Search Results (51)

Seismic multi-parameter full-waveform inversion (FWI) integrating velocity and density parameters can fully use the kinematic and dynamic information of observed data to reconstruct underground models. However, seismic multi-parameter FWI is a highly ill-posed problem due to the strong dependence on the initial model. An inaccurate initial model often leads to cycle skipping and convergence to local minima, resulting in poor inversion results. The introduction of prior information can regularize the inversion problem, not only improving the crosstalk phenomenon between parameters, but also effectively constraining the inversion parameters, enhancing the inversion efficiency. Multi-parameter FWI based on rock physical constraints can introduce prior information of underground media into the objective function of FWI. Taking a simple layered model as an example, the results show that the inversion strategy based on rock physical constraints can enhance the stability of inversion and obtain high-precision inversion results. Application to the international standard 1994BP model further confirms that the proposed inversion strategy has good applicability to complex geological models. Full article

Full waveform inversion (FWI) is an established precise velocity estimation tool for seismic exploration. Machine learning-based FWI could plausibly circumvent the long-standing cycle-skipping problem of traditional model-driven methods. The physics-guided self-supervised FWI is appealing in that it avoids having to make tedious efforts in terms of label generation for supervised methods. One way is to employ an inversion network to convert the seismic shot gathers into a velocity model. The objective function is to minimize the difference between the recorded seismic data and the synthetic data by solving the wave equation using the inverted velocity model. To further improve the efficiency, we propose a two-stage training strategy for the self-supervised learning FWI. The first stage is to pretrain the inversion network using a simultaneous source for a large-scale velocity model with high efficiency. The second stage is switched to modeling the separate shot gathers for an accurate measurement of the seismic data to invert the velocity model details. The inversion network is a partial convolution attention modified UNet (PCAMUNet), which combines local feature extraction with global information integration to achieve high-resolution velocity model estimation from seismic shot gathers. The time-domain 2D acoustic wave equation serves as the physical constraint in this self-supervised framework. Different loss functions are used for the two stages, that is, the waveform loss with time weighting for the first stage (simultaneous source) and the hybrid waveform with time weighting and logarithmic envelope loss for the second stage (separate source). Comparative experiments demonstrate that the proposed approach improves both inversion accuracy and efficiency on the Marmousi2 model, Overthrust model, and BP model tests. Moreover, the method exhibits excellent noise resistance and stability when low-frequency data component is missing. Full article

Elastic full waveform inversion (EFWI) is a crucial technique for retrieving high-resolution multi-parameter information. However, the lack of low-frequency components in seismic data may induce severe cycle-skipping phenomena in elastic full waveform inversion (EFWI). Recognizing the approximately linear relationship between the phase components of seismic data and the properties of subsurface media, we propose an Elastic Wave Phase Inversion in local-scale frequency–wavenumber domain (LFKEPI) method. This method aims to provide robust initial velocity models for EFWI, effectively mitigating cycle-skipping challenges. In our approach, we first employ a two-dimensional sliding window function to obtain local-scale seismic data. Following this, we utilize two-dimensional Fourier transforms to generate the local-scale frequency–wavenumber domain seismic data, constructing a corresponding elastic wave phase misfit. Unlike the Elastic Wave Phase Inversion in the frequency domain (FEPI), the local-scale frequency–wavenumber domain approach accounts for the continuity of seismic events in the spatial domain, enhancing the robustness of the inversion process. We subsequently derive the gradient operators for the LFKEPI methodology. Testing on the Marmousi model using a land seismic acquisition system and a simultaneous-source marine towed seismic acquisition system demonstrates that LFKEPI enables the acquisition of reliable initial velocity models for EFWI, effectively mitigating the cycle-skipping problem. Full article

Camshafts in internal combustion engines are critical components not only for mechanical functionality but also for dynamic performance and vibration characteristics. This study aims to present a comprehensive modal analysis of the camshaft by integrating both computational and experimental approaches, followed by the design of an innovative multi-mode camshaft mechanism that enhances fuel efficiency and optimizes engine performance. First, a computational model of the camshaft is built with a proper mesh structure with a mesh size of 2 mm. Modal analysis is performed on the computational model, and the critical modal parameters of the camshaft are obtained. Second, modal tests are performed on the camshaft, which reveal an error of 15% on the computationally predicted natural frequencies. Third, a model updating procedure is applied to improve the accuracy of the computational model. The critical material properties are determined based on a sensitivity analysis, and the structural optimization process is performed accordingly. The optimized model solution is compared to the experimental data, and the computational model is validated based on both natural frequencies and mode shapes. The comparison of experimentally and computationally estimated natural frequencies reveal a difference below 2%. Mode shapes are compared based on Modal Assurance Criteria (MAC), and it is determined that the values of the elements in the main diagonal of the MAC matrix are around 0.9. Finally, the validated model is used as a basis for an innovative camshaft mechanism. The proposed mechanism offers enhanced flexibility by integrating multiple valve actuation methods, including Variable Valve Timing (VVT), Variable Valve Lift (VVL), Variable Valve Duration (VVD), Cylinder Deactivation, and Skip Cycle methods into a single camshaft for the first time in the literature. Modal analysis is performed on the proposed multi-mode design, and it is observed that the modified design resembles the modal properties of the original design. Full article

CRISPR-Cas9 genome editing is a versatile platform for studying and treating various diseases. Homology-directed repair (HDR) with DNA donor templates serves as the primary pathway for gene correction in therapeutic applications, but its efficiency remains a significant challenge. This study investigates strategies to enhance gene correction efficiency using a T-shaped lipo-xenopeptide (XP)-based Cas9 RNP/ssDNA delivery system combined with various HDR enhancers. Nu7441, a known DNA-PKcs inhibitor, was found to be most effective in enhancing HDR-mediated gene correction. An over 10-fold increase in HDR efficiency was achieved by Nu7441 in HeLa-eGFPd2 cells, with a peak HDR efficiency of 53% at a 5 nM RNP concentration and up to 61% efficiency confirmed by Sanger sequencing. Surprisingly, the total gene editing efficiency including non-homologous end joining (NHEJ) was also improved. For example, Nu7441 boosted exon skipping via NHEJ-mediated splice site destruction by 30-fold in a DMD reporter cell model. Nu7441 modulated the cell cycle by reducing cells in the G1 phase and extending the S and G2/M phases without compromising cellular uptake or endosomal escape. The enhancement in genome editing by Nu7441 was widely applicable across several cell lines, several Cas9 RNP/ssDNA carriers (LAF-XPs), and also Cas9 mRNA/sgRNA/ssDNA polyplexes. These findings highlight a novel and counterintuitive role for Nu7441 as an enhancer of both HDR and total gene editing efficiency, presenting a promising strategy for Cas9 RNP-based gene therapy. Full article

Integration of old and recent experimental data consequences is needed to correct and help improve the hypothetical mechanism responsible for the stimulus–secretion coupling mechanism of glucose-induced insulin secretion. The main purpose of this review is to supply biochemical considerations about some of the metabolic pathways implicated in the process of insulin secretion. It is emphasized that glucose β-cells’ threshold to activate secretion (5 mM) might depend on the predominance of anaerobic glycolysis at this basal glucose concentration. This argues against the predominance of phosphoenolpyruvate (PEP) over mitochondrial pyruvate oxidation for the initiation of insulin secretion. Full quantitative and qualitative reproduction, except the threshold effect, of glucose-induced insulin release by a permeable methylated analog of succinic acid indicates that mitochondrial metabolism is enough for sustained insulin secretion. Mitochondrial PEP generation is skipped if the GABA-shunt pathway is exclusively coupled to the citric acid cycle, as proposed in the “GABA-shunt” model of stimulus–secretion coupling. Strong or maintained depolarization by KCl or sulfonylureas might induce the opening of β-cells Cx36 hemichannels, allowing the loss of adenine nucleotides and other metabolites, mimicking the effect of an excessive mitochondrial ATP demand. A few alterations of OxPhos (Oxidative Phosphorylation) regulation in human T2D islets have been described, but the responsible mechanism(s) is (are) not yet known. Finally, some experimental data arguing as proof of the relative irrelevance of the mitochondrial function in the insulin secretion coupling mechanism for the initiation and/or sustained stimulation of hormone release are discussed. Full article

Circular RNAs (circRNAs) are a class of unique transcripts characterized by a covalently closed loop structure, which differentiates them from conventional linear RNAs. The formation of circRNAs occurs co-transcriptionally and post-transcriptionally through a distinct type of splicing known as back-splicing, which involves the formation of a head-to-tail splice junction between a 5′ splice donor and an upstream 3′ splice acceptor. This process, along with exon skipping, intron retention, cryptic splice site utilization, and lariat-driven intron processing, results in the generation of three main types of circRNAs (exonic, intronic, and exonic–intronic) and their isoforms. The intricate biogenesis of circRNAs is regulated by the interplay of cis-regulatory elements and trans-acting factors, with intronic Alu repeats and RNA-binding proteins playing pivotal roles, at least in the formation of exonic circRNAs. Various hypotheses regarding pathways of circRNA turnover are forwarded, including endonucleolytic cleavage and exonuclease-mediated degradation; however, similarly to the inconclusive nature of circRNA biogenesis, the process of their degradation and the factors involved remain largely unclear. There is a knowledge gap regarding whether these processes are guided by universal pathways or whether each category of circRNAs requires special tools and particular mechanisms for their life cycles. Understanding these factors is pivotal for fully comprehending the biological significance of circRNAs. This review provides an overview of the various pathways involved in the biogenesis and degradation of different types of circRNAs and explores key factors that have beneficial or adverse effects on the formation and stability of these unique transcripts in higher eukaryotes. Full article

The circadian clock gene system plays a pivotal role in coordinating the daily rhythms of most metabolic processes. It is synchronized with the light–dark cycle and the eating–fasting schedule. Notably, the interaction between meal timing and circadian clock genes (CGs) allows for optimizing metabolic processes at specific times of the day. Breakfast has a powerful resetting effect on the CG network. A misaligned meal pattern, such as skipping breakfast, can lead to a discordance between meal timing and the endogenous CGs, and is associated with obesity and T2D. Conversely, concentrating most calories and carbohydrates (CH) in the early hours of the day upregulates metabolic CG expression, thus promoting improved weight loss and glycemic control. Recently, it was revealed that microorganisms in the gastrointestinal tract, known as the gut microbiome (GM), and its derived metabolites display daily oscillation, and play a critical role in energy and glucose metabolism. The timing of meal intake coordinates the oscillation of GM and GM-derived metabolites, which in turn influences CG expression, playing a crucial role in the metabolic response to food intake. An imbalance in the gut microbiota (dysbiosis) can also reciprocally disrupt CG rhythms. Evidence suggests that misaligned meal timing may cause such disruptions and can lead to obesity and hyperglycemia. This manuscript focuses on the reciprocal interaction between meal timing, GM oscillation, and circadian CG rhythms. It will also review studies demonstrating how aligning meal timing with the circadian clock can reset and synchronize CG rhythms and GM oscillations. This synchronization can facilitate weight loss and improve glycemic control in obesity and those with T2D. Full article

Full-waveform inversion (FWI) is one of the most promising techniques in current ground-penetrating radar (GPR) inversion methods. The least-squares method is usually used, minimizing the mismatch between the observed signal and the simulated signal. However, the cycle-skipping problem has become an urgent focus of this method because of the nonlinearity of the inversion problem. To mitigate the issue of local minima, the optimal transport problem has been introduced into full-waveform inversion in this study. The Wasserstein distance derived from the optimal transport problem is defined as the mismatch function in the FWI objective function, replacing the $L_2$ norm. In this study, the Wasserstein distance is computed by using entropy regularization and the Sinkhorn algorithm to reduce computational complexity and improve efficiency. Additionally, this study presents the objective function for dual-parameter full-waveform inversion of ground-penetrating radar, with the Wasserstein distance as the mismatch function. By normalizing with the Softplus function, the electromagnetic wave signals are adjusted to meet the non-negativity and mass conservation assumptions of the Wasserstein distance, and the convexity of the method has been proven. A multi-scale frequency-domain Wasserstein distance full-waveform inversion method based on the Softplus normalization approach is proposed, enabling the simultaneous inversion of relative permittivity and conductivity from ground-penetrating radar data. Numerical simulation cases demonstrate that this method has low initial model dependency and low noise sensitivity, allowing for high-precision inversion of relative permittivity and conductivity. The inversion results show that it, in particular, significantly improves the accuracy of conductivity inversion. Full article

This study compared the mechanical and physiological load placed on Division I female collegiate lacrosse athletes (1) with and without hormone contraceptive (HC) use and (2) with and without menstruation during training and games. Athletes’ (20.6 ± 1.5 years, HC users = 9, naturally cycling (NC) athletes = 9) workloads—total distance traveled (TD, m), max speed (km∙h⁻¹), accelerations (repetitions), decelerations (repetitions), and high-intensity distance (HID, m)—were measured through VX Sport wearable microtechnology in training sessions (n = 87/athlete) and games (n = 17/athlete). Analyses showed no statistical group differences based on HC use or not, and no differences during menstruation versus non-menstruation for training or games. However, while not statistically different, athletes taking HCs performed worse during menstruation, with a 5.1% decline in decelerations, 3.4% decline in TD and HID, 1.2% decline in max speed, and 1% decline in accelerations. NC athletes did not show this same decline with menses. Given that withdrawal bleeding exacerbates performance reduction of HC users, it may be beneficial for these athletes to consider skipping their withdrawal bleed if it is likely to coincide with a game. Further research needs to be carried out to see if these trends are consistent across other female athletes in other sports. Full article

This paper presents a procedure for determining the elastoplastic parameters of phase-field fracture of sintered material. The material considered was sintered steel Astaloy™ Mo+0.2C of three densities: 6.5, 6.8 and 7.1 g/${\mathrm{cm}}^3$. The stress–strain curve and Wöhler curve, which are experimentally obtained, are utilized for validation of the numerical simulations. For modelling of damage evolution, a CCPF (Convergence check phase-field) algorithm was used as a numerical framework. During calibration of the numerical parameters, two-dimensional as well as three-dimensional modelling was used. A comparison of different fatigue degradation functions known from the literature is also made. To improve the efficiency of numerical simulations of fatigue behaviour, the cycle skip technique is also employed. Full article

The current study proposed and evaluated “residual squeeze and excitation attention gate” (rSEAG), a novel network that can improve image quality by reducing distortion attributed to artifacts. This method was established by modifying the Cycle Generative Adversarial Network (cycleGAN)-based generator network using projection data for pre-reconstruction processing in digital breast tomosynthesis. Residual squeeze and excitation were installed in the bridge of the generator network, and the attention gate was installed in the skip connection between the encoder and decoder. Based on the radiation dose index (exposure index and division index) incident on the detector, the cases approved by the ethics committee and used for the study were classified as reference (675 projection images) and object (675 projection images). For the cases, unsupervised data containing a mixture of cases with and without masses were used. The cases were trained using cycleGAN with rSEAG and the conventional networks (ResUNet and U-Net). For testing, predictive processing was performed on cases (60 projection images) that were not used for learning. Images were generated using filtered backprojection reconstruction (kernel: Ramachandran and Lakshminarayanan) from projection data for testing data and without pre-reconstruction processing data (evaluation: in-focus plane). The distortion was evaluated using perception-based image quality evaluation (PIQE) analysis, texture analysis (feature: “Homogeneity” and “Contrast”), and a statistical model with a Gumbel distribution. PIQE has a low rSEAG value. Texture analysis showed that rSEAG and a network without cycleGAN were similar in terms of the “Contrast” feature. In dense breasts, ResUNet had the lowest “Contrast” feature and U-Net had differences between cases. The maximal variations in the Gumbel plot, rSEAG reduced the high-frequency ripple artifacts. In this study, rSEAG could improve distortion and reduce ripple artifacts. Full article

Infrared images are susceptible to interference from a variety of factors during acquisition and transmission, resulting in the inclusion of mixed noise, which seriously affects the accuracy of subsequent vision tasks. To solve this problem, we designed a mixed noise removal algorithm for infrared images based on improved CycleGAN. First, we proposed a ResNet-E Block that incorporates the EMA (Efficient Multi-Scale Attention Module) and build a generator based on it using the skip-connection structure to improve the network’s ability to remove mixed noise of different strengths. Second, we added the PSNR (Peak Signal-to-Noise Ratio) as an extra calculation item of cycle consistency loss, so that the network can effectively retain the detailed information of infrared images while denoising. Finally, we conducted experimental validation on both synthetic noisy images and real noisy images, which proved that our algorithm can effectively remove the mixed noise in infrared images and the denoising effect is better than other similar methods. Full article

Mountainous forests are pivotal in the global carbon cycle, serving as substantial reservoirs and sinks of carbon. However, generating a reliable estimate remains a considerable challenge, primarily due to the lack of representative in situ measurements and proper methods capable of addressing their complex spatial variation. Here, we proposed a deep learning-based method that combines Residual convolutional neural networks (ResNet) with in situ measurements, microwave (Sentinel-1 and VOD), and optical data (Sentinel-2 and Landsat) to estimate forest biomass and track its change over the mountainous regions. Our approach, integrating in situ measurements across representative elevations with multi-source remote sensing images, significantly improves the accuracy of biomass estimation in Tibet’s complex mountainous forests (R² = 0.80, root mean squared error = 15.8 MgC ha⁻¹). Moreover, ResNet, which addresses the vanishing gradient problem in deep neural networks by introducing skip connections, enables the extraction of complex spatial patterns from limited datasets, outperforming traditional optical-based or pixel-based methods. The mean value of forest biomass was estimated as 162.8 ± 21.3 MgC ha⁻¹, notably higher than that of forests at comparable latitudes or flat regions in China. Additionally, our findings revealed a substantial forest biomass carbon sink of 3.35 TgC year⁻¹ during 2015–2020, which is largely underestimated by previous estimates, mainly due to the underestimation of mountainous carbon stock. The significant carbon density, combined with the underestimated carbon sink in mountainous regions, emphasizes the urgent need to reassess mountain forests to better approximate the global carbon budget. Full article

Multi-class segmentation of unlabelled living cells in time-lapse light microscopy images is challenging due to the temporal behaviour and changes in cell life cycles and the complexity of these images. The deep-learning-based methods achieved promising outcomes and remarkable success in single- and multi-class medical and microscopy image segmentation. The main objective of this study is to develop a hybrid deep-learning-based categorical segmentation and classification method for living HeLa cells in reflected light microscopy images. A symmetric simple U-Net and three asymmetric hybrid convolution neural networks—VGG19-U-Net, Inception-U-Net, and ResNet34-U-Net—were proposed and mutually compared to find the most suitable architecture for multi-class segmentation of our datasets. The inception module in the Inception-U-Net contained kernels with different sizes within the same layer to extract all feature descriptors. The series of residual blocks with the skip connections in each ResNet34-U-Net’s level alleviated the gradient vanishing problem and improved the generalisation ability. The m-IoU scores of multi-class segmentation for our datasets reached 0.7062, 0.7178, 0.7907, and 0.8067 for the simple U-Net, VGG19-U-Net, Inception-U-Net, and ResNet34-U-Net, respectively. For each class and the mean value across all classes, the most accurate multi-class semantic segmentation was achieved using the ResNet34-U-Net architecture (evaluated as the m-IoU and Dice metrics). Full article