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  • Physics - Geophysics; Physics - Geophysics  (2)
  • CYP51 redox partner  (1)
  • Oxford University Press  (3)
  • American Association for the Advancement of Science
  • American Institute of Physics
  • Annual Reviews
  • Blackwell Publishing Ltd
  • 2020-2023  (3)
  • 1980-1984
  • 1925-1929
  • 1920-1924
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  • 2020-2023  (3)
  • 1980-1984
  • 1925-1929
  • 1920-1924
  • 2020-2024  (1)
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  • 1
    Publication Date: 2022-03-16
    Description: This article has been accepted for publication in Geophysical Journal International ©: The Authors 2021. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved. Uploaded in accordance with the publisher's self-archiving policy.
    Description: In a recent study (Jozinovi\'c et al, 2020) we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak ground motion intensity measures (IMs) at distant stations using only recordings from stations near the epicenter. The predictions are made without any previous knowledge concerning the earthquake location and magnitude. This approach differs from the standard procedure adopted by earthquake early warning systems (EEWSs) that rely on location and magnitude information. In the previous study, we used 10 s, raw, multistation waveforms for the 2016 earthquake sequence in central Italy for 915 events (CI dataset). The CI dataset has a large number of spatially concentrated earthquakes and a dense station network. In this work, we applied the CNN model to an area around the VIRGO gravitational waves observatory sited near Pisa, Italy. In our initial application of the technique, we used a dataset consisting of 266 earthquakes recorded by 39 stations. We found that the CNN model trained using this smaller dataset performed worse compared to the results presented in the original study by Jozinovi\'c et al. (2020). To counter the lack of data, we adopted transfer learning (TL) using two approaches: first, by using a pre-trained model built on the CI dataset and, next, by using a pre-trained model built on a different (seismological) problem that has a larger dataset available for training. We show that the use of TL improves the results in terms of outliers, bias, and variability of the residuals between predicted and true IMs values. We also demonstrate that adding knowledge of station positions as an additional layer in the neural network improves the results. The possible use for EEW is demonstrated by the times for the warnings that would be received at the station PII.
    Description: RISE (Union's Horizon 2020 research and innovation programme, grant agreement No.821115)
    Description: Published
    Description: 704–718
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Description: JCR Journal
    Keywords: Physics - Geophysics; Physics - Geophysics ; machine learning ; ground motion prediction ; seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 2
    Publication Date: 2022-05-26
    Description: © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Lamb, D. C., Hargrove, T. Y., Zhao, B., Wawrzak, Z., Goldstone, J. V., Nes, W. D., Kelly, S. L., Waterman, M. R., Stegeman, J. J., & Lepesheva, G. I. Concerning P450 evolution: structural analyses support bacterial origin of sterol 14α-demethylases. Molecular Biology and Evolution, (2020): msaa260, doi:10.1093/molbev/msaa260.
    Description: Sterol biosynthesis, primarily associated with eukaryotic kingdoms of life, occurs as an abbreviated pathway in the bacterium Methylococcus capsulatus. Sterol 14α-demethylation is an essential step in this pathway and is catalyzed by cytochrome P450 51 (CYP51). In M. capsulatus, the enzyme consists of the P450 domain naturally fused to a ferredoxin domain at the C-terminus (CYP51fx). The structure of M. capsulatus CYP51fx was solved to 2.7 Å resolution and is the first structure of a bacterial sterol biosynthetic enzyme. The structure contained one P450 molecule per asymmetric unit with no electron density seen for ferredoxin. We connect this with the requirement of P450 substrate binding in order to activate productive ferredoxin binding. Further, the structure of the P450 domain with bound detergent (which replaced the substrate upon crystallization) was solved to 2.4 Å resolution. Comparison of these two structures to the CYP51s from human, fungi, and protozoa reveals strict conservation of the overall protein architecture. However, the structure of an “orphan” P450 from nonsterol-producing Mycobacterium tuberculosis that also has CYP51 activity reveals marked differences, suggesting that loss of function in vivo might have led to alterations in the structural constraints. Our results are consistent with the idea that eukaryotic and bacterial CYP51s evolved from a common cenancestor and that early eukaryotes may have recruited CYP51 from a bacterial source. The idea is supported by bioinformatic analysis, revealing the presence of CYP51 genes in 〉1,000 bacteria from nine different phyla, 〉50 of them being natural CYP51fx fusion proteins.
    Description: The study was supported by National Institutes of Health (Grant No. R01 GM067871 to G.I.L.) and by a UK-USA Fulbright Scholarship and the Royal Society (to D.C.L.).
    Keywords: sterol biosynthesis ; evolution ; cytochrome P450 ; CYP51 redox partner ; crystallography
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 3
    Publication Date: 2022-10-28
    Description: Relative relocation methods are commonly used to precisely relocate earthquake clusters consisting of similar waveforms. Repeating waveforms are often recorded at volcanoes, where, however, the crust structure is expected to contain strong heterogeneities and therefore the 1D velocity model assumption that is made in most location strategies is not likely to describe reality. A peculiar cluster of repeating low-frequency seismic events was recorded on the south flank of Katla volcano (Iceland) from 2011. As the hypocentres are located at the rim of the glacier, the seismicity may be due to volcanic or glacial processes. Information on the size and shape of the cluster may help constraining the source process. The extreme similarity of waveforms points to a very small spatial distribution of hypocentres. In order to extract meaningful information about size and shape of the cluster, we minimize uncertainty by optimizing the cross-correlation measurements and relative-relocation process. With a synthetic test we determine the best parameters for differential-time measurements and estimate their uncertainties, specifically for each waveform. We design a relocation strategy to work without a predefined velocity model, by formulating and inverting the problem to seek changes in both location and slowness, thus accounting for azimuth, take-off angles and velocity deviations from a 1D model. We solve the inversion explicitly in order to propagate data errors through the calculation. With this approach we are able to resolve a source volume few tens of meters wide on horizontal directions and around 100 meters in depth. There is no suggestion that the hypocentres lie on a single fault plane and the depth distribution indicates that their source is unlikely to be related to glacial processes as the ice thickness is not expected to exceed few tens of meters in the source area.
    Description: Published
    Description: 1244–1257
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Description: JCR Journal
    Keywords: Physics - Geophysics; Physics - Geophysics
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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