DNA methylation meta-analysis confirms the division of the genome into two functional groups

Based on a meta-analysis of human genome methylation data, we tested a theoretical model in which aging is explained by the redistribution of limited resources in cells between two main tasks of the organism: its self-sustenance based on the function of the housekeeping gene group (HG) and functional differentiation, provided by the (IntG) integrative gene group. A meta-analysis of methylation of 100 genes, 50 in the HG group and 50 in IntG, showed significant differences ( p<0.0001) between our groups in the level of absolute methylation values of genes bodies and its promoters. We showed a reliable decrease of absolute methylation values in IntG with rising age in contrast to HG, where this level remained constant. The one-sided decrease in methylation in the IntG group is indirectly confirmed by the dispersion data analysis, which also decreased in the genes of this group. The imbalance between HG and IntG in methylation levels suggests that this IntG-shift is a side effect of the ontogenesis grownup program and the main cause of aging. The theoretical model of functional genome division also suggests the leading role of slow dividing and post mitotic cells in triggering and implementing the aging process.

USTClitho2.0: Updated Unified Seismic Tomography Models for Continental China Lithosphere from Joint Inversion of Body-Wave Arrival Times and Surface-Wave Dispersion Data

Xin et al. (2019) presented 3D seismic velocity models (VP and VS) of crust and uppermost mantle of continental China using seismic body-wave travel-time tomography, which are referred to as Unified Seismic Tomography Models for Continental China Lithosphere 1.0 (USTClitho1.0). Compared with previous models of continental China, the VP and VS models of USTClitho1.0 have the highest spatial resolution of 0.5°–1.0° in the horizontal direction and are useful for better understanding the complex tectonics of continental China. Although USTClitho1.0 is implicitly constrained by surface-wave data by using the VS model from surface-wave tomography and the converted VP model as initial models for body-wave travel-time tomography, the predicted surface-wave dispersion curves from USTClitho1.0 do not fit the observed data well. Here, we present updated 3D VP and VS models of the continental China lithosphere (USTClitho2.0) by joint inversion of body-wave arrival times and surface-wave dispersion data. Compared with the previous joint inversion scheme of Zhang et al. (2014), similar to Fang et al. (2016), it is further improved by including the sensitivity of surface-wave dispersion data to VP in the new joint inversion system. As a result, the shallow VP structure is also better imaged. In addition, the new joint inversion scheme considers the large topography variations between the eastern and western parts of China. Thus, USTClitho2.0 better resolves the upper-crustal structure of the Tibetan plateau. Compared with USTClitho1.0, USTClitho2.0 fits both body-wave arrival times and surface-wave dispersion data. Thus, the new velocity models are more accurate and can serve as a better reference model for regional-scale tomography and geodynamic studies in continental China.

On the Utility of Horizontal-to-Vertical Spectral Ratios of Ambient Noise in Joint Inversion with Rayleigh Wave Dispersion Curves for the Large-N Maupasacq Experiment

Horizontal-to-Vertical Spectral Ratios (HVSR) and Rayleigh group velocity dispersion curves (DC) can be used to estimate the shallow S-wave velocity (VS) structure. Knowing the VS structure is important for geophysical data interpretation either in order to better constrain data inversions for P-wave velocity (VP) structures such as travel time tomography or full waveform inversions or to directly study the VS structure for geo-engineering purposes (e.g., ground motion prediction). The joint inversion of HVSR and dispersion data for 1D VS structure allows characterising the uppermost crust and near surface, where the HVSR data (0.03 to 10s) are most sensitive while the dispersion data (1 to 30s) constrain the deeper model which would, otherwise, add complexity to the HVSR data inversion and adversely affect its convergence. During a large-scale experiment, 197 three-component short-period stations, 41 broad band instruments and 190 geophones were continuously operated for 6 months (April to October 2017) covering an area of approximately 1500km2 with a site spacing of approximately 1 to 3km. Joint inversion of HVSR and DC allowed estimating VS and, to some extent density, down to depths of around 1000m. Broadband and short period instruments performed statistically better than geophone nodes due to the latter’s gap in sensitivity between HVSR and DC. It may be possible to use HVSR data in a joint inversion with DC, increasing resolution for the shallower layers and/or alleviating the absence of short period DC data, which may be harder to obtain. By including HVSR to DC inversions, confidence improvements of two to three times for layers above 300m were achieved. Furthermore, HVSR/DC joint inversion may be useful to generate initial models for 3D tomographic inversions in large scale deployments. Lastly, the joint inversion of HVSR and DC data can be sensitive to density but this sensitivity is situational and depends strongly on the other inversion parameters, namely VS and VP. Density estimates from a HVSR/DC joint inversion should be treated with care, while some subsurface structures may be sensitive, others are clearly not. Inclusion of gravity inversion to HVSR/DC joint inversion may be possible and prove useful.

Characterization of Roll-Cladded Aluminum Plates With Laser-Based Surface-Acoustic-Wave Measurements

We investigated roll-cladded aluminum structures consisting of a layer of Al4045 on Al3003 with a non-destructive laser-ultrasound technique. We determined the thickness of the cladding layer based on the dispersion of the fundamental guided wave propagating along the surface. We analyzed eight surfaces, with cladding thicknesses ranging from 0 to approximately 400μm The inversion of the dispersion data to obtain the thickness yielded in consistent results, which we compare to reference values obtained from micrographs. For the optimization procedure we allowed the material properties to be free parameters, but constrained them to be equal for all samples.

Machine-Learning-Enabled Automatic Sonic Shear Processing

Flexural-dipole sonic logging has been widely used as the primary method to measure formation shear slowness because it can be applied in both fast and slow formations and can resolve azimuthal anisotropy. The flexural-dipole waveforms are dispersive borehole-guided waves that are sensitive to borehole geometry, mud, and formation properties, and therefore the processing techniques need to honor the physical dispersive signatures to obtain an accurate estimation of shear slowness. Traditional processing techniques are based on either a model-dependent method, in which an isotropic model is used as a reference to compensate for the dispersion effect, or a model-independent method, which optimizes nonphysical parameters to fit a simplified model to the field dispersion data extracted in the slowness-frequency domain. Many methods often require inputs, such as mud slowness, frequency bandpass filter, or an initial guess of formation shear. Consequently, these methods often fail to interpret the dispersion signature properly when those inputs are inaccurate or when the waveform quality is poor due to downhole logging noises. The users must manually tune the processing parameters and/or choose different methods as a workaround, which causes extra time and effort to obtain the result, hence imposes a significant challenge for automating sonic shear processing. We developed a physics-driven, machine-learning-based method for enhancing the interpretation of borehole sonic dipole data for wireline logging in an openhole scenario. A synthetic database is generated from an anisotropic root-finding, mode-search routine and used to train a neural network model as an accurate and efficient proxy. This neural network model can be used for real-time sensitivity analysis and performing inversion to the measured sonic dipole dispersion data to estimate relevant model parameters with associated uncertainties. We introduce how this trained model can be used to enhance the labeling and extraction of the dipole dispersion mode. We developed a new method that outperforms previous model-dependent and model-independent approaches because the new method introduces a mechanism to constrain the solution with physics that also has the capability to incorporate more complicated physical dispersion signatures. This new method does not rely on a good initial guess on mud slowness and formation shear slowness, nor any tuning parameter. This leads to significant progress toward fully automated sonic interpretation. The algorithm has been tested on field data for challenging borehole and geological conditions.

Estimation of the transversely isotropic formation parameters using flexural and quadrupole dispersion data in the fast formation

Borehole acoustic logging plays an important role in inverting the five Thomsen parameters of many formations characterized as a transversely isotropic medium with a vertical axis of symmetry (VTI). Generally, these parameters are obtained under certain assumptions, and the formation type defined by the relation of Thomsen parameters is not taken into consideration. We develop a method to determine the Thomsen parameters of all kinds of fast VTI formations from the first-order flexural and first-order quadrupole dispersion data by dividing the Thomsen parameters inversion process into two parts: low-frequency asymptotic slowness inversion of the flexural or quadrupole dispersion and Thomsen anisotropy parameters inversion by a constrained evolutionary optimization algorithm. Compared with traditional approaches, the new method is not only independent of any assumed correlation among the Thomsen anisotropy parameters but also provides a more accurate result than the inversion of either of them alone for both kinds of fast VTI formations. The inversion results obtained from either the flexural or quadrupole dispersion data alone are wrong if the formation type is incorrectly identified, such as misidentifying an isotropic formation as a special VTI formation. The new method can not only identify the type of fast VTI formation but also obtains the shear slowness in a special VTI formation, which cannot be obtained by existing methods. Accordingly, the application to synthetic examples validates the significance and necessity of the proposed joint method in the inversion of the Thomsen parameters.

Layered mantle flow beneath the Japan Sea and NE China from inversion of surface wave dispersion using rj-MCMC method

&lt;p&gt;The surface wave dispersion data with azimuthal anisotropy can be used to invert for the wavespeed azimuthal anisotropy, which provides essential dynamic information about depth-varying deformation of the Earth&amp;#8217;s interior. The traditional method to slove this inversion problem is a two-step process, i.e. inverting the isotropic wavespeed first, based on which the anisotropic part is solved successively. In this study, we try to simultaneously invert both the isotropic and anisotropic shear wave velocity using the rj-MCMC (reversible jump Markov Monte Carlo) algorithm, which allows sampling the model space in a transdimensional way.&lt;/p&gt;&lt;p&gt;Our resarch is conducted in the Northeast Aisa, including the East and Northeast China (EC and NEC), Korean Peninsula and the sea of Japan (see Fig. 1). The previous anisotropic and tomographic studies were mainly conducted on separated continents, lacking a panoramic view of geodynamics across the entire region. In this study, we construct a crustal and uppermantle model of the whole ragion based on the Rayleigh wave dispersion data collected by Fan et al. (2020, GRL), and acquire high-resolution patterns reflecting valuable geodynamic characteristics.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;img src=&quot;https://contentmanager.copernicus.org/fileStorageProxy.php?f=gepj.b0e3c3a9850061565790161/sdaolpUECMynit/12UGE&amp;app=m&amp;a=0&amp;c=795fbbfedd6847e1e6ec5631f617bb03&amp;ct=x&amp;pn=gepj.elif&amp;d=1&quot; alt=&quot;&quot;&gt;&lt;/p&gt;&lt;p&gt;Figure 1. Map of the NE Asia showing the main tectonic features. Major blocks: NEC = north-east China; EC = East China; KP = Korean Peninsula; KS = Korea Strait; SoJ = Sea of Japan; JI = Japanese Island. The gray area in the background delineates the major sedimentary basins with thickness no less than 1.5 km. Red volcano symbols denote the Late Cenozoic intraplate volcanoes, including: CBV = Changbaishan volcano; JPHV = Jingpohu volcano; LGV = Longgang volcano; XJDV = Xianjingdao volcano; CRV = ChugaRyong volcano; ULV = Ulleung volcano; HLV = Halla volcano; FJV = FukueJima volcano. Small red triangles show the locations of island arc volca-noes. The Japan Trench where the western Pacific Plate subducts, and the Ryukyu Trench where the Philippine Sea Plate subducts are outlined by black lines with white sawtooth. Interface depths of the subducting Pacific slab and Philippine Sea slab are marked by white and purple dashed lines, respectively, with depth annotation. The Tanlu fault zone (TLFZ) is represented by thin black lines.&lt;/p&gt;