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Over the past few years, drones have been utilized in a wide range of applications. However, the illegal operation of drones may pose a series of security risks to sensitive areas such as airports and military bases. Hence, it is vital to develop an effective method of identifying drones to address the above issues. Existing drone classification methods based on radio frequency (RF) signals have low accuracy or a high computational cost. In this paper, we propose a novel RF signal image representation scheme that incorporates a convolutional neural network (CNN), named the frequency domain Gramian Angular Field with a CNN (FDGAF-CNN), to perform drone classification. Specifically, we first compute the time–frequency spectrum of raw RF signals based on short-time Fourier transform (STFT). Then, the 1D frequency spectrum series is encoded as 2D images using a modified GAF transform. Moreover, to further improve the recognition performance, the images obtained from different channels are fused to serve as the input of a CNN classifier. Finally, numerous experiments were conducted on the two available open-source DroneRF and DroneRFa datasets. The experimental results show that the proposed FDGAF-CNN can achieve a relatively high classification accuracy of 98.72% and 98.67% on the above two datasets, respectively, confirming the effectiveness and generalization ability of the proposed method.

Epigenetic regulation is an important entry point to study the pathogenesis of selective fetal growth restriction (sFGR), and an understanding of the role of long noncoding RNAs (lncRNAs) in sFGR is lacking. Our study aimed to investigate the potential role of a lncRNA, metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), in sFGR using molecular biology experiments and gain- or loss-of-function assays. We found that the levels of MALAT1, ERRγ, and HSD17B1 were downregulated and that of miR-424 was upregulated in the placental shares of the smaller twins. Moreover, angiogenesis was impaired in the placental share of the smaller fetus and MALAT1 could regulate the paracrine effects of trophoblasts on endothelium angiogenesis and proliferation by regulating miR-424. In trophoblasts, MALAT1 could competitively bind to miR-424 to regulate the expression of ERRγ and HSD17B1, thus regulating trophoblast invasion and migration. MALAT1 overexpression could decrease apoptosis and promote proliferation, alleviating cell damage induced by hypoxia. Taken together, the downregulation of MALAT1 can reduce the expression of ERRγ and HSD17B1 by competitively binding to miR-424, impairing the proangiogenic effect of trophoblasts, trophoblast invasion and migration, and the ability of trophoblasts to compensate for hypoxia, which may be involved in the pathogenesis of sFGR through various aspects.

Abiotic stimuli severely restrict the growth and development of plants, resulting in massive losses in the quality and yield of crops. Exploring genes that can improve crop tolerance to abiotic stress is important. In a previous study, we found that overexpression of the Arabidopsis nucleotide-binding domain leucine-rich repeat (NB-LRR) gene AtRPM1(D505V) increased disease resistance in rice. In this research, we found that AtRPM1(D505V) transgenic plants were more sensitive to abscisic acid (ABA) than wild type (WT) plants. Abiotic-stress resistance in AtRPM1(D505V) transgenic plants was investigated. We found that AtRPM1(D505V) transgenic plants exhibited improved resistance to drought and salt stress; the phonotype and survival rates of transgenic rice were better than WT plants. The expression of stress responsive genes including OsDREB2A, OsDREB2B, OsRD22, and OsRD29A were significantly upregulated in AtRPM1(D505V) overexpressed plants than in WT plants. Moreover, the activities of catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD) were significantly increased in AtRPM1(D505V) overexpressed plants than in WT plants under drought and salt stress. Under cold stress, the expression of stress responsive genes and the activities of antioxidant enzymes in AtRPM1(D505V) transgenic plants were significantly lower than in WT plants. Our research demonstrated that AtRPM1(D505V) confers drought and salt resistance to transgenic rice. Therefore, AtRPM1(D505V) could act as a potential candidate gene to cultivate drought- and salt-tolerant plants.

To resolve the problem of the super-long steel structures producing high-temperature stress on the lower concrete roof column section and improve the structure’s seismic performance, in this paper, we introduce a type of composite damped hinge bearing (CDHB). Firstly, this paper introduces the detailed construction of the CDHB and elaborates on its working principle. The mechanical properties of a CDHB have been tested, including the monotonic loading test on the whole bearing and the reciprocal loading test on the built-in dampers. The CDHB’s working performance, rotation performance, and energy consumption performance under tensile and compressive forces are also discussed in this paper. The study results show that: (1) Generally, the vertical deformation of the CDHB increases linearly with the increase of compressive and tensile forces, while it decreases with the increase of initial displacement. (2) Under vertical loading, the CDHB can reach a turning angle of 0.6 rad. The bearing is more likely to rotate with the increase in initial displacement. (3) The ultimate displacement, maximum damping force, damping constant, and damping coefficient of the built-in dampers are all stable.

Depression is a complex mental disorder, affecting approximately 280 million individuals globally. The pathobiology of depression is not fully understood, and the development of new treatments is urgently needed. Dihydromyricetin (DHM) is a natural flavanone, mainly distributed in Ampelopsis grossedentata. DHM has demonstrated a protective role against cardiovascular disease, diabetes, liver disease, cancer, kidney injury and neurodegenerative disorders. In the present study, we examined the protective effect of DHM against depression in a chronic depression mouse model induced by corticosterone (CORT). Animals exposed to CORT displayed depressive-like behaviors; DHM treatment reversed these behaviors. Network pharmacology analyses showed that DHM’s function against depression involved a wide range of targets and signaling pathways, among which the inflammation-linked targets and signaling pathways were critical. Western blotting showed that CORT-treated animals had significantly increased levels of the advanced glycation end product (AGE) and receptor of AGE (RAGE) in the hippocampus, implicating activation of the AGE-RAGE signaling pathway. Furthermore, enzyme-linked immunosorbent assay (ELISA) detected a marked increase in the production of proinflammatory cytokines, interleukin-1 beta (IL-1β), IL-6 and tumor necrosis factor-alpha (TNFα) in the hippocampus of CORT-treated mice. DHM administration significantly counteracted these CORT-induced changes. These findings suggest that protection against depression by DHM is mediated by suppression of neuroinflammation, predominantly via the AGE-RAGE signaling pathway.

Depression, a mood disorder, affects one in fifteen adults, has multiple risk factors and is associated with complicated underlying pathological mechanisms. P-coumaric acid (p-CA), a phenolic acid, is widely distributed in vegetables, fruits and mushrooms. P-CA has demonstrated a protective role against oxidative stress and inflammation in various diseases, including cardiovascular disease, diabetes and cancer. In the current study, we investigated the protection of p-CA against depression and memory impairment in a corticosterone (CORT)-induced chronic depressive mouse model. CORT administration resulted in depression-like behaviors and memory impairment. P-CA treatment alleviated CORT-induced depression-related behaviors and memory impairment. Network pharmacology predicted that p-CA had multiple targets and mediated various signaling pathways, of which inflammation-associated targets and signaling pathways are predominant. Western blotting showed CORT-induced activation of the advanced glycation end product (AGE)-receptor of AGE (RAGE) (AGE-RAGE) signaling and increased expression of the proinflammatory cytokines interleukin-1 beta (IL-1β) and tumor necrosis factor-alpha (TNFα) in the hippocampus, while p-CA treatment inactivated AGE-RAGE signaling and decreased the levels of IL-1β and TNFα, suggesting that protection against depression and memory impairment by p-CA is mediated by the inhibition of inflammation, mainly via the AGE-RAGE signaling pathway. Our data suggest that p-CA treatment will benefit patients with depression.

Three-dimensional (3D) synthetic aperture radar (SAR) images can provide comprehensive 3D spatial information for environmental monitoring, high dimensional mapping and radar cross sectional (RCS) measurement. However, the SAR image obtained by the traditional matched filtering (MF) method has a high sidelobe and is easily disturbed by noise. In order to obtain high-quality 3D SAR images, sparse signal processing has been used in SAR imaging in recent years. However, the typical L1 regularization model is a biased estimation, which tends to underestimate the target intensity. Therefore, in this article, we present a 3D sparse SAR image reconstruction method combining the Cauchy penalty and improved alternating direction method of multipliers (ADMM). The Cauchy penalty is a non-convex penalty function, which can estimate the target intensity more accurately than L1. At the same time, the objective function maintains convexity via the convex non-convex (CNC) strategy. Compared with L1 regularization, the proposed method can reconstruct the image more accurately and improve the image quality. Finally, three indexes suitable for SAR images are used to evaluate the performance of the method under different conditions. Simulation and experimental results verify the effectiveness of the proposed method.

Three-dimensional (3D) synthetic aperture radar (SAR) imaging provides complete 3D spatial information, which has been used in environmental monitoring in recent years. Compared with matched filtering (MF) algorithms, the regularization technique can improve image quality. However, due to the substantial computational cost, the existing observation-matrix-based sparse imaging algorithm is difficult to apply to large-scene and 3D reconstructions. Therefore, in this paper, novel 3D sparse reconstruction algorithms with generalized Lq-regularization are proposed. First, we combine majorization–minimization (MM) and L1 regularization (MM-L1) to improve SAR image quality. Next, we combine MM and L1/2 regularization (MM-L1/2) to achieve high-quality 3D images. Then, we present the algorithm which combines MM and L0 regularization (MM-L0) to obtain 3D images. Finally, we present a generalized MM-Lq algorithm (GMM-Lq) for sparse SAR imaging problems with arbitrary q0≤q≤1 values. The proposed algorithm can improve the performance of 3D SAR images, compared with existing regularization techniques, and effectively reduce the amount of calculation needed. Additionally, the reconstructed complex image retains the phase information, which makes the reconstructed SAR image still suitable for interferometry applications. Simulation and experimental results verify the effectiveness of the algorithms.

Retinal diseases are a leading cause of visual loss and blindness, affecting a significant proportion of the population worldwide and having a detrimental impact on quality of life, with consequent economic burden. The retina is highly metabolically active, and a number of retinal diseases are associated with metabolic dysfunction. To better understand the pathogenesis underlying such retinopathies, new technology has been developed to elucidate the mechanism behind retinal diseases. Metabolomics is a relatively new “omics” technology, which has developed subsequent to genomics, transcriptomics, and proteomics. This new technology can provide qualitative and quantitative information about low-molecular-weight metabolites (M.W. < 1500 Da) in a given biological system, which shed light on the physiological or pathological state of a cell or tissue sample at a particular time point. In this article we provide an extensive review of the application of metabolomics to retinal diseases, with focus on age-related macular degeneration (AMD), diabetic retinopathy (DR), retinopathy of prematurity (ROP), glaucoma, and retinitis pigmentosa (RP).

Retinitis pigmentosa (RP) is a group of visual disorders caused by mutations in over 70 genes. RP is characterized by initial degeneration of rod cells and late cone cell death, regardless of genetic abnormality. Rod cells are the main consumers of oxygen in the retina, and after the death of rod cells, the cone cells have to endure high levels of oxygen, which in turn leads to oxidative damage and cone degeneration. Gypenosides (Gyp) are major dammarane-type saponins of Gynostemma pentaphyllum that are known to reduce oxidative stress and inflammation. In this project we assessed the protective effect of Gyp against cone cell death in the rpgrip1 mutant zebrafish, which recapitulate the classical pathological features found in RP patients. Rpgrip1 mutant zebrafish were treated with Gyp (50 µg/g body weight) from two-months post fertilization (mpf) until 6 mpf. Gyp treatment resulted in a significant decrease in cone cell death compared to that of untreated mutant zebrafish. A markedly low level of reactive oxygen species and increased expression of antioxidant genes were detected in Gyp-incubated mutant zebrafish eyes compared to that of untreated mutant zebrafish. Similarly, the activities of catalase and superoxide dismutase and the level of glutathione were significantly increased in Gyp-treated mutant zebrafish eyes compared to that of untreated mutant zebrafish. Gyp treatment also decreased endoplasmic reticulum stress in rpgrip1 mutant eyes. Expression of proinflammatory cytokines was also significantly decreased in Gyp-treated mutant zebrafish eyes compared to that of untreated mutant zebrafish. Network pharmacology analysis demonstrated that the promotion of cone cell survival by Gyp is possibly mediated by multiple hub genes and associated signalling pathways. These data suggest treatment with Gyp will benefit RP patients.

Upgrading ordinary streetlights to smart streetlights to help monitor traffic flow is a low-cost and pragmatic option for cities. Fine-grained classification of vehicles in the sight of smart streetlights is essential for intelligent transportation and smart cities. In order to improve the classification accuracy of distant cars, we propose a reformed YOLOv3 (You Only Look Once, version 3) algorithm to realize the detection of various types of automobiles, such as SUVs, sedans, taxis, commercial vehicles, small commercial vehicles, vans, buses, trucks and pickup trucks. Based on the dataset UA-DETRAC-LITE, manually labeled data is added to improve the data balance. First, data optimization for the vehicle target is performed to improve the generalization ability and position regression loss function of the model. The experimental results show that, within the range of 67 m, and through scale optimization (i.e., by introducing multi-scale training and anchor clustering), the classification accuracies of trucks and pickup trucks are raised by 26.98% and 16.54%, respectively, and the overall accuracy is increased by 8%. Secondly, label smoothing and mixup optimization is also performed to improve the generalization ability of the model. Compared with the original YOLO algorithm, the accuracy of the proposed algorithm is improved by 16.01%. By combining the optimization of the position regression loss function of GIOU (Generalized Intersection Over Union), the overall system accuracy can reach 92.7%, which improves the performance by 21.28% compared with the original YOLOv3 algorithm.

Non-contact driver respiration rate detection is a challenging problem in the Internet of Vehicles, because the automobile environment is much narrower, and thus the multipath effect is greater. To overcome these challenges, a 2.4 GHz continuous wave forward-scattering radar respiratory detection system is proposed based on the theory that the radar cross-section (RCS) of the human body changes with human breathing. We also analyze the impact of the multipath effect in the vehicle on the received radar signal and compare the output signal captured by a directional antenna with that captured by an omnidirectional antenna in the proposed system. In addition, the mean value of the received signal’s envelope is used to judge whether the driver’s posture is reasonable. Finally, compared with the existing contact respiratory detection system, the actual test results demonstrate the effectiveness of the proposed FSR system, and the driver respiration rates obtained by the proposed system are consistent with those obtained by the contact respiratory detection system.

Bioradar-based noncontact breathing detection technology has been widely studied due to its superior detection performance. In this paper, a breath detection mechanism based on the change in radar cross section (RCS) is proposed by using a forward scatter radar and the deduction of the mathematical model of the received signal. Furthermore, we completed human breathing detection experiments in an anechoic chamber and in an ordinary chamber; we obtained the breathing rate through envelope detection in cases where the human orientation angle was 0, 30, 60, and 90°. The analysis of the measured data shows that the theoretical model fits well with the measured results. Compared with the existing single-base radar detection schemes, the proposed scheme can detect human respiratory rates in different orientations.

Many commonly consumed foods, herbs and spices contain a complex array of naturally occurring bioactive molecules called phytochemicals, which may confer health benefits. In this study, the impact of LiuWei Zhuanggu Granules (LWZGG) on mineral metabolism in osteopenia development was evaluated. Results showed that serum estrogen, bone gla protein (BGP), and calcitonin (CT) levels, bone Ca, Zn and Cu levels, femur, lumbar vertebrae and trabecular bone density, tibia maximum stress and maximum bending strength were increased, and serum parathyroid hormone (PTH), serum and urine Ca, Zn and Cu levels were decreased in rat bone. It can be concluded that LWZGG is useful to improve bone quality in ovariectomized (OVX) rats.

Spectrum sensing remains a challenge in the context of cognitive radio networks (CRNs). Compared with traditional single-user sensing, cooperative spectrum sensing (CSS) exploits multiuser diversity to overcome channel fading, shadowing, and hidden terminal problems, which can effectively enhance the sensing performance and protect licensed users from harmful interference. However, for a large number of sensing nodes that need high bandwidth of the control channel for data transmitting, CSS increases cooperative overhead. To address this problem, we investigated the soft decision fusion strategy under a limited bandwidth of the control channel and proposed a simple quantization-based multibit data soft fusion rule for CSS for its simple structure and easily implementation. Under the quantization-based sensing strategy, each cooperative secondary user (SU) adopts an energy detector for local spectrum sensing. Each SU transmits quantized multibit data that sends local sensing information, instead of forwarding local one-bit hard decision results or original observation statistics, to the fusion center (FC). Furthermore, the closed-form expressions of the quantization levels and the quantization thresholds are analytically derived. Simulation results indicate that the detection performance of the proposed method approaches that of the conventional soft fusion rule with less cooperative overhead and outperforms the hard decision rules. Extensive simulations also show that multibit quantization fusion achieves a desirable tradeoff between the sensing performance and the control channel overhead for CSS.