Search Results (1,843)

Ground-penetrating radar (GPR) is an effective geophysical method for rapid and non-destructive detection. Directional borehole radar is the application of GPR in a borehole, which can determine the depth, orientation, and distance of the target from the borehole. The borehole radar azimuth recognition algorithm is based on the assumption of far-field plane waves. Therefore, in the near-field area where the target is closer to the borehole, the electromagnetic waves reflected by the target cannot be regarded as plane waves but will have a certain curvature. The plane wave assumption is not valid in this area, so the azimuth recognition algorithm will have significant errors, forming blind zones for directional borehole radar detection. This article uses the finite-difference time-domain (FDTD) algorithm to numerically simulate how blind zones affect directional borehole radar systems, identify the impact patterns, and minimize them. After calculation and numerical simulation verification, it has been found that when the center frequency of the antenna is 1 GHz, within 2 m of the target from the borehole, there is a significant error in azimuth recognition, which can be defined as the near-field region. Similarly, through numerical simulation verification, the optimal antenna center frequency is between 600 MHz and 1100 MHz. Oil-based mud is superior to water-based mud. The optimal antenna center frequency decreases as the target distance increases. Full article

Colorectal cancer (CRC) is a significant worldwide health problem due to its high prevalence, mortality rates, and frequent diagnosis at advanced stages. While diagnostic and therapeutic approaches have evolved, the underlying mechanisms driving CRC initiation and progression are not yet fully understood. Early detection is critical for improving patient survival, as initial cancer stages often exhibit epigenetic changes—such as DNA methylation—that regulate gene expression and tumor progression. Identifying DNA methylation patterns and key survival-related genes in CRC could thus enhance diagnostic accuracy and extend patient lifespans. In this study, we apply two of our recently developed methods for identifying differential methylation and analyzing survival using a sparse, finite mixture of accelerated failure time regression models, focusing on key genes and pathways in CRC datasets. Our approach outperforms two other leading methods, yielding robust findings and identifying novel differentially methylated cytosines. We found that CRC patient survival time follows a two-component mixture regression model, where genes CDH11, EPB41L3, and DOCK2 are active in the more aggressive form of CRC, whereas TMEM215, PPP1R14A, GPR158, and NAPSB are active in the less aggressive form. Full article

G-protein coupled receptors (GPCRs), the largest family of integral membrane proteins, enable cells to sense and appropriately respond to the environment through mediating extracellular signaling to intercellular messenger molecules. GPCRs’ pairing with a diverse array of G protein subunits and related downstream secondary messengers, combined with their ligand versatility-from conventional peptide hormone to numerous bioactive metabolites, allow GPCRs to comprehensively regulate metabolism and physiology. Consequently, GPCRs have garnered significant attention for their therapeutic potential in metabolic diseases. This review focuses on six GPCRs, GPR40, GPR120, GLP-1R, and ß-adrenergic receptors (ADRB1, ADRB2, and ADRB3), with GLP-1R recognized as a prominent regulator of system-level metabolism, while the roles of GPR40, GPR120 and ß-adrenergic receptors in central carbon metabolism and energy homeostasis are increasingly appreciated. Here, we discuss their physiological functions in metabolism, the current pharmacological landscape, and the intricacies of their signaling pathways via G protein and ß-arrestin activation. Additionally, we discuss the limitations of existing GPCR-targeted strategies for treating metabolic diseases and offer insights into future perspectives for advancing GPCR pharmacology. Full article

The non-destructive geophysical testing method ground penetrating radar (GPR), along with satellite image and air photo assessment, a review of the existing literature sources, and Holocaust survivor testimony, was used to document the location of potential mass graves in Alytus, Lithuania. In World War II, six million Jews were murdered, as were as many as five million other victims of Nazi Germany’s orchestrated persecution. In the summer of 1941, 8030 Jews (4.70 percent of Lithuania’s Jewish population) lived in Alytus County, where the town of Alytus is located. It is estimated that over 8000 Jews were murdered in Alytus County, including nearly the entire Jewish population of the town of Alytus. The murder of Jews from Alytus County accounts for approximately 4.2% of the total number of Lithuanian Jews killed in the Holocaust. Survivor testimony indicates that several thousand Jews from both the town and county were murdered and buried in the Vidzgiris Forest about 1000 m from the town center. In 2022, field reconnaissance at locations in the forest, which appeared to be disturbed in a 1944 German Luftwaffe air photograph, indicated that these disturbances were associated with natural geomorphic processes and not the Holocaust. Analysis of GPR data that was collected using a pulseEKKO Pro 500-megahertz groundpenetrating radar (GPR) system in 2022 in the vicinity of monuments erected in the forest to memorialize mass graves indicates that no mass graves were directly associated with these monuments. The 1944 air photograph contained two roads that traversed through and abruptly ended in the forest, which was the impetus for detailed field reconnaissance in that area. A segment of a 150 m long linear surface feature found in the forest was assessed using GPR, and based on the profile that was generated, it was determined that this feature is possibly a segment of a much more extensive mass grave. Testimony of a Holocaust survivor stated that as many as three burial trenches exist in this portion of the forest. Additional research using non-destructive GPR technology, air photograph and satellite image assessment, and the existing literature and testimony-based data are required for the Vidzgiris Forest to better define these and other potential mass graves and other Holocaust-related features. Full article

Rainfall–runoff models are crucial tools for managing water resources. The absence of reliable rainfall data in many regions of the world is a major limitation for these models, notably in many African countries, although some recent global rainfall products can effectively monitor rainfall from space. In Algeria, to identify a relevant modeling approach using this new source of rainfall information, the present research aims to (i) compare a conceptual model (GR4J) and seven machine learning algorithms (FFNN, ELM, LSTM, LSTM2, GRU, SVM, and GPR) and (ii) compare different types of precipitation inputs, including four satellite products (CHIRPS, SM2RAIN, GPM, and PERSIANN), one reanalysis product (ERA5), and observed precipitation, to assess which combination of models and precipitation data provides the optimal performance for river discharge simulation. The results show that the ELM, FFNN, and LSTM algorithms give the best performance (NSE > 0.6) for river runoff simulation and provide reliable alternatives compared to a conceptual hydrological model. The SM2RAIN-ASCAT and ERA5 rainfall products are as efficient as observed precipitation in this data-scarce context. Consequently, this work is the first step towards the implementation of these tools for the operational monitoring of surface water resources in Algeria. Full article

G protein-coupled receptors (GPRs), including pro-inflammatory GPR4 and ovarian cancer GPR1 (OGR1/GPR68), are involved in the pH sensing of the extracellular space and have been implicated in inflammatory bowel disease (IBD). Previous data show that a loss of GPR4 or OGR1 independently is associated with reduced intestinal inflammation in mouse models of experimental colitis. In the present manuscript, we investigated the impact of the simultaneous loss of GPR4 and OGR1 in animal models of IBD. To study the effects of combined loss of Gpr4 Ogr1 in IBD we used the well-established acute dextran sodium sulfate (DSS) and spontaneous Il10^−/− murine colitis models. Disease severity was assessed using multiple clinical scores (e.g., body weight loss, disease activity score, murine endoscopic index of colitis severity (MEICS) and histological analyses). Real-time quantitative polymerase chain reaction (qPCR), Western blot, and flow cytometry were used to investigate changes in pro-inflammatory cytokines expression and immune cells infiltration. We found that a combined loss of GPR4 and OGR1 significantly reduces colon inflammation in IBD relative to single deficiencies as evidenced by reduced body weight loss, disease score, CD4/CD8 ratio, and Il1β, Il6, and Tnf in the colon. Similarly, in the II10 deficiency model, the inflammation was significantly ameliorated upon the simultaneous deletion of GPR4 and OGR1, evidenced by a reduction in the MEICS score, colon length, Tnf and Il1β measurements, and a decrease in the number of macrophages in the colon, as compared to single deletions. Importantly, hydroxyproline levels were decreased close to baseline in Il10^−/− × Gpr4^−/− × Ogr1^−/− mice. Our findings demonstrate that the simultaneous loss of GRP4 and OGR1 functions exerts an additive effect on multiple parameters associated with colonic inflammation. These results further reinforce the hypothesis that chronic inflammatory acidosis is a driver of fibrosis and is dependent on GPR4 and OGR1 signaling. The inhibition of both GPR4 and OGR1 by pH-sensing receptor modulators may constitute as a potential therapeutic option for IBD, as both pH-sensing receptors appear to sustain inflammation by acting on complementary pro-inflammatory pathways. Full article

Ground penetrating radar (GPR) is a mature and important research method in the field of structural non-destructive testing. However, when the detection target scale is small and the amount of data collected is limited, it poses a serious challenge for this research method. In order to verify the applicability of typical one-dimensional radar signals combined with convolutional neural networks (CNN) in the non-destructive testing of concrete structures, this study created concrete specimens with embedded defects (voids, non-dense solids, and cracks) commonly found in concrete structures in a laboratory setting. High-frequency GPR equipment is used for data acquisition, A-scan data corresponding to different defects is extracted as a training set, and appropriate labeling is carried out. The extracted original radar signals were taken as the input of the CNN model. At the same time, in order to improve the sensitivity of the CNN models to specific damage types, the spectrums of A-scan are also used as part of the training datasets of the CNN models. In this paper, two CNN models with different dimensions are used to train the datasets and evaluate the classification results; one is the traditional one-dimensional CNN model, and the other is the classical two-dimensional CNN architecture AlexNet. In addition, the finite difference time domain (FDTD) model of three-dimensional complex media is established by gprMax, and the propagation characteristics of GPR in concrete media are simulated. The results of applying this method to both simulated and experimental data show that combining the A-scan data of ground penetrating radar and their spectrums as input with the CNN model can effectively identify different types of damage and defects inside the concrete structure. Compared with the one-dimensional CNN model, AlexNet has obvious advantages in extracting complex signal features and processing high-dimensional data. The feasibility of this method in the research field of damage detection of concrete structures has been verified. Full article

This study investigates highway pavement subsidence along Road 431, Israel, using an integrated geophysical framework that combines Interferometric Synthetic Aperture Radar (InSAR), Ground Penetrating Radar (GPR), and Electrical Resistivity Tomography (ERT). These methods address the limitations of standalone techniques by correlating surface subsidence patterns with subsurface anomalies. InSAR identified surface subsidence rates of up to −2.7 cm/year, pinpointing subsidence hotspots, while GPR detected disintegrated fill layers and air voids, and ERT revealed resistivity anomalies at depths of 50–100 m linked to karstic cavities and water infiltration. Validation through borehole drilling confirmed structural heterogeneity, specifically identifying karstic voids in limestone layers and weathered chalk layers that align with the geophysical findings. The findings highlight the complex interplay of geological and hydrological processes driving ground instability, exacerbated by groundwater fluctuations. This study demonstrates the novelty of combining surface and subsurface monitoring methods, offering a detailed diagnostic framework for understanding and mitigating geotechnical risks in transportation infrastructure. Full article

Objective: Cholesin is a recently discovered gut-derived hormone secreted by enterocytes upon dietary cholesterol uptake via the transmembrane sterol transporter Niemann–Pick disease C1-like intracellular cholesterol transporter 1 (NPC1L1). In the liver, cholesin activates G protein-coupled receptor 146 (GPR146), causing reduced cholesterol synthesis. In this exploratory, hypothesis-generating study based on post hoc analysis, human data on the cholesin system are presented. Methods: Mucosal biopsies were collected throughout the intestinal tract from 12 individuals with type 2 diabetes (T2D) and 12 healthy, matched controls. Upper small intestinal mucosal biopsies were collected from 20 individuals before and after Roux-en-Y gastric bypass (RYGB) surgery. Liver biopsies were collected from 12 men with obesity and 15 matched controls without obesity. Subcutaneous abdominal adipose tissue biopsies were collected from 20 men with type 1 diabetes (T1D). All biopsies underwent full mRNA sequencing. Results: Cholesin mRNA expression was observed throughout the intestinal tracts of the individuals with T2D and the controls, in the livers of men with and without obesity, and in adipose tissue of men with T1D. NPC1L1 mRNA expression was robust throughout the small intestines but negligible in the large intestines of both individuals with and without T2D. RYGB surgery induced the expression of NPC1L1 mRNA in the upper small intestine. GPR146 mRNA was expressed in the livers of men, both with and without obesity, and in the adipose tissue of men with T1D, but not in the intestines. Conclusions: Our results suggest a role of the cholesin system in human physiology, but whether it is perturbed in metabolic diseases remains unknown. Clinical trial registration numbers: NCT03044860, NCT03093298, NCT02337660, NCT03734718. Full article

Unreinforced masonry buildings are highly vulnerable to earthquake damage due to their limited ability to withstand lateral loads, compared to other structures. Therefore, a detailed assessment of the seismic response and resultant damage associated with such buildings becomes necessary. The present study employs machine learning models to effectively predict the seismic response and classify the damage level for a benchmark unreinforced masonry building. In this regard, eight regression-based models, namely, Linear Regression (LR), Stepwise Linear Regression (SLR), Ridge Regression (RR), Support Vector Machine (SVM), Gaussian Process Regression (GPR), Decision Tree (DT), Random Forest (RF), and Neural Networks (NN), were used to predict the building’s responses. Additionally, eight classification-based models, namely, Naïve Bayes (NB), Discriminant Analysis (DA), K-Nearest Neighbours (KNN), Adaptive Boosting (AB), DT, RF, SVM, and NN, were explored for the purpose of categorizing the damage states of the building. The material properties of the masonry and the earthquake intensity were considered as the input parameters. The results from the regression models indicate that the GPR model efficiently predicts the seismic response with larger coefficients of determination and smaller root mean square error values than other models. Among the classification-based models, the RF, AB, and NN models effectively classify the damage states with accuracy levels of 92.9%, 91.1%, and 92.6%, respectively. In conclusion, the overall performance of the non-parametric models, such as GPR, NN, and RF, was found to be better than that of the parametric models. Full article

Tree roots are vital for tree ecosystems; accurate root detection helps analyze the health of trees and supports the effective management of resources such as fertilizers, water and pesticides. In this paper, a deep learning-based ground-penetrating radar (GPR) inversion method is proposed to simultaneously image the spatial distribution of permittivity for subsurface tree roots and layered heterogeneous soils in real time. Additionally, a GPR simulation data set and a measured data set are built in this study, which were used to train inversion models and validate the effectiveness of GPR inversion methods.The introduced GPR inversion model is a pyramid convolutional network with vision transformer and edge inversion auxiliary task (PyViTENet), which combines pyramidal convolution and vision transformer to improve the diversity and accuracy of data feature extraction. Furthermore, by adding the task of edge inversion of the permittivity distribution of underground materials, the model focuses more on the details of heterogeneous structures. The experimental results show that, for the case of buried scatterers in layered heterogeneous soil, the PyViTENet performs better than other deep learning methods on the simulation data set. It can more accurately invert the permittivity of scatterers and the soil stratification. The most notable advantage of PyViTENet is that it can accurately capture the heterogeneous structural details of the soil within the layer since the soil around the tree roots in the real scene is layered soil and each layer of soil is also heterogeneous due to factors such as humidity, proportion of different soil particles, etc.In order to further verify the effectiveness of the proposed inversion method, this study applied the PyViTENet to GPR measured data through transfer learning for reconstructing the permittivity, shape, and position information of scatterers in the actual scene. The proposed model shows good generalization ability and accuracy, and provides a basis for non-destructive detection of underground scatterers and their surrounding medium. Full article

To address the significant impact of noise on the target detection performance of borehole radar (BHR), a key type of ground-penetrating radar (GPR), a denoising scheme based on the whale optimization algorithm (WOA) for adaptive variational mode decomposition (VMD) and multiscale principal component analysis (MSPCA) is proposed. This study initially conducts the modal decomposition of BHR data using an improved adaptive VMD method based on the WOA; it then automatically selects modes meeting specific frequency band standards. The correlation coefficients between these modes and the original signal are computed, discarding weakly correlated modes before signal reconstruction. Finally, MSPCA further suppresses noise, yielding denoised BHR data. Simulations show that the proposed scheme increases the signal-to-noise ratio by 17.964 dB or higher, surpassing the more established denoising techniques of robust principal component analysis (RPCA), MSPCA, and empirical mode decomposition (EMD), and obtains the most favorable results in terms of the RMSE and MSE metrics. The experimental results demonstrate that the proposed scheme more effectively suppresses vertical and random noise signals in BHR data. Both the numerical simulations and experimental results confirm the effectiveness of this scheme in noise reduction for BHR data. Full article

The axial-flow pump system has been widely applied to coastal drainage pump stations, but the hydraulic performance optimization based on the contraction angles of the inlet passage has not been studied. This paper combined the computational fluid dynamics (CFD) method, machine learning (ML) algorithms and genetic algorithm (GA) to find the optimal contraction angles of the inlet passage. The 125 sets of comprehensive objective function were obtained by the CFD method. Three contraction angles and comprehensive objective function values were regressed by three ML algorithms. After hyperparameter optimization, the Gaussian process regression (GPR) model had the highest R² = 0.958 in the test set and had the strongest generalization ability among the three models. The impact degree of the three contraction angles on the objective function of the GPR model was investigated by the Sobol sensitivity analysis method; the results indicated that the order of impact degree from high to low was ${\theta }_3>{\theta }_2>{\theta }_1$. The optimal objective function values of the GPR model and corresponding contraction angles were searched through GA; the maximum objective function value was 0.963 and corresponding contraction angles were ${\theta }_1=13.34°$, ${\theta }_2=28.36°$ and ${\theta }_3=3.64°$, respectively. The results of this study can provide reference for the optimization of inlet passages in coastal drainage pump systems. Full article

Slope failures caused by changes in soil moisture content have become a growing global concern, resulting in significant loss of life and economic damage. To ensure the stability of slopes, it is necessary to accurately monitor the moisture content and understand the complex interactions between soil, water, and slope behavior. This paper provides a comprehensive overview of advanced soil moisture detection techniques for unsaturated soil slopes, including point-scale measurements and geophysical methods. It first introduces the fundamental concepts of the soil–water characteristic curve (SWCC) and its influence on the shear strength and stability of unsaturated soil slopes. It then delves into the working principles and applications of various point-scale measurement techniques, such as time-domain reflectometry (TDR), frequency-domain reflectometry (FDR), and neutron probe methods. Additionally, this paper explores the use of geophysiDear Editor: The author has checked that the name and affiliation are accuratecal methods, including ground-penetrating radar (GPR), electrical resistivity tomography (ERT), and electromagnetic induction (EMI), for the non-invasive assessment of soil moisture conditions and slope stability monitoring. This review highlights the advantages of integrating multiple geophysical techniques, combined with traditional geotechnical and hydrological measurements, to obtain a more comprehensive understanding of the subsurface conditions and their influence on slope stability. Several case studies are presented to demonstrate the successful application of this integrated approach in various slope monitoring scenarios. The continued advancement in these areas will contribute to the development of more accurate, reliable, and widely adopted solutions for the assessment and management of slope stability risks. Full article

This paper presents a method to improve confidence-interval (CI) estimation using individual uncertainty measures and weighted feature decisions for cuff-less blood-pressure (BP) measurement. We obtained uncertainty using Gaussian process regression (GPR). The CI obtained from the GPR model is computed using the distribution of BP estimates, which provides relatively wide CIs. Thus, we proposed a method to obtain improved CIs for individual subjects by applying bootstrap and uncertainty methods using the cuff-less BP estimates of each subject obtained through GPR. This study also introduced a novel method to estimate cuff-less BP with high fidelity by determining highly weighted features using weighted feature decisions. The standard deviation of the proposed method’s mean error is 2.94 mmHg and 1.50 mmHg for systolic blood pressure (SBP) and (DBP), respectively. The mean absolute error results were obtained by weighted feature determination combining GPR and gradient boosting algorithms (GBA) for SBP (1.46 mmHg) and DBP (0.69 mmHg). The study confirmed that the BP estimates were within the CI based on the test samples of almost all subjects. The weighted feature decisions combining GPR and GBA were more accurate and reliable for cuff-less BP estimation. Full article