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This paper aims to investigate gap acceptance behavior during discretionary lane-changing maneuvers considering the heterogeneity among drivers and lane-changing urgency on a freeway segment from a microscopic perspective. It was found that there were statistically significant differences in gap acceptance behavior between changing lane to the left/right. The accelerated failure time(AFT) model, random parameter AFT model, and correlated random parameters with heterogeneity in means AFT model were established. The results show that the third model have the best goodness of fit. There is obvious mean heterogeneity in the gap acceptance models of different lane change directions, and the correlation between random parameters significantly affects the gap acceptance behavior. The urgency of lane change has a significant heterogeneous effect on the lagging gap acceptance behavior of lane-changing drivers and is significantly correlated with other random parameters. The study results can help improve the safety lane-changing modules for connected and autonomous vehicles.

Human-robot collaborative applications require scene representations that are kept up-to-date and facilitate safe motions in dynamic scenes. In this letter, we present an interactive distance field mapping and planning (IDMP) framework that handles dynamic objects and collision avoidance through an efficient representation. We define \textit{interactive} mapping and planning as the process of creating and updating the representation of the scene online while simultaneously planning and adapting the robot's actions based on that representation. Given depth sensor data, our framework builds a continuous field that allows to query the distance and gradient to the closest obstacle at any required position in 3D space. The key aspect of this work is an efficient Gaussian Process field that performs incremental updates and implicitly handles dynamic objects with a simple and elegant formulation based on a temporary latent model. In terms of mapping, IDMP is able to fuse point cloud data from single and multiple sensors, query the free space at any spatial resolution, and deal with moving objects without semantics. In terms of planning, IDMP allows seamless integration with gradient-based motion planners facilitating fast re-planning for collision-free navigation. The framework is evaluated on both real and synthetic datasets. A comparison with similar state-of-the-art frameworks shows superior performance when handling dynamic objects and comparable or better performance in the accuracy of the computed distance and gradient field. Finally, we show how the framework can be used for fast motion planning in the presence of moving objects. An accompanying video, code, and datasets are made publicly available https://uts-ri.github.io/IDMP.

The success of intelligent robotic missions relies on integrating various research tasks, each demanding distinct representations. Designing task-specific representations for each task is costly and impractical. Unified representations suitable for multiple tasks remain unexplored. My outline introduces a series of research outcomes of GP-based probabilistic distance field (GPDF) representation that mathematically models the fundamental property of Euclidean distance field (EDF) along with gradients, surface normals and dense reconstruction. The progress to date and ongoing future works show that GPDF has the potential to offer a unified solution of representation for multiple tasks such as localisation, mapping, motion planning, obstacle avoidance, grasping, human-robot collaboration, and dense visualisation. I believe that GPDF serves as the cornerstone for robots to accomplish more complex and challenging tasks. By leveraging GPDF, robots can navigate through intricate environments, understand spatial relationships, and interact with objects and humans seamlessly.

Accurate multi-step flight trajectory prediction plays an important role in Air Traffic Control, which can ensure the safety of air transportation. Two main issues limit the flight trajectory prediction performance of existing works. The first issue is the negative impact on prediction accuracy caused by the significant differences in data range. The second issue is that real-world flight trajectories involve underlying temporal dependencies, and existing methods fail to reveal the hidden complex temporal variations and only extract features from one single time scale. To address the above issues, we propose FlightPatchNet, a multi-scale patch network with differential coding for flight trajectory prediction. Specifically, FlightPatchNet first utilizes the differential coding to encode the original values of longitude and latitude into first-order differences and generates embeddings for all variables at each time step. Then, a global temporal attention is introduced to explore the dependencies between different time steps. To fully explore the diverse temporal patterns in flight trajectories, a multi-scale patch network is delicately designed to serve as the backbone. The multi-scale patch network exploits stacked patch mixer blocks to capture inter- and intra-patch dependencies under different time scales, and further integrates multi-scale temporal features across different scales and variables. Finally, FlightPatchNet ensembles multiple predictors to make direct multi-step prediction. Extensive experiments on ADS-B datasets demonstrate that our model outperforms the competitive baselines. Code is available at: https://github.com/FlightTrajectoryResearch/FlightPatchNet.

This paper introduces a novel method to estimate distance fields from noisy point clouds using Gaussian Process (GP) regression. Distance fields, or distance functions, gained popularity for applications like point cloud registration, odometry, SLAM, path planning, shape reconstruction, etc. A distance field provides a continuous representation of the scene defined as the shortest distance from any query point and the closest surface. The key concept of the proposed method is the transformation of a GP-inferred latent scalar field into an accurate distance field by using a reverting function related to the kernel inverse. The latent field can be interpreted as a smooth occupancy map. This paper provides the theoretical derivation of the proposed method as well as a novel uncertainty proxy for the distance estimates. The improved performance compared with existing distance fields is demonstrated with simulated experiments. The level of accuracy of the proposed approach enables novel applications that rely on precise distance estimation: this work presents echolocation and mapping frameworks for ultrasonic-guided wave sensing in metallic structures. These methods leverage the proposed distance field with a physics-based measurement model accounting for the propagation of the ultrasonic waves in the material. Real-world experiments are conducted to demonstrate the soundness of these frameworks.

Large-scale data missing is a challenging problem in Intelligent Transportation Systems (ITS). Many studies have been carried out to impute large-scale traffic data by considering their spatiotemporal correlations at a network level. In existing traffic data imputations, however, rich semantic information of a road network has been largely ignored when capturing network-wide spatiotemporal correlations. This study proposes a Graph Transformer for Traffic Data Imputation (GT-TDI) model to impute large-scale traffic data with spatiotemporal semantic understanding of a road network. Specifically, the proposed model introduces semantic descriptions consisting of network-wide spatial and temporal information of traffic data to help the GT-TDI model capture spatiotemporal correlations at a network level. The proposed model takes incomplete data, the social connectivity of sensors, and semantic descriptions as input to perform imputation tasks with the help of Graph Neural Networks (GNN) and Transformer. On the PeMS freeway dataset, extensive experiments are conducted to compare the proposed GT-TDI model with conventional methods, tensor factorization methods, and deep learning-based methods. The results show that the proposed GT-TDI outperforms existing methods in complex missing patterns and diverse missing rates. The code of the GT-TDI model will be available at https://github.com/KP-Zhang/GT-TDI.

Forest biomass is a crucial component of the global carbon budget in climate change studies. Therefore, it is essential to develop a credible way to estimate forest biomass as carbon stock. Our study used PALSAR-2 (ALOS-2) and Sentinel-2 images to drive the Random Forest regression model, which we trained with airborne lidar data. We used the model to estimate forest aboveground biomass (AGB) of two significant coniferous trees, Japanese cedar and Japanese cypress, in Ibaraki Prefecture, Japan. We used 48 variables derived from the two remote sensing datasets to predict forest AGB under the Random Forest algorithm, and found that the model that combined the two datasets performed better than models based on only one dataset, with R-2 = 0.31, root-mean-square error (RMSE) = 54.38 Mg ha(-1), mean absolute error (MAE) = 40.98 Mg ha(-1), and relative RMSE (rRMSE) of 0.35 for Japanese cedar, and R-2 = 0.37, RMSE = 98.63 Mg ha(-1), MAE = 76.97 Mg ha(-1), and rRMSE of 0.33 for Japanese cypress, over the whole AGB range. In the satellite AGB map, the total AGB of Japanese cedar in 17 targeted cities in Ibaraki Prefecture was 5.27 Pg, with a mean of 146.50 Mg ha(-1) and a standard deviation of 44.37 Mg ha(-1). The total AGB of Japanese cypress was 3.56 Pg, with a mean of 293.12 Mg ha(-1) and a standard deviation of 78.48 Mg ha(-1). We also found a strong linear relationship with between the model estimates and Japanese government data, with R-2 = 0.99 for both species and found the government information underestimates the AGB for cypress but overestimates it for cedar. Our results reveal that combining information from multiple sensors can predict forest AGB with increased accuracy and robustness.

Forest biomass is a crucial component of the global carbon budget in climate change studies. Therefore, it is essential to develop a credible way to estimate forest biomass as carbon stock. Our study used PALSAR-2 (ALOS-2) and Sentinel-2 images to drive the Random Forest regression model, which we trained with airborne lidar data. We used the model to estimate forest aboveground biomass (AGB) of two significant coniferous trees, Japanese cedar and Japanese cypress, in Ibaraki Prefecture, Japan. We used 48 variables derived from the two remote sensing datasets to predict forest AGB under the Random Forest algorithm, and found that the model that combined the two datasets performed better than models based on only one dataset, with R-2 = 0.31, root-mean-square error (RMSE) = 54.38 Mg ha(-1), mean absolute error (MAE) = 40.98 Mg ha(-1), and relative RMSE (rRMSE) of 0.35 for Japanese cedar, and R-2 = 0.37, RMSE = 98.63 Mg ha(-1), MAE = 76.97 Mg ha(-1), and rRMSE of 0.33 for Japanese cypress, over the whole AGB range. In the satellite AGB map, the total AGB of Japanese cedar in 17 targeted cities in Ibaraki Prefecture was 5.27 Pg, with a mean of 146.50 Mg ha(-1) and a standard deviation of 44.37 Mg ha(-1). The total AGB of Japanese cypress was 3.56 Pg, with a mean of 293.12 Mg ha(-1) and a standard deviation of 78.48 Mg ha(-1). We also found a strong linear relationship with between the model estimates and Japanese government data, with R-2 = 0.99 for both species and found the government information underestimates the AGB for cypress but overestimates it for cedar. Our results reveal that combining information from multiple sensors can predict forest AGB with increased accuracy and robustness.

Forest biomass is a crucial component of the global carbon budget in climate change studies. Therefore, it is essential to develop a credible way to estimate forest biomass as carbon stock. Our study used PALSAR-2 (ALOS-2) and Sentinel-2 images to drive the Random Forest regression model, which we trained with airborne lidar data. We used the model to estimate forest aboveground biomass (AGB) of two significant coniferous trees, Japanese cedar and Japanese cypress, in Ibaraki Prefecture, Japan. We used 48 variables derived from the two remote sensing datasets to predict forest AGB under the Random Forest algorithm, and found that the model that combined the two datasets performed better than models based on only one dataset, with R-2 = 0.31, root-mean-square error (RMSE) = 54.38 Mg ha(-1), mean absolute error (MAE) = 40.98 Mg ha(-1), and relative RMSE (rRMSE) of 0.35 for Japanese cedar, and R-2 = 0.37, RMSE = 98.63 Mg ha(-1), MAE = 76.97 Mg ha(-1), and rRMSE of 0.33 for Japanese cypress, over the whole AGB range. In the satellite AGB map, the total AGB of Japanese cedar in 17 targeted cities in Ibaraki Prefecture was 5.27 Pg, with a mean of 146.50 Mg ha(-1) and a standard deviation of 44.37 Mg ha(-1). The total AGB of Japanese cypress was 3.56 Pg, with a mean of 293.12 Mg ha(-1) and a standard deviation of 78.48 Mg ha(-1). We also found a strong linear relationship with between the model estimates and Japanese government data, with R-2 = 0.99 for both species and found the government information underestimates the AGB for cypress but overestimates it for cedar. Our results reveal that combining information from multiple sensors can predict forest AGB with increased accuracy and robustness.

With specific and inherent mRNA cleaving activity, small interfering RNA (siRNA) has been deemed promising therapeutics to reduce the exacerbation rate of asthma by inhibiting the expression and release of proinflammatory cytokines from airway epithelial cells (AECs). To exert the therapeutic effects of siRNA drugs, nano-formulations with high efficiency and safety are required to deliver these nucleic acids to the target cells. Herein, we exploited novel inhaled lipid nanoparticles (LNPs) targeting intercellular adhesion molecule-1 (ICAM-1) receptors on the apical side of AECs. This delivery system is meant to enhance the specific delivery efficiency of siRNA in AECs to prevent the expression of proinflammatory cytokines in AECs and the concomitant symptoms in parallel. A cyclic peptide that resembles part of the capsid protein of rhinovirus and binds to ICAM-1 receptors was initially conjugated with cholesterol and subsequently assembled with ionizable cationic lipids to form the LNPs (Pep-LNPs) loaded with siRNA against thymic stromal lymphopoietin (TSLP siRNA). The obtained Pep-LNPs were subjected to thorough characterization and evaluations in vitro and in vivo. Pep-LNPs significantly enhanced cellular uptake and gene silencing efficiency in human epithelial cells expressing ICAM-1 in vitro, exhibited AEC-specific delivery and improved the gene silencing effect in ovalbumin-challenged asthmatic mice after pulmonary administration. More importantly, Pep-LNPs remarkably downregulated the expression of TSLP in AECs, effectively alleviated inflammatory cell infiltration, and reduced the secretion of other proinflammatory cytokines, including IL-4 and IL-13, as well as mucus production in asthmatic mice. This study demonstrates that Pep-LNPs are safe and efficient to deliver siRNA drugs to asthmatic AECs and could potentially alleviate allergic asthma by inhibiting the overexpression of proinflammatory cytokines in the airway.

Whereas dedicated scene representations are required for each different task in conventional robotic systems, this paper demonstrates that a unified representation can be used directly for multiple key tasks. We propose the Log-Gaussian Process Implicit Surface for Mapping, Odometry and Planning (Log-GPIS-MOP): a probabilistic framework for surface reconstruction, localisation and navigation based on a unified representation. Our framework applies a logarithmic transformation to a Gaussian Process Implicit Surface (GPIS) formulation to recover a global representation that accurately captures the Euclidean distance field with gradients and, at the same time, the implicit surface. By directly estimating the distance field and its gradient through Log-GPIS inference, the proposed incremental odometry technique computes the optimal alignment of an incoming frame and fuses it globally to produce a map. Concurrently, an optimisation-based planner computes a safe collision-free path using the same Log-GPIS surface representation. We validate the proposed framework on simulated and real datasets in 2D and 3D and benchmark against the state-of-the-art approaches. Our experiments show that Log-GPIS-MOP produces competitive results in sequential odometry, surface mapping and obstacle avoidance.

Multimodal imaging-the ability to acquire images of an object through more than one imaging mode simultaneously-has opened additional perspectives in areas ranging from astronomy to medicine. In this paper, we report progress toward combining optical and magnetic resonance (MR) imaging in such a "dual" imaging mode. They are attractive in combination because they offer complementary advantages of resolution and speed, especially in the context of imaging in scattering environments. Our approach relies on a specific material platform, microdiamond particles hosting nitrogen vacancy (NV) defect centers that fluoresce brightly under optical excitation and simultaneously "hyperpolarize" lattice [Formula: see text] nuclei, making them bright under MR imaging. We highlight advantages of dual-mode optical and MR imaging in allowing background-free particle imaging and describe regimes in which either mode can enhance the other. Leveraging the fact that the two imaging modes proceed in Fourier-reciprocal domains (real and k-space), we propose a sampling protocol that accelerates image reconstruction in sparse-imaging scenarios. Our work suggests interesting possibilities for the simultaneous optical and low-field MR imaging of targeted diamond nanoparticles.

Successful CAR T cell therapy for the treatment of solid tumors requires exemplary CAR T cell expansion, persistence and fitness, and the ability to target tumor antigens safely. Here we address this constellation of critical attributes for successful cellular therapy by using integrated technologies that simplify development and derisk clinical translation. We have developed a CAR-CD19 T cell that secretes a CD19-anti-Her2 bridging protein. This cell therapy strategy exploits the ability of CD19-targeting CAR T cells to interact with CD19 on normal B cells to drive expansion, persistence and fitness. The secreted bridging protein potently binds to Her2-positive tumor cells, mediating CAR-CD19 T cell cytotoxicity in vitro and in vivo. Because of its short half-life, the secreted bridging protein will selectively accumulate at the site of highest antigen expression, ie. at the tumor. Bridging proteins that bind to multiple different tumor antigens have been created. Therefore, antigen-bridging CAR-CD19 T cells incorporate critical attributes for successful solid tumor cell therapy. This platform can be exploited to attack tumor antigens on any cancer.

Lan Tang, 1 Wenhui Jiang, 1 Lan Wu, 1 Xiaolan Yu, 1 Zheng He, 1 Weiguang Shan, 1 Lulu Fu, 1 Zhenhai Zhang, 2 Yunchun Zhao 3 1College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou, People’s Republic of China; 2Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Jiangsu Province Academy of Traditional Chinese Medicine, Nanjing, People’s Republic of China; 3Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, People’s Republic of ChinaCorrespondence: Zhenhai ZhangAffiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, 210018, People’s Republic of ChinaTel +86 13758190403Email david23932@163.comYunchun ZhaoWomen’s Hospital, School of Medicine, Zhejiang University, 1, Xueshi Road, Hangzhou, 310016, People’s Republic of ChinaTel +86 15858178673Email 5515059@zju.edu.cnBackground: Doxorubicin (DOX) is an anthracycline antibiotic that inhibits the growth of several solid and hematologic malignant tumors. Increasing the targeting ability of DOX and reducing the multi-drug resistance (MDR) of tumor cells to DOX are major aims for researchers.Purpose: In this study, to increase therapeutic efficiency, reduce the side effects and the MDR of tumor cells to DOX, D-alpha-tocopheryl polyethylene glycol 2000 succinate monoester (TPGS2000)-DOX prodrug micelles were developed by grafting DOX to TPGS2000 via an amide bond that release DOX in the slightly acidic conditions in tumor tissue.Materials and Methods: The TPGS2000-DOX micelles were constructed using polyethylene glycol 12-hydroxy stearate (Solutol HS15) as the carrier. The in vitro drug release profile and dilution stability of the nanomicelles were determined. The in vitro cytotoxicity and distribution of the nanomicelles in the tumor cells were also investigated. Moreover, we e

Edge computing, which sinks a large number of complex calculations into edge servers, can effectively meet the requirement of low latency and bandwidth efficiency and can be conducive to the development of the Internet of Vehicles (IoV). However, a large number of edge servers mean a big cost, especially for the 5G scenario in IoV, because of the small coverage of 5G base stations. Fortunately, coherent beamforming (CB) technology enables fast and long-distance transmission, which gives us a possibility to reduce the number of 5G base stations without losing the whole network performance. In this paper, we try to adopt the CB technology on the IoV 5G scenario. We suppose we can arrange roadside nodes for helping transferring tasks of vehicles to the base station based on the CB technology. We first give the mathematical model and prove that it is a NP-hard model that cannot be solved directly. Therefore, we design a heuristic algorithm for an Iterative Coherent Beamforming Node Design (ICBND) algorithm to obtain the approximate optimal solution. Simulation results show that this algorithm can greatly reduce the cost of communication network infrastructure.

Multimodal imaging-the ability to acquire images of an object through more than one imaging mode simultaneously-has opened additional perspectives in areas ranging from astronomy to medicine. In this paper, we report progress toward combining optical and magnetic resonance (MR) imaging in such a "dual" imaging mode. They are attractive in combination because they offer complementary advantages of resolution and speed, especially in the context of imaging in scattering environments. Our approach relies on a specific material platform, microdiamond particles hosting nitrogen vacancy (NV) defect centers that fluoresce brightly under optical excitation and simultaneously "hyperpolarize" lattice [Formula: see text] nuclei, making them bright under MR imaging. We highlight advantages of dual-mode optical and MR imaging in allowing background-free particle imaging and describe regimes in which either mode can enhance the other. Leveraging the fact that the two imaging modes proceed in Fourier-reciprocal domains (real and k-space), we propose a sampling protocol that accelerates image reconstruction in sparse-imaging scenarios. Our work suggests interesting possibilities for the simultaneous optical and low-field MR imaging of targeted diamond nanoparticles.

Pulmonary delivery of small interfering RNA (siRNA)-based drugs is promising in treating severe lung disorders characterized by the upregulated expression of disease-causing genes. Previous studies have shown that the sustained siRNA release in vitro can be achieved from polymeric matrix nanoparticles based on poly(lactide-co-glycolide) (PLGA) loaded with lipoplexes (LPXs) composed of cationic lipid and anionic siRNA (lipid-polymer hybrid nanoparticles, LPNs). Yet, the in vivo efficacy, potential for prolonging the pharmacological effect, disposition, and safety of LPNs after pulmonary administration have not been investigated. In this study, siRNA against enhanced green fluorescent protein (EGFP-siRNA) was either assembled with 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP) to form LPX or co-entrapped with DOTAP in PLGA nanoparticles to form LPNs. The disposition and clearance of LPXs and LPNs in mouse lungs were studied after intratracheal administration by using single-photon emission computed tomography/computed tomography (SPECT/CT) and gamma counting. Fluorescence spectroscopy, Western blot, and confocal laser scanning microscopy were used to evaluate the silencing of the EGFP expression mediated by the LPXs and LPNs after intratracheal administration to transgenic mice expressing the EGFP gene. The in vivo biocompatibility of LPXs and LPNs was investigated by measuring the cytokine level, total cell counts in bronchoalveolar lavage fluid, and observing the lung tissue histology section. The results showed that the silencing of the EGFP expression mediated by LPNs after pulmonary administration was both prolonged and enhanced as compared to LPXs. This may be attributed to the sustained release characteristics of PLGA, and the prolonged retention in the lung tissue of the colloidally more stable LPNs in comparison to LPXs, as indicated by SPECT/CT. The presence of PLGA effectively alleviated the acute inflammatory effect of cationic lipids to the lungs. Th

Lan Tang, 1 Wenhui Jiang, 1 Lan Wu, 1 Xiaolan Yu, 1 Zheng He, 1 Weiguang Shan, 1 Lulu Fu, 1 Zhenhai Zhang, 2 Yunchun Zhao 3 1College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou, People’s Republic of China; 2Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Jiangsu Province Academy of Traditional Chinese Medicine, Nanjing, People’s Republic of China; 3Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, People’s Republic of ChinaCorrespondence: Zhenhai ZhangAffiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, 210018, People’s Republic of ChinaTel +86 13758190403Email david23932@163.comYunchun ZhaoWomen’s Hospital, School of Medicine, Zhejiang University, 1, Xueshi Road, Hangzhou, 310016, People’s Republic of ChinaTel +86 15858178673Email 5515059@zju.edu.cnBackground: Doxorubicin (DOX) is an anthracycline antibiotic that inhibits the growth of several solid and hematologic malignant tumors. Increasing the targeting ability of DOX and reducing the multi-drug resistance (MDR) of tumor cells to DOX are major aims for researchers.Purpose: In this study, to increase therapeutic efficiency, reduce the side effects and the MDR of tumor cells to DOX, D-alpha-tocopheryl polyethylene glycol 2000 succinate monoester (TPGS2000)-DOX prodrug micelles were developed by grafting DOX to TPGS2000 via an amide bond that release DOX in the slightly acidic conditions in tumor tissue.Materials and Methods: The TPGS2000-DOX micelles were constructed using polyethylene glycol 12-hydroxy stearate (Solutol HS15) as the carrier. The in vitro drug release profile and dilution stability of the nanomicelles were determined. The in vitro cytotoxicity and distribution of the nanomicelles in the tumor cells were also investigated. Moreover, we e

Successful CAR T cell therapy for the treatment of solid tumors requires exemplary CAR T cell expansion, persistence and fitness, and the ability to target tumor antigens safely. Here we address this constellation of critical attributes for successful cellular therapy by using integrated technologies that simplify development and derisk clinical translation. We have developed a CAR-CD19 T cell that secretes a CD19-anti-Her2 bridging protein. This cell therapy strategy exploits the ability of CD19-targeting CAR T cells to interact with CD19 on normal B cells to drive expansion, persistence and fitness. The secreted bridging protein potently binds to Her2-positive tumor cells, mediating CAR-CD19 T cell cytotoxicity in vitro and in vivo. Because of its short half-life, the secreted bridging protein will selectively accumulate at the site of highest antigen expression, ie. at the tumor. Bridging proteins that bind to multiple different tumor antigens have been created. Therefore, antigen-bridging CAR-CD19 T cells incorporate critical attributes for successful solid tumor cell therapy. This platform can be exploited to attack tumor antigens on any cancer.

Edge computing, which sinks a large number of complex calculations into edge servers, can effectively meet the requirement of low latency and bandwidth efficiency and can be conducive to the development of the Internet of Vehicles (IoV). However, a large number of edge servers mean a big cost, especially for the 5G scenario in IoV, because of the small coverage of 5G base stations. Fortunately, coherent beamforming (CB) technology enables fast and long-distance transmission, which gives us a possibility to reduce the number of 5G base stations without losing the whole network performance. In this paper, we try to adopt the CB technology on the IoV 5G scenario. We suppose we can arrange roadside nodes for helping transferring tasks of vehicles to the base station based on the CB technology. We first give the mathematical model and prove that it is a NP-hard model that cannot be solved directly. Therefore, we design a heuristic algorithm for an Iterative Coherent Beamforming Node Design (ICBND) algorithm to obtain the approximate optimal solution. Simulation results show that this algorithm can greatly reduce the cost of communication network infrastructure.

Pulmonary delivery of small interfering RNA (siRNA)-based drugs is promising in treating severe lung disorders characterized by the upregulated expression of disease-causing genes. Previous studies have shown that the sustained siRNA release in vitro can be achieved from polymeric matrix nanoparticles based on poly(lactide-co-glycolide) (PLGA) loaded with lipoplexes (LPXs) composed of cationic lipid and anionic siRNA (lipid-polymer hybrid nanoparticles, LPNs). Yet, the in vivo efficacy, potential for prolonging the pharmacological effect, disposition, and safety of LPNs after pulmonary administration have not been investigated. In this study, siRNA against enhanced green fluorescent protein (EGFP-siRNA) was either assembled with 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP) to form LPX or co-entrapped with DOTAP in PLGA nanoparticles to form LPNs. The disposition and clearance of LPXs and LPNs in mouse lungs were studied after intratracheal administration by using single-photon emission computed tomography/computed tomography (SPECT/CT) and gamma counting. Fluorescence spectroscopy, Western blot, and confocal laser scanning microscopy were used to evaluate the silencing of the EGFP expression mediated by the LPXs and LPNs after intratracheal administration to transgenic mice expressing the EGFP gene. The in vivo biocompatibility of LPXs and LPNs was investigated by measuring the cytokine level, total cell counts in bronchoalveolar lavage fluid, and observing the lung tissue histology section. The results showed that the silencing of the EGFP expression mediated by LPNs after pulmonary administration was both prolonged and enhanced as compared to LPXs. This may be attributed to the sustained release characteristics of PLGA, and the prolonged retention in the lung tissue of the colloidally more stable LPNs in comparison to LPXs, as indicated by SPECT/CT. The presence of PLGA effectively alleviated the acute inflammatory effect of cationic lipids to the lungs. Th

Focus on the safety of herbal medicines has mainly been directed towards the presence of intrinsic toxicity, as found in the cases of renal and hepatic dysfunction caused by aristolochic acids. However, contamination from extrinsic hazards may impart an even greater reduction in their safety and efficacy. This study reveals that pesticides were present in the majority (88%) of a comprehensive cross-section (n = 1771) of herbal medicine samples. Alarmingly, more than half (59%) contained pesticides over the European Pharmacopoeia (EP) limit, and 43% of them contained 35 varieties of banned, extremely toxic pesticides, eight of which were detected at levels over 500 times higher than the default Maximum Residue Limit (MRL). DDTs, carbofuran, and mevinphos were confirmed as being among the most risk-inducing pesticides by three different risk assessment methods, reported to produce carcinogenic, genotoxic, reproductive, and developmental effects, in addition to carrying nephrotoxicity and hepatotoxicity. In light of these findings, and withstanding that extrinsic hazards can be controlled unlike intrinsic toxicity, the authors here strongly recommend the application of herbal medicine quality-control measures and solutions to safeguard against a neglected but certainly potentially serious health risk posed to the majority of the global population that consumes herbal medicines.

We develop a novel approach to explain why AdaBoost is a successful classifier. By introducing a measure of the influence of the noise points (ION) in the training data for the binary classification problem, we prove that there is a strong connection between the ION and the test error. We further identify that the ION of AdaBoost decreases as the iteration number or the complexity of the base learners increases. We confirm that it is impossible to obtain a consistent classifier without deep trees as the base learners of AdaBoost in some complicated situations. We apply AdaBoost in portfolio management via empirical studies in the Chinese market, which corroborates our theoretical propositions.

With the continually increasing water depth of offshore structures being moored, it become harder to study the behaviour of such deep water mooring systems adopting model test with conventional scale ratio due to the limited depth and width of available model basins. According to this condition, an alternative approach is equivalent truncation method, which has been proved to be a feasible method. However, the effect of truncation factor is still not clear, when the truncation factor is too small, it is difficult to get a satisfied mooring system owing to the effect of the line dynamics and other factors. This paper presents one taut mooring system of semi-submersible in full water depth of 1200m. Model test research using scale ratio of 1:62.5 is carried out in Deep Water Ocean Basin of CSSRC, which can satisfy the full depth model test demand which need the water depth 19.2m in model scale. And the mooring systems in water depth of 750m and 250m are initially truncated designed based on the static equivalent characteristics similar criteria, then optimize the truncation mooring system through iteration process via static load optimization and dynamic optimization. Truncation model test are numerical reconstructed, then extrapolated to full depth, which are compared with the full depth results subsequently. The comparison results show that the extrapolated results based on 750m water depth are coincident well with the full depth results, but the extrapolated results based on 250m do not dovetail with the full depth results. According to the comparison results, the effect of truncation factor to predict full depth results is analysed, pilot study show that aiming at this mooring system scheme, when the truncation factor is more than about 0.45, the predicted results based on truncation model test can be reliable.

Edge computing, which sinks a large number of complex calculations into edge servers, can effectively meet the requirement of low latency and bandwidth efficiency and can be conducive to the development of the Internet of Vehicles (IoV). However, a large number of edge servers mean a big cost, especially for the 5G scenario in IoV, because of the small coverage of 5G base stations. Fortunately, coherent beamforming (CB) technology enables fast and long-distance transmission, which gives us a possibility to reduce the number of 5G base stations without losing the whole network performance. In this paper, we try to adopt the CB technology on the IoV 5G scenario. We suppose we can arrange roadside nodes for helping transferring tasks of vehicles to the base station based on the CB technology. We first give the mathematical model and prove that it is a NP-hard model that cannot be solved directly. Therefore, we design a heuristic algorithm for an Iterative Coherent Beamforming Node Design (ICBND) algorithm to obtain the approximate optimal solution. Simulation results show that this algorithm can greatly reduce the cost of communication network infrastructure.

Pulmonary formulations have been attracting much attention because of their direct effects on respiratory diseases, but also their non-invasive administration for the treatment of systemic diseases. When developing such formulations, they are typically first investigated in mice. As there are various pulmonary administration methods, the researcher has to decide on the best quantitative method for their preclinical investigations among candidate methods, both for total delivery and distribution within the lung lobes. In this study, we investigated the deposition and distribution of siRNA loaded PLGA nanoparticles (NPs) in the different lung lobes via three widely used pulmonary administration methods: intratracheal instillation, intratracheal spraying and intranasal instillation. The NPs were radiolabeled with 111In, administered and a single photon emission computed tomography (SPECT/CT) whole body scan performed. Quantitative image volume of interest (VOI) analysis of all inhalation related organs was performed, plus sub-organ examinations using dissection and gamma counting. Intratracheal instillation and intratracheal spraying deposited >95% and >85% of radiolabeled NPs in the lung, respectively. However, the lung lobe distribution of the NPs was inhomogeneous. Intranasal instillation deposited only ∼28% of the dose in the lungs, with even larger inhomogeneity and individual variation between animals. Furthermore, there was a high deposition of the NPs in the stomach. Intratracheal instillation and intratracheal spraying deposit a large number of NPs in the lungs, and are thus useful to test therapeutic effects in preclinical animal studies. However, the inhomogeneous distribution of formulation between lung lobes needs to be considered in the experimental design. Intranasal instillation should not be used as a means of pulmonary administration.

In this letter, we present an interactive probabilistic mapping framework for a mobile manipulator picking objects from a pile. The aim is to map the scene, actively decide where to go next and which object to pick, make changes to the scene by picking the chosen object, and then map these changes alongside. The proposed framework uses a novel dynamic Gaussian Process (GP) Implicit Surface method to incrementally build and update the scene map that reflects environment changes. Actively the framework provides the next-best-view, balancing the need for picking object reachability with map information gain (IG). To enforce a priority of visiting boundary segments over unknown regions, the IG formulation includes an uncertainty gradient-based frontier score by exploiting the GP kernel derivative. This leads to an efficient strategy that addresses the often conflicting requirement of unknown environment exploration and object picking exploitation given a limited execution horizon. We demonstrate the effectiveness of our framework with software simulation and real-life experiments.

In this letter, we introduce the Log-Gaussian Process Implicit Surface (Log-GPIS), a novel continuous and probabilistic mapping representation suitable for surface reconstruction and local navigation. Our key contribution is the realisation that the regularised Eikonal equation can be simply solved by applying the logarithmic transformation to a GPIS formulation to recover the accurate Euclidean distance field (EDF) and, at the same time, the implicit surface. To derive the proposed representation, Varadhan's formula is exploited to approximate the non-linear Eikonal partial differential equation (PDE) of the EDF by the logarithm of a linear PDE. We show that members of the Matern covariance family directly satisfy this linear PDE. The proposed approach does not require post-processing steps to recover the EDF. Moreover, unlike sampling-based methods, Log-GPIS does not use sample points inside and outside the surface as the derivative of the covariance allow direct estimation of the surface normals and distance gradients. We benchmarked the proposed method on simulated and real data against state-of-the-art mapping frameworks that also aim at recovering both the surface and a distance field. Our experiments show that Log-GPIS produces the most accurate results for the EDF and comparable results for surface reconstruction and its computation time still allows online operations.

Face recognition has been an active and vital topic among computer vision community for a long time. Previous researches mainly focus on loss functions used for facial feature extraction network, among which the improvements of softmax-based loss functions greatly promote the performance of face recognition. However, the contradiction between the drastically increasing number of face identities and the shortage of GPU memories is gradually becoming irreconcilable. In this paper, we thoroughly analyze the optimization goal of softmax-based loss functions and the difficulty of training massive identities. We find that the importance of negative classes in softmax function in face representation learning is not as high as we previously thought. The experiment demonstrates no loss of accuracy when training with only 10\% randomly sampled classes for the softmax-based loss functions, compared with training with full classes using state-of-the-art models on mainstream benchmarks. We also implement a very efficient distributed sampling algorithm, taking into account model accuracy and training efficiency, which uses only eight NVIDIA RTX2080Ti to complete classification tasks with tens of millions of identities. The code of this paper has been made available https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc., Comment: 8 pages, 9 figures

Pulmonary delivery of small interfering RNAs (siRNAs) has shown promising results for the treatment of lung diseases with gene disorders. The aim of this work was to evaluate the effect of processing-induced thermal and shear stresses during the spray drying process on the solid-state properties, the chemical integrity and the bioactivity of spray-dried siRNA powder intended for inhalation. To this end, inhalable siRNA dry powders composed of EGFP-siRNA and mannitol were prepared by using a lab-scale spray drier. Scanning electron microscopy (SEM), laser diffraction, and X-ray powder diffraction (XRPD) were used to characterize the solid-state properties of the spray-dried siRNA-mannitol powders. High performance liquid chromatography (HPLC) and flow cytometry were exploited to assess the chemical stability and cellular transfection efficiency of siRNA formulations, respectively. The results showed that the spray-dried particles changed from spherical to irregular shape with an increase in the inlet temperature. The high inlet temperature and intensive atomization conditions resulted in more agglomerates in the spray-dried particles. XRPD analysis indicated that the presence of siRNA affected the polymorphic form of mannitol in the spray-dried powder. Compromised chemical stability and cell transfection efficiency of siRNA were observed with an increase in the thermal stress and shear stress during the spray drying process. The chemical stability of siRNA in liquid state was more prone to thermal stress when compared to the stability in the solid-state. In conclusion, stable siRNA based particles for inhalation purposes could be produced using the spray drying technology.

Lung cancer is a complex disease caused by a multitude of genetic and environmental factors. The progression of lung cancer involves dynamic changes in the genome and a complex network of interactions between cancer cells with multiple, distinct cell types that form tumors. Combination therapy using different pharmaceuticals has been proven highly effective due to the ability to affect multiple cellular pathways involved in the disease progression. However, the currently used drug combination designs are primarily based on empirical clinical studies, and little attention has been given to dosage regimens, i.e. how administration routes, onsets, and durations of the combinations influence the therapeutic outcome. This is partly because combination therapy is challenged by distinct physicochemical properties and in vivo pharmacokinetics/pharmacodynamics of the individual pharmaceuticals, including small molecule drugs and biopharmaceuticals, which make the optimization of dosing and administration schedule challenging. This article reviews the recent advances in the design and development of combinations of pharmaceuticals for the treatment of lung cancer. Focus is primarily on rationales for the selection of specific combination therapies for lung cancer treatment, and state of the art of delivery technologies and dosage regimens for the combinations, tested in preclinical and clinical trials.

LinLi Luo,* Juan Ni,* Lan Wu, Dong Luo Department of Anesthesiology, West China Second Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China  *These authors contributed equally to this work and should be considered co-first authors Abstract: Anesthetic management of patients with preexisting diseases is challenging and individualized approaches need to be determined based on patients' complications. We report here a case of ultrasound-guided epidural anesthesia in combination with low-dose ketamine during cesarean delivery on a parturient with severe malformations of the skeletal system and airway problems. The ultrasound-guided epidural anesthesia was performed in the L1–L2 space, followed by an intravenous administration of ketamine (0.5 mg/kg) for sedation and analgesia. Satisfactory anesthesia was provided to the patient and spontaneous ventilation was maintained during the surgery. The mother and the baby were discharged 5 days after surgery, no complications were reported for either of them. Our work demonstrated that an ultrasound-guided epidural anesthesia combined with low-dose ketamine can be used to successfully maintain spontaneous ventilation and provide effective analgesia during surgery and reduce the risk of postoperative anesthesia-related pulmonary infection. Keywords: anesthesia, regional, cesarean delivery, ketamine, ultrasound-guided

In this paper, we investigate the possibility to use two tilings of the hyperbolic plane as basic frame for devising a way to input texts in Chinese characters into messages of cellphones, smartphones, ipads and tablets., Comment: 28 pages, 40 figures

Background In order to induce a potent and cross-reactive neutralizing antibody (nAb), an effective envelope immunogen is crucial for many viral vaccines, including the vaccine for the human immunodeficiency virus (HIV). The Chinese equine infectious anemia virus (EIAV) attenuated vaccine has controlled the epidemic of this virus after its vaccination in over 70 million equine animals during the last 3 decades in China. Data from our past studies demonstrate that the Env protein of this vaccine plays a pivotal role in protecting horses from both homologous and heterogeneous EIAV challenges. Therefore, the amino acid sequence information from the Chinese EIAV attenuated vaccine, in comparison with the parental wild-type EIAV strains, was applied to modify the corresponding region of the envelope glycoprotein of HIV-1 CN54. The direction of the mutations was made towards the amino acids conserved in the two EIAV vaccine strains, distinguishing them from the two wild-type strains. The purpose of the modification was to enhance the immunogenicity of the HIV Env. Results The induced nAb by the modified HIV Env neutralized HIV-1 B and B'/C viruses at the highest titer of 1:270. Further studies showed that a single amino acid change in the C1 region accounts for the substantial enhancement in induction of anti-HIV-1 neutralizing antibodies. Conclusions This study shows that an HIV envelope modified by the information of another lentivirus vaccine induces effective broadly neutralizing antibodies. A single amino acid mutation was found to increase the immunogenicity of the HIV Env.

Background In order to induce a potent and cross-reactive neutralizing antibody (nAb), an effective envelope immunogen is crucial for many viral vaccines, including the vaccine for the human immunodeficiency virus (HIV). The Chinese equine infectious anemia virus (EIAV) attenuated vaccine has controlled the epidemic of this virus after its vaccination in over 70 million equine animals during the last 3 decades in China. Data from our past studies demonstrate that the Env protein of this vaccine plays a pivotal role in protecting horses from both homologous and heterogeneous EIAV challenges. Therefore, the amino acid sequence information from the Chinese EIAV attenuated vaccine, in comparison with the parental wild-type EIAV strains, was applied to modify the corresponding region of the envelope glycoprotein of HIV-1 CN54. The direction of the mutations was made towards the amino acids conserved in the two EIAV vaccine strains, distinguishing them from the two wild-type strains. The purpose of the modification was to enhance the immunogenicity of the HIV Env. Results The induced nAb by the modified HIV Env neutralized HIV-1 B and B'/C viruses at the highest titer of 1:270. Further studies showed that a single amino acid change in the C1 region accounts for the substantial enhancement in induction of anti-HIV-1 neutralizing antibodies. Conclusions This study shows that an HIV envelope modified by the information of another lentivirus vaccine induces effective broadly neutralizing antibodies. A single amino acid mutation was found to increase the immunogenicity of the HIV Env.

Several hundred taste buds develop and mature in the trench walls of the rat's vallate papilla during the first 3 months after birth. The objective of this investigation of taste bud development was to determine: (i) whether the vallate papilla has local growth zones where new taste buds form, (ii) whether new taste buds arise by the division of mature taste buds, (iii) how many days are required for a new taste bud to mature, and (iv) whether a taste pore forms as the taste bud reaches a critical volume. Camera lucida drawings were made of more than 4000 iron hematoxylin-stained, serially sectioned, vallate taste buds. The relative abundance of immature taste buds declined exponentially with age, from 18% of the mature taste buds at day 15 to 2% at day 90. At days 21, 33 and 45 most of the immature taste buds (those lacking a taste pore) were located in growth zones at the anterior and posterior extremes of the vallate trench. A mean of 10.5 days was required for the maturation of each cohort of immature taste buds present at days 15, 21, 33 and 45. Vallate taste buds were added de novo; fission of mature taste buds was rare. Taste buds varied widely in the taste bud volume at which a pore formed and in the final volume of the taste bud.

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