Computationally Efficient DOA Tracking Algorithm in Monostatic MIMO Radar with Automatic Association

We consider the problem of tracking the direction of arrivals (DOA) of multiple moving targets in monostatic multiple-input multiple-output (MIMO) radar. A low-complexity DOA tracking algorithm in monostatic MIMO radar is proposed. The proposed algorithm obtains DOA estimation via the difference between previous and current covariance matrix of the reduced-dimension transformation signal, and it reduces the computational complexity and realizes automatic association in DOA tracking. Error analysis and Cramér-Rao lower bound (CRLB) of DOA tracking are derived in the paper. The proposed algorithm not only can be regarded as an extension of array-signal-processing DOA tracking algorithm in (Zhang et al. (2008)), but also is an improved version of the DOA tracking algorithm in (Zhang et al. (2008)). Furthermore, the proposed algorithm has better DOA tracking performance than the DOA tracking algorithm in (Zhang et al. (2008)). The simulation results demonstrate effectiveness of the proposed algorithm. Our work provides the technical support for the practical application of MIMO radar

An Integrative Pharmacology Model for Decoding the Underlying Therapeutic Mechanisms of Ermiao Powder for Rheumatoid Arthritis

As a systemic inflammatory arthritis disease, rheumatoid arthritis (RA) is complex and hereditary. Traditional Chinese medicine (TCM) has evident advantages in treating complex diseases, and a variety of TCM formulas have been reported that have effective treatment on RA. Clinical and pharmacological studies showed that Ermiao Powder, which consists of Phellodendron amurense Rupr. (PAR) and Atractylodes lancea (Thunb.) DC. (ALD), can be used in the treatment of RA. Currently, most studies focus on the anti-inflammatory mechanism of PAR and ALD and are less focused on their coordinated molecular mechanism. In this research, we established an integrative pharmacological strategy to explore the coordinated molecular mechanism of the two herbs of Ermiao Powder in treating RA. To explore the potential coordinated mechanism of PAR and ALD, we firstly developed a novel mathematical model to calculate the contribution score of 126 active components and 85 active components, which contributed 90% of the total contribution scores that were retained to construct the coordinated functional space. Then, the knapsack algorithm was applied to identify the core coordinated functional components from the 85 active components. Finally, we obtained the potential coordinated functional components group (CFCG) with 37 components, including wogonin, paeonol, ethyl caffeate, and magnoflorine. Also, functional enrichment analysis was performed on the targets of CFCG to explore the potential coordinated molecular mechanisms of PAR and ALD. The results indicated that the CFCG could treat RA by coordinated targeting to the genes involved in immunity and inflammation-related signal pathways, such as phosphatidylinositol 3‑kinase/protein kinase B signaling pathway, mitogen-activated protein kinase signaling pathway, tumor necrosis factor signaling pathway, and nuclear factor-kappa B signaling pathway. The docking and in vitro experiments were used to predict the affinity and validate the effect of CFCG and further confirm the reliability of our method. Our integrative pharmacological strategy, including CFCG identification and verification, can provide the methodological references for exploring the coordinated mechanism of TCM in treating complex diseases and contribute to improving our understanding of the coordinated mechanism

Electric field control of deterministic current-induced magnetization switching in a hybrid ferromagnetic/ferroelectric structure

All-electrical and programmable manipulations of ferromagnetic bits are highly pursued for the aim of high integration and low energy consumption in modern information technology1, 2, 3. Methods based on the spin–orbit torque switching4, 5, 6 in heavy metal/ferromagnet structures have been proposed with magnetic field7, 8, 9, 10, 11, 12, 13, 14, 15, and are heading toward deterministic switching without external magnetic field16, 17. Here we demonstrate that an in-plane effective magnetic field can be induced by an electric field without breaking the symmetry of the structure of the thin film, and realize the deterministic magnetization switching in a hybrid ferromagnetic/ferroelectric structure with Pt/Co/Ni/Co/Pt layers on PMN-PT substrate. The effective magnetic field can be reversed by changing the direction of the applied electric field on the PMN-PT substrate, which fully replaces the controllability function of the external magnetic field. The electric field is found to generate an additional spin–orbit torque on the CoNiCo magnets, which is confirmed by macrospin calculations and micromagnetic simulations

Lifestyle factors, metabolic factors and socioeconomic status for pelvic organ prolapse: a Mendelian randomization study

Abstract Background Previous observational studies have reported that lifestyle factors, metabolic factors and socioeconomic status are associated with the development of female pelvic organ prolapse (POP); however, whether these associations are causal remains unclear. The current study aimed to assess the causal effect of lifestyle factors, metabolic factors and socioeconomic status on POP risk. Methods We conducted a two-sample Mendelian randomization (MR) study based on summary-level data from the largest available genome-wide association studies (GWAS) to evaluate whether lifestyle factors, metabolic factors and socioeconomic status are causally related to POP. We used single nucleotide polymorphisms that are strongly associated with exposure at the genome-wide significance level (P < 5 × 10–8) as instrumental variables from genome-wide association studies. The method of random-effect inverse-variance weighting (IVW) was used as the primary analysis method, supplemented with the weighted median, MR-Egger and the MR pleiotropy residual sum and outlier applied to verify the MR assumptions. Two-step MR was conducted to investigate potential intermediate factors that are on the causal pathway from exposure to POP. Results There were associations with POP for genetically predicted waist-to-hip ratio (WHR) (odds ratio (OR) 1.02, 95% confidence interval (CI) 1.01–1.03 per SD-increase, P < 0.001), WHR adjusted for body mass index (WHRadjBMI) (OR 1.017, 95% CI 1.01–1.025 per SD-increase, P < 0.001) and education attainment (OR 0.986, 95% CI 0.98–0.991 per SD-increase) in the meta-analysis. Additionally, genetically predicted coffee consumption (OR per 50% increase 0.67, 95% CI 0.47–0.96, P = 0.03), vigorous physical activity (OR 0.83, 95% CI 0.69–0.98, P = 0.043) and high-density lipoprotein cholesterol (HDL-C) (OR 0.91, 95% CI 0.84–0.98 per SD-increase, P = 0.049) were inversely associated with POP in the FinnGen Consortium. The mediation analysis showed that the indirect effects of education attainment on POP were partly mediated by WHR and WHRadjBMI, with a mediated proportion of 27% and 13% in the UK Biobank study, respectively. Conclusions Our study provides MR evidence of a robust causal association of WHR, WHRadjBMI and education attainment with POP

Bi-HRNet: A Road Extraction Framework from Satellite Imagery Based on Node Heatmap and Bidirectional Connectivity

Today, with the rapid development of the geographic information industry, automatic road extraction from satellite imagery is a basic requirement. Most existing methods have been designed based on binary segmentation. However, these methods do not consider the topological features of road networks, which include point, edge, and direction. In this study, a topology-based multi-task convolution network is designed, namely Bi-HRNet, which can effectively learn the key features of nodes and their directions. First, the proposed network learns the node heatmap of roads, and then the pixel coordinates are extracted from the node heatmap via non-maximum suppression (NMS). At the same time, the connectivity between nodes is predicted. To improve the integrity and accuracy of connectivity, we propose a bidirectional connectivity prediction strategy, which can learn the bidirectional categories instead of direction angles. The bidirectional categories are designed based on “top-to-bottom” and “bottom-to-top” strategies, which can improve the accuracy of the connectivity between nodes. To illustrate the effectiveness of the proposed Bi-HRNet, we compare our method with several methods on different datasets. The experiments show that our method achieves a state-of-the-art performance and significantly outperforms various previous methods

Classification of Small-Scale Eucalyptus Plantations Based on NDVI Time Series Obtained from Multiple High-Resolution Datasets

Eucalyptus, a short-rotation plantation, has been expanding rapidly in southeast China in recent years owing to its short growth cycle and high yield of wood. Effective identification of eucalyptus, therefore, is important for monitoring land use changes and investigating environmental quality. For this article, we used remote sensing images over 15 years (one per year) with a 30-m spatial resolution, including Landsat 5 thematic mapper images, Landsat 7-enhanced thematic mapper images, and HJ 1A/1B images. These data were used to construct a 15-year Normalized Difference Vegetation Index (NDVI) time series for several cities in Guangdong Province, China. Eucalyptus reference NDVI time series sub-sequences were acquired, including one-year-long and two-year-long growing periods, using invested eucalyptus samples in the study region. In order to compensate for the discontinuity of the NDVI time series that is a consequence of the relatively coarse temporal resolution, we developed an inverted triangle area methodology. Using this methodology, the images were classified on the basis of the matching degree of the NDVI time series and two reference NDVI time series sub-sequences during the growing period of the eucalyptus rotations. Three additional methodologies (Bounding Envelope, City Block, and Standardized Euclidian Distance) were also tested and used as a comparison group. Threshold coefficients for the algorithms were adjusted using commission–omission error criteria. The results show that the triangle area methodology out-performed the other methodologies in classifying eucalyptus plantations. Threshold coefficients and an optimal discriminant function were determined using a mosaic photograph that had been taken by an unmanned aerial vehicle platform. Good stability was found as we performed further validation using multiple-year data from the high-resolution Gaofen Satellite 1 (GF-1) observations of larger regions. Eucalyptus planting dates were also estimated using invested eucalyptus samples and the Root Mean Square Error (RMSE) of the estimation was 84 days. This novel and reliable method for classifying short-rotation plantations at small scales is the focus of this study

Effects of Extraction Methods on the Characteristics, Physicochemical Properties and Sensory Quality of Collagen from Spent-Hens Bones

The present study used acetic acid, sodium hydroxide, and pepsin extract acid-soluble collagen (ASC), alkali-soluble collagen (ALSC), and pepsin-soluble collagen (PSC) from the bones of spent-hens, and the effects of three extraction methods on the characteristics, processing properties, antioxidant properties and acceptability of chicken bone collagen were compared. The results showed that the extraction rates of ASC, ALSC and PSC extracted from bones of spent-hens were 3.39%, 2.42% and 9.63%, respectively. The analysis of the amino acid composition, sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), Fourier transform infrared spectroscopy (FTIR), and ultraviolet full spectrum showed that the collagen extracted by the three methods had typical collagen characteristics and stable triple-helix structure, but the triple helical structure of PSC is more stable, and acid and alkaline extraction seems to have adverse effects on the secondary structure of chicken bone collagen. Differential scanning calorimetry (DSC) and scanning electron microscopy (SEM) scanning showed that PSC had higher thermal stability and more regular, loose, and porous microstructure. In addition, PSC has good processing properties, in vitro antioxidant activity, and organoleptic acceptability. Therefore, enzymatic hydrolysis was still one of the best methods to prepare collagen from bones of spent-hens, and enzyme-soluble collagen has wider application prospects in functional food and medicine and also provides an effective way for the high-value comprehensive utilization of waste chicken bone by-products