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Deep-learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep-learning full waveform inversion method that after the neural network has been trained can image CO2 saturation and its uncertainty in real time. Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO2 saturation models and once fully trained directly estimates CO2 saturation as a function of subsurface location. We further quantify uncertainties of CO2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof-of-concept study, the CO2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep-learning inversion experiments using noise-free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep-learning inversion can estimate 2D distributions of CO2 fairly well even in the presence of Gaussian random noise and (2) both CO2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep-learning inversion method can serve as a robust real-time monitoring tool for geological carbon storage and/or other time-varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.

This study demonstrates an integrated approach using a seismic-coloured inversion to derive the hydrocarbon potential of a field in the Upper Assam basin. The well-log analysis indicated the presence of hydrocarbon in the form of gas, oil, and water in sandstones with thin streaks of limestone and shale layers. The Acoustic Impedance (AI) model was generated from post-stack seismic data using a coloured inversion. Shale volume (Vshale), density, porosity and water saturation are the petrophysical parameters spatially populated using a multilayered feed-forward neural network on the inversion-based AI model. The estimated range of model-derived parameters varies as Velocity: 2136–5034 m/s, Vshale: 0–48%, Density: 2.14–2.72 gm/cc, Porosity: 6–29%, Water saturation: 9–28%, and demonstrates a reasonable correlation to well-log data. Seismic attribute for thin-fault likelihood was used to interpret the major faults. From the combined analysis based on interpreted faults and modelled parameters, the northwestern part of the study area displays a thick hydrocarbon-bearing zone (due to increasing thickness and reservoir quality). This interpretation is based on the consideration that faults are open. Thus, assuming continuity in the sequence, this northwestern region can be considered as one of the promising candidates for further exploration. [ABSTRACT FROM AUTHOR]

The extension of the seismic bandwidth to lower frequencies enhances impedance contrasts that can be poorly represented by the broadband acquisition wavelet. Furthermore, long filters that are used to shape the wavelet of processed data can cause issues with noise, phase and interference between seismic events. In this paper, we use a mathematical technique known as mollification to resolve impedance variations with the highest detail allowed by the bandwidth of the data. The mollifier is integrated and windowed to match the low‐frequency content of the data to yield a convenient conversion to relative impedance. Synthetic data created from wedge models show that the windowed mollifier provides an improved representation of the impedance profile. This is replicated by application to an acoustic well log and a regular seismic dataset recorded in the Southern North Sea as well as a broadband dataset recorded in the North Sea. [ABSTRACT FROM AUTHOR]

Reflectivity inversion is a key step in reservoir prediction. Conventional sparse‐spike deconvolution assumes that the reflectivity (reflection coefficient series) is sparse and solves for the reflection coefficients by an L1‐norm inversion process. Spectral inversion is an alternative to sparse‐spike deconvolution, which is based on the odd–even decomposition algorithm and can accurately identify thin layers and reduce the wavelet tuning effect without using constraints from logging data, from horizon interpretations or from an initial model of the reflectivity. In seismic processing, an error exists in wavelet extraction because of complex geological structures, resulting in the low accuracy of deconvolution and inversion. Blind deconvolution is an effective method for solving the problem mentioned above, which comprises seismic wavelet and reflectivity sequence, assuming that the wavelets that affect some subsets of the seismic data are approximately the same. Therefore, we combined blind deconvolution with spectral inversion to propose blind spectral inversion. Given an initial wavelet, we can calculate the reflectivity based on spectral inversion and update the wavelet for the next iteration. During the update processing, we add the smoothness of the wavelet amplitude spectrum as a regularization term, thus reducing the wavelet oscillation in the time domain, increasing the similarity between inverted and initial wavelets, and improving the stability of the solution. The blind spectral inversion method inherits the wavelet robustness of blind deconvolution and high resolution of spectral inversion, which is suitable for reflectivity inversion. Applications to synthetic and field seismic datasets demonstrate that the blind spectral inversion method can accurately calculate the reflectivity even when there is an error in wavelet extraction. [ABSTRACT FROM AUTHOR]

Over the past decades, surface wave methods have been routinely employed to retrieve the physical characteristics of the first tens of meters of the subsurface, particularly the shear wave velocity profiles. Traditional methods rely on the application of the multichannel analysis of surface waves to invert the fundamental and higher modes of Rayleigh waves. However, the limitations affecting this approach, such as the 1D model assumption and the high degree of subjectivity when extracting the dispersion curve, motivate us to apply the elastic full‐waveform inversion, which, despite its higher computational cost, enables leveraging the complete information embedded in the recorded seismograms. Standard approaches solve the full‐waveform inversion using gradient‐based algorithms minimizing an error function, commonly measuring the misfit between observed and predicted waveforms. However, these deterministic approaches lack proper uncertainty quantification and are susceptible to get trapped in some local minima of the error function. An alternative lies in a probabilistic framework, but, in this case, we need to deal with the huge computational effort characterizing the Bayesian approach when applied to non‐linear problems associated with expensive forward modelling and large model spaces. In this work, we present a gradient‐based Markov chain Monte Carlo full‐waveform inversion where we accelerate the sampling of the posterior distribution by compressing data and model spaces through the discrete cosine transform. Additionally, a proposal is defined as a local, Gaussian approximation of the target density, constructed using the local Hessian and gradient information of the log posterior. We first validate our method through a synthetic test where the velocity model features lateral and vertical velocity variations. Then we invert a real dataset from the InterPACIFIC project. The obtained results prove the efficiency of our proposed algorithm, which demonstrates to be robust against cycle‐skipping issues and able to provide reasonable uncertainty evaluations with an affordable computational cost. [ABSTRACT FROM AUTHOR]

Surface geometry inversion of geophysical data has recently been introduced as an effective approach for generating surface‐based geological models. The models obtained through surface geometry inversion clearly delineate the contacts between distinct rock units, making them easily interpretable by geologists. Surface geometry inversion has shown promising preliminary results in other works, but the practical application of surface geometry inversion on real geophysical data has not been thoroughly investigated. To move towards a better understanding of the practicalities involved, we applied surface geometry inversion to a real magnetic dataset acquired over two kimberlite pipes located in north‐central Botswana. The objective was to assess the effectiveness and limitations of the surface geometry inversion approach in accurately characterizing the subsurface geometry and identifying the boundaries of the kimberlite pipes. We first perform an anomaly separation approach to isolate the magnetic anomalies associated with the kimberlite pipes. A surface geometry inversion algorithm was applied to the original and separated datasets using various initial models and other control parameters. Several tests were performed to investigate the effects that data processing, initial models, and other parameter choices have on the surface geometry inversion results. We successfully recover the geometry, extension and dip of the two kimberlite pipes. We discuss the results of our various tests and provide advice for practitioners interested in applying surface geometry inversion methods to their data. Our work indicates that surface geometry inversion can be used as a complementary approach to voxel inversion, and we propose an iterative surface geometry inversion algorithm as a possible alternative approach to voxel inversion for simple geological scenarios. This work provides valuable insights into the appropriate application of surface geometry inversion on real geophysical datasets. [ABSTRACT FROM AUTHOR]

Most Event Related Potential studies investigating the time course of visual processing have focused mainly on the N170 component. Stimulus orientation affects the N170 amplitude for faces but not for objects, a finding interpreted as reflecting holistic/configural processing for faces and featural processing for objects. Furthermore, while recent studies suggest where on the face people fixate impacts the N170, fixation location effects have not been investigated in objects. A data-driven mass univariate analysis (all time points and electrodes) was used to investigate the time course of inversion and fixation location effects on the neural processing of faces and houses. Strong and widespread orientation effects were found for both faces and houses, from 100-350ms post-stimulus onset, including P1 and N170 components, and later, a finding arguing against a lack of holistic processing for houses. While no clear fixation effect was found for houses, fixation location strongly impacted face processing early, reflecting retinotopic mapping around the C2 and P1 components, and during the N170-P2 interval. Face inversion effects were also largest for nasion fixation around 120ms. The results support the view that facial feature integration (1) depends on which feature is being fixated and where the other features are situated in the visual field, (2) occurs maximally during the P1-N170 interval when fixation is on the nasion and (3) continues past 200ms, suggesting the N170 peak, where weak effects were found, might be an inflexion point between processes rather than the end of a feature integration into a whole process. [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]

The Cerro Prieto basin, a tectonically active pull-apart basin, hosts significant geothermal resources currently being exploited in the Cerro Prieto Geothermal Field (CPGF). Consequently, natural tectonic processes and anthropogenic activities contribute to three-dimensional surface displacements in this pull-apart basin. Here, we obtained the Cerro Prieto Step-Over 3D surface velocity field (3DSVF) by accomplishing a weighted least square algorithm inversion from geometrically quasi-orthogonal airborne UAVSAR and RADARSAT-2, Sentinel 1A satellite Synthetic Aperture-Radar (SAR) imagery collected from 2012 to 2016. The 3DSVF results show a vertical rate of 150 mm/yr and 40 mm/yr for the horizontal rate, where for the first time, the north component displacement is achieved by using only the Interferometric SAR time series in the CPGF. Data integration and validation between the 3DSVF and ground-based measurements such as continuous GPS time series and precise leveling data were achieved. Correlating the findings with recent geothermal energy production revealed a subsidence rate slowdown that aligns with the CPGF's annual vapor production. [ABSTRACT FROM AUTHOR]

Tree trunks with thick bark can be expected to serve as the potential water storage capacity. Moisture content is one of the most important indicators to monitor the physiological condition of tree trunks in the development of agriculture and forestry. Therefore, it is crucial to accurately obtain the thickness and relative dielectric constant of the layered medium in tree trunks. The growth and health status of trees can be evaluated to optimize the water management strategies. To this end, this study aims to invert the dielectric parameters of tree trunk layered structure using radar detection. Non-invasive detection was realized for the internal structure of tree trunk. The electrical characteristics of different layers were obtained for the medium layers in the tree trunk. According to Snell's law, the physiological structure of tree trunks were combined to construct the propagation model of radar signals in the layered structure of tree trunks. Electromagnetic properties of different media layers in tree trunks were considered, such as the thickness and relative dielectric constant differences of bark, sapwood, and heartwood. These key parameters were then evaluated using radar echo signals. In addition, the accurate inversion was also performed on the dielectric parameters of tree trunks using sparse decomposition. K-SVD dictionary training was combined to process and analyze radar signals. A software defined radio (SDR) platform was used to build a tree trunk detection radar system in the experiment. The accuracy of detection was further improved to easily adjust the operating frequency of the radar, and then modify the signal processing under different environments. Layer stripping algorithm was selected to invert the dielectric constant and thickness of the layered structure in the tree trunks. The echo signals detected by radar were collected to evaluate the performance of the improved model. A comparative experiments were conducted on the radar detection under two conditions: aliasing and no aliasing in the echo signal. In the absence of aliasing, the inversion errors of the relative dielectric constant and layer thickness of the tree trunk were within 2.93% and 3.5%, respectively; In the case of echo aliasing, the inversion errors of relative dielectric constant and thickness were 7.52% and 7.61%, respectively. The experimental results show that the robustness and practicality were achieved to accurately estimate the thickness and relative dielectric constant of the layered medium in tree trunks under different signal environments. Application prospects were also offered to promote the plant physiology. The finding can provide the accurate data support to monitor the tree health status. Effective technical references can also be provided to promote the garden management and water control in the sustainable agriculture and forestry. [ABSTRACT FROM AUTHOR]

Lithology classification is a fundamental aspect of reservoir classification. Due to the limited availability of core samples, computational modelling methods for lithology classification based on indirect measurements are required. The main challenge for standard clustering methods is the complex vertical dependency of sedimentological sequences as well as the spatial coupling of well logs. Machine learning methods, such as recurrent neural networks, long short‐term memory and bidirectional long short‐term memory, can account for the spatial correlation of the measured data and the predicted model. Based on these developments, we propose a novel approach using two distinct models: a self‐attention‐assisted bidirectional long short‐term memory model and a multi‐head attention‐based bidirectional long short‐term memory model. These models consider spatial continuity and adaptively adjust the weight in each step to improve the classification using the attention mechanism. The proposed method is tested on a set of real well logs with limited training data obtained from core samples. The prediction results from the proposed models and the benchmark one are compared in terms of the accuracy of lithology classification. Additionally, the weight matrices from both attention mechanisms are visualized to elucidate the correlations between depth steps and to help analyse how these mechanisms contribute to improved prediction accuracy. The study shows that the proposed multi‐head attention‐based bidirectional long short‐term memory model improves classification, especially for thin layers. [ABSTRACT FROM AUTHOR]

Least‐squares reverse time migration is an increasingly popular technique for subsurface imaging, especially in the presence of complex geological structures. However, elastic least‐squares reverse time migration algorithms face significant practical and numerical challenges when migrating multi‐component seismic data acquired from irregular topography. Many associated issues can be avoided by abandoning the Cartesian coordinate system and migrating the data to a generalized topographic coordinate system conformal to surface topology. We introduce a generalized anisotropic elastic least‐squares reverse time migration methodology that uses the numerical solutions of tensorial elastodynamics for propagating wavefields in computational domains influenced by free‐surface topography. We define a coordinate mapping assuming unstretched vertically translated meshes that transform an irregular physical domain to a regular computational domain on which calculating numerical elastodynamics solutions is straightforward. This allows us to obtain numerical solutions of forward and adjoint elastodynamics and generate subsurface images directly in topographic coordinates using a tensorial energy‐norm imaging condition. Numerical examples demonstrate that the proposed generalized elastic least‐squares reverse time migration algorithm is suitable for generating high‐quality images with reduced artefacts and better balanced reflectivity that can accurately explain observed data acquired from topography in a medium characterized by arbitrary heterogeneity and anisotropy. Finally, the computational cost of our method is comparable to that of an equivalent Cartesian elastic least‐squares reverse time migration numerical implementation. [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]

In this study, we introduce elliptic inversion and investigate general properties and basic concepts of this inversion in l∞-plane or R2M, the plane equipped with the l∞-metric. In addition, we give some properties such as cross ratio and harmonic conjugates of the elliptic inversion in R²M. [ABSTRACT FROM AUTHOR]

When adaptive phenotypic variation or quantitative trait loci map within an inverted segment of a chromosome, researchers often despair because the suppression of crossing over will prevent the discovery of selective target genes that established the rearrangement. If an inversion polymorphism is old enough, then the accumulation of gene conversion tracts offers the promise that quantitative trait loci or selected loci within inversions can be mapped. The inversion polymorphism of Drosophila pseudoobscura is a model system to show that gene conversion analysis is a useful tool for mapping selected loci within inversions. D. pseudoobscura has over 30 different chromosomal arrangements on the third chromosome (Muller C) in natural populations and their frequencies vary with changes in environmental habitats. Statistical tests of five D. pseudoobscura gene arrangements identified outlier genes within inverted regions that had potentially heritable variation, either fixed amino acid differences or differential expression patterns. We use genome sequences of the inverted third chromosome (Muller C) to infer 98,443 gene conversion tracts for a total coverage of 142 Mb or 7.2× coverage of the 19.7 Mb chromosome. We estimated gene conversion tract coverage in the 2,668 genes on Muller C and tested whether gene conversion coverage was similar among arrangements for outlier vs non-outlier loci. Outlier genes had lower gene conversion tract coverage among arrangements than the non-outlier genes suggesting that selection removes exchanged DNA in the outlier genes. These data support the hypothesis that the third chromosome in D. pseudoobscura captured locally adapted combinations of alleles prior to inversion mutation events. [ABSTRACT FROM AUTHOR]

Understanding the genetic basis of species differences in flowering time and inflorescence architecture can shed light on speciation and molecular breeding. Melastoma shows rapid speciation, with about 100 species formed in the past few million years, and, meanwhile, possesses high ornamental values. Two largely sympatric and closely related species of this genus, M. candidum and M. normale, differ markedly in flowering time and flower number per inflorescence. Here, we constructed an F2 population between M. candidum and M. normale, and used extreme bulks for flowering time and flower number per inflorescence in this population to identify genomic regions underlying the two traits. We found high differentiation on nearly the whole chromosome 7 plus a few regions on other chromosomes between the two extreme bulks for flowering time. Large chromosomal inversions on chromosome 7 between the two species, which contain flowering-related genes, can explain recombinational suppression on the chromosome. We identified 1872 genes with one or more highly differentiated SNPs between the two bulks for flowering time, including CSTF77, FY, SPA3, CDF3, AGL8, AGL15, FHY1, COL9, CIB1, FKF1 and FAR1, known to be related to flowering. We also identified 680 genes with one or more highly differentiated SNPs between the two bulks for flower number per inflorescence, including PNF, FIL and LAS, knows to play important roles in inflorescence development. These large inversions on chromosome 7 prevent us from narrowing down the genomic region(s) associated with flowering time differences between the two species. Flower number per inflorescence in Melastoma appears to be controlled by multiple genes, without any gene of major effect. Our study indicates that large chromosomal inversions can hamper the identification of the genetic basis of important traits, and the inflorescence architecture of Melastoma species may have a complex genetic basis. [ABSTRACT FROM AUTHOR]

Atmospheric methane concentrations rose rapidly over the past decade and surged in 2020-2022 but the causes have been unclear. We find from inverse analysis of GOSAT satellite observations that emissions from the wet tropics drove the 2010-2019 increase and the subsequent 2020-2022 surge, while emissions from northern mid-latitudes decreased. The 2020-2022 surge is principally contributed by emissions in Equatorial Asia (43%) and Africa (30%). Wetlands are the major drivers of the 2020-2022 emission increases in Africa and Equatorial Asia because of tropical inundation associated with La Niña conditions, consistent with trends in the GRACE terrestrial water storage data. In contrast, emissions from major anthropogenic emitters such as the United States, Russia, and China are relatively flat over 2010-2022. Concentrations of tropospheric OH (the main methane sink) show no long-term trend over 2010-2022 but a decrease over 2020-2022 that contributed to the methane surge. [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]

Studying the source characteristics of doublet or multiple earthquake sequences presents significant challenges in seismology, especially with short time intervals between events. On November 14, 2021, a doublet earthquake (Mw 6.0 and Mw 6.1) occurred near Fin city, southern Iran, within a span of less than two minutes and 10 km apart. We employed the Kinematic Waveform Inversion (KIWI) procedure to determine the point and extended source parameters of these events, using a multistep inversion approach for stable solutions. Our analysis highlighted the directivity of the earthquakes: the first event exhibited bilateral directivity, causing a rupture area that reached the surface, while the second event showed unilateral westward directivity, supported by waveform amplitude differences observed at various stations. This directivity analysis plays an essential role in seismic hazard studies. Our findings regarding the source parameters of these recent doublet earthquakes in the Fin region align well with regional geological trends and fault patterns. However, retrieving the main fault plane for the second earthquake was challenging due to the complexities of the waveform. Moment tensor decomposition revealed significant non-double-couple components for the second event, indicating the complexity inherent in analyzing doublet events. This study underscores the critical role of precise waveform analysis and robust inversion techniques in understanding complex seismic events. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to explore the feasibility of using GF-1 satellite data to retrieve the concentration of dissolved nitrous oxide (N2O) in water so as to provide an effective way to realize low-cost and high-efficiency real-time monitoring of water quality. Methods The 1st and 5th drainage ditches in Qingtongxia Irrigation District of Ningxia were selected as the research objects, and the reflectance and water quality parameters of GF-1 satellite image band, which were highly correlated with the concentration of dissolved N2O in the drainage ditches, were selected as independent variables, and the optimal combination of independent variables was determined by optimal subset screening method. Multiple linear regression, BP neural network and support vector machine models were respectively constructed to predict and compare the concentration of dissolved N2O in water. [Results] The water temperature (T) and dissolved organic carbon (DOC) were the main factors affecting the concentration of dissolved N2O in water, and satellite bands such as near infrared (NIR) were significantly correlated with the variation trend of dissolved N2O concentration in water. When the independent variable including 7 factors such as T and NIR. the model had the best prediction effect. Among the three models, the R of BP neural network model was 0.64. which had the highest prediction accuracy. [Conclusion] There is a complex correlation between GF-1 satellite data and water quality parameters and dissolved N2O concentration in water bodies, and BP neural network can use GF-1 satellite data to retrieve dissolved N2O concentration in water bodies with high accuracy. [ABSTRACT FROM AUTHOR]

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Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal 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.)

The properties of the first kilometers of the Martian atmospheric Planetary Boundary Layer have until now been measured by only a few instruments and probes. InSight offers an opportunity to investigate this region through seismoacoustics. On six occasions, its seismometers recorded short low‐frequency waveforms, with clear dispersion between 0.4 and 4 Hz. These signals are the air‐to‐ground coupling of impact‐generated infrasound, which propagated in an low‐altitude atmospheric waveguide. Their group velocity depends on the structure of effective sound speed in the boundary layer. Here, we conduct a Bayesian inversion of effective sound speed up to 2,000 m altitude using the group velocity measured for events S0981c, S0986c and S1034a. The inverted effective sound speed profiles are in good agreement with estimates provided by the Mars Climate Database. Differences between inverted and modeled profiles can be attributed to a local wind variation in the impact→station direction, of amplitude smaller than 2 m/s. Plain Language Summary: The Martian Planetary Boundary Layer corresponds to the first few kilometers of the atmosphere. The InSight lander offers the opportunity to investigate its properties via the coupling of seismic and acoustic waves. Impact‐generated infrasound waves were recorded for the first time on Mars by the seismometers of the InSight mission. These infrasound waves propagated in an atmospheric waveguide in the first kilometers above the Martian surface, and thus present a frequency‐dependent group velocity. This frequency‐dependence, also known as a dispersion relation, is influenced by the structure of the speed of sound in the waveguide. Here, we use group velocity measured for events S0981c, S0986c and S1034a to invert the variations of effective sound speed between 0 and 2,000 m altitude. For the three events, the inverted profiles are in good agreement with estimates provided by the Mars Climate Database using global climate modeling. The differences between inverted and modeled profiles can be attributed to a local variation in wind in the impact→station direction, with magnitude smaller than 2 m/s. Key Points: InSight recorded impact‐generated infrasound on Mars. Their group velocity is sensitive to the structure of the atmospheric boundary layerWe conduct a Bayesian inversion of effective sound speed profiles with altitude based on group velocities measured for three impact eventsThe inverted profiles provide an indirect measurement of the Martian boundary layer, and validate models of the Mars Climate Database [ABSTRACT FROM AUTHOR]

Under Priabonian (latest Eocene) far-field compression, the major Jurassic graben systems and their proximal areas experienced domal uplift in the Netherlands and northeastern Belgium. It has been proposed that this flexural uplift was simultaneous with flexural subsidence of the surrounding areas. This hypothesis is, however, only based on the correlation of few widely spaced boreholes without supporting biostratigraphic or seismic data. For this study, a seismic line and cross-boundary borehole log correlation panel, comprising three biostratigraphically analyzed boreholes, were used to critically examine the abovementioned hypothesis. The seismic line and correlation panel run from the southern flank of the uplifted West Netherlands Basin, also known as the Voorne Trough, towards the south. They both show a gradual thickening of the Priabonian sequences towards the Voorne Trough, confirming the flexural subsidence hypothesis. The underlying Bartonian and overlying lowermost Rupelian units do not share the abovementioned thickness trend, which highlights the particularity of Priabonian basin dynamics. Within the Priabonian depocenter, plenty of reworked organic material was deposited, mostly of Bartonian age, providing further evidence of the simultaneity of subsidence and uplift in nearby regions. We consider it very likely that subsidence and uplift were part of the same vertical surface deformation process. The results of this study are consistent with the mechanism of lithospheric folding under far-field compression by the convergence between Africa and Europe. A Priabonian N-S compressional stress-field would also be consistent with fault, joint and vein analyses performed at the British Isles west of the study area. As the regional stress-field changed around the Eocene–Oligocene boundary, the vertical surface movements of the study area also changed.Graphical Abstract: Under Priabonian (latest Eocene) far-field compression, the major Jurassic graben systems and their proximal areas experienced domal uplift in the Netherlands and northeastern Belgium. It has been proposed that this flexural uplift was simultaneous with flexural subsidence of the surrounding areas. This hypothesis is, however, only based on the correlation of few widely spaced boreholes without supporting biostratigraphic or seismic data. For this study, a seismic line and cross-boundary borehole log correlation panel, comprising three biostratigraphically analyzed boreholes, were used to critically examine the abovementioned hypothesis. The seismic line and correlation panel run from the southern flank of the uplifted West Netherlands Basin, also known as the Voorne Trough, towards the south. They both show a gradual thickening of the Priabonian sequences towards the Voorne Trough, confirming the flexural subsidence hypothesis. The underlying Bartonian and overlying lowermost Rupelian units do not share the abovementioned thickness trend, which highlights the particularity of Priabonian basin dynamics. Within the Priabonian depocenter, plenty of reworked organic material was deposited, mostly of Bartonian age, providing further evidence of the simultaneity of subsidence and uplift in nearby regions. We consider it very likely that subsidence and uplift were part of the same vertical surface deformation process. The results of this study are consistent with the mechanism of lithospheric folding under far-field compression by the convergence between Africa and Europe. A Priabonian N-S compressional stress-field would also be consistent with fault, joint and vein analyses performed at the British Isles west of the study area. As the regional stress-field changed around the Eocene–Oligocene boundary, the vertical surface movements of the study area also changed. [ABSTRACT FROM AUTHOR]

Background: Duchenne Muscular Dystrophy (DMD) is an X-linked disorder caused by mutations in the DMD gene, with large deletions being the most common type of mutation. Inversions involving the DMD gene are a less frequent cause of the disorder, largely because they often evade detection by standard diagnostic methods such as multiplex ligation probe amplification (MLPA) and whole exome sequencing (WES). Case presentation: : Our research identified two intrachromosomal inversions involving the dystrophin gene in two unrelated families through Long-read sequencing (LRS). These variants were subsequently confirmed via Sanger sequencing. The first case involved a pericentric inversion extending from DMD intron 47 to Xq27.3. The second case featured a paracentric inversion between DMD intron 42 and Xp21.1, inherited from the mother. In both cases, simple repeat sequences (SRS) were present at the breakpoints of these inversions. Conclusions: Our findings demonstrate that LRS is an effective tool for detecting atypical mutations. The identification of SRS at the breakpoints in DMD patients enhances our understanding of the mechanisms underlying structural variations, thereby facilitating the exploration of potential treatments. [ABSTRACT FROM AUTHOR]

The Ghare-Aghaj potash resource is one of the largest halokinetic-type potash-bearing salt deposits in the northern part of the central Iran zone, within the Zanjan Province. This near-surface deposit originates in a sedimentary environment rich in siliciclastic sediments during the relaxation phase between tectonic activities. To explore such massive evaporates, because of their low density contrast compared to the surrounding rocks, a ground-based gravity survey proves to be an ideal strategy. In this study, we use a mixed Lp–Lq norm regularization in a constrained inversion process to generate compact and smooth models in three dimensions. This approach leads to the accurate detection of deposit boundaries and also localizes the prospect zone. First, we employ this method in a synthetic case, representing a simple vertical prism shape resembling evaporates' general geometry, such as salt domes and potash mineralization. This assessment examines how well it can recover these geometric structures with lower density contrast at varying depths. Subsequently, we apply this method in a real-case scenario. The findings detect potash mineralization within a salt wall structure at about 10–20 m below the surface, considering the challenging topographical conditions. Smooth reconstructed models, while accurately recovering the background properties, fail to recover the boundaries of the target ore. Conversely, blocky reconstructed models sharply recover the geometry of the potash mineralization but with less precision in the recovery of the background properties. The inversion results illustrate a salt dome within an anticline, showing a vertical expansion of around 110 m and a lateral expansion of 160 m. These results are in good concordance with the well data, indicating a salt distribution with a vertical expansion ranging from 11 to 115 m below the surface. [ABSTRACT FROM AUTHOR]

Sharing of emotional states is reduced for individuals wearing face coverings, but the mechanism behind this reduction remains unknown. Here, we investigated if face occlusion by masks reduces empathy by disrupting configural processing of emotional faces. Participants rated their empathy for happy and neutral faces which were presented in upright or inverted orientation and wore opaque, clear, or no face masks. Empathy ratings were reduced for masked faces (opaque or clear) as well as for inverted faces. Importantly, face inversion disrupted empathy more for faces wearing opaque masks relative to those wearing clear or no masks, which stands in contrast to the predictions generated by the classic configural processing models. We discuss these data within the context of classic and novel configural face perception models, and highlight that studying inverted occluded faces presents an informative case worthy of further investigation. [ABSTRACT FROM AUTHOR]

A thorough understanding of adaptation and speciation requires model organisms with both a history of ecological and phenotypic study as well as a complete set of genomic resources. In particular, high-quality genome assemblies of ecological model organisms are needed to assess the evolution of genome structure and its role in adaptation and speciation. Here, we generate new genomes of cactophilic Drosophila , a crucial model clade for understanding speciation and ecological adaptation in xeric environments. We generated chromosome-level genome assemblies and complete annotations for seven populations across Drosophila mojavensis , Drosophila arizonae , and Drosophila navojoa. We use these data first to establish the most robust phylogeny for this clade to date, and to assess patterns of molecular evolution across the phylogeny, showing concordance with a priori hypotheses regarding adaptive genes in this system. We then show that structural evolution occurs at constant rate across the phylogeny, varies by chromosome, and is correlated with molecular evolution. These results advance the understanding of the D. mojavensis clade by demonstrating core evolutionary genetic patterns and integrating those patterns to generate new gene-level hypotheses regarding adaptation. Our data are presented in a new public database (cactusflybase.arizona.edu), providing one of the most in-depth resources for the analysis of inter- and intraspecific evolutionary genomic data. Furthermore, we anticipate that the patterns of structural evolution identified here will serve as a baseline for future comparative studies to identify the factors that influence the evolution of genome structure across taxa. [ABSTRACT FROM AUTHOR]

In certain tetraploid species resulting from interspecific hybridization, one parent's subgenome is known to selectively undergo DNA loss. The molecular mechanisms behind this remain unclear. In our study, we compared the genomes of a standard diploid species with two allotetraploid species from the Xenopus genus, both possessing L (longer) and S (shorter) homoeologous subgenomes. We observed substantial gene losses and intergenic DNA deletions in both the S and L subgenomes of the tetraploid species. Gene losses were around 1,000 to 3,000 for L and 4,000 to 6,000 for S, with especially prominent losses in the S subgenome. Many of these losses likely occurred shortly after interspecific hybridization in both L/S subgenomes. We also deduced frequent large inversions in the S subgenome. Upon reassessing transposon dynamics using updated genome databases, we reaffirmed heightened DNA transposon activity during the hybridization, as previously reported. We next investigated whether S subgenome-biased DNA loss could be correlated with the activation of DNA transposons following hybridization. Notably, distinct patterns were observed in the dynamics of DNA transposons between the L and S subgenomes. Several DNA transposon subfamilies correlated positively with DNA deletions in the S subgenome and negatively in the L subgenome. Based on these results, we propose a model that, upon and after hybridization between two related diploid Xenopus species, the mixture of their genomes resulted in the derepression of DNA transposons, especially in the S subgenome, leading to selective DNA loss in the S subgenome. [ABSTRACT FROM AUTHOR]

We present a new inversion scheme for 2D magnetotelluric data. In contrast to established approaches, it is based on a mesh‐free formulation of the Quasi‐Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS) iteration which uses the cost function gradient to implicitly construct approximations of the Hessian inverse to update the unknown conductivity. We introduce conventional first–order regularization as well as second–order regularization where inversions based on the latter are more appropriate for sparse data and can be read as maximum likelihood estimation of the unknown conductivity. We apply first–order finite element method (FEM) discretizations of the inversion scheme, forward and adjoint problems, where the latter is required for the construction of the cost function gradients. We allow for unstructured first–order triangular meshes supporting an enhanced ground level resolution including topographical features and coarsening at the far field leading to significant reduction in computational costs from using structured mesh. Formulating the inversion iteration in continuous form prior to discretization eliminates bias due to local refinements in the mesh and gives way for computationally efficient sparse matrix techniques in the implementation. A keystone in the new scheme is the multi‐grid approximation of the Hessian of the regularizations to construct efficient preconditioning for the inversion iteration. The method is applied to the Commeni4 benchmark and two field data sets. Tests show that for both first and second–order regularization an anisotropic approach is important to address the vast differences in horizontal and vertical spatial scale which in conventional approaches is implicitly introduced through the elongated shape of grid cells. Plain Language Summary: We have developed a new inversion scheme for 2D MT data. Like most inversion methods, the cost function is composed of data misfit and a measure of the smoothness of the model (regularization). Inversion solutions are found by taking the derivative of the cost function and setting it to zero to minimize the function. The minimizing equation is solved iteratively using a quasi‐Newton method. Different to most other approaches, our method uses a discretize last strategy. Only once the quasi‐Newton iteration is derived, will discretization occur using an irregular triangular finite element mesh. The mesh is fine at ground level, very fine near measurement stations and extremely coarse at the edges of the model leading to significant reductions in computational costs compared to regular mesh. An additional feature of our method is two different types of regularization, conventional first‐order regularization and second‐order regularization, both with spatial anisotropy. This leads to multiple inversion solutions showing the variation of allowable solutions. Testing on benchmark and field data shows our method's robustness and its potential for real‐world applications. Key Points: New 2D MT inversion with both first and second–order anisotropic regularization for resolving different spacial characteristicsThe discretize‐last‐strategy implements a mesh‐free, gradient based inversion schemeMulti‐resolution preconditioning with efficient sparse matrix techniques accelerates computations [ABSTRACT FROM AUTHOR]

Considering the bijections of an arbitrary group onto itself which commute with all inner automorphisms, we establish the general properties. In particular, the automorphisms constitute the group that includes the group of central automorphisms. Also, we fully describe for the dihedral groups , with . [ABSTRACT FROM AUTHOR]

The Yerrida Basin in the south-eastern Capricorn Orogen, WA is a Proterozoic sedimentary basin that is prospective for base metals. A regional TEMPEST airborne electromagnetics (AEM) survey over the Capricorn Orogen, coinciding magnetotelluric (MT) survey and available downhole electrical property measurements provide an opportunity to compare the electrical conductivity of the rocks, and the electromagnetic (EM) responses of near surface and deeper sedimentary packages of the Yerrida Basin. Of particular interest is assessing whether it is possible to obtain reliable structural and stratigraphic information from, and to what depth, such as is required for exploration of basin terrains. The MT data were modelled using a 3D inversion algorithm, and the AEM data were modelling using a 1D inversion algorithm. An integrated interpretation of the two datasets allows surface geology to be mapped and for a conductivity section to be created to several kilometres depth. Electrical property measurements showed that one of the stratigraphic units, the Johnson Cairn Formation, is anomalously conductive and hence is readily mapped allowing faults, folds, thickness variations etc to be inferred from the inversions. The underlying and resistive Juderina Formation also gives rise to EM responses, albeit less strong. The results suggest significant modification of the published geological map of the area. The study demonstrates the usefulness of EM methods for exploring sedimentary terrains, but this is greatly facilitated by the presence of conductive stratigraphic units. While not always common practice in green fields exploration, it is recommended that electrical petrophysical measurements are made before committing to large scale EM surveys to better constrain interpretations. [ABSTRACT FROM AUTHOR]

The magnetotelluric (MT) method is increasingly being applied to mineral exploration under cover with several case studies showing that mineral systems can be imaged from the lower crust to the near surface. Driven by this success, the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP) is delivering long-period data on a 0.5° grid across Australia, and derived continental scale resistivity models that are helping to drive investment in mineral exploration in frontier areas. Part of this investment includes higher-resolution broadband MT surveys to enhance the resolution of features of interest and improve targeting. To help gain best value for this investment it is important to understand the ability and limitations of MT to resolve features on different scales. Here we present synthetic modelling of continuous, narrow, near-vertical faults 500 m to 1500 m wide with a resistivity 100–200 times less than the background rock resistivity using the ModEM software, and show that for such a situation a station spacing of around 14 km across strike is sufficient to resolve these into the upper crust. However, the vertical extent of these features is not well constrained, with near-vertical planar features commonly resolved as two separate features. This highlights the need for careful interpretation of anomalies in MT inversion. In particular, in an exploration scenario, it is important to consider that a lack of interconnectivity between a lower crustal/upper mantle conductor and conductors higher up in the crust and the surface might be apparent only, and may not necessarily reflect reduced mineral prospectivity. [ABSTRACT FROM AUTHOR]

Transversely isotropic media with a tilted symmetry axis (TTI) exits widely underground due to tectonic movement and mineral orientation. Traditional full waveform inversion (FWI) based on isotropic media or transversely isotropic media with a vertical symmetry axis (VTI) cannot deal with such situations. To address this limitation, TTI–based FWI was developed. However, its practical application faces challenges in estimating the symmetry axis tilt angle θt${{\theta }_{\mathrm{t}}}$. Previous studies have generally assumed that θt${{\theta }_{\mathrm{t}}}$ is equal to the strata dip angle, which is incorrect in complex structures such as salt domes and magmatic intrusion zones. Another theoretically robust way to estimate θt${{\theta }_{\mathrm{t}}}$ is to treat it as the parameter to be inverted, but there are still some problems unresolved. First, the parameter θt${{\theta }_{\mathrm{t}}}$ increases the nonlinearity of the inversion process, and its impact mechanism on inversion is not yet clear. Second, there is severe crosstalk (also known as trade‐off or coupling) between parameters, but the current parameter decoupling technique for TTI–based FWI is not mature. To address the first problem, we assess the interaction between θt${{\theta }_{\mathrm{t}}}$ and other parameters by analysing the radiation patterns in the TTI background. Our analysis reveals that θt${{\theta }_{\mathrm{t}}}$ is most coupled by S‐wave vertical velocity vs0${{v}_{{\mathrm{s}}0}}$, and θt${{\theta }_{\mathrm{t}}}$ substantially affects anisotropic parameters ε$\varepsilon $ and δ$\delta $. Therefore, we conclude that a good inversion of velocity parameters is a prerequisite for recovering θt${{\theta }_{\mathrm{t}}}$, and only after θt${{\theta }_{\mathrm{t}}}$ recovers can ε$\varepsilon $ and δ$\delta $ be recovered. This conclusion provides a natural perspective for solving the second problem. We therefore propose a multi‐step and multi‐parameter joint inversion strategy to gradually improve the quality of parameter inversion and weaken their coupling. Numerical experiments demonstrate that our strategy achieves more accurate inversion results compared to previously proposed multi‐parameter inversion strategies. Finally, the application to the field OBN dataset acquired from the South China Sea verifies the practicality of our method. [ABSTRACT FROM AUTHOR]

The construction of an accurate and high‐resolution reservoir parameter model is crucial for reservoir characterization. However, due to the band‐limited characteristics of seismic data, the inversion results heavily rely on the accuracy of the initial model. Most existing techniques for constructing an initial model interpolate well logging data within the stratigraphic framework, neglecting the effect of the stratigraphic sequence, which compromises the reliability of the initial model. The stratigraphic sequence is essential for dividing stratigraphic evolution stages and defining a geological relationship between reservoirs within the stratigraphic framework. Therefore, an initial model construction method constrained by stratigraphic sequence representation is proposed for pre‐stack seismic inversion. The process begins with establishing the stratigraphic framework using horizon and fault data. Subsequently, the collaborative sparse representation algorithm is used to learn a joint dictionary that captures the relationship of structural features between seismic data and stratigraphic sequence from the well logging data. In the process of seismic data representation, the stratigraphic sequence is accurately represented in three‐dimensional space by sharing sparse coefficients in the joint dictionary. Finally, the elastic parameter model is constructed by integrating the stratigraphic framework, stratigraphic sequence and well logging data, providing a reliable initial model for pre‐stack seismic inversion. The main innovation of the proposed method is the three‐dimensional representation of the stratigraphic sequence. A synthetic example demonstrates that the proposed method produces a more accurate initial model than conventional interpolation methods. Additionally, when applied to field data, it yields satisfactory results even without complete S‐wave velocity well logging data. [ABSTRACT FROM AUTHOR]

Full waveform inversion stands at the forefront of seismic imaging technologies, pivotal in retrieving high‐resolution subsurface velocity models. Its application is especially profound when imaging complex geologies such as salt bodies, which are regions notoriously challenging, yet essential given their hydrocarbon potential. However, with the power of full waveform inversion comes the intrinsic challenge of estimating the associated uncertainties. Such uncertainties are crucial in understanding the reliability of subsurface models, particularly in terrains like subsalt regions. Addressing this, we advocate for a nuanced approach employing the Stein variational gradient descent algorithm. Through a judicious use of a limited number of velocity model particles and the integration of random field‐based perturbations, our methodology provides a local representation of the uncertainties inherent in full waveform inversion. Our evaluations, based on the Marmousi model, showcase the robustness of the proposed technique. Yet, it is our exploration into salt‐intensive terrains, leveraging data from the Sigsbee 2A synthetic model and the Gulf of Mexico, that emphasizes the method's versatility. Findings indicate pronounced uncertainties along salt boundaries and in the deeper subsalt sediments, contrasting the minimal uncertainties in non‐salt terrains. However, anomalies like salt canyons present unique challenges, potentially due to the interplay of multi‐scattering effects. Emphasizing the scalability and cost‐effectiveness of this approach, we highlight its potential for large‐scale industrial applications in full waveform inversion, while also underscoring the necessity for prudence when integrating these uncertainty insights into subsequent seismic‐driven geological and reservoir modelling. [ABSTRACT FROM AUTHOR]

To undertake a systematic analysis of 17 medical attention and time-loss lateral ankle ligament sprain (LALS) events from televised Australian professional netball games during the 2020–2023 seasons. Case series. Three analysts independently assessed the video footage and then convened to review and discuss each case until a consensus was reached. When in possession (7 cases) a player was commonly performing an agility-based manoeuvre to break free from an opponent and reposition themselves to be a passing option (5/7 cases). When out of possession (10 cases) a player was either attempting to intercept a pass (6 cases) or marking an opponent (4 cases). Players tended to land on the anterior one-third of the plantar surface of the foot - forefoot or shoe tip (7 cases). Players often landed on either the ground (7 cases) or the opponent's shoe then the ground (8 cases). In 9 cases the ankle-foot was considered to be in a neutral alignment in the frontal plane at landing. At the estimated index frame the players' weight tended to be all on the foot on the injured side (11 cases) or favouring the foot on the injured side (5 cases). Inversion and adduction was a common injury mechanism. Plantar-flexion was rarely involved. Landing on the anterior one-third of the plantar surface of the foot and subsequent weight transference onto the injured limb side was more important than ankle-foot inversion at initial ground contact. Exercises involving external perturbations that challenge the control of frontal and transverse plane ankle-foot motion and improve proprioception, neuromuscular control, and dynamic balance are warranted. • In 59% of cases, players were involved in a collision of some type with an opponent. • The foot of the injured player contacted the foot of an opponent in 59% of cases. • Injured players tended to land on the forefoot (41% of cases) and transfer weight onto the injured limb side (94% of cases). • Inversion and adduction was common (42% of cases) while plantar-flexion was rarely involved. [ABSTRACT FROM AUTHOR]

Biofertilizers are promising technologies for achieving sustainable agriculture. However, high-temperature tolerance is a constraint that limits the function of microbial inoculants. To characterize the genetic changes responsible for the high-temperature tolerance of rhizobia, mutant screening was performed using Bradyrhizobium diazoefficiens USDA110. The wild-type cells were mutagenized with carbon-ion irradiation, and two mutant strains, designated M10 and M14, were obtained after a three-day heat-shock treatment at 43 °C. In particular, M14 showed superior growth at 36 °C, at which temperature growth of the wild type was extremely slow, whereas M14 grew more slowly than the wild type at 32 °C. Whole-genome sequencing revealed that M10 had seven point mutations, whereas M14 had eight point mutations together with a 1.27 Mb inversion. RNA sequencing showed that the number of differentially expressed genes greatly exceeded the actual number of induced mutations. In M14, a gene cluster associated with pyruvate metabolism was markedly downregulated, probably because of disjunction with the promoter region after inversion, and was considered to be the cause of the slow growth rate of M14 at 32 °C. Notably, transmembrane proteins, including porins, were enriched among the genes upregulated in both M10 and M14. M14 was confirmed to retain symbiotic functions with soybeans. These results indicate that high-temperature tolerance was conferred by random mutagenesis while the symbiotic functions of rhizobia was maintained. [ABSTRACT FROM AUTHOR]

The structural similarity of biological, organic and inorganic substances in the nanoworld, on the one hand, blurs the lines between them, and on the other, creates new opportunities for digital modeling. The fundamental characteristic of the nanoworld is its spatiotemporal structure as a set of stable connections that ensure integrity, adaptation and preservation of the basic properties of a self-organized dynamic system under internal and external influences. The goal of the work is to develop an integrative methodology for modeling 3D structures, within the framework of which to create a meta-theory of digital modeling of physical reality.Human activity in real and/or virtual space creates cognitive problems that are caused by a hybrid subject environment. However, the commonality of biological, organic and inorganic substances in the nanoworld makes their structures similar, which made it possible to develop an integrative methodology for modeling 3D structures of information sources of various natures. The methodology is based on the complementarity of paradigms and tools for digital modeling of physical reality, which are united by physical and social ontology.The harmony of perception of triads of conjugate opposites in information sources of various natures is the basis of a hybrid approach to the integrative metatheory of digital modeling of physical reality. Therefore, in extreme conditions, the connection of hybrid thinking with the holographic nature of memory, which also has a semiotic nature, is important. The prospects and innovative potential of the methodology for modeling 3D structures and the complementarity of metamodels of physical reality are discussed. [ABSTRACT FROM AUTHOR]

Abstract Background Duchenne Muscular Dystrophy (DMD) is an X-linked disorder caused by mutations in the DMD gene, with large deletions being the most common type of mutation. Inversions involving the DMD gene are a less frequent cause of the disorder, largely because they often evade detection by standard diagnostic methods such as multiplex ligation probe amplification (MLPA) and whole exome sequencing (WES). Case presentation : Our research identified two intrachromosomal inversions involving the dystrophin gene in two unrelated families through Long-read sequencing (LRS). These variants were subsequently confirmed via Sanger sequencing. The first case involved a pericentric inversion extending from DMD intron 47 to Xq27.3. The second case featured a paracentric inversion between DMD intron 42 and Xp21.1, inherited from the mother. In both cases, simple repeat sequences (SRS) were present at the breakpoints of these inversions. Conclusions Our findings demonstrate that LRS is an effective tool for detecting atypical mutations. The identification of SRS at the breakpoints in DMD patients enhances our understanding of the mechanisms underlying structural variations, thereby facilitating the exploration of potential treatments.

La corrupción global genera desconfianza, distorsiona la competencia y obstaculiza el crecimiento económico. Actos como el soborno y el nepotismo benefician a quienes participan, limitando las oportunidades y desalentando la ética empresarial. En este sentido, esta investigación tuvo como objetivo profundizar la comprensión de la relación entre la corrupción y la inversión privada en el Perú. Metodológicamente, se trató de una revisión bibliográfica, de tipo narrativo, donde se utilizaron las bases de datos Scopus, Web of Science, Scielo y Dialnet para la búsqueda de documentos, utilizando las palabras clave "corrupción" e "inversión privada", sobre publicaciones entre 2010-2020. Las categorías fueron preestablecidas para el análisis documental, siendo estas: a) Impacto económico de la corrupción; b) Marco legal y medidas anticorrupción y c) Buenas prácticas y casos de éxito; El resultado más relevante sugiere que la corrupción genera desconfianza entre los inversionistas, limitando las oportunidades de negocios éticos y transparentes, desacelerando así el crecimiento económico y la innovación en el país. Las conclusiones destacan que la falta de confianza generada por la corrupción dificulta la toma de decisiones de inversión y reduce la disposición de los inversionistas a comprometer sus recursos en proyectos en el país.

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 full-waveform 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 wavefield-adapted 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 structure.

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]

Abstract The polar cod, Boreogadus saida, is an abundant and ubiquitous forage fish and a crucial link in Arctic marine trophic dynamics. Our objective was to unravel layers of genomic structure in B. saida from Canadian waters, specifically screening for potential hybridization with the Arctic cod, Arctogadus glacialis, large chromosomal inversions, and sex-linked regions, prior to interpreting population structure. Our analysis of 53,384 SNPs in 522 individuals revealed hybridization and introgression between A. glacialis and B. saida. Subsequent population level analyses of B. saida using 12,305 SNPs in 511 individuals revealed three large (ca. 7.4–16.1 Mbp) chromosomal inversions, and a 2 Mbp region featuring sex-linked loci. We showcase population structuring across the Western and Eastern North American Arctic, and subarctic regions ranging from the Hudson Bay to the Canadian Atlantic maritime provinces. Genomic signal for the inferred population structure was highly aggregated into a handful of SNPs (13.8%), pointing to potentially important adaptive evolution across the Canadian range. Our study provides a high-resolution perspective on the genomic structure of B. saida, providing a foundation for work that could be expanded to the entire circumpolar range for the species.