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Non-spherical gravity plays a crucial role in the LEO satellite orbit determination and prediction. In recent years, several new gravity models have been proposed with more comprehensive ground and space-borne data. The impact of the gravity models has been extensively studied while its impact on the orbit prediction has not attracted enough attention. With the risen of the mega LEO constellation, new applications such as the LEO navigation requires real-time precise orbit, which increases the importance of the precise orbit prediction. In this study, we selected six popular gravity models, namely JGM3, EGM2008, EGM96, EIGEN2, GL04C, and GGM03S, and compared their performance in different LEO orbit predictions. The comparison results indicate that there is no single optimal gravity model for all LEO orbit prediction scenarios. For short-term prediction, JGM3, EGM2008, GL04C models perform better while in long-term prediction JGM3, EGM96, EIGEN2 have more potential. The results also reveal that the optimal model changed with time. In addition, the impact of the gravity order on the orbit prediction is investigated, the results indicate that for satellites with lower orbital heights, the gravitational field order required to achieve a certain truncation error is higher than for satellites with higher orbital heights. The authors also explore the effect of gravitational field-associated permanent tides on orbital prediction. In one day, for satellites with an orbital altitude of about 970km, the effect of permanent tides on 3D RMS is 6.92m; for satellites around 710km, the effect of permanent tides on 3D RMS is 4.20m; for satellites around 970km, the effect of permanent tides on 3D RMS is 2.07m.

Wi-Fi fingerprint positioning is widely used because of its ready hardware and high accuracy. However, its application is considerably restricted by time-consuming and labor-intensive works of offline collection and irregular fluctuation of signals. To address the above problems, we proposed a novel method to deploy the Wi-Fi fingerprint database based on implicit crowdsourcing and improved the weighted k-nearest neighbor (WKNN) algorithm to eliminate the influence of neighbor mismatching and device heterogeneity. First, ordinary users continuously gather Wi-Fi information instead of collecting one point after another. Meanwhile, video surveillance cameras record users’ trajectories without any intervention and use monocular vision based on plane constraints to obtain users’ location at the moment of each scanning. At the localization phase, the morphology similarity distance instead of the Euclidean distance is used to measure the similarity of signals to solve the problem of device heterogeneity. Outlier detection is also utilized for a secondary selection of neighbor points. Finally, geometric and signal morphology similarity distances are used to determine the combined weight of all neighbors after the dimensionless treatment. Results of the experiments conducted in a real indoor environment show that the proposed strategy improves the efficiency of fingerprint collection and achieves higher positioning accuracy.

Dynamics of optically excited plasmonic nanoparticles are presently understood as a series of scattering events involving the initiation of nanoparticle breathing oscillations. According to established models, these are caused by statistical heat transfer from thermalized electrons to the lattice. An additional contribution by hot-electron pressure accounts for phase mismatches between theory and experimental observations. However, direct experimental studies resolving the breathing-oscillation excitation are still missing. We used optical transient-absorption spectroscopy and time-resolved single-particle X-ray diffractive imaging to access the electron system and lattice. The time-resolved single-particle imaging data provided structural information directly on the onset of the breathing oscillation and confirmed the need for an additional excitation mechanism for thermal expansion. We developed a new model that reproduces all of our experimental observations. We identified optically induced electron density gradients as the initial driving source.

CD3+CD56+ natural killer T (NKT)-like cells are a group of CD3+ T cells sharing characteristics of NK and T cells and constitute a major component of host antitumor immune response in human cancer. However, the nature, function and clinical relevance of CD3+CD56+ NKT-like cells in human gastric cancer (GC) remain unclear. In this study, we showed that the frequencies of CD3+CD56+NKT-like cells in GC tumors were significantly decreased and low levels of tumor-infiltrating CD3+CD56+ NKT-like cells were positively correlated with poor survival and disease progression. Most CD3+CD56+NKT-like cells in GC tumors were CD45RA-CD27+/- central/effectormemory cells with decreased activity and lower expression levels of CD69, NKG2D and DNAM-1 than those in non-tumor tissues. We further observed that tumor-infiltrating CD3+CD56+ NKT-like cells had impaired effector function as shown by decreased IFN-γ, TNF-α, granzyme B and Ki-67 expression. Moreover, in vitro studies showed that soluble factors released from GC tumors could induce the functional impairment of CD3+CD56+ NKT-like cells. Collectively, our data indicate that decreased tumorinfiltrating CD3+CD56+ NKT-like cells with impaired effector function are associated with tumor progression and poor survival of GC patients, which may contribute to immune escape of GC.

Lifang Wei,1,* Ye Li,2 Yue Yu,1 Minmin Xu,1 Huilan Chen,1 Lijie Li,1 Ting Peng,1 Kang Zhao,1 Yongze Zhuang3,* 1Department of Nephrology, The Third People’s Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, People’s Republic of China; 2The Third People’s Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, People’s Republic of China; 3Department of Nephrology, 900 Hospital of the Joint Logistics Team, PLA, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, People’s Republic of China*These authors contributed equally to this workCorrespondence: Lifang Wei; Yongze Zhuang Tel +86 591 62091295Fax +86 591 22869300Email liwlf2008@163.com; Doczhuang@aliyun.comAbstract: Obesity-related glomerulopathy (ORG) is a secondary glomerular disease caused by obesity, with clinical manifestations such as proteinuria and glomerulomegaly. Currently, the high incidence of obesity brings a change in the spectrum of kidney diseases across the globe, including China. ORG has become another important secondary nephropathy leading to end-stage renal disease (ESRD), and its incidence has increased significantly. This trend is bound to bring about a serious socioeconomic burden. Therefore, it is urgent to study its pathogenesis and intervention measures. Currently, the occurrence and development mechanisms in ORG are complicated by many factors, which are still unclear. In the past 20 years, with the continuous intensive research on mechanisms such as hypoxia in the metabolic process, immune inflammation, and pyroptosis, there have been new advances in the mechanism of ORG, especially the important role of inflammation in podocyte injury and its impact on the progress of ORG. Here, we briefly review the possible pathogenic role of the inflammasome in the podocyte damage in ORG and summarize the possible therapeutical strategies targeting inflammasome.Keywords: obesity, NLRP3 inflammasome, ORG, podocyte, Chinese herbal medicines

Yue Zhuang, Tiantian Zhao, Xuanrong Shao School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan City, Hubei Province, People’s Republic of ChinaCorrespondence: Tiantian ZhaoSchool of Safety Science and Emergency Management, Wuhan University of Technology, 122 Luoshi Road, Wuhan City, Hubei Province, People’s Republic of ChinaEmail ztt220@whut.edu.cnPurpose: Taking COVID-19 as an example, this paper explores the mechanism of WeChat’s impact on risk information transmission in social media and builds a model of WeChat’s impact on public risk perception based on risk communication.Methods: Research primarily utilised the questionnaire survey method to collect and analyse public attitudes towards COVID-19 to achieve the research goals. We checked the adequacy of measurements and tested the hypotheses with regression analysis based on 801 participants in China.Results: The results indicated that the levels of the public WeChat COVID-19 risk information attention and trust had a significant positive impact on the public’s risk perception and willingness of pandemic prevention. The improved public risk perception of COVID-19 as a mediator enhanced the public pandemic prevention ability, which allowed WeChat to play a more effective role in major disaster emergency.Keywords: COVID-19, WeChat, public risk perception, pandemic prevention

Yifang Chen,1,* Yang Li,1,* Han Guo,1,2,* Zhaoqi Zhang,1,2 Jiayu Zhang,1,2 Xue Dong,1 Yi Liu,3 Yuan Zhuang,3 Yong Zhao1,4 1Department of State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, People’s Republic of China; 2Savaid Medical School, University of Chinese Academy of Sciences, Beijing, People’s Republic of China; 3Department of Blood Transfusion, First Medical Center of Chinese PLA General Hospital, Beijing, People’s Republic of China; 4Department of State Key Laboratory of Membrane Biology, Institute for Stem Cell and Regeneration, Chinese Academy of Science, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yong Zhao; Yuan Zhuang Email zhaoy@ioz.ac.cn; zhuangyuan0215@126.comBackground: Neutrophils present great diverse phenotypes in various microenvironments and play different immune regulatory functions. Neutrophils generally classified into inflammatory phenotype N1 and anti-informatory phenotype N2. Our recent studies showed that IL-23 alone stimulated neutrophils to express IL-17A, IL-17F and IL-22 and displayed a gene transcriptional profile similar to Th17 cells. In the present study, we tried to identify potential cytokines to promote IL-23-induced neutrophil polarization.Methods: Mouse bone marrow-derived neutrophils and human peripheral blood neutrophils were treated with IL-23 (10 ng/mL) plus IL-18 (25 ng/mL) to induce Th17-like subset in vitro and detected by real-time PCR, flow cytometry, ELISA, immunofluorescence and RNA-seq assays. In vivo, collagen-induced arthritis (CIA) mouse model and EL4 tumor-bearing mouse model were used to characterize the potential roles of N(IL-23+IL-18) in inflammation and tumor.Results: Real-time PCR, ELISA and flow cytometry assays showed that IL-18 could significantly enhance IL-23-induced IL-17A, IL-17F and IL-22 expressions in mouse and human neutrophils in a synergistic way, although IL-18 alone failed to induce these cytokines expression. RNA-seq and molecular studies showed that the polarization of N(IL-23+IL-18) is mainly mediated by the JNK/p38-STAT3-BATF signaling pathway. Adoptive transfer of the induced N(IL-23+IL-18) neutrophils significantly accelerated the tumor growth in EL4 tumor-bearing mice and enhanced disease progression in the CIA mouse model. IL-17A-deficient N(IL-23+IL-18) neutrophils failed to enhance the CIA pathogenesis in this model, suggesting that IL-17A may be involved in the N(IL-23+IL-18) neutrophils-promoted arthritis in mice.Conclusion: The Th17-type subpopulation N(IL-23+IL-18) has pro-tumor and pro-inflammatory properties. Recognizing the different functional polarization of neutrophils would significantly help us to understand the distinctive protective/pathological roles of neutrophils in physiological and different pathological situations.Keywords: IL-23, IL-18, neutrophils, polarization, tumor, arthritis

Shijuan Zhang,1 Yuhong Wei,2 Jinxia Liu,3 Yutian Zhuang1 1Department of Critical Care Medicine, Yidu Central Hospital of Weifang, Weifang, Shandong, 262500, People’s Republic of China; 2Department of Gastroenterology First Ward, Yidu Central Hospital of Weifang, Weifang, Shandong, 262500, People’s Republic of China; 3Department of Neurology First Ward, Yidu Central Hospital of Weifang, Weifang, Shandong, 262500, People’s Republic of ChinaCorrespondence: Yutian ZhuangDepartment of Critical Care Medicine, Yidu Central Hospital of Weifang, Weifang, Shandong, 262500, People’s Republic of ChinaTel +86-13573698900Fax +86-0536-3277731Email zhuang76349@163.comIntroduction: Sepsis is a heterogeneous syndrome with a life-long threat caused by infection. This study aimed to investigate the clinical function of miR-940 and its influence on cardiomyocyte models.Methods: The relative expression of miR-940 was assessed by qRT-PCR and the roles in the clinical diagnosis of miR-940 were revealed by the ROC curve. The relationship between miR-940 and clinical parameters was validated by Pearson analysis. The sepsis rat models were established by treatment with cecal ligation and perforation (CLP) and clinical items including left ventricular systolic pressure (LVSP), left ventricular and end-diastolic pressure (LVEDP), maximum rate of increase/decrease in left ventricular blood pressure (± dp/dtmax) as well as troponin (cTnl), creatine kinase isoenzyme (CK-MB), TNF-α, IL-1β, and IL-6 were detected.Results: The finding of qRT-PCR accentuated that the relative expression of miR-940 was significantly decreased in sepsis patients and CLP-stimulated models. The ROC curve proposed that miR-940 could be a satisfactory diagnostic biomarker for sepsis patients. Pearson analysis reinforced the expression of miR-940 was negatively associated with the PCT, WBC, CRP, Scr, SOFA score, and APACHE II score. The outcome of CLP-steered rat verified that overexpression of miR-940 inhibited the detrimental effects of CLP on myocardial dysfunction and inflammation reactions.Conclusion: The downregulation of miR-940 was reported and it might be an underlying diagnostic marker in sepsis patients. Overexpression of miR-940 protected myocardial function from damage and inflammation induced by CLP.Keywords: miR-940, sepsis, inflammation, myocardial disorder

Qingshan Huang,1 Lijun Xie,2 Liyan Huang,2 Weili Wei,2 Haiying Li,2 Yunfang Zhuang,2 Xinxiu Liu,2 Shuqiang Chen,2 Sufang Zhang2 1Musculoskeletal Tumor Center, Peking University People’s Hospital, Beijing, 100044, People’s Republic of China; 2Department of Ultrasound, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, People’s Republic of ChinaCorrespondence: Sufang ZhangDepartment of Ultrasound, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, People’s Republic of ChinaTel/Fax +86-591-87981961Email zsufang@fjmu.edu.cnQingshan HuangMusculoskeletal Tumor Center, Peking University People’s Hospital, Beijing, 100044, People’s Republic of ChinaTel/Fax +86-10-88324471Email hqingshan@pku.edu.cnBackground: High-resolution ultrasound is the first choice for the diagnosis of thyroid nodules, but it is still difficult to distinguish between follicular thyroid carcinoma (FTC) and follicular adenoma (FA). Our research aimed to develop and validate an ultrasonic diagnostic model for differentiating FTC from FA.Methods: This study retrospectively analyzed 196 patients who were diagnosed as FTC (n=83) and FA (n=113). LASSO regression analysis was used to screen clinical and ultrasonic features. Multivariate logistic regression analysis was used to establish the ultrasonic diagnostic model of FTC. Nomogram was used for the visualization of diagnostic models. C-index, ROC, and calibration curves analysis were used to evaluate the accuracy of the diagnostic model. Decision curve analysis (DCA) was used to evaluate the net benefits of the ultrasonic diagnostic model for FTC diagnosis under different threshold probabilities. The bootstrap method was used to verify the ultrasonic diagnostic model.Results: After Lasso regression analysis, 10 clinical and ultrasonic features were used to construct the ultrasonic diagnostic model of FTC. The C-index and AUC of the model were 0.868 and 0.860, respectively. DCA showed that the ultrasonic model had good clinical application value. The C-index in the validation group was 0.818, which was close to the C-index in the model.Conclusion: Ultrasonic diagnostic model constructed with 10 clinical and ultrasonic features can better distinguish FTC from FA.Keywords: follicular thyroid carcinoma, follicular adenoma, diagnostic, ultrasonic

Yongliang Li,1,* Mi Tian,2,* Dongqing Zhang,1 Yifei Zhuang,1 Zhimin Li,1 Shenqi Xie,1 Keyu Sun1 1Department of Emergency, Minhang Hospital, Fudan University, Shanghai, 200040, People’s Republic of China; 2Department of Intensive Care Unit, Huashan Hospital, Fudan University, Shanghai, 200040, People’s Republic of China*These authors contributed equally to this workCorrespondence: Keyu SunDepartment of Emergency, Minhang Hospital, Fudan University, Shanghai, 200040, People’s Republic of ChinaTel +86 18930536861Email hoceima@126.comBackground: Long non-coding RNAs (lncRNAs) have been reported to play critical roles in human tumours, including gallbladder carcinoma (GBC). However, their biological functions and molecular mechanisms in tumorigenesis and progression remain largely unknown.Methods: Quantitative polymerase chain reaction (qPCR) was used to verify the expression of lncRNA myosin light chain kinase antisense RNA 1 (MYLK-AS1) in 120 pairs of GBC tissues and paired adjacent non-tumour tissues, as well as in six different GBC cell lines (NOZ, EH-GB1, OCUG-1, GBC-SD, SGC-996 and QBC-939). Cell counting kit 8 was applied to explore cell proliferation and drug sensitivity assays. The target miRNAs (miR) of MYLK-AS1 and downstream target genes were predicted using Starbase 3.0 software and confirmed by double luciferase reporting test. The expression of proteins was assessed using Western blot assay.Results: Here, we demonstrated that MYLK-AS1 was significantly upregulated and correlated with a poor prognosis and poor clinical characteristics in GBC. Furthermore, the forced expression of MYLK-AS1 significantly promoted GBC cell proliferation and resistance to gemcitabine in vitro. Mechanistically, MYLK-AS1 functioned as an efficient miR-217 sponge, thereby releasing the inhibition of enhancer of zeste 2 polycomb repressive complex 2 (EZH2) subunit expression. MYLK-AS1 promoted GBC cell proliferation and resistance to gemcitabine by upregulating EZH2 expression, and EZH2 was confirmed as a direct target of miR-217.Discussion: Our results confirmed that the chemoresistant driver MYLK-AS1 might be a promising candidate as a therapeutic target for the treatment of advanced GBC.Keywords: chemoresistance, GBC, lncRNA, proliferation

Recovering key aspects of an incoming photon stream during projected FACET-II experiments, such as their energy distributions and the original electron beam's parameters, remains an unsolved computational problem. This paper utilized data from simulated plasma wakefield acceleration betatron radiation experiments and electron-positron pair production to determine which methods could most reliably reconstruct these key properties. The data from these two cases provided a large range of photon energies to help increase confidence in each of the tested methods. In both cases, we compared the performance of maximum likelihood estimation (MLE), a statistical technique used to determine unknown parameters from the distribution of observed data, neural networks, which detect patterns between datasets through repeated training, and a hybrid approach combining the two. Furthermore, in the electron-positron production case, the paper also compared QR decomposition, a matrix decomposition method. The betatron radiation case demonstrated the effectiveness of a hybrid ML-MLE approach, while the electron-positron pair production case illustrated the effectiveness of the ML model in the face of noise. As such, the ML-MLE hybrid approach proved to be the most generalizable of the methods., 22 pages, 29 figures

We review the recent development of cross-media intelligence, analyze its new trends and challenges, and discuss future prospects of cross-media intelligence. Cross-media intelligence is focused on the integration of multi-source and multi-modal data. It attempts to use the relationship between different media data for high-level semantic understanding and logical reasoning. Existing cross-media algorithms mainly follow the paradigm of “single media representation” to “multimedia integration”, in which the two processes of feature learning and logical reasoning are relatively disconnected. It is unlikely to synthesize multi-source and multi-level semantic information to obtain unified features, which hinders the mutual benefits of the reasoning and learning process. This paradigm is lack of the process of explicit knowledge accumulation and multi-level structure understanding. At the same time, it restricts the interpretability and robustness of the model. We interpret new representation method, i. e., visual knowledge. Visual knowledge driven cross-media intelligence has the features of multi-level modeling and knowledge reasoning. Its built-in mechanisms can implement operations and reconstruction visually, which learns knowledge alignment and association. To establish a unified way of knowledge representation learning, the theory of visual knowledge has been illustrated as mentioned below: 1) we introduce three key factors of visual contexts, i. e., concept, visual relationship, and visual reasoning. Visual knowledge has capable of knowledge representations abstraction and multiple knowledge complementing. Visual relations represent the relationship between visual concepts and provide an effective basis for more complex cross-media visual reasoning. We demonstrate visual-based spatio-temporal and causal relationships, but the visual relationship is not limited to these categories. We recommend that the pairwise visual relationships should be extended to multi-objects cascade relationships and the integrated spatio-temporal and causal representations effectively. Visual knowledge is derived of visual concepts and visual relationships, enabling more interpretive and generalized high-level cross-media visual reasoning. Visual knowledge develops a structured knowledge representation, a multi-level basis for visual reasoning, and realizes an effective demonstration for neural network decisions. Broadly, the referred visual reasoning includes a variety of visual operations, such as prediction, reconstruction, association and decomposition. 2) We discuss the applications of visual knowledge, and introduce detailed analysis on their future challenges. We select three applications of those are structured representation of visual knowledge, operation and reasoning of visual knowledge, and cross-media reconstruction and generation. Visual knowledge is predicted to resolve the ambiguity problems in relational descriptions and suppress data bias effectively. It is worth noting that these three specific applications are involved some cross-media intelligence examples of visual knowledge only. Although hand-crafted features are less capable of abstracting multimedia data than deep learning features, these descriptors tend to be more interpretable. The effective integration of hand-crafted features and deep learning features for cross-media representation modeling is a typical application of visual knowledge representation in the context of cross-media intelligence. The structured representation of visual knowledge contributes to the improvement of model interpretability. 3) We analyze the advantages of visual knowledge. It aids to achieve a unified framework driven by both data and knowledge, learn explainable structured representations, and promote cross-media knowledge association and intelligent reasoning. Thanks to the development of visual knowledge based cross-media intelligence, more emerging cross-media intelligence applications will be developed. The decision-making assistance process is more credible through the structural and multi-granularity representation of visual knowledge and the integrated optimization of multi-source and cross-domain data. The reasoning process can be reviewed and clarified, and the model generalization ability can be improved systematically. These factors provide a new powerful pivot for the evolution of cross-media intelligence. Visual knowledge can improve the generative models greatly and enhance the application of simulation technology. Future visual knowledge can be used as a prior to improve the rendering of scenes, realize interactive visual editing tools and controllable semantic understanding of scene objects. A data-driven and visual knowledge derived graphics system will be focused on the integration of the strengths of data and rules, semantic features extraction of visual data, model complexity optimization, simulation improvement, and realistic and sustainable content in new perspectives and new scenarios.