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Simulated Stereoelectroencephalography dataset used for the validation of High-Frequency Oscillation detector in Epilepsy (Roehri et al. 2017 Plos One, What are the assets and weaknesses of HFO detectors? A benchmark framework based on realistic simulations).
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Abstract Background Recent, large trials of high-frequency oscillation (HFO) versus conventional ventilation (CV) in acute respiratory distress syndrome (ARDS) reported negative results. This could be explained by an HFO-induced right ventricular (RV) dysfunction/failure due to high intrathoracic pressures and hypercapnia. We hypothesized that HFO strategies aimed at averting/attenuating hypercapnia, such as “low-frequency” (i.e., 4 Hz) HFO and 4-Hz HFO with tracheal-gas insufflation (HFO-TGI), may result in an improved RV function relative to “high-frequency” (i.e., 7 Hz) HFO (which may promote hypercapnia) and similar RV function relative to lung protective CV. Methods We studied 17 patients with moderate-to-severe ARDS [PaO2-to-inspiratory O2 fraction ratio (PaO2/FiO2)
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Parity-time (PT) symmetry breaking provides an excellent tool for single-mode oscillation by exploiting the interplay between gain and loss. Previously, the oscillation mode is fixed because the breaking of PT symmetry cannot be manipulated precisely. In this paper, we propose and experimentally demonstrate a selective PT-symmetry optoelectronic oscillator (OEO), which shows wideband tunability and ultra-high side mode suppression ratio (SMSR). The tunability of the proposed OEO is attributed to selection of different modes to break PT symmetry by using a widely tunable microwave photonic filter (MPF). The large roll-off of the MPF greatly enhances the gain difference between the selected mode and competing modes. Consequently, both the output power and the SMSR of the OEO are increased. In the experiment, the oscillation frequency can be tuned over 40 GHz. The output power of the selected mode is enhanced by 12.91 dB, and the maximal SMSR is as high as 71.41 dB. Further, the measured phase noise of the OEO at 17.74 GHz is -129 dBc/Hz at the 10 kHz offset frequency. Exploration of the selective PT-symmetry breaking provides the possibility of developing classes of widely tunable OEOs with ultra-high SMSR and excellent phase noise performance.
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We suggest the main principals and functional units of the parallel chemical computer, namely, (i) a generator (which is a network of coupled oscillators) of oscillatory dynamic modes, (ii) a unit which is able to recognize these modes (a ‘reader’) and (iii) a decision-making unit, which analyses the current mode, compares it with the external signal and sends a command to the mode generator to switch it to the other dynamical regime. Three main methods of the functioning of the reader unit are suggested and tested computationally: (a) the polychronization method, which explores the differences between the phases of the generator oscillators; (b) the amplitude method which detects clusters of the generator and (c) the resonance method which is based on the resonances between the frequencies of the generator modes and the internal frequencies of the damped oscillations of the reader cells. Pro and contra of these methods have been analysed.
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The combination of nonlinear and integrated photonics enables Kerr frequency comb generation in stable chip-based micro-resonators. Such a comb system will revolutionize applications, including multi-wavelength lasers, metrology, and spectroscopy. Aluminum gallium arsenide (AlGaAs) exhibits very high material nonlinearity and low nonlinear loss. However, difficulties in device processing and low device effective nonlinearity made Kerr frequency comb generation elusive. Here, we demonstrate AlGaAs-on-insulator as a nonlinear platform at telecom wavelengths with an ultra-high device nonlinearity. We show high-quality-factor (Q>105) micro-resonators where optical parametric oscillations are achieved with milliwatt-level pump threshold powers, which paves the way for on-chip pumped comb generation.
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Abstract Background Circadian rhythms comprise oscillating molecular interactions, the disruption of the homeostasis of which would cause various disorders. To understand this phenomenon systematically, an accurate technique to identify oscillating molecules among omics datasets must be developed; however, this is still impeded by many difficulties, such as experimental noise and attenuated amplitude. Results To address these issues, we developed a new algorithm named Maximal Information Coefficient-based Oscillation Prediction (MICOP), a sine curve-matching method. The performance of MICOP in labeling oscillation or non-oscillation was compared with four reported methods using Mathews correlation coefficient (MCC) values. The numerical experiments were performed with time-series data with (1) mimicking of molecular oscillation decay, (2) high noise and low sampling frequency and (3) one-cycle data. The first experiment revealed that MICOP could accurately identify the rhythmicity of decaying molecular oscillation (MCC > 0.7). The second experiment revealed that MICOP was robust against high-level noise (MCC > 0.8) even upon the use of low-sampling-frequency data. The third experiment revealed that MICOP could accurately identify the rhythmicity of noisy one-cycle data (MCC > 0.8). As an application, we utilized MICOP to analyze time-series proteome data of mouse liver. MICOP identified that novel oscillating candidates numbered 14 and 30 for C57BL/6 and C57BL/6 J, respectively. Conclusions In this paper, we presented MICOP, which is an MIC-based algorithm, for predicting periodic patterns in large-scale time-resolved protein expression profiles. The performance test using artificially generated simulation data revealed that the performance of MICOP for decaying data was superior to that of the existing widely used methods. It can reveal novel findings from time-series data and may contribute to biologically significant results. This study suggests that MICOP is an ideal approach for detecting and characterizing oscillations in time-resolved omics data sets.
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Abstract Background Circadian rhythms comprise oscillating molecular interactions, the disruption of the homeostasis of which would cause various disorders. To understand this phenomenon systematically, an accurate technique to identify oscillating molecules among omics datasets must be developed; however, this is still impeded by many difficulties, such as experimental noise and attenuated amplitude. Results To address these issues, we developed a new algorithm named Maximal Information Coefficient-based Oscillation Prediction (MICOP), a sine curve-matching method. The performance of MICOP in labeling oscillation or non-oscillation was compared with four reported methods using Mathews correlation coefficient (MCC) values. The numerical experiments were performed with time-series data with (1) mimicking of molecular oscillation decay, (2) high noise and low sampling frequency and (3) one-cycle data. The first experiment revealed that MICOP could accurately identify the rhythmicity of decaying molecular oscillation (MCC > 0.7). The second experiment revealed that MICOP was robust against high-level noise (MCC > 0.8) even upon the use of low-sampling-frequency data. The third experiment revealed that MICOP could accurately identify the rhythmicity of noisy one-cycle data (MCC > 0.8). As an application, we utilized MICOP to analyze time-series proteome data of mouse liver. MICOP identified that novel oscillating candidates numbered 14 and 30 for C57BL/6 and C57BL/6 J, respectively. Conclusions In this paper, we presented MICOP, which is an MIC-based algorithm, for predicting periodic patterns in large-scale time-resolved protein expression profiles. The performance test using artificially generated simulation data revealed that the performance of MICOP for decaying data was superior to that of the existing widely used methods. It can reveal novel findings from time-series data and may contribute to biologically significant results. This study suggests that MICOP is an ideal approach for detecting and characterizing oscillations in time-resolved omics data sets.
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Abstract Background Recent, large trials of high-frequency oscillation (HFO) versus conventional ventilation (CV) in acute respiratory distress syndrome (ARDS) reported negative results. This could be explained by an HFO-induced right ventricular (RV) dysfunction/failure due to high intrathoracic pressures and hypercapnia. We hypothesized that HFO strategies aimed at averting/attenuating hypercapnia, such as “low-frequency” (i.e., 4 Hz) HFO and 4-Hz HFO with tracheal-gas insufflation (HFO-TGI), may result in an improved RV function relative to “high-frequency” (i.e., 7 Hz) HFO (which may promote hypercapnia) and similar RV function relative to lung protective CV. Methods We studied 17 patients with moderate-to-severe ARDS [PaO2-to-inspiratory O2 fraction ratio (PaO2/FiO2)
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Abstract This study investigated with observations and modeling using daily data of Long-Wave Radiation (RLW) the favorable phase to the convective activity associated with the low-frequency intrassazonal atmospheric variability known with Madden-Julian Oscillation (MJO) between the Indian Pacific region (INDI-PAC) and Northeastern Brazil (NEB) from November to May period of 1982 to 2013. The model used was the Global Ocean-Land-Atmosphere Model (OLAMV3.3) and observations were used for data generated by satellites by the Climate Prediction Center / National Ocean Atmospheric Admistration. The objective was to verify the time of the zonal band shift of these convective oscillations from its initial pulse, INDI-PAC region until reaching NEB. The data filtering method known as Lanczos Filter was applied in the band of 20-70 days in order to eliminate high oscillations, synoptic scales) and low frequencies (annual or more). Spectral characteristics using Wavelet analyzes showed that these oscillations between 20-70 days, from November to May, have a maximum energy spectrum in the observed data centered in the period of 8-16 pentads, end of December and beginning of April. The OLAM showed an advance of this maximum energy peak in the INDI-PAC region and an extension on the date of arrival in the NEB, meaning a delay, being that date for the end of April and May of this maximum of energy on the NEB.
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We demonstrate numerically that the temporal periodic modulation of the pump can excite regular transverse spatiotemporal intensity patterns in broad-area lasers. The Floquet stability analysis is used to characterize dynamics of modulated systems. Pattern formation occurs when the modulation frequency is approximately equal to the relaxation frequency or twice the value of this frequency. Instability of homogeneous oscillations leads to the formation of regular optical patterns such as stripes and hexagons. The characteristic sizes of the observed patterns are in good agreement with predictions of the Floquet analysis.
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