Search

Previous studies have highlighted the significant role of complement activation in kidney injuries induced by rhabdomyolysis, intravascular hemolysis, sepsis, and ischemia-reperfusion. Nevertheless, the specific role and mechanism of complement activation in acute kidney injury (AKI) caused by wasp venom remain unclear. The aim of this study was to elucidate the specific complement pathway activated and investigate complement activation in AKI induced by wasp venom. In this study, a complement-depleted mouse model was used to investigate the role of complement in wasp venom-induced AKI. Mice were randomly categorized into control, cobra venom factor (CVF), AKI, and CVF + AKI groups. Compared to the AKI group, the CVF + AKI group showed improved pathological changes in kidneys and reduced blood urea nitrogen (BUN) levels. The expression levels of renal complement 3 (C3), complement 5 (C5), complement 1q (C1q), factor B (FB), mannose-binding lectin (MBL), and C5b-9 in AKI group were upregulated compared with the control group. Conversely, the renal tissue expression levels of C3, C5, C1q, FB, MBL, and C5b-9 were decreased in the CVF + AKI group compared to those in the AKI group. Complement activation occurs through all three pathways in AKI induced by wasp venom. Furthermore, complement depletion by CVF attenuates wasp venom-induced nephrotoxicity, suggesting that complement activation plays a primary role in the pathogenesis of wasp venom-induced AKI. [ABSTRACT FROM AUTHOR]

Due to their important role in maintaining temperature and soil moisture, agricultural plastic covers have been widely utilized around the globe for improving crop-growing conditions, which include both plastic-covered greenhouses (PCGs) and plastic-mulched farmlands (PMFs). However, it is a challenging and long-neglected issue to separate PCGs from PMFs due to their spectral similarity. The objective of this study is to propose a deep semantic segmentation model for accurate PCG and PMF mapping based on very high-resolution satellite images and to improve the model's spatial generalization capability using a transfer learning strategy. Specifically, the proposed semantic segmentation model has an encoder-decoder structure, where the encoder is composed of a new convolutional neural network for discriminative spatial feature learning, while the decoder utilizes a multi-task strategy to improve the predictions on the boundaries. Meanwhile, a transfer learning framework is adopted to increase mapping performance and generalization ability under limited samples. Experimental results in several typical regions across the Eurasian continent show that the proposed model could separate PCGs from PMFs accurately with a mean overall accuracy of 94.49% and an average mIoU of 0.8377. Ablation studies verify the role of encoder-decoder and transfer learning strategy in improving classification performance. [ABSTRACT FROM AUTHOR]

The visual perception of augmented reality (AR) geovisualization is significantly different from traditional controllable 2D and 3D visualization. In this study, we extended the rendering styles of color variables to include natural material color (NMC) and illuminating material color (IMC) and extended the size to include linear size (LS) and angular size (AS). Outdoor AR geovisualization user experiments were conducted, examining the guidance characteristics of five static variables (NMC, IMC, shape, AS, LS) and two dynamic variables (vibration, flicker). The results showed that the two dynamic variables provided the highest guidance, and among all the static variables, the order of guidance was shape, IMC, AS, NMC, and finally LS. This is a new finding that is different from the color, size, and shape guidance order in 2D visualization and the color, shape, and size order in 3D visualization. The results could be utilized to guide the selection of visual variables for symbol design in AR geovisualization. [ABSTRACT FROM AUTHOR]

To avoid crowd evacuation simulations depending on 2D environments and real data, we propose a framework for crowd evacuation modeling and simulation by applying deep reinforcement learning (DRL) and 3D physical environments (3DPEs). In 3DPEs, we construct simulation scenarios from the aspects of geometry, semantics and physics, which include the environment, the agents and their interactions, and provide training samples for DRL. In DRL, we design a double branch feature extraction combined actor and critic network as the DRL policy and value function and use a clipped surrogate objective with polynomial decay to update the policy. With a unified configuration, we conduct evacuation simulations. In scenarios with one exit, we reproduce and verify the bottleneck effect of congested crowds and explore the impact of exit width and agent characteristics (number, mass and height) on evacuation. In scenarios with two exits and a uniform (nonuniform) distribution of agents, we explore the impact of exit characteristics (width and relative position) and agent characteristics (height, initial location and distribution) on agent exit selection and evacuation. Overall, interactive 3DPEs and unified DRL enable agents to adapt to different evacuation scenarios to simulate crowd evacuation and explore the laws of crowd evacuation. [ABSTRACT FROM AUTHOR]

The urbanization worldwide leads to the rapid increase of solid waste, posing a threat to environment and people's wellbeing. However, it is challenging to detect solid waste sites with high accuracy due to complex landscape, and very few studies considered solid waste mapping across multi-cities and in large areas. To tackle this issue, this study proposes a novel deep learning model for solid waste mapping from very high resolution remote sensing imagery. By integrating a multi-scale dilated convolutional neural network (CNN) and a Swin-Transformer, both local and global features are aggregated. Experiments in China, India and Mexico indicate that the proposed model achieves high performance with an average accuracy of 90.62%. The novelty lies in the fusion of CNN and Transformer for solid waste mapping in multi-cities without the need for pixel-wise labelled data. Future work would consider more sophisticated methods such as semantic segmentation for fine-grained solid waste classification. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) has been widely used in emergency response, self-driving and city-scale 3D mapping and navigation. Recent deep-learning based feature point extractors have demonstrated superior performance in dealing with the complex environmental challenges (e.g. extreme lighting) while the traditional extractors are struggling. In this paper, we have successfully improved the robustness and accuracy of a monocular visual SLAM system under various complex scenes by adding a deep learning based visual localization thread as an augmentation to the visual SLAM framework. In this thread, our feature extractor with an efficient lightweight deep neural network is used for absolute pose and scale estimation in real time using the highly accurate georeferenced prior map database at 20cm geometric accuracy created by our in-house and low-cost LiDAR and camera integrated device. The closed-loop error provided by our SLAM system with and without this enhancement is 1.03m and 18.28m respectively. The scale estimation of the monocular visual SLAM is also significantly improved (0.01 versus 0.98). In addition, a novel camera-LiDAR calibration workflow is also provided for large-scale 3D mapping. This paper demonstrates the application and research potential of deep-learning based vision SLAM with image and LiDAR sensors. [ABSTRACT FROM AUTHOR]

Overexpression of CD30 has been reported on the surface of some T-cell lymphomas, especially on Hodgkin's lymphoma (HL) and anaplastic large cell lymphoma (ALCL). CD30 targeted immunotherapy has good clinical therapy response. We have produced a novel antibody drug conjugates (ADCs)-anti-CD30-LDM, which shows attractive tumour-targeting capability and extremely potent antitumor efficacy. To further investigate biological characteristics and promote clinical translation of anti-CD30-LDM, we constructed a radiolabeled 123I-anti-CD30-LDM to evaluate the biodistribution characteristics. The anti-CD30-LDM was radioiodinated by the Iodogen method. The radiochemical purity of 123I-anti-CD30-LDM was more over 98%, and the specific activity of 240.5 MBq/mg. The stability and the specificity of 123I-anti-CD30-LDM were evaluated in vitro. Cellular binding assays were used to evaluate the binding capabilities in CD30-positive Karpas299 cells and CD30-negative Raji cells. B-NDG mice bearing Karpas 299 and Raji xenografts were used for in vivo biodistribution studies. Our results demonstrated that anti-CD30-LDM as an ideal ADC targeted to CD30, which was labelled easily with 123I and obtained the sufficient yields. The 123I-anti-CD30-LDM preserved specific binding to CD30 in vitro and uptake in tumour xenografts in B-NDG mice. These results are encouraging for anti-CD30-LDM as a promising clinical translational candidate for various CD30 positive lymphomas and other diseases. [ABSTRACT FROM AUTHOR]