Search Results (10)

Background/Objective: Despite available SARS-CoV-2 vaccines, coverage gaps persist due to unequal distribution and limited access. Microarray patches offer a promising solution to address these challenges, providing a safer and easier-to-use alternative. We present a randomised, double-blind Phase I clinical trial evaluating the SARS-CoV-2 spike protein subunit vaccine, HexaPro, delivered via a high-density microarray patch (HD-MAP). Methods: Forty-four healthy adults aged 18–50 years were assigned to receive either 0 µg, 15 µg, or 45 µg of HexaPro via the HD-MAP, with the primary objective of assessing safety and tolerability. Results: The HD-MAP HexaPro vaccine was found to be safe and well tolerated, with only mild adverse events reported. Following vaccination significant increases in spike-specific IgG titers were observed by 7 days and remained stable through day 90. This IgG response effectively neutralised multiple SARS-CoV-2 variants. Additionally, the HexaPro HD-MAP was stable for up to 12 months at 40 °C. Conclusions: These findings support the continued clinical development of HD-MAPs as an alternative vaccination strategy. Full article

High-definition maps (HDMaps) are critical for safe autonomy on structured roads. Yet traditional production—relying on dedicated mapping fleets and manual quality control—is costly and slow, impeding large-scale, frequent updates. Recently, standard-definition maps (SDMaps) derived from remote sensing have been adopted as priors to support HDMap perception, lowering cost but struggling with subtle urban changes and localization drift. We propose Loop-MapNet, a self-evolving, multimodal, closed-loop mapping framework. Loop-MapNet effectively leverages surround-view images, LiDAR point clouds, and SDMaps; it fuses multi-scale vision via a weighted BiFPN, and couples PointPillars BEV and SDMap topology encoders for cross-modal sensing. A Transformer-based bidirectional adaptive cross-attention aligns SDMap with online perception, enabling robust fusion under heterogeneity. We further introduce a confidence-guided masked autoencoder (CG-MAE) that leverages confidence and probabilistic distillation to both capture implicit SDMap priors and enhance the detailed representation of low-confidence HDMap regions. With spatiotemporal consistency checks, Loop-MapNet incrementally updates SDMaps to form a perception–mapping–update loop, compensating remote-sensing latency and enabling online map optimization. On nuScenes, within 120 m, Loop-MapNet attains 61.05% mIoU, surpassing the best baseline by 0.77%. Under extreme localization errors, it maintains 60.46% mIoU, improving robustness by 2.77%; CG-MAE pre-training raises accuracy in low-confidence regions by 1.72%. These results demonstrate advantages in fusion and robustness, moving beyond one-way prior injection and enabling HDMap–SDMap co-evolution for closed-loop autonomy and rapid SDMap refresh from remote sensing. Full article

This study addresses the optimization of a camera-based bird’s eye view (BEV) segmentation technique that operates in real-time within an embedded system environment while maintaining high accuracy despite limited computational resources. Specifically, it examines three technical approaches for BEV segmentation in autonomous driving: depth-based methods, MLP-based methods, and transformer-based methods, focusing on key techniques such as lift–splat–shoot, HDMapNet, and BEVFormer. A mathematical analysis of these methods is conducted, followed by a comparative performance evaluation using the nuScenes dataset. The optimization process was carried out in three stages: accuracy improvement, latency reduction, and model size optimization. In the first stage of the process, the three modules for BEV segmentation (encoder, view transformation, and decoder) were selected with the goal of maximizing mIoU performance. In the second stage, environmental variable optimization was performed through input resolution adaptation and data augmentation to improve accuracy. Finally, in the third stage, model compression was applied to minimize model size and latency for efficient deployment on embedded systems. Experimental results from the first stage show that the lift–splat–shoot view transformation model, based on the InternImage-B encoder and EfficientNet-B0 decoder, achieved the highest performance with 54.9 mIoU at an input image size of $448\times 800$. Notably, the lift–splat–shoot view transformation model with the InternImage-T encoder and EfficientNet-B0 decoder demonstrated performance of 53.1 mIoU while achieving high efficiency (51.7 ms and 159.5 MB, respectively). The application of the second stage revealed that increasing the input resolution does not always lead to improved accuracy, and there is an optimal resolution size depending on the model. In this study, the best performance was achieved with an input image size of $448\times 800$. During the third stage, FP16 quantization enabled a 50% reduction in memory size and decreased latency while maintaining similar or identical mIoU performance. When deployed on the NVIDIA AGX Orin device, which operates under power constraints, energy efficiency improved, although it resulted in higher latency under certain power supply conditions. As a result, the InternImage encoder-based lift–splat–shoot technique was shown to achieve the highest accuracy performance relative to latency and model size. This approach outperformed the original method by achieving a 29.2% higher mIoU while maintaining similar latency performance and reducing memory size by 32.2%. Full article

Microarray patches (MAPs) have the potential to be a safer, more acceptable, easier-to-use, and more cost-effective means for the administration of vaccines than injection by needle and syringe. Here, we report findings from a randomized, partially double-blinded, placebo-controlled Phase I trial using the Vaxxas high-density MAP (HD-MAP) to deliver a measles rubella (MR) vaccine. Healthy adults (N = 63, age 18–50 years) were randomly assigned 1:1:1:1 to four groups: uncoated (placebo) HD-MAPs, low-dose MR HD-MAPs (~3100 median cell-culture infectious dose [CCID₅₀] measles, ~4300 CCID₅₀ rubella); high-dose MR-HD-MAPs (~9300 CCID₅₀ measles, ~12,900 CCID₅₀ rubella); or a sub-cutaneous (SC) injection of an approved MR vaccine, MR-Vac (≥1000 CCID₅₀ per virus). The MR vaccines were stable and remained viable on HD-MAPs when stored at 2–8 °C for at least 24 months. When MR HD-MAPs stored at 2–8 °C for 24 months were transferred to 40 °C for 3 days in a controlled temperature excursion, loss of potency was minimal, and MR HD-MAPs still met World Health Organisation (WHO) specifications. MR HD-MAP vaccination was safe and well-tolerated; any systemic or local adverse events (AEs) were mild or moderate. Similar levels of binding and neutralizing antibodies to measles and rubella were induced by low-dose and high-dose MR HD-MAPs and MR-Vac. The neutralizing antibody seroconversion rates on day 28 after vaccination for the low-dose HD-MAP, high-dose HD-MAP and MR-Vac groups were 37.5%, 18.8% and 35.7%, respectively, for measles, and 37.5%, 25.0% and 35.7%, respectively, for rubella. Most participants were seropositive for measles and rubella antibodies at baseline, which appeared to negatively impact the number of participants that seroconverted to vaccines delivered by either route. The data reported here suggest HD-MAPs could be a valuable means for delivering MR-vaccine to hard-to-reach populations and support further development. Clinical trial registry number: ACTRN12621000820808. Full article

On campus, the complexity of the environment and the lack of regulatory constraints make it difficult to model the environment, resulting in less efficient motion planning algorithms. To solve this problem, HD-Map-guided sampling-based motion planning is a feasible research direction. We proposed a motion planning algorithm for autonomous vehicles on campus, called HD-Map-guided rapidly-exploring random tree (HDM-RRT). In our algorithm, A collision risk map (CR-Map) that quantifies the collision risk coefficient on the road is combined with the Gaussian distribution for sampling to improve the efficiency of algorithm. Then, the node optimization strategy of the algorithm is deeply optimized through the prior information of the CR-Map to improve the convergence rate and solve the problem of poor stability in campus environments. Three experiments were designed to verify the efficiency and stability of our approach. The results show that the sampling efficiency of our algorithm is four times higher than that of the Gaussian distribution method. The average convergence rate of the proposed algorithm outperforms the RRT* algorithm and DT-RRT* algorithm. In terms of algorithm efficiency, the average computation time of the proposed algorithm is only 15.98 ms, which is much better than that of the three compared algorithms. Full article

The high-density microneedle array patch (HD-MAP) is a promising alternative vaccine delivery system device with broad application in disease, including SARS-CoV-2. Skin reactivity to HD-MAP applications has been extensively studied in young individuals, but not in the >65 years population, a risk group often requiring higher dose vaccines to produce protective immune responses. The primary aims of the present study were to characterise local inflammatory responses and barrier recovery to HD-MAPs in elderly skin. In twelve volunteers aged 69–84 years, HD-MAPs were applied to the forearm and deltoid regions. Measurements of transepidermal water loss (TEWL), dielectric permittivity and erythema were performed before and after HD-MAP application at t = 10 min, 30 min, 48 h, and 7 days. At all sites, TEWL (barrier damage), dielectric permittivity (superficial water);, and erythema measurements rapidly increased after HD-MAP application. After 7 days, the mean measures had recovered toward pre-application values. The fact that the degree and chronology of skin reactivity and recovery after HD-MAP was similar in elderly skin to that previously reported in younger adults suggests that the reactivity basis for physical immune enhancement observed in younger adults will also be achievable in the older population. Full article

The ongoing coronavirus disease 2019 (COVID-19) pandemic continues to disrupt essential health services in 90 percent of countries today. The spike (S) protein found on the surface of the causative agent, the SARS-CoV-2 virus, has been the prime target for current vaccine research since antibodies directed against the S protein were found to neutralize the virus. However, as new variants emerge, mutations within the spike protein have given rise to potential immune evasion of the response generated by the current generation of SARS-CoV-2 vaccines. In this study, a modified, HexaPro S protein subunit vaccine, delivered using a needle-free high-density microarray patch (HD-MAP), was investigated for its immunogenicity and virus-neutralizing abilities. Mice given two doses of the vaccine candidate generated potent antibody responses capable of neutralizing the parental SARS-CoV-2 virus as well as the variants of concern, Alpha and Delta. These results demonstrate that this alternative vaccination strategy has the potential to mitigate the effect of emerging viral variants. Full article

High-definition (HD) maps determine the location of the vehicle under limited visibility based on the location information of safety signs detected by sensors. If a safety sign disappears or changes, incorrect information may be obtained. Thus, map data must be updated daily to prevent accidents. This study proposes a map update system (MUS) framework that maps objects detected by a road map detection system and the object present in the HD map. Based on traffic safety signs notified by the Korean National Police Agency, 151 types of objects, including traffic signs, traffic lights, and road markings, were annotated manually and semi-automatically. Approximately 3,000,000 annotations were trained based on the you only look once (YOLO) model, suitable for real-time detection by grouping safety signs with similar properties. The object coordinates were then extracted from the mobile mapping system point cloud, and the detection location accuracy was verified by comparing and evaluating the center point of the object detected in the MUS. The performance of the groups with and without specified properties was compared and their effectiveness was verified based on the dataset configuration. A model trained with a Korean road traffic dataset on our testbed achieved a group model of 95% mAP and no group model of 70.9% mAP. Full article

Alternative delivery systems such as the high-density microarray patch (HD-MAP) are being widely explored due to the variety of benefits they offer over traditional vaccine delivery methods. As vaccines are dry coated onto the HD-MAP, there is a need to ensure the stability of the vaccine in a solid state upon dry down. Other challenges faced are the structural stability during storage as a dried vaccine and during reconstitution upon application into the skin. Using a novel live chimeric virus vaccine candidate, BinJ/DENV2-prME, we explored a panel of pharmaceutical excipients to mitigate vaccine loss during the drying and storage process. This screening identified human serum albumin (HSA) as the lead stabilizing excipient. When bDENV2-coated HD-MAPs were stored at 4 °C for a month, we found complete retention of vaccine potency as assessed by the generation of potent virus-neutralizing antibody responses in mice. We also demonstrated that HD-MAP wear time did not influence vaccine deposition into the skin or the corresponding immunological outcomes. The final candidate formulation with HSA maintained ~100% percentage recovery after 6 months of storage at 4 °C. Full article

The high-definition map (HD-map) of road structures is crucial for the safe planning and control of autonomous vehicles. However, generating and updating such maps requires intensive manual work. Simultaneous localization and mapping (SLAM) is able to automatically build and update a map of the environment. Nevertheless, there is still a lack of SLAM method for generating vector-based road structure maps. In this paper, we propose a vector-based SLAM method for the road structure mapping using vehicle-mounted multibeam LiDAR. We propose using polylines as the primary mapping element instead of grid maps or point clouds because the vector-based representation is lightweight and precise. We explored the following: (1) the extraction and vectorization of road structures based on multiframe probabilistic fusion; (2) the efficient vector-based matching between frames of road structures; (3) the loop closure and optimization based on the pose-graph; and (4) the global reconstruction of the vector map. One specific road structure, the road boundary, is taken as an example. We applied the proposed mapping method to three road scenes, ranging from hundreds of meters to over ten kilometers and the results are automatically generated vector-based road boundary maps. The average absolute pose error of the trajectory in the mapping is 1.83 m without the aid of high-precision GPS. Full article