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Abstract Optical encryption strategies utilizing fully coherent light have been widely explored but often face challenges such as speckle noise and beam instabilities. In this work, we introduce a novel protocol for multi-channel optical information encoding and encryption using vectorial spatial coherence engineering of a partially coherent light beam. By characterizing the beam’s spatial coherence structure with a $$2 \times 2$$ 2 × 2 coherence matrix, we demonstrate independent control over the three components of the coherence Stokes vector. This allows for three-channel optical information encoding and encryption, with applications in color image representation. Unlike existing methods based on fully coherent light modulations, our approach utilizes a two-point dependent coherence Stokes vector, proving resilient to random noise in experimental scenarios. Our findings provide a robust foundation for higher-dimensional optical encoding and encryption, addressing limitations associated with partially coherent light in complex environments.

Mobile laser scanning (MLS) point clouds have increasingly been a significant data source for acquiring accurate three-dimensional (3D) semantic information from complex scenes. However, most current point cloud semantic segmentation methods heavily depend on a huge number of manually labeled training samples, which is labor-intensive and time-consuming. To address the above challenge, a weakly-supervised MLS point cloud semantic segmentation method integrated with data augmentation and active learning strategies is proposed (termed DAAL-WS). By taking advantage of our previously presented multi-branch weakly-supervised network (WSPointNet) in weakly supervisory signals and ensemble predictions, the DAAL-WS integrates WSPointNet with two essential components, i.e., an elevation-calibrated Mix3D (EC-Mix3D) data augmentation strategy and a point-level ensemble prediction-based (PLEP) active learning strategy. Specifically, the EC-Mix3D data augmentation strategy leverages elevation information to calibrate sub-point clouds and generates new contextual scenes by mixing the elevation-calibrated sub-point clouds, thereby augmenting the training point cloud distribution. Designed to reduce labeling redundancy, the PLEP active learning strategy selects the most important labeled points for model training. This strategy first measures the uncertainty for each unlabeled point by ensemble predictions and then employs a feature-distance suppression module to select the significant and discriminating unlabeled points for manual labeling. The proposed DAAL-WS method was evaluated on three public MLS datasets, including Toronto3D, Paris-Lille-3D, and WHU-MLS datasets, on which DAAL-WS obtained a competitive performance over fully-supervised baselines using only 0.015 %, 0.03 %, and 0.03 % labeled points, with mean Intersection over Union (mIoU) scores of 81.91 %, 82.59 %, and 60.36 %, respectively.

Underground coal gasification is currently the highest-temperature (over 1200 ℃) unconventional development method for fossil energy. Underground gasification of coal in the medium-deep (refer to the depth of 800～1500 m in this paper) has obvious advantages in improving the gasification pressure and reducing the geological safety risks. Scientific prediction of safe width of gasification cavity is important to ensure stable gasification operation. Since the method of calculating the safe width of gasification cavity based on Controlled Retreat Injection Point (CRIP) process has not yet been established, targeted research is needed to ensure the smooth implementation of field tests. Once the top plate of the gasification cavity is “exposed” to high temperature behind the gasification cavity. The generation position of thermal stresses inside the rock and the influence law of differences in the thermal expansion coefficient of particles and matrix on the magnitude of thermal stresses were studied by numerical simulation under compressive stress constraints. the thermal damage mechanism of rock under high temperature was clarified combined with the scanning electron microscope results of rock after high temperature treatment. According to the characteristics of CRIP gasification process, a thin plate model of gasification cavity roof considering the influence of high temperature was established, and a method for calculating the safe width of the gasification cavity was proposed in combination with the theory of “key layer”. The study revealed that, rock thermal damage was caused by the interaction and synergy of rock physicochemical reactions and thermal stress, and the rock undergone irregular deformation at high temperatures. Microstructural changes of rock caused by the thermal damage were the fundamental cause of changes in rock mechanical and physical properties. The maximum tensile thermal stress in rock occurred at the grain interfaces or in grains with small coefficient of thermal expansion. The maximum tensile thermal stress increased rapidly with decreasing the thermal expansion coefficient of the grain when the ratio of the thermal expansion coefficient of the grain to that of the matrix is in the range of [0.01−1). The microcracks of mudstone was developed when heated to 200 ℃, the crack development was more obvious when heated to 400 ℃, which mainly along the edge of the particles rupture. The number of cracks was increased and the size was larger when heated to 600−800 ℃. The larger cracks and numerous pores were formed when heated to 1000 ℃. The crack connectivity increased significantly at 1200 °C, and the stomatal development was larger. Due to the effect of high temperature, the step constant for the thin plate model is no longer a constant, and specific values need to be determined based on the extent of thermal damage to the roof of gasification cavity and the location of the hard rock layer on the top plate. The safe width of gasification cavity was affected by temperature. In the study, the calculated safe widths of sandstone roof at 35 and 1000 °C were 34.3 m and 14.1 m, respectively, with a difference of 58.9%. while the safe widths at 35 and 1000 °C were 16.7 m and 15.9 m, respectively, with a 4.8% difference. Lastly, a method of determining the longitudinal target area of coal seam was proposed from the perspective of reducing the risk of roof collapse and improving gasification control. When the coal seam thickness exceeded half of the gasification cavity safe width, it was suggested to design the longitudinal target area of horizontal well at a location not more than half of the safe width of gasification cavity from the top of the coal.

As the key structure of industrial reducers, the planetary gear system will be subject to cyclic load and impact load during the operation, which will cause metal fatigue peeling off the surface of gears and form pitting fault. When the system is subject to the impact load, the severe fluctuation of vibration signal will disturb the fault identification of the system. To solve this problem, a diagnostic index is developed based on the dynamic characteristics analysis of the planetary gear system with pitting fault under impact conditions. Firstly, the contact stiffness model of gear pairs is established based on the analytic geometry method and the Hertz contact theory. Secondly, the impact load is introduced, and the contact force between sun gears and planet gears is solved by the impact function method. Finally, based on the spectrum distribution of the system dynamic response signal, the energy index of the sideband centroid is proposed, which realizes the accurate identification of the fault impact and the load impact.

Given its capacity to generate 2-D fine images of observation areas, very high resolution (VHR) synthetic aperture radar (SAR) has become increasingly popular in myriad fields, including remote sensing, geoscience, and surveillance. In this article, we present a comprehensive overview of VHR SAR systems and imaging to highlight the related research and promote further development. First, we provide a summary of VHR SAR applications and summarize the typical airborne and spaceborne systems. Subsequently, a detailed overview is provided to demonstrate the range VHR technologies by introducing dechirp processing, stepped frequency, and microwave photonic. In addition, VHR imaging methods on different platforms, i.e., ideal trajectory, airborne, and spaceborne, are reviewed. Finally, the challenges and prospects of VHR SAR are presented to inform future research directions. This work provides a valuable resource to improve the system design and signal processing of VHR SAR.

Semantic segmentation is an essential task in polarimetric synthetic aperture radar (PolSAR) image interpretation. To address the issue of insufficient measurement ability of single-view similarity, an unsupervised semantic segmentation method for PolSAR images based on multiview similarity is proposed to estimate the number-of-classes (NoC) and perform classification. NoC estimation is commonly neglected in semantic segmentation methods, to ensure rationality, a NoC estimation method before clustering is proposed based on multiview polarimetric rotation domain features and the visual assessment of tendency method for PolSAR images without supervision. Then, the norm distance, geodesic distance, maximum likelihood distance, and generalized likelihood ratio test distance based on the statistical characteristics of PolSAR images are comprehensively analyzed. Various advantages of different distances are integrated to combine the multiview vector information and scattering information to construct six multikernel similarity matrices. Subsequently, the consensus similarity network fusion method is utilized to further strengthen the discriminative ability of the similarity matrices. In addition, efficient superpixel segmentation is also adopted to reduce the speckle noise. Finally, based on the estimated NoC and the fused similarity matrix, spectral clustering is utilized to obtain semantic segmentation results. Extensive experiments are conducted on two AIRSAR datasets and one Gaofen-3 dataset demonstrate that the proposed method can effectively combine the spatial neighborhood similarity information and achieve higher semantic segmentation accuracy.

An L-band geosynchronous synthetic aperture radar (GEO SAR) is susceptible to the ionospheric scintillation induced by ionospheric irregularities due to its lower carrier frequency. In particular, the drifting characteristic of the ionospheric irregularities makes this issue more complicated because the drifting velocity is comparable with the scanning velocity of ionospheric penetration points (IPPs) of GEO SAR. In this article, a drifting ionospheric scintillation simulator (DISS) is devised for producing L-band GEO SAR raw data affected by the drifting ionospheric scintillation phase and amplitude errors. The DISS is divided into a static scintillation transfer function (STF) generator, an IPP drifting offset indexer and a GEO SAR echo generator. Each azimuth echo of a point target is modulated by the complex STF versus the drifting IPP coordinate. Finally, two simulation experiments of the point-target and extended-target scenes are conducted to validate the effectiveness of the DISS. The results indicate that the drifting ionospheric irregularities present absolutely different artifacts and more serious decorrelations in L-band GEO SAR images, compared with the traditional L-band low-Earth-orbit (LEO) SAR.

To examine flavor variations in Xuanwei ham due to different cooking methods, we selected one-year cured Xuanwei ham and applied four techniques: dry frying (DF), baking (BA), steaming (ST), and boiling (BO). Organoleptic evaluation revealed ST received the highest overall sensory score. High-performance liquid chromatography (HPLC) revealed that the total nucleotide content was significantly different (P 1). This study aimed to support comprehensive research on the flavor characteristics of cooked Xuanwei ham and guide the selection of appropriate processing methods.

Wood-leaf separation from terrestrial laser scanning (TLS) is a crucial prerequisite for quantifying many biophysical properties and understanding ecological functions. In this study, we propose a novel multi-directional collaborative convolutional neural network (MDC-Net) that takes the original 3D coordinates and useful features from prior knowledge (prior features) as input, and outputs the semantic labels of TLS point clouds. The MDC-Net contains two key units: (1) a multi-directional neighborhood construction (MDNC) unit to obtain more representative neighbors and enable directionally aware feature encoding in the subsequent local feature extraction, to mitigate occlusion effects; (2) a collaborative feature encoding (CFE) unit is introduced to incorporate useful features from prior knowledge into the network through a collaborative cross coding to enhance the discrimination for thin structures (e.g. small branches and leaf). The MDC-Net is evaluated on five plots from forests in Guangxi, China, with different branch architectures and leaf distributions. Experimental results showed that the MDC-Net achieved an OA of 0.973 and a mIoU of 0.821 and outperformed other related methods. We believe the MDC-Net would facilitate the usage of TLS in ecology studies for quantifying tree size and morphology and thus promote the development of relevant ecological applications.

Oil-rich coal is a special coal resource with a tar yield ≥ 7% and a combination of coal, oil, and gas attributes. It is an important resource and development direction for the clean utilization of coal, and has important scientific value in alleviating the shortage of national oil and gas resources and promoting the breakthrough development of coal chemical industry. There are a large number of tar-rich coal resources in the eastern basin of Xinjiang Uygur Autonomous Region. In order to find out the occurrence and distribution of tar-rich coal, the coal accumulation and occurrence characteristics of tar-rich coal were studied by means of sedimentary environment analysis, proximate analysis of coal, ultimate analysis of coal, and Gray-King assay for coal. The geological block method was used to estimate the resources of tar-rich coal. The results show that the tar-rich coal seams in the Santanghu Basin are mainly distributed in the upper section of the Badaowan Formation (J1b2), the Sangonghe Formation (J1s) and the lower section of the Xishanyao Formation (J2x1), the tar yield is about 13.67%, which is high tar-rich coal. The tar-rich coal seams in the Turpan-Hami Basin are developed in the middle section of the Xishanyao Formation (J2x2), and the tar yield is about 7.6%, which are tar-bearing coal and tar-rich coal. The tar-rich coal in the area is generally characterized by ultra-low to medium-low water content, low to medium-low ash content, medium-high to high volatile content, high tar yield, rich in oil generating components such as vitrinite and lipitnite, low degree of coalification, and formed in the lacustrine-deltaic sedimentary environment. The potential of rich oil coal resources in the study area is large. The resources of tar-rich coal in the Santanghu Basin is estimated to be 67.083 billion tonnes above a depth of 2 000 m, and the resources of tar-rich coal in the Turpan-Hami Basin is estimated to be 41.755 billion tonnes above a depth of 2 000 m, and the average thickness of the coal seams is more than 9 m. In order to effectively utilize the development of oil rich coal resources, the technical assumption of Underground Coal Pyrolysis (UCP) is proposed. The UCP refers to the establishment of heat introduction and product production channels in underground coal seams through petroleum engineering technology. Through artificial heating, coal undergoes in-situ carbonization reactions. The pyrolysis products include tar, gas (CH4, H2, light hydrocarbon C2+, etc.), and water. The carbon element in coal is mainly left underground in the form of semicoke, and the generated carbon dioxide can be used to drive oil in the Santanghu and Turpan-Hami Basins. The remaining carbon dioxide can be buried in-situ using the semicoke layer. The UCP achieves a clean conversion of coal resources through “hydrogen extraction and carbon retention”, which is expected to become a technical direction for the low-carbon green development of tar-rich coal.

Myocardial ischemia-reperfusion (MI/R) injury is common in patients who undergo revascularization therapy for myocardial infarction, often leading to cardiac dysfunction. Carbon monoxide (CO) has emerged as a therapeutic molecule due to its beneficial properties such as anti-inflammatory, anti-apoptotic, and mitochondrial biogenesis-promoting properties. However, its clinical application is limited due to uncontrolled release, potential toxicity, and poor targeting efficiency. To address these limitations, a peroxynitrite (ONOO−)-triggered CO donor (PCOD585) is utilized to generate a poly (lactic-co-glycolic acid) (PLGA)-based, biomimetic CO nanogenerator (M/PCOD@PLGA) that is coated with the macrophage membrane, which could target to the ischemic area and neutralize proinflammatory cytokines. In the ischemic area, local produced ONOO− triggers the continuous release of CO from M/PCOD@PLGA, which efficiently ameliorates MI/R injury by clearing harmful ONOO−, attenuating the inflammatory response, inhibiting cardiomyocyte apoptosis, and promoting mitochondrial biogenesis. This study provides a novel insight into the safe therapeutic use of CO for MI/R injury by utilizing a novel CO donor combined with biomimetic technology. The M/PCOD@PLGA nanogenerator offers targeted delivery of CO to the ischemic area, minimizing potential toxicity and enhancing therapeutic efficacy.

IntroductionTaraxacum mongolicum (TM) is a kind of medicinal and edible homologous plant which is included in the catalogue of feed raw materials in China. It is rich in polyphenols, flavonoids, polysaccharides and other active substances, and shows many benefits to livestock, poultry and aquatic products. The study aimed to assess the potential of TM aqueous extract (TMAE) as a substitute for poultry AGPs. MethodsA total of 240 one-day-old Arbor Acker broilers were randomly assigned to four groups and fed a basal diet (Con) supplemented with 500, 1000, and 2000 mg/kg TMAE (Low, Medium, and High groups). The growth performance of the broilers was measured on day 21 and day 42. At the end of the trial, the researchers measured slaughter performance and collected serum, liver, spleen, ileum, and intestinal contents to investigate the effects of TMAE on serum biochemistry, antioxidant capacity, immune function, organ coefficient, intestinal morphology, flora composition, and short-chain fatty acids (SCFAs). ResultsThe results showed that broilers treated with TMAE had a significantly higher average daily gain from 22 to 42 days old compared to the Con group. Various doses of TMAE resulted in different levels of improvement in serum chemistry. High doses increased serum alkaline phosphatase and decreased creatinine. TMAE also increased the antioxidant capacity of serum, liver, and ileum in broilers. Additionally, middle and high doses of TMAE enhanced the innate immune function of the liver (IL-10) and ileum (Occludin) in broilers. Compared to the control group, the TMAE treatment group exhibited an increase in the ratio of villi length to villi crypt in the duodenum. TMAE increased the abundance of beneficial bacteria, such as Alistipes and Lactobacillus, while reducing the accumulation of harmful bacteria, such as Colidextracter and Sellimonas. The cecum's SCFAs content increased with a medium dose of TMAE. Supplementing broiler diets with TMAE at varying doses enhanced growth performance and overall health. The most significant benefits were observed at a dose of 1000 mg/kg, including improved serum biochemical parameters, intestinal morphology, antioxidant capacity of the liver and ileum, immune function of the liver and ileum, and increased SCFAs content. Lactobacillus aviarius, norank_f_norank_o__Clostridia_UCG-014, and Flavonifractor are potentially dominant members of the intestinal microflora. ConclusionIn conclusion, TMAE is a promising poultry feed additive and 1000 mg/kg is an effective reference dose.

Summary: The efficacy of COVID-19 vaccination relies on the induction of neutralizing antibodies, which can vary among vaccine recipients. In this study, we investigated the potential factors affecting the neutralizing antibody response by combining plasma and urine proteomics and gut microbiota analysis. We found that activation of the LXR/FXR pathway in plasma was associated with the production of ACE2-RBD-inhibiting antibodies, while urine proteins related to complement system, acute phase response signaling, LXR/FXR, and STAT3 pathways were correlated with neutralizing antibody production. Moreover, we observed a correlation between the gut microbiota and plasma and urine proteins, as well as the vaccination response. Based on the above data, we built a predictive model for vaccination response (AUC = 0.85). Our study provides insights into characteristic plasma and urine proteins and gut microbiota associated with the ACE2-RBD-inhibiting antibodies, which could benefit our understanding of the host response to COVID-19 vaccination.

Dislocation of the metatarsophalangeal joint (MTPJ) by trauma commonly occurs in adults. Most dislocations of the MTPJ could be reduced by closed reduction. However, isolated traumatic irreducible dislocation of the fifth MTPJ is an extremely rare injury, particularly in children. We report the case of a 10-year-old boy with irreducible dislocation of the fifth MTPJ who presented with a dorsiflexion injury of the right foot 1 year previously. Closed reduction was attempted but failed. Computed tomography showed the dorsolateral dislocation of the fifth MTPJ. We performed an open reduction and metatarsal bone osteotomy, with a short osteotomy at approximately 0.8 cm. The osteotomy was adjusted to a reduction of the MTPJ and fixation by a lock compression plate. The distal growth plate in the metatarsal bone was protected to avoid pre-closure of the growth plate. There were no instances of dislocation or signs of avascular necrosis of the head of the metatarsal bone. The results of this study demonstrated that open reduction and metatarsal bone osteotomy could be an optional treatment for irreducible dislocation of the fifth MTPJ in children. We should pay more attention to the distal growth plate in the metatarsal bone to avoid pre-closure of the growth plate.

Laser impact dynamic compaction experiments and 2D multi-particle finite element method (MPFEM) simulations were carried out on aluminum–copper (Al–Cu) binary-based composite powders. Al–Cu compacts with densities of about 95% were successfully prepared at the maximum laser energy of 1.8 J. Research trends indicate that higher power lasers could nearly achieve the theoretical densities. Analysis of the influence laws of laser energy and copper powder mass fraction on the densities of Al–Cu compacts was carried out, and the effects of microstructure and microhardness of Al–Cu compacts on their mechanical properties were investigated, as well as the stresses and velocities in the dynamic compaction process of Al–Cu composite powders were analyzed. The results showed that the relative densities of composite powder compacts of Al-5 wt%Cu gradually exceeded those of Al-10 wt%Cu and Al-15 wt%Cu with the increase of laser energy. In laser impact dynamic compaction the mechanism of inter-particle joining was solid-state press-welding, and the increase in the hardness of the Al–Cu compacts could be attributed to the inherent hardness of the hard particles. The upper particles have a localized maximum velocity of more than 300 m/s, which met the explosion welding velocity, thus localized cold welding occurred and contributed to the inter-particle connection.

Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated.

This study employs the reflection symmetry decomposition (RSD) method to extract polarization scattering features from ground object images, aiming to determine the optimal data input scheme for deep learning networks in polarimetric synthetic aperture radar classification. Eight distinct polarizing feature combinations were designed, and the classification accuracy of various approaches was evaluated using the classic convolutional neural networks (CNNs) AlexNet and VGG16. The findings reveal that the commonly employed six-parameter input scheme, favored by many researchers, lacks the comprehensive utilization of polarization information and warrants attention. Intriguingly, leveraging the complete nine-parameter input scheme based on the polarization coherence matrix results in improved classification accuracy. Furthermore, the input scheme incorporating all 21 parameters from the RSD and polarization coherence matrix notably enhances overall accuracy and the Kappa coefficient compared to the other seven schemes. This comprehensive approach maximizes the utilization of polarization scattering information from ground objects, emerging as the most effective CNN input data scheme in this study. Additionally, the classification performance using the second and third component total power values (P2 and P3) from the RSD surpasses the approach utilizing surface scattering power value (PS) and secondary scattering power value (PD) from the same decomposition.

Polarimetric features extracted from polarimetric synthetic aperture radar (PolSAR) images contain abundant back-scattering information about objects. Utilizing this information for PolSAR image classification can improve accuracy and enhance object monitoring. In this paper, a deep learning classification method based on polarimetric channel power features for PolSAR is proposed. The distinctive characteristic of this method is that the polarimetric features input into the deep learning network are the power values of polarimetric channels and contain complete polarimetric information. The other two input data schemes are designed to compare the proposed method. The neural network can utilize the extracted polarimetric features to classify images, and the classification accuracy analysis is employed to compare the strengths and weaknesses of the power-based scheme. It is worth mentioning that the polarized characteristics of the data input scheme mentioned in this article have been derived through rigorous mathematical deduction, and each polarimetric feature has a clear physical meaning. By testing different data input schemes on the Gaofen-3 (GF-3) PolSAR image, the experimental results show that the method proposed in this article outperforms existing methods and can improve the accuracy of classification to a certain extent, validating the effectiveness of this method in large-scale area classification.

Accurate estimation of above-ground biomass (AGB) in forested areas is essential for studying forest ecological functions, surface carbon cycling, and global carbon balance. Over the past decade, models that harness the distinct features of multi-source remote sensing observations for estimating AGB have gained significant popularity. It is worth exploring the differences in model performance by using simple and fused data. Additionally, quantitative estimation of the impact of high-cost laser point clouds on satellite imagery of varying costs remains largely unexplored. To address these challenges, model performance and cost must be considered comprehensively. We propose a comprehensive assessment based on three perspectives (i.e., performance, potential and limitations) for four typical AGB-estimation models. First, different variables are extracted from the multi-source and multi-resolution data. Subsequently, the performance of four regression methods is tested for AGB estimation with diverse indicator combinations. Experimental results prove that the combination of multi-source data provides a highly accurate AGB regression model. The proposed regression and variables rating approaches can flexibly integrate other data sources for modeling. Furthermore, the data cost is discussed against the AGB model performance. Our study demonstrates the potential of using low-cost satellite data to provide a rough AGB estimation for larger areas, which can allow different remote sensing data to meet different needs of forest management decisions.

Nickel powders of nominal sizes (i.e., 50 nm, 300 nm, and 2 μm) were pre-coated on copper substrates and then subjected to laser shock processing (LSP) for producing nickel coating. The shockwave consolidation behavior, microstructure, and bonding performance were investigated as a function of particle size. Results indicated that nano-coating had better surface quality, decreased microstructural defects (pores and cracks), and higher bonding strength (approximately twice the hardness value) than its micro-counterpart. Microstructure analysis revealed that nano/submicron particles were consolidated by a mixed solid-liquid sintering behavior, whereas micron particles were consolidated only by solid-state sintering under laser shock. A mixed microstructure of nanocoating was obtained, consisting of solid-state atomic bonds between solid-state sintered particles and amorphous phases between liquid-phase sintered particles. After LSP, the cold shock characteristics of LSP could not only reduce the overall grain growth and oxidation of nano coating, but also induce fine nanocrystalline at the inter-particle interface of the micron coating by dynamic recrystallization.

ABSTRACTWith the rapid development of reality capture methods, such as laser scanning and oblique photogrammetry, point cloud data have become the third most important data source, after vector maps and imagery. Point cloud data also play an increasingly important role in scientific research and engineering in the fields of Earth science, spatial cognition, and smart cities. However, how to acquire high-quality three-dimensional (3D) geospatial information from point clouds has become a scientific frontier, for which there is an urgent demand in the fields of surveying and mapping, as well as geoscience applications. To address the challenges mentioned above, point cloud intelligence came into being. This paper summarizes the state-of-the-art of point cloud intelligence, with regard to acquisition equipment, intelligent processing, scientific research, and engineering applications. For this purpose, we refer to a recent project on the hybrid georeferencing of images and LiDAR data for high-quality point cloud collection, as well as a current benchmark for the semantic segmentation of high-resolution 3D point clouds. These projects were conducted at the Institute for Photogrammetry, the University of Stuttgart, which was initially headed by the late Prof. Ackermann. Finally, the development prospects of point cloud intelligence are summarized.

BackgroundDespite the well-grounded benefits of physical activity (PA), poor compliance with the PA guidelines has been reported among head and neck cancer (HNC) patients. Mobile health (mHealth)-based interventions can help cancer survivors increase their PA levels and increase the reach or efficiency of rehabilitation services. However, there is limited knowledge about the needs and perceptions of HNC patients regarding these interventions. This study explored the perceptions and needs of HNC patients regarding mHealth-based PA programs before developing such interventions to ensure their improved effectiveness.Study designA constructivist qualitative study.MethodsWe purposively selected 17 adult HNC patients aged 40–80 years to determine their needs and perceptions of future mHealth-based PA programs. Semi-structured face-to-face interviews were conducted, and the data were analyzed via thematic analysis. The report followed the Consolidated Criteria for Qualitative Research Reports guidelines.ResultsFour themes were analyzed from the interview transcripts regarding the needs and perceptions of mHealth-based PA programs: (1) functionality needs; (2) system usage requirements; (3) social support; and (4) perceived barriers and facilitators. HNC patients expect highly customized and specialized mHealth interventions that consider individual factors, address their concerns about security, network, and cost, and prefer improved comfort. Moreover, they expect to receive support from their healthcare providers, families, and peers.ConclusionThe study provides pragmatic ready-to-use recommendations to design interventions for inactive HNC patients to achieve the recommended PA levels. Future mHealth interventions should be tailored according to the needs of the HNC patients by utilizing perceived facilitators and removing perceived barriers to help them engage in PA actively.

Alongside widely accepted geometric information, airborne laser bathymetry (ALB) typically captures the temporal profile sample (waveforms) of both the emitted laser pulses and their echoes. These waveforms provide radiometric properties (backscattering intensity) of sensed targets and assist with precise strip registration, classification of fine ground cover (sediment), and advanced geometric modelling. However, effective intensity data extraction is essential because the intensity provided by the LAS file is mixed with invalid signals, multiple echo intensity, and intermittent intensity variations caused by improper interference of automatic gain control, making it unusable for direct analysis. To address this issue, a flexible index sharing mechanism between waveform, coordinate, and intensity data is constructed for interference intensity tracking. A novel waveform classification-based approach is proposed to efficiently extract ALB intensity by dividing waveform data into six categories using morphological differences and topographic data of waveforms. To ensure accurate analysis, duplicate and invalid waveforms are eliminated, leaving only genuine intensity readings. Additionally, a triple spline fit is employed to restore oversaturated waveform segments that were previously suppressed due to exceeding the device's maximum measurable limit. To address the problem of mixing multiple return intensities, waveforms are decomposed using various decomposition models. This approach ensures that different waveform categories retain their respective varying return intensities. The approach is then tested on an ALB dataset collected using Optech Aquarius around Yuanzhi Island in the South China Sea. The results demonstrate a considerable advancement in data quality when compared to LAS file intensity products with a reduction in maximum deviation of 883 [digital number, DN], standard deviation of 350 [DN], and mean absolute error of 310 [DN].

Wavenumber domain algorithms have unique advantages in processing highly squinted synthetic aperture radar data. This article studies the performance of three commonly used wavenumber domain algorithms including the classical wavenumber domain (CWD) algorithm, extended wavenumber domain (EWD) algorithm, and squint wavenumber domain (SWD) algorithm. First, the wavenumber domain signal expression under the zero-Doppler and acquisition-Doppler reference geometries are both derived. Second, the internal relationship between three wavenumber domain algorithms is analyzed. A new interpretation of the relationship between the three algorithms and an interpolation strategy are given. The analysis not only provides a deeper understanding of the three algorithms, but also provides a basis for comparing them. Then, the performance of the three wavenumber domain algorithms is evaluated from the perspectives of computational complexity, image quality, and geometric position through theoretical analysis and simulation experiments. Aiming at the problem that the range and azimuth profiles are not orthogonal, a method to calculate resolution and extract profile is proposed. The results show that all three algorithms can obtain a well-focused images if full-resolution interpolation is performed, and the computational complexities of CWD and SWD are less than that of EWD.

The full Poincaré (FP) beam, encompassing all possible polarization states in its beam cross-section, has demonstrated advantages in various applications. However, conventional FP beams are typically considered as spatially fully coherent, rendering them sensitive to disturbances in the propagation path and susceptible to speckle effects. In this work, we propose an alternative approach to synthesize the optical beam with a FP polarization state through the spatial coherence engineering of a partially coherent beam. In this process, the FP polarization state is initially encoded into the spatial coherence structure of the beam source. We demonstrate that during the encoding process, the vector nature of the beam transitions from the FP polarization state to the spatial coherence structure of the source. However, during the propagation of the partially coherent beam, the vectorness reverts to the polarization state, resulting in the re-emergence of the encoded FP polarization in the output plane. We illustrate that the synthesized FP polarization state, achieved through spatial coherence engineering, is highly robust against obstructions in the propagation path. Furthermore, we examine the effect of the spatial coherence area of the beam on the quality of the recovered FP polarization state. The findings of this work can have valuable applications in optical trapping and optical imaging in complex environments.

The innovative synthesis of 3,8-dibromo-2,9-dinitro-5,6-dihydrodiimidazo [1,2-a:2′,1′-c]pyrazine and 3,9-dibromo-2,10-dinitro-6,7-dihydro-5H-diimidazo [1,2-a:2′,1′-c][1,4]diazepine is described in this study. The tricyclic fused molecular structures are formed by the respective amalgamation of piperazine and homopiperazine with the imidazole ring containing nitro. Compound 1 and 2 possess excellent high-density physical properties (ρ1 = 2.49 g/cm3, ρ2 = 2.35 g/cm3) due to the presence of a fused ring structure and Br atom. In addition to their high density, they have high decomposition temperatures (Td > 290 °C) which means that they have excellent thermal stability and can be used as potential heat-resistant explosives. Low mechanical sensitivities (IS > 40 J, FS > 360 N) are observed. The twinning structure of 2 was resolved by X-ray diffraction. Non-covalent interaction analysis, Hirshfeld surfaces, 2D fingerprint plot, and Electrostatic potential analysis were used to understand the intramolecular interactions in relation to physicochemical properties. The unique structures of this type of compound provide new potential for the evolution of energetic materials.

Heavy metals (HMs) are the toxic pollutants in urban environment, and their sources are complex. Dust might be a good carrier of HMs into ecosystem and human. In this study, 48 dust samples were collected in Nanjing, an industrial city and transportation hub in the Yangtze River Delta, China. The concentrations, spatial distribution, sources and risks of heavy metals (Cr, Ni, Cu, Zn, Cd and Pb) in dust were determined and analyzed. The results showed that although the health risks of some HMs had decreased, Cr, Zn, and Cd had high concentrations and high risks on ecosystem/human. And thus, the total risks of the target HMs were higher than the threshold of non-risks. Especially, children may face the highest possible risks due to the frequent hand-oral ingestion when children play. The hot spot regions of the HMs were mainly in the industrial district in the north, urban, and rural region in south, relating with the industrial, traffic, and agricultural sources, respectively. The analysis for the risks of individual sources further confirmed these sources should be further controlled. The results in this study will provide information on the priority of HMs’ monitoring and source management.

Abstract Background Cystic angiomatosis is a rare benign disease manifesting as multiple lytic and sclerotic bone lesions, described as the proliferation of vascular and lymphatic channels lined by a single layer of endothelial cells. However, the potential pathogenetic mechanism of the disease still remains unknown. Here, we reported a case of cystic angiomatosis with multifocal bone lesion evaluated by whole exome sequencing. Case description In this presentation, we reported a case of an 11-year-old boy with pain in his chest. Computed tomography (CT) revealed the multiple lytic of the bone in the ribs, clavicle, vertebra thoracalis, skull, mandibula, shoulder blade, etc. The blood test showed ALP to be 393U/L and VEGF to be 287.26 pg/ml. The patient was performed with an open biopsy in the ribs and was diagnosed with cystic angiomatosis. Besides, the whole exome sequencing reported the single-nucleotide substitutions in the coding region of BRIP1, CHEK2, GRM4, and MUC16. Then, the upregulated genes involved CASC15, CENPF, ABCA13, ALK, BLM, and FGFR3. Conclusions In this article, we report a rare case of cystic angiomatosis in a child with abnormal VEGF and ALP reported by peripheral blood examination. The whole exome sequencing could provide the reference for the potential molecular mechanism in the diagnosis and treatment of cystic angiomatosis.

Bizzare parosteal osteochondromatous proliferation (BPOP), first introduced by Nora et al., in 1983, s an uncommon benign bone disease that manifests as a parosteal bone mass that frequently develops in the location of short bones. In our study, we reported an 11-year-old child with BPOP in the second finger of the right hand and the bone lesion was gradually increasing after 14 months of follow-up. Radiographic imaging confirmed an osseous and lobulated mass from the proximal phalanx cortex without the connection with medullary canal of finger. Due to the aggressive nature of the imaging, bone biopsies were performed with ''blue bone'' pathological findings typical of BPOP, with complete resection of the bone exostosis and extended resection of the bone cortex to reduce the recurrence of the lesion. Then, the microarray data of GSE19357 in BPOP was downloaded and analyzed as a result of differentially expressed genes (DEGs), resulting in 11 up-regulated genes including HAPLN1, ACAN, FMOD, DKK3, and P4HA3 with the calculating criteria of P 2. STRING databases were performed to reveal the protein–protein interaction network (PPI) among DEGs. There were 21 nodes and 50 edges with an average local clustering coefficient of 0.624 and PPI enrichment p-value of 3.83e−13. The molecular function in Gene Ontology was highly enriched in Lipoprotein lipase activity, Phospholipase a1 activity, Phosphatidylserine 1-acylhydrolase activity, and Carboxylic acid binding. In Cytoscape, there were three sub-networks of genes with Plugin MCODE, including the relationship of PPARG, FABP4, PRKAR2B, TGFB3 FMOD and DKK3, SULF1, THBS3, HAPLN1 and P4HA3, and LIPG, LPL and PCK1. In this article, we reported a rare case of BPOP in a child with a bone lesion. The DEGs GSE19357 in BPOP were then analyzed and evaluated for the potential molecular mechanism in the diagnosis and treatment of BPOP.

Spatially resolved proteomics is an emerging approach for mapping proteome heterogeneity. Here, the authors report a method based on the combination of hydrogel-based tissue transformation with mass spectrometry-based proteomics, that enables proteome profiling with a lateral resolution of 160 µm.

Abstract Determination of malignancy in thyroid nodules remains a major diagnostic challenge. Here we report the feasibility and clinical utility of developing an AI-defined protein-based biomarker panel for diagnostic classification of thyroid nodules: based initially on formalin-fixed paraffin-embedded (FFPE), and further refined for fine-needle aspiration (FNA) tissue specimens of minute amounts which pose technical challenges for other methods. We first developed a neural network model of 19 protein biomarkers based on the proteomes of 1724 FFPE thyroid tissue samples from a retrospective cohort. This classifier achieved over 91% accuracy in the discovery set for classifying malignant thyroid nodules. The classifier was externally validated by blinded analyses in a retrospective cohort of 288 nodules (89% accuracy; FFPE) and a prospective cohort of 294 FNA biopsies (85% accuracy) from twelve independent clinical centers. This study shows that integrating high-throughput proteomics and AI technology in multi-center retrospective and prospective clinical cohorts facilitates precise disease diagnosis which is otherwise difficult to achieve by other methods.

Objectives: This study aimed to explore the effects of 25-hydroxy vitamin D (25[OH]D) on T lymphocyte subsets and sputum smear conversion during antituberculosis (TB) treatment. Methods: A total of 120 newly diagnosed patients with active pulmonary TB were collected and classified into vitamin D sufficiency group, vitamin D insufficiency group, and vitamin D deficiency group according to serum 25(OH)D levels. The clinical data and sputum smear conversion were collected and serum 25(OH)D and T lymphocyte subsets were also measured and compared. Results: Our data showed that 25(OH)D levels reached the lowest point at two months of anti-TB treatment. Significant differences existed in the increase of CD4+ and CD8+ T cells on the basis of vitamin D levels. The vitamin D sufficiency group had a significantly higher increase of CD4+ T cells during six months of anti-TB treatment and CD8+ T cells after four months of anti-TB treatment than the other groups. Vitamin D had no effect on the time-to-sputum smear conversion (vitamin D sufficiency group: adjusted hazard ratio: 1.27, 95% confidence interval [CI]: 0.78–2.06); vitamin D insufficiency group: adjusted hazard ratio: 1.05, 95% CI: 0.63–1.75)]. Conclusion: Through null effects on sputum smear conversion, vitamin D may have a beneficial effect on the increase of CD4+ and CD8+ T cells during anti-TB treatment.

Abstract Background The ketogenic diet (KD) has anti-tumor and anti-diabetic effects in addition to its anti-epileptic role. It could also improve cardiac function and attenuate neurological insult. However, the effect of KD on blood perfusion or tissue recovery after ischemia remains largely unknown. Thus, we observed blood flow and ischemic tissue recovery following hind limb ischemia (HLI) in mice. Methods C57 mice were fed with either a KD or normal diet (ND) for 2 weeks, before inducing hind limb ischemia, blood perfusion of ischemic limb tissue was observed at 0, 7, and 21 days post operation. Results KD not only decreased blood perfusion of ischemic limb tissue but also delayed muscle recovery after ischemia, induced muscle atrophy of non-ischemic tissue compared to mice fed with ND. Furthermore, KD delayed wound healing at the surgical site and aggravated inflammation of the ischemic tissue. At the cellular level, KD altered the metabolic status of limb tissue by decreasing glucose and ketone body utilization while increasing fatty acid oxidation. Following ischemia, glycolysis, ketolysis, and fatty acid utilization in limb tissue were all further reduced by KD, while ketogenesis was mildly increased post KD in this mice model. Conclusion The KD may cause impaired tissue recovery after ischemia and possible muscle atrophy under a prolonged diet. Our results hint that patients with limb ischemia should avoid ketogenic diet.

Three-dimensional (3D) object detection utilizes numerous onboard sensors to determine the position, size, and motion information of surrounding objects. Recently, some researchers have utilized HD maps in 3D object detection for LiDAR point clouds. However, existing LiDAR–map fusion detection methods simply take the HD map as an additional input and more prior information in the map is underexplored. To address this limitation, we employ the HD map in data augmentation for LiDAR 3D detection to eliminate the bias of distributions between the augmented training and test sets. Furthermore, we propose a map-enhanced 3D object detection network, called MENet, for LiDAR point clouds. Compared with the standard LiDAR-only detector in bird’s eye view (BEV), MENet extends LiDAR-based baselines by using a map encoder to encode the input rasterized HD map into a BEV feature. In addition, it uses a multimodal fuser to aggregate the map BEV feature with the LiDAR BEV feature extracted in the LiDAR stream. MENet achieved a mean average precision (mAP) of 56.90% and nuScenes detection score (NDS) of 63.43%, improving the mAP and NDS of its LiDAR-only backbone by 4.18% and 2.22%, respectively. Although the HD map is necessary at training time, the map-enhanced 3D detector can work robustly and perform better than the original baseline when there was no map input at test time. Experimental results further indicate that the proposed map plugins can improve the performance of both anchor-based and anchor-free models on different datasets. The code will be open-source at https://github.com/WHU-USI3DV/MENet.

In this study, a 3 × 3 microfiber coupler sensing structure is fabricated by the fusion taper technology, and the mode coupling distribution of its light energy is numerically simulated by the beam propagation method. Results confirmed that the sensing structure produces regular and stable mode coupling within an 11–25 µm effective coupling region. The 3 × 3 microfiber coupler is used as a light splitter. In addition, it can also perform refractive index (RI) sensing. The RI characteristics of the proposed sensor are further verified experimentally. Experimental findings reveal that the RI sensitivity is inversely proportional to the effective coupling region diameter, and the sensitivity is 5223.83 nm/RIU when the effective coupling region diameter is 11 µm.

With the rapid expansion of urban areas in both horizontal and vertical directions, the complicated building structural changes challenge the existing 3D change detection methods. The existing 3D change detection methods are mainly based on local differences and rely on setting thresholds and rules, and face difficulties when determining complex change types. In this paper, to solve these problems, we present a building object extraction method using change indicators and an object-based change type determination approach. The key steps are as follows: (1) point-level change indicators are generated using the local geometric differences between the point clouds from two epochs; (2) change indicators are used to guide the process of region growing and graph cuts for building object extraction; and (3) the object-based change types are determined by a random forest classifier, relying on the elaborate features of the building objects. Experiments were carried out on a simulated dataset and a real airborne laser scanning (ALS) dataset. The proposed method achieved the best performance on the simulated dataset, and the average precision and recall on the real ALS dataset reached 91.1% and 85.3% respectively, which demonstrates the effectiveness of the proposed method. This work enables the 3D updating of urban building maps and can be applied to building safety monitoring and identification of potential illegal structures.

Forest field inventory plays a crucial role in forestry management and the estimation of carbon circular economy, as it provides information on forest parameters, assesses carbon storage, and identifies the impact factors of ecological change. Terrestrial Laser Scanning (TLS) and Mobile Mapping Systems (MMS) are commonly used for forest field inventory, but they are unable to verify the inventory results in real time. With real-time forest inventory, forestry workers can immediately verify if all trees have been correctly extracted without requiring expertise in point clouds. The advences of miniaturized, low-cost 3D sensors (such as solid-state laser scanners and Inertial Measurement Units [IMUs]) and edge computing units have made it possible to achieve real-time forest inventory using a compact, low-cost helmet. To this end, this paper presents a real-time automated forest field inventory method, which is validated on a compact, low-cost helmet-based laser scanning system. Firstly, a Fast Candidate Tree Detection (FCTD) approach is proposed to identify individual trees as candidates by utilizing a novel 2D corner detection technique based on point cloud projection, taking into account point density and geometry features. Secondly, a Spatiotemporal Consistency-based Tree Parameter Estimation (SCTPE) method is proposed to estimate the tree parameters by considering both the current submap and submaps that have been scanned in real-time. The proposed method was tested in three typical forest areas in Wuhan, China, where the main tree species present include Sapindaceae, dawn redwoods, and Platycladus. The results showed that the proposed method achieves high accuracy in tree detection (recall=0.97,precision=0.94,F=0.96). The average error and root-mean-square error (RMSE) of DBH are 0.033 m and 0.038 m, which outperforms the existing one-circle fitting model. The average error and RMSE of tree height are 0.231 m and 0.294 m. Overall, these results demonstrate the high potential of the helmet-based laser scanning system for real-time forest field inventory.

Abstract With the advancement of manoeuvrability and the increasement of coherent processing interval (CPI), the observed target might be of multiple motion stages. Therefore, most existing long‐time coherent integration (LTCI) algorithms based on the assumption of single‐motion stage is not proper any more. To address this problem, this letter proposes an effective motion parameters estimation algorithm, which is the key step of LTCI, for the manoeuvring weak target with multiple motion stages based on range symmetry transform and short‐time fractional Fourier transform (RST‐STFrFT). The effectiveness of the proposed algorithm is verified by signal‐level simulations.

The multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has developed rapidly since its discovery. At the same time, the low-disturbance and high-gain requirements of the MIMO system are continuing to increase. Through the application of digital beamforming (DBF) techniques, the multidimensional waveform encoding (MWE) technique can play a key role in MIMO systems, which can greatly improve the system’s performance, especially the multi-mission capability of radar. Intrapulse beamsteering in elevation is a typical form of multi-dimensional waveform encoding which can greatly improve the transmitting efficiency and multi-mission performance of radar. However, because of the high sensitivity of the DBF technique to height, there is significant deterioration in performance in the presence of terrain undulations. The OFDM (Orthogonal Frequency Division Multiplexing) technique is widely used in communication. Due to the similarity of radar and communication systems and the great waveform diversity of OFDM signals, the OFDM radar has recently begun to emerge as a new radar system, simultaneously, the orthogonality of OFDM signals is in the time and frequency domains, and is not affected by terrain undulation. So, this paper proposes a novel radar mode combining intrapulse beamsteering in elevation and OFDM-Chirp signals, that is, the combination of “beam orthogonality” and “waveform orthogonality”, which can greatly improve the performance and fault tolerance to interference signals. In this manuscript, the system working mode and signal processing flow are introduced in detail, and simulations for both point targets and distributed targets are carried out to verify the feasibility of the proposed mode. Simultaneously, a comparison experiment is carried out, which shows the high level of fault tolerance to terrain undulation and the high potential of the proposed radar mode in Earth observation.

Using carbon dioxide as a gasification agent for underground coal gasification (UCG) can not only reduce carbon dioxide emissions but is also expected to lead to a new natural gas technology revolution and ensure national energy security. To explore the effect of the oxygen content in oxygen-enriched carbon dioxide gasification agents on the results of gasification experiments, underground gasification experiments under different oxygen-enrichment conditions were designed, and quantitative parameters were used to analyze and evaluate the gas produced in the gasification experiments. The results showed that as the oxygen content in the oxygen-enriched carbon dioxide gasification agent increased, the CO and H2 in the combustible gas gradually increased, and the calorific value of the combustible gas also slowly increased, reaching a peak value under the gasification condition of 60% oxygen concentration, and then decreased slightly; the product formation rate and the gas production per unit mass of coal fluctuated. The coal consumption rate increased with time and was relatively stable. According to theoretical calculations for the gasification energy recovery evaluation system, the overall energy recovery rate was 56.34%, and the energy utilization rate was relatively high. Research on quantitative indicators based on gas production data has good practical significance for evaluating the gasification efficiency of UCG, which can be used to better evaluate and control the reaction process of UCG.

Thyroid nodules occur in about 60% of the population. A major challenge in thyroid nodule diagnosis is to distinguish between follicular adenoma (FA) and carcinoma (FTC). Here, we present a comprehensive thyroid spectral library covering five types of thyroid tissues. This library includes 121 960 peptides and 9941 protein groups. This spectral library can be used to quantify up to 7863 proteins from thyroid tissues, and can also be used to develop parallel reaction monitoring (PRM) assays for targeted protein quantification. Next, to stratify follicular thyroid tumours, we compared the proteomes of 24 FA and 22 FTC samples, and identified 204 differentially expressed proteins (DEPs). Our data suggest altered ferroptosis pathways in malignant follicular carcinoma. In all, 31 selected proteins effectively distinguished follicular tumours. Of those DEPs, nine proteins were further verified by PRM in an independent cohort of 18 FA and 19 FTC. Together, we present a comprehensive spectral library for DIA and targeted proteomics analysis of thyroid tissue specimens, and identified nine proteins that could potentially distinguish FA and FTC.

Abstract Multidimensional and flexible radar resource transmission in time, frequency and space domains is strongly demanded for advanced digital array radar to meet the challenging observation requirements. In this study, a high degrees of freedom (DOF) transmitting strategy is proposed for pulsed‐coherent frequency diverse array (FDA) radar. The flexible beam scanning property of coherent FDA radar is well developed by combining with spatial transmit array weighting and temporal piecewise waveform design. The proposed method fully exploits the transmitting DOFs of coherent FDA radar, which not only enables the transmit beampattern shaping but also promises the ability of spatial waveform diversity and transmitting energy allocation. Since the spatial transmitting signal bandwidth is preset, a received signal processing method with a variable sampling rate is proposed to avoid over sampling in all receiving channels. Simulation results validate the effectiveness of the proposed method and further demonstrate its potential applications.

Glioblastoma is the most malignant primary brain tumor, the prognosis of which remains dismal even with aggressive surgical, medical, and radiation therapies. Glioblastoma stem cells (GSCs) promote therapeutic resistance and cellular heterogeneity due to their self-renewal properties and capacity for plasticity. To understand the molecular processes essential for maintaining GSCs, we performed an integrative analysis comparing active enhancer landscapes, transcriptional profiles, and functional genomics profiles of GSCs and non-neoplastic neural stem cells (NSCs). We identified sorting nexin 10 (SNX10), an endosomal protein sorting factor, as selectively expressed in GSCs compared with NSCs and essential for GSC survival. Targeting SNX10 impaired GSC viability and proliferation, induced apoptosis, and reduced self-renewal capacity. Mechanistically, GSCs utilized endosomal protein sorting to promote platelet-derived growth factor receptor β (PDGFRβ) proliferative and stem cell signaling pathways through posttranscriptional regulation of the PDGFR tyrosine kinase. Targeting SNX10 expression extended survival of orthotopic xenograft–bearing mice, and high SNX10 expression correlated with poor glioblastoma patient prognosis, suggesting its potential clinical importance. Thus, our study reveals an essential connection between endosomal protein sorting and oncogenic receptor tyrosine kinase signaling and suggests that targeting endosomal sorting may represent a promising therapeutic approach for glioblastoma treatment.

BackgroundAcceptability and perception of the COVID-19 vaccine among different social groups have been the subject of several studies. However, little is known about foreign medical students in Chinese universities.AimThis study, therefore, fills the literature gap using a focus group technique to assess the acceptance and perception of the COVID-19 vaccine among foreign medical students in China.MethodsThe study adopted an online cross-sectional survey method following the Chinese universities' lockdowns to collect the data between March and April 2022. A data collection questionnaire was developed, and then the link was shared with the respondents through key informants in different universities in China to obtain the data. The data collection process only included foreign medical students who were in China from May 2021 to April 2022. The authors received a total of 403 responses from the respondents. During data processing, we excluded 17 respondents since they were not in China while administering the questionnaire to enhance the data validity. The authors then coded the remaining 386 respondents for the estimation process. We finally applied the multilinear logistics regression technique to model the COVID-19 vaccine acceptance with the response or influencing factors, including the mediating factors among the foreign medical students in China.ResultsThe data statistics show that 4.9% of the respondents were younger than 20 years, 91.5% were 20–40 years old, and 3.6% were older than 40 years; 36.3% of respondents were female subjects and 63.7% were male subjects. The results also show that the respondents are from six continents, including the African continent, 72.4%, Asia 17.4%, 3.1% from Europe, 2.8% from North America, 1.6% from Australia, and 2.3% from South America. The mediation analysis for the gender variable (β = 0.235, p = 0.002) suggests that gender is a significant channel in COVID-19 vaccine acceptance and perception among foreign medical students in China. Also, the main analysis shows that opinion on the safety of the vaccine (β = 0.081, p = 0.043), doses of the vaccine to receive (β = 0.175, p = 0.001), vaccine safety with some side effects (β = 0.15, p = 0.000), and the possibility of acquiring COVID-19 after vaccination (β = 0.062, p = 0.040) are all positive factors influencing vaccine acceptability and perception. Also, the home continent (β = −0.062, p = 0.071) is a negative factor influencing COVID-19 vaccine acceptance and perception. Furthermore, the finding shows that fear perceptions has affected 200 (51.81%) respondents. The medical students feared that the vaccines might result in future implications such as infertility, impotence, and systemic health conditions such as cardiovascular, respiratory, or deep vein thrombosis. In addition, 186 (48.19%) students feared that the vaccines were intended to shorten life expectancy.ConclusionCOVID-19 vaccination acceptability and perception among medical students in China is high, most predominantly due to their knowledge of medicine composition formulation. Despite widespread acceptance by the general public and private stakeholders, we concluded that vaccination resistance remains a significant factor among medical students and trainees. The study further adds that in considering the COVID-19 vaccine, the factor of the home continent plays a significant role in vaccine hesitancy among foreign medical students. Also, knowledge, information, and education are important pillars confronting new medicine administered among medical trainees. Finally, there is a low rate of COVID-19 vaccine hesitancy among foreign medical students in China. The study, therefore, recommends targeted policy strategies, including sensitization, detailed public information, and education, especially for medical colleges and institutions on the COVID-19 vaccination, to achieve 100%. Furthermore, the study recommends that future researchers explore other factors influencing accurate information and education for successful COVID-19 vaccination implementation.