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Abstract Currently, there is a lack of effective treatments for spinal cord injury (SCI), a debilitating medical condition associated with enduring paralysis and irreversible neuronal damage. Extradural decompression of osseous as well as soft tissue components has historically been the principal objective of surgical procedures. Nevertheless, this particular surgical procedure fails to tackle the intradural compressive alterations that contribute to secondary SCI. Here, we propose an early intrathecal decompression strategy and evaluate its role on function outcome, tissue sparing, inflammation, and tissue stiffness after SCI. Durotomy surgery significantly promoted recovery of hindlimb locomotor function in an open‐field test. Radiological analysis suggested that lesion size and tissue edema were significantly reduced in animals that received durotomy. Relative to the group with laminectomy alone, the animals treated with a durotomy had decreased cavitation, scar formation, and inflammatory responses at 4 weeks after SCI. An examination of the mechanical properties revealed that durotomy facilitated an expeditious restoration of the injured tissue's elastic rigidity. In general, early decompressive durotomy could serve as a significant strategy to mitigate the impairments caused by secondary injury and establish a more conducive microenvironment for prospective cellular or biomaterial transplantation.

Abstract Spinal cord injury (SCI) remains a severe condition with an extremely high disability rate. The challenges of SCI repair include its complex pathological mechanisms and the difficulties of neural regeneration in the central nervous system. In the past few decades, researchers have attempted to completely elucidate the pathological mechanism of SCI and identify effective strategies to promote axon regeneration and neural circuit remodeling, but the results have not been ideal. Recently, new pathological mechanisms of SCI, especially the interactions between immune and neural cell responses, have been revealed by single-cell sequencing and spatial transcriptome analysis. With the development of bioactive materials and stem cells, more attention has been focused on forming intermediate neural networks to promote neural regeneration and neural circuit reconstruction than on promoting axonal regeneration in the corticospinal tract. Furthermore, technologies to control physical parameters such as electricity, magnetism and ultrasound have been constantly innovated and applied in neural cell fate regulation. Among these advanced novel strategies and technologies, stem cell therapy, biomaterial transplantation, and electromagnetic stimulation have entered into the stage of clinical trials, and some of them have already been applied in clinical treatment. In this review, we outline the overall epidemiology and pathophysiology of SCI, expound on the latest research progress related to neural regeneration and circuit reconstruction in detail, and propose future directions for SCI repair and clinical applications.

Abstract Spinal cord injury (SCI) involves diverse injury responses in different cell types in a temporally and spatially specific manner. Here, using single-cell transcriptomic analyses combined with classic anatomical, behavioral, electrophysiological analyses, we report, with single-cell resolution, temporal molecular and cellular changes in crush-injured adult mouse spinal cord. Data revealed pathological changes of 12 different major cell types, three of which infiltrated into the spinal cord at distinct times post-injury. We discovered novel microglia and astrocyte subtypes in the uninjured spinal cord, and their dynamic conversions into additional stage-specific subtypes/states. Most dynamic changes occur at 3-days post-injury and by day-14 the second wave of microglial activation emerged, accompanied with changes in various cell types including neurons, indicative of the second round of attacks. By day-38, major cell types are still substantially deviated from uninjured states, demonstrating prolonged alterations. This study provides a comprehensive mapping of cellular/molecular pathological changes along the temporal axis after SCI, which may facilitate the development of novel therapeutic strategies, including those targeting microglia.

Accurate and reliable trajectory prediction is crucial for autonomous vehicles to achieve safe and efficient operation. Vehicles perceive the historical trajectories of moving objects and make predictions of behavioral intentions for a future period of time. With the predicted trajectories of moving objects such as obstacle vehicles, pedestrians, and non-motorized vehicles as inputs, self-driving vehicles can make more rational driving decisions and plan more reasonable and safe vehicle motion behaviors. However, due to traffic environments such as intersection scenes with highly interdependent and dynamic attributes, the task of motion anticipation becomes challenging. Existing works focus on the mutual relationships among vehicles while ignoring other potential essential interactions such as vehicle–traffic rules. These studies have not yet deeply explored the intensive learning of interactions between multi-agents, which may result in evaluation deviations. Aiming to meet these issues, we have designed a novel framework, namely trajectory prediction with attention-based spatial–temporal graph convolutional networks (TPASTGCN). In our proposal, the multi-agent interaction mechanisms, including vehicle–vehicle and vehicle–traffic rules, are meticulously highlighted and integrated into one homogeneous graph by transferring the time-series data of traffic lights into the spatial–temporal domains. Through integrating the attention mechanism into the adjacency matrix, we effectively learn the different strengths of interactive association and improve the model’s ability to capture critical features. Simultaneously, we construct a hierarchical structure employing the spatial GCN and temporal GCN to extract the spatial dependencies of traffic networks. Profiting from the gated recurrent unit (GRU), the scene context in temporal dimensions is further attained and enhanced with the encoder. In such a way, the GCN and GRU networks are fused as a features extractor module in the proposed framework. Finally, the future potential trajectories generation tasks are performed by another GRU network. Experiments on real-world datasets demonstrate the superior performance of the scheme compared with several baselines.

Background Persistent vertebral artery occlusion caused by compression of cervical facet joint originated osteophyte is exceptional rare. The authors sought to achieve adequate decompression of the vertebral artery (VA) with less stability decrease and movement restriction via the anterior approach, and to the authors' knowledge, no case of anterior decompression of this condition has been reported, and combination of intraoperative indocyanine green (ICG) angiography in the setting of VA decompression is also rare. Case presentation A 77‐year‐old man presented continuous vertigo, unsteady gait and dysphagia with no relationship to the head movement. Preoperative computed tomography angiography (CTA) and digital substraction angiography (DSA) examination revealed the left vertebral artery was severely compressed at C4‐5 level with approximately 95% occlusion due to a left C4‐5 facet joint originated large osteophyte. Successful anterior decompression was performed without fusion and intraoperative ICG fluorescence angiography proved excellent blood flow. After surgery, vertebrobasilar insufficiency symptoms remarkably improved with no neurological deficits and no recurrence at 12 months' follow‐up. Conclusions The authors' therapeutic strategy of anterior decompression was successful in treating VA compression due to facet joint overgrowth with adequate exposure, no stability decrease and movement restriction, and lower rates of neck pain and blood loss.

Severe traumatic spinal cord injury (SCI) leads to long-lasting oligodendrocyte death and extensive demyelination in the lesion area. Oligodendrocyte progenitor cells (OPCs) are the reservoir of new mature oligodendrocytes during damaged myelin regeneration, which also have latent potential for neurogenic regeneration and oligospheres formation. Whether oligospheres derived OPCs can differentiate into neurons and the neurogenesis potential of OPCs after SCI remains unclear. In this study, primary OPCs cultures were used to generate oligospheres and detect the differentiation and neurogenesis potential of oligospheres. In vivo, SCI models of juvenile and adult mice were constructed. Combining the single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing (RNA-seq), bioinformatics analysis, immunofluorescence staining, and molecular experiment, we investigated the neurogenesis potential and mechanisms of OPCs in vitro and vivo. We found that OPCs differentiation and oligodendrocyte morphology were significantly different between brain and spinal cord. Intriguingly, we identify a previously undescribed findings that OPCs were involved in oligospheres formation which could further differentiate into neuron-like cells. We also firstly detected the intermediate states of oligodendrocytes and neurons during oligospheres differentiation. Furthermore, we found that OPCs were significantly activated after SCI. Combining scRNA-seq and bulk RNA-seq data from injured spinal cord, we confirmed the neurogenesis potential of OPCs and the activation of endoplasmic reticulum stress after SCI. Inhibition of endoplasmic reticulum stress could effectively attenuate OPCs death. Additionally, we also found that endoplasmic reticulum may regulate the stemness and differentiation of oligospheres. These findings revealed the neurogenesis potential of OPCs from oligospheres and injured spinal cord, which may provide a new source and a potential target for spinal cord repair.

Abstract Combination of computed tomography (CT) radiography and deep learning to predict subsequent osteoporotic vertebral compression fracture (OVCF) has not been reported. To do so, we analyzed retrospectively CT images from 103 patients who experienced twice OVCF in Tongji Hospital from 2011 to 2022. Meanwhile, CT images from 70 age‐matched osteoporotic patients without vertebral fracture were used as the negative control. Convolutional neural network was used for classification and the Adam optimizer combining the momentum and exponentially weighted moving average gradients methods were used to update the weights of the networks. In the prediction model, we split 80% data of each type of the patient as the training group, while the other 20% was held as the independent testing group. We found that the number of subsequent fracture in women is higher than that in men (81 vs. 22). Additionally, the incidence rate of adjacent vertebral fracture is higher than that of remote vertebral fracture (64.1 vs. 35.9%), while the onset time of the former was 11.9 ± 12.8 months, significantly less than 22.3 ± 18.2 months of the latter (p

Due to the nonlinearity and dynamics of transportation systems, traffic signal control (TSC) in urban traffic networks has always been an important challenge. In recent years, model predictive control (MPC) has shown extraordinary potential in TSC due to its outstanding ability to model dynamic systems. However, the relatively complex online computing, lack of reasonable setpoints for target solving, and uncertainty of traffic network hinder MPC from being further applied. To address these problems, we propose a hierarchical, distributed, and robust model predictive control (HD-RMPC) framework for urban TSC. At the slow-update layer, the road network is dynamically divided into several subareas according to regional attributes and real-time traffic demand. Meanwhile, the volume is coordinated in a robust way for the purpose of traffic equilibrium and overflow prevention. Then, the set-point matrix of each subarea is calculated to equalize the flow in the subarea. This distributed framework guarantees the real-time performance of MPC in urban traffic networks. At the fast-update layer, we adopt an improved prediction model by explicit modeling of the disturbance and reduce the prediction error. Finally, the objective function is reconstructed and solved at the control layer to obtain the optimal control law. Through continuous and asynchronous optimization of the set point and prediction model, the framework significantly improves the control effect. Simulation evaluation based on a real-world road network demonstrates that the proposed HD-RMPC method outperforms all baselines and maintains excellent real-time performance.

To meet the demand for cooperative signal control at oversaturated heterogeneous traffic flow intersections containing CAVs and HVs, cooperative control including dedicated CAV lanes has been explored to improve intersection safety capacity and reduce vehicle delays while avoiding uncertain HV driving behaviour. However, this approach does not fully exploit CAV network connectivity advantages and intersection spatial and temporal resources. Here, an oversaturated heterogeneous traffic flow signal control model based on a variable virtual waiting zone with a dedicated CAV lane is proposed. Within the model, CAVs going straight or left share a dedicated CAV lane, a CAV variable virtual waiting zone is within the intersection ahead of the dedicated CAV lane, and CAVs and HVs share the straight-through lane. The model framework has three layers. The upper layer optimizes the barrier time using a rolling time domain scheme. The middle layer optimizes the phase duration and variable virtual waiting zone switching time based on the fixed phase sequence, returning the vehicle delay to the upper optimization model. The lower layer performs CAV grouping and trajectory planning in the dedicated CAV lane based on signal timing and variable virtual waiting zone duration, returning the CAV delays to the middle level.

Recently, short-term traffic state prediction for urban transportation networks has become a popular topic. However, due to the uncontrollable and unpredictable elements of special events, it is difficult to get abundant data and desired predictions under such condition. As k-nearest neighbor (KNN) has a competitive advantage over other approaches, it could predict traffic state based on a small correlative part of data. Thus, a special event-based KNN (SEKNN) model is proposed for the short-term traffic state prediction with three key points presented in this paper. First, the evolution of the traffic states is redefined as a multipart object, state unit, which includes the benchmark state and the trend vector. Second, to select the nearest neighbors, the state distances of the state units are designed to be compatible with the benchmark states and the trend vectors by fusing the Euclidean distance and the cosine distance. Finally, the prediction results are forced to adjust the benchmark states based on the prediction function using the Gaussian weighted method. The proposed SEKNN is implemented in the district of the Beijing Workers' Stadium (257 links), where special events occur frequently. The results show that the proposed model performs significantly better under special events than the other traditional machine-learning approaches and state-of-the-art deep-learning approaches.

In the geogenetic overburden excavation of underground space projects (such as comprehensive pipe corridors or subways), the foundation stress is in an unloading state. The effect(s) from the unloading on the coefficient of subsidence in the loess area should be considered. In this study, to explore the effect of unloading on the collapsibility of loess, the collapsible loess in the Guanzhong area was considered as the research object. An expression for the unloading collapse coefficient was established based on the unloading stress ratio, unloading collapse ratio, and other parameters. The influence of the unloading on the loess collapse coefficient was studied using an indoor collapsibility test, and the function form and parameters for the expression were determined. As combined with the field test, the accuracy of the expression for the unloading coefficient was verified based on the test value for the specific collapsibility, calculated value for the specific collapsibility, and calculated value for the unloading collapsibility.

Traffic prediction is a critical aspect of many real-world scenarios that requires accurate traffic status predictions, such as travel demand prediction. The emergence of online car-hailing activities has given people greater mobility and makes intercity travel more frequent. The increase in online car-hailing demand has often led to a supply–demand imbalance where there is a mismatch between the immediate availability of car-hailing services and the number of passengers in certain areas. Accurate prediction of online car-hailing demand promotes efficiencies and minimizes resources and time waste. However, many prior related studies often fail to fully utilize spatiotemporal characteristics. With the development of newer deep-learning models, this paper aims to solve online car-hailing problems with an ST-transformer model. The spatiotemporal characteristics of online car-hailing data are analyzed and extracted. The study region is divided into subareas, and the demand for each subarea is summed at a specific time interval. Historical demand of the areas is used to predict future demand. The results of the ST-transformer outperformed other baseline models, namely, VAR, SVR, LSTM, LSTNet, and transformers. The validated results suggest that the ST-transformer is more capable of capturing spatiotemporal characteristics compared to the other models. Additionally, compared to others, the model is less affected by data sparsity.

Ridesplitting, a form of ridesourcing in which riders with similar origins and destinations are matched, is an effective mode of sustainable transportation. In recently years, ridesplitting has spread rapidly worldwide and plays an increasingly important role in intercity travel. However, intercity ridesplitting has rarely been studied. In this paper, we use observe intercity ridesplitting data between Yinchuan and Shizuishan in China and building environment data based on a geographic information system (GIS) to analyse temporal, spatial and other characteristics. Then, we divide the study area into grids and explore the contributing factors that affect the intercity ridesplitting matching success rate. Based on these significant factors, we develop a binary logistic regression (BLR) model and predict the intercity ridesplitting matching success rate. The results indicate that morning peak, evening peak, weekends and weekdays, precipitation and snowfall, population density, some types of points of interest (POI), travel time and the advance appointment time are significant factors. In addition, the prediction accuracy of the model is more than 78%, which shows that the factors studied in this paper have good explanatory power. The results of this study can help in understanding the characteristics of intercity ridesplitting and provide a reference for improving the intercity ridesplitting matching success rate.

Recently, short-term traffic prediction under conditions with corrupted or missing data has become a popular topic. Since a road section has predictive power regarding the adjacent roads at a specific location, this paper proposes a novel hybrid convolutional long short-term memory neural network model based on critical road sections (CRS-ConvLSTM NN) to predict the traffic evolution of global networks. The critical road sections that have the most powerful impact on the subnetwork are identified by a spatiotemporal correlation algorithm. Subsequently, the traffic speed of the critical road sections is used as the input to the ConvLSTM to predict the future traffic states of the entire network. The experimental results from a Beijing traffic network indicate that the CRS-ConvLSTM outperforms prevailing deep learning (DL) approaches for cases that consider critical road sections and the results validate the capability and generalizability of the model when predicting with different numbers of critical road sections.