Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network

An automatic system that utilizes data analytics and machine learning to identify adult American eel in data obtained by imaging sonars is created in this study. Wavelet transform has been applied to de-noise the ARIS sonar data and a convolutional neural network model has been built to classify eels and non-eel objects. Because of the unbalanced amounts of data in laboratory and field experiments, a transfer learning strategy is implemented to fine-tune the convolutional neural network model so that it performs well for both the laboratory and field data. The proposed system can provide important information to develop mitigation strategies for safe passage of out-migrating eels at hydroelectric facilities

Acoustic Green's Function Extraction in the Ocean

The acoustic Green's function (GF) is the key to understanding the acoustic properties of ocean environments. With knowledge of the acoustic GF, the physics of sound propagation, such as dispersion, can be analyzed; underwater communication over thousands of miles can be understood; physical properties of the ocean, including ocean temperature, ocean current speed, as well as seafloor bathymetry, can be investigated. Experimental methods of acoustic GF extraction can be categorized as active methods and passive methods. Active methods are based on employment of man-made sound sources. These active methods require less computational complexity and time, but may cause harm to marine mammals. Passive methods cost much less and do not harm marine mammals, but require more theoretical and computational work. Both methods have advantages and disadvantages that should be carefully tailored to fit the need of each specific environment and application. In this dissertation, we study one passive method, the noise interferometry method, and one active method, the inverse filter processing method, to achieve acoustic GF extraction in the ocean. The passive method of noise interferometry makes use of ambient noise to extract an approximation to the acoustic GF. In an environment with a diffusive distribution of sound sources, sound waves that pass through two hydrophones at two locations carry the information of the acoustic GF between these two locations; by listening to the long-term ambient noise signals and cross-correlating the noise data recorded at two locations, the acoustic GF emerges from the noise cross-correlation function (NCF); a coherent stack of many realizations of NCFs yields a good approximation to the acoustic GF between these two locations, with all the deterministic structures clearly exhibited in the waveform. To test the performance of noise interferometry in different types of ocean environments, two field experiments were performed and ambient noise data were collected in a 100-meter deep coastal ocean environment and a 600-meter deep ocean environment. In the coastal ocean environment, the collected noise data were processed by coherently stacking five days of cross-correlation functions between pairs of hydrophones separated by 5 km, 10 km and 15 km, respectively. NCF waveforms were modeled using the KRAKEN normal mode model, with the difference between the NCFs and the acoustic GFs quantified by a weighting function. Through waveform inversion of NCFs, an optimal geoacoustic model was obtained by minimizing the two-norm misfit between the simulation and the measurement. Using a simulated time-reversal mirror, the extracted GF was back propagated from the receiver location to the virtual source, and a strong focus was found in the vicinity of the source, which provides additional support for the optimality of the aforementioned geoacoustic model. With the extracted GF, dispersion in experimental shallow water environment was visualized in the time-frequency representation. Normal modes of GFs were separated using the time-warping transformation. By separating the modes in the frequency domain of the time-warped signal, we isolated modal arrivals and reconstructed the NCF by summing up the isolated modes, thereby significantly improving the signal-to-noise ratio of NCFs. Finally, these reconstructed NCFs were employed to estimate the depth-averaged current speed in the Florida Straits, based on an effective sound speed approximation. In the mid-deep ocean environment, the noise data were processed using the same noise interferometry method, but the obtained NCFs were not as good as those in the coastal ocean environment. Several highly possible reasons of the difference in the noise interferometry performance were investigated and discussed. The first one is the noise source composition, which is different in the spectrograms of noise records in two environments. The second is strong ocean current variability that can result in coherence loss and undermine the utility of coherent stacking. The third one is the downward refracting sound speed profile, which impedes strong coupling between near surface noise sources and the near-bottom instruments. The active method of inverse filter processing was tested in a long-range deep-ocean environment. The high-power sound source, which was located near the sound channel axis, transmitted a pre-designed signal that was composed of a precursor signal and a communication signal. After traveling 1428.5 km distance in the north Pacific Ocean, the transmitted signal was detected by the receiver and was processed using the inverse filter. The probe signal, which was composed of M sequences and was known at the receiver, was utilized for the GF extraction in the inverse filter; the communication signal was then interpreted with the extracted GF. With a glitch in the length of communication signal, the inverse filter processing method was shown to be effective for long-range low-frequency deep ocean acoustic communication. In summary, this dissertation explored two creative methods to extract the acoustic GFs in the ocean. The extracted acoustic GFs were utilized both for studying the physical properties of the ocean and for underwater communication. The study combined experimental data analysis and numerical simulation, using various signal processing techniques. This work is valuable in both passive acoustic remote sensing and active acoustic communication.</p

Acoustic Green\u27s Function Extraction in the Ocean

The acoustic Green\u27s function (GF) is the key to understanding the acoustic properties of ocean environments. With knowledge of the acoustic GF, the physics of sound propagation, such as dispersion, can be analyzed; underwater communication over thousands of miles can be understood; physical properties of the ocean, including ocean temperature, ocean current speed, as well as seafloor bathymetry, can be investigated. Experimental methods of acoustic GF extraction can be categorized as active methods and passive methods. Active methods are based on employment of man-made sound sources. These active methods require less computational complexity and time, but may cause harm to marine mammals. Passive methods cost much less and do not harm marine mammals, but require more theoretical and computational work. Both methods have advantages and disadvantages that should be carefully tailored to fit the need of each specific environment and application. In this dissertation, we study one passive method, the noise interferometry method, and one active method, the inverse filter processing method, to achieve acoustic GF extraction in the ocean. The passive method of noise interferometry makes use of ambient noise to extract an approximation to the acoustic GF. In an environment with a diffusive distribution of sound sources, sound waves that pass through two hydrophones at two locations carry the information of the acoustic GF between these two locations; by listening to the long-term ambient noise signals and cross-correlating the noise data recorded at two locations, the acoustic GF emerges from the noise cross-correlation function (NCF); a coherent stack of many realizations of NCFs yields a good approximation to the acoustic GF between these two locations, with all the deterministic structures clearly exhibited in the waveform. To test the performance of noise interferometry in different types of ocean environments, two field experiments were performed and ambient noise data were collected in a 100-meter deep coastal ocean environment and a 600-meter deep ocean environment. In the coastal ocean environment, the collected noise data were processed by coherently stacking five days of cross-correlation functions between pairs of hydrophones separated by 5 km, 10 km and 15 km, respectively. NCF waveforms were modeled using the KRAKEN normal mode model, with the difference between the NCFs and the acoustic GFs quantified by a weighting function. Through waveform inversion of NCFs, an optimal geoacoustic model was obtained by minimizing the two-norm misfit between the simulation and the measurement. Using a simulated time-reversal mirror, the extracted GF was back propagated from the receiver location to the virtual source, and a strong focus was found in the vicinity of the source, which provides additional support for the optimality of the aforementioned geoacoustic model. With the extracted GF, dispersion in experimental shallow water environment was visualized in the time-frequency representation. Normal modes of GFs were separated using the time-warping transformation. By separating the modes in the frequency domain of the time-warped signal, we isolated modal arrivals and reconstructed the NCF by summing up the isolated modes, thereby significantly improving the signal-to-noise ratio of NCFs. Finally, these reconstructed NCFs were employed to estimate the depth-averaged current speed in the Florida Straits, based on an effective sound speed approximation. In the mid-deep ocean environment, the noise data were processed using the same noise interferometry method, but the obtained NCFs were not as good as those in the coastal ocean environment. Several highly possible reasons of the difference in the noise interferometry performance were investigated and discussed. The first one is the noise source composition, which is different in the spectrograms of noise records in two environments. The second is strong ocean current variability that can result in coherence loss and undermine the utility of coherent stacking. The third one is the downward refracting sound speed profile, which impedes strong coupling between near surface noise sources and the near-bottom instruments. The active method of inverse filter processing was tested in a long-range deep-ocean environment. The high-power sound source, which was located near the sound channel axis, transmitted a pre-designed signal that was composed of a precursor signal and a communication signal. After traveling 1428.5 km distance in the north Pacific Ocean, the transmitted signal was detected by the receiver and was processed using the inverse filter. The probe signal, which was composed of M sequences and was known at the receiver, was utilized for the GF extraction in the inverse filter; the communication signal was then interpreted with the extracted GF. With a glitch in the length of communication signal, the inverse filter processing method was shown to be effective for long-range low-frequency deep ocean acoustic communication. In summary, this dissertation explored two creative methods to extract the acoustic GFs in the ocean. The extracted acoustic GFs were utilized both for studying the physical properties of the ocean and for underwater communication. The study combined experimental data analysis and numerical simulation, using various signal processing techniques. This work is valuable in both passive acoustic remote sensing and active acoustic communication

Waveform modeling and inversion of ambient noise cross-correlation functions in a coastal ocean environment

The article of record as published may be found at https://doi.org/10.1121/1.4928303Theoretical studies have shown that cross-correlation functions (CFs) of time series of ambient noise measured at two locations yield approximations to the Green's functions (GFs) that describe propagation between those locations. Specifically, CFs are estimates of weighted GFs. In this paper, it is demonstrated that measured CFs in the 20–70 Hz band can be accurately modeled as weighted GFs using ambient noise data collected in the Florida Straits at ∼100 m depth with horizontal separations of 5 and 10 km. Two weighting functions are employed. These account for (1) the dipole radiation pattern produced by a near-surface source, and (2) coherence loss of surface-reflecting energy in time-averaged CFs resulting from tidal fluctuations. After describing the relationship between CFs and GFs, the inverse problem is considered and is shown to result in an environmental model for which agreement between computed and simulated CFs is good

FTO-mediated m6A demethylation regulates GnRH expression in the hypothalamus via the PLCβ3/Ca2+/CAMK signalling pathway

Abstract N6-methyladenosine (m6A) plays a crucial role in the development and functional homeostasis of the central nervous system. The fat mass and obesity-associated (FTO) gene, which is highly expressed in the hypothalamus, is closely related to female pubertal development. In this study, we found that m6A methylation decreased in the hypothalamus gradually with puberty and decreased in female rats with precocious puberty. FTO expression was increased at the same time. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) showed that the m6A methylation of PLCβ3, a key enzyme of the Ca2+ signalling pathway, was decreased significantly in the hypothalamus in precocious rats. Upregulating FTO increased PLCβ3 expression and activated the Ca2+ signalling pathway, which promoted GnRH expression. Dual-luciferase reporter and MeRIP-qPCR assays confirmed that FTO regulated m6A demethylation of PLCβ3 and promoted PLCβ3 expression. Upon overexpressing FTO in the hypothalamic arcuate nucleus (ARC) in female rats, we observed advanced puberty onset. Meanwhile, PLCβ3 and GnRH expression in the hypothalamus increased significantly, and the Ca2+ signalling pathway was activated. Our study demonstrates that FTO enhances GnRH expression, which promotes puberty onset, by regulating m6A demethylation of PLCβ3 and activating the Ca2+ signalling pathway

Phosphorylation of 338SSYY341 regulates specific interaction between Raf-1 and MEK1

The present study characterizes the interaction between the Raf-1 kinase domain and MEK1 and examines whether the magnitude of their interaction correlates to the ability of Raf to phosphorylate MEK1. Here we show that the minimal domain required for the Raf kinase activity starts from tryptophan 342. Maximal binding of the Raf kinase domain to MEK1 and its kinase activity are achieved upon phosphorylation of the region (338)SSYY(341) in response to 4beta-12-O-tetradecanoylphorbol-13-acetate (TPA), or mutation of Y340Y341 to aspartic acids. Conversely, the TPA-stimulated MEK binding and kinase activity are diminished when this region is deleted or Ser(338) and Ser(339) are mutated to alanines. We also show that the integrity of the Raf ATP-binding site is necessary for the interaction between Raf-1 and MEK1. Furthermore, two MEK-binding sites are identified; the first is localized between amino acids 325 and 349, and the second is within the region between amino acids 350 and 648. Separately, the binding of each site to MEK1 is weak, but in a cis context, they give rise to a much stronger association, which can be further stimulated by TPA. Finally, we find that tryptophan 342, which is conserved among the Raf family and other protein kinases, is essential for the Ser(338) phosphorylation of the full-length Raf and its binding to MEK1. Taken together, our results indicate that the phosphorylation of Ser(338) and Tyr(341) on Raf exerts an important effect on reconfiguring the two MEK-binding sites. As a result, these two sites coordinate to form a high affinity MEK-binding epitope, leading to a marked increase in Raf kinase activity

Ocean remote sensing with acoustic daylight: Lessons from experiments in the Florida Straits

Ambient and shipping noise in the ocean provides acoustic illumination, which can be used, similarly to daylight in the atmosphere, to characterize the environment. Phase information, which is particularly sensitive to sound speed variations and current velocity, can be retrieved from noise observations by the process known as noise interferometry. Approximations to acoustic Green's functions, which describe sound propagation between two locations, are estimated by cross-correlating time series of diffuse noise measured at those locations. Noise-interferometry-based approximations to Green's functions can be used as the basis for a variety of inversion algorithms, thereby providing a purely passive alternative to active-source ocean acoustic remote sensing. This paper gives an overview of results from noise interferometry experiments conducted in the Florida Straits at 100 m depth in December 2012, and at 600 m depth in September/October 2013. Under good conditions for noise interferometry, estimates of cross-correlation functions are shown to allow one to perform advanced phase-coherent signal processing techniques to: perform waveform inversions; estimate currents by exploiting nonreciprocity; perform time-reversal/back-propagation calculations; and investigate modal dispersion using time-warping techniques. Conditions which are favorable for noise interferometry are identified and discussed. [Work supported by NSF and ONR.