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Range Cell Migration Correction (RCMC) represents an important advancement in the estimation of moving target parameters and imaging of targets in high-resolution Synthetic Aperture Radar (SAR) systems. When the motion of a target or platform becomes complex, the traditional low-order RCMC method may no longer be suitable. Meanwhile, the existing high-order RCMC method based on parameterization is susceptible to issues such as model mismatch and high computational complexity. Additionally, its performance may decrease significantly under a low Signal-to-Noise Ratio (SNR). This research utilizes Extended Kalman Filter (EKF) to track the phase responsible for RCM and develop a phase compensation function to achieve RCMC. The proposed approach is model-independent and can track high-order components in the phase, thereby enabling high-order RCMC of moving targets in SAR. In addition, EKF can filter signals during phase tracking to effectively lower the SNR threshold of the proposed method. Thus, this method offers broad applicability, moderate computational complexity, and the ability to correct non-negligible high-order residual range cell migrations, thereby distinguishing it from traditional methods. This study thoroughly explains the principles and mathematical model behind the proposed method, demonstrating its effectiveness and superiority through multiple sets of simulations and measured data processing.

Abstract Background Carbon and water use efficiencies (CUE and WUE, respectively) are vital indicators of the adaptability of plants to environmental conditions. However, the effects of grazing and climate change on the spatiotemporal changes in CUE and WUE in Qinghai–Tibet Plateau grasslands (QTPG) are still unclear. Results Using the enhanced Biome-BGCMuSo model in combination with observed data, we estimated and analyzed the spatiotemporal variations in CUE and WUE and their responses to grazing in QTPG from 1979 to 2018. The mean annual CUE was 0.7066 in QTPG from 1979 to 2018 under the actual climate scenario. In general, the grassland CUE was low in the southeast and high in the northwest. Grazing generally decreased CUE in QTPG from 1979 to 2018, and there was an increasing trend in the difference in CUE between the grazing and nongrazing scenarios. The difference in CUE was generally greater in the northwest than in the southeast. The mean annual WUE was 0.5591 g C/kg H2O in QTPG from 1979 to 2018 under the actual climate scenario. After 2000, the grassland WUE exhibited a fluctuating upward trend. In general, the grassland WUE was greater in the southeast than in the northwest. Grazing generally decreased WUE in QTPG from 1979 to 2018, and there was an increasing trend in the difference in WUE between the grazing and nongrazing scenarios. The difference in WUE was generally greater in the northwest than in the southeast. Conclusions The findings of this study suggested that the spatiotemporal changes in CUE and WUE in QTPG were closely related to changes in the natural environment and grazing management.

Abstract Immunocompromised individuals are particularly vulnerable to viral infections and reactivation, especially endogenous herpes viruses such as Epstein-Barr virus (EBV), a member of oncogenic gamma-herpesviruses, which are commonly linked to pneumonia and consequently significant morbidity and mortality. In the study of human and animal oncogenic gammaherpesviruses, the murine gamma-herpesviruses-68 (MHV-68) model has been applied, as it can induce pneumonia in immunocompromised mice. Mesenchymal stem cell (MSC) treatment has demonstrated therapeutic potential for pneumonia, as well as other forms of acute lung injury, in preclinical models. In this study, we aim to investigate the therapeutic efficacy and underlying mechanisms of human bone marrow-derived MSC (hMSC) on MHV-68-induced pneumonia. We found that intravenous administration of hMSCs significantly reduced lung damages, diminished inflammatory mediators and somehow inhibited MHV-68 replication. Furthermore, hMSCs treatment can regulate innate immune response and induce macrophage polarization from M1 to M2 phenotype, could significantly alter leukocyte infiltration and reduce pulmonary fibrosis. Our findings with co-culture system indicated that hMSCs effectively reduced the secretion of of inflammation-related factors and induced a shift in macrophage polarization, consistent with in vivo results. Further investigations revealed that hMSCs treatment suppressed the activation of macrophage ROS/NLRP3 signaling pathway in vivo and in vitro. Moreover, administration of MCC950, a selective NLRP3 inhibitor has been shown to effectively reduce ROS production and subsequently alleviate inflammation induced by MHV-68. Taken together, our work has shown that hMSCs can effectively protect mice from lethal MHV-68 pneumonia, which may throw new light on strategy for combating human EBV-associated pneumonia.

Advanced waveform design schemes have been widely employed for radar and sonar remote sensing analysis such as target detection and separation, where significant range sidelobe is a main factor that limits the improvement of analysis performance. As an extensional type of Golay complementary waveforms, complementary sets are a waveform design scenario of concern that shows more diversity in the design of transmission order, and results in a different distribution of range sidelobes. This work proposes an oversampled generalized Prouhet–Thue–Morse (OGPTM) method for the transmitted signal design of complementary sets, with comprehensive analysis to the influence on the sidelobe distribution. Based on this idea and our previous work, we further put forward a pointwise multiplication processor (PMuP) to integrate two delay-Doppler maps of oversampled complementary sets, which achieve much better sidelobe suppression performance on high-speed target detection with range migration.

Holographic synthetic aperture radar (HoloSAR) introduces a cutting-edge three-dimensional (3-D) imaging mode to the field of synthetic aperture radar (SAR), enriching the scattering information of targets by observing them across multiple spatial dimensions. However, independent phase errors among baselines, such as those caused by platform jitter and measurement inaccuracies, pose significant challenges to imaging quality. The phase gradient autofocus (PGA) method effectively estimates phase errors, but struggles to accurately estimate the linear component, causing vertical shift in HoloSAR subaperture imaging result. Therefore, this paper proposes a PGA-based phase error compensation method for HoloSAR to address the vertical shift issue caused by linear phase errors. This method can achieve phase error correction in both the echo domain and image domain with enhanced efficiency. Experimental results of simulated targets and real data from the GOTCHA system demonstrate the effectiveness and practicality of the proposed method.

Visual Simultaneous Localization and Mapping (V-SLAM) plays a crucial role in the development of intelligent robotics and autonomous navigation systems. However, it still faces significant challenges in handling highly dynamic environments. The prevalent method currently used for dynamic object recognition in the environment is deep learning. However, models such as Yolov5 and Mask R-CNN require significant computational resources, which limits their potential in real-time applications due to hardware and time constraints. To overcome this limitation, this paper proposes ADM-SLAM, a visual SLAM system designed for dynamic environments that builds upon the ORB-SLAM2. This system integrates efficient adaptive feature point homogenization extraction, lightweight deep learning semantic segmentation based on an improved DeepLabv3, and multi-view geometric segmentation. It optimizes keyframe extraction, segments potential dynamic objects using contextual information with the semantic segmentation network, and detects the motion states of dynamic objects using multi-view geometric methods, thereby eliminating dynamic interference points. The results indicate that ADM-SLAM outperforms ORB-SLAM2 in dynamic environments, especially in high-dynamic scenes, where it achieves up to a 97% reduction in Absolute Trajectory Error (ATE). In various highly dynamic test sequences, ADM-SLAM outperforms DS-SLAM and DynaSLAM in terms of real-time performance and accuracy, proving its excellent adaptability.

Abstract Climate change is a vital driver of biodiversity patterns and species distributions, understanding how organisms respond to climate change will shed light on the conservation of endangered species. In this study, the MaxEnt model was used to predict the potential suitable area of 12 threatened medicinal plants in the QTP (Qinghai‐Tibet Plateau) under the current and future (2050s, 2070s) three climate scenarios (RCP2.6, RCP4.5, RCP8.5). The results showed that the climatically suitable habitats for the threatened medicinal plants were primarily found in the eastern, southeast, southern, and some parts of the central regions on the QTP. Moreover, 25% of the threatened medicinal plants would have reduced suitable habitat areas within the next 30–50 years in the different future global warming scenarios. Among these medicinal plants, RT (Rheum tanguticum) would miss the most habitat (98.97%), while the RAN (Rhododendron anthopogonoides) would miss the least habitat (10.15%). Nevertheless, 33.3% of the threatened medicinal plants showed an increase in their future habitat area because of their physiological characteristics which are more adaptable to a wide range of climates. The climatic suitable habitat for 50% of the threatened medicinal plants would migrate to higher altitudes or higher latitudes regions. This study provides a data foundation for the conservation of biodiversity and wild medicinal plants on the QTP.

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) detection, known as SIC NOMA, enjoys higher spectral efficiency (SE), but suffers increased processing complexity and additional error propagation compared to Non-SIC NOMA, in which the receivers directly detect their own signals from the superposed signal without SIC detection. We propose a joint Mode Selection and Power Allocation (MSPA) scheme that can flexibly realize different kinds of tradeoffs while guaranteeing minimum rate requirements (MRRs) for two-user MISO-NOMA downlink. Specifically, we first obtain rate region boundaries that guarantee two users’ MRRs for two candidate modes: maximum-ratio transmission (MRT)/SIC NOMA and minimum mean square error beamforming (MMSE-BF)/Non-SIC NOMA. We then construct an expression of power allocation factors (transmit power factors) that can satisfy the two users’ MRRs for MRT/SIC NOMA (MMSE-BF/Non-SIC NOMA) by introducing a parameter λ. Finally, we achieve outer boundary of the union of the two modes' rate regions by mode selection and derive expected users' rates on it. Numerical results demonstrate that the proposed MSPA can improve SE and flexibly achieve different kinds of tradeoffs by adjusting λ, while guaranteeing the two users’ MRRs simultaneously for high signal to noise ratio (SNR) scenario and alternately guaranteeing the two users’ MRRs for low SNR scenario.

Objective The complex tibial plateau fractures involving both medial and posterolateral columns are of frequent occurrence in clinics, but the existing fixation system cannot deal with medial and posterolateral fragments simultaneously. Therefore, a novel locking buttress plate named as medial and posterior column plate (MPCP) was designed in this study to fix the simultaneous medial and posterolateral tibial plateau fractures. Meanwhile, the comparative finite element analysis (FEA) was conducted to investigate the discrepancy between MPCP and traditional multiple plates (MP + PLP) in their biomechanical characteristics. Methods Two 3D finite element models of simultaneous medial and posterolateral tibial plateau fracture fixed with MPCP and MP + PLP system, respectively, was constructed. To imitate the axial stress of knee joint in ordinary life, diverse axial forces with 100, 500, 1000, and 1500 N were applied in the two fixation models, and then the equivalent displacement and stress nephograms and values were obtained. Results The similar trend of displacement and stress increasing with the loads was observed in the two fixation models. However, several heterogeneities of displacement and stress distribution were found in the two fixation models. The max displacement and von Mises stress values of plates, screws, and fragments in the MPCP fixation model were significantly smaller than that in the MP + PLP fixation model, except for the max‐shear stress values. Conclusion As a single locking buttress plate, the MPCP system showed the excellent benefit on improving the stability of the simultaneous medial and posterolateral tibial plateau fractures, compared with the traditional double plate fixation system. However, the excessive shear stress around screw holes should be paid attention to prevent trabecular microfracture and screw loosening.

Frequency synchronization error, as one of the inevitable technical challenges in distributed synthetic aperture radar (SAR), has different impacts on different SAR systems. Multi-monostatic SAR is a typical distributed configuration where frequency synchronization errors are tiny in distributed airborne and low earth orbit (LEO) SAR systems. However, due to the long time delay and long synthetic aperture time, the imaging performance of a multi-monostatic geosynchronous (GEO) SAR system is affected by frequency oscillator errors. In this paper, to investigate the frequency synchronization problem in this configuration, we firstly model the echo signals with the frequency synchronization errors, which can be divided into fixed frequency errors and random phase noise. Secondly, we talk about the impacts of the two kinds of errors on imaging performance. To solve the problem, we thirdly propose an autofocus back-projection (ABP) algorithm, which adopts the coordinate descent method and iteratively adjusts the phase error estimation until the image reaches its maximum sharpness. Based on the characteristics of the frequency synchronization errors, we further propose the Node ABP (NABP) algorithm, which greatly reduces the amount of storage and computation compared to the ABP algorithm. Finally, simulations are carried out to validate the effectiveness of the ABP and NABP algorithms.

This article proposes an along-track swarm synthetic aperture radar (ATS-SAR) system to accomplish the high frame rate and enhance imaging resolution simultaneously. Unlike the current along-track multistatic SAR (Multi-SAR), each platform of the proposed ATS-SAR only collects part of the aperture data. Then, the bistatic pair acquisitions of ATS-SAR are transformed into virtual monostatic subapertures, and a large aperture is combined in a short time. Considering the practical motion state difference of each individual platform, various ATS-SAR echo models are thoroughly investigated and established. Furthermore, a subaperture collaboration imaging algorithm for ATS-SAR (SACIm-ATS) based on sparse constraints is also proposed. An effective phase compensation function is designed to improve echo sparsity by homogenizing the ATS-SAR echo. Then, the compressed sensing method can be utilized to accurately estimate more azimuth data, obtaining a higher azimuth resolution. Simulations and a real measured experiment are carried out to verify the effectiveness of the proposed ATS-SAR and the SACIm-ATS algorithm. Compared with the state-of-the-art imaging algorithms, the proposed SACIm-ATS algorithm can significantly enhance the ATS-SAR imaging performance.

Abstract In the urban environment, the radar is easily affected by multipath (MP) returns. The problem of detecting point‐like targets in the urban environment with the existence of resolvable MP returns under the background of heterogeneous clutter is addressed. Two adaptive detectors have been devised with a combination of prior information on the environment and echoes of target. The detectors can use the MP echoes in all MP prediction range cells for detection or in some available paths for signal detection by the available path selection algorithm that eliminates the weak signal propagation paths. Remarkably, the proposed decision schemes exhibit the desirable constant false alarm rate property with respect to the unknown parameters of the heterogeneous interference. The performance assessment, conducted on simulated data in terms of detection probability also in comparison with conventional solutions, highlights the effectiveness of the MP exploitation, while the MP signal is strong and shows the detection performance enhancement potential.

Abstract Background Neoadjuvant programmed death receptor-1 (PD-1) inhibitors have drawn increasing attention in locally advanced head and neck squamous cell carcinoma (HNSCC). In this study, we investigated the safety and efficacy of gemcitabine and cisplatin (GP), combined with a PD-1 inhibitor, in patients with locally advanced HNSCC. Materials and methods A total of 23 eligible patients were administered two cycles of toripalimab and GP followed by surgical resection. The primary endpoints were safety, treatment-related adverse events (TRAEs), and non-operation delay rates. The secondary endpoints consisted of pathological complete response (pCR) rate, major pathological response (MPR) rate, objective response rate (ORR), and R0 resection rate. Results The incidence of TRAEs from grades 1 to 4 was 43.5%, 34.8%, 13.0%, and 8.7%, respectively. Grade 3/4 TRAEs included neutropenia, fatigue, hyperglycemia, nausea and vomiting, decreased appetite, rash, and diarrhea. No treatment-related surgical delay was observed. The radiographic response rates were 5.0% (CR), 40.0% (PR), and 55.0% (SD). The ORR reached 45.0%. Eighteen patients underwent successful surgical resection. The R0 resection rate was 100%. The pathological response rates were 16.7% (pCR), 27.8% (MPR, two of five near-pCR), 16.7% (PPR), and 38.8% (NPR). CD4, CD8, CD20, and CD38 expression in the tumors significantly increased after neoadjuvant chemotherapy. The increase in CD20 levels after neoadjuvant treatment in patients with pCR/MPR was significantly higher than in patients with PPR/NPR. Conclusion Triweekly neoadjuvant toripalimab-GP is feasible and achieves promising pCR and MPR rates in patients with resectable locally advanced HNSCC. Trial registration Chinese clinical trial registry, ChiCTR2100043743, Registered 27 Febrary 2021- Retrospectively registered, http://www.chictr.org.cn/showproj.aspx?proj=120570

BackgroundWilson’s disease (WD) is a recessive genetic disorder characterized by copper metabolism dysfunction. It is difficult to obtain an accurate diagnosis due to its variable clinical presentation. This study aimed to describe the clinical characteristics and diagnostic particularities in a series of Chinese WD patients.MethodsThe medical records of 371 patients with WD retrieved from January 2005 to December 2020 were retrospectively reviewed.ResultsThe incidence of WD has a male predominance in the adult population. However, the difference in sex distribution is not significant in the pediatric population. Females have an earlier symptom onset than males. The most common initial symptoms were neuropsychiatric manifestations both in the pediatric population (49.7%) and adult population (69.8%), and there was a male predominance (61.8%). Eighty-two percent of patients presented with more than two neurologic symptoms. Fifty-two (14%) patients presented with psychiatric symptoms. The most common WD phenotype was the neuropsychiatric form (48%). The age of onset occurred earlier in patients with the hepatic phenotype than in those with the neuropsychiatric phenotype. Moreover, there was a significant difference in sex distribution regarding phenotype. Females presented with a hepatic phenotype more often than males, and the neuropsychiatric phenotype occurred more frequently in males with an older onset age. Further study showed that the age at onset was a deciding factor for predicting the neuropsychiatric phenotype among the hepatic phenotype. However, sex did not correlate with the phenotype.ConclusionMales seem to have a higher disease susceptibility, with symptom onset later than females. Males frequently present with a neuropsychiatric phenotype, while females present with a hepatic phenotype. Age at onset was a deciding factor for predicting the WD phenotype. Further studies focusing on the effect of estrogens on the pathology of WD are suggested.

Airborne circular synthetic aperture radar (CSAR) can realize high-resolution imaging of the scene over 360 degrees azimuth angle variation. Aiming at the problem of focusing of buildings for the airborne CSAR, this paper first analyzes the phase errors of CSAR buildings focusing in detail, and the analytic relationship between the scatterer height and azimuth focusing quality is deduced. Then, a focusing method of CSAR buildings based on the back projection algorithm is proposed. This method adopts the processing strategy of multi-layers imaging, and it is able to improve azimuth focusing quality of the buildings which have large height dimension. The proposed method is especially suitable for the high-resolution imaging and monitoring of the urban site with high-rise buildings in the airborne CSAR scenario. The correctness of the theoretical analysis and the validity of the proposed method are verified by using both simulation results and Ku-band airborne CSAR real data processing results.

In the practical application of Tomographic Synthetic Aperture Radar (TomoSAR), the number of acquisitions is usually restricted due to their expensive cost. A coprime TomoSAR technique was proposed to reduce the required number of acquisitions by sparsely distributing acquisitions and elongating baseline aperture. To guarantee a reliable tomogram, this study aims to determine the minimum number of acquisitions for the coprime TomoSAR when adopting a subspace method for performing the tomographic reconstruction. However, the performance of the subspace method depends on multiple parameters. In light of this, the selection of acquisition times has to comprehensively weigh the effects of all these parameters on the reconstruction performance. To this end, a prerequisite for reliable reconstruction is established by quantifying the relationship between the sample eigenvalues and all related parameters. Compared with conventional estimation approaches for the minimum number of acquisitions, the proposed approach has twofold advantages of containing all related parameters and of having a closed-form expression. Finally, the simulation experiments verify that the number of acquisitions estimated by our approach is close to the minimum and can guarantee reconstruction reliability.

Abstract Background Cytomegalovirus (CMV) pneumonia is a major cause of morbidity and mortality in immunodeficiency individuals, including transplant recipients and Acquired Immune Deficiency Syndrome patients. Antiviral drugs ganciclovir (GCV) and phosphonoformate (PFA) are first-line agents for pneumonia caused by herpesvirus infection. However, the therapy suffers from various limitations such as low efficiency, drug resistance, toxicity, and lack of specificity. Methods The antiviral drugs GCV and PFA were loaded into the pH-responsive nanoparticles fabricated by poly(lactic-co-glycolic acid) (PLGA) and 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), and further coated with cell membranes derived from bone marrow mesenchymal stem cells to form artificial stem cells, namely MPDGP. We evaluated the viral suppression effects of MPDGP in vitro and in vivo. Results MPDGP showed significant inflammation tropism and efficient suppression of viral replication and virus infection-associated inflammation in the CMV-induced pneumonia model. The synergistic effects of the combination of viral DNA elongation inhibitor GCV and viral DNA polymerase inhibitor PFA on suppressing the inflammation efficiently. Conclusion The present study develops a novel therapeutic intervention using artificial stem cells to deliver antiviral drugs at inflammatory sites, which shows great potential for the targeted treatment of pneumonia. To our best knowledge, we are the first to fabricate this kind of artificial stem cell to deliver antiviral drugs for pneumonia treatment. Graphical Abstract

In this paper, we address the problem of covariance matrix estimation for radar adaptive detection under non-Gaussian clutter. Traditional model-based estimators may suffer from performance loss due to the mismatch between real data and assumed models. Therefore, we resort to a data-driven deep-learning method and propose a covariance matrix estimation method based on a complex-valued convolutional neural network (CV-CNN). Moreover, a real-valued (RV) network with the same framework as the proposed CV network is also constructed to serve as a natural competitor. The obtained clutter covariance matrix estimation based on the network is applied to the adaptive normalized matched filter (ANMF) detector for performance assessment. The detection results via both simulated and real sea clutter illustrate that the estimator based on CV-CNN outperforms other traditional model-based estimators as well as its RV competitor in terms of probability of detection (PD).

Inverse synthetic aperture radar (ISAR) imaging can be improved by processing Range-Instantaneous Doppler (RID) images, according to a method proposed in this paper that uses neural networks. ISAR is a significant imaging technique for moving targets. However, scatterers span across several range bins and Doppler bins while imaging a moving target over a large accumulated angle. Defocusing consequently occurs in the results produced by the conventional Range Doppler Algorithm (RDA). Defocusing can be solved with the time-frequency analysis (TFA) method, but the resolution performance is reduced. The proposed method provides the neural network with more details by using a string of RID frames of images as input. As a consequence, it produces better resolution and avoids defocusing. Furthermore, we have developed a positional encoding method that precisely represents pixel positions while taking into account the features of ISAR images. To address the issue of an imbalance in the ratio of pixel count between target and non-target areas in ISAR images, we additionally use the idea of Focal Loss to improve the Mean Squared Error (MSE). We conduct experiments with simulated data of point targets and full-wave simulated data produced by FEKO to assess the efficacy of the proposed approach. The experimental results demonstrate that our approach can improve resolution while preventing defocusing in ISAR images.

To solve the problem of the moving target synthetic aperture radar (SAR) imaging from an incomplete echo, we proposed a novel SAR imaging method for the moving target with the azimuth missing data (MTIm-AMD) in this article. Instead of directly reconstructing the moving target image using the sparse SAR imaging method, we estimate and reconstruct the noncooperative moving target's complete echo from the azimuth incomplete echo based on its sparsity. At first, the noncooperative moving target's motion parameters are estimated from the incomplete echo. Then, to ensure that the complete echo can be well reconstructed using the compressed sensing method, these parameters are exploited to design a phase compensation function. Finally, using the reconstructed data, the fine-focused moving target can be obtained via the traditional SAR imaging algorithm. The simulation and experiment data results verify the effectiveness of the proposed MTIm-AMD method and demonstrate that the moving target can be fine imaging when the echo signal-to-noise ratio (SNR) is higher than $-$20 dB and the azimuth missing ratio (AMR) is less than 70%. This implies that the proposed MTIm-AMD method achieves satisfactory robustness to echo SNR and can handle most AMR cases.

The neutrino closure method is often used to obtain kinematics of semileptonic decays with one unreconstructed particle in hadron collider experiments. The kinematics of decays can be deducted by a twofold ambiguity with a quadratic equation. To resolve the twofold ambiguity, a novel method based on machine learning (ML) is proposed. We study the effect of different sets of features and regressors on the improvement of reconstructed invariant mass squared of ℓν system (q2). The result shows that the best performance is obtained by using the flight vector as the features and the multilayer perceptron (MLP) model as the regressor. Compared with the random choice, the MLP model improves the resolution of reconstructed q2 by ~40%. Furthermore, the possibility of using this method on various semileptonic decays is shown.

Background The degradation of alpine meadows has induced substantial losses of soil organic carbon (SOC) on the Tibetan Plateau. A commonly-used method for rehabilitating degraded alpine meadows in this region is establishing cultivated grasslands through sowing seed mixtures, but its impact on the biochemical stability of SOC has remained inadequately explored. Methods In this study, a total of 20 composited 0-20 cm soil samples were collected from a heavily degraded alpine meadow (DM) and three adjacent cultivated grasslands established for 3 years (CG3), 12 years (CG12), and 17 years (CG17) on the eastern Tibetan Plateau, and the SOC pool was separated into labile C pool I (LOC I), labile C pool II (LOC II), and recalcitrant C pool (ROC) in order to investigate changes in contents of SOC fractions that have different biochemical stabilities after the establishment of cultivated grassland. Results Although the establishment of cultivated grasslands led to increases in soil total organic C content, the increase was only significant in samples with 17 years of cultivation. We found that the contents of the three SOC fractions were higher at CG3 and CG12 compared with those in the DM, and the differences were only significant for soil LOC II. By comparison, 17 years of cultivation led to significant increases in all of the SOC fraction contents. The results implied that different cultivation years had distinct impacts on SOC fractions in cultivated grasslands, and longer cultivation years contributed to accumulated soil ROC. The recalcitrance index of SOC in the DM was higher than that at CG3 and CG12, but lower than that at CG17. This was possibly due to the generally low litter quality of cultivated grasslands, which led to a slow release of complex compounds to soils. Moreover, it was observed that soil C:N ratio was a potential indicator of SOC biochemical stability because of their close correlation. Conclusions Our findings suggest that the long-term establishment of cultivated grasslands on DM is a promising solution to recovering both the quantity and stability of SOC on the Tibetan Plateau.

Microwave radar has advantages in detection accuracy and robustness, and it is an area of active research in unmanned ground vehicles. However, the existing conventional automotive corner radar, which employs real-aperture antenna arrays, has limitations in terms of observable angle and azimuthal resolution. This paper proposes a novel 3D ArcSAR method to address this issue, which combines rotational synthetic aperture radar (SAR) and direction estimation algorithms. The method aims to reconstruct 3D images of 360° scenes and offers distinctive advantages in both azimuthal and altitudinal sensing. Nevertheless, due to the unique structural characteristics of vehicle SAR, it is limited to receiving only a single snapshot signal for 3D sensing. We propose a resolution algorithm based on ArcSAR and the iterative adaptive approach (IAA) to resolve the limitation. Furthermore, the errors in altitude angle estimation of the proposed algorithm and conventional algorithms are analyzed under various conditions, including different target spacing and signal-to-noise ratio (SNR). Finally, we design and implement a prototype of the 3D ArcSAR sensing system, which utilizes a millimeter-wave MIMO radar system and a rotating scanning mechanical system. The experimental results obtained from this prototype effectively validate the effectiveness of the proposed method.

Stable and reliable autonomous localization technology is fundamental for realizing autonomous driving. Localization systems based on global positioning system (GPS), cameras, LIDAR, etc., can be affected by building occlusion or drastic changes in the environment. These effects can degrade the localization accuracy and even cause the problem of localization failure. Localizing ground-penetrating radar (LGPR) as a new type of localization can rely only on robust subsurface information for autonomous localization. LGPR is mostly a 2D-2D registration process. This paper describes the LGPR as a slice-to-volume registration (SVR) problem and proposes an end-to-end TSVR-Net-based regression localization method. Firstly, the information of different dimensions in 3D data is used to ensure the high discriminative power of the data. Then the attention module is added to the design to make the network pay attention to important information and high discriminative regions while balancing the information weights of different dimensions. Eventually, it can directly regress to predict the current data location on the map. We designed several sets of experiments to verify the method’s effectiveness by a step-by-step analysis. The superiority of the proposed method over the current state-of-the-art LGPR method is also verified on five datasets. The experimental results show that both the deep learning method and the increase in dimensional information can improve the stability of the localization system. The proposed method exhibits excellent localization accuracy and better stability, providing a new concept to realize the stable and reliable real-time localization of ground-penetrating radar images.

Due to significant electromagnetic interference, radar interruptions, and other factors, Azimuth Missing Data (AMD) may occur in Synthetic Aperture Radar (SAR) echo, resulting in severe defocusing and even false targets. An important approach to solving this problem is to utilize Compressed Sensing (CS) methods on AMD echo to reconstruct complete echo, which can be abbreviated as the AMD Imaging Algorithm (AMDIA). However, the State-of-the-Art AMDIA (SOA-AMDIA) do not consider the influence of motion phase errors, resulting in an unacceptable estimation error of the complete echo reconstruction. Therefore, in order to enhance the practical applicability of AMDIA, this article proposes an improved AMDIA using Sparse Representation Autofocusing (SRA-AMDIA). The proposed SRA-AMDIA aims to accurately focus the imaging result, even in the Phase Error AMD (PE-AMD) echo case. Firstly, a Phase-Compensation Function (PCF) based on the phase history of the scene centroid is designed. When the PCF is multiplied with the PE-AMD echo in the range-frequency domain, a coarse-focused sparse representation signal can be obtained in the range-Doppler domain. However, due to the influence of unknown PE, the sparsity of this sparse representation signal is unsatisfying, breaking the sparse constraints requirement of the CS method. Therefore, we introduced a minimum entropy autofocusing algorithm to autofocus this sparse representation signal. Next, the estimated PE is compensated for this sparse representation signal, and a more sparse representation signal is obtained. Hence, the non-PE complete echo can be reconstructed. Finally, the estimated complete echo can be used with classic imaging algorithms to obtain high-resolution imaging results under the PE-AMD condition. Simulation and real measured data have verified the effectiveness of the proposed SRA-AMDIA.

Abstract Here, a novel off‐grid direction‐of‐arrival (DOA) estimation algorithm for nested arrays in the context of sparse recovery is proposed. The framework of existing off‐grid approaches is to correct the on‐grid parameter with a grid mismatch variable. From another perspective, we treat the direction‐of‐arrival deviation as the steering vector deviation equivalently, and the concept of steering vector uncertainty set is introduced to approximate the desired steering vector. Based on this idea, an optimization problem with respect to the uncertainty set for the purpose of steering vector reconstruction is formulated. After that, phase‐ambiguity resolving and phase‐difference approximation are orderly performed on the optimized uncertainty set to extract the off‐grid direction of arrival. Even though a coarse grid is given, simulation results are presented to demonstrate the performance advantage of the proposed direction‐of‐arrival estimation algorithm compared with several existing approaches.

Due to the substantial electromagnetic interference, radar interruptions, and other factors, the SAR system may fail to receive valid data in some azimuth areas. This phenomenon is known as Azimuth Missing Data (AMD). If classical SAR imaging algorithms are performed directly using AMD echo, the imaging results may be defocused or even display false targets, which seriously affects the accuracy of the image. Thus, we proposed a Sub-echo Segmentation and Reconstruction Azimuth Missing Data SAR Imaging Algorithm (SSR-AMDIA) to solve the problem of incomplete echo SAR imaging in this article. Instead of using the motion compensation step of the Polar Format algorithm (PFA) to recover the full echo from the AMD echo, the proposed SSR-AMDIA eliminates the effect of the planar approximation in PFA and expands the maximum depth of focus (DOF). The raw AMD echo was first subjected to range compression and Range Cell Migration Correction (RCMC), after which the AMD-RCMC echo was divided along the range direction. Then, we constructed a series of phase compensation functions based on the sub-segment AMD-RCMC echoes to guarantee the perfect recovery of the full RCMC echoes corresponding to the sub-scenes. Finally, we combined them to obtain the complete RCMC echo, and an excellent focused imaging result was then obtained via azimuth compression. Simulation and experimental data verified the effectiveness of the proposed algorithm. Furthermore, we derived the mathematical expressions for the two-dimensional maximum DOFs of the proposed algorithm. In contrast to the State-Of-the-Art (SOA) AMDIA, the SSR-AMDIA can obtain a superior imaging performance in a larger imaging scope under the conditions of most AMD cases.

An enhanced Doppler resolution and sidelobe suppression have long been practical issues for moving target detection using Golay complementary waveforms. In this paper, Golay complementary waveform radar returns are combined with a proposed processor, the pointwise thresholding processor (PTP). Compared to the pointwise minimization processor (PMP) illustrated in a previous work, which could only achieve a Doppler resolution comparable to existing methods, this approach essentially increases the Doppler resolution to a very high level in theory. This study also introduced a further filtering process for the delay-Doppler map of the PTP, and simulations verified that the method results in a delay-Doppler map virtually free of range sidelobes.

Accurate identification of desert vegetation dynamics in arid regions is challenging because of its complex composition of grass species, obscure boundary with non-desert vegetation, and high sensitiveness to climatic variations. This study examined the ability of optimized multi-endmember spectral mixture analysis (MESMA) for monitoring desert vegetation degradation, recovery, and greening in a dryland basin of Northwest China using Landsat time series data from the 1990s to 2016. Eight modeled endmember fractions were generated using the best endmember model with the lowest fraction error and root mean square error (RMSE). Abundances of non-desert vegetation, desert vegetation, soil, and impervious surface areas were incorporated based on the eight original fractions and validated using high spatial resolution images. Finally, the post-classification comparison approach was used to detect desert vegetation degradation, recovery, and greening. Results show that: (1) More than 97% of the land pixels were modeled successfully into eight endmember fractions for each period with the mean RMSE less than 0.01. All four simulated abundances had high correlations (r = 0.89–0.96) with the corresponding reference data, indicating good performance of MESMA in this study; (2) Desert vegetation increased dramatically (772.68 km2) during the 26-year period. The major change was desert vegetation recovery with a total area of 10,705 km2, followed by degradation with a total area of 4,715 km2. The greening area was the smallest, covering only 1,509 km2; (3) Increased precipitation was the major contributor for desert vegetation greening in the west of upper region while decreased precipitation was the major contributor for the degradation in the west of lower region. Anthropogenic factors (e.g., improvement of irrigation, crop expansion) were major contributors for the change in desert vegetation in the middle region. This research demonstrates that MESMA is promising in detecting desert vegetation dynamics in semi-arid and arid regions.

Ship detection with polarimetric synthetic aperture radar (PolSAR) images has attracted a lot of attention in recent years. However, modeling the distribution of clutter is a complicated task. This article introduces a distribution independent ship detector for PolSAR images. First, in order to improve the detection performance, the multichannel PolSAR data are projected onto a 1-D space utilizing an adaptive linear filter. The design of linear filter is modeled as a nonconvex optimization problem with the principle of maximization target-to-clutter ratio, which is solved by an iteration optimization algorithm. Then, the convergence and computational complexities of the proposed algorithm are theoretically analyzed. After that, a distribution independent detector with a bounded constant false alarm rate property is proposed to distinguish ships from sea clutter. The detection threshold is calculated based on the Markov inequality without modeling the statistical distribution of clutter. Experiments are carried out on real Radarsat-2 and AirSAR data to test the proposed detector. The results demonstrate that the proposed detector, which takes the distribution independent and unsupervised properties as the main advantages, also achieves comparable detection performance with state-of-the-art methods. Moreover, additional experiments verify the robustness of the proposed detector to the initialization of the algorithm, even though the optimization problem is nonconvex. Finally, the effects on detection results caused by polarization characteristics are investigated to give a further explanation about linear polarization enhancement.

The purpose of sparse unmixing (SU) is to find the optimal spectral subset from the spectral library and uses this subset to model each pixel in the hyperspectral data. The existing SU methods concern Gaussian noise a lot and focus less on the varied intensity of Gaussian noise in different bands and other types of noise, e.g., impulse noise and deadlines. Besides, the high coherence of the spectral library limits the performance of SU. Given the aforementioned problems, this article proposes a new method, called bandwise model based on spectral prior information (BMSPI). This proposed BMSPI models the Gaussian noise across different spectral bands and the other types of mixed noise under the maximum a posteriori probability framework, and decreases the effect of high coherence in the spectral library with the spectral prior information. The alternating direction method of multipliers is adopted to solve the BMSPI. The results of the simulated and real data experiments show that the bandwise model can suppress noises of different types effectively, and the spectral prior information is conducive to guide SU. The advantage of BMSPI is that the mentioned information is used completely. Thus, the accuracy of abundance estimation is improved.

IntroductionPrevious studies have reported a close relationship between cancer and microbes, particularly gut and tumor microbiota; however, the presence of tumor microbiome in nasopharyngeal carcinoma (NPC) and its role in the prognosis of NPC remain unclear.MethodsWe collected 64 samples including tissues from 50 patients with NPC (NPC group) and 14 patients with chronic nasopharyngitis (control group) receiver operating characteristics and we applied 16S ribosome RNA gene sequencing of all samples to assess microbiome profiles and immunohistochemistry to detect tumor microbiome in NPC.ResultsPatients in the control group harbored higher species diversity than those in the NPC group; however, the beta diversity was more distinct in the NPC group. In total, three genera with statistically significant differences between the two groups were identified. The area under the receiver operating characteristics (ROC) curve (AUC) was calculated using the relative abundance of these three significant genera, and a value of 0.842 was achieved. Furthermore, Turicibacter was confirmed as a potentially independent prognostic factor for NPC patients, and the progression-free survival (PFS) was markedly prolonged in patients with a low relative abundance of Turicibacter compared to patients with a high relative abundance of this genus (cutoff: 0.0046, hazard ratio: 5.10, 95% confidence interval: 2.04–12.77, p = 0.004).ConclusionsThe present study provided strong evidence of a correlation between tumor microbiome and NPC; the tumor microbiome may be considered a biomarker for early NPC diagnosis. Turicibacter potentially served as a independently prognostic indicator for NPC patients.

Global warming has a great impact on the activities of terrestrial vegetation. A consensus has been reached that the global vegetation is greening from the 1980–2010s. However, the trends of global vegetation are highly uncertain after 2000. Therefore, we used multi-source remote sensing vegetation index (VI), climate data, and Mann–Kendall trend analysis to explore the global vegetation trend and its uncertainty from 2001 to 2016. The effects of climate on the changes in vegetation were also investigated. We found that GIMMS-based VIs exhibited decreasing trends. By contrast, MODIS-based VIs and GLOBMAP LAI tended to increase. Evergreen broad-leaf forest contributed the most to the uncertainty of global vegetation trends, and the uncertainty of December–January–February and September–October–November was higher than that in the other seasons. The correlation of forest VI and temperature was the highest in March–April–May, whereas the correlation of non-forest VI and precipitation was higher than that of the forest. The anomalies of GIMMS-based VIs and mean annual precipitation were more consistent in the evergreen broad-leaf forest, woody savannas, mixed forest, evergreen needle-leaf forest, and deciduous needle-leaf forest than those in biomes under the impact of 2015–2016 El Niño.

Coprime arrays can highly increase degree-of-freedom (DOF) by exploiting the equivalent virtual signal. However, since the corresponding virtual array constructed by the coprime array is always a non-uniform linear array (non-ULA), most existing direction-of-arrival (DOA) estimation algorithms fail to utilize all received information and result in performance degradation. To address this issue, we propose a novel interpolation approach for coprime arrays to convert the virtual array into a ULA with which all received information can be efficiently utilized. In this paper, we consider a weighted covariance matrix fitting criterion to formulate a semi-definite programming (SDP) problem with respect to the interpolated virtual signal. After that, we can reconstruct a Hermitian Toeplitz covariance matrix corresponding to the interpolated ULA in a gridless manner, and the number of detectable targets is ulteriorly increased with the reconstructed covariance matrix. The proposed approach is hyperparameter-free so that the tedious process of selecting regularization parameters is avoided. Numerical experiments validate the superiority of the proposed interpolation-based DOA estimation algorithm in terms of DOF characteristic, resolution ability and estimation accuracy compared with several existing techniques.

Estimating the grassland carbon budget is critically important for ensuring that grassland resources are used sustainably. However, the spatiotemporal dynamics of the carbon budget and the response to grazing have not yet been characterized in Qinghai grasslands. Here, we estimated the gross primary productivity (GPP) and net ecosystem exchange (NEE) in Qinghai grasslands using the improved Biome-BGCMuSo model to characterize the spatiotemporal dynamics of the carbon budget and the response to grazing in this region from 1979 to 2018. The GPP of Qinghai grasslands fluctuated during the study period, with an average annual value of 118.78 gC/m2. The NEE of Qinghai grasslands fluctuated from 1979 to 2018, with an average value of −5.16 gC/m2. After 2,000, GPP increased, and NEE decreased in a fluctuating manner. There were clear regional differences in GPP and NEE. GPP was low in most areas of Qinghai, and GPP was high in eastern and southern Qinghai. The southern, southeastern, and northeastern parts of Qinghai were mainly carbon sinks, and the northwestern part of Qinghai and the region between the southeastern and northeastern parts of Qinghai were mainly carbon sources. Grazing generally decreased GPP and increased NEE in Qinghai grasslands from 1979 to 2018. There was spatial heterogeneity in the effect of grazing on GPP and NEE. Under grazing, GPP and NEE were significantly decreased mainly in eastern Qinghai, and GPP and NEE were significantly increased mainly in southern and eastern Qinghai. NEE was most affected by grazing in eastern Qinghai. The results of this study aid our understanding of the mechanism driving variation in the grassland carbon budget and provide new data that could be used to support local grassland management.

We review the two- and three-body baryonic B decays with the dibaryon (BB′¯) as the final states. Accordingly, we summarize the experimental data of the branching fractions, angular asymmetries, and CP asymmetries. Using the W-boson annihilation (exchange) mechanism, the branching fractions of B⟶BB′¯ are shown to be interpretable. In the approach of perturbative QCD counting rules, we study the three-body decay channels. In particular, we review the CP asymmetries of B⟶BB′¯M, which are promising to be measured by the LHCb and Belle II experiments. Finally, we remark the theoretical challenges in interpreting BB−⟶pp¯ρ− and BB−⟶pp¯μ−ν¯μ.

Ground penetrating radar (GPR) is one of the most generally used underground sensing equipment, but it is frequently contaminated by clutter and noise during data acquisition, which has a significant impact on the detection performance of buried targets. The purpose of this letter is to present a novel clutter suppression method based on the principal component Gaussian curvature decomposition (PCGCD). First, the GPR B-scan data are divided into different sub-components using principal component analysis (PCA). Then, a Gaussian curvature decomposition (GCD) method is proposed, which can be applied to PCA domain subspaces to recover more target structure information from random noise. The PCGCD method’s performance is evaluated using both numerical simulation and real-world GPR datasets. The visualization and quantitative results demonstrated our method’s superiority in protecting the underground target structure, removing complex random noise, and improving the detection ability of buried targets.

Assuming uncertain multipath return, this paper considers the robust joint transmit waveform and the receiving filter bank design of a multiple-input–multiple-output (MIMO) radar for multipath exploitation. The actual multipath return is considered to belong to an uncertain set, and we focus on the worst-case optimization of the signal-to-interference-plus-noise ratio (SINR) in the output of the filter bank. The design is cast as a non-convex max–min problem, which is very hard to solve. To tackle it, an equivalent reformulation is utilized and a cyclic optimization paradigm is devised. At each iteration, the filter’s optimization problem is equal to a set of separate solvable problems, the closed-form solution to which can be given directly. Moreover, we have shown that the max–min problem for the waveform optimization belongs to the area of generalized fractional programming, and it can be globally solved by resorting to Dinkelbach’s algorithm. Through simulations, the superiority of the proposed algorithm is demonstrated via a number of examples.

Abstract Background In recent years, some traction-assisted approaches have been introduced to facilitate endoscopic submucosal dissection (ESD) procedures by reducing the procedure time and risks related to the procedure. However, the relative advantages of traction-assisted endoscopic submucosal dissection (T-ESD) are still being debated. This study aimed to assess the efficacy of T-ESD for the treatment of superficial gastrointestinal neoplasms. Methods We searched MEDLINE, Embase, and Cochrane library up to March 31, 2019 for randomized controlled trials (RCTs) comparing T-ESD and conventional endoscopic submucosal dissection (C-ESD) for superficial gastrointestinal neoplasms. The main endpoints are en bloc resection, complete resection, procedure time, perforation, and delayed bleeding. Pooled risk ratio (RR), Peto odds ratio (OR), and mean difference (MD) were calculated to compare T-ESD and C-ESD. This study is registered with PROSPERO, number CRD42018108135. Results A total of 7 RCTs with 1007 patients were included in this meta-analysis. There were no significant differences between the T-ESD and C-ESD groups in the pooled estimate of en bloc resection, complete resection, and delayed bleeding (RR = 1.00, 95% CI 0.99, 1.01, I 2 = 0%, P = 0.66; RR = 1.00, 95% CI 0.98, 1.03, I 2 = 0%, P = 0.81; OR = 0.95, 95% CI 0.48, 1.86, I 2 = 19%, P = 0.87,respectively). The pooled estimate indicated that the procedure time was significantly shorter in the T-ESD group (MD = − 16.19, 95% CI − 29.24, − 3.13, I 2 = 87%, P = 0.02) than in the C-ESD group. Compared to C-ESD, T-ESD was associated with lower incidence of perforation (OR = 0.32, 95% CI 0.11, 0.91, I 2 = 0%, P = 0.03). Conclusions T-ESD is a safe and effective treatment option with a low perforation rate and shorter procedure time than C-ESD for superficial gastrointestinal neoplasms. Future multi-center (including European populations), randomized controlled trials of larger sample size and long-term outcomes of T-ESD are required.

Compressed sensing (CS) is a sparsity-driven and regularization-based approach. The CS theory has led to superior imaging methods, offering a number of benefits, including increased image quality and robustness to the limitations in data quantity. In such imaging methods, if the targets are not originally sparse (not sparse in the canonical basis), sparse representation is required. However, in synthetic aperture radar (SAR) imaging scenarios, owing to the potentially random phase of the scene, it is very difficult to find bases to sparsify a complex-valued and extended scene. In this paper, an improved framework to handle the magnitude and phase of the scene separately is proposed. Although the phase of the scene is potentially random, the magnitude can usually be sparsely represented. In the proposed framework, besides the sparsity, the real-valued information of the magnitude and the coefficient distribution of the sparse representation are also utilized. In the reconstruction process, the magnitude of the scene is constrained to be real-valued, and the coefficients of the sparse representation are limited in the low-frequency area. A novel gradient-based optimization algorithm is developed to solve the proposed framework effectively. Since more a priori knowledge is utilized in the proposed algorithm, it can reduce the computational complexity and improve the reconstruction precision. The simulation results and real data results from an airborne SAR system are presented to show the superiority of the proposed algorithm.