[Objective]This study aims to develop and evaluate a low-cost suppression jamming experimental platform using software-defined radio(SDR)technology.The platform's ability to generate multiple types of suppression jamming signals in a controlled wired-loop environment was assessed.The proposed platform provides a flexible,configurable,and cost-effective solution for evaluating GNSS receiver anti-jamming performance,while avoiding the complexity and high cost of commercial jamming equipment.[Methods]A suppression jamming experimental platform was designed and implemented using GNU Radio and HackRF hardware.It features a modular architecture comprising parameter configuration,jamming signal generation,signal selection,waveform monitoring,and radio frequency(RF)transmission modules.Four representative suppression jamming waveforms were generated:continuous wave,frequency-swept,pulse,and band-limited Gaussian noise jamming.Jamming parameters such as center frequency,sweep characteristics,pulse repetition pattern,and output power could be configured via software interfaces.To verify signal generation accuracy,a spectrum analyzer and oscilloscope were used to evaluate the frequency-domain characteristics and pulse-modulation timing performance of the generated signals.The platform was further integrated into a wired-loop test environment,enabling direct injection of jamming signals into the RF input of GNSS receivers.This configuration provides a repeatable and controllable testing environment,eliminating uncertainties caused by wireless propagation.To assess jamming effectiveness,comparative experiments were conducted between the proposed low-cost SDR platform and a commercial high-cost jammer under identical test conditions.The carrier-to-noise density ratio(C/N0)variation and attenuation characteristics of the receiver were selected as the primary evaluation metrics.In addition,dynamic interference experiments were performed to investigate platform performance under motion conditions and evaluate its capabilities in continuous jamming signal generation.[Results]Experimental verification demonstrated that the generated suppression jamming signals exhibit spectral characteristics and temporal behaviors consistent with theoretical design expectations.Measured center frequencies,bandwidths,sweep patterns,and pulse timing parameters showed good agreement with configured values,confirming the accuracy of signal generation and modulation processes.Comparative testing indicated that the proposed SDR platform and the commercial jammer produced similar interference effects on GNSS receivers.Within the GPS L1 frequency band and over a jammer-to-signal ratio range of 30-60 dB,both systems resulted in nearly identical trends of receiver C/N0 degradation.As interference intensity increased,both platforms progressively deteriorated signal quality,eventually leading to receiver tracking failure and signal loss-of-lock.The observed attenuation characteristics and loss-of-lock thresholds exhibited strong consistency between the two jamming sources.Dynamic experiments further demonstrated that the proposed platform can continuously generate stable suppression jamming signals during motion without considerable frequency drift or power fluctuation.The platform effectively degraded receiver tracking performance and maintained stable interference throughout the test.These results verify the reliability and practicality of the proposed system for dynamic anti-jamming experiments.[Conclusions]A low-cost SDR-based suppression jamming experimental platform was successfully developed and validated.Experimental results demonstrate that the proposed platform accurately generates multiple suppression jamming waveforms,achieving interference effects comparable to commercial high-cost jammers in the GPS L1 band.The platform offers several advantages,including low implementation cost,flexible architecture,convenient software configuration,and strong scalability.By supporting various suppression jamming modes and providing stable operation in both static and dynamic environments,it serves as an effective experimental tool for evaluating GNSS receiver anti-jamming performance,analyzing interference mechanisms,and other applications in navigation research.

Precision airdrop enables rapid, safe, and accurate delivery of supplies and delicate equipment under extreme environmental and transportation constraints. For high-precision landing, accurate parafoil canopy attitude measurement is essential to improve touchdown stability and accuracy. This paper proposes a vision-based parafoil canopy attitude estimation method for controlled airdrop systems without requiring attitude sensors to be mounted on the canopy. A semi-physical relative attitude measurement simulation system is also designed. The proposed method uses a cooperative target to identify the parafoil operating state and estimate the initial canopy attitude. It then tracks feature points on the canopy surface and applies a projection-operator-based formulation to the corresponding 3D points. In this way, continuous canopy attitude measurement is achieved without installing sensors on the parafoil canopy. Semi-physical simulation experiments are conducted to verify the proposed method. The results show that the method has good practicality and measurement accuracy. The maximum RMSE of the measured parafoil attitude is 0.29°.

Array self-position determination methods based on multiple emitter data can avoid significant deviations of vehicle satellite navigation in harsh environments. However, existing array self-position determination methods show decrease in performance under multipath environments. To deal with this problem, we propose an array self-position determination method based on orthogonal grid matching with the spatial differencing method. Specifically, the direction of arrival (DOA) of direct path and multipath signals are respectively estimated by array spatial differencing method. The matching accuracy is enhanced by utilizing the prior information of direct path signal. After calculating correlation coefficients of different sources, estimated angles with high correlation are then classified into the same set. Then, the noise subspace of each angle set is reconstructed and the position is estimated by grid matching with the orthogonal property between the noise subspaces and the characteristic steering vectors. The matching results of redundant angle sets are removed as non-matching items, thus averting positioning deviations. The simulation results demonstrate that the computational complexity of the proposed method is comparable to that of the signal subspace fitting (SSF). Moreover, in terms of positioning precision, the proposed method outperforms multiple signal classification with enhanced spatial smoothing (ESSMUSIC), initial signal fitting (ISF), and SSF.

With the advancement of multi-constellation GNSS, tightly coupled GPS/BDS/INS integration has become a prominent outdoor navigation solution. However, it faces challenges including inaccurate receiver clock error modeling and increased susceptibility to observation faults. This paper proposes a novel solution using Between-Satellite Single Difference (BSSD) methodology combined with a Fault Detection and Exclusion (FDE) strategy. The established BSSD-based tightly coupled model accounts for GPS-BDS Inter-System Bias (ISB), effectively eliminating errors from receiver clock inaccuracies and enabling shared reference satellites across both systems. A specialized two-step FDE scheme was developed: the first step identifies and removes multiple faulty observations, including the reference satellite, while the second step implements comprehensive refinement. Experimental results demonstrate that the proposed BSSD-based integration significantly enhances positioning accuracy, achieving up to 74.6% improvement over conventional tightly coupled approaches. Furthermore, the two-step FDE algorithm proved effective across various simulated fault scenarios, ensuring both precision and reliability for the integrated navigation system. The method effectively addresses key limitations in current multi-GNSS/INS integration while maintaining robust performance under challenging conditions.

Functional profiling of whole-metagenome shotgun sequencing (WMS) enables our understanding of microbe-host interactions. We demonstrate microbial functional information loss by current annotation methods at both the taxon and community levels, particularly at lower read depths. To address information loss, we develop a framework, RFW (reference-based functional profile inference on WMS), that utilizes information from genome functional annotations and taxonomic profiles to infer microbial function abundances from WMS. Furthermore, we provide an algorithm for absolute abundance change quantification between groups as part of the RFW framework. By applying RFW to several datasets related to autism spectrum disorder and colorectal cancer, we show that RFW augments downstream analyses, such as differential microbial function identification and association analysis between microbial function and host phenotype. RFW is open source and freely available at https://github.com/Xingyinliu-Lab/RFW.

The rotary vector (RV) reducer is one of the widely used mechanical components in industrial systems, specifically in robots. The stability of the transmission performance of the RV reducer is crucial for the efficient operation of industrial equipment. The manufacturing and assembly errors of various components of the RV reducer during the production process are important factors that affect the transmission performance. However, in previous research work, the coupling effect of multiple errors on the transmission accuracy of RV reducer has not been fully considered. Furthermore, a vague relationship between system transmission errors and various errors also has not been thoroughly discussed, which presents a challenge to analyze and optimize the errors of components using the simulation technology of virtual prototype. Therefore, we propose a novel approach to use the response surface method (RSM) to investigate the transmission accuracy of RV reducer. Firstly, based on the constructed virtual prototype of RV reducer, the individual effects of different original errors on the overall transmission error are analyzed. Secondly, a response surface approximation model using RSM is constructed to analyze the effect of multiple error interactions on the transmission accuracy of the RV reducer, and the potential functional relationship between multiple error factors and the overall transmission error is also explored. Finally, the authenticity of the proposed approach is verified by setting up some comparative experiments. This study provides a reference for the efficient analysis and optimization of the transmission accuracy of RV reducers.

针对弱信号环境B1C信号难以跟踪以及积分时间难以延长的问题,设计了一种基于低阶扩展卡尔曼滤波(EKF)联合的相干与非相干积分组合跟踪算法.首先,分析了B1C信号结构,建立了数据和导频通道相关器联合跟踪模型;其次,根据B1C在弱信号环境下的变化特点,设计了相干与非相干积分组合跟踪算法;最后,利用模拟信号发生器生成-144 dBm的B1C弱信号,进行了仿真实验验证.实验结果表明,低阶EKF联合跟踪算法采用 20 ms长相干积分,载波与伪码跟踪误差分别为 0.014 rad与 0.027 chip.采用相干积分与非相干积分组合积分后,载波与伪码跟踪误差均提高 14%以上,且对更长积分时间的适应性明显提高,能够改善B1C弱信号的跟踪能力.

美国全球定位系统(Global Positioning System,GPS)现代化建设使得L1频点同时支持播发L1 C/A和L1 C信号,具有同频双信号的特点,为GPS信号捕获技术研究提供了更多可能.针对GPS L1 C信号测距码长、捕获运算量大、耗时久的问题,利用L1 C/A与L1 C信号之间码相位延时和Doppler频率的关系,提出并实现了一种基于GPS L1 C/A辅助的L1 C快速捕获算法.该算法首先对L1 C/A信号进行捕获跟踪,获得码相位和Doppler频率;再通过L1 C/A信号Doppler频率剥离L1 C信号载波,同时利用L1 C/A码相位产生多个相位偏移修正的L1 C码序列;最后通过不同相位偏移的L1 C码序列最大相关积分值的判定,获取L1 C捕获结果.通过实测数据对算法性能进行测试,结果表明:相较未使用辅助信息的捕获算法,该算法可以有效提高接收机对L1 C信号的捕获速度,对单颗L1 C卫星信号捕获耗时下降约85.56％,具有一定的工程意义.

Due to the inherent vulnerability of GNSS and the increasing complexity of the electromagnetic environment in recent years, GNSS signals are facing a growing threat from types of jamming interference. Early detection of GNSS interference is crucial to provide timely warnings before degradation in navigation and positioning functions occurs. This paper presents a novel approach for GNSS jamming interference detection using the statistical analysis, while also the influence of Automatic Gain Control (AGC) on detection is analyzed. By comparing the similarities of mean value derived from signals under normal and potential jamming conditions, hypothesis testing is introduced to detect the presence of jamming. The detection results are evaluated on Continuous Wave (CW), Chirp, and Additive White Gaussian Noise (AWGN) jamming signals by a semi-physical simulation platform. The results demonstrate that the approach exhibits a high detection rate under low JSR conditions.

To solve the problem that the IMU nodes of the traditional factor graph are unable to be corrected in time and the positioning accuracy decreases in the condition of satellite failure, an improved preintegration method for the vehicle navigation system is proposed. Different from the current factor graph optimization method, the proposed method that involves corrective parameters of visual sensors is introduced. The structure of the system is changed when GNSS is unavailable and the calculated information of Lidar is used to compensate for IMU errors. The sliding window is used to improve the real-time performance of the system. The experimental results show that the proposed algorithm can improve the positioning accuracy of vehicle navigation when GNSS is unavailable. Compared with the traditional factor graph integrated navigation algorithm, the positioning accuracy of the proposed preintegration method is improved by more than 16% in GNSS failure environments.

Basophils are a rare type of granulocyte in peripheral blood. Owing to their accessibility in circulation and similarities to mast cells, basophils were considered a tool to gain insight into the function of mast cells. However, recent studies have uncovered that basophils have unique biology, specifically in activation, recruitment, and potential biomarkers. Accordingly, some previously unrecognized functions, particularly in neuroimmunology, have been found, suggesting a role of basophils in inflammatory and pruritic disorders. In this review, we aim to present an overview of basophil biology to show how basophils contribute to certain pruritic skin diseases.

Autonomous aerial refueling (AAR) has generated great interest in recent years. However, much research has focused on the vision-based close docking stage; few studies have been conducted on the navigation algorithm for the rendezvous and following stages. High-precision relative navigation in following stage can provide favourable conditions for successful docking. Aiming at precise relative navigation in the complex high dynamic environment of aerial refueling rendezvous and following stages, a two-stage adaptive filtering architecture is exploited in this paper. An adaptive main Kalman filter (AKF) is realized for ambiguity eliminated GNSS/INS tightly coupled integrated system, and a robust adaptive subfilter is developed for GNSS individually. Particularly, aiming at the influence of pseudorange observation multipath outliers and state abnormal disturbances in unmanned air vehicle- (UAV-) tanker proximity, an INS-aided bifactor robust and classified factor adaptive filtering (IBRCAF) algorithm for single-frequency ambiguity resolution is proposed. Finally, the effectiveness of the algorithm is verified by the simulation experiments for UAV-tanker. The results indicate that the IBRCAF algorithm can efficiently suppress the influence of pseudorange multipath gross errors and abnormal state disturbances and greatly raise the success rate of ambiguity resolution, and the two-stage adaptive filtering algorithm of IBRCAF-AKF can significantly improve relative navigation performance and achieve centimeter-level accuracy.

Background Osteosarcoma (OS) is the most common malignant bone tumor, which often has lung metastasis. The survival rate after tumor metastasis is very low. Treatments including the antitumor immunocompetence of innate immune cells are found favorable for OS tumor. However, as a major means of early tumor detection, there are still few studies on T cells in peripheral blood of OS patients. Methods OS cells were implanted into the femoral bone marrow cavity of C3H/HeN mice to construct OS model. The proportion of T lymphocyte subsets in peripheral blood of mice was detected by flow cytometry on the 14th and 21st days after modeling. Results Compared with sham operation group, on the 14th day after operation, the proportion of γδT cells and CD4+ regulatory T cells (Tregs) showed significantly higher level while other subsets didn’t have obvious difference. On the 21st day after operation, the proportion of CD3+ T cells, γδ T cells, CD4+ Tregs and Tregs/Th17 ratio were obviously higher than that of sham operation group. Meanwhile, while CD4+ T cells and CD8+ T cells were lower, suggesting the happen of peripheral immunosuppression. Comparing the T lymphocytes subsets on the 14th and 21st day after modeling in the tumor group, it was found that CD3+ T cells and CD4+ Tregs on the 21st day after modeling were significantly higher than those on the 14th day while CD4+ T cells, CD8+ T cells decreased significantly, indicating the changes of T lymphocytes in peripheral blood of OS mice are related to tumor development. Conclusions The formation and development of femoral OS is accompanied by the disorder of peripheral blood T lymphocyte subsets and peripheral immunosuppression.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that causes coronavirus disease 2019 (COVID-19), the respiratory illness responsible for the COVID-19 pandemic. SARS-CoV-2 is a positive-stranded RNA virus belongs to Coronaviridae family. The viral genome of SARS-CoV-2 contains around 29.8 kilobase with a 5′-cap structure and 3′-poly-A tail, and shows 79.2% nucleotide identity with human SARS-CoV-1, which caused the 2002-2004 SARS outbreak. As the successor to SARS-CoV-1, SARS-CoV-2 now has circulated across the globe. There is a growing understanding of SARS-CoV-2 in virology, epidemiology, and clinical management strategies. In this study, we verified the existence of two 18-22 nt small viral RNAs (svRNAs) derived from the same precursor in human specimens infected with SARS-CoV-2, including nasopharyngeal swabs and formalin-fixed paraffin-embedded (FFPE) explanted lungs from lung transplantation of COVID-19 patients. We then simulated and confirmed the formation of these two SARS-CoV-2-Encoded small RNAs in human lung epithelial cells. And the potential pro-inflammatory effects of the splicing and maturation process of these two svRNAs in human lung epithelial cells were also explored. By screening cytokine storm genes and the characteristic expression profiling of COVID-19 in the explanted lung tissues and the svRNAs precursor transfected human lung epithelial cells, we found that the maturation of these two small viral RNAs contributed significantly to the infection associated lung inflammation, mainly via the activation of the CXCL8, CXCL11 and type I interferon signaling pathway. Taken together, we discovered two SARS-CoV-2-Encoded small RNAs and investigated the pro-inflammatory effects during their maturation in human lung epithelial cells, which might provide new insight into the pathogenesis and possible treatment options for COVID-19.

随着全球导航卫星系统(GNSS)信号体制的不断更新,接收机应用环境越来越复杂,多径误差抑制技术也在不断发展进步.其中,基于改进基带的多径抑制算法由于成本和效果综合较优,受到了国内外研究人员的重视.论文首先阐述了 GNSS多径抑制的基本原理,综述了传统的GNSS参量式和非参量式多径抑制算法的研究现状和实现方式,然后探讨分析了新体制信号中提高抗多径性能的方法,总结了当前GNSS多径抑制基带处理研究中的主要问题,最后展望了其未来的发展趋势.

合作目标方案在辅助飞行器着陆、物资投递、工程量测中具有重要应用价值.为解决单目视觉定位任务中非充分光照条件下合作目标识别不准确、提取过程易丢失等问题,提出了结合改进自适应局部阈值二值化与形态学约束的轮廓提取方案.以图像明度通道值作为灰度化依据,提出使用自适应局部阈值的方法对像素点进行二值化处理,结合使用形态学约束条件筛除冗余轮廓与Douglas-Peucker算法拟合轮廓边缘,可在非充分光照条件下实现对合作目标的准确提取.实验结果表明,该算法在光照不足、光照不均条件下具有高达92.3％的标记召回率,单帧解算耗时不超过2×10-4ms,兼顾了鲁棒性与实时性的使用需求.

Multipath interference seriously degrades the performance of Global Navigation Satellite System (GNSS) positioning in an urban canyon. Most current multipath mitigation algorithms suffer from heavy computational load or need external assistance. We propose a multipath mitigation algorithm based on the steepest descent approach, which has the merits of less computational load and no need for external aid. A new ranging code tracking loop is designed based on the steepest descent method, which can save an early branch or a late branch compared with the narrow-spacing correlation method. The power of the Non-Line-of-Sight (NLOS) signal is weaker than that of the Line-of-Sight (LOS) signal when the LOS signal is not obstructed and with a relatively high Carrier Noise Ratio (CNR). The peak position in the X-axis of the ranging code autocorrelation function does not move with the NLOS interference. Meanwhile, the cost function is designed according to this phenomenon. The results demonstrate that the proposed algorithm outperforms the narrow-spacing correlation and the Multipath Estimated Delay Locked Loop (MEDLL) in terms of the code multipath mitigation and computation time. The Standard Deviation (STD) of the tracking error with the proposed algorithm is less than 0.016 chips. Moreover, the computation time of the proposed algorithm in a software defined receiver is shortened by 24.21% compared with the narrow-spacing correlation.

Background Angiotensin-converting enzyme 2 (ACE2) is known as a tumor suppressor and lowly expressed in most cancers. The expression pattern and role of ACE2 in breast cancer (BC) have not been deeply elucidated. Methods A systematic pan-cancer analysis was conducted to assess the expression pattern and immunological role of ACE2 based on RNA-sequencing (RNA-seq) data downloaded from The Cancer Genome Atlas (TCGA). The correlation of ACE2 expression and immunological characteristics in the BC tumor microenvironment (TME) was evaluated. The role of ACE2 in predicting the response to therapeutic options was estimated. Moreover, the pharmacodynamic effect of angiotensin-(1-7) (Ang-1-7), the product of ACE2, on chemotherapy and immunotherapy was evaluated on the BALB/c mouse BC model. In addition, the plasma samples from BC patients receiving neoadjuvant chemotherapy were collected and subjected to the correlation analysis of the expression level of Ang-1-7 and the response to neoadjuvant chemotherapy. Results ACE2 was lowly expressed in BC tissues compared with that in adjacent tissues. Interestingly, ACE2 was shown the highest correlation with immunomodulators, tumor-infiltrating immune cells (TIICs), cancer immunity cycles, immune checkpoints, and tumor mutation burden (TMB) in BC. In addition, a high level of ACE2 indicated a low response to endocrine therapy and a high response to chemotherapy, anti-ERBB therapy, antiangiogenic therapy and immunotherapy. In the mouse model, Ang-1-7 sensitized mouse BC to the chemotherapy and anti-PD-1 immunotherapy, which revealed its significant anti-tumor effect. Moreover, a high plasma level of Ang-1-7 was associated with a better response to neoadjuvant chemotherapy. Conclusions ACE2 identifies immuno-hot tumors in BC, and its enzymatic product Ang-1-7 sensitizes BC to the chemotherapy and immunotherapy by remodeling the TME.

故障预测与健康管理(PHM)对提高无人机的可靠性具有重要意义.针对北斗短报文机载PHM通信中数据量与传输效率之间的矛盾,提出并实现了一种机载PHM通信中的北斗卫星导航系统(BDS)优先级分包传输方法.该方法基于传输信息优先级思路设计了分包传输策略,平衡了通信频度与容量不足的矛盾,然后利用丢包重传机制提高了通信的可靠性,并实现了可视化通信应用.实验结果表明,所提策略可在规定时间内实现PHM信息的可靠传输,验证了所设计的BDS优先级分包传输方法在机载PHM信息传输方面的可靠性.

北斗三号播发的B1C信号采用二进制偏移载波(BOC)调制方式和10 ms周期的伪随机码,可以与现存L1频段上的导航信号共有载波频率,同时提高导航信号的跟踪精度和伪距测量精度.然而,BOC调制会带来相关函数多峰性问题,10 ms周期的伪随机码会极大增加信号捕获的计算量,影响捕获速度,同时长码的跟踪需要更小带宽的跟踪环路,这对捕获精度提出更高要求.为此,根据北斗B1C信号的特点,在利用ASPeCT技术消除BOC信号相关副峰的基础上,兼顾捕获的快速性与精度,提出一种北斗BIC信号快速高精度捕获算法.所提算法先利用多级搜索和降采样策略快速实现信号的粗捕获,将捕获频率范围缩小,再利用多项式曲线拟合的方法进行多普勒频率精捕获,提高捕获精度.在多级搜索过程中,相关计算融合ASPeCT技术,通过本地码与副载波调制码构造无副峰相关函数,确保搜索过程无模糊度.所提捕获算法经接收前端采集的北斗B1C信号测试,实验表明,该捕获方法对相关副峰的消除程度可达到86.70％,多普勒频率误差缩小到1 Hz以内,捕获耗时相对缩短了近2/3,实现了快速高精度捕获,保障了后续跟踪算法的成功率.