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With the deep integration of the modern automotive industry and artificial intelligence technologies, connected and automated vehicles (CAVs) have emerged as a key breakthrough for improving traffic safety and operational efficiency. This study proposes a distributed multi-vehicle cooperative trajectory planning and control framework for ramp merging and diverging scenarios, integrating Deep Neural Networks (DNNs) with Model Predictive Control (MPC). The methodology consists of three key components: First, a distributed cooperative architecture based on dynamic topology is constructed to effectively reduce communication loads; second, a feature point-based Cubic Bézier Curve trajectory generation method is proposed, enabling flexible path planning with reduced reliance on high-precision maps; finally, a DNN-accelerated MPC solving strategy (NN-MPC) is designed. This strategy employs an offline-trained deep neural network to approximate the online optimization process, supplemented by a terminal Safety Check mechanism and a dynamic surrounding vehicle selection algorithm. Experimental results demonstrate that the proposed method successfully reproduces the planning capability of offline high-precision MPC in ramp merging and diverging scenarios while reducing computation time to the millisecond level. It effectively overcomes the myopic decision-making problem of traditional real-time algorithms, achieving smoother conflict resolution and higher traffic efficiency. Notably, quantitative validation confirms that this cooperative framework achieves an approximate 30% reduction in average travel delay compared to the non-cooperative baseline. This study confirms the engineering advantages of the hybrid architecture under dynamic high-density traffic flows, significantly enhancing the system’s real-time response capability while balancing the safety and riding comfort of cooperative driving.

Silent magnetic resonance angiography (S-MRA) is primarily utilized to assess the blood flow in aneurysms and parent vessels of treated intracranial aneurysms. This study aimed to compare the diagnostic value of S-MRA and three-dimensional time of flight (3D-TOF) MRA for unruptured intracranial aneurysms. We included patients diagnosed with unruptured intracranial aneurysms using digital subtraction angiography (DSA) who subsequently underwent S-MRA and 3D-TOF MRA. Two independent neuroimaging and neurointerventional doctors evaluated the DSA images and measured aneurysm dimensions. Using DSA results as the gold standard, we determined the sensitivity and specificity of S-MRA and 3D-TOF MRA, as well as their accuracy in measuring aneurysm size and identifying aneurysms with daughter sacs. We detected a total of 41 intracranial aneurysms (in 37 patients) on both S-MRA and 3D-TOF MRA, with both techniques achieving a sensitivity and specificity of 100%. For aneurysm height, the intraclass correlation coefficient (ICC) was 0.977 ( P  < 0.001) between S-MRA and DSA, and 0.908 ( P  < 0.001) between 3D-TOF MRA and DSA. For neck width, the ICC was 0.663 ( P  < 0.001) between S-MRA and DSA, and 0.563 ( P  < 0.001) between 3D-TOF MRA and DSA. In terms of daughter aneurysm detection , 3D-TOF MRA Sensitivity 40%; specificity 92%: positive predictive value 100%; S-MRA sensitivity 60%; specificity 89%; positive predictive value 42%. In conclusion, S-MRA and 3D-TOF MRA did not significantly differ in aneurysm detection ability. For the detection of aneurysm with dauthger sacs indicators, the sensitivity is also higher.

Background and purposeIndividuals with intracranial artery occlusion have high rates of ischaemic events and recurrence. It has been challenging to identify patients who had high-risk stroke using a simple, valid and non-invasive screening approach. This study aimed to investigate whether fluid-attenuated inversion recovery (FLAIR) vascular hyperintensity (FVH), a specific imaging sign on the FLAIR sequence, could be a predictor of ischaemic events in a population with internal carotid artery (ICA) or middle cerebral artery (MCA) occlusion.MethodsWe retrospectively analysed 147 patients (mean 60.43±12.83 years) with 149 lesions, including 37 asymptomatic and 112 symptomatic cases of ICA or MCA occlusion. Symptomatic occlusion was considered if ischaemic events were present in the relevant territory within 90 days. FVH Alberta Stroke Program Early Computed Tomography Score (FVH-ASPECTS: 0–7, with 0 indicating absence of FVH and 7 suggesting prominent FVH) and collateral circulation grade were assessed for each participant. Multivariable logistic regression analysis was performed to detect independent markers associated with symptomatic status.ResultsA lower FVH-ASPECTS was associated with a more favourable collateral circulation grade (rho=−0.464, p<0.0001). The FVH-ASPECTS was significantly lower in the asymptomatic occlusion group than in the symptomatic occlusion group (p<0.0001). FVH-ASPECTS (Odd ratio, 2.973; 95% confidence interval, 1.849 to 4.781; p<0.0001) was independently associated with symptomatic status after adjustment for age, sex, lesion location and collateral circulation grade in the multivariate logistic regression. The area under the curve was 0.861 for the use of FVH-ASPECTS to identify symptomatic occlusion.ConclusionsThe ability to discriminate symptomatic from asymptomatic occlusion suggests that FVH may be a predictor of stroke. As a simple imaging sign, FVH may serve as a surrogate for haemodynamic impairments and can be used to identify high-risk stroke cases early in ICA or MCA occlusion.

Objectives We aim to investigate whether cerebral small vessel disease (cSVD) imaging markers correlate with deep medullary vein (DMV) damage in small vessel occlusion acute ischemic stroke (SVO-AIS) patients. Methods The DMV was divided into six segments according to the regional anatomy. The total DMV score (0–18) was calculated based on segmental continuity and visibility. The damage of DMV was grouped according to the quartiles of the total DMV score. Neuroimaging biomarkers of cSVD including white matter hyperintensity (WMH), cerebral microbleed (CMB), perivascular space (PVS), and lacune were identified. The cSVD score were further analyzed. Results We included 229 SVO-AIS patients, the mean age was 63.7 ± 23.1 years, the median NIHSS score was 3 (IQR, 2–6). In the severe DMV burden group (the 4th quartile), the NIHSS score grade (6 (3–9)) was significantly higher than other groups ( p  < 0.01). The grade scores for basal ganglia PVS (BG-PVS) were positively correlated with the degree of DMV ( R  = 0.67, p  < 0.01), rather than centrum semivole PVS (CS-PVS) ( R  = 0.17, p  = 0.1). In multivariate analysis, high CMB burden (adjusted odds ratio [aOR], 25.38; 95% confidence interval [CI], 1.87–345.23) was associated with severe DMV scores. In addition, BG-PVS was related to severe DMV burden in a dose-dependent manner: when BG-PVS score was 3 and 4, the aORs of severe DMV burden were 18.5 and 12.19, respectively. Conclusion The DMV impairment was associated with the severity of cSVD, which suggests that DMV burden may be used for risk stratification in SVO-AIS patients. Clinical relevance statement The DMV damage score, based on the association between small vessel disease and the deep medullary veins impairment, is a potential new imaging biomarker for the prognosis of small vessel occlusion acute ischemic stroke, with clinical management implications. Key Points • The damage to the deep medullary vein may be one mechanism of cerebral small vessel disease. • Severe burden of the basal ganglia perivascular space and cerebral microbleed is closely associated with significant impairment to the deep medullary vein. • The deep medullary vein damage score may reflect a risk of added vascular damage in small vessel occlusion acute ischemic stroke patients.

Aims Our purpose is to assess the role of cerebral small vessel disease (SVD) in prediction models in patients with different subtypes of acute ischemic stroke (AIS). Methods We enrolled 398 small‐vessel occlusion (SVO) and 175 large artery atherosclerosis (LAA) AIS patients. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days. MRI was performed to assess white matter hyperintensity (WMH), perivascular space (PVS), lacune, and cerebral microbleed (CMB). Logistic regression (LR) and machine learning (ML) were used to develop predictive models to assess the influences of SVD on the prognosis. Results In the feature evaluation of SVO‐AIS for different outcomes, the modified total SVD score (Gain: 0.38, 0.28) has the maximum weight, and periventricular WMH (Gain: 0.07, 0.09) was more important than deep WMH (Gain: 0.01, 0.01) in prognosis. In SVO‐AIS, SVD performed better than regular clinical data, which is the opposite of LAA‐AIS. Among all models, eXtreme gradient boosting (XGBoost) method with optimal index (OI) has the best performance to predict excellent outcome in SVO‐AIS. [0.91 (0.84–0.97)]. Conclusions Our results revealed that different SVD markers had distinct prognostic weights in AIS patients, and SVD burden alone may accurately predict the SVO‐AIS patients' prognosis. Characteristics of cerebral small vessel disease (CSVD) in acute ischemic stroke (AIS) patients can affect outcomes at 90 days. Meanwhile, different imaging markers of CSVD have different weights of impact on large artery atherosclerosis and small vessel occlusion subtype AIS.

The interaction effect between collateral circulation and ischemic core size on stroke outcomes has been highlighted in acute ischemic stroke (AIS). However, biomarkers that assess the magnitude of this interaction are still lacking. We aimed to present a new imaging marker, the collateral-core ratio (CCR), to quantify the interaction effect between these factors and evaluate its ability to predict functional outcomes using machine learning (ML) in AIS. Patients with AIS caused by anterior circulation large vessel occlusion (LVO) were recruited from a prospective multicenter study. CCR was calculated as collateral perfusion volume/ischemic core volume. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days. An ML model was built and tested with a tenfold cross-validation using nine clinical and four imaging variables with mRS score 3–6 as unfavorable outcomes. Among 129 patients, CCR was identified as the most important variable. The prediction model incorporating clinical factors, ischemic core volume, collateral perfusion volume, and CCR showed better discriminatory power in predicting unfavorable outcomes than the model without CCR (mean C index 0.853 ± 0.108 versus 0.793 ± 0.133, P  = 0.70; mean net reclassification index 52.7% ± 32.7%, P  < 0.05). When patients were divided into two groups based on their CCR value with a threshold of 0.73, unfavorable outcomes were significantly more prevalent in patients with CCR ≤ 0.73 than in those with CCR > 0.73. CCR is a robust predictor of functional outcomes, as identified by ML, in patients with acute LVO. The prediction model that incorporated CCR improved the model’s ability to identify unfavorable outcomes. ClinicalTrials.gov Identifier: NCT02580097.

Animatable 3D reconstruction has significant applications across various fields, primarily relying on artists' handcraft creation. Recently, some studies have successfully constructed animatable 3D models from monocular videos. However, these approaches require sufficient view coverage of the object within the input video and typically necessitate significant time and computational costs for training and rendering. This limitation restricts the practical applications. In this work, we propose a method to build animatable 3D Gaussian Splatting from monocular video with diffusion priors. The 3D Gaussian representations significantly accelerate the training and rendering process, and the diffusion priors allow the method to learn 3D models with limited viewpoints. We also present the rigid regularization to enhance the utilization of the priors. We perform an extensive evaluation across various real-world videos, demonstrating its superior performance compared to the current state-of-the-art methods.

We propose ProTracker, a novel framework for accurate and robust long-term dense tracking of arbitrary points in videos. Previous methods relying on global cost volumes effectively handle large occlusions and scene changes but lack precision and temporal awareness. In contrast, local iteration-based methods accurately track smoothly transforming scenes but face challenges with occlusions and drift. To address these issues, we propose a probabilistic framework that marries the strengths of both paradigms by leveraging local optical flow for predictions and refined global heatmaps for observations. This design effectively combines global semantic information with temporally aware low-level features, enabling precise and robust long-term tracking of arbitrary points in videos. Extensive experiments demonstrate that ProTracker attains state-of-the-art performance among optimization-based approaches and surpasses supervised feed-forward methods on multiple benchmarks. The code and model will be released after publication.

Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (M pro ) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity. Structure-based generative chemistry is crucial in computer-aided drug discovery. Here, authors propose PMDM, a conditional generative model for 3D molecule generation tailored to specific targets. Extensive experiments demonstrate that PMDM can effectively generate rational bioactive molecules

Rapid and accurate state of health (SOH) estimation of retired batteries is a crucial pretreatment for reuse and recycling. However, data-driven methods require exhaustive data curation under random SOH and state of charge (SOC) retirement conditions. Here, we show that the generative learning-assisted SOH estimation is promising in alleviating data scarcity and heterogeneity challenges, validated through a pulse injection dataset of 2700 retired lithium-ion battery samples, covering 3 cathode material types, 3 physical formats, 4 capacity designs, and 4 historical usages with 10 SOC levels. Using generated data, a regressor realizes accurate SOH estimations, with mean absolute percentage errors below 6% under unseen SOC. We predict that assuming uniform deployment of the proposed technique, this would save 4.9 billion USD in electricity costs and 35.8 billion kg CO 2 emissions by mitigating data curation costs for a 2030 worldwide battery retirement scenario. This paper highlights exploiting limited data for exploring extended data space using generative methods, given data can be time-consuming, expensive, and polluting to retrieve for many estimation and predictive tasks. Data scarcity and heterogeneity impede the estimation of retired battery capacity. Here, Tao et al. propose a generative learning method that extends measured data space, potentially reducing curation time, cost and facilitating their sustainable reuse and recycling.