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Abstract The vacancy effect in quantum spin liquid (QSL) has been extensively studied. A finite density of random vacancies in the Kitaev model can lead to a pileup of low-energy density of states (DOS), which is generally experimentally determined by a scaling behavior of thermodynamic or magnetization quantities. Here, we report detailed muon spin relaxation (μSR) results of H3LiIr2O6, a Kitaev QSL candidate with vacancies. The absence of magnetic order is confirmed down to 80 mK, and the spin fluctuations are found to be persistent at low temperatures. Intriguingly, the time-field scaling law of longitudinal-field (LF)-μSR polarization is observed down to 0.1 K. This indicates a dynamical scaling, whose critical exponent of 0.46 is excellently consistent with the scaling behavior of specific heat and magnetization data. All the observations point to the finite DOS with the form $$N(E)\sim {E}^{-\nu }$$ N ( E ) ∼ E − ν , which is expected for the Kitaev QSL in the presence of vacancies. Our μSR study provides a dynamical fingerprint of the power-law low-energy DOS and introduces a crucial new insight into the vacancy effect in QSL.

Objective We aimed to screen and construct a predictive model for pregnancy loss in polycystic ovary syndrome (PCOS) patients through machine learning methods.Methods We obtained the endometrial samples from 33 PCOS patients and 7 healthy controls at the Reproductive Center of the Second Hospital of Lanzhou University from September 2019 to September 2020. Liquid chromatography tandem mass spectrometry (LCMS/MS) was conducted to identify the differentially expressed proteins (DEPs) of the two groups. Gene Ontology (GO) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to analyze the related pathways and functions of the DEPs. Then, we used machine learning methods to screen the feature proteins. Multivariate Cox regression analysis was also conducted to establish the prognostic models. The performance of the prognostic model was then evaluated by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). In addition, the Bootstrap method was conducted to verify the generalization ability of the model. Finally, linear correlation analysis was performed to figure out the correlation between the feature proteins and clinical data.Results Four hundred and fifty DEPs in PCOS and controls were screened out, and we obtained some pathways and functions. A prognostic model for the pregnancy loss of PCOS was established, which has good discrimination and generalization ability based on two feature proteins (TIA1, COL5A1). Strong correlation between clinical data and proteins were identified to predict the reproductive outcome in PCOS.Conclusion The model based on the TIA1 and COL5A1 protein could effectively predict the occurrence of pregnancy loss in PCOS patients and provide a good theoretical foundation for subsequent research.

Abstract Establishing prediction models of pregnancy outcomes for recurrent pregnancy loss women at specific gestational weeks will provide patients and physicians with more precise information, ultimately leading to time and cost savings associated with unnecessary revisits. Therefore, our aim was to develop a prediction model for first trimester pregnancy loss in RPL patients. We used ultrasound indices during the first trimester of pregnancy in combination with demographic characteristics and commonly used serum markers. The independent risk factors for each week were as follows: age and P in the fifth week; age, mGSD and CRL in the sixth week; age, hCG and CRL in the seventh week; CRL in the eighth week; mGSD and CRL in ninth week. The corresponding AUC was 0.671, 0.796, 0.872, 0.871, 0.813, respectively. There is a linear relationship between age and first trimester pregnancy loss. hCG 48.3 mm in ninth gestational week was associated with a higher risk of pregnancy loss. These models and thresholds may help physicians and patients make more informed decisions together. Further studies are needed to confirm the results.

A tight sandstone gas reservoir is characterized by low porosity and permeability. The existence of fractures can improve the permeability of the reservoir, and fractures are a vital storage space and migration channel of oil and gas. The development of fractures is also conducive to forming a fracture network during hydraulic fracturing. The prediction of fractures can provide an essential basis for developing tight sandstone gas reservoirs. The variation of seismic amplitude with azimuth can provide information on vertical fractures in the reservoir. This study proposed an improved inversion method of azimuth amplitude difference for the HTI (transversely isotropy with a horizontal axis) medium and combined with the rock physical theory, predicted fracture weakness parameter, characterizing the fracture properties. Conventional inversion methods invert elastic and fracture parameters simultaneously. The improved azimuth amplitude difference method introduces a reference azimuth, builds the track set of azimuth amplitude difference to eliminate the isotropic background, and inverts only the fracture weakness parameters related to the anisotropic terms. Using the only azimuth anisotropic response, the sensitivity of fracture identification and the accuracy of fracture parameter inversion are improved. The application of the field data shows that the sensitivity of the proposed inversion method to the prediction of fracture parameters is improved compared with the conventional inversion method, and the predicted fracture weakness is consistent with the permeability well logs and has a significant correlation with the gas production of tight sandstone gas reservoirs. Therefore, the prediction of fracture distribution and development degree based on the proposed method can provide a reliable indicator for the identification and development of gas-bearing favorable areas in tight sandstone reservoirs.

Abstract Traumatic brain injury (TBI) is one of the leading causes of death and disability worldwide. However, effective diagnostic, therapeutic and prognostic biomarkers are still lacking. Our research group previously revealed through high-throughput sequencing that the serum exosomes miR-133a-3p, miR-206, and miR-549a-3p differ significantly in severe TBI (sTBI), mild or moderate TBI (mTBI), and control groups. However, convincing experimental evidence is lacking. To solve this problem, we used qPCR in this study to further verify the expression levels of serum exosomes miR-133a-3p, miR-206 and miR-549a-3p in TBI patients. The results showed that the serum exosomes miR-206 and miR-549a-3p showed good predictive value as biomarkers of TBI. In addition, in order to further verify whether serum exosomes miR-206 and miR-549a-3p can be used as potential biomarkers in patients with TBI and to understand the mechanism of their possible effects, we further determined the contents of SOD, BDNF, VEGF, VEGI, NSE and S100β in the serum of TBI patients. The results showed that, serum exosomes miR-206 and miR-549a-3p showed good correlation with BDNF, NSE and S100β. In conclusion, serum exosomes miR-206 and miR-549a-3p have the potential to serve as potential biomarkers in patients with TBI.

Abstract Quantum confinement is a restriction on the motion of electrons in a material to specific region, resulting in discrete energy levels rather than continuous energy bands. In certain materials, quantum confinement could dramatically reshape the electronic structure and properties of the surface with respect to the bulk. Here, in the recently discovered kagome superconductors CsV3Sb5, we unveil the dominant role of quantum confinement in determining their surface electronic structure. Combining angle-resolved photoemission spectroscopy (ARPES) measurement and density-functional theory simulation, we report the observations of two-dimensional quantum well states due to the confinement of bulk electron pocket and Dirac cone to the nearly isolated surface layer. The theoretical calculations on the slab model also suggest that the ARPES observed spectra are almost entirely contributed by the top two layers. Our results not only explain the disagreement of band structures between the recent experiments and calculations, but also suggest an equally important role played by quantum confinement, together with strong correlation and band topology, in shaping the electronic properties of this material.

Abstract Objective We aimed to investigate the significance of autophagy proteins and their association with clinical data on pregnancy loss in polycystic ovary syndrome (PCOS), while also constructing predictive models. Methods This study was a secondary analysis. we collected endometrial samples from 33 patients with polycystic ovary syndrome (PCOS) and 7 patients with successful pregnancy control women at the Reproductive Center of the Second Hospital of Lanzhou University between September 2019 and September 2020. Liquid chromatography tandem mass spectrometry was employed to identify expressed proteins in the endometrium of 40 patients. R was use to identify differential expression proteins(DEPs). Subsequently, Metascape was utilized for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Multivariate Cox analysis was performed to analyze autophagy proteins associated with reproductive outcomes, while logistic regression was used for analyzing clinical data. Linear correlation analysis was conducted to examine the relationship between autophagy proteins and clinical data. We established prognostic models and constructed the nomograms based on proteome data and clinical data respectively. The performance of the prognostic model was evaluated by the receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Results A total of 5331 proteins were identified, with 450 proteins exhibiting significant differential expression between the PCOS and control groups. A prognostic model for autophagy protein was developed based on three autophagy proteins (ARSA, ITGB1, and GABARAPL2). Additionally, another prognostic model for clinical data was established using insulin, TSH, TPOAB, and VD3. Our findings revealed a significant positive correlation between insulin and ARSA (R = 0.49), as well as ITGB1 (R = 0.3). Conversely, TSH exhibited a negative correlation with both ARSA (-0.33) and ITGB1 (R = -0.26). Conclusion Our research could effectively predict the occurrence of pregnancy loss in PCOS patients and provide a basis for subsequent research.

The Arxan Volcanic Field (AVF) is an active volcanic region in Northeast Asia, and its last eruption occurred approximately 2000 years ago. Its eruption mechanism remains unknown. To investigate the shallow magma system beneath the volcanic cones in the AVF, we deployed a dense seismic array consisting of 227 portable seismographs and conducted high-resolution ambient noise tomography (ANT). The results of checkerboard test (CRT) and restoring resolution test (RRT) demonstrate that our imaging results are reliable. These results reveale significant slow-velocity anomalies at depths of 5~9 km below the Tianchi caldera and GD1213 volcano in Arxan, with the highest anomaly reaching up to approximately 15%. These anomalies suggest partial melting in a shallow magma chamber, indicating ongoing volcanic activity in the AVF. The velocity of the magma chamber corresponding to a melt fraction of approximately 7.4~12.9%. Therefore, the presence of the magma chamber poses potential hazards to the Arxan region, including volcanic eruptions and their associated risks.

Understanding the impact of creep on the long-term mechanical features of concrete is crucial, and constructing an accurate prediction model is the key to exploring the development of concrete creep under long-term loads. Therefore, in this study, three machine learning (ML) models, a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Machine (XGBoost), are constructed, and the Hybrid Snake Optimization Algorithm (HSOA) is proposed, which can reduce the risk of the ML model falling into the local optimum while improving its prediction performance. Simultaneously, the contributions of the input features are ranked, and the optimal model’s prediction outcomes are explained through SHapley Additive exPlanations (SHAP). The research results show that the optimized SVM, RF, and XGBoost models increase their accuracies on the test set by 9.927%, 9.58%, and 14.1%, respectively, and the XGBoost has the highest precision in forecasting the concrete creep. The verification results of four scenarios confirm that the optimized model can precisely capture the compliance changes in long-term creep, meeting the requirements for forecasting the nature of concrete creep.

The Changbai volcano, a globally recognized hotspot of volcanic activity, has garnered significant attention due to its persistent seismicity and ongoing magma activity. The volcano’s discontinuities and magma dynamics have raised concerns about the likelihood of future eruptions, which would likely result in substantial ecological, climatic, and economic impacts. Consequently, a comprehensive understanding of the Changbai volcanic system is essential for mitigating the risks associated with volcanic activity. In recent years, the P-wave coda autocorrelation method has gained popularity in lithosphere exploration as a reliable technique for detecting reflection coefficients. Additionally, the Common Reflection Point stacking approach has been employed to superimpose reflection signals in a spatial grid, enabling continuous observation of reflection coefficients in the study area. However, the accuracy of this approach is heavily reliant on better spatial data coverage. To better understand the internal dynamics of the Changbai volcano, we applied this approach to a densely packed short-period seismic array with an average station spacing of less than 1 km. Our results were constrained using waveform data of reflection coefficients and Moho dip angles. Our findings revealed a discontinuity in the Moho, which may indicate a conduit for mantle magma entering the crust. Furthermore, we identified two low-velocity anomalies within the crust, likely representing a magma chamber comprising molten and crystallized magma. Notably, our results also provided a clear definition of the lithosphere–asthenosphere boundary.

Abstract Background The relationship between Mg (magnesium), Cu (copper), and K (potassium) intakes and the risk of rheumatoid arthritis (RA) remains limited. The aim of present study was to examine the associations between Mg, Cu and K intakes with RA. Methods Using data from the National Health and Nutrition Examination Survey (NHANES) 2003–2018, we examined the association between Mg, Cu and K intakes and the risk of RA among US adults. After adjustment for age, sex, race, BMI, educational level, smoking history, alcohol consumption, family Poverty Income Ratio (PIR), diabetes and total daily energy intake, logistic regression models and smooth curve fitting were applied to examine the associations of Mg, Cu and K intakes with RA. Results A total of 18,338 participants were included (1,008 participants with RA). The multivariate adjusted ORs (95% CI) of RA were [0.66 (0.51, 0.84)], [0.76 (0.60, 0.97)], and [0.75 (0.58, 0.97)] in the highest versus lowest quartile of magnesium intakes, respectively. A nonlinear association between Cu intakes and RA was found. When Cu intake (ln) was between 0.6–2.2 mg, the risk of RA reduced by 26% for every 1 mg increase of intake in Cu [0.74 (0.58, 0.96)]. Conclusions Higher Mg, Cu and K intakes may be inversely associated with the risk of RA among US adults, and an inverse L-shaped association between dietary Cu and RA was found.

Abstract Space groups describing the symmetry of lattice structure allow the emergence of fermionic quasiparticles with various degeneracy in the band structure. Theoretical efforts have predicted many materials hosting fermions with the highest degeneracy, i.e., eightfold fermions, yet lacking experimental realization. Here, we explore the band degeneracies in TaCo2Te2 crystals. Through systematic experimental and theoretical analyses, we establish TaCo2Te2 as a nonsymmorphic crystal with negligible spin–orbit coupling (SOC) and long-range magnetic order. These critical properties guarantee the realization of practical eightfold fermions and fourfold van Hove singularity, as directly observed by photoemission spectroscopy. TaCo2Te2 serves as a topological quantum critical platform, which can be tuned into various magnetic, topologically trivial, and nontrivial phases by adding strain, magnetic field, or SOC. The latter is demonstrated by our first-principles calculations, which show that enhancing SOC in TaCo2Te2 will promote the experimental observation of bulk hourglass fermions. Our results establish TaCo2Te2 as a platform to explore the interplay between symmetry and band topology.

BackgroundDespite the crucial role of Chest pain centers (CPCs) in acute myocardial infarction (AMI) management, China's mortality rate for ST-segment elevation myocardial infarction (STEMI) has remained stagnant. This study evaluates the influence of CPC quality control indicators on mortality risk in STEMI patients receiving primary percutaneous coronary intervention (PPCI) during the COVID-19 pandemic.MethodsA cohort of 664 consecutive STEMI patients undergoing PPCI from 2020 to 2022 was analyzed using Cox proportional hazards regression models. The cohort was stratified by Killip classification at admission (Class 1: n = 402, Class ≥2: n = 262).ResultsAt a median follow-up of 17 months, 35 deaths were recorded. In Class ≥2, longer door-to-balloon (D-to-B) time, PCI informed consent time, catheterization laboratory activation time, and diagnosis-to-loading dose dual antiplatelet therapy (DAPT) time were associated with increased mortality risk. In Class 1, consultation time (notice to arrival) under 10 min reduced death risk. In Class ≥2, PCI informed consent time under 20 min decreased mortality risk.ConclusionCPC quality control metrics affect STEMI mortality based on Killip class. Key factors include time indicators and standardization of CPC management. The study provides guidance for quality care during COVID-19.

Red Monascus pigments (MPs) are a large group of polyketides from the fungus Monascus which have been widely used as food colorants. In this study, a variety of red MPs congeners were prepared to explore promising water-soluble candidates for application in liquid food formulations. The results showed that by combining the two-stage, low-pH fermentation strategy with a downstream purification step of fractional crystallization, precursors of red MPs, namely monascorubrin and rubropunctatin, were obtained with a purity of 91.9%. Then, via the azaphilic addition reaction, 18 types of red MPs congeners carrying different amino acid moieties (MPs-aa) were semi-synthesized. Compared to rubropunctamine and monascorubramine, the water solubility, pH and thermal stability of MPs-aa were improved greatly. MPs-His, MPs-Phe, MPs-Tyr and MPs-Trp were identified to be the most resistant to pasteurization. These findings provide water-soluble red MPs candidates with high thermal stability and an attractive approach for their large scale production.

ABSTRACTSatellite remote sensing has been widely utilized for petroleum detection offshore since oil spill is normally widespread. However, the onshore hydrocarbon detection is insufficient because the lower spectral resolution of previous satellites made it difficult to identify small scales of seepages. In this study, a dataset of the High-Resolution 5 satellite (GF-5) of China was chosen to directly detect hydrocarbon seeps in the Karamay area. First, an IDL program was used to remove the vertical tripe noise of the image to ensure the high quality of the image. Second, use principal component analysis (PCA) was combined with the classification and regression trees (CART) method RuleGen to process the image. The detected hydrocarbon information was enhanced by RGB color synthesis. The results show that the methods can detect hydrocarbon effectively. Field verification results show that the accuracy is 86.5%. This study provides an effective method for hydrocarbon detection using GF-5 data.

Magnetotelluric (MT) signals exhibit the characteristics of being weak and having a wide frequency band. The acquired field data are susceptible to various types of noise, which poses challenges in accurate identification and processing. Currently, there exist numerous MT data processing methods; however, they lack efficiency and physical meaning. To address this issue and improve the signal-to-noise ratio of the acquired data, this study proposes a MT data processing method based on cepstral analysis. By employing cepstral analysis on the MT data, the cepstrum is obtained, and an appropriate truncation position is selected for processing. The experimental results demonstrate that this method obtains smoother and more continuous apparent resistivity curves with fewer errors. Compared with other methods, the cepstral analysis method can effectively suppress different types of MT noise, and the method is simple and efficient with clear physical significance.

BackgroundCatastrophic health expenditures (CHE) can trigger illness-caused poverty and compound poverty-caused illness. Our study is the first regional comparative study to analyze CHE trends and health inequality in eastern, central and western China, exploring the differences and disparities across regions to make targeted health policy recommendations.MethodsUsing data from China's Household Panel Study (CFPS), we selected Shanghai, Henan and Gansu as representative eastern-central-western regional provinces to construct a unique 5-year CHE unbalanced panel dataset. CHE incidence was measured by calculating headcount; CHE intensity was measured by overshoot and CHE inequality was estimated by concentration curves (CC) and the concentration index (CI). A random effect model was employed to analyze the impact of household head socio-economic characteristics, the household socio-economic characteristics and household health utilization on CHE incidence across the three regions.ResultsThe study found that the incidence and intensity of CHE decreased, but the degree of CHE inequality increased, across all three regions. For all regions, the trend of inequality first decreased and then increased. We also revealed significant differences across the eastern, central and western regions of China in CHE incidence, intensity, inequality and regional differences in the CHE influencing factors. Affected by factors such as the gap between the rich and the poor and the uneven distribution of medical resources, families in the eastern region who were unmarried, use supplementary medical insurance, and had members receiving outpatient treatment were more likely to experience CHE. Families with chronic diseases in the central and western regions were more likely to suffer CHE, and rural families in the western region were more likely to experience CHE.ConclusionsThe trends and causes of CHE varied across the different regions, which requires a further tilt of medical resources to the central and western regions; improved prevention and financial support for chronic diseases households; and reform of the insurance reimbursement policy of outpatient medical insurance. On a regional basis, health policy should not only address CHE incidence and intensity, but also its inequality.

In this study, a Pt@Co-Al LDH hybrid structure was fabricated by assembling the metal precursor PtCl62− with the exfoliated LDH nanosheets followed by in situ reduction by NaBH4. The morphology, composition and microstructure of the hybrid were characterized by FESEM, HRTEM, XRD, XPS and BET techniques. Pt nanoparticles (NPs) with an average particle size of 3.1 nm were successfully and uniformly loaded on the surface of LDH nanosheets. The catalytic activity of the Pt@Co-Al LDH hybrid was tested for the reduction of 4-nitrophenol, which is one of the most frequent pollutants in wastewater effluent from the pharmaceutical and textile industries. The hybrid displays superior catalytic activity and stability in the reduction of 4-NP under environmental conditions with NaBH4 as a reducing agent. The hybrid can be recovered in a simple way and still shows high catalytic activity after five reuses.

Abstract The Central Asian Orogenic Belt is bounded on the north by the Siberian Craton and on the south by the North China Craton and the Tarim Craton. It is one of the largest Phanerozoic accretionary orogenic belts on Earth. Since the early Paleozoic, its eastern part has experienced the compound orogenesis and mineralization of three major tectonic systems: the closure of the Paleo-Asian Ocean, the closure of the Mongolian–Okhotsk Ocean, and the subduction of the Paleo-Pacific Plate. From Zhangwu County in the south to East-Ujimqin Banner in the north, a 500 km magnetotelluric profile adjacent to Northeast China has been studied. With 100 sites of magnetotelluric data processing and analysis, we apply a two dimensional inversion in TE and TM modes and obtain a resistivity model up to a 100 km depth. We have discovered two high-resistivity anomalies with opposite dip directions in the upper mantle on both sides of the Solonker Suture Zone, which provide an evidence of the bi-directional subduction pattern of the oceanic crust and the position of the closure of the Paleo-Asian Ocean. In addition, the whole study area presents an approximate basin-range coupling relationship. In the northern part of the study area, the low-resistivity anomalies below it have an apparent north-dipping characteristic, which may be related to the asthenosphere upwelling from west to east. In addition, they may be related to the upwelling of mantle materials, and provide sources of ore-forming material for the Baiyinnuoer mining area through post-collision extension. In the central part of the study area, there are several large-scale high-resistivity anomalies below the Baolidao Belt. The different dip directions reveal the experiences of several subductions and collisions. In the southern part of the study area, the Bainaimiao Belt is located between the southern margin of the Songliao Basin and the northern margin of North China Craton. The main resistivity anomalies below are all south-dipping. Graphical Abstract

ObjectiveTo analyze the characteristics of thoracic aorta injury in road traffic accidents, to provide data reference for forensic identification.MethodsThe data of 27 traffic accident death cases with thoracic aorta injury were analyzed according to relevant parameters including sex, age, mode of transportation, and thoracic aorta injury.ResultsAortic injury in traffic accidents was significantly more in males than females, and 74.1% cases were in the age range of 31-70 years. The most common mode of transportation was the motorcycle, followed by electric bike, most of which crashed with trucks. Most cases were accompanied by rib fractures and lung injuries. Thoracic aorta injury was the most common in ascending aorta, followed by aortic arch and thoracic aorta. Ascending aorta injury was most likely to occur in the range of 0-

Abstract Lorentz‐violating type‐II Dirac nodal line semimetals (DNLSs), hosting curves of band degeneracy formed by two dispersion branches with the same sign of slope, represent a novel state of matter. While being studied extensively in theory, convincing experimental evidence of type‐II DNLSs remain elusive. Recently, vanadium‐based kagome materials have emerged as a fertile ground to study the interplay between lattice symmetry and band topology. This work studies the low‐energy band structure of double‐kagome‐layered CsV8Sb12 and identifies it as a scarce type‐II DNLS protected by mirror symmetry. This work observes multiple DNLs consisting of type‐II Dirac cones close to or almost at the Fermi level via angle‐resolved photoemission spectroscopy (ARPES), which provides an electronic explanation for the nonsaturating magnetoresistance effect as observed. First‐principles theory analyses show that spin‐orbit coupling only opens a small gap, resulting in effectively gapless ARPES spectra, yet generating large spin Berry curvature. These type‐II DNLs, together with the interaction between a low‐energy van Hove singularity and quasi‐one‐dimensional band as observed in the same material, suggest CsV8Sb12 as an ideal platform for exploring novel transport properties.

Abstract Background Burnout is a growing problem among medical staff worldwide and empathy has been described as an essential competence to attenuate burnout. Previous studies found job satisfaction and job commitment were affected by the empathy and associated with burnout. This study explores the effect and mechanism of empathy on burnout on medical staff and investigates the mediating role of job satisfaction and job commitment in the relationship between empathy and burnout among medical staff. Methods Based on a self-administered questionnaire which included the Maslach Burnout Inventory (MBI) to measure burnout, 335 responses from medical staff in Tianjin City, China, yielded data on socio-demographic characteristics, empathy, burnout, job satisfaction and job commitment. Bivariate correlation and structured equation modeling (SEM) analyzed the relationships between empathy, job satisfaction, job commitment and burnout multi-group invariant analysis was used to evaluate whether the model was consistent across different type and level of hospitals and different job and employment type subgroups. Results A total of 202 (60.3%) medical staff had low level burnout, 115 (34.3%) staff had the moderate level and 18 (5.4%) staff had the high level burnout. The results of the SEM showed that empathy not only had a direct negative effect on burnout ( $$\beta =-0.401, P 0.05) and hospital type ( $$\Delta {\chi }^{2}$$ Δ χ 2 = 7.748, p > 0.05), but was inconsistent across hospital level ( $$\Delta {\chi }^{2}$$ Δ χ 2 = 42.930, p

Monte Carlo-based sampling methods (MCMC) can be used to solve inverse problems affecting ground penetrating radar (GPR) data. However, due to their high computational complexity, they have not been widely used in practical applications. This article uses neural network methods to replace the computationally complex forward problem of Monte Carlo methods. However, the neural network method is an approximation of the accurate formula method, and this may introduce model errors. In order to reduce the impact of model errors, in this study, we incorporate the Squeeze-and-Excitation (SE) attention mechanism into Convolutional Neural Networks (CNN) to further improve the accuracy of the network. Moreover, with the statistical advantages of the MCMC method, model errors can be explained during the inversion process, further reducing their impact. We apply the proposed method to solve the inversion problem of crosshole ground-penetrating radar travel time data. Compared with commonly used approximate forward models, the method proposed in this paper has better accuracy. The results of data experiments indicate that this method can effectively invert the velocity of underground media.

Traveltime picking accuracy is frequently affected by incoherent or random data noise. Within this context, we put forth a new denoising method called iterative interferometric denoising filtering. This method leverages the pseudo-Wigner distribution function to capture the offset and time-symmetric patterns of source wavelets convolved in seismic signals. Incoherent or random noises without this characteristic are eliminated via this approach. The processed data have waveform information distortion and more frequency components. However, the traveltime information can be considered correct, and the improved signal-to-noise ratio makes traveltime picking much more convenient. Our method’s practical applications in a synthetic and in two field datasets show that this technology can increase the signal-to-noise ratio, and the picked traveltime information can be used in traveltime tomography. These two field datasets were collected near the Aqaba Gulf and the Qademah fault, located in King Abdullah Economic City.

Horizontal fractures are one of the factors that significantly affect the ultimate productivity of shale oil reservoirs. However, the prediction of horizontal fractures by using seismic methods remains a challenge, which is due to the complex elastic and seismic responses that are associated with horizontal fractures. A framework that predicts horizontal fractures by seismic rock physical methods has been developed in the present study. A shale model is then proposed to quantify the shale elastic responses that are associated with the properties of the horizontal fractures. The modeling results that are based on the logging data validated the applicability of the proposed model, and the predicted fracture properties could be used to evaluate the development of horizontal fractures. According to the framework of the Poisson impedance, a horizontal fracture indicator is suggested to represent the logging-derived fracture density in terms of a combination of elastic properties. By using seismic-inverted elastic properties, the obtained indicator enabled an estimation of zones with the potential development of horizontal fractures. The established indicator showed a good correlation with the fracture density and could be used as an effective indicator in the prediction of horizontal fractures in shale oil reservoirs. Furthermore, seismic data applications show a consistency between the development of horizontal fractures and the production status of the boreholes. This result highlights the importance of horizontal fractures for the ultimate productivity and emphasizes the applicability of the proposed methods.

Tight sandstones produce an increasing amount of natural gas worldwide. Apart from identifying the gas enrichment, the predictions of lithology and permeable zones are crucial for the prediction of tight gas sandstones. In the present study, a seismic inversion method is developed based on rock physical modeling, by which it is possible to directly predict the lithology and pore structure in tight formations. The double-porosity model is used as a modeling tool in considering complex pore structures. Based on the model, the microfracture porosity is then predicted using logging data, which are used as a factor to estimate microfractures. Parameters representing the lithology and pore structure are proposed and estimated using logging data analyses and rock physical modeling based on the framework of the Poisson impedance. Thereafter, a new AVO equation is established and extended to the form of an elastic impedance for a direct prediction of the lithology and pore structure parameters. Real data applications show that the indicators of lithology and permeable zones are consistent with the production status. They agree with the petrophysical properties measured in wellbores, thereby proving the applicability of the proposed method for the effective characterization of tight gas sandstones.

In this paper, we review the research progress of domestic and foreign scientists on the relationship between the occurrence of strong earthquakes and crustal heterogeneity, and analyze the advantages and problems of the main research methods. Further analysis shows that the main methods to study the structure of the focal region and seismogenic mechanism of strong earthquakes are as follows: relocating the high-precision focal location of aftershocks by the relative localization method and obtaining the high-resolution seismic wave velocity structure in the focal area by seismic tomography. Different from the mechanism of strong earthquakes in fluid-triggered subduction zones, strong earthquakes occur in or at the boundary of high-velocity anomaly that is easy to accumulate stress, and the weakening of the low-velocity and high-conductivity layer in the middle and lower crust is the main factor that triggers strong earthquakes. Insights into the crustal heterogeneity, seismogenic structure, and seismicity in seismogenic areas can provide important data for earthquake prevention and disaster reduction.

In a sedimentary environment, the conventional one-dimensional (1D) inversion based on the horizontal layered model has difficulty restoring the resistivity distribution of the inclined strata when a coal seam has some dip angle or a small interval between layers. In such cases, the inversion resistivity exhibits horizontal discontinuities, which cannot accurately represent actual geological conditions. Therefore, in view of the good horizontal continuity of the underground electrical structure of sedimentary strata, we propose a high-resolution inversion method based on weighted horizontal and vertical constraints. As a quasi-two-dimensional (2D) inversion, this not only ensures the horizontal continuity of resistivity and recovers the inclined strata, but also improves the vertical resolution. Because the constrained factor has a significant influence on the inversion result, different constrained factors are applied in the horizontal and vertical directions to adjust the constraint strength on the model parameters of each layer and the continuity of the layer interface. In the numerical experiments, we design synthetic models with different tilt angles and layer spacings to test the inversion method and optimize the constrained factors used for coal seam detection. Finally, the transient electromagnetic (TEM) field data processing results in Inner Mongolia show that the resistivity distributions of sedimentary strata can be accurately restored by the new method, and the inversion results are consistent with known geological information.

IntroductionPopliteal cysts are secondary to degenerative changes in the knee joint. After total knee arthroplasty (TKA), 56.7% of patients with popliteal cysts at 4.9 years follow-up remained symptomatic in the popliteal area. However, the result of simultaneous arthroscopic cystectomy and unicompartmental knee arthroplasty (UKA) was uncertain.Case presentationA 57-year-old man was admitted to our hospital with severe pain and swelling in his left knee and the popliteal area. He was diagnosed with severe medial unicompartmental knee osteoarthritis (KOA) with a symptomatic popliteal cyst. Subsequently, arthroscopic cystectomy and unicompartmental knee arthroplasty (UKA) were performed simultaneously. A month after the operation, he returned to his normal life. There was no progression in the lateral compartment of the left knee and no recurrence of the popliteal cyst at the 1-year follow-up.ConclusionFor KOA patients with a popliteal cyst seeking UKA, simultaneous arthroscopic cystectomy and UKA are feasible with great success if managed appropriately.

Full-polarimetric ground penetrating radar (FP-GPR) can measure the ability of an object to change the polarization of electromagnetic waves. Compared to the traditional GPR, it has a stronger capability to identify underground objects. In recent years, a series of polarization decomposition methods have been applied to the FP-GPR data processing to obtain the polarimetric attributes and enhance the capability of targets identification. Different polarimetric attributes characterize different features of a target but there is still no effective way to integrate these attributes and take their respective advantages for target classification. In this article, we propose a particle center AdaBoost (PCAD) method and achieve the multiparameter adaptive target identification. The experimental results indicate that the PCAD method can automatically select suitable parameters during the training process for different targets. Compared to the single-parameter classification and the AdaBoost methods based on the traditional average and Bagging method, the PCAD method presents higher correct rates in classification. Finally, the proposed method is applied to landmine detection. The results demonstrate that the landmine is a composite scatterer that can generate surface scattering signals on its surface and dipole and volume scattering signals from its interior; based on the color-coded two-dimensional image by PCAD, we can distinguish landmines from other targets.

ABSTRACT Inhaled corticosteroids (ICS) are commonly prescribed first-line treatments for asthma and chronic obstructive pulmonary disease (COPD). Recent evidence has shown that ICS use is associated with changes in the airway microbiome, which may impact clinical outcomes such as potential increased risk for pneumonia in COPD. Although the immunomodulatory effects of corticosteroids are well appreciated, whether ICS could directly influence the behavior of respiratory tract bacteria has been unknown. In this pilot study we explored the effects of fluticasone proprionate, a commonly prescribed inhaled corticosteroid, on respiratory bacteria with an expanded focus on Klebsiella pneumoniae, a species previously implicated in fluticasone-associated pneumonia in COPD. We observed significant effects of fluticasone proprionate on growth responses of K. pneumoniae, as well as other bacterial species isolated from asthmatic patients. Fluticasone-exposed K. pneumoniae displayed altered expression of several bacterial genes and reduced the metabolic activity of bronchial epithelial cells and their expression of human β-defensin 2. Targeted assays identified a fluticasone metabolite from fluticasone-exposed K. pneumoniae cells, suggesting this species may be capable of metabolizing fluticasone proprionate. Collectively, these observations support the hypothesis that specific members of the airway microbiota possess the functional repertoire to respond to or potentially utilize corticosteroids in their microenvironment. These findings lay a foundation for novel research directions into the potential direct effects of ICS, often prescribed long term to patients, on the broader airway microbial community and on the behavior of specific microbial species implicated in asthma and COPD outcomes. IMPORTANCE Inhaled corticosteroids are widely prescribed for many respiratory diseases, including asthma and COPD. While they benefit many patients, corticosteroids can also have negative effects. Some patients do not improve with treatment and even experience adverse side effects. Recent studies have shown that inhaled corticosteroids can change the make-up of bacteria in the human respiratory tract. However, whether these medications can directly impact the behavior of such bacteria has been unknown. Here, we explored the effects of fluticasone propionate, a commonly prescribed inhaled corticosteroid, on Klebsiella pneumoniae and other airway bacteria of interest, including primary species isolated from adult asthma patients. We provide evidence of growth responses to direct fluticasone exposure in culture and further examined fluticasone’s effects on K. pneumoniae, including gene expression changes and effects of fluticasone-exposed bacteria on airway cells. These findings indicate that members of the human airway bacterial community possess the functional ability to respond to corticosteroids, which may have implications for the heterogeneity of treatment response observed clinically.

Parkinson's disease (PD) is a common neurodegenerative disease, and there is still a lack of effective diagnostic and treatment methods. This study aimed to search for hub genes that might serve as diagnostic or therapeutic targets for PD. All the analysis was performed in R software. The expression profile data of PD (number: GSE7621) was acquired from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) associated with PD were screened by the “Limma” package of the R software. Key genes associated with PD were screened by the “WGCNA” package of the R software. Target genes were screened by merging the results of “Limma” and “WGCNA.” Enrichment analysis of target genes was performed by Gene Ontology (GO), Disease Ontology (DO), and Kyoto Enrichment of Genes and Genomes (KEGG). Machine learning algorithms were employed to screen for hub genes. Nomogram was constructed using the “rms” package. And the receiver operating characteristic curve (ROC) was plotted to detect and validate our prediction model sensitivity and specificity. Additional expression profile data of PD (number: GSE20141) was acquired from the GEO database to validate the nomogram. GSEA was used to determine the biological functions of the hub genes. Finally, RPL3L, PLEK2, PYCRL, CD99P1, LOC100133130, MELK, LINC01101, and DLG3-AS1 were identified as hub genes of PD. These findings can provide a new direction for the diagnosis and treatment of PD.

Abstract Background Catastrophic health expenditures (CHE) are out-of-pocket payments (OOP) that exceed a predefined percentage or threshold of a household’s resources, usually 40 %, that can push households into poverty in China. We analyzed the trends in the incidence and intensity, and explored the determinants, of CHE, and proposed policy recommendation to address CHE. Methods A unique 5-year national urban-rural panel database was constructed from the China Family Panel Studies (CFPS) surveys. CHE incidence was measured by calculating headcount (percentage of households incurring CHE to the total household sample) and intensity was measured by overshoot (degree by which an average out of pocket health expenditure exceeds the threshold of the total sample). A linear probability model was employed to assess the trend in the net effect of the determinants of CHE incidence and a random effect logit model was used to analyse the role of the characteristics of the household head, the household and household health utilization on CHE incidence. Results CHE determinants vary across time and geographical location. From 2010 to 2018, the total, urban and rural CHE incidence all showed a decreasing tend, falling from 14.7 to 8.7 % for total households, 12.5–6.6 % in urban and 16.8–10.9 % in rural areas. CHE intensity decreased in rural (24.50–20.51 %) and urban (22.31–19.57 %) areas and for all households (23.61–20.15 %). Inpatient services were the most important determinant of the incidence of CHE. For urban households, the random effect logit model identified household head (age, education, self-rated health); household characteristics (members 65 + years, chronic diseases, family size and income status); and healthcare utilization (inpatient and outpatient usage) as determinants of CHE. For rural areas, the same variables were significant with the addition of household head’s sex and health insurance. Conclusions The incidence and intensity of CHE in China displayed a downward trend, but was higher in rural than urban areas. Costs of inpatient service usage should be a key intervention strategy to address CHE. The policy implications include improving the economic level of poor households, reforming health insurance and reinforcing pre-payment hospital insurance methods.

Spinon excitations of a Kagome quantum spin liquid are expected to give rise to a magnetic continuum in Raman spectroscopy. Here, the authors report a magnetic Raman continuum in the Kagome spin liquid candidate Cu3Zn(OH)6FBr, in contrast to a sharp magnon Raman peak in the Kagome antiferromagnet EuCu3(OH)6Cl3.

Abstract Background Uncemented allograft prosthesis composite (APC) has been applied for tumorous bone defect reconstruction in the proximal femur. However, the long-term results are rarely reported. This study aimed to evaluate long-term outcomes of uncemented APC. Methods Eighteen patients who received uncemented APC reconstruction in the proximal femur after tumor resections were retrospectively reviewed. Results The average resection length was 110 mm (80–154) and the average follow-up was 106.7 months (65–141). Bone union achieved in all patients with an average duration of 7.6 months (5–10). The average HHS, MSTS score and gluteus medius strength at one-year follow-up were 88.0 (80–94), 25.2 (22–28) and 4 (3–5), respectively. While at the last follow-up, the HHS, MSTS score and gluteus medius strength were 83.0 (48–100), 24.0 (10–30) and 4 (2–5), respectively. Five intraoperative fractures were fixed with cerclage wires. Two postoperative periprosthetic and prosthetic fractures received a revision. Three local recurrent patients received a secondary surgery. One of these three lung metastatic patients underwent lung metastatic tumor resection. Another two patients were diagnosed with both bone and lung metastases, only one of them underwent amputation. Two greater trochanteric fractures received no treatment. There were10 severe, 3 moderate and 5 mild allograft resorptions without treatment. Conclusion Uncemented APC is a reliable reconstruction for neoplastic bone defect of the proximal femur, especially for the young patient who expected long-life expectancy and good function. Though allograft resorption and trochanteric fracture are the common complications, they seem no effect on the function.

Mare Smythii is a special basin, which is one of the oldest mare basins with the considerably young mare basalts and the high density of floor-fractured craters. In this article, the microwave sounder, named CELMS, onboard Chang'E-2 lunar orbiter was first invested to assess the thermophysical characteristics of the floor materials in mare Smythii. Based on the numerical simulation with the radiative transfer model and the previous geological results, several special findings are noted as follows. First, the substrate temperature in mare Smythii is likely fairly high, and a special temperature structure is provided. Second, there likely exists an unrevealed deposit in the upper layer of the regolith with strong thermal absorption ability. Third, the CELMS results show a strong correlation with the subsurface features obtained by lunar radar sounder data and the surface wrinkle ridges. These findings will be of essential importance to strengthen knowing the thermal and magmatic evolutions of the lunar maria.

Abstract Background Migrant workers are a susceptible population to the hepatitis b virus (HBV) and a vulnerable spot in China’s immunization procedures. There is no free HBV immunization program for migrant workers in China, so understanding migrant workers’ motivation to receive the HBV vaccine is the first step in designing effective immunization policies. Methods A fully specified protection motivation theory (PMT) model of HBV vaccination intention among migrant workers was specified. Data were collected through a cross-sectional survey of 406 migrant workers in three migrant-dense industries in Tianjin, China. Principal component factor analysis was used to produce PMT factors and nested binary logistic regression modeling was applied to assess the associations between protection motivation and HBV vaccination intention of migrant workers. Results The nested binary logistic regression model suggested that the severity factor and self-efficacy factor were positively related to HBV vaccination intention (OR = 2.15, 95% CI: 1.25–3.71; OR = 2.75, 95% CI: 1.62–4.66) while the response costs was negatively related to the HBV vaccination motivation (OR = 0.50, 95% CI: 0.29–0.83). The socio-demographic variables showed that younger, married and good self-rated health status participants were statistically associated with the intention of taking the HBV vaccine. Sex, education level and income group were not significantly associated with vaccination intention. The migrant-industry variables showed that migrant location had a strong effect on migrant workers’ vaccination intention. Conclusion Socio-demographic, migrant-industry variables and PMT factors (severity, self-efficacy and response costs) were statistically associated with migrant workers’ intention to vaccinate. Our results suggest that health policy makers should provide more information to migrants on HBV severity; inform migrant workers on where, when and how to get the HBV vaccine; tap into work organizations as a location for vaccinations; and identify migrant worker subgroups for targeted interventions.

The existence of multiple reflections brings difficulty to seismic data processing and interpretation in seismic reflection exploration. Parabolic Radon transform is widely used in multiple attenuation because it is easily implemented, highly robust and efficient. However, finite seismic acquisition aperture of seismic data causes energy diffusion in the Radon domain, which leads to multiple residuals. In this paper, we propose a sparse parabolic Radon transform with the nonconvex Lq1-Lq2(0

Compared with primary and local tumor, metastasis is more difficult to resect completely and remain the leading cause of death associated with solid tumors. It is therefore pressing to develop effective strategies to prevent and suppress tumor metastasis in its early stages. Heparanase overexpression is significantly associated with increased malignancy and metastasis. Here, we develop a novel heparanase degradable heparin-based microcarrier, HBMA, for specific staining, targeting, and inhibiting tumor cells with high metastatic potential. HBMA is fabricated through self-assembling of three components, positively charged fluorescent gold nanoclusters (KG-AuNCs), heparin polymers (HP), and antisense miRNA-21 oligonucleotides (AM-21), with each component playing multiple roles. Results of confocal microscopy and flow cytometry show that this agent has an excellent capability to label metastatic tumor cells with high selectivity. On the therapeutic side, the results of cell viability assay, wound healing assay, and plate colony formation assay verify that HBMA induces a significant inhibition effect towards the proliferation and migration of the selectively target tumor cells. The heparanase responsive AM-21 delivery, the specific staining, and the efficient tumor cell activity suppression highlight HBMA as a promising cancer theranostic agent to prevent tumor metastasis.

AbstractSeismic dispersion and fluid mobility attributes are used to characterize a volcanic gas reservoir in the Songliao Basin of China. A rock physics model is constructed to describe poroelastic behaviors associated with heterogeneous fluids saturation within the volcanic gas reservoirs, where velocity dispersion and attenuation of propagating waves are attributed to the wave-induced fluid flow described by the patchy saturation theory. Modeling results indicate that the frequency-dependent bulk modulus at the seismic frequency is more sensitive to gas saturation than the P-wave velocity dispersion. Accordingly, a new inversion method is developed to compute bulk-modulus-related dispersion attribute DK for improved characterization of volcanic gas reservoirs. Synthetic tests indicate that DK is more sensitive than traditional P-wave dispersion attribute DP to the variations of reservoir properties. The high value of dispersion attribute DK indicates the volcanic gas reservoirs with high porosity and gas saturation. At the same time, fluid mobility attribute FM can discriminate the volcanic gas reservoir as DK. Field data applications illustrate that DK and FM exhibit anomalies to the gas zones in the volcanic gas reservoir on the cross-well section. However, DK is more robust than FM to identify favorable zones on horizontal slices for specific target layers. Overall, rock physical modeling provides insights into the poroelastic behaviors of volcanic gas reservoirs, and inversion for seismic dispersion attribute DK improves hydrocarbon detection in the volcanic gas reservoir.

Pores and microfractures provide storage spaces and migration pathways for gas accumulation in tight sandstones with low porosity and permeability, acting as one of the controlling factors of gas production. The development of a rational rock physics model is essential for better understanding the elastic responses of tight sandstone with complex pore structures. Accordingly, seismic characterization of pores and microfractures based on the rock physics model provides valuable information in predicting high-quality tight gas sandstone reservoirs. This paper proposes a rock-physics-based approach to compute the pore–microfracture indicator (PMI) from elastic properties for pore structure evaluation in tight sandstones. The PMI is achieved based on the axis rotation of the elastic parameter space using well-log data. The rotation angle is determined by finding the maximum correlation between the linearized combination of the elastic parameters and the introduced factor associated with total porosity and microfracture porosity. The microfracture porosity is then estimated with an inversion scheme based on the double-porosity model. Finally, the optimized rotation angle is employed to compute the PMI with seismic data. The obtained results are of great benefit in predicting the permeable zones, providing valuable information for sweet spot characterization in tight gas sandstone reservoirs.

Characterizing gas hydrate-bearing marine sediments using seismic methods is essential for locating potential hydrate resources. However, most existing pre-stack seismic inversion methods estimate the properties of sediments containing gas hydrates without considering specific characteristics associated with gas hydrate occurrences. In the present study, a pore-filling–solid matrix decoupling amplitude variation with offset (AVO) formula is proposed to represent seismic reflectivity in terms of properties associated with gas hydrates. Based on the rock physics relationships of solid substitution, the parameters introduced into the decoupling AVO equation estimate the concentration of gas hydrates with different occurrences, including pore fillings mixed with water and solid components forming part of the dry sediment frame. A theoretical model test indicates that seismic attributes obtained with the decoupling AVO inversion are superior to the conventional wave velocities-related properties in predicting gas hydrate saturations. A realistic model test further validates the applicability of the proposed method in characterizing a gas hydrate system with varying concentrations and layer thickness. By adjusting the tuning parameters, the configurations and concentrations of the gas hydrate system can be identified using the obtained attributes. Therefore, the presented method provides a useful tool for the characterization of gas hydrate-bearing sediments.

Sufficient gas accumulation is an essential factor that controls the effective development of tight sandstone gas reservoirs that are generally characterized by low porosity and permeability. Seismic methods are important for predicting potential gas areas in tight sandstones. However, the complex relationships between rock physical properties and gas saturation make gas enrichment estimation with seismic methods challenging. Nonetheless, seismic velocity dispersion using a wave-induced fluid flow mechanism can enable gas identification by utilizing the associated dispersion attributes. This paper proposes a method for improved gas identification using a new fluid dispersion attribute obtained by incorporating the decoupled fluid-solid seismic amplitude variation with offset representation into the frequency-dependent inversion scheme. Numerical analyses and synthetic data tests confirmed the enhanced sensitivity of the fluid dispersion attribute to gas saturation compared to the conventionally used compressional wave velocity dispersion attribute. Field data applications further validated the ability of the proposed fluid dispersion attribute to improve gas prediction in tight sandstone reservoirs. The results of the measurements enable rational interpretation of the geological significance of assessments of reservoir properties from gas-producing wellbores. The proposed fluid dispersion attribute is a reliable indicator for gas prediction and represents a useful tool for characterizing tight sandstone reservoirs.

Gas hydrates are considered a potential energy source for the future. Rock physics modeling provides insights into the elastic response of sediments containing gas hydrates, which is essential for identifying gas hydrates using well-log data and seismic attributes. This paper establishes a rock physics model (RPM) by employing effective medium theories to quantify the elastic properties of sediments containing gas hydrates. Specifically, the proposed RPM introduces critical gas hydrate saturation for various modeling schemes. Such a key factor considers the impact of gas hydrates on sediment stiffnesses during the dynamic process of the gas hydrate accumulating as pore fillings and part of the solid components. Theoretical modeling illustrates that elastic characteristics of the sediments exhibit distinct variation trends determined by critical gas hydrate saturation. Numerical tests of the model based on the well-log data confirm that the proposed technique can be employed to rationally predict gas hydrate saturation using the elastic properties. The compressional wave velocity model is also developed to estimate the gas hydrate saturation, which gives reliable fit results to core measurement data. The proposed methods could improve our understanding of the elastic behaviors of gas hydrates, providing a practical approach to estimating their concentrations.

Quantitative estimates of reservoir parameters and elastic anisotropy using seismic methods is essential for characterizing shale oil reservoirs. Rock physics models were established to quantify elastic anisotropy associated with clay properties, laminated microstructures, and bedding fractures at different scales in shale. The inversion schemes based on the built rock physics models were proposed to estimate reservoir parameters and elastic anisotropy using well log data. Based on the back propagation neural network framework, the obtained rock physical inversion results were used to establish the nonlinear models between elastic properties and reservoir parameters and elastic anisotropy of shale. The established correlations were applied for quantitative seismic interpretation, converting seismic inversion results to the reservoir parameters and elastic anisotropy to characterize the shale oil reservoir comprehensively. The predicted elastic anisotropy of the shale matrix reflects the lamination degree and the mechanical properties of the shale, which is critical for the effective implementation of hydraulic fracturing. The calculated elastic anisotropy of the shale provides more accurate models for seismic modeling and inversion. The obtained bedding fracture parameters provide insights into reservoir permeability. Therefore, the proposed method provides valuable information for identifying favorable oil zones in the study area.

Summary: Dysregulated glycine metabolism is emerging as a common denominator in cardiometabolic diseases, but its contribution to atherosclerosis remains unclear. In this study, we demonstrate impaired glycine-oxalate metabolism through alanine-glyoxylate aminotransferase (AGXT) in atherosclerosis. As found in patients with atherosclerosis, the glycine/oxalate ratio is decreased in atherosclerotic mice concomitant with suppression of AGXT. Agxt deletion in apolipoprotein E-deficient (Apoe−/−) mice decreases the glycine/oxalate ratio and increases atherosclerosis with induction of hepatic pro-atherogenic pathways, predominantly cytokine/chemokine signaling and dysregulated redox homeostasis. Consistently, circulating and aortic C-C motif chemokine ligand 5 (CCL5) and superoxide in lesional macrophages are increased. Similar findings are observed following dietary oxalate overload in Apoe−/− mice. In macrophages, oxalate induces mitochondrial dysfunction and superoxide accumulation, leading to increased CCL5. Conversely, AGXT overexpression in Apoe−/− mice increases the glycine/oxalate ratio and decreases aortic superoxide, CCL5, and atherosclerosis. Our findings uncover dysregulated oxalate metabolism via suppressed AGXT as a driver and therapeutic target in atherosclerosis.

Gas identification using seismic data is challenging for tight gas reservoirs with low porosity and permeability due to the complicated poroelastic behaviors of tight sandstone. In this study, the Chapman theory was used to simulate the dispersion and attenuation caused by the squirt flow of fluids in the complex pore spaces, which are assumed to consist of high aspect-ratio pores (stiff pores) and low aspect-ratio microcracks (soft pores). The rock physics modeling revealed that as the gas saturation varies, P-wave velocity dispersion and attenuation occurs at seismic frequencies, and it tends to move to high frequencies as the gas saturation increases. The velocity dispersion of the tight gas sandstone causes a frequency-dependent contrast in the P-wave impedance between the tight sandstone and the overlying mudstone, which consequently leads to frequency-dependent incidence reflection coefficients across the interface. In the synthetic seismic AVO modeling conducted by integrating the rock physics model and the propagator matrix method, the variations in the amplitudes and phases of the PP reflections can be observed for various gas saturations. The tests of the frequency-dependent AVO inversion of these synthetic data revealed that the magnitude of the inverted P-wave dispersion attribute can be used to indicate gas saturation in tight sandstone reservoirs. The applications of the frequency-dependent AVO inversion to the field pre-stacked seismic data revealed that the obtained P-wave dispersion attribute is positively correlated with the gas production from the pay zone at the well locations. Thus, the methods of the rock physics modeling and the frequency-dependent AVO inversion conducted in this study have good potential for the evaluation of the gas saturation in tight gas sandstone reservoirs.