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Background Spinal cord injury (SCI) patients display disruption of gut microbiome, and gut dysbiosis exacerbate neurological impairment in SCI models. Cumulative data support an important role of gut microbiome in SCI. Here, we investigated the hypothesis that fecal microbiota transplantation (FMT) from healthy uninjured mice into SCI mice may exert a neuroprotective effect. Results FMT facilitated functional recovery, promoted neuronal axonal regeneration, improved animal weight gain and metabolic profiling, and enhanced intestinal barrier integrity and GI motility in SCI mice. High-throughput sequencing revealed that levels of phylum Firmicutes , family Christensenellaceae , and genus Butyricimonas were reduced in fecal samples of SCI mice, and FMT remarkably reshaped gut microbiome. Also, FMT-treated SCI mice showed increased amount of fecal short-chain fatty acids (SCFAs), which correlated with alteration of intestinal permeability and locomotor recovery. Furthermore, FMT downregulated IL-1β/NF-κB signaling in spinal cord and NF-κB signaling in gut following SCI. Conclusion Our study demonstrates that reprogramming of gut microbiota by FMT improves locomotor and GI functions in SCI mice, possibly through the anti-inflammatory functions of SCFAs. 4wtf5ApZ8ToEwi5YEe-D32 Video Abstract

The increase in Cervical Spondylosis cases and the expansion of the affected demographic to younger patients have escalated the demand for X-ray screening. Challenges include variability in imaging technology, differences in equipment specifications, and the diverse experience levels of clinicians, which collectively hinder diagnostic accuracy. In response, a deep learning approach utilizing a ResNet-34 convolutional neural network has been developed. This model, trained on a comprehensive dataset of 1235 cervical spine X-ray images representing a wide range of projection angles, aims to mitigate these issues by providing a robust tool for diagnosis. Validation of the model was performed on an independent set of 136 X-ray images, also varied in projection angles, to ensure its efficacy across diverse clinical scenarios. The model achieved a classification accuracy of 89.7%, significantly outperforming the traditional manual diagnostic approach, which has an accuracy of 68.3%. This advancement demonstrates the viability of deep learning models to not only complement but enhance the diagnostic capabilities of clinicians in identifying Cervical Spondylosis, offering a promising avenue for improving diagnostic accuracy and efficiency in clinical settings.

Salt stress represents a critical abiotic constraint that severely impedes plant growth and agricultural productivity. While plant growth-promoting rhizobacteria (PGPR) demonstrate potential in enhancing plant salt tolerance, their precise mechanisms remain incompletely elucidated. This study systematically investigates the mechanistic basis by which PGPR inoculation ameliorates salt stress in rice seedlings through modulation of rhizosphere microbiota and root exudate profiles. We inoculated rice seedlings with five monocultures ( sp., sp., etc.) and a synthetic consortium (T6) under salt stress conditions. Growth parameters, rhizobacterial communities (via 16S rRNA sequencing), and root exudates (untargeted metabolomics) were comparatively analyzed against uninoculated controls (CK). PGPR inoculation significantly promoted rice seedling growth under salt stress. Treatments T2-T6 exhibited substantial increases in key biomass parameters-including root length, plant height, and dry weight-relative to the CK control. Concurrently, elevated chlorophyll content and enhanced photosynthetic efficiency were observed. Inoculated plants also displayed significantly higher activities of antioxidant enzymes (Superoxide dismutase, Peroxidase, Catalase activity) (SOD, POD, CAT) and proline (Pro) accumulation in both leaves and roots, coupled with a marked reduction in Malondialdehyde, indicating effective mitigation of oxidative damage. PGPR inoculation altered rhizosphere bacterial community composition, reducing overall alpha-diversity. Notably, the relative abundance of dominant bacterial phyla (e.g., , ) and beneficial genera (e.g., , ) increased significantly. These microbial shifts showed positive correlations with improved plant physiological status, suggesting a synergistic role in promoting seedling growth under salt stress. Root exudate metabolomics revealed a substantial number of differentially abundant metabolites in inoculated plants compared to CK, encompassing lipids, hormones, and signaling molecules. Crucially, the production of these specific exudates correlated with the enrichment of dominant bacterial taxa in the rice rhizosphere. Metabolic pathway analysis indicated significant enrichment primarily within Nucleotide metabolism and Purine metabolism pathways (belonging to the Metabolism superclass) and ABC transporter pathways (within Environmental Information Processing). The T6 consortium treatment induced enrichment across a significantly greater number of key metabolic pathways compared to single-strain inoculations. PGPR inoculation enhances rice seedling growth and salt tolerance by: (1) optimizing rhizosphere microbiota (enriching dominant phyla and beneficial genera); (2) recruiting stress-mitigating microbial consortia; and (3) stimulating root exudates enriched in nucleotide/purine metabolism and ABC transporters. The superior efficacy of the T6 consortium underscores the advantage of synergistic microbial interactions. Collectively, these findings reveal plant-microbe-metabolite mechanisms underlying PGPR-mediated salt tolerance, providing a foundation for developing salinized soil remediation strategies.

Environmental perception plays a fundamental role in decision-making and is crucial for ensuring the safety of autonomous driving. A pressing challenge is the online evaluation of perception uncertainty, a crucial step towards ensuring the safety and the industrialization of autonomous driving. High-definition maps offer precise information about static elements on the road, along with their topological relationships. As a result, the map can provide valuable prior information for assessing the uncertainty associated with static elements. In this paper, a method for evaluating perception uncertainty online, encompassing both static and dynamic elements, is introduced based on the high-definition map. The proposed method is as follows: Firstly, the uncertainty of static elements in perception, including the uncertainty of their existence and spatial information, was assessed based on the spatial and topological features of the static environmental elements; secondly, an online assessment model for the uncertainty of dynamic elements in perception was constructed. The online evaluation of the static element uncertainty was utilized to infer the dynamic element uncertainty, and then a model for recognizing the driving scenario and weather conditions was constructed to identify the triggering factors of uncertainty in real-time perception during autonomous driving operations, which can further optimize the online assessment model for perception uncertainty. The verification results on the nuScenes dataset show that our uncertainty assessment method based on a high-definition map effectively evaluates the real-time perception results’ performance.

Traumatic spinal cord injury (tSCI) is a severe injury to the central nervous system that is categorized into primary and secondary injuries. Among them, the local microenvironmental imbalance in the spinal cord caused by secondary spinal cord injury includes accumulation of cytokines and chemokines, reduced angiogenesis, dysregulation of cellular energy metabolism, and dysfunction of immune cells at the site of injury, which severely impedes neurological recovery from spinal cord injury (SCI). In recent years, single-cell techniques have revealed the heterogeneity of multiple immune cells at the genomic, transcriptomic, proteomic, and metabolomic levels after tSCI, further deepening our understanding of the mechanisms underlying tSCI. However, spatial information about the tSCI microenvironment, such as cell location and cell-cell interactions, is lost in these approaches. The application of spatial multi-omics technology can solve this problem by combining the data obtained from immunohistochemistry and multiparametric analysis to reveal the changes in the microenvironment at different times of secondary injury after SCI. In this review, we systematically review the progress of spatial multi-omics techniques in the study of the microenvironment after SCI, including changes in the immune microenvironment and discuss potential future therapeutic strategies.

For the robust fault-tolerant control of the controllable suspension system, a control strategy driven by knowledge-data fusion is proposed. Firstly, the boundary fuzziness between perturbation type uncertainty and gain type fault is analyzed, and then a data-driven method is introduced to avoid the state estimation of system uncertainty and fault. The proximal policy optimization algorithm in reinforcement learning is selected to construct a “data control law”, to deal with uncertainty and fault. On the other hand, based on the classical sky-hook control, the “knowledge control law” for system performance optimization is designed, taking into account the nonlinear and non-stationary characteristics of the system. Furthermore, the dependency between robust fault tolerance and performance optimization control is revealed, and the two control laws are fused by numerical multiplication, to realize the performance matching optimization control of robust fault tolerance of controllable suspension system driven by knowledge-data fusion. Finally, the effectiveness and feasibility of the proposed method are verified by the simulation and real-time experiment of non-stationary excitation and near-stationary excitation under the combination of uncertainty and fault.

To improve the path tracking accuracy and yaw stability of distributed drive autonomous vehicles (DDAVs) under extreme working conditions, a cooperative lateral motion control method based on the dynamic boundary is proposed to prevent different road adhesion conditions from affecting the motion stability of DDAVs. Based on the analysis of the DDAV lateral dynamics system coordination mechanism, a dynamic boundary considering the pavement adhesion coefficient is proposed, and the Lateral Motion Synergistic Control System (LMSCS) is designed. The LMSCS is divided into the coordination, control, and executive layers. The coordination layer divides the control domain into the stable, quasi-stable, and unstable domains by the dynamic boundary, and coordinates the control strength of the path following control and yaw stability control. In the control layer, the path following control and yaw stability control laws are designed based on the global fast terminal sliding mode. The executive layer estimates the expected steering wheel angle and expected additional wheel torque. Joint simulations under double line shifting conditions confirmed that LMSCS reflects the impact of the road attachment conditions and improves the path tracking accuracy and vehicle yaw stability. The LMSCS has better overall performance than existing lateral motion control methods.

To improve the multidirectional motion control accuracy and driving stability of Distributed Drive Autonomous Vehicles (DDAVs) under extreme conditions, the extreme speed estimation method based on dynamic boundary and the multidirectional motion coupling control law design method based on multi degrees of freedom vehicle dynamic model are proposed. The extreme speed estimation method identifies the stable state of DDAVs by the dynamic boundary composed of yaw rate, sideslip angle and roll angle, and then estimates the extreme speed of the vehicle. The design method of multidirectional motion coupling control law adopts eight-degrees-of-freedom (8-DOF) vehicle dynamic model to design path tracking control law, speed tracking control law, yaw stability control law, and active suspension control law at the same time, so as to realize multidirectional motion coupling control. Based on the above method, the Multidirectional Motion Coupling Control System (MMCCS) of DDAVs is designed. The effectiveness of the proposed method is verified by double-line-shifting and serpentine driving simulations under different road adhesion conditions. The superiority of the method is proved by comparing the existing integrated control method.

Deep neural network algorithms have achieved impressive performance in object detection. Real-time evaluation of perception uncertainty from deep neural network algorithms is indispensable for safe driving in autonomous vehicles. More research is required to determine how to assess the effectiveness and uncertainty of perception findings in real-time.This paper proposes a novel real-time evaluation method combining multi-source perception fusion and deep ensemble. The effectiveness of single-frame perception results is evaluated in real-time. Then, the spatial uncertainty of the detected objects and influencing factors are analyzed. Finally, the accuracy of spatial uncertainty is validated with the ground truth in the KITTI dataset. The research results show that the evaluation of perception effectiveness can reach 92% accuracy, and a positive correlation with the ground truth is found for both the uncertainty and the error. The spatial uncertainty is related to the distance and occlusion degree of detected objects.

Background Neurogenic bowel dysfunction (NBD) is a major physical and psychological problem in patients with spinal cord injury (SCI), and gut dysbiosis is commonly occurs in SCI. Here, we document neurogenic bowel management of male patients with chronic traumatic complete SCI in our centre and perform comparative analysis of the gut microbiota between our patients and healthy males. Methods A total of 43 male patients with chronic traumatic complete SCI (20 with quadriplegia and 23 with paraplegia) and 23 healthy male adults were enrolled. Clinical data and fresh stool specimens were collected from all participants. Face-to-face interviews were conducted to survey the neurogenic bowel management of 43 patients with SCI. Gut microbiomes were analysed by sequencing of the V3–V4 region of the 16S rRNA gene. Results NBD was common in adult male patients with chronic traumatic complete SCI. Patients with quadriplegia exhibited a longer time to defecate than did those with paraplegia and had higher NBD scores and heavier neurogenic bowel symptoms. The diversity of the gut microbiota in the SCI group was reduced, and the structural composition was different from that of the healthy adult male group. The abundance of Veillonellaceae and Prevotellaceae increased, while Bacteroidaceae and Bacteroides decreased in the SCI group. The abundance of Bacteroidaceae and Bacteroides in the quadriplegia group and Acidaminococcaceae, Blautia, Porphyromonadaceae, and Lachnoclostridium in the paraplegia group were significantly higher than those in the healthy male group. Serum biomarkers (GLU, HDL, CR, and CRP), NBD defecation time and COURSE had significant correlations with microbial community structure. Microbial community structure was significantly associated with serum biomarkers (GLU, HDL, CR, and CRP), NBD defecation time, and COURSE. Conclusions This study presents a comprehensive landscape of the gut microbiota in adult male patients with chronic traumatic complete SCI and documents their neurogenic bowel management. Gut microbiota dysbiosis in SCI patients was correlated with serum biomarkers and NBD symptoms.