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Abstract Oxidative stress (OS) is strongly implicated in the pathophysiology of major depressive disorder (MDD) but the molecular mechanisms remain largely unknown. The purpose of this study is to identify genes related to both OS and MDD, and further to evaluate the utility of these genes as diagnostic markers and potential treatment targets. We searched datasets related to MDD from the Gene Expression Omnibus (GEO) database for differentially expressed genes (DEGs) also related to OS according to GeneCards. Bioinformatics analyses and machine learning algorithms were used to identify hub genes mediating OS–MDD interactions. A summary data-based Mendelian randomization (SMR) approach was employed to identify possible causal genes for MDD from blood tissue eQLT data. These investigations identified 32 genes mediating OS–MDD interactions, while SMR analysis identified KCNE1 (OR = 1.057, 95%CI = 1.013–1.102, P value = 0.010), MAPK3 (OR = 1.023, 95%CI = 1.004–1.043, P value = 0.020), and STIP1 (OR = 0.792, 95%CI = 0.641–0.979, P value = 0.031) as OS-related causal genes for MDD. These genes may thus serve as useful diagnostic markers and potential therapeutic targets.

Fusarium graminearum is the main pathogenic fungus causing wheat head blight. It not only causes severe crop yield reduction, but also produces fungal toxins such as deoxynivalenol (DON) and 15-acetyl deoxynivalenol (15-ADON), which are dangerous food pollutants to people and livestock. For the genetic functional study of Fusarium graminearum, it is often necessary to culture mycelium on LTB medium in the laboratory to investigate its regulatory effect on DON and 15-ADON synthesis. However, the detection of DON and 15-ADON in the mycelium is focused on extracellular, with less focus on the detection of intracellular. Simultaneous detection of intracellular and extracellular DON and 15-ADON can provide more in-depth information of fungal toxin production and secretion functions. In the study, a method based on gas chromatography-mass spectrometry (GC-MS) for simultaneous detection of DON and 15-ADON was developed. The solvents for extracting DON and 15-ADON can be completely separated and the calibration curves of each extract show good linear relationship within a certain range. The mass spectrometric characteristic scan shows high ionization efficiency for both DON and 15-ADON. The retention time of DON and 15-ADON in chromatogram are 6.98 and 7.68 min, respectively. Qualitative analysis of DON was performed using m/z 295, 235, and 193 under single ion monitor (SIM) mode, while m/z 392, 235, and 193 were used for 15-ADON. The highest intensity ion m/z 235 was selected for the quantification of DON, and m/z 193 for the quantification of 15-ADON. This method was used to detect the intracellular and extracellular levels of DON and 15-ADON in wild-type PH-1 and tri5 mutant with DON biosynthesis deficiency. The intracellular DON and 15-ADON levels in PH-1 are determined to be (149.13±9.15) μg/g and (1833.31±185.33) μg/g, extracellular levels are (5910.35±468.23) μg/g and (45222.12±2726.81) μg/g, respectively. The study identified a mutant strain with the deletion of the first enzyme involved in the synthesis pathway of DON and 15-ADON，both DON and 15-ADON are not detected in the extracellular and intracellular of the tri5 mutant. Additionally, this research confirms that after 7 days of cultivation in LTB medium, the wild-type strain exhibits higher levels of 15-ADON in both intracellular and extracellular compared to DON, with extracellular levels of DON and 15-ADON being higher than intracellular levels. This method can provide a methodological reference for the functional study of toxin synthesis in Fusarium graminearum.

Abstract Purpose Although the potential association between autoimmune thyroiditis and papillary thyroid cancer (PTC) has been acknowledged, whether the clinicopathological features of PTC will be affected by thyroid autoantibodies remains unknown. Patients and methods We conducted a case-control study to investigate the association of thyroid autoantibodies with clinicopathological characteristics of PTC in 15,305 patients (including 11,465 females and 3,840 males) from 3 medical centers in the central province of China. Logistic regression and restricted cubic spline models were performed to analyze the association of thyroid autoantibodies with clinicopathological features of PTC. Results In total, out of the 15,305 patients enrolled in this study, 10,087 (65.9%) had negative thyroid autoantibodies, while 5,218(34.1%) tested positive thyroid autoantibodies. Among these individuals, 1,530(10.0%) showed positivity for TPOAb only, 1,247(8.2%) for TGAb only and a further 2,441(15.9%) exhibited dual positivity for both TPOAb and TGAb combined. Thyroid autoantibodies level demonstrated significant correlations with certain aggressive features in PTC. Specifically, TGAb level displayed a direct correlation to an increased likelihood of multifocality, bilateral tumor, extrathyroidal extension, lymph node metastasis, as well as more than five affected lymph nodes. However, TPOAb level exhibited an inverse association with the risk associated with extrathyroidal extension, lymph node metastasis, and more than five affected lymph nodes. Conclusion Elevated level of TGAb were positively correlated with the risk of aggressive features in PTC, while high level of TPOAb were inversely associated with the risk of extrathyroidal extension and lymph node metastasis.

Abstract The concept of Yin-Yang, originating in ancient Chinese philosophy, symbolizes two opposing but complementary forces or principles found in all aspects of life. This concept can be quite fitting in the context of extracellular vehicles (EVs) and inflammatory diseases. Over the past decades, numerous studies have revealed that EVs can exhibit dual sides, acting as both pro- and anti-inflammatory agents, akin to the concept of Yin-Yang theory (i.e., two sides of a coin). This has enabled EVs to serve as potential indicators of pathogenesis or be manipulated for therapeutic purposes by influencing immune and inflammatory pathways. This review delves into the recent advances in understanding the Yin-Yang sides of EVs and their regulation in specific inflammatory diseases. We shed light on the current prospects of engineering EVs for treating inflammatory conditions. The Yin-Yang principle of EVs bestows upon them great potential as, therapeutic, and preventive agents for inflammatory diseases. Graphical Abstract

Abstract Messenger RNA (mRNA) has emerged as a promising therapeutic molecule with numerous clinical applications in treating central nervous system disorders, tumors, COVID-19, and other diseases. mRNA therapies must be encapsulated into safe, stable, and effective delivery vehicles to preserve the cargo from degradation and prevent immunogenicity. Exosomes have gained growing attention in mRNA delivery because of their good biocompatibility, low immunogenicity, small size, unique capacity to traverse physiological barriers, and cell-specific tropism. Moreover, these exosomes can be engineered to utilize the natural carriers to target specific cells or tissues. This targeted approach will enhance the efficacy and reduce the side effects of mRNAs. However, difficulties such as a lack of consistent and reliable methods for exosome purification and the efficient encapsulation of large mRNAs into exosomes must be addressed. This article outlines current breakthroughs in cell-derived vesicle-mediated mRNA delivery and its biomedical applications. Graphical Abstract

Effective hurricane preparedness and response demand a thorough understanding of the impact on mobility patterns. While existing studies have explored mobility disruptions caused by hurricanes, very few have delved into the impact, considering both mobility vulnerability and recovery, on a state level. Utilising mobile phone location data, this research examines the mobility patterns of Florida residents throughout Hurricane Ian. The findings reveal that the hurricane profoundly disturbed the mobility patterns of Floridians. The state experienced a maximum average daily mobility reduction of 63.41%, with certain neighbourhoods coming to a complete standstill at 100% cessation in mobility. On average, Florida neighbourhoods needed 2.61 days for mobility recovery, though this period stretched to as long as 92 days in the most severely affected areas. The bivariate map highlights a prevailing trend: neighbourhoods with prolonged recovery periods also witnessed more substantial reductions in mobility. This dual disadvantage underscores the critical need for increased focus on these neighbourhoods. Furthermore, our findings highlight the significance of factoring in forecasted hurricane paths when analysing mobility impacts, as we noted more substantial effects on neighbourhoods along the predicted trajectory.

Abstract Anaplastic lymphoma kinase (ALK) fusion-positive colorectal cancer (CRC) is a rare and chemotherapy-refractory subtype that lacks established and effective treatment strategies. Additionally, the efficacy and safety of ALK inhibitors (ALKi) in CRC remain undetermined. Herein, we examined a series of ALK-positive CRC patients who underwent various lines of ALKi treatment. Notably, we detected an ALK 1196M resistance mutation in a CRC patient who received multiple lines of chemotherapy and ALKi treatment. Importantly, we found that Brigatinib and Lorlatinib demonstrated some efficacy in managing this patient, although the observed effectiveness was not as pronounced as in non-small cell lung cancer cases. Furthermore, based on our preliminary analyses, we surmise that ALK-positive CRC patients are likely to exhibit inner resistance to Cetuximab. Taken together, our findings have important implications for the treatment of ALK-positive CRC patients.

Delirium in critical ill patients is a complex and common neurological syndrome in the intensive care unit (ICU) that is caused by a range of structural or functional abnormalities. ICU Delirium is associated with reduced compliance, prolonged hospital stays, greater use or delayed withdrawal of sedatives, higher rates and durations of mechanical ventilation, and higher rates of mortality. The aetiology and pathogenesis of ICU delirium are unclear, and the lack of better prediction, prevention, and treatment measures leads to a non-standardized control of delirium. By searching the relevant literature, we aim in this narrative review to describe progress in the pathogenesis, predictive biomarkers, diagnosis, and treatment of ICU delirium.

[Significance]As the research focus of future agricultural machinery, agricultural wheeled robots are developing in the direction of intelligence and multi-functionality. Advanced environmental perception technologies serve as a crucial foundation and key components to promote intelligent operations of agricultural wheeled robots. However, considering the non-structured and complex environments in agricultural on-field operational processes, the environmental information obtained through conventional 2D perception technologies is limited. Therefore, 3D environmental perception technologies are highlighted as they can provide more dimensional information such as depth, among others, thereby directly enhancing the precision and efficiency of unmanned agricultural machinery operation. This paper aims to provide a detailed analysis and summary of 3D environmental perception technologies, investigate the issues in the development of agricultural environmental perception technologies, and clarify the future key development directions of 3D environmental perception technologies regarding agricultural machinery, especially the agricultural wheeled robot.[Progress]Firstly, an overview of the general status of wheeled robots was introduced, considering their dominant influence in environmental perception technologies. It was concluded that multi-wheel robots, especially four-wheel robots, were more suitable for the agricultural environment due to their favorable adaptability and robustness in various agricultural scenarios. In recent years, multi-wheel agricultural robots have gained widespread adoption and application globally. The further improvement of the universality, operation efficiency, and intelligence of agricultural wheeled robots is determined by the employed perception systems and control systems. Therefore, agricultural wheeled robots equipped with novel 3D environmental perception technologies can obtain high-dimensional environmental information, which is significant for improving the accuracy of decision-making and control. Moreover, it enables them to explore effective ways to address the challenges in intelligent environmental perception technology. Secondly, the recent development status of 3D environmental perception technologies in the agriculture field was briefly reviewed. Meanwhile, sensing equipment and the corresponding key technologies were also introduced. For the wheeled robots reported in the agriculture area, it was noted that the applied technologies of environmental perception, in terms of the primary employed sensor solutions, were divided into three categories: LiDAR, vision sensors, and multi-sensor fusion-based solutions. Multi-line LiDAR had better performance on many tasks when employing point cloud processing algorithms. Compared with LiDAR, depth cameras such as binocular cameras, TOF cameras, and structured light cameras have been comprehensively investigated for their application in agricultural robots. Depth camera-based perception systems have shown superiority in cost and providing abundant point cloud information. This study has investigated and summarized the latest research on 3D environmental perception technologies employed by wheeled robots in agricultural machinery. In the reported application scenarios of agricultural environmental perception, the state-of-the-art 3D environmental perception approaches have mainly focused on obstacle recognition, path recognition, and plant phenotyping. 3D environmental perception technologies have the potential to enhance the ability of agricultural robot systems to understand and adapt to the complex, unstructured agricultural environment. Furthermore, they can effectively address several challenges that traditional environmental perception technologies have struggled to overcome, such as partial sensor information loss, adverse weather conditions, and poor lighting conditions. Current research results have indicated that multi-sensor fusion-based 3D environmental perception systems outperform single-sensor-based systems. This superiority arises from the amalgamation of advantages from various sensors, which concurrently serve to mitigate individual shortcomings.[Conclusions and Prospects]The potential of 3D environmental perception technology for agricultural wheeled robots was discussed in light of the evolving demands of smart agriculture. Suggestions were made to improve sensor applicability, develop deep learning-based agricultural environmental perception technology, and explore intelligent high-speed online multi-sensor fusion strategies. Currently, the employed sensors in agricultural wheeled robots may not fully meet practical requirements, and the system's cost remains a barrier to widespread deployment of 3D environmental perception technologies in agriculture. Therefore, there is an urgent need to enhance the agricultural applicability of 3D sensors and reduce production costs. Deep learning methods were highlighted as a powerful tool for processing information obtained from 3D environmental perception sensors, improving response speed and accuracy. However, the limited datasets in the agriculture field remain a key issue that needs to be addressed. Additionally, multi-sensor fusion has been recognized for its potential to enhance perception performance in complex and changeable environments. As a result, it is clear that 3D environmental perception technology based on multi-sensor fusion is the future development direction of smart agriculture. To overcome challenges such as slow data processing speed, delayed processed data, and limited memory space for storing data, it is essential to investigate effective fusion schemes to achieve online multi-source information fusion with greater intelligence and speed.

This paper provides a detailed description of the development and experimental results of the supercritical flow experiment payload carried on the TZ-6 cargo spacecraft, as well as a systematic verification of the out-of-cabin deployment experiment. The technical and engineering indicators of the payload deployment experiment are analyzed, and the functional modules of the payload are shown. The paper provides a detailed description of the design, installation location, size, weight, temperature, illumination, pressure, radiation, control, command reception, telemetry data, downlink data, and experimental procedures for the out-of-cabin payload in the extreme conditions of space. The paper presents the annular liquid surface state and temperature oscillation signals obtained from the space experiment and conducts ground matching experiments to verify the results, providing scientific references for the design and condition setting of space experiments and comparisons for the experimental results to obtain the flow field structure under supercritical conditions. The paper provides a specific summary and discussion of the space fluid science experiment project, providing useful references for future long-term in-orbit scientific research using cargo spacecraft.

Background The aim of this study is to design a deep learning (DL) model to preoperatively predict the occurrence of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC). Methods This research collected preoperative ultrasound (US) images and clinical factors of 611 PTMC patients. The clinical factors were analyzed using multivariate regression. Then, a DL model based on US images and clinical factors was developed to preoperatively predict CLNM. The model’s efficacy was evaluated using the receiver operating characteristic (ROC) curve, along with accuracy, sensitivity, specificity, and the F1 score. Results The multivariate analysis indicated an independent correlation factors including age ≥55 (OR = 0.309, p < 0.001), tumor diameter (OR = 2.551, p = 0.010), macrocalcifications (OR = 1.832, p = 0.002), and capsular invasion (OR = 1.977, p = 0.005). The suggested DL model utilized US images achieved an average area under the curve (AUC) of 0.65, slightly outperforming the model that employed traditional clinical factors (AUC = 0.64). Nevertheless, the model that incorporated both of them did not enhance prediction accuracy (AUC = 0.63). Conclusions The suggested approach offers a reference for the treatment and supervision of PTMC. Among three models used in this study, the deep model relied generally more on image modalities than the data modality of clinic records when making the predictions.

Abstract The biological barriers of the body, such as the blood–brain, placental, intestinal, skin, and air‐blood, protect against invading viruses and bacteria while providing necessary physical support. However, these barriers also hinder the delivery of drugs to target tissues, reducing their therapeutic efficacy. Extracellular vesicles (EVs), nanostructures with a diameter ranging from 30 nm to 10 μm secreted by cells, offer a potential solution to this challenge. These natural vesicles can effectively pass through various biological barriers, facilitating intercellular communication. As a result, artificially engineered EVs that mimic or are superior to the natural ones have emerged as a promising drug delivery vehicle, capable of delivering drugs to almost any body part to treat various diseases. This review first provides an overview of the formation and cross‐species uptake of natural EVs from different organisms, including animals, plants, and bacteria. Later, it explores the current clinical applications, perspectives, and challenges associated with using engineered EVs as a drug delivery platform. Finally, it aims to inspire further research to help bioengineered EVs effectively cross biological barriers to treat diseases.

Abstract Although the incidence of thyroid cancer is increasing year by year, most patients, especially those with differentiated thyroid cancer, can usually be cured with surgery, radioactive iodine, and thyroid-stimulating hormone suppression. However, treatment options for patients with poorly differentiated thyroid cancers or radioiodine-refractory thyroid cancer have historically been limited. Altered energy metabolism is one of the hallmarks of cancer and a well-documented feature in thyroid cancer. In a hypoxic environment with extreme nutrient deficiencies resulting from uncontrolled growth, thyroid cancer cells utilize “metabolic reprogramming” to satisfy their energy demand and support malignant behaviors such as metastasis. This review summarizes past and recent advances in our understanding of the reprogramming of glucose metabolism in thyroid cancer cells, which we expect will yield new therapeutic approaches for patients with special pathological types of thyroid cancer by targeting reprogrammed glucose metabolism.

Abstract Prostate cancer (PCa) is a non-cutaneous malignancy in males with wide variation in incidence rates across the globe. It is the second most reported cause of cancer death. Its etiology may have been linked to genetic polymorphisms, which are not only dominating cause of malignancy casualties but also exerts significant effects on pharmacotherapy outcomes. Although many therapeutic options are available, but suitable candidates identified by useful biomarkers can exhibit maximum therapeutic efficacy. The single-nucleotide polymorphisms (SNPs) reported in androgen receptor signaling genes influence the effectiveness of androgen receptor pathway inhibitors and androgen deprivation therapy. Furthermore, SNPs located in genes involved in transport, drug metabolism, and efflux pumps also influence the efficacy of pharmacotherapy. Hence, SNPs biomarkers provide the basis for individualized pharmacotherapy. The pharmacotherapeutic options for PCa include hormonal therapy, chemotherapy (Docetaxel, Mitoxantrone, Cabazitaxel, and Estramustine, etc.), and radiotherapy. Here, we overview the impact of SNPs reported in various genes on the pharmacotherapy for PCa and evaluate current genetic biomarkers with an emphasis on early diagnosis and individualized treatment strategy in PCa.

Abstract Liver sinusoidal endothelial cells (LSECs) play a pivotal role in maintaining liver homeostasis and influencing the pathological processes of various liver diseases. However, neither LSEC-specific hallmark genes nor a LSEC promoter-driven Cre mouse line has been introduced before, which largely restricts the study of liver diseases with vascular disorders. To explore LSEC-specific hallmark genes, we compared the top 50 marker genes between liver endothelial cells (ECs) and liver capillary ECs and identified 18 overlapping genes. After excluding globally expressed genes and those with low expression percentages, we narrowed our focus to two final candidates: Oit3 and Dnase1l3. Through single-cell RNA sequencing (scRNA-seq) and analysis of the NCBI database, we confirmed the extrahepatic expression of Dnase1l3. The paired-cell sequencing data further demonstrated that Oit3 was predominantly expressed in the midlobular liver ECs. Subsequently, we constructed inducible Oit3-CreERT2 transgenic mice, which were further crossed with ROSA26-tdTomato mice. Microscopy validated that the established Oit3-CreERT2-tdTomato mice exhibited significant fluorescence in the liver rather than in other organs. The staining analysis confirmed the colocalization of tdTomato and EC markers. Ex-vivo experiments further confirmed that isolated tdTomato+ cells exhibited well-differentiated fenestrae and highly expressed EC markers, confirming their identity as LSECs. Overall, Oit3 is a promising hallmark gene for tracing LSECs. The establishment of Oit3-CreERT2-tdTomato mice provides a valuable model for studying the complexities of LSECs in liver diseases.

In order to improve the precision of phase recognition and reduce the rate of misdetection, this paper applies the deep learning method to automatic phase recognition. In this paper, an automatic seismic phase recognition model based on the Bi-LSTM network is designed. To test the performance of this model, the STEAD dataset is used for training and testing, and this model is compared with the traditional STA/LTA and AIC methods. The experimental results show that, compared to STA/LTA and AIC methods, the Bi-LSTM network can reduce the misdetection rate by about 8–15%, and improve the RSEM; especially, the prediction error of S-wave is greatly reduced.

Most space shuttle fuel tanks use a center column to hold the Propellant Management Device (PMD). This paper analyzes the gas–liquid interface state in the tanks with a central column during microgravity experiments conducted in the Chinese Space Station. It launches an extended study to investigate the gas–liquid interface state under different gravity conditions. Using the perturbation method and boundary layer theory, we numerically calculated the morphology of the gas–liquid interface under varying gravity conditions based on the Young–Laplace equation. The results were then compared to those obtained from existing commercial software and were found to be consistent. Based on this, the study develops two types of calculation procedures. The first procedure generates the corresponding shape of the liquid surface by inputting the height of the liquid surface endpoints and the gravity level. The second procedure is based on the targeting method and generates the corresponding liquid surface by inputting the volume of the liquid in the storage tank and the gravity level. The procedures were used to analyze the variation of gas–liquid interface properties under different gravity conditions. This study offers theoretical support for liquid management in aerospace engineering fuel tanks.

Abstract One challenge for realizing high-efficiency electrocatalysts for CO2 electroreduction is lacking in comprehensive understanding of potential-driven chemical state and dynamic atomic-configuration evolutions. Herein, by using a complementary combination of in situ/operando methods and employing copper single-atom electrocatalyst as a model system, we provide evidence on how the complex interplay among dynamic atomic-configuration, chemical state change and surface coulombic charging determines the resulting product profiles. We further demonstrate an informative indicator of atomic surface charge (φ e) for evaluating the CO2RR performance, and validate potential-driven dynamic low-coordinated Cu centers for performing significantly high selectivity and activity toward CO product over the well-known four N-coordinated counterparts. It indicates that the structural reconstruction only involved the dynamic breaking of Cu–N bond is partially reversible, whereas Cu–Cu bond formation is clearly irreversible. For all single-atom electrocatalysts (Cu, Fe and Co), the φ e value for efficient CO production has been revealed closely correlated with the configuration transformation to generate dynamic low-coordinated configuration. A universal explication can be concluded that the dynamic low-coordinated configuration is the active form to efficiently catalyze CO2-to-CO conversion.

The microthruster is generally chosen as the actuator of the drag-free satellite control system, and the layout directly affects the stability of the satellite control. Due to the extremely rigorous objectives of drag-free control, a design method for the redundant thruster layout is proposed in this paper to improve the control stability of the satellite and reduce the allocation error of the control system. First, the factors corresponding to the allocation errors of redundant thruster layouts are analyzed. And the minimum number of thrusters required for satellite control is determined through the calculation method of thruster redundancy. Then, the design criterion of reducing the allocation error and increasing the satellite control stability is put forward. To demonstrate the effectiveness and feasibility of the design method proposed in this paper, the space gravitational wave detection satellite is taken as a simulation case. Thrust distribution models corresponding to the designed thruster layouts are established, and the results show that a proper layout can not only improve stability but also reduce fuel consumption. The research is looking forward to providing a reference for designing the microthruster layout of the gravitational wave detection satellite in the future.

The chemokine-like factor (CKLF)-like MARVEL transmembrane domain-containing (CMTM) family includes CMTM1–8 and CKLF, and they play key roles in the hematopoietic, immune, cardiovascular, and male reproductive systems, participating in the physiological functions, cancer, and other diseases associated with these systems. CMTM family members activate and chemoattract immune cells to affect the proliferation and invasion of tumor cells through a similar mechanism, the structural characteristics typical of chemokines and transmembrane 4 superfamily (TM4SF). In this review, we discuss each CMTM family member’s chromosomal location, involved signaling pathways, expression patterns, and potential roles, and mechanisms of action in pancreatic, breast, gastric and liver cancers. Furthermore, we discuss several clinically applied tumor therapies targeted at the CMTM family, indicating that CMTM family members could be novel immune checkpoints and potential targets effective in tumor treatment.

In view of the future development trend of ocean altimetry satellites, the design idea of Ka/C dual frequency combination system is proposed, that is, the mode of using an altimeter antenna to contain Ka and C frequencies (35.7 and 5.3 GHz, respectively) for combined ranging. First, the random error model of altimeter measuring the distance from the altimeter phase center to the sea surface is given, and the analysis shows that the ionospheric correction error is one of the important factors. Second, the calculation of typical ionospheric parameters shows that the influence of ionospheric higher order terms on altimeter ranging is below the mm level, and its influence can be ignored. The rigorous expression for the first-order and second-order term correction of ionosphere error are given, respectively, by using dual frequency and three frequencies. The computational experiment of Jason-2 and SARAL satellite show that the maximum magnitude of ionospheric error correction in Ku band can reach 11 cm, while the maximum magnitude of ionospheric error correction in Ka band can reach 2 cm. For high-precision applications, ionospheric corrections must be made for single Ka frequency. After Ka/C dual frequency combination is adopted, error correction can be directly conducted without global ionospheric map (GIM) model, and its accuracy will be further improved than GIM model. Under 1 Hz sampling conditions, the ionospheric correction accuracy of Ka/C combination can be better than 2.5 mm, basically eliminating the influence of the ionosphere, and the total ranging accuracy can reach 3.5 cm considering the propagation error.