., from 3 to 13s (in gray). Due to the temperature sensitivity of fluorescence [23], F.I. was decreased (or increased) as heat pulse was given (or ceased). Left, bottom: enlarged view of the graph showing SL on top from 6 to 10s. HSOs are clearly seen coexisting with [Ca2+]i-dependent spontaneous beating. Right, top: FFT analysis for the changes in F.I. from 4.7 to 12.5s. Right, bottom: same as in Right, top for the changes in SL. Note that while only one peak is present for F.I., two different components, i.e., slow and fast components, are seen for SL (with the former corresponding to that for F.I.; see arrows). (D) Left: effects of IR laser irradiations on the **frequency** of Ca2+ transient. Closed red circles with error bars, average values; open circles in various colors without error bars, individual data. ∗Pfrequency of HSOs. Closed black circles with error bars, average values; open circles in various colors without error bars, individual data. The HSO **frequency** was not significantly changed during the course of heating (both 1st and 2nd heating). Data obtained 1.5, 5 and 8.5s after the onset of heating were analyzed. n=24 (12 cells).
...HSOs in spontaneously beating cardiomyocytes. (A) Epi-illumination image of a myocyte expressed with AcGFP in Z-disks. Fluo-8 loaded into the myocyte. Laser center is indicated (closed red circle). A yellow arrow indicates the sarcomere for the analysis in (C). (B) Top: changes in temperature induced by IR laser irradiation for 10s. Bottom: relationship of the distance from laser center and ΔT. Given the distance from the laser center, the temperature in the sarcomeres, indicated by the yellow arrow in (A), was estimated to be 39.7°C. (C) Left, top: time-dependent changes in SL and F.I. in the myocyte in (A) upon IR laser irradiation (2nd irradiation; see Fig. S1A for data of 1st and 2nd irradiations). F.I. was obtained from the whole myocyte in the view window. Heat pulse was given for 10s, i.e., from 3 to 13s (in gray). Due to the temperature sensitivity of fluorescence [23], F.I. was decreased (or increased) as heat pulse was given (or ceased). Left, bottom: enlarged view of the graph showing SL on top from 6 to 10s. HSOs are clearly seen coexisting with [Ca2+]i-dependent spontaneous beating. Right, top: FFT analysis for the changes in F.I. from 4.7 to 12.5s. Right, bottom: same as in Right, top for the changes in SL. Note that while only one peak is present for F.I., two different components, i.e., slow and fast components, are seen for SL (with the former corresponding to that for F.I.; see arrows). (D) Left: effects of IR laser irradiations on the frequency of Ca2+ transient. Closed red circles with error bars, average values; open circles in various colors without error bars, individual data. ∗Pheating on the frequency of HSOs. Closed black circles with error bars, average values; open circles in various colors without error bars, individual data. The HSO frequency was not significantly changed during the course of heating (both 1st and 2nd heating). Data obtained 1.5, 5 and 8.5s after the onset of heating were analyzed. n=24 (12 cells).
...In the present study, we investigated the effects of infra-red laser irradiation on sarcomere dynamics in living neonatal cardiomyocytes of the rat. A rapid increase in temperature to >∼38°C induced [Ca2+]i-independent high-**frequency** (∼5–10Hz) sarcomeric auto-**oscillations** (Hyperthermal Sarcomeric **Oscillations**; HSOs). In myocytes with the intact sarcoplasmic reticular functions, HSOs coexisted with [Ca2+]i-dependent spontaneous beating in the same sarcomeres, with markedly varying **frequencies** (∼10 and ∼1Hz for the former and latter, respectively). HSOs likewise occurred following blockade of the sarcoplasmic reticular functions, with the amplitude becoming larger and the **frequency** lower in a time-dependent manner. The present findings suggest that in the mammalian heart, sarcomeres spontaneously **oscillate** at higher **frequencies** than the sinus rhythm at temperatures slightly above the physiologically relevant levels....Summary of the effects of heating on the cross-bridge attachment rate and the properties of HSOs. Our mathematical model predicts that heating increases amplitude and decreases **frequency** via an increase in the attachment rate constant of cross-bridges, α. Arrows, directions of change. See [19,20] for details of our model.
...HSOs in cardiomyocytes with blockade of the SR functions. (A) Top, left: epi-illumination image of a myocyte expressed with AcGFP in Z-disks. Fluo-8 was loaded into the myocyte, and 200μM ryanodine and 4μM thapsigargin were present. Laser center is indicated (closed red circle). The yellow arrow indicates the sarcomere used for the analysis. Top, right: time-dependent changes in temperature induced by IR laser irradiation for 10s (2nd irradiation; see Fig. S1B for data of 1st, 2nd and 3rd irradiations). Given the distance from the laser center, the temperature in the sarcomeres indicated by the yellow arrow in Top, left was estimated to be 40.8°C. Bottom, left: enlarged view of the graph (2–6s) showing the occurrence of HSOs. Note that HSOs were induced in a delayed fashion (∼1.5s after the onset of heating). Bottom, right: enlarged view of the graph showing HSOs at the steady-state. Note no periodic F.I. (i.e., [Ca2+]i) changes in any of the graphs, due to the presence of ryanodine and thapsigargin. (B) Effect of heating on peak SLs during HSOs. IR laser irradiation (for 10s each) was applied three times consecutively. Minimal and maximal SLs, obtained at the peak of shortening and lengthening, respectively. (C) Effect of heating on the amplitude of HSOs. Amplitude was defined as maximal SL (at the peak of lengthening) minus minimal SL (at the peak of shortening) [cf. (B)]. (D) Effect of heating on the frequency of HSOs. (E) Effect of heating on the sarcomere shortening velocity during HSOs. Velocity was calculated based on our previous study employing SL nanometry [13]. In (B), (C), (D) and (E), data obtained 1.5, 5 and 8.5s after the onset of heating (i.e., beginning, middle and end of heating) were analyzed for 2nd and 3rd heating. Due to time delay of the appearance of HSOs [see (A) and Fig. S1B], data obtained 4, 6.5 and 9s after the onset of heating (i.e., beginning, middle and end of heating) were analyzed for 1st heating. ∗P<0.05 compared with the corresponding value obtained in the beginning of heating. #P<0.05 compared with the corresponding value obtained in the middle of heating. †P<0.05 compared with the corresponding value during the 1st heating. ‡P<0.05 compared with the corresponding value during the 2nd heating.
... In the present study, we investigated the effects of infra-red laser irradiation on sarcomere dynamics in living neonatal cardiomyocytes of the rat. A rapid increase in temperature to >∼38°C induced [Ca2+]i-independent high-**frequency** (∼5–10Hz) sarcomeric auto-**oscillations** (Hyperthermal Sarcomeric **Oscillations**; HSOs). In myocytes with the intact sarcoplasmic reticular functions, HSOs coexisted with [Ca2+]i-dependent spontaneous beating in the same sarcomeres, with markedly varying **frequencies** (∼10 and ∼1Hz for the former and latter, respectively). HSOs likewise occurred following blockade of the sarcoplasmic reticular functions, with the amplitude becoming larger and the **frequency** lower in a time-dependent manner. The present findings suggest that in the mammalian heart, sarcomeres spontaneously **oscillate** at higher **frequencies** than the sinus rhythm at temperatures slightly above the physiologically relevant levels.

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Contributors: Miguel Antonio Aon, Sonia Cortassa, Brian O’Rourke

Date: 2006-01-01

Loss of correlation exhibited by mitochondrial **oscillators** in the high-**frequency**, low-amplitude domain of the power spectrum after treatment affecting ROS production, scavenging, or spreading. Isolated cardiomyocytes loaded with TMRM as described for the other experiments were treated for 30min with 64μM 4′Cl-DZP (n=4, two experiments) or 15μM rotenone (n=4, two experiments) or for 2h with 135μM TMPyP (n=4, two experiments). Treated cells were imaged at 110-ms time resolution and the TMRM fluorescence time series analyzed by PSA. (A) We determined the Pearson correlation coefficient, r, of the high-**frequency**, low-amplitude region of the spectrum as that represented by **frequencies** >0.3Hz. Represented are the absolute values of r. We determined 0.3Hz as delimiting the high-**frequency** region on the basis of analysis of white noise spectra since it contains 93% of the data points (panel E; the dashed line points out the 0.3Hz **frequency**, which to the right corresponds to the high-**frequency**, low-amplitude domain of the spectrum, as in panels B–E). This analysis was also applied to randomized time series (see Fig. 4 B) or mitochondria **oscillating** outside the mitochondrial cluster (panel D). The region of the spectrum >0.3Hz corresponds to random behavior characterized by r=0.051±0.001 (n=4) as opposed to r=0.70±0.05 (n=4) exhibited by the mitochondrial network under control conditions (panel B) or an **oscillating** mitochondrial cluster (panel C). In panels B–E, the dashed lines represent the linear fitting for the two separate regions of the spectrum to emphasize that the change in slope mainly happens in the high **frequency** domain (see also Fig. 5). In panel A, WN is white noise and MOC stands for mitochondria outside the cluster.
...Mitochondria can behave as individual **oscillators** whose dynamics may obey collective, network properties. We have shown that cardiomyocytes exhibit high-amplitude, self-sustained, and synchronous **oscillations** of bioenergetic parameters when the mitochondrial network is stressed to a critical state. Computational studies suggested that additional low-amplitude, high-**frequency** **oscillations** were also possible. Herein, employing power spectral analysis, we show that the temporal behavior of mitochondrial membrane potential (ΔΨm) in cardiomyocytes under physiological conditions is oscillatory and characterized by a broad **frequency** distribution that obeys a homogeneous power law (1/fβ) with a spectral exponent, β=1.74. Additionally, relative dispersional analysis shows that mitochondrial oscillatory dynamics exhibits long-term memory, characterized by an inverse power law that scales with a fractal dimension (Df) of 1.008, distinct from random behavior (Df=1.5), over at least three orders of magnitude. Analysis of a computational model of the mitochondrial **oscillator** suggests that the mechanistic origin of the power law behavior is based on the inverse dependence of amplitude versus **frequency** of **oscillation** related to the balance between reactive oxygen species production and scavenging. The results demonstrate that cardiac mitochondria behave as a network of coupled **oscillators** under both physiological and pathophysiological conditions....The Fundamental Organization of Cardiac Mitochondria as a Network of Coupled **Oscillators**...Frequency and amplitude modulation of the mitochondrial oscillator model through changes in the balance between ROS production and ROS scavenging. (A) Oscillation periods of 25ms and 143ms are shown for SOD concentrations of 0.75μM and 1.07μM, respectively. The model parameters used to run the simulations for shunt=0.0744 (defined as the fraction of the electron flow in the respiratory chain diverted to the generation of superoxide anion, O2−) were concentration of respiratory chain carriers, ρREN=2.5×10−6mM; concentration of F1F0 ATPase, ρF1=2.03×10−3mM; [Ca2+]i=0.1μM; Kcc=0.01mM; kSOD1=2.4×106mM−1s−1; kCAT1=1.7×104mM−1s−1; GT=0.5mM; maximal rate of the adenine nucleotide translocase, VmaxANT=5mMs−1; maximal rate of the mitochondrial Na-Ca exchanger, VmaxNaCa= 0.015mMs−1. The O2−concentrations correspond to the mitochondrial matrix space and were calculated as described in Cortassa et al. (14). Remaining parameters were set as described in Cortassa et al. (10) and Cortassa et al. (14). (B) Under similar parametric conditions, the frequency and amplitude of the oscillations in O2− delivered to the cytoplasm as a function of the fractional O2− production in the high-frequency domain (ms). Within the oscillatory region (shaded), the oscillatory period constantly decreased, whereas the amplitude reached a peak and then decreased as a function of the increase in ROS production. A similar analysis was performed in the low-frequency domain (seconds to minutes) for SOD concentrations of 1.87μM (not shown). In the latter case, the amplitude of O2− (0.86mM) did not change as a function of the shunt (from 0.05 to 0.25), whereas the period decreased from 276s to 62s, respectively.
...Physiological and pathophysiological behaviors of the mitochondrial network in heart cells. Freshly isolated ventricular cardiomyocytes were loaded with 100nM TMRM and imaged by two photon microscopy (150-ms time resolution) as described in Materials and Methods. The results obtained from a stack of 3720 images are shown (see the video of this experiment in Supplementary Material). Before the mitochondrial network reaches criticality (9,15,30), the ΔΨm (as measured by TMRM) **oscillates** at high **frequencies** and small amplitudes. After criticality, the network behavior evolves into “pathophysiological” behavior characterized by low-**frequency**, high-amplitude **oscillations** (8,10). The return plot of the time series shown in the inset was calculated by representing the fluorescent signal, Xn, with a lag of 150ms with respect to itself, Xn+1. This graph allows a quick visualization of the richness of high-**frequency**, low-amplitude **oscillations** present in the physiological as opposed to the low-**frequency**, high-amplitude **oscillations** present in the pathophysiological regime.
...**Frequency** and amplitude modulation of the mitochondrial **oscillator** model through changes in the balance between ROS production and ROS scavenging. (A) **Oscillation** periods of 25ms and 143ms are shown for SOD concentrations of 0.75μM and 1.07μM, respectively. The model parameters used to run the simulations for shunt=0.0744 (defined as the fraction of the electron flow in the respiratory chain diverted to the generation of superoxide anion, O2−) were concentration of respiratory chain carriers, ρREN=2.5×10−6mM; concentration of F1F0 ATPase, ρF1=2.03×10−3mM; [Ca2+]i=0.1μM; Kcc=0.01mM; kSOD1=2.4×106mM−1s−1; kCAT1=1.7×104mM−1s−1; GT=0.5mM; maximal rate of the adenine nucleotide translocase, VmaxANT=5mMs−1; maximal rate of the mitochondrial Na-Ca exchanger, VmaxNaCa= 0.015mMs−1. The O2−concentrations correspond to the mitochondrial matrix space and were calculated as described in Cortassa et al. (14). Remaining parameters were set as described in Cortassa et al. (10) and Cortassa et al. (14). (B) Under similar parametric conditions, the **frequency** and amplitude of the **oscillations** in O2− delivered to the cytoplasm as a function of the fractional O2− production in the high-**frequency** domain (ms). Within the oscillatory region (shaded), the oscillatory period constantly decreased, whereas the amplitude reached a peak and then decreased as a function of the increase in ROS production. A similar analysis was performed in the low-**frequency** domain (seconds to minutes) for SOD concentrations of 1.87μM (not shown). In the latter case, the amplitude of O2− (0.86mM) did not change as a function of the shunt (from 0.05 to 0.25), whereas the period decreased from 276s to 62s, respectively.
...Loss of correlation exhibited by mitochondrial oscillators in the high-frequency, low-amplitude domain of the power spectrum after treatment affecting ROS production, scavenging, or spreading. Isolated cardiomyocytes loaded with TMRM as described for the other experiments were treated for 30min with 64μM 4′Cl-DZP (n=4, two experiments) or 15μM rotenone (n=4, two experiments) or for 2h with 135μM TMPyP (n=4, two experiments). Treated cells were imaged at 110-ms time resolution and the TMRM fluorescence time series analyzed by PSA. (A) We determined the Pearson correlation coefficient, r, of the high-frequency, low-amplitude region of the spectrum as that represented by frequencies >0.3Hz. Represented are the absolute values of r. We determined 0.3Hz as delimiting the high-frequency region on the basis of analysis of white noise spectra since it contains 93% of the data points (panel E; the dashed line points out the 0.3Hz frequency, which to the right corresponds to the high-frequency, low-amplitude domain of the spectrum, as in panels B–E). This analysis was also applied to randomized time series (see Fig. 4 B) or mitochondria oscillating outside the mitochondrial cluster (panel D). The region of the spectrum >0.3Hz corresponds to random behavior characterized by r=0.051±0.001 (n=4) as opposed to r=0.70±0.05 (n=4) exhibited by the mitochondrial network under control conditions (panel B) or an oscillating mitochondrial cluster (panel C). In panels B–E, the dashed lines represent the linear fitting for the two separate regions of the spectrum to emphasize that the change in slope mainly happens in the high frequency domain (see also Fig. 5). In panel A, WN is white noise and MOC stands for mitochondria outside the cluster.
...PSA of TMRM fluorescence time series from the mitochondrial network of cardiomyocytes. Experiments were carried out as described in the legend of Fig. 1 and Materials and Methods. The time series of TMRM fluorescence was subjected to FFT as described in Materials and Methods. (A) PSA: The power spectrum was obtained from the FFT of the TMRM signal as the double log plot of the amplitude (power) versus the **frequency**. This relationship obeys a homogeneous power law (1/fβ; with f, **frequency**, and β, the spectral exponent) and is statistically self-similar, which means that there is no dominant **frequency**. The PSA reveals a broad spectrum of **oscillation** in normally polarized mitochondria with a spectral exponent of β=1.79, whereas a random process (white noise) gives a β ∼ 0, meaning that there is no relationship between the amplitude and the **frequency** in a random signal. A β=1.0 (Supplementary Material, Fig. S3) or 2.0 (Fig. 4 B, bottom panels) corresponds to pink or brown noise, respectively. The inverse power law spectrum arises from the coupling of **frequency** and amplitude in an orderly statistical sequence. The periods, in seconds or milliseconds, at the bottom of panel A are intended to facilitate the interpretation of the high- and low-**frequency** domains of the spectrum. (B) When the time series of the TMRM fluorescent signal is randomized (mid, left), we obtain a value of β close to zero (mid, right) as opposed to a β=1.79 in the nonrandomized signal (right, top). The spectral exponent β=1.79 (right, top) is consistent with long-range correlations that after signal randomization becomes white noise, with loss of correlation properties β=0.25 (≅ 0) (mid, right) (12).
...PSA of TMRM fluorescence time series from the mitochondrial network of cardiomyocytes. Experiments were carried out as described in the legend of Fig. 1 and Materials and Methods. The time series of TMRM fluorescence was subjected to FFT as described in Materials and Methods. (A) PSA: The power spectrum was obtained from the FFT of the TMRM signal as the double log plot of the amplitude (power**) versus **the frequency. This relationship obeys a homogeneous power law (1**/fβ; with** f, frequency, and β, the spectral exponent) and is statistically self-similar, which means that there is no dominant frequency. The PSA reveals a broad spectrum of oscillation in normally polarized mitochondria with a spectral exponent of β=1.79, whereas a random process (white noise) gives a β ∼ 0, meaning that there is no relationship between the amplitude and the frequency in a random signal. A β=1.0 (Supplementary Material, Fig. S3) or 2.0 (Fig. 4 B, bottom panels) corresponds to pink or brown noise, respectively. The inverse power law spectrum arises from the** coupling** of frequency and amplitude in an orderly statistical sequence. The periods, in seconds or milliseconds, at the bottom of panel A are intended to facilitate the interpretation of the high- and low-frequency domains of the spectrum. (B) When the time series of the TMRM fluorescent signal is randomized (mid, left), we obtain a value of β close to zero (mid, right) as opposed to a β=1.79 in the nonrandomized signal (right, top). The spectral exponent β=1.79 (right, top) is consistent with long-range correlations that after signal randomization becomes white noise, with loss of correlation properties β=0.25 (≅ 0) (mid, right) (12).
...Mitochondria can behave as individual **oscillators** whose dynamics may obey collective, network properties. We have shown that cardiomyocytes exhibit high-amplitude, self-sustained, and synchronous oscillations of bioenergetic parameters when the mitochondrial network is stressed to a critical state. Computational studies suggested that additional low-amplitude, high-**frequency** oscillations were also possible. Herein, employing power spectral analysis, we show that the temporal behavior of mitochondrial membrane potential (ΔΨm) in cardiomyocytes under physiological conditions is oscillatory and characterized by a broad **frequency** distribution that obeys a homogeneous power law (1/fβ) with a spectral exponent, β=1.74. Additionally, relative dispersional analysis shows that mitochondrial oscillatory dynamics exhibits long-term memory, characterized by an inverse power law that scales with a fractal dimension (Df) of 1.008, distinct from random behavior (Df=1.5), over at least three orders of magnitude. Analysis of a computational model of the mitochondrial **oscillator** suggests that the mechanistic origin of the power law behavior is based on the inverse dependence of amplitude versus **frequency** of oscillation related to the balance between reactive oxygen species production and scavenging. The results demonstrate that cardiac mitochondria behave as a network of coupled **oscillators** under both physiological and pathophysiological conditions....Physiological and pathophysiological behaviors of the mitochondrial network in heart cells. Freshly isolated ventricular cardiomyocytes were loaded with 100nM TMRM and imaged by two photon microscopy (150-ms time resolution) as described in Materials and Methods. The results obtained from a stack of 3720 images are shown (see the video of this experiment in Supplementary Material). Before the mitochondrial network reaches criticality (9,15,30), the ΔΨm (as measured by TMRM) oscillates at high frequencies and small amplitudes. After criticality, the network behavior evolves into “pathophysiological” behavior characterized by low-frequency, high-amplitude oscillations (8,10). The return plot of the time series shown in the inset was calculated by representing the fluorescent signal, Xn, with a lag of 150ms with respect to itself, Xn+1. This graph allows a quick visualization of the richness of high-frequency, low-amplitude oscillations present in the physiological as opposed to the low-frequency, high-amplitude oscillations present in the pathophysiological regime.
...Inverse power law behavior and the amplitude versus **frequency** relationship exhibited by the mitochondrial **oscillator**. (A) **Oscillations** were simulated with our computational model of the mitochondrial **oscillator** ((10); see also Fig. S4 in Supplementary Material). The double log graph of the amplitude versus **frequency** (1/period) was plotted from ΔΨm **oscillations** with amplitudes in the range of 2–124mV and periods ranging from 70 to 430ms, respectively. The simulations for shunt=0.1 and SOD concentrations from 0.9×10−4 to 1.3×10−3 were performed with the set of parameters described in the legend of Fig. 2 (see Cortassa et al. (10) and Cortassa et al. (14)) for detailed parameter descriptions). B) From the simulations, we selected five oscillatory periods in the high **frequency** domain (between 70 and 300ms) and one from the low-**frequency** (1-min period) domain and attributed each one of them proportionally to a network composed by 500 mitochondria as described in Supplementary Material (see also Fig. S5). A matrix containing a total of 500 columns (mitochondria) and 6,000 rows was constructed. The time steps represented by the rows correspond to a fixed integration step of 20ms for the numerical integration of the system of ordinary differential equations (see Supplementary Material). We applied RDA and PSA to the average value of each row of the matrix at, e.g., time 1, T1, that represents the experimental average value of fluorescent intensity of the ΔΨm probe (corresponding to mV) obtained every 110ms from 500 mitochondria (on average) from each image of our stack.
...Inverse power law behavior and the amplitude versus frequency relationship exhibited by the mitochondrial oscillator. (A) Oscillations were simulated with our computational model of the mitochondrial oscillator ((10); see also Fig. S4 in Supplementary Material). The double log graph of the amplitude versus frequency (1/period) was plotted from ΔΨm oscillations with amplitudes in the range of 2–124mV and periods ranging from 70 to 430ms, respectively. The simulations for shunt=0.1 and SOD concentrations from 0.9×10−4 to 1.3×10−3 were performed with the set of parameters described in the legend of Fig. 2 (see Cortassa et al. (10) and Cortassa et al. (14)) for detailed parameter descriptions). B) From the simulations, we selected five oscillatory periods in the high frequency domain (between 70 and 300ms) and one from the low-frequency (1-min period) domain and attributed each one of them proportionally to a network composed by 500 mitochondria as described in Supplementary Material (see also Fig. S5). A matrix containing a total of 500 columns (mitochondria) and 6,000 rows was constructed. The time steps represented by the rows correspond to a fixed integration step of 20ms for the numerical integration of the system of ordinary differential equations (see Supplementary Material). We applied RDA and PSA to the average value of each row of the matrix at, e.g., time 1, T1, that represents the experimental average value of fluorescent intensity of the ΔΨm probe (corresponding to mV) obtained every 110ms from 500 mitochondria (on average) from each image of our stack.
... Mitochondria can behave as individual **oscillators** whose dynamics may obey collective, network properties. We have shown that cardiomyocytes exhibit high-amplitude, self-sustained, and synchronous **oscillations** of bioenergetic parameters when the mitochondrial network is stressed to a critical state. Computational studies suggested that additional low-amplitude, high-**frequency** **oscillations** were also possible. Herein, employing power spectral analysis, we show that the temporal behavior of mitochondrial membrane potential (ΔΨm) in cardiomyocytes under physiological conditions is oscillatory and characterized by a broad **frequency** distribution that obeys a homogeneous power law (1/fβ) with a spectral exponent, β=1.74. Additionally, relative dispersional analysis shows that mitochondrial oscillatory dynamics exhibits long-term memory, characterized by an inverse power law that scales with a fractal dimension (Df) of 1.008, distinct from random behavior (Df=1.5), over at least three orders of magnitude. Analysis of a computational model of the mitochondrial **oscillator** suggests that the mechanistic origin of the power law behavior is based on the inverse dependence of amplitude versus **frequency** of **oscillation** related to the balance between reactive oxygen species production and scavenging. The results demonstrate that cardiac mitochondria behave as a network of coupled **oscillators** under both physiological and pathophysiological conditions.

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Contributors: Csaba Bagyinka, Gabriella Pankotai-Bodó, Rui M.M. Branca, Mónika Debreczeny

Date: 2014-10-31

Snapshots of **oscillations**. The original movies are presented in the Supplementary material. In the left panel (Supplementary movie-2), white circles evolved from the middle of the original blue circles. These spread over the whole reaction volume and the colors then reversed again. By the end, the whole reaction volume had become blue. In the right panel (Supplementary movie-3), two propellers evolved after fronts had undergone damping. These propellers continued to rotate for several hours. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
...Bulk **oscillation** of hydrogenase. Absorption change of an unbuffered hydrogenase solution containing 2 mM benzyl viologen and 200 nM hydrogenase, with 50 μl (blue line) or 100 μl (red line) of gaseous H2 added at the beginning of the reaction. The measurement was performed in an anaerobic cell with a path-length of 0.6 mm. After addition of H2, the cell was vigorously shaken in order to distribute the H2 evenly in the solution. Stirring the reaction did not affect the **oscillation** behavior of the reaction: stirred (blue line) and non-stirred (red line) reactions both exhibited **oscillations**. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
...The hydrogenase-catalyzed oxidation of H2 includes an autocatalytic step in the reaction cycle. The reaction also exhibits different pH dependence in the H2 oxidation and in the proton reduction directions. This is not only due to the pH titration of the amino acid side chains as protons are also either the substrates or the products of the reaction. Utilizing the autocatalytic nature of the hydrogenase reaction and the multiple roles of protons therein, together with appropriate limitation of the substrate (gaseous H2) supply, oscillations can be induced in the system. The reaction oscillates both in space and in time, and can last for days with decreasing **frequency** until reaching chemical equilibrium. Of all biological oscillating systems described so far, this one is the simplest in that it has the fewest biological components....**Oscillating** reaction...The hydrogenase-catalyzed oxidation of H2 includes an autocatalytic step in the reaction cycle. The reaction also exhibits different pH dependence in the H2 oxidation and in the proton reduction directions. This is not only due to the pH titration of the amino acid side chains as protons are also either the substrates or the products of the reaction. Utilizing the autocatalytic nature of the hydrogenase reaction and the multiple roles of protons therein, together with appropriate limitation of the substrate (gaseous H2) supply, **oscillations** can be induced in the system. The reaction **oscillates** both in space and in time, and can last for days with decreasing **frequency** until reaching chemical equilibrium. Of all biological **oscillating** systems described so far, this one is the simplest in that it has the fewest biological components....The experiment was performed in a thin-layer reaction chamber [13], which was flushed with gaseous N2 for 10 min. After the air was replaced by N2, the gas inlet was closed and gaseous H2 was injected into the atmosphere (final H2 concentration 10%). The movie was accelerated 100-fold (1 s in the movie corresponds to 100 s in real time). The total length of the movie is 30 s. The whole movie is repeated and white squares are positioned onto the regions where local **oscillations** are clearly visible.
... The hydrogenase-catalyzed oxidation of H2 includes an autocatalytic step in the reaction cycle. The reaction also exhibits different pH dependence in the H2 oxidation and in the proton reduction directions. This is not only due to the pH titration of the amino acid side chains as protons are also either the substrates or the products of the reaction. Utilizing the autocatalytic nature of the hydrogenase reaction and the multiple roles of protons therein, together with appropriate limitation of the substrate (gaseous H2) supply, **oscillations** can be induced in the system. The reaction **oscillates** both in space and in time, and can last for days with decreasing **frequency** until reaching chemical equilibrium. Of all biological **oscillating** systems described so far, this one is the simplest in that it has the fewest biological components.

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Contributors: Zhaoliang Gao, Haifang Cai, Jing Wan, Kun Cai

Date: 2016-01-01

(a) The initial simulation model for a nano rotation–**oscillation** transmission system, in which the two straight 16-layer outer (10, 10) carbon tubes (orange parts) are fixed. Initially, there are 16 layers of atoms on each end of the inner tube beyond the constraint of the stators. The lower left end (grey part, 16-layer) of the curved inner (5, 5) carbon nanotube has a constant input rotational speed, i.e., ωin, and the upper right end has an output rotational **frequency**, i.e., ωout. The value of gap is the axial distance between the upper right end of the inner tube and stator 2. Between the two outer tubes, the mid part of the inner tube with n layers is curved. And the radius of the curved axis, i.e., R, is the distance between O and O∗. θ is the central angle, and equal 90° in this study. After relaxation (b, c and d), near stator 1, the inner tubes do not keep co-axial with the outer tubes and the included angle is ∼2° (see the sections within dashed boxes). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
...In a nano bearing, a curved inner carbon nano tube (CNT) constrained by two short outer CNTs will have an oscillation along the curved axis of the tube when a specified rotational velocity is input on one end of the inner tube. It is found that the free end has periodic axial translational oscillation and the amplitude of oscillation is very high when the **frequency** of the input rotational velocity is close to an eigen/resonance **frequency** of the system, i.e., energy absorption of the inner tube from the interaction between the inner and outer tubes. Higher curvature of the inner tube leads to higher value of fundamental **frequency** of the system. The free end of the inner tube also has obvious torque oscillation. Both of the axial translational oscillation and torque oscillation of the free end can be used as output signals of the system as working in a nano signal generator. The mid part of the inner tube, i.e., the part between two outer tubes, has obvious in-plane vibration, which indicates that the present nano bearing is a two-dimensional device....Histories of gap and amplitudes of gap in Model 2 with respect to input rotational frequency during [2.8, 3.0] ns.
...In a nano bearing, a curved inner carbon nano tube (CNT) constrained by two short outer CNTs will have an **oscillation** along the curved axis of the tube when a specified rotational velocity is input on one end of the inner tube. It is found that the free end has periodic axial translational **oscillation** and the amplitude of **oscillation** is very high when the **frequency** of the input rotational velocity is close to an eigen/resonance **frequency** of the system, i.e., energy absorption of the inner tube from the interaction between the inner and outer tubes. Higher curvature of the inner tube leads to higher value of fundamental **frequency** of the system. The free end of the inner tube also has obvious torque **oscillation**. Both of the axial translational **oscillation** and torque **oscillation** of the free end can be used as output signals of the system as working in a nano signal generator. The mid part of the inner tube, i.e., the part between two outer tubes, has obvious in-plane vibration, which indicates that the present nano bearing is a two-dimensional device....Histories of gap and amplitudes of gap in Model 2 with respect to input rotational **frequency** during [2.8, 3.0] ns.
...(a) The initial simulation model for a nano rotation–oscillation transmission system, in which the two straight 16-layer outer (10, 10) carbon tubes (orange parts) are fixed. Initially, there are 16 layers of atoms on each end of the inner tube beyond the constraint of the stators. The lower left end (grey part, 16-layer) of the curved inner (5, 5) carbon nanotube has a constant input rotational speed, i.e., ωin, and the upper right end has an output rotational frequency, i.e., ωout. The value of gap is the axial distance between the upper right end of the inner tube and stator 2. Between the two outer tubes, the mid part of the inner tube with n layers is curved. And the radius of the curved axis, i.e., R, is the distance between O and O∗. θ is the central angle, and equal 90° in this study. After relaxation (b, c and d), near stator 1, the inner tubes do not keep co-axial with the outer tubes and the included angle is ∼2° (see the sections within dashed boxes). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
... In a nano bearing, a curved inner carbon nano tube (CNT) constrained by two short outer CNTs will have an **oscillation** along the curved axis of the tube when a specified rotational velocity is input on one end of the inner tube. It is found that the free end has periodic axial translational **oscillation** and the amplitude of **oscillation** is very high when the **frequency** of the input rotational velocity is close to an eigen/resonance **frequency** of the system, i.e., energy absorption of the inner tube from the interaction between the inner and outer tubes. Higher curvature of the inner tube leads to higher value of fundamental **frequency** of the system. The free end of the inner tube also has obvious torque **oscillation**. Both of the axial translational **oscillation** and torque **oscillation** of the free end can be used as output signals of the system as working in a nano signal generator. The mid part of the inner tube, i.e., the part between two outer tubes, has obvious in-plane vibration, which indicates that the present nano bearing is a two-dimensional device.

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Contributors: Shubhi Bansal, Prosenjit Sen

Date: 2016-09-01

Comparison of axisymmetric **oscillations** and non-axisymmetric **oscillations** observed for **frequencies** 40Hz/60Hz and 50Hz respectively, for the same actuation voltage 74Vrms. This figure depicts the existence of a local minimum at 50Hz where non-axisymmetric modes can be observed at lower voltages.
...Number of cycles required for mixing of droplets at different actuation **frequencies** for 115Vrms using non-axisymmetric modes. Below 55Hz, only k=2 mode **oscillations** exist and the number of cycles required for mixing increase with the **frequency**. Beyond 55Hz, other higher **oscillation** modes exist.
...Mixing of droplets of DI water (8μl) and diluted orange food colour droplet (2μl) using non-asymmetric **oscillations** with 115Vrms and **frequencies** (A) 35Hz (mode k=2) and (B) 85Hz (mode k=3). [supplementary videos available]. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
...The dependence of oscillation dynamics of a sessile droplet on the actuation parameters (voltage and **frequency**) in AC electrowetting which leads to the manifestation of non-axisymmetric oscillation patterns were investigated through experiments and theoretical modeling. The symmetrical nature of the electrowetting force leads to a circular three phase contact line for low actuation voltages. At higher actuation voltages, despite of symmetrical actuation force the contact line showed a transition from axisymmetric to non-axisymmetric oscillations. We found a good match between the experimentally determined region in the actuation parameter space where non-axisymmetric modes are dominant and the theoretically modeled parametric instability region derived from the Mathieu equation. The results showed that these non-axisymmetric modes are degenerate sectoral modes defined by the spherical harmonic functions. In contrast to axisymmetric oscillations, for non-axisymmetric oscillations the variation of contact angle and base radius remained in-phase between successive resonant modes. Finally, mixing by these parametric oscillations was investigated and the best mixing time was approximately 2% of the diffusive mixing time....Change in base radius of an drop at different **frequencies** for 35Vrms. Axisymmetric **oscillations** at 35Vrms voltage are found to have a resonance peak at 25Hz (having an average contact angle θa∼113°).
...Hue distribution of unmixed droplets (left) and well-mixed droplets (right) **actuated **with voltage 115Vrms and frequency 30Hz (mode k=2). Orange hue peak increases and background hue peak decreases while going from unmixed to well-mixed state. Standard deviation is shown as a measure for progression of the mixing process. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
...The dependence of **oscillation** dynamics of a sessile droplet on the actuation parameters (voltage and **frequency**) in AC electrowetting which leads to the manifestation of non-axisymmetric **oscillation** patterns were investigated through experiments and theoretical modeling. The symmetrical nature of the electrowetting force leads to a circular three phase contact line for low actuation voltages. At higher actuation voltages, despite of symmetrical actuation force the contact line showed a transition from axisymmetric to non-axisymmetric **oscillations**. We found a good match between the experimentally determined region in the actuation parameter space where non-axisymmetric modes are dominant and the theoretically modeled parametric instability region derived from the Mathieu equation. The results showed that these non-axisymmetric modes are degenerate sectoral modes defined by the spherical harmonic functions. In contrast to axisymmetric **oscillations**, for non-axisymmetric **oscillations** the variation of contact angle and base radius remained in-phase between successive resonant modes. Finally, mixing by these parametric **oscillations** was investigated and the best mixing time was approximately 2% of the diffusive mixing time....Non-axisymmetric **oscillations**...Change in base radius of **an drop at **different frequencies for 35Vrms. Axisymmetric oscillations at 35Vrms voltage are found to have a resonance peak at 25Hz (having an average contact angle θa∼113°).
...Comparison of theoretical and experimental actuation parameter values (voltage and frequency) defining a region for obtaining non-asymmetric mode k=2. For droplet oscillaion at frequencies near axisymmetric resonance frequency of 25Hz (got at average contact angle θa∼113° and voltage 35Vrms), voltages lower than theoretical voltages are sufficent to obtain non-axisymmetric modes.
...Number of cycles required for mixing of droplets at different actuation frequencies for 115Vrms using non-axisymmetric modes. Below 55Hz, only k=2 mode oscillations exist and the number of cycles required for mixing increase with the frequency. Beyond 55Hz, other higher oscillation modes exist.
...**Oscillation** patterns (top view) of a 8μl droplet at different voltages and **frequencies** (a) mode k=0 at 35Hz, 74Vrms; (b) mode k=2 at 35Hz, 117Vrms; (c) mode k=3 at 100Hz, 117Vrms. [supplementary videos available].
...Region of instability where non-axisymmteric mode amplitudes are dominant in the parameter space of nondimensionalized modal amplitude (3dR/R)total with frequency. The experimental data represents the mode cross-over points (i.e. data points where amplitude of mode k=2 becomes larger than amplitude of mode k=0).
... The dependence of **oscillation** dynamics of a sessile droplet on the actuation parameters (voltage and **frequency**) in AC electrowetting which leads to the manifestation of non-axisymmetric **oscillation** patterns were investigated through experiments and theoretical modeling. The symmetrical nature of the electrowetting force leads to a circular three phase contact line for low actuation voltages. At higher actuation voltages, despite of symmetrical actuation force the contact line showed a transition from axisymmetric to non-axisymmetric **oscillations**. We found a good match between the experimentally determined region in the actuation parameter space where non-axisymmetric modes are dominant and the theoretically modeled parametric instability region derived from the Mathieu equation. The results showed that these non-axisymmetric modes are degenerate sectoral modes defined by the spherical harmonic functions. In contrast to axisymmetric **oscillations**, for non-axisymmetric **oscillations** the variation of contact angle and base radius remained in-phase between successive resonant modes. Finally, mixing by these parametric **oscillations** was investigated and the best mixing time was approximately 2% of the diffusive mixing time.

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Contributors: Gianluca Gatti, Michael J. Brennan, Ivana Kovacic

Date: 2010-05-15

This paper describes the dynamic behaviour of a coupled system which includes a nonlinear hardening system driven harmonically by a shaker. The shaker is modelled as a linear single degree-of-freedom system and the nonlinear system under test is modelled as a hardening Duffing **oscillator**. The mass of the nonlinear system is much less than the moving mass of the shaker and thus the nonlinear system has little effect on the shaker dynamics. The nonlinearity is due to the geometric configuration consisting of a mass suspended on four springs, which incline as they are extended. Following experimental validation, the model is used to explore the dynamic behaviour of the system under a range of different conditions. Of particular interest is the situation when the linear natural **frequency** of the nonlinear system is less than the natural **frequency** of the shaker such that the **frequency** response curve of the nonlinear system bends to higher **frequencies** and thus interacts with the resonance **frequency** of the shaker. It is found that for some values of the system parameters a complicated **frequency** response curve for the nonlinear system can occur; closed detached curves can appear as a part of the overall amplitude-**frequency** response. These detached curves can lie outside or inside the main resonance curve, and a physical explanation for their occurrence is given....On the interaction of the responses at the resonance **frequencies** of a nonlinear two degrees-of-freedom system...Supplimentary material to “On the interaction of the responses at the resonance frequencies of a nonlinear two degrees-of-freedom system”.
...Coupled **oscillators**...FRCs of the normalised relative displacement W as a function of the normalised **frequency**, Ω, for γ=2×10−3, ζs=0.046,ζ=0.026 and for different values of the normalised primary resonance of the **oscillator**: (a) ω0=1.4, (b) ω0=1.1, (c) ω0=0.7, (d) ω0=0.5, (e) ω0=0.3, (f) ω0=0.1. Stable solution (blue solid line), unstable solution (red dashed line). Numerical solution by integrating Eqs. (4a) and (4b) for μ=0.001 (black ‘∘’).
...Supplimentary material to “On the interaction of the responses at the resonance **frequencies** of a nonlinear two degrees-of-freedom system”.
...Experimental results for a stepped-sine input to the shaker with a constant force amplitude. Wires with a low initial tension: (a) Absolute displacement of the support structure, (b) Relative displacement between the suspended mass and support structure. Wires with a high initial tension: (c) Absolute displacement of the support structure, (d) Relative displacement between the suspended mass and support structure. Increasing frequency (magenta ‘ +’), decreasing frequency (green ‘×’).
...FRCs of the normalised relative displacement W as a function of the normalised frequency, Ω, for γ=2×10−3, ζs=0.046,ζ=0.026 and for different values of the normalised primary resonance of the oscillator: (a) ω0=1.4, (b) ω0=1.1, (c) ω0=0.7, (d) ω0=0.5, (e) ω0=0.3, (f) ω0=0.1. Stable solution (blue solid line), unstable solution (red dashed line). Numerical solution by integrating Eqs. (4a) and (4b) for μ=0.001 (black ‘∘’).
... This paper describes the dynamic behaviour of a coupled system which includes a nonlinear hardening system driven harmonically by a shaker. The shaker is modelled as a linear single degree-of-freedom system and the nonlinear system under test is modelled as a hardening Duffing **oscillator**. The mass of the nonlinear system is much less than the moving mass of the shaker and thus the nonlinear system has little effect on the shaker dynamics. The nonlinearity is due to the geometric configuration consisting of a mass suspended on four springs, which incline as they are extended. Following experimental validation, the model is used to explore the dynamic behaviour of the system under a range of different conditions. Of particular interest is the situation when the linear natural **frequency** of the nonlinear system is less than the natural **frequency** of the shaker such that the **frequency** response curve of the nonlinear system bends to higher **frequencies** and thus interacts with the resonance **frequency** of the shaker. It is found that for some values of the system parameters a complicated **frequency** response curve for the nonlinear system can occur; closed detached curves can appear as a part of the overall amplitude-**frequency** response. These detached curves can lie outside or inside the main resonance curve, and a physical explanation for their occurrence is given.

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Contributors: V.Yu. Bychkov, Yu.P. Tyulenin, M.M. Slinko, V.N. Korchak

Date: 2007-04-11

Oscillatory behaviour during methane oxidation over a cobalt foil has been studied using on-line mass-spectrometry and video recording of the colour of the catalyst surface. It was demonstrated that during oscillatory behaviour, periodic transitions of the catalyst surface from an oxidised state (dark colour) to a reduced state (light colour) occurred together with the variation of the catalyst temperature. **Oscillations** over the cobalt foil appeared at higher temperatures (860–950°C), and had longer periods, in comparison with oscillatory behaviour which was observed earlier over a nickel foil. The application of TGA in combination with TPR experiments revealed the differences in nickel and cobalt redox properties which are responsible for the variation in the properties of the **oscillations**. Forced **oscillations** could be obtained in a low temperature region (700–860°C), if bare chromel and alumel wires were spot-welded separately to the cobalt foil. It was shown that in this case the chromel wire induced oscillatory behaviour of the whole cobalt foil. Complicated mixed mode **oscillations** detected at higher temperatures were shown to be the result of the coupling of high **frequency** **oscillations** produced by the unshielded chromel–alumel thermocouple and low **frequency** **oscillations** appearing over the cobalt foil....Temperature **oscillations** which were detected on the Co foil with the modified thermocouple (see Fig. 8). (T=880°C, inlet mixture 20% O2 in CH4, 30ml/min.)
...Forced **oscillations**...(a) Regular autonomous **oscillations** at 925°C, flow rate 15ml/min. (a) O2×10, (b) CO2+20, (c) CO+20, (d) H2+40 and (e) CH4+20. (b) Regular autonomous **oscillations** at 860°C, flow rate 10ml/min after the heating to 925°C and slow cooling down to 860°C. (a) O2×2, (b) CO2+10, (c) CO+20, (d) H2+40 and (e) CH4+70.
...Synchronous temperature **oscillations** of two Ni foils. Inlet mixture 20% O2 in CH4, 32ml/min.
...Forced temperature **oscillations** over a Co foil in the presence of a Ni foil. (Inlet mixture: 20% O2 in CH4, 21ml/min.)
...Oscillatory behaviour during methane oxidation over a cobalt foil has been studied using on-line mass-spectrometry and video recording of the colour of the catalyst surface. It was demonstrated that during oscillatory behaviour, periodic transitions of the catalyst surface from an oxidised state (dark colour) to a reduced state (light colour) occurred together with the variation of the catalyst temperature. Oscillations over the cobalt foil appeared at higher temperatures (860–950°C), and had longer periods, in comparison with oscillatory behaviour which was observed earlier over a nickel foil. The application of TGA in combination with TPR experiments revealed the differences in nickel and cobalt redox properties which are responsible for the variation in the properties of the oscillations. Forced oscillations could be obtained in a low temperature region (700–860°C), if bare chromel and alumel wires were spot-welded separately to the cobalt foil. It was shown that in this case the chromel wire induced oscillatory behaviour of the whole cobalt foil. Complicated mixed mode oscillations detected at higher temperatures were shown to be the result of the coupling of high **frequency** oscillations produced by the unshielded chromel–alumel thermocouple and low **frequency** oscillations appearing over the cobalt foil....**Oscillations**...The appearance of autonomous **oscillations** at 860°C. (a) O2×5, (b) CO2+30 (c) CO+40, (d) H2+80 and (e) CH4+90.
... Oscillatory behaviour during methane oxidation over a cobalt foil has been studied using on-line mass-spectrometry and video recording of the colour of the catalyst surface. It was demonstrated that during oscillatory behaviour, periodic transitions of the catalyst surface from an oxidised state (dark colour) to a reduced state (light colour) occurred together with the variation of the catalyst temperature. **Oscillations** over the cobalt foil appeared at higher temperatures (860–950°C), and had longer periods, in comparison with oscillatory behaviour which was observed earlier over a nickel foil. The application of TGA in combination with TPR experiments revealed the differences in nickel and cobalt redox properties which are responsible for the variation in the properties of the **oscillations**. Forced **oscillations** could be obtained in a low temperature region (700–860°C), if bare chromel and alumel wires were spot-welded separately to the cobalt foil. It was shown that in this case the chromel wire induced oscillatory behaviour of the whole cobalt foil. Complicated mixed mode **oscillations** detected at higher temperatures were shown to be the result of the coupling of high **frequency** **oscillations** produced by the unshielded chromel–alumel thermocouple and low **frequency** **oscillations** appearing over the cobalt foil.

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Contributors: Noriaki Ashida, Takehiko Ueyama, Kyoko Rikitake, Yasuhito Shirai, Mika Eto, Takeshi Kondoh, Eiji Kohmura, Naoaki Saito

Date: 2008-12-03

Oscillatory translocation of γPKC-KN-GFP with co-expression of PKC subtype (δ or γ) and effects of various inhibitors on **oscillation** **frequency** of γPKC-KN-GFP
...We found that stimulation of P2Y2 receptor (P2Y2R), which is endogenously expressed in CHO-K1 cells, induced intracellular calcium ([Ca2+]i) **oscillation** with a low **frequency** of 11.4±2.7mHz. When CHO-K1 cells expressing GFP-tagged kinase-negative γPKC (γPKC-KN-GFP), which is a neuron-specific subtype of PKC, were stimulated with UDP, γPKC-KN-GFP, but not wild-type γPKC (γPKC-GFP) showed an oscillatory translocation. The oscillatory translocation of γPKC-KN-GFP corresponded with [Ca2+]i **oscillation**, which was not observed in the cells expressing γPKC-GFP. We examined the mechanism of P2Y2R-induced [Ca2+]i **oscillation** pharmacologically. γPKC-KN-GFP **oscillation** was stopped by an extracellular Ca2+ chelator, EGTA, an antagonist of P2Y2R, Suramin, and store-operated calcium channel (SOC) inhibitors, SKF96365 and 2-ABP. Taken together, P2Y2R-induced [Ca2+]i **oscillation** in CHO-K1 cells is related with Ca2+ influx through SOC, whose function may be negatively regulated by γPKC. This [Ca2+]i **oscillation** was distinct from that induced by metabotropic glutamate receptor 5 (mGluR5) stimulation in the **frequency** (72.3±5.3mHz) and in the regulatory mechanism....Oscillatory translocation of γPKC-KN-GFP with co-expression of PKC subtype (δ or γ) and effects of various inhibitors on oscillation **frequency** of γPKC-KN-GFP
...Ca2+ **oscillation**...We found that stimulation of P2Y2 receptor (P2Y2R), which is endogenously expressed in CHO-K1 cells, induced intracellular calcium ([Ca2+]i) oscillation with a low **frequency** of 11.4±2.7mHz. When CHO-K1 cells expressing GFP-tagged kinase-negative γPKC (γPKC-KN-GFP), which is a neuron-specific subtype of PKC, were stimulated with UDP, γPKC-KN-GFP, but not wild-type γPKC (γPKC-GFP) showed an oscillatory translocation. The oscillatory translocation of γPKC-KN-GFP corresponded with [Ca2+]i oscillation, which was not observed in the cells expressing γPKC-GFP. We examined the mechanism of P2Y2R-induced [Ca2+]i oscillation pharmacologically. γPKC-KN-GFP oscillation was stopped by an extracellular Ca2+ chelator, EGTA, an antagonist of P2Y2R, Suramin, and store-operated calcium channel (SOC) inhibitors, SKF96365 and 2-ABP. Taken together, P2Y2R-induced [Ca2+]i oscillation in CHO-K1 cells is related with Ca2+ influx through SOC, whose function may be negatively regulated by γPKC. This [Ca2+]i oscillation was distinct from that induced by metabotropic glutamate receptor 5 (mGluR5) stimulation in the **frequency** (72.3±5.3mHz) and in the regulatory mechanism....Oscillatory translocation of γPKC-KN-GFP in response to UTP or UDP and regulation of [Ca2+]i **oscillation** by kinase-activity of γPKC, in CHO-K1 cells. Time courses of γPKC-KN-GFP and γPKC-GFP translocation between the cytoplasm and the plasma membrane stimulated with 100μM UTP (A) or UDP (C) (n≥5). The ratio (fluorescence intensity at the plasma membrane/fluorescence intensity in the cytoplasm) are calculated and plotted at each time point. Representative video of γPKC-GFP and γPKC-KN-GFP after UDP stimulation are available as supplemental Video 1 (600s) and Video 2 (600s), respectively. The numbers of oscillatory translocation (stimulated with 100μM UTP for 15min) of γPKC-GFP, γPKC-KN-GFP, and γPKC-GFP treated with 100μM Go6983 (n≥3) (B). Change of [Ca2+]i after stimulation with UDP are monitored as 340/380 ratio of the fluorescence in CHO-K1 cells (loaded with 5μM Fura 2-AM) expressing γPKC-GFP, γPKC-KN-GFP, or GFP alone (D) (n≥3).
...Different mechanism and regulation between P2Y2R-induced (left) and mGluR5-induced (right) [Ca2+]i **oscillation** through kinase-activity of γPKC and δPKC. The stimulatory pathways are shown as arrows. Inhibitory regulations are indicated by inhibitory arrow. Broken arrows indicate the pathways not involved in the regulation of [Ca2+]i **oscillation**.
... We found that stimulation of P2Y2 receptor (P2Y2R), which is endogenously expressed in CHO-K1 cells, induced intracellular calcium ([Ca2+]i) **oscillation** with a low **frequency** of 11.4±2.7mHz. When CHO-K1 cells expressing GFP-tagged kinase-negative γPKC (γPKC-KN-GFP), which is a neuron-specific subtype of PKC, were stimulated with UDP, γPKC-KN-GFP, but not wild-type γPKC (γPKC-GFP) showed an oscillatory translocation. The oscillatory translocation of γPKC-KN-GFP corresponded with [Ca2+]i **oscillation**, which was not observed in the cells expressing γPKC-GFP. We examined the mechanism of P2Y2R-induced [Ca2+]i **oscillation** pharmacologically. γPKC-KN-GFP **oscillation** was stopped by an extracellular Ca2+ chelator, EGTA, an antagonist of P2Y2R, Suramin, and store-operated calcium channel (SOC) inhibitors, SKF96365 and 2-ABP. Taken together, P2Y2R-induced [Ca2+]i **oscillation** in CHO-K1 cells is related with Ca2+ influx through SOC, whose function may be negatively regulated by γPKC. This [Ca2+]i **oscillation** was distinct from that induced by metabotropic glutamate receptor 5 (mGluR5) stimulation in the **frequency** (72.3±5.3mHz) and in the regulatory mechanism.

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Contributors: Ondřej Kučera, Daniel Havelka

Date: 2012-09-01

Spontaneous mechanical **oscillations** were predicted and experimentally proven on almost every level of cellular structure. Besides morphogenetic potential of oscillatory mechanical force, **oscillations** may drive vibrations of electrically polar structures or these structures themselves may **oscillate** on their own natural **frequencies**. Vibrations of electric charge will generate **oscillating** electric field, role of which in morphogenesis is discussed in this paper. This idea is demonstrated in silico on the conformation of two growing microtubules....Schematic position of spontaneous mechanical **oscillations** in cells within the **frequency** spectrum. Passive and active **oscillations** are connected with the concept of linear and nonlinear **oscillator**, respectively.
...Schematic position of spontaneous mechanical oscillations in cells within the frequency spectrum. Passive and active oscillations are connected with the concept of linear and nonlinear oscillator, respectively.
...Spontaneous mechanical oscillations were predicted and experimentally proven on almost every level of cellular structure. Besides morphogenetic potential of oscillatory mechanical force, oscillations may drive vibrations of electrically polar structures or these structures themselves may oscillate on their own natural **frequencies**. Vibrations of electric charge will generate oscillating electric field, role of which in morphogenesis is discussed in this paper. This idea is demonstrated in silico on the conformation of two growing microtubules. ... Spontaneous mechanical **oscillations** were predicted and experimentally proven on almost every level of cellular structure. Besides morphogenetic potential of oscillatory mechanical force, **oscillations** may drive vibrations of electrically polar structures or these structures themselves may **oscillate** on their own natural **frequencies**. Vibrations of electric charge will generate **oscillating** electric field, role of which in morphogenesis is discussed in this paper. This idea is demonstrated in silico on the conformation of two growing microtubules.

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