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Scanning radar angular super-resolution technology is based on the relationship between the target and antenna pattern, and a deconvolution method is used to obtain angular resolution capabilities beyond the real beam. Most current angular super-resolution methods are based on ideal distortion-free antenna patterns and do not consider pattern changes in the actual process due to the influence of factors such as radar radome, antenna measurement errors, and non-ideal platform motion. In practice, an antenna pattern often has unknown errors, which can result in reduced target resolution and even false target generation. To address this problem, this paper proposes an angular super-resolution imaging method for airborne radar with unknown antenna errors. First, based on the Total Least Square (TLS) criterion, this paper considers the effect of the pattern error matrix and derive the corresponding objective function. Second, this paper employs the iterative reweighted optimization method to solve the objective function by adopting an alternative iteration solution idea. Finally, an adaptive parameter update method is introduced for algorithm hyperparameter selection. The simulation and experimental results demonstrate that the proposed method can achieve super-resolution reconstruction even in the presence of unknown antenna errors, promoting the robustness of the super-resolution algorithm.

Abstract Background Sleep loss is a common public health problem that causes hyperalgesia, especially that after surgery, which reduces the quality of life seriously. Methods The 48-h sleep restriction (SR) mouse model was created using restriction chambers. In vivo imaging, transmission electron microscopy (TEM), immunofluorescence staining and Western blot were performed to detect the status of the blood-spinal cord barrier (BSCB). Paw withdrawal mechanical threshold (PWMT) was measured to track mouse pain behavior. The role of infiltrating regulatory T cells (Tregs) and endothelial cells (ECs) in mouse glycolysis and BSCB damage were analyzed using flow cytometry, Western blot, CCK-8 assay, colorimetric method and lactate administration. Results The 48-h SR made mice in sleep disruption status and caused an acute damage to the BSCB, resulting in hyperalgesia and neuroinflammation in the spinal cord. In SR mice, the levels of glycolysis and glycolysis enzymes of ECs in the BSCB were found significantly decreased [CON group vs. SR group: CD31+Glut1+ cells: p < 0.001], which could cause dysfunction of ECs and this was confirmed in vitro. Increased numbers of infiltrating T cells [p < 0.0001] and Treg population [p < 0.05] were detected in the mouse spinal cord after 48-h SR. In the co-cultured system of ECs and Tregs in vitro, the competition of Tregs for glucose resulted in the glycolysis disorder of ECs [Glut1: p < 0.01, ENO1: p < 0.05, LDHα: p < 0.05; complete tubular structures formed: p < 0.0001; CCK8 assay: p < 0.001 on 24h, p < 0.0001 on 48h; glycolysis level: p < 0.0001]. An administration of sodium lactate partially rescued the function of ECs and relieved SR-induced hyperalgesia. Furthermore, the mTOR signaling pathway was excessively activated in ECs after SR in vivo and those under the inhibition of glycolysis or co-cultured with Tregs in vitro. Conclusions Affected by glycolysis disorders of ECs due to glucose competition with infiltrating Tregs through regulating the mTOR signaling pathway, hyperalgesia induced by 48-h SR is attributed to neuroinflammation and damages to the barriers, which can be relieved by lactate supplementation.

Microwave Photonic (MWP) radars have remarkably improved traditional microwave radar hardware architectures using photonics devices. With the exceptional physical properties of photonic devices, MWP radars can emit ultra-wideband, high-linearity, high-quality linear frequency modulation signals, allowing ultra-high-resolution target imaging and detection. Different target regions exhibit distinct responses to different frequency signals during target imaging and detection due to their diverse structures and characteristics. Therefore, MWP radars have the potential to generate pseudo-color images based on scattering differences, further enhancing the information retrieval capability of MWP Synthetic Aperture Radar (MWP-SAR). Pseudo-color images generated using traditional remote sensing techniques cannot achieve centimeter-level resolution. Therefore, we propose a method for generating pseudo-color images while maintaining the resolution of MWP-SAR. The algorithm first determines an optimal sub-band echo search model and subsequently employs the optimal sub-band search algorithm to process the ultra-wideband echoes to obtain sub-band echo channels with the largest scattering characteristic differences. The multi-sub-band images are then color-composited to generate pseudo-color images that best describe the target scattering characteristics. However, to ensure the high resolution of MWP-SAR, a fusion model is established to combine the full-resolution SAR image with the multi-sub-band image. Finally, full-resolution pseudo-color images are successfully synthesized using the measured airborne MWP-SAR data, validating the effectiveness of the algorithm. This algorithm enables MWP-SAR to obtain more target information during imaging, offering assistance in implementing imaging radar and microwave vision.

Parameter extraction of radar signals is an important but challenging task in electronic warfare. In the modern electromagnetic environment, the radiation sources greatly increase, causing different radar signals to overlap, making the parameter extraction of radar signals difficult. Meanwhile, using radar signal parameter extraction methods that are not suitable for dealing with overlapping signals can lead to serious errors in this case. To address this, we propose a parameter extraction network for overlapping radar signals using modulation recognition-guided semantic segmentation. Specifically, we first design an encoder–decoder to segment overlapping radar signals, which uses channel rearrangement and modulation type filtering to increase the accuracy of segmentation. In this encoder–decoder, channel rearrangement is an optimization of convolution operation, aiming to increase the perceptual field while reducing feature information loss. And modulation type filtering can convert the results of semantic segmentation into masks corresponding to each radar signal, increasing the accuracy of segmentation. After the encoder–decoder, signal segmentation masks are obtained. Then, we compress these segmentation masks in the time and frequency domains, and extract the span of them to achieve accurate extraction of the pulsewidth and bandwidth of each radar signal. The experiments validate the feasibility of the proposed method.

Forward-looking imaging and target detection are highly desirable in many military and civilian fields, such as search and rescue, sea surface surveillance, airport surveillance, and guidance. However, during the processing procedure, imaging and target detection are usually regarded as two independent parts, which means that the imaging result will directly affect the detection performance. In this article, the LRSD-ADMM-net is proposed to achieve simultaneous super-resolution imaging and target detection for forward-looking scanning radar. First, low-rank and sparse constraints as regularization norms are incorporated to establish objective function, and the alternating direction multiplier method is used to solve simultaneous super-resolution and target detection problems. Then, the solving process is expanded into a neural network, where the weight parameters of each level are obtained through adaptive learning. At last, experiments are conducted to verify the effectiveness of the proposed method.

ABSTRACTThree-dimensional (3D) printing has been profoundly changing the production mode of traditional industries. However, this technique is usually limited to metre-scale fabrication, which prevents large-scale 3D printing (LS3DP) applications such as the manufacturing of buildings, aircraft, ships, and rockets. LS3DP faces great challenges, particularly, it not only requires confronting problems not yet solved by conventional 3D printing, such as the inability to print functional structures due to limitations by single-material manufacturing, but also needs to overcome the size effect limitation of large-scale printing. Here, we systematically review the state of the art in the integration of materials and technologies in LS3DP. We also demonstrate some disruptive engineering cases of LS3DP in the field of construction. The challenges and strategies for overcoming size constraints to achieve LS3DP of functional structures are discussed, including multifunctional 3D printing processes from nano- to large-scale and large-scale 4D printing processes, diverse printable materials and sustainable structures, horizontal and vertical size-independent printers, collaborative and intelligent control of the entire process, and extreme environment printing. These strategies can provide tremendous opportunities for the fully automated, intelligent, and unmanned production of these different material megastructures and internal multiscale multifunctional components such as buildings/structures, aerospace vehicles, and marine equipment.

BackgroundPhosphorus in the soil is mostly too insoluble for plants to utilize, resulting in inhibited aboveground biomass, while Carex can maintain their aboveground biomass through the presence of dauciform roots. However, dauciform roots lead to both morphological and physiological changes in the root system, making their primary mechanism unclear.MethodsA greenhouse experiment was conducted on three Carex species, in which Al-P, Ca-P, Fe-P, and K-P were employed as sole phosphorus sources. The plants were harvested and assessed after 30, 60 and 90 days.Results(1) The density of dauciform roots was positively correlated with root length and specific root length, positively influencing aboveground biomass at all three stages. (2) The aboveground phosphorus concentration showed a negative correlation with both dauciform root density and aboveground biomass in the first two stages, which became positive in the third stage. (3) Aboveground biomass correlated negatively with the aboveground Al concentration, and positively with Ca and Fe concentration (except Al-P). (4) Root morphological traits emerged as critical factors in dauciform roots’ promotion of aboveground biomass accumulation.ConclusionDespite the difference among insoluble phosphorus, dauciform roots have a contributing effect on aboveground growth status over time, mainly by regulating root morphological traits. This study contributes to our understanding of short-term variation in dauciform roots and their regulatory mechanisms that enhance Carex aboveground biomass under low available phosphorus conditions.

The shrinkage characteristic of porous materials is an important consideration in the drying process, as it can significantly impact the texture of the dried product and energy utilization. This phenomenon is influenced by numerous factors, including the structure of the cells, drying conditions, and the glass transition temperature. To gain a deeper understanding of the drying process, it is necessary to develop theoretical models that account for the simultaneous heat and mass transfer processes at the cellular level, as well as simulation tools to analyze the associated changes in drying morphology. In this paper, we highlight several key factors affecting shrinkage during the drying of porous materials, and also outline drying modeling, morphological simulation, and drying technology design considerations to provide guidance for improving the drying quality of porous materials as well as energy conversion efficiency.

Abstract Background The availability of physical activity (PA) facilities in neighborhoods is hypothesized to influence cardiovascular disease (CVD), but evidence from individual-level long-term cohort studies is limited. We aimed to assess the association between neighborhood exposure to PA facilities and CVD incidence. Methods A total of 4658 participants from the Chinese Multi-provincial Cohort Study without CVD at baseline (2007–2008) were followed for the incidence of CVD, coronary heart disease (CHD), and stroke. Availability of PA facilities was defined as both the presence and the density of PA facilities within a 500-m buffer zone around the participants’ residential addresses. Time-dependent Cox regression models were performed to estimate the associations between the availability of PA facilities and risks of incident CVD, CHD, and stroke. Results During a median follow-up of 12.1 years, there were 518 CVD events, 188 CHD events, and 355 stroke events. Analyses with the presence indicator revealed significantly lower risks of CVD (hazard ratio [HR] 0.80, 95% confidence interval ([CI] 0.65–0.99) and stroke (HR 0.76, 95% CI 0.60–0.97) in participants with PA facilities in the 500-m buffer zone compared with participants with no nearby facilities in fully adjusted models. In analyses with the density indicator, exposure to 2 and ≥ 3 PA facilities was associated with 35% (HR 0.65, 95% CI 0.47–0.91) and 28% (HR 0.72, 95% CI 0.56–0.92) lower risks of CVD and 40% (HR 0.60, 95% CI 0.40–0.90) and 38% (HR 0.62, 95% CI 0.46–0.84) lower risks of stroke compared with those without any PA facilities in 500-m buffer, respectively. Effect modifications between presence of PA facilities and a history of hypertension for incident stroke (P = 0.049), and a history of diabetes for incident CVD (P = 0.013) and stroke (P = 0.009) were noted. Conclusions Residing in neighborhoods with better availability of PA facilities was associated with a lower risk of incident CVD. Urban planning intervention policies that increase the availability of PA facilities could contribute to CVD prevention.

Studying the apportionment and sharing of agricultural water prices is necessary for clarifying the relationship between agricultural water price apportionment and government responsibility and ensuring the benign operation of irrigated areas. From the perspective of the versatility of irrigation water, irrigation benefits are classified as internal or external; the degree of benefit is measured using the proportion of irrigation water per cubic metre of internal and external benefits. This is used as an allocation coefficient to reasonably share prices based on farmers’ water price tolerance, build agricultural water price apportionment and sharing models, and calculate the range of water prices borne by farmers and the government in the Baojixia Irrigation District in the Shaanxi Province of China. Results showed that the apportionment coefficients of farmers and government were 0.85 and 0.15, respectively. For grain crops, the farmers' price range was 0.115–0.508 yuan/m3, while that of the government was 0.516–0.909 yuan/m3; for cash crops, these values were 0.566–3.009 and 0.154–0.458 yuan/m3, respectively. The results of this study support the formulation of agricultural water prices and provide a theoretical reference for reducing the water burden of farmers and promoting the high-quality development of the regional economy HIGHLIGHTS Study on the apportionment and sharing of agricultural water prices; The agricultural irrigation benefit is divided into direct benefit and indirect benefit according to the degree of influence.; The emergy theory method was used to calculate the benefit value of agricultural irrigation.; Using the benefit apportionment method, the apportionment ratio of different subjects was calculated, and the apportionment model of agricultural water price was constructed.; Considering farmers' water bearability, research on agricultural water sharing models and analysis of agricultural water price share the scale.;

Abstract Background Enhancer of zeste homolog 2 (EZH2), the key catalytic subunit of polycomb repressive complex 2 (PRC2), is overexpressed and plays an oncogenic role in various cancers through catalysis-dependent or catalysis-independent pathways. However, the related mechanisms contributing to ovarian cancer (OC) are not well understood. Methods The levels of EZH2 and H3K27me3 were evaluated in 105 OC patients by immunohistochemistry (IHC) staining, and these patients were stratified based on these levels. Canonical and noncanonical binding sites of EZH2 were defined by chromatin immunoprecipitation sequencing (ChIP-Seq). The EZH2 solo targets were obtained by integrative analysis of ChIP-Seq and RNA sequencing data. In vitro and in vivo experiments were performed to determine the role of EZH2 in OC growth. Results We showed that a subgroup of OC patients with high EZH2 expression but low H3K27me3 exhibited the worst prognosis, with limited therapeutic options. We demonstrated that induction of EZH2 degradation but not catalytic inhibition profoundly blocked OC cell proliferation and tumorigenicity in vitro and in vivo. Integrative analysis of genome-wide chromatin and transcriptome profiles revealed extensive EZH2 occupancy not only at genomic loci marked by H3K27me3 but also at promoters independent of PRC2, indicating a noncanonical role of EZH2 in OC. Mechanistically, EZH2 transcriptionally upregulated IDH2 to potentiate metabolic rewiring by enhancing tricarboxylic acid cycle (TCA cycle) activity, which contributed to the growth of OC. Conclusions These data reveal a novel oncogenic role of EZH2 in OC and identify potential therapeutic strategies for OC by targeting the noncatalytic activity of EZH2.

Abstract Background Xp22.31 deletion and duplication have been described in various studies, but different laboratories interpret pathogenicity differently. Objectives Our study aimed to refine the genotype–phenotype associations between Xp22.31 copy number variants in fetuses, with the aim of providing data support to genetic counseling. Methods We retrospectively analyzed karyotyping and single nucleotide polymorphism array results from 87 fetuses and their family members. Phenotypic data were obtained through follow-up visits. Results The percentage of fetuses carrying the Xp22.31 deletions (9 females, 12 males) was 24.1% (n = 21), while duplications (38 females, 28 males) accounted for 75.9% (n = 66). Here, we noted that the typical region (from 6.4 to 8.1 Mb, hg19) was detected in the highest ratio, either in the fetuses with deletions (76.2%, 16 of 21) or duplications (69.7%, 46 of 66). In female deletion carriers, termination of pregnancy was chosen for two fetuses, and the remaining seven were born without distinct phenotypic abnormalities. In male deletion carriers, termination of pregnancy was chosen for four fetuses, and the remaining eight of them displayed ichthyosis without neurodevelopmental anomalies. In two of these cases, the chromosomal imbalance was inherited from the maternal grandfathers, who also only had ichthyosis phenotypes. Among the 66 duplication carriers, two cases were lost at follow-up, and pregnancy was terminated for eight cases. There were no other clinical findings in the rest of the 56 fetuses, including two with Xp22.31 tetrasomy, for either male or female carriers. Conclusion Our observations provide support for genetic counseling in male and female carriers of Xp22.31 copy number variants. Most of them are asymptomatic in male deletion carriers, except for skin findings. Our study is consistent with the view that the Xp22.31 duplication may be a benign variant in both sexes.

A wide variety of abundant plant leaves exist in nature, and the wettability of their surfaces is formed to adapt to diverse external environments. In this paper we will focus on the factors influencing the wettability of various plant leaves prevalent in nature. And we hope to investigate the interfacial problems of plants from a mechanical point of view. It is found that there are many factors affecting the surface wettability of leaves, such as chemical composition, surface microstructures, hierarchical structures, and growth age. Different influencing factors have different contributions to the change of surface wettability. The surface wax composition influences the surface wettability from a chemical point of view while the hierarchical structure consisting of nanostructures and micron structures also influences the wettability from a structural point of view. Also as the growth age of the plant increases, there is a combined effect on the chemical composition and microstructure of the leaves. Then we discuss the surface/interface mechanics of droplets on various plant leaves and analyze the wetting properties of droplets on different substrates. Finally, we hope that the surface/interface mechanics of plant leaves may be systematically utilized in the future for the preparation of multifunctional biomimetic materials, realizing the crossover of chemistry, biology, mechanics, and other materials science fields.

Magnesium silicate hydrate (M-S-H) samples were prepared utilizing reactive magnesia and quartz with six various Mg/Si molar ratios by the autoclaved curing system. The characteristics and microstructure of M-S-H samples were examined using XRD, TGA, FTIR, SEM and 29Si MAS NMR. Additionally, silica fume was utilized as the siliceous raw material to prepare the M-S-H samples that served as controls. The results showed quartz can be utilized as siliceous raw material for preparing M-S-H. Compared with the samples employing silica fume, the M-S-H samples utilizing quartz as the siliceous material had a layered structure and a significant amount of silicate polymerization. Moreover, the SEM images of M-S-H utilizing quartz as the raw material exhibited a denser structure when compared to the microstructure of granular morphology of M-S-H with silica fume. Remarkably, the existence of quartz as a siliceous raw material led to the production of M-S-H with an antigorite structure. At the same time, the 29Si MAS NMR observations revealed that quartz as a siliceous raw material is favorable for the conversion of Q2 to Q3, resulting in improvement of polymerization degree of M-S-H silicone tetrahedron. The study demonstrated that it is the feasible to prepare M-S-H by replacing silica fume with quartz, which can provide a reference and choice for the preparation of green cementitious materials.

Abstract In the natural habitats of China, dauciform roots were only described in degraded alpine meadows. It was found that the presence of dauciform roots of Carex filispica was related to the advantage of multiple functional traits after trampling, reflecting short‐term resistance. However, the long‐term response of dauciform roots to trampling and the recovery of C. filispica with and without dauciform roots to trampling require further studies. In this study, different intensities of trampling (0, 50, 200 and 500 passages) were performed in an alpine meadow. One year later, individuals with and without dauciform roots were separated and their functional traits related to the economic spectrum of leaves and roots were measured as a reflection of recovery from trampling. The results showed that: (1) 1 year after trampling, the number of dauciform roots showed an increase with trampling intensity; (2) 1 year later, there was no significant difference in the response of economic spectrum traits among trampling intensities, or between plants with and without dauciform roots; (3) the number of dauciform roots was positively correlated with the leaf area of both individuals with and without dauciform roots, as well as with the biomass of those without dauciform roots; and (4) plants with more resource‐conservative roots showed an advantage after trampling recovery: specifically, plants with dauciform roots showed such an advantage in the control group, which was lost with a leaning towards resource‐acquisitive roots and an increased density of dauciform roots once trampled. In contrast, plants without dauciform roots showed a significant advantage of conservative roots only after trampling. In conclusion, the presence of dauciform roots is related to the plants' position on the root economic spectrum, thereby influencing the recovery of C. filispica from trampling. Carex filispica showed strong recovery from trampling after 1 year, which makes it an adequate choice for ecological restoration in alpine meadows. Dauciform roots showed a positive correlation with the aboveground growth of both plants with and without them, however, it requires a lab‐controlled study to confirm whether there is indeed a positive effect on the growth of neighbouring plants.

Background and aimsSpecial root structures that can dissolve insoluble phosphorus locked in soil are supposed to contribute not only to the growing status of themselves but also to the neighbouring plants. However, whether dauciform roots have any effect on the neighbouring plants and how does it respond to meadow degradation had not been studied.MethodsAlpine meadows with different degradation statuses were selected and the functional traits of Carex filispica and the co-occurring species Polygonum viviparum were measured to explore their response to degradation, as well as the response of Polygonum viviparum to the dauciform roots of Carex filispica.ResultsThe results showed that 1) the number of dauciform roots decreased with the intensifying degradation, positively related to available phosphorus in the soil and negatively related to the aboveground phosphorus of Carex filispica. 2) Carex filispica and Polygonum viviparum are similar in specific leaf area and specific root area, yet different in the phosphorus content. The available phosphorus in the soil was negatively related to the aboveground phosphorus of Carex filispica and positively related to that of Polygonum viviparum. 3) When lightly degraded, the proportion of dauciform roots had positive effects on the aboveground resource-acquiring traits of Polygonum viviparum, which were no longer significant at heavy degradation. 4) Polygonum viviparum and Carex filispica without dauciform roots have similar performance: a decrease of belowground carbon with the increasing degradation, and a trend toward resource conservation with the increasing proportion of dauciform roots, which did not exist in Carex filispica with dauciform roots.ConclusionOur study found that dauciform roots had a beneficial effect on the resource acquisition of their neighbouring plants. However, due to the uncontrollable nature of natural habitats, whether this effect is stable and strong enough to be performed in ecological restoration requires further lab-controlled studies.

Abstract Background Hip or knee osteoarthritis (OA) is one of the main causes of disability worldwide and occurs mostly in the older adults. Total hip or knee arthroplasty is the most effective method to treat OA. However, severe postsurgical pain leading to a poor prognosis. So, investigating the population genetics and genes related to severe chronic pain in older adult patients after lower extremity arthroplasty is helpful to improve the quality of treatment. Methods We collected blood samples from elderly patients who underwent lower extremity arthroplasty from September 2020 to February 2021 at the Drum Tower Hospital Affiliated to Nanjing University Medical School. The enrolled patients provided measures of pain intensity using the numerical rating scale on the 90th day after surgery. Patients were divided into the case group (Group A) and the control group (Group B) including 10 patients respectively by the numerical rating scale. DNA was isolated from the blood samples of the two groups for whole-exome sequencing. Results In total, 661 variants were identified in the 507 gene regions that were significantly different between both groups (P

Maritime surveillance is extensively concerned by worldwide authorities, in which ship recognition in synthetic aperture radar (SAR) images is a significant and fundamental component. Though some development has been achieved in the SAR ship recognition task, two areas remain inadequately explored, which are the comprehensive utilization of multiscale features and the deployment of the prior knowledge of the ship shape. In this article, a novel ship augmented attention network (SA$^{2}$Net) for ship recognition is proposed, which comprehensively utilizes the multiscale features and integrates the ship shape prior to the end-to-end network. On one hand, due to the unequal effects of different scales, a scale attention module is proposed to adaptively select and assign weights to desired feature scales while disregarding irrelevant scales. Moreover, a feature weaving module (FWM) is constructed to merge semantic and detailed features produced by the high-to-low backbone, enriching representations across all scales of ship targets. On the other hand, in order to incorporate the priory knowledge of the ship shape into the network, we develop a feature augmentation module (FAM) to further boost the ship recognition accuracy. This module can provide rectangular receptive fields that align with the shape of ships, wherein a limitation encountered with traditional square convolutions. Comprehensive experiments on representative three- and six-category OpenSARShip tasks and seven-category FUSAR-Ship tasks show that our SA$^{2}$Net demonstrates superior performance when compared to the current state-of-the-art methods.

In the spectrum-congested environment, weak target obscuration can be addressed by simultaneously shaping the ambiguity function and waveform spectrum based on cognitive radar a priori information. In this article, we investigate joint waveform design to suppress slow-time ambiguity function (STAF) disturbance power and overlapping frequency band energy. Thus, a novel design criterion involving minimizing the interested range-Doppler blocks of STAF and energy spectrum density stopband is constructed, which is subject to the waveform energy and peak-to-average ratio constraints. To cope with the resulting complex quartic optimization problem, a waveform design approach is proposed which utilizes the iterative sequential quartic optimization algorithm framework to obtain a closed-form solution at each iteration. Finally, the designed waveforms can suppress the interested range-Doppler block level while satisfying the spectral coexistence requirements. Numerical simulation results verify that the proposed method has higher STAF disturbance suppression performance than state-of-the-art methods. Meanwhile, this method also possesses the ability to reject narrow-band spectrum interference.

Radar operation mode recognition holds an increasingly critical place in electronic countermeasure as well as in remote sensing. However, the overlapped waveform parameters pose huge challenges to performing the radar operation mode recognition task in severe electromagnetic environments, particularly with large measurement errors or small sample lengths. By analyzing the timing patterns of a single radar pulse parameter and the correlation characteristics of multiple radar pulse parameters, this article first provides a revolutionary representation of the radar operating state by integrating interpulse parameter characteristics. Subsequently, a multifeature fused stream-level recognition framework with an attention mechanism, named residual-and-shrinkage ConvNet, is proposed to identify typical radar operating states. This tailored-made deep learning framework can effectively extract the timing and correlative features, which are conducive to pattern classification. The results of numerical experiments suggest that the proposed approach affords superior performance for the operation mode recognition task, even when the measurement error is large and the sample length is small, signifying the proposed method is strongly robust and time-efficient.

Regularization methods, including single constraint regularization and joint constraints regularization, have been applied to radar forward-looking super-resolution imaging. However, the ill-posedness of the antenna measurement matrix is serious, which degrades the imaging performance in low signal-to-noise ratio (SNR) conditions. In our work, the following two-step regularization strategy is proposed to achieve super-resolution imaging in low SNR conditions: 1) in the first step, a projection regularization method is designed to repair the ill-posed antenna measurement matrix by truncating and modifying singular values, which mitigates the ill-posedness of the deconvolution process and suppresses noise amplification and 2) in the second step, based on the repaired convolution model, the $L_{1}$ norm is introduced for sparse targets to improve the radar azimuth resolution. The iteratively reweighted norm solver is employed to solve the optimization problem. The superiority of the proposed two-step strategy is analyzed from the perspectives of singular value decomposition. The effectiveness of the proposed strategy is verified by simulated and experimental data.

IntroductionThe drought and phosphorus deficiency have inevitably become environmental issues globally in the future. The analysis of plants functional trait variation and response strategies under the stress of phosphorus deficiency and drought is important to explore their ability to respond to potential ecological stress.MethodsIn this study, Carex breviculmis was selected as the research object, and a 14-week pot experiment was conducted in a greenhouse, with two phosphorus treatment (add 0.5mmol/L or 0.05μmol/L phosphorus) and four drought treatment (add 0-5%PEG6000), totaling eight treatments. Biomass allocation characteristics, leaf anatomical characteristics, biochemical parameters, root morphology, chemical element content, and photosynthetic parameters were measured.ResultsThe results showed that the anatomical characteristics, chemical elements, and photosynthetic parameters of Carex breviculmis responded more significantly to main effect of phosphorus deficiency. Stomatal width, leaf phosphorus content and maximum net photosynthetic rate decreased by 11.38%, 59.39%, 38.18% significantly (p0.05). Biomass allocation characteristics and root morphology responded more significantly to main effect of drought. Severe drought significantly decreased leaf fresh weight by 61% and increased root shoot ratio by 223.3% compared to the control group (p

Abstract Background Tanshinone IIA (TSA), a major lipophilic component extracted from the roots of Salvia miltiorrhiza Bunge, has been widely used in China for its various biological activities. However, its effect on ovarian reserve in aged mice was not studied elsewhere. Objectives This study aimed to explore the effect of TSA on the ovarian reserve of aged mice as well as young mice. Forty weeks old mice (N = 40) were considered as aged group compared to 4 weeks old mice (N = 40), and these groups were subdivided into four subgroups (N = 10) to receive different doses of TSA (0, 10, 20, and 40 μg/g/day). Methods The effect of TSA was evaluated by counting follicular number by histological examination. Basal serum levels of FSH, LH, E2, and anti‐Mullerian hormone (AMH) were measured by ELISA. Moreover, the expression levels of antioxidant genes (CAT, Nrf2, GPX1), gap junction (Cx37), ERK1/2, and Smad5 family gene were examined at both mRNA (qPCR) and protein levels (western blot). Results Follicular number, level of AMH and E2, and the expression of CAT, Nrf2, and GPX1 genes increased significantly (p

Abstract Palpation localization is essential for detecting physiological parameters of the radial artery for pulse diagnosis of Traditional Chinese Medicine (TCM). Detecting signal or applying pressure at the wrong location can seriously affect the measurement of pulse waves and result in misdiagnosis. In this paper, we propose an effective and high accuracy regression model using 3-dimensional convolution neural networks (CNN) processing near-infrared picture sequences to locate radial artery upon radius at the wrist. Comparing with early studies using 2-dimensional models, 3Dcnn introduces temporal features with the third dimension to leverage pulsation rhythms, and had achieved superior performance accuracy as 0.87 within 50 pixels at testing resolution of 1024 × 544. Model visualization shows that the additional dimension of the temporal convolution highlights dynamic changes within image sequences. This study presents the great potential of our constructed model to be applied in real wrist palpation location scenarios to bring the key convenience for pulse diagnosis.

Abstract Background The brain area stimulated during repetitive transcranial magnetic stimulation (rTMS) treatment is important in altered states of consciousness. However, the functional contribution of the M1 region during the treatment of high‐frequency rTMS remains unclear. Objective The aim of this study was to examine the clinical [the Glasgow coma scale (GCS) and the coma recovery scale‐revised (CRS‐R)] and neurophysiological (EEG reactivity and SSEP) responses in vegetative state (VS) patients following traumatic brain injury (TBI) before and after a protocol of high‐frequency rTMS over the M1 region. Methods Ninety‐nine patients in a VS following TBI were recruited so that their clinical and neurophysiological responses could be evaluated in this study. These patients were randomly allocated into three experimental groups: rTMS over the M1 region (test group; n = 33), rTMS over the left dorsolateral prefrontal cortex (DLPFC) (control group; n = 33) and placebo rTMS over the M1 region (placebo group; n = 33). Each rTMS treatment lasted 20 min and was carried out once a day. The duration of this protocol was a month with 20 treatments (5 times per week) occurring with that time. Results We found that the clinical and neurophysiological responses improved after treatment in the test, control, and placebo groups; the improvement was highest in the test group compared to that in the control and placebo groups. Conclusions Our results demonstrate an effective method of high‐frequency rTMS over the M1 region for consciousness recovery after severe brain injury.

Multistatic airborne SAR (MuA-SAR) benefits from the ability to flexibly adjust the positions of multiple transmitters and receivers in space, which can shorten the synthetic aperture time to achieve the required resolution. To ensure both imaging efficiency and quality of different system spatial configurations and trajectories, the fast factorized back projection (FFBP) algorithm is proposed. However, if the FFBP algorithm based on polar coordinates is directly applied to the MuA-SAR system, the interpolation in the recursive fusion process will bring the problem of redundant calculations and error accumulation, leading to a sharp decrease in imaging efficiency and quality. In this paper, a unified Cartesian fast factorized back projection (UCFFBP) algorithm with a multi-level regional attention strategy is proposed for MuA-SAR fast imaging. First, a global Cartesian coordinate system (GCCS) is established. Through designing the rotation mapping matrix and phase compensation factor, data from different bistatic radar pairs can be processed coherently and efficiently. In addition, a multi-level regional attention strategy based on maximally stable extremal regions (MSER) is proposed. In the recursive fusion process, only the suspected target regions are paid more attention and segmented for coherent fusion at each fusion level, which further improves efficiency. The proposed UCFFBP algorithm ensures both the quality and efficiency of MuA-SAR imaging. Simulation experiments verified the effectiveness of the proposed algorithm.

Abstract In China, dauciform roots were hardly studied and only reported in alpine meadows, where sedges showed a different tendency from other functional groups such as grasses and forbs with degradation. In addition, Carex species were proved to have shifting scaling relationships among LES (leaf economics spectrum) traits under disturbance. So, are these unique performances of sedges related to the presence of dauciform roots, and if so, how? An alpine meadow dominated by Carex filispica in Baima Snow Mountain was selected, and quantitative trampling was performed (0, 50, 200, and 500 passes). The cover and dauciform root properties of Carex filispica were measured, as well as the morphological, chemical traits and biomass of leaves and roots, their correlations and the differences between individuals with and without dauciform roots were analyzed. After the trampling, individuals with dauciform roots showed multiple resource‐acquisitive traits: Larger, thicker leaves, more aboveground biomass, higher efficiency of nutrient utilization, and slenderer roots. Additionally, they had a tighter correlation among belowground biomass, morphological and chemical traits, as well as dauciform root properties and morphology of leaves, suggesting that their traits were more related than those without dauciform roots. The presence of dauciform roots in Carex filispica was related to advantages in multiple traits after trampling, which is consistent with and might be responsible for the unique performances of sedges.

Hard gold has excellent physical and chemical properties, and is widely used in jewelry and the electronics industry. The deposition of hard gold has long been focused on cyanide-based aqueous electrolytes, which are highly toxic with relatively low electroplating efficiency due to the coreduction of hydrogen. Ionic liquids have recently been studied as a solvent for the electrodeposition of various metals. Herein, choline chloride–urea ionic liquid was used as the solvent with 5,5-dimethylhydantoin as the complexing agent and Co2+ was added to produce cobalt-hardened gold deposits. The addition of Co2+ promoted grain size refinement and improved the hardness of deposits. With 62.5 mg/L Co2+ present in the electrolyte, the coating showed a high degree of hardness (2.3 GPa) and relatively good corrosion resistance.

Objectives The present study aimed to develop and validate nomograms to predict the survival of patients with breast invasive micropapillary carcinoma (IMPC) to aid objective decision-making.Design Prognostic factors were identified using Cox proportional hazards regression analyses and used to construct nomograms to predict overall survival (OS) and breast cancer-specific survival (BCSS) at 3 and 5 years. Kaplan-Meier analysis, calibration curves, the area under the curve (AUC) and the concordance index (C-index) evaluated the nomograms’ performance. Decision curve analysis (DCA), integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to compare the nomograms with the American Joint Committee on Cancer (AJCC) staging system.Setting Patient data were collected from the Surveillance, Epidemiology, and End Results (SEER) database. This database holds data related to the incidence of cancer acquired from 18 population-based cancer registries in the US.Participants We ruled out 1893 patients and allowed the incorporation of 1340 patients into the present study.Results The C-index of the AJCC8 stage was lower than that of the OS nomogram (0.670 vs 0.766) and the OS nomograms had higher AUCs than the AJCC8 stage (3 years: 0.839 vs 0.735, 5 years: 0.787 vs 0.658). On calibration plots, the predicted and actual outcomes agreed well, and DCA revealed that the nomograms had better clinical utility compared with the conventional prognosis tool. In the training cohort, the NRI for OS was 0.227, and for BCSS was 0.182, while the IDI for OS was 0.070, and for BCSS was 0.078 (both p

Sufficient synthetic aperture radar (SAR) target images are very important for the development of research works. However, available SAR target images are often limited in practice, which hinders the progress of SAR application. In this article, we propose an azimuth-controllable generative adversarial network to generate precise SAR target images with an intermediate azimuth between two given SAR images' azimuths. This network mainly contains three parts: 1) generator, 2) discriminator, and 3) predictor. Through the proposed specific network structure, the generator can extract and fuse the optimal target features from two input SAR target images to generate an SAR target image. Then, a similarity discriminator and an azimuth predictor are designed. The similarity discriminator can differentiate the generated SAR target images from the real SAR images to ensure the accuracy of the generated while the azimuth predictor measures the difference of azimuth between the generated and the desired to ensure the azimuth controllability of the generated. Therefore, the proposed network can generate precise SAR images, and their azimuths can be controlled well by the inputs of the deep network, which can generate the target images in different azimuths to solve the small sample problem to some degree and benefit the research works of SAR images. Extensive experimental results show the superiority of the proposed method in azimuth controllability and accuracy of SAR target image generation.

The search for low-altitude targets becomes a more challenging task for airborne phased array radar, due to the limited system resources and serious ground clutter. In this article, an optimal search strategy using the digital elevation model (DEM) is proposed to allow for a fast and high-probability searches for low-altitude targets. First, a clutter-free detection region is formed by introducing the DEM. Compared with the traditional methods, the detection probability of the low-altitude targets can be improved. Second, the search problem is transformed into a constrained multiobjective optimization problem. The proposed strategy can simultaneously minimize the search time and maximize the target detection probability. Finally, two intelligent evolutionary algorithms are compared to verify the proposed strategy, and the proposed strategy is also testified by utilizing real DEM data of Dujiangyan, Sichuan, China. Simulated results indicate that the proposed optimal search framework improves the detection probability and reduces time cost for low-altitude targets.

Marine surveillance radar is widely used in marine monitoring for its ability of observing sea surface all-time and all-weather. However, the radar target detection performance is seriously affected by the existence of sea clutter. In this article, we propose a new sea clutter suppression method based on machine learning approach. We first employ a cyclic structure network with a pair of generative adversarial networks to sufficiently learn the characteristics of sea clutter, which converts the problem of sea clutter suppression as a transformation from the clutter radar data domain to the clutter-free radar data domain. In addition, we propose a target-consistency loss for the cost function of the network to effectively preserve the target information while suppressing the sea clutter. Therefore, the proposed method can not only effectively remove the sea clutter from the radar data but also protect the target information from being damaged during sea clutter suppression, thereby achieving excellent sea clutter suppression performance. Experimental results have shown the superiorities of the proposed sea clutter suppression method on both simulated and measured marine surveillance radar data.

Video synthetic aperture radar (SAR) is attracting more and more attention because of its continuous imaging capability for ground scene of interest under any weather conditions and any time of the day. To reduce the sampling amount of video SAR, image processing can be formulated into a low-tubal-rank tensor recovery problem. In this article, we proposed an orthogonal rank-1 tensor pursuit (ORTP) algorithm to solve the low-tubal-rank tensor recovery problem in video SAR imaging. The proposed ORTP algorithm is an extension of the orthogonal rank-1 matrix pursuit algorithm in the matrix sensing problem from the matrix case to the tensor case under a tubal-rank model. It is capable of reconstructing the target tensor efficiently without requiring any prior information about the prespecified or pre-estimated tensor tubal-rank value. To achieve this, rank-1 basis tensors and weight tensors of the target tensor are estimated iteratively, and the residual error between the observed tensor and the estimated tensor through linear mapping is utilized as the stop condition. We theoretically prove the convergence and correctness of the proposed ORTP method. The methodology was tested on synthetic data, real video data, and video SAR data. These tests show that the proposed approach outperforms other video SAR imaging algorithms and low-rank tensor recovery algorithms.

Prevalent copy number alteration is the most prominent genetic characteristic associated with ovarian cancer (OV) development, but its role in immune evasion has not been fully elucidated. In this study, we identified RAD21, a key component of the cohesin complex, as a frequently amplified oncogene that could modulate immune response in OV. Through interrogating the RAD21-regulated transcriptional program, we found that RAD21 directly interacts with YAP/TEAD4 transcriptional corepressors and recruits the NuRD complex to suppress interferon (IFN) signaling. In multiple clinical cohorts, RAD21 overexpression is inversely correlated with IFN signature gene expression in OV. We further demonstrated in murine syngeneic tumor models that RAD21 ablation potentiated anti–PD-1 efficacy with increased intratumoral CD8+ T cell effector activity. Our study identifies a RAD21–YAP/TEAD4–NuRD corepressor complex in immune modulation, and thus provides a potential target and biomarker for precision immunotherapy in OV.

ABSTRACT: Background: Livestock and poultry skeletal muscle development was regulated by a variety of genetic factors playing an essential role in this process. In our de novo sequencing, a missense mutation in ISPD exon 2 showed a selected signature for well-developed muscles. However, the relationship between this mutation and muscle phenotypes remains unclear. Results: Based on the genotype bias of this missense mutation in gamecock chickens, we compared the cross-section of muscle fibers among the individuals with different genotypes with the results showing a genotype preference of this missense mutation in the chickens with hypertrophic muscle fibers. Comparing TT- and CC-genotype ISPD showed that TT- genotype was associated with heavier skeletal muscle and a better effect on myogenesis and myofiber stability. At cellular level, ISPD was identified to inhibit myoblast proliferation, differentiation and antagonize of muscular atrophy. Conclusion: It can be concluded that the missense mutation of Arg84Lys in ISPD was associated with well-developed muscles and demonstrated to improve chicken muscles’ development.

BackgroundThis study aimed to construct a nomogram for Breast sarcoma (BS) to predict the prognosis of patients with BS accurately and provide a theoretical basis for individualized treatment.MethodsPatients selected from the Surveillance, Epidemiology and End Results (SEER) database from 2000 to 2018 were assigned to a training group (TG, n = 696) and an internal validation group (IVG, n = 299) at a 7:3 ratio. Cox regression analysis was performed on the TG, and statistically significant factors were used to establish a nomogram to predict 3-, 5-, and 10-year overall survival (OS). The nomogram’s predictive power was validated using data from patients who attended our institution as the external validation group (EVG, n =79).ResultsCox regression analysis identified five factors, which were used to construct the nomogram. Good prediction accuracy was demonstrated using calibration curves. The concordance (C) indices for TG = 0.804 (95% confidence interval (CI) 0.777–0.831) and IVG = 0.761 (0.716–0.806) were higher than those based on 8th American Joint Committee on Cancer (AJCC8) stage: TG = 0.695 (0.660–0.730), IVG = 0.637 (0.584–0.690). The EVG also had a high C-index: 0.844 (0.768–0.920). Decision curve analysis showed that nomogram has larger net benefits than the AJCC8. The Kaplan–Meier curves of the nomogram-based risk groups showed significant differences (p < 0.001).ConclusionsThe nomogram could accurately predict 3-, 5-, and 10-year OS and provided nomogram-based risk stratification, which could help physicians to personalize treatment plans for patients with BS.

Yellow-feathered chickens have great nutritional value and are widely and traditionally used in China, on an industrial scale as broilers. The presence of intestinal microbes has been shown to correlate with poultry performance and serves as an essential reservoir of antibiotic resistance genes (ARGs). Antibiotic resistance is a major public health concern. Here, we investigated functional characteristics of the gut microbiome of indigenous Chinese yellow-feathered chickens (the Huiyang Bearded, Xinghua, Huaixiang, Zhongshan Shanlan, Qingyuan Partridge, and Yangshan chickens) through metagenomic sequencing and reconstructed 409 draft genomes, including 60 novel species and 6 novel genera. Furthermore, we assessed the functions of the intestinal microbial communities and examined the ARGs within them. The results showed that the microbial populations of yellow-feathered broilers were primarily dominated by Bacteroidetes and Firmicutes at the phylum level and Bacteroides at the genus level. Furthermore, the Qingyuan Partridge chicken showed a significantly higher abundance of Prevotella than the other five breeds of chicken. Principal coordinates analysis indicated significant differences in the structures of microbial communities and ARGs, based on the binary Jaccard distance, among the six chicken breeds. Moreover, 989 ARGs conferring tetracycline, multidrug, and aminoglycoside resistance were identified, which represented more than 80% of the faecal resistomes; the most abundant gene in the yellow-feathered chickens was tet(Q). In addition, we found the greatest abundance of resistance genes in Xinghua chickens, indicating that Xinghua chickens are highly resistant to antibiotics. Overall, our findings revealed differences in the gut microbial community structure of indigenous Chinese yellow-feathered broiler breeds and the composition and characteristics of ARGs and antibiotic resistance that enabled us to reconstruct the yellow-feathered chicken gut microbial community genomes. The current data significantly improves our knowledge of the gut microbiome and antibiotic resistance of popular broiler breeds in China.

Objective: This study aimed to examine whether integrating risk-enhancing factors into the Chinese Society of Cardiology-recommended clinical risk assessment tool (i.e., the CSC model) for atherosclerotic cardiovascular disease (ASCVD) might improve 10-year ASCVD risk stratification in Chinese adults. Methods: A total of 4910 Chinese participants who were 50–79 years of age and free of cardiovascular disease in the 2007–2008 Survey from the Chinese Multi-provincial Cohort Study were included. We assessed the updated model’s clinical utility (i.e., Harrell’s C-index and net reclassification improvement [NRI]) by adding risk-enhancing factors individually or the number of risk-enhancing factors to the CSC model, for all individuals or those at intermediate risk. Risk-enhancing factors, including a family history of CVD, triglycerides ≥2.3 mmol/L, high-sensitivity C-reactive protein ≥2 mg/L, Lipoprotein (a) ≥50 mg/dL, non-high-density lipoprotein cholesterol ≥4.9 mmol/L, overweight/obesity, and central obesity, were evaluated. ASCVD events were defined as a composite endpoint comprising ischemic stroke and acute coronary heart disease events (including nonfatal acute myocardial infarction and all coronary deaths). Results: During a median 10-year follow-up, 449 (9.1%) ASCVD events were recorded. Addition of ≥2 risk-enhancing factors to the CSC model yielded a significant improvement in the C-index (1.0%, 95% confidence interval [CI]: 0.2–1.7%) and a modest improvement in the NRI (2.0%, 95% CI: −1.2–5.4%) in the total population. For intermediate-risk individuals, particularly individuals at high risk of developing ASCVD, significant improvements in NRI were observed after adding ≥2 risk-enhancing factors (17.4%, 95% CI: 5.6–28.5%) to the CSC model. Conclusions: Addition of ≥2 risk-enhancing factors refined 10-year ASCVD risk stratification, particularly for intermediate-risk individuals, supporting their potential in helping tailor targeted interventions in clinical practice.

This study aims to explore the regulatory effect of S-ketamine on the mechanical allodynia, anxiety-like behaviors and microglia activation in adult male rats exposed to an animal model of post-traumatic stress disorder (PTSD). The rat PTSD model was established by the exposure to single-prolonged stress (SPS), and 1 day later, rats were intraperitoneally injected with 5 mg/kg S-ketamine or normal saline, respectively. Paw withdrawal mechanical threshold was measured 2 days before, and 1, 3, 5, 7, 10, 14, 21 and 28 days after injection to assess mechanical allodynia in the SPS-exposed rats. For anxiety-like behaviors, the open field test and elevated plus maze test were performed at 7 and 14 days after S-ketamine treatment in the SPS-exposed rats, respectively. SPS-induced rats presented pronounced mechanical allodynia and anxiety-like behaviors, which were alleviated by S-ketamine treatment. After behavioral tests, rats were sacrificed for collecting the anterior cingulate cortex (ACC), prefrontal cortex (PFC), dorsal striatum, and periaqueductal gray (PAG). Protein levels of TNF-α, IL-1β, p-NF-κB, and NF-κB in brain regions were examined by Western blot. In addition, microglia activation in each brain region was determined by immunofluorescence staining of the microglia-specific biomarker Iba-1. Interestingly, pro-inflammatory cytokines were significantly upregulated in the dorsal striatum and PAG, rather than ACC and PFC. Activated microglia was observed in the dorsal striatum and PAG as well, and upregulated p-NF-κB was detected in the dorsal striatum. Inflammatory response, phosphorylation of NF-κB and microglia activation in certain brain regions were significantly alleviated by S-ketamine treatment. Collectively, S-ketamine is a promising drug in alleviating mechanical allodynia, anxiety-like behaviors, and pro-inflammatory responses in discrete brain regions in a model of PTSD.

Real aperture radar (RAR) usually sweeps a wide sector to continuously observe the scenario of interest. Because its angular resolution is limited by the size of the antenna aperture, target reconstruction methods are widely applied to obtain super-resolution radar images. However, the wide-sector processing mode suffers from high operational complexity because of the high-dimensional matrix inversion. Even worse, for the targets located at the scene edge, its echo data are received less than half beamwidth. The incomplete echo data will lead to the deformation of the reconstructed targets by existing reconstruction methods. To overcome the two problems, a data extrapolation-based parallel iterative adaptive approach is proposed to fast reconstruct the targets in the whole sector without the distortion at the scene edge. First, the echo model of RAR is repaired to remedy the model error. Then, based on the correlation of the echo data within one beamwidth, an autoregressive model is adopted to extrapolate the data of the missing half beamwidth. Finally, a parallel iterative adaptive approach method is proposed to efficiently recover the targets by exploiting the regular characteristics of the repaired steering matrix. Simulations and experimental data are applied to verify the proposed method.

It is greatly significant to achieve radar forward-looking region imaging. Due to the limitation of phase ambiguity and small Doppler gradient in forward-looking region, synthetic aperture radar and Doppler beam sharpening cannot work for forward-looking imaging, while real aperture radar (RAR) has arbitrary imaging geometry. Nevertheless, restricted by the antenna aperture, azimuth resolution of RAR is coarse, super-resolution technology is required to improve its azimuth resolution. Exploiting the sparse prior information of the target, the super-resolution problem can be transformed into an $L_1$ norm minimization problem mathematically. Iterative reweighted algorithm can effectively solve the $L_1$ norm minimization problem by replacing $L_1$ norm with reweighted $L_2$ norm and computing the weight in each iteration. However, it suffers from a large computational load due to the repeated multiplications and inversions of large matrices. In this article, a fast azimuth super-resolution imaging method of RAR based on iterative reweighted least squares (IRLS) with linear sketching (LS) was proposed to achieve fast super-resolution imaging of RAR. The LS theory is employed to compress echo matrix and antenna measurement matrix into much smaller matrices via multiplying them by an embedded matrix. Then, the IRLS solver was utilized to address the reconstructed objective function. Much of the expensive computation can then be performed on the smaller matrices, thereby accelerating the algorithm. Simulations and experimental data prove that the proposed algorithm can offer a time complexity reduction without loss of imaging performance.

Total variation-sparse (TV-sparse)-based multiconstraint devonvolution method has been used to realize superresolution imaging and preserve target contour information simultaneously of radar forward-looking imaging. However, due to the existence of matrix inversion, it suffers from high computational complexity, which restricts the ability of radar real-time imaging. In this article, an Gohberg-Semencul (GS) decomposition-based fast TV-sparse (FTV-sparse) method is proposed to reduce the computational complexity of TV-sparse method. The acceleration strategy utilizes the low displacement rank features of Toeplitz matrix, realizing fast matrix inversion by using a GS representation. It reduces the computational complexity of traditional TV-sparse method from O(N3) to O(N2), benefiting for improvement of the computing efficiency. The simulation and experimental data processing results show that the proposed FTV-sparse method has almost no resolution loss compared with the traditional TV sparse method. Hardware test results show that the proposed FTV-sparse method significantly improves the computational efficiency of TVsparse method.

Spaceborne-airborne multistatic synthetic aperture radar (SA-MuSAR) has the ability to provide high-resolution forward-looking imagery for receivers, but it relies on careful design of the geometric configuration (GC). In this article, a forward-looking GC optimization design method is proposed to obtain a high-quality fused image with limited observation time. First, the relationship between the spatial resolution and GC is illustrated by the wavenumber spectrum distribution of SA-MuSAR. Second, GC evaluators depending on the distribution of multiple wavenumber spectrum data are proposed. The GC design problem of coherent SA-MuSAR is transformed into a constrained multiobjective optimization problem. An intelligent evolutionary algorithm is adopted to optimize the wavenumber spectrum distribution. With the proposed method, high-quality forward-looking imagery can be obtained with a short observation time. Numerical simulations are carried out to verify the effectiveness of the proposed method.

A multistatic unmanned aerial vehicle-borne synthetic aperture radar (MuUAV-SAR) plays an important role in the applications of environmental monitoring and disaster warning because its distributed platforms can provide high-resolution imagery by fusing the multiple measurements. However, the flight paths of the multiple platforms are limited for such an unmanned system since the flight safety and the path length are basic conditions for guaranteeing the effective observation. This article first studies the observation signal model of the MuUAV-SAR imaging system and then analyzes the factors that determine the imaging resolution, while these factors are all determined by the flight path of unmanned aerial vehicles (UAVs). Second, an MuUAV-SAR imaging path planning problem is established as a constrained multiobjective optimization problem (CMOP), which considers the navigation and imaging performance of the UAV in the process of completing the path planning task in detail. For this CMOP, a heuristic search method is proposed to solve it, which can ensure that each step achieves local optimum, and it can also list all feasible solutions to meet the application requirements for selection. Finally, experimental results verify the effectiveness and practicability of the proposed heuristic path planning method.

With the development of economic globalization, coastal harbors have become an increasingly important gateway for international trade. Synthetic aperture radar (SAR) is an important microwave sensor with high resolution imaging capability. Harbor detection in SAR images is of great significance for timely obtaining coastal intelligence of a country. However, due to its complexity and diversity, harbor detection is challenging. In this article, a harbor detection method based on multidirectional one-dimensional scanning is proposed. First, discrete coastline to obtain control points, and multiple directions are selected at each control point, values of scanned pixels in each direction are recorded to obtain the representation vector. Then, a one-dimensional convolutional neural network for harbor features identification of representation vectors is designed. Finally, the harbors are located by clustering feature points. The proposed method can extract vectors to characterize the distribution of land, waters, and wharfs. Therefore, the problem of object detection in two-dimensional images is transformed into the identification of representation vectors. The method is effective for harbors with various scales and forms and has low computational complexity. Experimental results on spaceborne SAR images demonstrate the effectiveness of the proposed method.

Synthetic aperture radar (SAR) is a microwave remote sensing system. It has a broad scope of applications in both military and civilian fields. Benefited from the latest advances in deep learning, SAR automatic target recognition technology has made an excellent breakthrough However, most existing methods ignore the large variation of scattering characteristics of SAR target images with different azimuths, which limits the performance and practical application. The SAR images under different azimuths contain distinct feature information, and the images under adjacent azimuths are correlated in terms of features. Therefore, extracting the feature information of images under adjacent azimuths and leveraging their correlation can improve the recognition performance. In this article, we proposed a multiview attention convolutional neural network with long short-term memory (LSTM) network to extract and fuse the features from images with adjacent azimuths. It adopts multiple convolutional modules to extract deep features from each single-view SAR image and spatial attention module to locate the information of the target and suppress the useless noise. Then, the LSTM module performs feature fusion based on the correlation of features obtained from adjacent azimuths. Finally, based on these multiview images, deep features are extracted and fused to obtain precise recognition results. Experiments are performed on the moving and stationary target acquisition and recognition dataset, and the results have verified the effectiveness of the proposed method.

Plant nitrogen (N) uptake preference is a key factor affecting plant nutrient acquisition, vegetation composition and ecosystem function. However, few studies have investigated the contribution of different N sources to plant N strategies, especially during the process of primary succession of a glacial retreat area. By measuring the natural abundance of N isotopes (δ15N) of dominant plants and soil, we estimated the relative contribution of different N forms (ammonium-NH4+, nitrate-NO3− and soluble organic N-DON) and absorption preferences of nine dominant plants of three stages (12, 40 and 120 years old) of the Hailuogou glacier retreat area. Along with the chronosequence of primary succession, dominant plants preferred to absorb NO3− in the early (73.5%) and middle (46.5%) stages. At the late stage, soil NH4+ contributed more than 60.0%, In addition, the contribution of DON to the total N uptake of plants was nearly 19.4%. Thus, the dominant plants’ preference for NO3− in the first two stages changes to NH4+ in the late stages during primary succession. The contribution of DON to the N source of dominant plants should not be ignored. It suggests that the shift of N uptake preference of dominant plants may reflect the adjustment of their N acquisition strategy, in response to the changes in their physiological traits and soil nutrient conditions. Better knowledge of plant preferences for different N forms could significantly improve our understanding on the potential feedbacks of plant N acquisition strategies to environmental changes, and provide valuable suggestions for the sustainable management of plantations during different successional stages.

In recent years, sparse direction-of-arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar has attracted extensive attention and been extensively studied, especially the method based on the classic least absolute shrinkage and selection operator (LASSO) estimator. The alternating-direction method of multipliers (ADMM) is an effective method for solving this problem at the cost of introducing an additional user parameter. To avoid introducing an additional user parameter, this paper adopts an equivalent transformation in the form of the generalized SParse Iterative Covariance-based Estimation (qSPICE) cost function to obtain a mean squared minimized form of the cost function. Then, the problem is transformed into a sparse optimization problem in the form of a weighted LASSO. Next, this unconstrained optimization problem is decomposed into three subproblems, which are solved separately to reduce the dimension of each problem and thus reduce the overall computational complexity based on ADMM. Simulation results and measured data indicate that the proposed method significantly outperforms the traditional super-resolution DOA estimation method and ADMM-LASSO method and slightly outperforms qSPICE in terms of resolution and sidelobe suppression capability. In addition, the proposed method has a much lower computational complexity and substantially fewer iterations than qSPICE.

Intervertebral disc degeneration (IDD) has been considered as the primary pathological mechanism that underlies low back pain. Understanding the molecular mechanisms underlying human IDD is imperative for making strategies to treat IDD-related diseases. Herein, we report the molecular programs, lineage progression patterns, and paths of cellular communications during the progression of IDD using single-cell RNA sequencing (scRNA-seq) on nucleus pulposus (NP) cells from patients with different grades of IDD undergoing discectomy. New subtypes of cells and cell-type-specific gene signatures of the metabolic homeostatic NP cells (Met NPC), adhesive NP cells (Adh NPC), inflammatory response NP cells (IR NPC), endoplasmic reticulum stress NP cells (ERS NPC), fibrocartilaginous NP cells (Fc NPC), and CD70 and CD82+ progenitor NP cells (Pro NPC) were identified. In the late stage of IDD, the IR NPC and Fc NPC account for a large proportion of NPC. Importantly, immune cells including macrophages, T cells, myeloid progenitors, and neutrophils were also identified, and further analysis showed that significant intercellular interaction between macrophages and Pro NPC occurred via MIF (macrophage migration inhibitory factor) and NF-kB signaling pathways during the progression of IDD. In addition, dynamic polarization of macrophage M1 and M2 cell subtypes was found in the progression of IDD, and gene set functional enrichment analysis suggested a significant role of the macrophage polarization in regulating cell metabolism, especially the Pro NPC. Finally, we found that the NP cells in the late degenerative stage were mainly composed of the cell types related to inflammatory and endoplasmic reticulum (ER) response, and fibrocartilaginous activity. Our results provided new insights into the identification of NP cell populations at single-cell resolution and at the relatively whole-transcriptome scale, accompanied by cellular communications between immune cells and NP cells, and discriminative markers in relation to specific cell subsets. These new findings present clues for effective and functional manipulation of human IDD-related bioremediation and healthcare.

Enhancer RNAs (eRNAs) are noncoding RNAs that synthesized at active enhancers. eRNAs have important regulatory characteristics and appear to be significant for maintenance of cell identity and information processing. Series of functional eRNAs have been identified as potential therapeutic targets for multiple diseases. Nevertheless, the role of eRNAs on intervertebral disc degeneration (IDD) is still unknown yet. Herein, we utilized the nucleus pulposus samples of patients and identified a key eRNA (LINC02569) with the Arraystar eRNA Microarray. LINC02569 mostly locates in nucleus and plays an important role in the progress of IDD by activating nuclear factor kappa-B (NF-κB) signaling pathway. We used a cationic polymer brush coated carbon nanotube (oCNT-pb)-based siRNA delivery platform that we previously designed, to transport LINC02569 siRNA (si-02569) to nucleus pulposus cells. The siRNA loaded oCNT-pb accumulated in nucleus pulposus cells with lower toxicity and higher transfection efficiency, compared with the traditional siRNA delivery system. Moreover, the results showed that the delivery of si-02569 significantly alleviated the inflammatory response in the nucleus pulposus cells via inhibiting P65 phosphorylation and preventing its transfer into the nucleus, and meanwhile alleviated cell senescence by decreasing the expression of P21. Altogether, our results highlight that eRNA (LINC02569) plays important role in the progression of IDD and could be a potential therapeutic target for alleviation of IDD.