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- Kuisma, H, et al.
(författare)
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Parity associates with chromosomal damage in uterine leiomyomas
- 2021
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12:1, s. 5448-
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Tidskriftsartikel (refereegranskat)abstract
- Mechanical forces in a constrained cellular environment were recently established as a facilitator of chromosomal damage. Whether this could contribute to tumorigenesis is not known. Uterine leiomyomas are common neoplasms that display relatively few chromosomal aberrations. We hypothesized that if mechanical forces contribute to chromosomal damage, signs of this could be seen in uterine leiomyomas from parous women. We examined the karyotypes of 1946 tumors, and found a striking overrepresentation of chromosomal damage associated with parity. We then subjected myometrial cells to physiological forces similar to those encountered during pregnancy, and found this to cause DNA breaks and a DNA repair response. While mechanical forces acting in constrained cellular environments may thus contribute to neoplastic degeneration, and genesis of uterine leiomyoma, further studies are needed to prove possible causality of the observed association. No evidence for progression to malignancy was found.
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- Khalili, M., et al.
(författare)
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A 3D simulation study for monitoring water content in a porous storage
- 2023
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Ingår i: 84th EAGE Annual Conference and Exhibition. - : European Association of Geoscientists and Engineers, EAGE. ; , s. 3309-3313
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Konferensbidrag (refereegranskat)abstract
- In this study, the neural network is used to estimate the amount of water stored in a porous reservoir from seismic data. To generate the training data for the neural network, a coupled poroviscoelastic-viscoelastic wave propagation model is solved using a three-dimensional (3D) discontinuous Galerkin method coupled with an Adams-Bashforth time stepping scheme. In addition, the effect of the unknown source wavelet is normalized using a deconvolution-based approach. Results indicate that the proposed neural network approach is applicable to estimate the wave content of a porous reservoir with a variety of noise amplitudes while uninteresting parameters can be successfully ignored.
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- Lähivaara, T., et al.
(författare)
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Monitoring of water table level and volume of water in a porous storage by seismic data
- 2023
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Ingår i: 29th European Meeting of Environmental and Engineering Geophysics, Held at Near Surface Geoscience Conference and Exhibition 2023, NSG 2023. - : EAGE Publications bv.
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Konferensbidrag (refereegranskat)abstract
- Neural networks provide an attractive framework to monitor the water table level and the volume of stored water in porous media from seismic data in an automated, fast and cost-efficient manner. In this work, a subsurface reservoir is modeled as a coupled three-dimensional poroviscoelastic-viscoelastic medium. The wave propagation from source to receiver(s) is numerically simulated using a nodal discontinuous Galerkin method coupled with an Adams-Bashforth time-stepping scheme on a graphics processing unit cluster. The wave field solver is used to generate databases for the neural network model to estimate the water table level and actual volume of water. We use a deconvolution-based approach to normalize the effect from the source wavelet. The results demonstrate the capacity of the fully connected neural network for estimating both the water table level and the volume of stored water in the porous storage reservoir from both synthetic and measured data.
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