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We have developed two novel axial anisotropic inversion codes for magnetotelluric (MT) data: a full axial inversion and a decoupled axial inversion. Both codes are based on the data space Gauss–Newton inversion method used in the WSINV3DMT code, enhanced with OpenMP parallelization and Intel MKL PARDISO direct solver for improved computational efficiency. The full axial inversion searches for three axial anisotropic resistivity elements ρ xx , ρ yy , and ρ zz . Based on our prior influence studies, we identified contrasting influence patterns: ρ xx has a strong influence on Zxy and Zyy, whereas ρ yy has a strong influence on Zyx and Zxx, while ρ zz has negligible impact on all responses. We, therefore, proposed a decoupled axial inversion. This novel technique splits the full axial inversion into two independent modes: ρ xx - and ρ yy -modes. The ρ xx -mode requires just the Zxy and Zyy responses to recover ρ xx , while the ρ yy -mode requires the other two responses. By splitting the inversion, each mode requires half the data parameters and reduces model parameters to one-third compared to the full axial inversion. The substantial parameter reduction is the key factor leading to significantly faster processing times and lower memory requirements. Both codes were validated with the synthetic model. Utilizing OpenMP and direct solvers, the full axial inversion processed 256 MT stations in 65 min per iteration or a total of 195 min for three iterations to converge. The decoupled inversion achieved significantly faster processing, requiring just 32 min per iteration or a total of 96 min to finish the inversion. In addition, the decoupled inversion requires just a fraction of the memory used in the full axial inversion. This makes it practical to operate even on the standard personal computers of current technology. We further applied the decoupled inversion to real MT data acquired in the Northwestern Pacific Ocean. The resulting 3-D inverted ρ xx and ρ yy models support the existence of the anisotropy occurring at 60–120 km depth beneath the ocean floor, in agreement with previous studies. [ABSTRACT FROM AUTHOR]

Introduction: Mucopolysaccharidosis type VI (MPSVI), an autosomal recessive lysosomal storage disorder caused by pathogenic variants in ARSB gene. Usually, whole exome sequencing (WES) can identify these variants, and if WES failed to detect causative variants, whole-genome sequencing (WGS) may be considered to investigate deep intronic variations and structural alterations in patients. Methods: Whole-exome sequencing (WES) and whole genome sequencing (WGS) were performed in a Chinese family having a boy with suspected diagnosis of MPS with macrocephaly, coarse facial features, broad forehead, thick lips, frontal bossing, craniosynostosis, blue spots, frequent upper respiratory infections, inguinal hernia, and dysostosis multiplex. Lysosomal enzymatic assays for leucocytes were used to assess the activity of arylsulfatase B of the boy's leucocytes. Sanger sequencing and karyotyping analysis were used to validate the variants identified in the boy and his parents. Results: This boy diagnosed with MPSVI based on clinical phenotypes and laboratory biochemical assays, and WES identified only a maternally inherited missense variant, c.908G>T (p.Gly303Val), in the ARSB gene. By performing WGS, we found a paracentric inversion involving chromosome 5q14.1q13.2 (78180730-138771424 inv), disrupting the ARSB gene on the proband and his father. The inversion was confirmed through karyotyping analysis, and the breakpoints were validated by agarose gel electrophoresis and Sanger sequencing. Disscussion: This study reminds us that WGS should be done when WES failed to achieve a molecular diagonosis, and it also underscores the importance of WGS especially in cases of high clinical suspicion. [ABSTRACT FROM AUTHOR]

Velocity gradient is an important parameter for interpreting tomographic velocity field and identifying geological boundaries. It is usually translated from the results of seismic travel-time tomography. Recent researches show seismic polarization direction appears to be a promising data source for obtaining velocity gradient field directly. However, what remains unclear is the sensitivity of polarization direction to velocity gradient, which causes difficulty for correctly inverting polarization direction data. To clarify this problem, the sensitivity of velocity gradient parameters on polarization direction is discussed in this paper. It was found that the sensitivity of polarization direction is related to the spatial position of the model parameter. The further the parameter position is from the sensor, the lower the sensitivity is. Such nonuniform distribution of sensitivity may cause distortion of inversion results with incomplete projection data. Based on this analysis, adjustment factors are introducing to the polarization direction inversion algorithm for correctly inverting polarization direction data. Numerical tests are conducted to verify our theoretical analysis and inversion algorithm. Test results show that our theoretical analysis is accurate in both homogeneous velocity field and near velocity interfaces. The inversion method with the adjustment factor can more accurately recover the velocity gradient, offering a promising approach for geological boundary imaging. [ABSTRACT FROM AUTHOR]

In this article we study some structures related to a family of arbeli possessing a common incircle. The structures comprise two perspective triangles which we characterize. They comprise also a parabola carrying the vertices of one of these triangles, and a rectangular hyperbola carrying the perspectors of the triangles. In addition we show that an arbitrary triangle defines in an intrinsic manner a unique arbelos and examine some structures related to sequences of arbeli of this family resulting by a standard process. [ABSTRACT FROM AUTHOR]

We developed an inversion program to apply the finite volume method (FVM) to consider the topography. In the 3D magnetotelluric (MT) forward modeling, we must consider the effect of the topography because it appears as a distortion of the data. FVM is a numerical analysis method that can consider topography. We adopted FVM and the H-forming equation as a governing equation for efficient and mathematically plain implementation. We also adopted the adjoint state method (ASM) to update model parameters in the inversion. ASM can easily and efficiently calculate the gradient vector of the objective function for the inversion without calculating the Jacobi matrix, which needs large memory and computation time. We verified the program using test models. In the forward modeling, it was confirmed that the program could perform the analysis efficiently and adequately considering topography and subsurface resistivity heterogeneity. Furthermore, we conducted the inversion using a synthetic open dataset and could reconstruct the true model of the dataset. The comparison with the previous work revealed that the computation time was comparable even though we calculated it with a personal computer. [ABSTRACT FROM AUTHOR]

Sedimentary basins, integral to Earth's geological history and energy resource exploration, undergo complex changes driven by sedimentation, subsidence and geological processes. Gravity anomaly inversion is a crucial technique offering insights into subsurface structures and density variations. Our study addresses the challenge of complex subsurface structure assessment by leveraging deep neural networks to invert observed gravity anomalies. Optimization approaches traditionally incorporate known density distributions obtained from borehole data or geological logging for inverting basement depth in sedimentary basins using observed gravity anomalies. Our study explores the application of deep neural networks in accurate architectural assessment of sedimentary basins and demonstrates their significance in mineral and hydrocarbon exploration. Recent years have witnessed a surge in the use of machine learning in geophysics, with deep learning models playing a pivotal role. Integrating deep neural networks, such as the feedforward neural networks, has revolutionized subsurface density distribution and basement depth estimation. This study introduces a deep neural network specifically tailored for inverting observed gravity anomalies to estimate two‐dimensional basement relief topographies in sedimentary basins. To enhance computational efficiency, a one‐dimensional discrete cosine transform based discretization approach is employed. Synthetic data, generated using non‐Gaussian fractals, compensates for the scarcity of true datasets for training the deep neural network model. The algorithm's robustness is validated through noise introduction with comparisons against an efficient and traditional global optimization‐based approach. Gravity anomalies of real sedimentary basins further validate the algorithm's efficacy, establishing it as a promising methodology for accurate and efficient subsurface imaging in geological exploration. [ABSTRACT FROM AUTHOR]

The joint inversion of Rayleigh and Love waves plays a crucial role in mitigating the non‐uniqueness of surface wave inversion results and enhancing the stability of these inversions. Existing approaches to the joint inversion of Rayleigh and Love wave dispersion curves, which rely on conventional objective functions, often struggle with complex stratigraphic configurations and yield results of limited accuracy. This study introduces two novel nonlinear joint inversion techniques for Rayleigh and Love waves: Pearson correlation coefficient and thickness mean sharing. The Pearson correlation coefficient approach employs the Pearson correlation coefficient and alternating iterative objective functions to synchronize the shear wave velocity structures derived from Rayleigh and Love waves, thereby enhancing the accuracy of the joint inversion. Conversely, the thickness mean sharing method computes an average of the thickness values obtained in each iteration of the inversion, utilizing the traditional joint inversion objective function. Tests on three distinct stratigraphic structures—characterized by increasing velocity, high‐speed hard interlayers and low‐speed soft interlayers—as well as on measured data, demonstrate that the proposed methods significantly improve the stability and accuracy of nonlinear joint inversion. [ABSTRACT FROM AUTHOR]

Interpreting gravity anomalies caused by fault formations is associated with hydrocarbon systems, mineralized areas and hazardous zones and is the main goal of this research. To achieve an effective and robust model over the geologically faulted structures from gravity anomalies, we present a nature‐inspired hybrid algorithm, which synergizes the physics of the particle swarm optimization and gravitational search algorithm with variable inertia weights. The basic principle of developed particle swarm optimization and gravitational search algorithm method is to synergistically use the exploratory strengths of gravitational search algorithm with the exploitation capacity of particle swarm optimization in order to optimize and enhance the effectiveness by both algorithms. The technique has been tested on synthetic gravity data with varying settings of noises over geologically faulted structure before being applied to field data taken from Ahiri‐Cherla and Aswaraopet master fault present in Pranhita–Godavari valley, India. The optimization process is further refined through normalized Gaussian probability density functions, confidence intervals, histograms and correlation matrices to quantify uncertainty, stability, sensitivity and resolution. When dealing with field data, the true model is never known; in these circumstances, the quality of the outcome can only be inferred from the uncertainty in the mean model. The research utilizes a 68.27% confidence intervals to identify a location where the probability density function is more dominant. This region is then used to evaluate the mean model, which is expected to be more appropriate and closer to the genuine model. Correlation matrices further provide a clear demonstration of the strong connection between layer parameters. The results suggest that particle swarm optimization and gravitational search algorithm is less affected by model parameters and yields geologically more consistent outcomes with little uncertainty in the model, aligning well with the available results. The analysed results show that the method we came up with works well and is stable when it comes to solving the two‐dimensional gravity inverse problem. Future research may involve extending the approach to three‐dimensional inversion problems, with potential improvements in computational efficiency and search accuracy for global optimization methods. [ABSTRACT FROM AUTHOR]

The electrical properties of rocks are widely used in the geophysical exploration of natural resources, such as minerals, hydrocarbons and groundwater. In mining exploration, the primary goal is to map electrically anomalous geological features associated with different mineralization styles, such as clay alteration haloes, metal oxides and sulphides, weathered crystalline rocks or fractured zones. As such, the reconciliation of geophysical data with geological information (geochemistry, mineralogy, texture and lithology) is a critical step and can be performed based on petrophysical properties collected either on core samples or as downhole measurements. Based on data from 189 diamond drill cores collected for uranium exploration in the Athabasca Basin (Saskatchewan, Canada), this paper presents a case study of reconciliation of downhole resistivity probing with core sample geochemistry and short‐wave infrared spectroscopy (350–2500 nm) through three successive steps: (i) multivariate analysis of resistivity and other petrophysical properties (porosity, density) against geochemical and infrared spectroscopy information to characterize electrical properties of rocks with respect to other physical parameters, (ii) a machine‐learning workflow integrating geochemistry and spectral signatures in order to infer synthetic resistivity logs along with uncertainties. The best model in the basin was Light Gradient‐Boosting Machine with pairwise log‐ratio, which yielded a coefficient of determination R2 = 0.80 (root mean square error = 0.16), and in the basement, support vector regression with data fusion of infrared spectroscopy and pairwise log‐ratios on geochemistry yielded R2 = 0.82 (root mean square error = 0.35); (iii) the best model was then fitted on an area that was excluded from the original dataset (Getty Russell property) in order to infer synthetic resistivity logs for that zone. Software code is publicly available. This workflow can be re‐used for the valorization of legacy datasets. [ABSTRACT FROM AUTHOR]

Cracks are extensively existing in rocks and play a significant role in the acoustic anisotropy of cracked rocks. Rocks in nature are affected by pore pressure, whereas the crack deformation with pore pressure and the impacts of the crack deformation on the anisotropic acoustic properties remain little known. Combining the theoretical model with the laboratory measurements of the anisotropic velocities of artificial sandstone samples with and without aligned penny‐shaped cracks, we invert for the crack parameters that characterize the crack deformation as a function of pore pressure and theoretically simulate the impacts of pore pressure–induced variation in the crack parameters on the anisotropic velocities. The results show that with increasing pore pressure, the inverted crack porosity increases exponentially, whereas the inverted crack aspect ratio decreases exponentially and the two crack parameters are linearly correlated. Moreover, model calculation demonstrates that the anisotropic velocities exhibit distinct reductions with the variation in the crack parameters caused by increasing pore pressure. In particular, the reduction in the velocity of the shear wave travelling parallel to the crack plane with polarization perpendicular to the crack plane is the most pronounced. We also demonstrate that the effects of the pore pressure–induced increasing crack porosity on the anisotropic velocities are more pronounced than the impacts of the decreasing crack aspect ratio. The findings not only reveal the variation of the crack geometry with pore pressure and the effects of the crack deformation on the anisotropic velocities of the cracked rocks but also can provide theoretical support for improving the characterization of the cracks through seismic survey. [ABSTRACT FROM AUTHOR]

This paper explores the application of machine learning techniques, specifically deep learning, to the inverse problem of marine controlled‐source electromagnetic data. A novel approach is proposed that combines the convolutional neural network and recurrent neural network architectures to reconstruct layered electrical resistivity variation beneath the seafloor from marine controlled‐source electromagnetic data. The approach leverages the strengths of both convolutional neural network and recurrent neural network, where convolutional neural network is used for recognizing and classifying features in the data, and recurrent neural network is used to capture the contextual information in the sequential data. We have built a large synthetic dataset based on one‐dimensional forward modelling of a large number of resistivity models with different levels of electromagnetic structural complexity. The combined learning of convolutional neural network and recurrent neural network is used to construct the mapping relationship between the marine controlled‐source electromagnetic data and the resistivity model. The trained network is then used to predict the distribution of resistivity in the model by feeding it with marine controlled‐source electromagnetic responses. The accuracy of the proposed approach is examined using several synthetic scenarios and applied to a field dataset. We explore the sensitivity of deep learning inversion to different electromagnetic responses produced by resistive targets distributed at different depths and with varying levels of noise. Results from both numerical simulations and field data processing consistently demonstrate that deep learning inversions reliably reconstruct the subsurface resistivity structures. Moreover, the proposed method significantly improves the efficiency of electromagnetic inversion and offers significant performance advantages over traditional electromagnetic inversion methods. [ABSTRACT FROM AUTHOR]

This article introduces the concept of inversion as a rhetorical-political strategy used to redescribe climate concerns from being sacrificed in favour of profitability to seeing that profitability necessitates climate concerns. Drawing on discourse theory and rhetorical analysis, the article analyses discursive struggles in the dominant discourse of fossil-fuel growth in Norway, from 2013 to 2019. By inverting the image of fossil-fuel dependency from growth and success to loss and stagnation in the Norwegian public discourse on fossil fuels and climate risk, those who challenge the hegemonic pro-fossil fuel-investments system pave the way for an alternative description of an ideal welfare state. As such, inversion, as a practice and rhetorical-political strategy may help us understand the strategic manoeuvring of the environmental movements as counter-hegemonic forces. [ABSTRACT FROM AUTHOR]

We develop a three‐dimensional inversion code to image the resistivity distribution of the subsurface from frequency‐domain controlled‐source electromagnetic data. Controlled‐source electromagnetic investigations play an important role in many different geophysical prospecting applications. To evaluate controlled‐source electromagnetic data collected with complex measurement setups, advanced three‐dimensional modelling and inversion tools are required.We adopt a preconditioned non‐linear conjugate gradient algorithm to enable three‐dimensional inversion of impedance tensor and vertical magnetic transfer function data produced by multiple sets of two independent active sources. Forward simulations are performed with a finite‐element solver. Increased sensitivities at source locations can optionally be counteracted with a weighting function in the regularization term to reduce source‐related anomalies in the resistivity model. We investigate the capabilities of the inversion code using one synthetic and one field example. The results demonstrate that we can produce reliable subsurface models, although data sets from single pairs of independent sources remain challenging. [ABSTRACT FROM AUTHOR]

In order to improve the identification and characterization of underground fractured reservoirs, seismic inversion for elastic properties and fracture indicators is required. To improve the accuracy of seismic inversion, model constraints are necessary. Model constraints of P‐ and S‐wave moduli can be provided by well logging data; however, model constraints of fracture weaknesses are often unavailable. To obtain model constraints of fracture weaknesses, we propose a two‐stage inversion method, which is implemented as (1) estimating azimuthal elastic impedance (AEI) and fracture facies using partially incidence‐angle‐stacked seismic data at different azimuths; and (2) using the estimated azimuthal elastic impedance to predict P‐ and S‐wave moduli, density and fracture weaknesses, which is constrained by models constructed using the estimated fracture facies. In the first stage, we use Gaussian mixture prior distribution to obtain azimuthal elastic impedance of different incidence angles and azimuths, and we also predict fracture facies combining the obtained azimuthal elastic impedance and seismic data. In the second stage, we implement the Bayesian maximum a posterior inversion for estimating unknown parameter vectors. We apply the proposed inversion method to noisy synthetic seismic data, which illustrates the inversion method is robust even in the case of a signal‐to‐noise ratio of 1. Tests on real data reveal that reliable results of P‐ and S‐wave moduli and fracture weaknesses are obtained, which verifies that the inversion method is a valuable tool for generating reliable fracture indicators from azimuthal seismic data for identifying underground fractured reservoirs. [ABSTRACT FROM AUTHOR]

The Baribis Fault is an important active fault in West Java, Indonesia. This fault has recently attracted the attention of many parties since some fault segments pass through densely populated areas, raising the risk of shallow earthquakes. The long-offset resistivity tomography method was applied to image the active Baribis Faults. This method clearly showed the subsurface geometry, including the contact characteristics of the Baribis Fault near the subsurface. The long-offset resistivity tomography surveys were acquired using multi-electrodes and multi-nodes, and the data acquisition was wirelessly controlled via a WiFi connection. The pole–dipole resistivity tomography image shows a 35∘ dip overhang structure of the Baribis thrust fault near the Jatigede area in Middle Eastern West Java, with a strike fault segment in a relative East–West direction. However, the other long-offset tomography images in northeastern West Java, near the Conggeang–Sumedang area, show the oblique thrust fault phenomena of the Baribis Fault with a strike in the Northeast–Southwest direction. The stress caused by the Cimandiri–Lembang regional strike–slip fault likely influences the dynamics of the Baribis Fault in the close area of Sumedang. [ABSTRACT FROM AUTHOR]

Transparency ( Z s d ) can most intuitively reflect changes in marine ecosystems, therefore; the data of Z s d is crucial to protect marine ecosystems. However, there is still a relative lack of long-term sequence data on Z s d for coastal turbid waters. Satellite remote sensing inversion provides an efficient means of obtaining long-term, large-scale Z s d . The method proposed by Lee et al. is currently one of the most widely used methods, which is divided into clear water and turbid water models based on the 670 nm remote sensing reflectance ( R r s ). In this study, we employed an improved model building upon the aforementioned method. The improved model simulates the continuous transition from clear to turbid water, which allows for automatic adjustment of model weights based on a logistic curve. Utilizing this improved model, this study inverts Z s d within 100 km of the eastern coast of China from 2003 to 2023 using MODIS Aqua Level 2 R r s data. Z s d shows an increasing trend with the distance from the coast, with high Z s d in the northern Yellow Sea, the southern Shandong Peninsula, and the far shore of the East China Sea, and the low Z s d in the coast of Bohai Sea and northern Jiangsu. As for the long-term changes, the number of pixels with significantly increased Z s d and those with significantly decreased Z s d and no significant changes accounted for 20.84%, 1.14%, and 78.02%, respectively. The order of seasonal Z s d is summer > autumn > spring > winter, and the seasonal variability amplitude increases synchronously with the offshore distance of seawater on the whole. Interestingly, the correlation between Z s d and the annual runoff of rivers exhibits spatial differentiation among six typical estuaries: there are positive correlations in northern, whereas negative correlations in the south. Additionally, the Z s d in five of six estuaries have negative correlations with annual sediment transport. Overall, this study not only provides more accurate and continuous data of Z s d for nearshore turbid waters compared to those obtained by the original model, but also offers valuable insights on the spatiotemporal variation in the Z s d of large-scale seawater. [ABSTRACT FROM AUTHOR]

Inversions are thought to play a key role in adaptation and speciation, suppressing recombination between diverging populations. Genes influencing adaptive traits cluster in inversions, and changes in inversion frequencies are associated with environmental differences. However, in many organisms, it is unclear if inversions are geographically and taxonomically widespread. The intertidal snail, Littorina saxatilis, is one such example. Strong associations between putative polymorphic inversions and phenotypic differences have been demonstrated between two ecotypes of L. saxatilis in Sweden and inferred elsewhere, but no direct evidence for inversion polymorphism currently exists across the species range. Using whole genome data from 107 snails, most inversion polymorphisms were found to be widespread across the species range. The frequencies of some inversion arrangements were significantly different among ecotypes, suggesting a parallel adaptive role. Many inversions were also polymorphic in the sister species, L. arcana, hinting at an ancient origin. [ABSTRACT FROM AUTHOR]

Local adaptation is facilitated by loci clustered in relatively few regions of the genome, termed genomic islands of divergence. The mechanisms that create and maintain these islands and how they contribute to adaptive divergence is an active research topic. Here, we use sockeye salmon as a model to investigate both the mechanisms responsible for creating islands of divergence and the patterns of differentiation at these islands. Previous research suggested that multiple islands contributed to adaptive radiation of sockeye salmon. However, the low‐density genomic methods used by these studies made it difficult to fully elucidate the mechanisms responsible for islands and connect genotypes to adaptive variation. We used whole genome resequencing to genotype millions of loci to investigate patterns of genetic variation at islands and the mechanisms that potentially created them. We discovered 64 islands, including 16 clustered in four genomic regions shared between two isolated populations. Characterisation of these four regions suggested that three were likely created by structural variation, while one was created by processes not involving structural variation. All four regions were small (< 600 kb), suggesting low recombination regions do not have to span megabases to be important for adaptive divergence. Differentiation at islands was not consistently associated with established population attributes. In sum, the landscape of adaptive divergence and the mechanisms that create it are complex; this complexity likely helps to facilitate fine‐scale local adaptation unique to each population. [ABSTRACT FROM AUTHOR]

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Inversions are balanced structural variants that often remain undetected in genetic diagnostics. We present a female proband with a de novo Chromosome 15 paracentric inversion, disrupting MEIS2 and NUSAP1. The inversion was detected by short-read genome sequencing and confirmed with adaptive long-read sequencing. The breakpoint junction analysis revealed a 96 bp (bp) deletion and an 18 bp insertion in the two junctions, suggesting that the rearrangement arose through a replicative error. Transcriptome sequencing of cultured fibroblasts revealed normal MEIS2 levels and 0.61-fold decreased expression of NUSAP1. Furthermore, three fusion transcripts were detected and confirmed by Sanger sequencing. Heterozygous loss of MEIS2 (MIM# 600987) is associated with a cleft palate, heart malformations, and intellectual impairment, which overlap with the clinical symptoms observed in the proband. The observed fusion transcripts are likely non-functional, and MEIS2 haploinsufficiency is the likely disease causative mechanism. Altogether, this study's findings illustrate the importance of including inversions in rare disease diagnostic testing and highlight the value of long read sequencing for the validation and characterization of such variants. [ABSTRACT FROM AUTHOR]

Delineation of structural variants (SVs) at sequence resolution in highly repetitive genomic regions has long been intractable. The sequence properties, origins, and functional effects of classes of genomic rearrangements such as ring chromosomes and Robertsonian translocations thus remain unknown. To resolve these complex structures, we leveraged several recent milestones in the field, including (1) the emergence of long-read sequencing, (2) the gapless telomere-to-telomere (T2T) assembly, and (3) a tool (BigClipper) to discover chromosomal rearrangements from long reads. We applied these technologies across 13 cases with ring chromosomes, Robertsonian translocations, and complex SVs that were unresolved by short reads, followed by validation using optical genome mapping (OGM). Our analyses resolved 10 of 13 cases, including a Robertsonian translocation and all ring chromosomes. Multiple breakpoints were localized to genomic regions previously recalcitrant to sequencing such as acrocentric p-arms, ribosomal DNA arrays, and telomeric repeats, and involved complex structures such as a deletion-inversion and interchromosomal dispersed duplications. We further performed methylation profiling from long-read data to discover phased differential methylation in a gene promoter proximal to a ring fusion, suggesting a long-range position effect (LRPE) with heterochromatin spreading. Breakpoint sequences suggested mechanisms of SV formation such as microhomology-mediated and non-homologous end-joining, as well as non-allelic homologous recombination. These methods provide some of the first glimpses into the sequence resolution of Robertsonian translocations and illuminate the structural diversity of ring chromosomes and complex chromosomal rearrangements with implications for genome biology, prediction of LRPEs from integrated multi-omics technologies, and molecular diagnostics in rare disease cases. [Display omitted] Genomic rearrangements in repetitive regions have long eluded sequence-based characterization by standard genomic methods. We leveraged long-read sequencing to resolve the architecture of ring chromosomes, Robertsonian translocations, and complex balanced rearrangements. We discovered unexpected complexity, mechanistic signatures, and DNA methylation changes, with implications for molecular diagnosis and genome biology. [ABSTRACT FROM AUTHOR]

Sieruela rutidosperma, the Fringed Spiderflower, is a medicinal weed in the family Cleomaceae. We obtained the sequence of the complete plastid genome (157,073 bp), which has two inverted repeats (IRs) (26,083 bp each), a large single-copy (LSC) region (86,423 bp), and a small single-copy (SSC) region of (18,484 bp). The genome includes 132 genes, including eight rDNA genes, 37 tDNA genes, and 87 protein-coding genes. We identified an 18.8 kb inversion in Coalisina paradoxa and a 19.5 kb inversion in Gynandropsis gynandra. Tarenaya hassleriana is the closest relative of S. rutidosperma, which was formerly included in Cleome. Cleomella is the out-group to other Cleomaceae. Comparison with other genera of Cleomaceae, Brassicaceae, and Capparaceae confirms that the last is the sister group to the first two. Identification of SNP-rich regions in the cpDNA genomes of Cleomaceae may provide useful molecular means for species identification. [ABSTRACT FROM AUTHOR]

The characterization of the subsurface profile properties that are susceptible to liquefaction during earthquakes is of great importance in accident prevention. Field data can be used in a stochastic inverse problem to estimate the material properties using Markov chain Monte Carlo Methods (McMC) that incorporates prior knowledge about the unknown parameters as well as the data available. Here, we numerically study the Random Walk (RW) and Differential Evolution (DE) variations of the Metropolis algorithm in the context of seismic modeling considering the viscoelastic wave equation. The algorithms are tested with data from the 1987 Superstition Hills earthquake recorded at the Wildlife Site. The results show the effectiveness and accuracy of these algorithms, with emphasis to DE which reached convergence earlier compared to RW. [ABSTRACT FROM AUTHOR]

The ongoing convergence between the Indian and Eurasian plates has formed the Himalayan mountain range, which has its highest peak at Mount Everest. A study of the detailed crustal shear velocity structure beneath the Mount Everest region that is present at the boundary between Nepal and Tibet will improve our understanding of the mountain-building process and plate dynamics in this collision zone. In this study, 250 earthquake data have been used to compute receiver functions at three seismic stations (NNAMC, EVN and RBSH) operated in the study region. Receiver functions have been modelled through different approaches, namely H-κ stacking, Neighbourhood Algorithm and joint inversion. The results reveal a thicker crust (~52–58 km) with average shear velocity (Vs) ~3.35–3.46 km/s and felsic-to-intermediate composition (Vp/Vs: 1.722–1.744) in the study region. We also observed a low Vs in the lower crust (~3.6–3.7 km/s) with upper mantle Vs ~4.3–4.6 km/s. The ratio of mountain surface elevation to crustal root is found to be between ~0.14 and 0.2, which is well correlated with the younger orogen (<100 Myr). At ~20–24 km depth, a mid-crustal low-velocity zone is present beneath the study region, which could be due to the presence of partial melt at that depth. Research highlights: Inversion of receiver function and surface wave dispersion data reveals a thicker crust (~52–58 km) with average shear velocity (Vs) ~3.35–3.46 km/s and felsic-to-intermediate composition (Vp/Vs: 1.72–1.74) beneath the Mount Everest region. Low Vs observed in the lower crust (~3.6–3.7 km/s). R-value (Ratio of mountain surface relief to crustal root) is ~0.14 to 0.2, well correlated with the younger orogens. A prominent Low-Velocity Zone (LVZ) associated with the Main Himalayan Thrust (MHT) is observed at ~20–24 km depth. [ABSTRACT FROM AUTHOR]

The Indo-Gangetic Plain (IGP) is characterized by thick sediments, predominantly comprising alluvial deposits, which can amplify seismic waves generated by earthquakes in the Himalayan region located to the north of the plain. The presence of loose sediments can indeed pose significant seismic hazards, mainly due to phenomena like soil liquefaction. These sediments pose a threat to densely populated Delhi and NCR regions, which are 200 km away from the plate boundary of India and the Eurasian plate. Scientists are concerned about people's safety in mitigating damage caused by high-rise buildings and loose sediments in the IGP region. Reliable knowledge of the sedimentary layer's thickness and velocity structure is crucial for investigating buried active faults, understanding significant destruction, and risk assessment. Sedimentary basins are also crucial for geo-resources such as hydrocarbon and geothermal energy. This research estimated the structure of the sedimentary layer beneath four stations in the Chandigarh–Ambala region in IGP using the high-frequency receiver function (PRF) technique. The study found that the sedimentary layer thickness varies significantly, with values from 2.0 to 3.0 km beneath the IGP and increasing northward. Shallow shear velocity (Sv) in the column of sediments below the Siwalik Himalaya ranges from 2.8 to 2.9 km/s, which can be utilized for assessing earthquake ground-motion sites. The study provides new perceptions of the geodynamic processes and seismotectonic structure of the Himalayan region, allowing for better identification of the earthquake hypocenter and assessment of seismic hazards. The shear wave velocity models estimated from this research can also be beneficial for assessing seismic hazards and earthquake-resistant construction. Estimates of the crustal thickness values from waveform inversion of the PRF at individual stations reveal that the Moho depth varies between 44 and 50 km in the Indo-Gangetic Plain. From Siwalik Himalaya to the higher Himalaya, it ranges from 44 to 65 km. The depth of Moho increases from the Indo-Gangetic plain towards the lesser Himalaya. [ABSTRACT FROM AUTHOR]

The processing of potential field datasets requires many steps; one of them is the inverse modeling of potential field data. Using a measurement dataset, the purpose is to evaluate the physical and geometric properties of an unidentified model in the subsurface. Because of the ill-posedness of the inverse problem, the determination of an acceptable solution requires the imposition of a regularization term to stabilize the inversion process. We also need a regularization parameter that determines the comparative weights of the stabilization and data fit terms. This work offers an evaluation of automated strategies for the estimation of the regularization parameter for underdetermined linear inverse problems. We look at the methods of generalized cross validation, active constraint balancing (ACB), the discrepancy principle, and the unbiased predictive risk estimator. It has been shown that the ACB technique is superior by applying the algorithms to both synthetic data and field data, which produces density models that are representative of real structures and demonstrate the method's supremacy. Data acquired over the chromite deposit in Camaguey, Cuba, are utilized to corroborate the procedures for the inversion of experimental data. The findings gathered from the three-dimensional inversion of gravity data from this region demonstrate that the ACB approach gives appropriate estimations of anomalous density structures and depth resolution inside the subsurface. [ABSTRACT FROM AUTHOR]

Having been a seemingly unreachable ideal for decades, 3-D full-waveform inversion applied to massive seismic datasets has become reality in recent years. Often achieving unprecedented resolution, it has provided new insight into the structure of the Earth, from the upper few metres of soil to the entire globe. Motivated by these successes, the technology is now being translated to medical ultrasound and non-destructive testing. Despite remarkable progress, the computational cost of fullwaveform inversion continues to be a major concern. It limits the amount of data that can be exploited, and it largely inhibits quantitative and comprehensive uncertainty analyses. These notes complement a presentation on recent developments in full-waveform inversion that are intended to reduce computational cost and assimilate more data, thereby improving tomographic resolution. The suite of strategies includes flexible and user-friendly spectral-element simulations, the design of wavefieldadapted meshes that harness prior information on wavefield geometry, dynamic mini-batch optimisation that naturally takes advantage of data redundancies, and collaborative multi-scale updating to jointly constrain crustal and mantle stru [ABSTRACT FROM AUTHOR]

In this study, we present an artificial neural network (ANN)-based approach for travel-time tomography of a volcanic edifice under sparse-ray coverage. We employ ray tracing to simulate the propagation of seismic waves through the heterogeneous medium of a volcanic edifice, and an inverse modeling algorithm that uses an ANN to estimate the velocity structure from the "observed" travel-time data. The performance of the approach is evaluated through a 2-dimensional numerical study that simulates i) an active source seismic experiment with a few (explosive) sources placed on one side of the edifice and a dense line of receivers placed on the other side, and ii) earthquakes located inside the edifice with receivers placed on both sides of the edifice. The results are compared with those obtained from conventional damped linear inversion. The average Root Mean Square Error (RMSE) between the input and output models is approximately 0.03 km/s for the ANN inversions, whereas it is about 0.4 km/s for the linear inversions, demonstrating that the ANN-based approach outperforms the classical approach, particularly in situations with sparse ray coverage. Our study emphasizes the advantages of employing a relatively simple ANN architecture in conjunction with second-order optimizers to minimize the loss function. Compared to using first-order optimizers, our ANN architecture shows a ~25% reduction in RMSE. The ANNbased approach is computationally efficient. We observed that even though the ANN is trained based on completely random velocity models, it is still capable of resolving previously unseen anomalous structures within the edifice with about 5% anomalous discrepancies, making it a potentially valuable tool for the detection of low velocity anomalies related to magmatic intrusions or mush. [ABSTRACT FROM AUTHOR]

The dynamic cone penetrometer (DCP) provides local soil resistance information. The difference in the vertical and horizontal data resolution (centimetric vs. multi‐metric) makes it difficult to spatialize the DCP data directly. This study uses a high‐resolution Vs$V_s$ section, extracted by the seismic surface‐wave method, as the auxiliary and physical constraint for mapping the DCP index (DCPI). Geostatistical formalism (kriging and cokriging) is used. The associated measurement error of the seismic surface‐wave data is also included in the cokriging system, that is, the cokriging with variance of measurement error (CKVME). The proposed methods are validated for the first time on a test site designed and constructed for this study, with known geotechnical perspectives. Seismic and high‐intensity DCP campaigns were performed on the test site. The results show that with decimating the number of DCP soundings, the kriging approach is no longer capable of estimating the lateral variation in the test site, and the root‐mean‐square error (RMSE) value of the kriging section is increased by 87%$87\%$. With the help of Vs$V_s$ sections constraining the lateral variability model, the RMSE values of the cokriging and the CKVME sections are increased by 25%$25\%$ and 17%$17\%$. [ABSTRACT FROM AUTHOR]

The proposed 3D stitching of spatially constrained Common Midpoint (CMP) 1D inversions is a method that integrates 1D laterally constrained inversion of marine Controlled-Source Electromagnetic (CSEM) data in the CMP domain to generate a cube of resistivities from 3D surveys. An interpretive model is built in the form of a set of columns of homogeneous cells that form a 3D resistivity grid. The proposed methodology is an iterative process that updates the entire model until it minimizes the data misfit between the real and synthetic data. We connect the model cells by smoothing regularization applied in all three directions, which generates stable solutions. Additionally, we evaluate the inversion result constrained by hard information, for example, well log resistivity data. Two applications to synthetic data and the inversion of a real data set illustrate the method. The synthetic data were generated from 3D models, one with two resistive targets at different depths and a second with a target inside a conductive layer over a resistive basement. The real data were gathered off the southeast coast of Brazil, in an area of gas hydrate accumulation. The results indicate that the method can generate useful approximations to the resistivity structures under the survey area in a much shorter time than that of a full 3D inversion. [ABSTRACT FROM AUTHOR]

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Satellites with dual‐frequency Global Navigation Satellite Systems (GNSS) receivers can measure integrated electron density, known as slant Total Electron Content (sTEC), between the receiver and transmitter. Precise relative variations of sTEC are achievable using phase measurements on L1 and L2 frequencies, yielding an accuracy of around 0.1 TECU or better. However, CubeSats like Spire LEMUR, with simpler setups (e.g., patch antennas) and code noise in the order of several meters, face limitations in accuracy. Their precision, determined by phase observations, remains in the 0.1–0.3 TECU range. With a substantial number of observations and comprehensive coverage of lines of sight between Low Earth Orbit (LEO) and GNSS satellites, global electron density can be reconstructed from sTEC measurements. Utilizing 27 satellites from various missions, including Swarm, Gravity Recovery And Climate Experiment Follow‐On, Jason‐3, Sentinel 1/2/3, COSMIC‐2, and Spire CubeSats, a cubic B‐spline expansion in magnetic latitude, magnetic local time, and altitude is employed to model the logarithmic electron density. Hourly snapshots of the three‐dimensional electron density are generated, adjusting the model parameters through non‐linear least squares based on sTEC observations. Results demonstrate that including Spire significantly enhances estimates, showcasing exceptional agreement with in situ observations from Swarm and Defense Meteorological Satellite Program LEO satellites. The model outperforms contemporary climatological models, such as International Reference Ionosphere (IRI)‐2020 and the neural network‐based NET model. Validation efforts include comparisons with ground‐based sTEC measurements, space‐based vertical TEC from Jason‐3 altimetry, and global TEC maps from the Center for Orbit Determination in Europe and the German Research Center for Geosciences (GFZ). Key Points: Slant Total Electron Content (TEC) from 27 LEO satellites, including Spire LEMURE CubeSats, is used to model the electron density in the ionosphere/plasmasphereExcellent agreement is achieved in validating against in situ electron density measured by Swarm and DMSP and altimeter vTEC from Jason‐3Comparison to up‐to‐date artificial neural network models is performed [ABSTRACT FROM AUTHOR]

In seismic exploration, particularly within the domain of oil and gas reservoirs, the accurate imaging of complex fault blocks and the identification of structural traps are important. Geological risk factors, including the implementation of structural traps, reservoir delineation, and precise target drilling, require immediate attention in practical exploration. Addressing these factors involves two primary challenges: ensuring imaging accuracy and minimizing structural distortions. This study introduces a high‐resolution velocity modelling technique with fault control, specifically developed to mitigate misties between seismic image and well‐log data and improve the accuracy of seismic depth imaging and well depth correlation. The method offers a targeted solution to the challenges of implementing structural traps, delineating reservoirs and executing precise drilling operations. By incorporating fault control, it accounts for the structural complexity of subsurface media, enabling an accurate inversion of velocity variations across fault blocks. This approach ensures that velocity models, constrained by geological and structural models, exhibit a high degree of consistency. Utilizing fault‐controlled travel time inversion, the method resolves mistier between seismic imaging and well‐log data, guaranteeing the precision of velocity models and imaging. The methodology provides reliable seismic data for target evaluation, effectively reducing exploration risks and improving the accuracy of velocity modelling. [ABSTRACT FROM AUTHOR]

The threat posed by the increasing concentration of carbon dioxide (CO2) in the atmosphere motivates a detailed and precise estimation of CO2 emissions and removals over the globe. This study refines the spatial resolution of the CAMS/LSCE inversion system, achieving a global resolution of 0.7° latitude and 1.4° longitude, or three times as many grid boxes as the current operational setup. In a 2‐year inversion assimilating the midday clear‐sky retrievals of the column‐averaged dry air mole fraction of carbon dioxide (XCO2) from NASA's second Orbiting Carbon Observatory (OCO‐2), the elevated resolution demonstrates an improvement in the representation of atmospheric CO2, particularly at the synoptic timescale, as validated against independent surface measurements. Vertical profiles of the CO2 concentration differ slightly above 22 km between resolutions compared to AirCore profiles, and highlight differences in the vertical distribution of CO2 between resolutions. However, this disparity is not evident for XCO2, as evaluated against independent reference ground‐based observations. Global and regional estimates of natural fluxes for 2015–2016 are similar between the two resolutions, but with North America exhibiting a higher natural sink at high resolution for 2016. Overall, both inversions seem to yield reasonable estimates of global and regional natural carbon fluxes. The increase in calculation time is less than the increase in the number of operations and in the volume of input data, revealing greater efficiency of the code executed on a graphics processing unit. This allows us to make this higher resolution the new standard for the CAMS/LSCE system. Plain Language Summary: Human activities have significantly increased the amount of carbon dioxide (CO2) in the atmosphere, a major driver of climate change. Accurately quantifying CO2 emissions and removals, known as fluxes, is crucial for implementing effective mitigation strategies. Inverse models are computer programs that analyze large amounts of CO2 observations to estimate surface fluxes that best match these observations in space and time. While satellites provide extremely precise CO2 observations all around the Earth, most inverse models lack the resolution to fully utilize these data at a large scale. Our study doubled the horizontal resolution of our inverse model, enhancing its performance and spatial precision when using data from the OCO‐2 satellite. Thanks to graphics processing unit acceleration, the computational cost remained manageable. This improved resolution is now being implemented in the European Copernicus Atmosphere Monitoring Service, with ongoing efforts to further improve the resolution. This advancement promises a more detailed understanding of global CO2 dynamics, supporting climate change mitigation efforts. Key Points: We upgraded our global atmospheric inverse system to 0.7° latitude by 1.4° longitude with a modest computational overheadThe resolution increase improves CO2 transport representation, benefiting coastal stations the mostWhile global flux estimates in 2015–2016 were similar across resolutions, the high resolution attributes higher sinks to land than ocean [ABSTRACT FROM AUTHOR]

Amplitude versus offset attribute inversion primarily utilizes the change in amplitude with offset to extract lithologic information of the reservoir. We performed high resolution imaging using three‐dimensional seismic data and simulated four different models of gas hydrate and free gas to optimize the selection of sensitive attributes for gas hydrate characterization. We optimized the selection of sensitive attributes for gas hydrate characterization. Our research identified the gradient parameter G as a highly sensitive attribute for characterizing gas hydrate reservoirs. By comparing theoretical models with drilling site data, we predicted the saturation variations of gas hydrate based on the amplitude of G and summarized the amplitude versus offset characteristics and sensitive attributes of gas hydrate at different saturation levels. The results offer valuable insights for identification of gas hydrate in seismic data and provide a reference for their characterization. [ABSTRACT FROM AUTHOR]

Genomic structural variants (SVs) play a crucial role in adaptive evolution, yet their average fitness effects and characterization with pangenome tools are understudied in wild animal populations. We constructed a pangenome for House Finches (Haemorhous mexicanus), a model for studies of host-pathogen coevolution, using long-read sequence data on 16 individuals (32 de novo-assembled haplotypes) and one outgroup. We identified 887,118 SVs larger than 50 base pairs, mostly (60%) involving repetitive elements, with reduced SV diversity in the eastern US as a result of its introduction by humans. The distribution of fitness effects of genome-wide SVs was estimated using maximum likelihood approaches and revealed that SVs in both coding and noncoding regions were on average more deleterious than smaller indels or single nucleotide polymorphisms. The reference-free pangenome facilitated identification of a > 10-My-old, 11-megabase-long pericentric inversion on chromosome 1. We found that the genotype frequencies of the inversion, estimated from 135 birds widely sampled temporally and geographically, increased steadily over the 25 y since House Finches were first exposed to the bacterial pathogen Mycoplasma gallisepticum and showed signatures of balancing selection, capturing genes related to immunity and telomerase activity. We also observed shorter telomeres in populations with a greater number of years exposure to Mycoplasma. Our study illustrates the utility of long-read sequencing and pangenome methods for understanding wild animal populations, estimating fitness effects of genome-wide SVs, and advancing our understanding of adaptive evolution through structural variation. [ABSTRACT FROM AUTHOR]

Identification of preferential flow‐paths, such as fractures, is required for various issues in geosciences. When chemicals are injected into the subsurface, monitoring the resulting structural and chemical changes remains a challenge. The ability of geophysical tomography to tackle this problem is not fully explored due to the lack of numerical methods suitable for modeling narrow structures. We explore how discrete representation of preferential flow‐paths provides innovative ways to invert electrical resistivity data collected during reagent injection at a contaminated site. The data set is inverted with a scheme where a new fracture is added at every iteration. This allows identifying newly created narrow conductive structures from the field data collected before and after injection. Fracture location remains overall consistent despite using different starting points for the fracture search. A prior constraint on fracture length improves convergence. These results show the potential of discrete inversion for identifying narrow structures from electrical resistivity monitoring. Plain Language Summary: In some geoscience activities, such as water extraction, geological storage, and contaminant fate, chemical solutions are injected into wells, which potentially leads to modify the system properties. Identifying preferential flow‐paths is therefore necessary even though monitoring structural and chemical changes in the subsurface remains a challenge. Electrical geophysical imaging techniques may be adapted to provide information for this purpose. However, their potential is not fully explored due to the limitations of numerical models to represent small‐scale structures. In this work, we explore the representation of preferential flow‐paths by discrete elements for the inversion of electrical resistivity data collected during a reagent injection at a contaminated site. The inversion scheme based on discrete forward simulations successively identifies small‐scale structures and focuses on evaluating their localization and length. The results show the need for further efforts to use discrete electrical resistivity inversion in the presence of narrow structures. Key Points: Standard modeling strategies are not well suited to identify narrow geological structures from cross‐borehole electrical data inversionInversion scheme based on discrete‐dual‐porosity forward simulations enable to image narrow preferential flow‐paths from electrical dataPrior assumptions are needed to invert the position and length of narrow preferential flow‐paths [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Sex chromosomes evolve from an autosomal pair after the acquisition of a sex-determining gene. The primary sex chromosomes are homomorphic in both sexes and often undergo heteromorphism in either sex (XY in males or ZW in females) in association with chromosome rearrangements such as inversion, which creates a non-recombining region, called a stratum. Then, multiple strata may form by sequential inversions and extend the non-recombining region, where gene divergence accelerates, and degeneration of the Y or W chromosome progressively occurs. Summary: In contrast to the conventional theory, we propose a shortcut in heteromorphic sex chromosome evolution, where an autosomal pair directly evolves into a heteromorphic sex chromosome pair. We illustrate this with two frog cases where Y chromosome or autosome, which is morphologically inverted, was introgressed from another species through interspecific hybridization, instantly forming a new heteromorphic sex chromosome pair. This event resulted in a distinct non-recombining region immediately after hybridization. Key Messages: The introduction of an inverted chromosome from a different species may be associated with benefits in morphology, breeding behavior, hybrid viability, sex determination, and recovery of the sex ratio of the hybrids. We discuss the molecular mechanisms driving preferential mutations in the introduced, inverted chromosome through interspecific hybridization. [ABSTRACT FROM AUTHOR]