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Epilepsy is one of the most common symptoms of many neurological disorders. The typical excessive, synchronous and aberrant firing of neurons originating from different cerebral areas cause spontaneous recurrent epileptic seizures. Prolonged epilepsy can lead to neuronal damage and cell death. The mechanisms underlying epileptic pathogenesis and neuronal death remain unclear. Ferroptosis is a newly defined form of regulated cell death that is characterized by the overload of intracellular iron ions, leading to the accumulation of lethal lipid-based reactive oxygen species (ROS). To date, studies have mainly focused on its role in tumors and various neurological disorders, including epilepsy. Current research shows that inhibition of ferroptosis is likely to be an effective therapeutic approach for epilepsy. In this review, we outline the pathogenesis of ferroptosis, regulatory mechanisms of ferroptosis, related regulatory molecules, and their effects on epilepsy, providing a new direction for discovering new therapeutic targets in epilepsy.

Structural variants (SVs) represent a major source of genetic diversity and are related to numerous agronomic traits and evolutionary events; however, their comprehensive identification and characterization in cucumber ( Cucumis sativus L.) have been hindered by the lack of a high-quality pan-genome. Here, we report a graph-based cucumber pan-genome by analyzing twelve chromosome-scale genome assemblies. Genotyping of seven large chromosomal rearrangements based on the pan-genome provides useful information for use of wild accessions in breeding and genetic studies. A total of ~4.3 million genetic variants including 56,214 SVs are identified leveraging the chromosome-level assemblies. The pan-genome graph integrating both variant information and reference genome sequences aids the identification of SVs associated with agronomic traits, including warty fruits, flowering times and root growth, and enhances the understanding of cucumber trait evolution. The graph-based cucumber pan-genome and the identified genetic variants provide rich resources for future biological research and genomics-assisted breeding. Increasing studies have suggested that single reference genome is insufficient to capture all variations in the genome. Here, the authors report a graph-based cucumber pan-genome by analyzing 12 chromosome-scale assemblies and reveal variations associated with agronomic traits and domestication.

Rapeseed ( Brassica napus ) is the second most important oilseed crop in the world but the genetic diversity underlying its massive phenotypic variations remains largely unexplored. Here, we report the sequencing, de novo assembly and annotation of eight B. napus accessions. Using pan-genome comparative analysis, millions of small variations and 77.2–149.6 megabase presence and absence variations (PAVs) were identified. More than 9.4% of the genes contained large-effect mutations or structural variations. PAV-based genome-wide association study (PAV-GWAS) directly identified causal structural variations for silique length, seed weight and flowering time in a nested association mapping population with ZS11 (reference line) as the donor, which were not detected by single-nucleotide polymorphisms-based GWAS (SNP-GWAS), demonstrating that PAV-GWAS was complementary to SNP-GWAS in identifying associations to traits. Further analysis showed that PAVs in three FLOWERING LOCUS C genes were closely related to flowering time and ecotype differentiation. This study provides resources to support a better understanding of the genome architecture and acceleration of the genetic improvement of B. napus . The assembly of eight high-quality rapeseed genomes allows identification of presence and absence variations (PAVs) and small variations. PAV-based genome-wide association analysis uncovered causal variations for agronomic traits and ecotype differentiation.

Compared to purely serial robots or cable-driven parallel robots (CDPRs), cable-driven hybrid robots (CDHRs) combine the advantages of both, addressing their limitations and enabling the execution of complex tasks. The series-parallel coupling structure increases the complexity of the system, complicating modeling, calibration, and force-closure workspace (FCW) analysis. This study develops a CDHR system equipped with various sensors and proposes methods for series-parallel coupling modeling, workspace analysis, and self-calibration of complex systems. First, the modular design requirements for the CDHR are analyzed, comprising an 8-cable parallel drive and a 4-degree-of-freedom serial manipulator. Second, a kinematic model of the CDHR with series-parallel coupling was derived, and the positions of the dynamic anchor seats were optimized using an optimization algorithm. Based on these optimized results, a modeling and analysis method for the statics and FCW is proposed. Furthermore, considering the complex and interdependent structural parameters of the system, a method for the self-calibration of the system parameters and trajectory planning for the CDHR is presented. Finally, experimental validation on both simulations and a physical prototype confirmed the effectiveness of the proposed methods. The developed prototype and the proposed method provide a basis for high-precision operations in large spaces, operations in dangerous/extreme environments, and automated operations in logistics/warehousing.

Cable-driven parallel robots (CDPRs) are attractive for large-space manipulation because of their lightweight structure, large workspace, and reconfigurability. However, existing systems still face three practical challenges: limited modularity of the mechanical architecture, repeated calibration after reconfiguration, and insufficient integration between visual perception and grasp execution. To address these issues, this paper presents a modular cable-driven parallel robot (MCDPR), together with its kinematic modeling, vision-based self-calibration, and visual grasping methods. First, a modular mechanical architecture is developed in which the drive, sensing, and cable-guiding functions are integrated to support rapid assembly/disassembly, convenient debugging, and cable anti-slack operation. Second, a pulley-considered multilayer kinematic model is established, and a vision-based self-calibration method is proposed to identify the structural parameters after assembly using onboard sensing and AprilTag observations, thereby reducing the number of recalibrations required during robot operation after reconfiguration. Third, a vision-guided bin-picking method is developed by combining RGB-D perception, coordinate transformation, and the calibrated robot model. Simulation and prototype experiments are conducted to validate the proposed system. A software/hardware combined validation framework is established, in which the CoppeliaSim-based simulation and the hardware prototype are used together to verify the proposed design and methods. In simulation, self-calibration reduces the Euclidean grasping position error from 0.371 mm to 0.048 mm and the orientation error from 0.071° to 0.004°. In experiments, the relative position error is reduced by 58.33% after self-calibration.

The coupling between the key parameters of rock and soil particle composition and slurry diffusion paths exerts a significant influence on actual grouting effectiveness. Based on the spherical penetration grouting model for Bingham cement grout, this study optimizes the fractal permeability model by coupling the characteristic particle size, porosity, and tortuosity, overcoming the deficiency of single-factor porosity consideration in existing permeability models. Unlike existing studies that only use experimentally measured permeability coefficients, this study employs a physically meaningful permeability model that realizes the synergistic coupling of soil particle composition, pore microstructure, and macroscopic permeability, and further establishes a penetration grouting mechanism that integrates the actual slurry diffusion path tortuosity into the classical spherical diffusion framework. A novel high-precision volume measurement method for grouting stone bodies based on point cloud 3D reconstruction is proposed, and a COMSOL-based visual numerical simulation program is developed by embedding the above coupling permeability model. The accuracy of the optimized mechanism is verified by a combination of model tests, numerical simulations, and theoretical analysis, which makes up for the existing grouting mechanism for loose gravelly soil failing to consider the synergistic influence of rock–soil particle composition parameters and the actual diffusion path. The research results indicate the following: (1) Adopting loose gravelly soil—which is more consistent with actual field conditions—as the grouted medium can effectively predict the reinforcement effect of heterogeneous media in grouting engineering. (2) Compared with theoretical values calculated by mechanisms that ignore the effect of the diffusion paths, those derived from the grouting mechanism that couples the rock and soil characteristic particle size with the Bingham cement grout diffusion path are closer to the experimental values. (3) The visual simulation results exhibit high morphological consistency with the actual grouting stone bodies, and the vast majority of the grout diffusion range falls within the numerical simulation domain. The findings of this study provide targeted theoretical and technical guidance for grouting design under complex geological conditions of loose gravelly soil layers.

The morphological changes in mountain glaciers are effective in indicating the environmental climate change in the alpine ice sheet. Aiming at the problems of single monitoring index and low prediction accuracy of mountain glacier deformation at present, this study takes Meili Mountain glacier in western China as the research object and uses InSAR technology to construct the mountain glacier deformation time series and 3D deformation field from January 2020 to December 2021. The relationship between glacier deformation and elevation, slope, aspect, glacier albedo, surface organic carbon content, and rainfall was revealed by grey correlation analysis. The GA-BP neural network prediction model is established from the perspective of multiple factors to predict the deformation of Meili Mountain glacier. The results showed that: The deformation of Meili Mountain glacier has obvious characteristics of spatio-temporal differentiation; the cumulative maximum deformation quantity of glaciers in the study period is −212.16 mm. After three-dimensional decomposition, the maximum deformation quantity of glaciers in vertical direction, north–south direction and east–west direction is −125.63 mm, −77.03 mm, and 107.98 mm, respectively. The average annual deformation rate is between −94.62 and 75.96 mm/year. The deformation of Meili Mountain glacier has a gradient effect, the absolute value of deformation quantity is larger when the elevation is below 4500 m, and the absolute value of deformation quantity is smaller when it is above 4500 m. The R2, MAPE, and RMSE of the GA-BP neural network to predict the deformation of Meili glacier are 0.86, 1.12%, and 10.38 mm, respectively. Compared with the standard BP algorithm, the prediction accuracy of the GA-BP neural network is significantly improved, and it can be used to predict the deformation of mountain glaciers.

The occurrence of frequent debris flow catastrophes in the mountainous regions of southwest China has necessitated the inclusion of debris flow disaster analysis and prevention as an essential component in the planning and construction of mountainous roadways. Daxilada watershed is located in the south of Mabian Yi Zuzizhixian, Leshan City, Sichuan Province, and the proposed Leshan-Xichang Expressway (Lexi Expressway) will pass through the upper reaches of Daxilada watershed. It is essential to consider that the presence of debris flows within the Daxilada watershed could have detrimental effects on the construction and functionality of the proposed Luoshanxi Bridge. This study examined the Daxilada watershed as a case study and analyzed the factors contributing to debris flow formation in the area. This analysis was based on field investigations, remote sensing interpretation, and experimental analysis. Additionally, the study utilized the Massflow software to simulate debris flow movement. It integrated the simulation results to determine the potential hazards the Daxilada Gully debris flow posed to the line project. This study found that Daxilada Gully meets debris flow formation conditions. The simulation results demonstrated that during the debris flow activity, there would be a maximum deposition depth of 2.1 m in the proposed Engineering Agency, which may lead to siltation and blocking disaster of the line project. Concerning the parameters related to the debris flow with a frequency of one in a hundred years, in conjunction with the outcomes obtained from numerical simulation, it would provide the design basis of the cross-flow cross-section of the proposed bridge. In a quantitative analysis of the blockage situation in the gully, debris flow deposits have the potential to cause damage to the line project. Debris flow deposits block the gully, but the risk of blockage is small. The study results have specific reference values for the debris flow prevention and control project of Lexi Expressway and offer valuable insights for the prevention and mitigation of similar disasters in relative projects.

Debris flow, the most extensive and most severe geological hazard along the Karakoram Highway, frequently blocks the Karakoram Highway. Based on the methods of field measurement, indoor statistical analysis and theoretical research, this paper discusses the relationship between the four types of debris flow along the Karakoram Highway. The four types are the rain type, the rain glacier type, the glacier ice lake break type and the freeze–thaw type, and their particle characteristics and gully slope are also considered in the discussion. The results are as follows: (1) The provenance particle size of debris flow is controlled by the type of debris flow. Generally, the provenance average particle equivalent diameter of the debris flow induced by the glacier ice lake type is relatively small, followed by the freeze–thaw type and glacier ice lake break type, and the equivalent diameter of the debris flow induced by the rain type is relatively large; (2) The gully slope coefficient of the debris flow C along the Karakoram Highway is greater than 1, and it increases with the increase in gully slope α, that is, the larger C is, the steeper the gully slope will be; (3) The gully slope coefficient C and the average particle equivalent diameter D of the four types of debris flow are distributed in the ellipse with them as the axis. This ellipse quantitatively describes the relationship between the gully slope of the four types of debris flow and the corresponding provenance particle characteristics. This paper analyzes the formation and causes of debris flow along the Karakoram Highway. It accurately understands the scientific connotation of debris flow formation in the surface matrix layer and improves the diversity, stability, and sustainability of the ecosystem. The paper also proposes ideas and suggestions for promoting the ecological protection and restoration of the Karakoram Highway. Therefore, the research has a certain theoretical significance and practical application value for the appropriate selection and rational design of the debris flow prevention projects along the China–Pakistan Highway.

To address the limitation of traditional machine learning models in explaining the rockburst intensity prediction process, this study proposes an interpretable rockburst intensity prediction model. The model was developed using 350 sets of actual rockburst sample data to explore the impact of input metrics on the final rockburst intensity level. The collected data underwent pre-processing using the isolation forest algorithm and synthetic minority oversampling technique. The random forest model was optimized through 5-fold cross-validation and the Optuna framework, resulting in the establishment of an Optuna-random forest (Op-RF) model that generates decision rules through its internal decision tree, utilizing the properties of the random forest model. The model was further interpreted using the Shapley additive explanations algorithm, both locally and globally. The results demonstrate that the proposed model achieved an area under curve score of 0.984. In comparison to eight other machine learning models, the proposed Op-RF model demonstrated superior accuracy, precision, recall, and F1 score. The model provides a transparent explanation of the prediction process, linking impact characteristics to the final output. Additionally, a cloud deployment method for the rockburst intensity prediction model is provided and its effectiveness is demonstrated through engineering verification. The proposed model offers a new approach to the application of machine learning in rockburst intensity prediction.