Machine Learning Analysis Reveals Abnormal Static and Dynamic Low-Frequency Oscillations Indicative of Long-Term Menstrual Pain in Primary Dysmenorrhea Patients

Abstract. Cenozoic stable carbon (δ13C) and oxygen (δ18O) isotope ratios of deep-sea foraminiferal calcite co-vary with the 405 kyr eccentricity cycle, suggesting a link between orbital forcing, the climate system, and the carbon cycle. Variations in δ18O are partly forced by ice-volume changes that have mostly occurred since the Oligocene. The cyclic δ13C–δ18O co-variations are found in both ice-free and glaciated climate states, however. Consequently, there should be a mechanism that forces the δ13C cycles independently of ice-dynamics. In search of this mechanism, we simulate the response of several key components of the carbon cycle to orbital forcing in the Long-term Ocean-atmosphere-Sediment CArbon cycle Reservoir model (LOSCAR). We force the model by changing the burial of organic carbon in the ocean with various astronomical solutions and noise, and study the response of the main carbon cycle tracers. Consistent with previous work, the simulations reveal that low frequency oscillations in the forcing are preferentially amplified relative to higher frequencies. However, while oceanic δ13C mainly varies with a 405 kyr period in the model, the dynamics of dissolved inorganic carbon in the oceans and of atmospheric CO2 are dominated by the 2.4 Myr cycle of eccentricity. This implies that the total ocean and atmosphere carbon inventory is strongly influenced by carbon cycle variability that exceeds the time scale of the 405 kyr period (such as silicate weathering). To test the applicability of the model results, we assemble a long (~ 22 Myr) δ13C and δ18O composite record spanning the Eocene to Miocene (34 to 12 Ma) and perform spectral analysis to assess the presence of the 2.4 Myr cycle. We find that, while the 2.4 Myr cycle appears to be overshadowed by long-term changes in the composite record, it is is present as an amplitude modulator of the 405 and 100 kyr eccentricity cycles.

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Predicting Vasovagal Responses: A Model-Based and Machine Learning Approach

Frontiers in Neurology ◽

10.3389/fneur.2021.631409 ◽

2021 ◽

Vol 12 ◽

Author(s):

Theodore Raphan ◽

Sergei B. Yakushin

Keyword(s):

Machine Learning ◽

Beta Blockers ◽

Vasovagal Syncope ◽

Low Frequency ◽

Tilt Test ◽

Learning Approach ◽

Clear Understanding ◽

Low Frequency Oscillations ◽

Separating Hyperplane ◽

Machine Learning Approach

Vasovagal syncope (VVS) or neurogenically induced fainting has resulted in falls, fractures, and death. Methods to deal with VVS are to use implanted pacemakers or beta blockers. These are often ineffective because the underlying changes in the cardiovascular system that lead to the syncope are incompletely understood and diagnosis of frequent occurrences of VVS is still based on history and a tilt test, in which subjects are passively tilted from a supine position to 20° from the spatial vertical (to a 70° position) on the tilt table and maintained in that orientation for 10–15 min. Recently, is has been shown that vasovagal responses (VVRs), which are characterized by transient drops in blood pressure (BP), heart rate (HR), and increased amplitude of low frequency oscillations in BP can be induced by sinusoidal galvanic vestibular stimulation (sGVS) and were similar to the low frequency oscillations that presaged VVS in humans. This transient drop in BP and HR of 25 mmHg and 25 beats per minute (bpm), respectively, were considered to be a VVR. Similar thresholds have been used to identify VVR's in human studies as well. However, this arbitrary threshold of identifying a VVR does not give a clear understanding of the identifying features of a VVR nor what triggers a VVR. In this study, we utilized our model of VVR generation together with a machine learning approach to learn a separating hyperplane between normal and VVR patterns. This methodology is proposed as a technique for more broadly identifying the features that trigger a VVR. If a similar feature identification could be associated with VVRs in humans, it potentially could be utilized to identify onset of a VVS, i.e, fainting, in real time.

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Assessing the relationship between low-frequency oscillations of global hydro-climate indices and long-term precipitation throughout the United States

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-20-0022.1 ◽

2020 ◽

Author(s):

Jason Giovannettone

Keyword(s):

Climate Variability ◽

Low Frequency ◽

Sliding Window ◽

The United States ◽

Lag Time ◽

Climate Indices ◽

Total Precipitation ◽

Low Frequency Oscillations ◽

The Us

AbstractAs many locations throughout the US have recently experienced periods of extreme wet and dry conditions, an attempt is made to better understand the relationships between long-term total precipitation and climate variability. Correlations between total precipitation at over 1,200 US sites and low-frequency oscillations of the mean activity of 30 hydro-climate indices (HCIs) are analyzed using correlation analysis and sliding window sizes on the order of years to reduce the effects of high-frequency variability in the time series. The strength and significance of each relationship are assessed using the Pearson’s correlation coefficient ( r ), Leave-One-Out Cross Validation, and a Monte-Carlo approach, respectively. The sliding window size, lag time, and beginning month were varied to produce the optimal correlation at each site; a 60-month sliding window and lag times of 12 and 48 months resulted in the strongest correlations. Correlations with 7 and 8 HCIs at each lag time, respectively, were regionally delineated. The MJO represents the dominant HCI at the 12-month lag time throughout most of the western half of the US, while ENSO revealed strong links to annual and longer-term total precipitation in the East and West, respectively. Other HCIs, such as the NAO and the PDO, demonstrated dominance over much smaller and more well-defined regions within the Southwest and the South, respectively. The final results of this study allow a greater understanding of potential links between climate variability and long-term precipitation in the US, leading to potentially improved predictions of the onset and persistence of future extreme meteorological events at longer lead times.

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The 405 kyr and 2.4 Myr eccentricity components in Cenozoic carbon isotope records

Climate of the Past ◽

10.5194/cp-15-91-2019 ◽

2019 ◽

Vol 15 (1) ◽

pp. 91-104 ◽

Cited By ~ 5

Author(s):

Ilja J. Kocken ◽

Margot J. Cramwinckel ◽

Richard E. Zeebe ◽

Jack J. Middelburg ◽

Appy Sluijs

Keyword(s):

Carbon Cycle ◽

Dissolved Inorganic Carbon ◽

Low Frequency ◽

Orbital Forcing ◽

Ice Dynamics ◽

Ice Volume ◽

Low Frequency Oscillations ◽

Amplitude Modulator ◽

Sediment Carbon

Abstract. Cenozoic stable carbon (δ13C) and oxygen (δ18O) isotope ratios of deep-sea foraminiferal calcite co-vary with the 405 kyr eccentricity cycle, suggesting a link between orbital forcing, the climate system, and the carbon cycle. Variations in δ18O are partly forced by ice-volume changes that have mostly occurred since the Oligocene. The cyclic δ13C–δ18O co-variation is found in both ice-free and glaciated climate states, however. Consequently, there should be a mechanism that forces the δ13C cycles independently of ice dynamics. In search of this mechanism, we simulate the response of several key components of the carbon cycle to orbital forcing in the Long-term Ocean-atmosphere-Sediment CArbon cycle Reservoir model (LOSCAR). We force the model by changing the burial of organic carbon in the ocean with various astronomical solutions and noise and study the response of the main carbon cycle tracers. Consistent with previous work, the simulations reveal that low-frequency oscillations in the forcing are preferentially amplified relative to higher frequencies. However, while oceanic δ13C mainly varies with a 405 kyr period in the model, the dynamics of dissolved inorganic carbon in the oceans and of atmospheric CO2 are dominated by the 2.4 Myr cycle of eccentricity. This implies that the total ocean and atmosphere carbon inventory is strongly influenced by carbon cycle variability that exceeds the timescale of the 405 kyr period (such as silicate weathering). To test the applicability of the model results, we assemble a long (∼22 Myr) δ13C and δ18O composite record spanning the Eocene to Miocene (34–12 Ma) and perform spectral analysis to assess the presence of the 2.4 Myr cycle. We find that, while the 2.4 Myr cycle appears to be overshadowed by long-term changes in the composite record, it is present as an amplitude modulator of the 405 and 100 kyr eccentricity cycles.

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