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Abstract Object detection in drone aerial images is challenging due to the difficulty of small object detection and complex backgrounds. To address these issues, this paper proposes an improved object detection model, perception and target capture detector (PTCDet), to increase detection accuracy and robustness in complex scenes. Specifically, the proposed multiple feature extraction attention (MFEA) module significantly enhances the ability of the model to detect small objects through multidimensional feature map augmentation. The weighted perceptive field augmentation (WPFA) module is designed to improve the contextual awareness and feature representation of the model, optimizing detection accuracy for small objects. Based on the multiscale feature fusion structure, an enhanced scale fusion detection (ESFD) module is used to improve small object detection by generating larger scale feature maps. Ultimately, the inner focaler IoU loss (INFL) function effectively accelerates the regression of detection bounding boxes, enhancing the generalization ability and overall detection performance of the model. The experimental results on three public datasets demonstrate that PTCDet outperforms other detection algorithms. For example, on the VisDrone dataset, compared with the baseline model YOLOv8, map@0.5 and map@0.5:0.95 are improved by 6.21% and 4.21%, respectively. PTCDet exhibits excellent performance in addressing complex backgrounds and small object detection, providing an effective and robust solution for object detection tasks in drone aerial images.

Abstract Aerial image target detection is essential for urban planning, traffic monitoring, and disaster assessment. However, existing detection algorithms struggle with small target recognition and accuracy in complex environments. To address this issue, this paper proposes an improved model based on YOLOv8, named MPE-YOLO. Initially, a multilevel feature integrator (MFI) module is employed to enhance the representation of small target features, which meticulously moderates information loss during the feature fusion process. For the backbone network of the model, a perception enhancement convolution (PEC) module is introduced to replace traditional convolutional layers, thereby expanding the network’s fine-grained feature processing capability. Furthermore, an enhanced scope-C2f (ES-C2f) module is designed, utilizing channel expansion and stacking of multiscale convolutional kernels to enhance the network’s ability to capture small target details. After a series of experiments on the VisDrone, RSOD, and AI-TOD datasets, the model has not only demonstrated superior performance in aerial image detection tasks compared to existing advanced algorithms but also achieved a lightweight model structure. The experimental results demonstrate the potential of MPE-YOLO in enhancing the accuracy and operational efficiency of aerial target detection. Code will be available online (https://github.com/zhanderen/MPE-YOLO).

In order to maintain the supply adequacy of energy for the Antarctic integrated energy system, which in turn supports normal scientific research activities and the life safety of researchers, it is imperative that the reliability criterion be applied in planning issues. In this paper, a reliability-constrained planning model for the Antarctic electricity-heat integrated energy system is proposed, thus the optimal allocation of the wind turbines, photovoltaic, diesel engine, battery storage system, and Hydrogen storage system are obtained. Firstly, the reliability-oriented planning model applicable to the Antarctic integrated energy system is constructed. In particular, the improved Gaussian mixture model is introduced to accurately describe the probabilistic characteristics of renewable energy in Antarctica considering its unique geographical location and operating environment. Then, a rolling horizon based Sequential Monte Carlo method is developed to address the problem of intractable reliability constraints in the planning formulation. More specifically, the explicit reliability function is exacted by approximation function, which can be easily added to the planning model. Finally, the online-offline numerical approach is proposed to speed up the online computation. The validity and feasibility of the proposed method is verified on the actual Antarctic energy system. The results indicate that the optimal allocation results calculated by proposed method can guarantee the reliable supply of the Antarctic energy system.

Assessment of social-ecological system resilience is a reliable means of characterizing the system’s ability to respond and recover in response to crisis events. This study takes Northwest China as the research area and aims to assess the spatiotemporal evolutionary trends of social-ecological system resilience levels. Driving force-Pressure-Meteorological constraint-State-Impact-Response(DPWSIR) model is proposed to construct the social-ecological system resilience evaluation system innovatively. The G1 entropy weight method is used to determine the index weight, and the comprehensive index method is used to calculate the social-ecological system resilience level values, evaluate the resilience of arid cities social-ecological systems in Northwest China from 2015 to 2021, and determine the resilience grades. The results demonstrate that: (1) The core status of regional central cities is highlighted, in general, the “center-periphery” spatial structure characteristics gradually stabilize. The resilience levels of regional central cities, such as Hohhot, Baotou, Xi’an, Lanzhou, Xining, Yinchuan, and Urumqi, are as high as 0.37, 0.43, 0.414, 0.41, 0.43, 0.58, and 0.39, respectively. (2) In the study area, the meteorological constraint resilience index showed a “flat-to-sudden decline” trend, with the inflection point of the sharp decline occurring in 2020. The decline in the resilience index value in arid cities is caused by the increase in precipitation and temperature, which is manifested as the result of “warming and humidification”. (3) Meteorological factors, as the direct influencing factors of sandstorms, are the most critical to the assessment of the resilience of arid cities social-ecological systems. The relative percentage difference (RPD) is used to characterize the relative difference between the traditional model (DPSIR) and the improved model (DPWSIR). In areas for example Jinchang(29%), which is close to the dust sources and greatly affected by sandstorms, the differences in the social-ecological systems resilience are large and the RPD values are significant. Therefore, the DPWSIR model is more suitable for arid cities. This study provides scientific reference for the formulation of ecological environment protection policies and resilience improvement strategies for cities affected by sandstorms.

Abstract Background Clopidogrel resistance profoundly increases the risk of major cardiovascular events in coronary artery disease (CAD) patients. Here, we comprehensively analyse global m6A modification alterations in clopidogrel-resistant (CR) and non-CR patients. Methods After RNA isolation, the RNA transcriptome expression (lncRNA, circRNA, and mRNA) was analysed via RNA-seq, and m6A peaks were identified by MeRIP-seq. The altered m6A methylation sites on mRNAs, lncRNAs, and circRNAs were identified, and then, GO and KEGG pathway analyses were performed. Through joint analysis with RNA-seq and MeRIP-seq data, differentially expressed mRNAs harbouring differentially methylated sites were identified. The changes in m6A regulator levels and the abundance of differentially methylated sites were measured by RT-PCR. The identification of m6A-modified RNAs was verified by m6A-IP-qPCR. Results The expression of 2919 hypermethylated and 2519 hypomethylated mRNAs, 192 hypermethylated and 391 hypomethylated lncRNAs, and 375 hypermethylated and 546 hypomethylated circRNAs was shown to be altered in CR patients. The m6A peaks related to CR indicated lower mark density at the CDS region. Functional enrichment analysis revealed that inflammatory pathways and insulin signalling pathways might be involved in the pathological processes underlying CR. The expression of mRNAs (ST5, KDM6B, GLB1L2, and LSM14B), lncRNAs (MSTRG.13776.1 and ENST00000627981.1), and circRNAs (hsa_circ_0070675_CBC1, hsa-circRNA13011-5_CBC1, and hsa-circRNA6406-3_CBC1) was upregulated in CR patients, while the expression of mRNAs (RPS16 and CREG1), lncRNAs (MSTRG.9215.1), and circRNAs (hsa_circ_0082972_CBC1) was downregulated in CR patients. Moreover, m6A regulators (FTO, YTHDF3, and WTAP) were also differentially expressed. An additional combined analysis of gene expression and m6A peaks revealed that the expression of mRNAs (such as ST5, LYPD2, and RPS16 mRNAs) was significantly altered in the CR patients. Conclusion The expression of m6A regulators, the RNA transcriptome, and the m6A landscape was altered in CR patients. These findings reveal epitranscriptomic regulation in CR patients, which might be novel therapeutic targets in future.

BackgroundWhile increasing concerns arise about the health effects of environmental pollutants, the relationship between blood manganese (Mn) and sarcopenia has yet to be fully explored in the general population.ObjectiveThis study aims to investigate the association between blood manganese (Mn) levels and sarcopenia in adults.MethodsIn our study, we evaluated 8,135 individuals aged 18–59 years, utilizing data from the National Health and Nutrition Examination Survey (NHANES) spanning 2011 to 2018. We employed generalized additive model (GAM) to discern potential non-linear relationships and utilized the two-piecewise linear regression model to probe the association between blood Mn levels and sarcopenia.ResultsAfter adjusting for potential confounders, we identified non-linear association between blood Mn levels and sarcopenia, with an inflection point at 13.45 μg/L. The effect sizes and the confidence intervals on the left and right sides of the inflection point were 1.006 (0.996 to 1.048) and 1.082 (1.043 to 1.122), respectively. Subgroup analysis showed that the effect sizes of blood Mn on sarcopenia have significant differences in gender and different BMI groups.ConclusionOur results showed that a reverse U-shaped curve between blood Mn levels and sarcopenia, with an identified the inflection point at blood Mn level of 13.45 μg/L.

Abstract In order to solve the problem of excessive degradation rate and insufficient biocompatibility of magnesium‐based bone implants, a polyphenol (EGCG) induced hydroxyapatite (HA) coating was prepared on the surface of AZ31 alloy. The physical and chemical properties and corrosion resistance of the coating were analysed in depth, and its biocompatibility was preliminarily explored in vitro. The results showed that the polyphenol (EGCG) conversion coating constructed on the AZ31 could successfully induce the formation of HA by complexing the phenolic hydroxyl group with calcium ions. The electrochemical and long‐term immersion experiments showed that the corrosion resistance of EGCG/HA composite coating was significantly improved. The self‐corrosion current density, hydrogen evolution and the increase of pH value of AZ31‐EGCG/HA were significantly lower than those of AZ31. On the basis of inhibiting the excessive corrosion of the substrate, the composite coating significantly improves the compatibility of pre‐osteoblasts, supports the adhesion and spreading and effectively reduces the haemolysis rate to less than 5%. The preparation method of the coating is simple, low cost and suitable for complex shape surfaces, which can significantly improve the corrosion resistance and biocompatibility of the AZ31 substrate. It is expected to provide a solution for the surface modification of magnesium‐based bone implants.

Abstract Since the distributed MIMO radar (DMR) has widely spread transmitters and receivers, it can provide higher target detection probability, as well as superior target tracking and localization performance than the monostatic/bistatic radar systems. An effective radar resource allocation scheme can optimise the DMR system parameter and obtain better system performance. In this paper, a critical but limited system resource is optimised, that is, select a subset of the active radar antennas. In this scenario, the convolutional neural network of the antenna subset selection for target localization (LCNN‐ASS) algorithm in the DMR is proposed based on two free switching policies. The proposed algorithm is immune to the failure of a single policy and selects antenna subsets from the entire sets with a remarkable computational speed. Therefore, the proposed algorithm increases the flexibility of resource scheduling over traditional algorithms. Simulation experiments and performance analysis demonstrate the localization performance and flexibility of the LCNN‐ASS algorithm.

Abstract An ultimate goal of electron paramagnetic resonance (EPR) spectroscopy is to analyze molecular dynamics in place where it occurs, such as in a living cell. The nanodiamond (ND) hosting nitrogen-vacancy (NV) centers will be a promising EPR sensor to achieve this goal. However, ND-based EPR spectroscopy remains elusive, due to the challenge of controlling NV centers without well-defined orientations inside a flexible ND. Here, we show a generalized zero-field EPR technique with spectra robust to the sensor’s orientation. The key is applying an amplitude modulation on the control field, which generates a series of equidistant Floquet states with energy splitting being the orientation-independent modulation frequency. We acquire the zero-field EPR spectrum of vanadyl ions in aqueous glycerol solution with embedded single NDs, paving the way towards in vivo EPR.

Aiming at the low observable target detection under sea clutter backgrounds, this paper emphasizes the exploration of distinguishable full-polarization features between target and sea clutter echoes. To overcome the shortcomings of the existing polarization feature-based methods, the full-polarization features of sea clutter are modeled and analyzed in detail by using Van Zyl polarization decomposition. Then, three polarimetric features (the relative surface scattering energy, the relative dihedral scattering energy and the relative diffuse scattering energy) are extracted from the fully polarimetric radar sea clutter echoes, which improve the feature differences between sea clutter and targets. And a tri-polarimetric feature detector with constant false alarm rate (CFAR) is constructed based on the fast convex hull learning algorithm. The experimental results on the real measured IPIX radar datasets prove that the proposed full-polarization feature detector obtains more competitive detection performance and lower computational complexity than the several existing feature-based detectors.

Remote sensing image dehazing aims to enhance the visibility of hazy images and improve the quality of remote sensing imagery, which is essential for various applications such as object detection and classification. However, the lack of paired data in remote sensing image dehazing enhances the applications of unpaired image-to-image translation methods. Nonetheless, the considerable parameter size of such methods often leads to prolonged training times and substantial resource consumption. In this work, we propose SPRGAN, a novel approach leveraging Enhanced Perlin Noise-Based Generative Adversarial Networks (GANs) with Rotation Invariance to address these challenges. Firstly, we introduce a Spatial-Spectrum Attention (SSA) mechanism with Skip-Attention (SKIPAT) to enhance the model’s ability to interpret and process spectral information in hazy images. Additionally, we have significantly reduced computational overhead to streamline processing. Secondly, our approach combines Perlin Noise Masks in pre-training to simulate real foggy conditions, thereby accelerating convergence and enhancing performance. Then, we introduce a Rotation Loss (RT Loss) to ensure the model’s ability to dehaze images from different angles uniformly, thus enhancing its robustness and adaptability to diverse scenarios. At last, experimental results demonstrate the effectiveness of SPRGAN in remote sensing image dehazing, achieving better performance compared to state-of-the-art methods.

Zoonotic coronaviruses (CoVs) caused major human outbreaks in the last two decades. One of the biggest challenges during future CoV disease is ensuring rapid detection and diagnosis at the early phase of a zoonotic event, and active surveillance to the zoonotic high-risk CoVs appears the best way at the present time to provide early warnings. However, there is neither an evaluation of spillover potential nor diagnosis tools for the majority of CoVs. Here, we analyzed the viral traits, including population, genetic diversity, receptor and host species for all 40 alpha- and beta-CoV species, where the human-infecting CoVs are from. Our analysis proposed 20 high-risk CoV species, including 6 of which jumped to human, 3 with evidence of spillover but not to human and 11 without evidence of spillover yet, which prediction were further supported by an analysis of the history of CoV zoonosis. We also found three major zoonotic sources: multiple bat-origin CoV species, the rodent-origin sub-genus Embecovirus and the CoV species AlphaCoV1. Moreover, the Rhinolophidae and Hipposideridae bats harbour a significantly higher proportion of human-threatening CoV species, whereas camel, civet, swine and pangolin could be important intermediate hosts during CoV zoonotic transmission. Finally, we established quick and sensitive serologic tools for a list of proposed high-risk CoVs and validated the methods in serum cross-reaction assays using hyper-immune rabbit sera or clinical samples. By comprehensive risk assessment of the potential human-infecting CoVs, our work provides a theoretical or practical basis for future CoV disease preparedness.

Narrowband interference and wideband interference are both common jamming signals against synthetic aperture radar, which can degrade the signal severely. To suppress interference effectively, an interference suppression method based on short-time fractional Fourier transform (STFrFT) is proposed. After transforming the signal into the time–frequency domain through STFrFT, an adaptive gain coefficient is determined for the instantaneous frequency spectrum at every certain time. The gain coefficient can be preserved while suppressing the interference. Finally, we obtain the useful signal by inverse STFrFT. The simulation and performance analysis show the effectiveness and validity of the proposed algorithm for measured data.

The information of cropland is obtained efficiently and accurately as the basis for achieving precision agriculture (PA). As the boundary between cropland and other types of land in remote sensing images with different resolutions is fuzzy, the characteristics of cropland are easily confused when extracting cropland, resulting in inaccurate identification and extraction of cropland under large and complex backgrounds and rough localization of marginal areas. We proposed a two-path multiscale attention self-supervised network with the perception of four directions in pixel space, called the multiscale bilateral spatial direction-aware network (MBSDANet), to solve these problems and improve the model’s ability to extract cropland in small samples. One path extracts attentional feature maps by spatial directions to preserve detailed direction-aware information and generate high-resolution features; the other path obtains local-to-global information through pyramid pooling and attention awareness to capture dense multiscale cropland features to separate targets in complex contexts. The features of the two branches at different levels are fused by weighting the multi-aware information. Triangular self-supervised and boundary-aware losses are used to achieve fine segmentation and extraction of cropland in small samples. We tested the extraction method on cropland in Denmark and the Hebei Province of China, demonstrating its effectiveness and generalization. Compared to other neural network models, MBSDANet achieves better accuracy with a precision of 0.9481, an IoU of 0.8937, and an F1 score of 0.9438.

High-resolution sea-surface surveillance radars are designed to find two classes of small targets, low-velocity and floating small targets on the sea surface and low-altitude small targets over the sea surface. Existing feature-based detectors using amplitude and Doppler spectra features of returns can fast detect the first class of small targets. But they often fail to find the second class of small targets due to their small radar cross-sections and low signal-to-clutter ratios (SCR). This article proposes a fast dual trifeature-based detector to find the two classes of small targets. At the low-velocity trifeature-based detector, the existing trifeature-based detector is used to find the first class of small targets. At the high-velocity trifeature-based detector, a new trifeature-based detector is used to detect the second class of small targets by utilizing three new features from the median normalized Doppler amplitude spectrum (MNDAS). Owing to the ability of the MNDAS to suppress sea clutter, the high-velocity trifeature-based detector can effectively find the second class of small targets with low SCRs. The detected results in the two trifeature-based detectors are combined into the binary output of the detector in terms of the “OR” operation. Finally, the proposed detector is verified and compared with the existing detectors by the open and recognized IPIX and CSIR radar databases and one unmanned aerial vehicle radar dataset. Besides the detection performance for the two classes of small targets, a merit of the proposed detector is of low-complexity decision because no time-frequency features are available in detection.

The detection of small objects in Unmanned Aerial Vehicle (UAV) images, which contain a large number of objects with extremely small pixels, has been a major challenge over the last few decades. Owing to the lack of sufficiently detailed representation of object features, conventional detectors based on deep learning are unsatisfactory when detecting small objects. Meanwhile, the flight altitude and the shooting angle of the UAV always changing, which leads to an uneven dispersion of the multiple objects and a varying density of small objects. In response to the issues mentioned above, a novel lightweight Cross-layer Triple-branch Parallel Fusion Network (CTPFNet) is proposed to improve the real-time detection accuracy of small objects under UAV images. Firstly, a novel downsampling structure called the Inverse Residual Pooling Cascade (IRPC) module is proposed to obtain richer feature information about small objects. Secondly, we construct an improved global feature extraction structure Efficient Layer Aggregation Networks-Transformer (ELAN-Trans) to enhance the association between global features. Then, we design the Hybrid Dilated Depth Separable-Spatial Pyramid Pooling-Fast (HDDS-SPPF) to capture more contextual information by using the dilated convolution operations. To enhance the cross-scale transfer fusion of low-level original features containing more detailed localization information and deep-level deep features containing more semantic information, we propose a CTPF module embedded into the neck region for the secondary feature reuse of adjacent feature maps with varying resolutions. Extensive experiments on the public VisDrone2021-DET dataset show that the proposed model achieves significant performance gains with fewer parameters.

ABSTRACTDriven by improvements in satellite internet and Low Earth Orbit (LEO) navigation augmentation, the integration of communication and navigation has become increasingly common, and further improving navigation capabilities based on communication constellations has become a significant challenge. In the context of the existing Orthogonal Frequency Division Multiplexing (OFDM) communication systems, this paper proposes a new ranging signal design method based on an LEO satellite communication constellation. The LEO Satellite Communication Constellation Block-type Pilot (LSCC-BPR) signal is superimposed on the communication signal in a block-type form and occupies some of the subcarriers of the OFDM signal for transmission, thus ensuring the continuity of the ranging pilot signal in the time and frequency domains. Joint estimation in the time and frequency domains is performed to obtain the relevant distance value, and the ranging accuracy and communication resource utilization rate are determined. To characterize the ranging performance, the Root Mean Square Error (RMSE) is selected as an evaluation criterion. Simulations show that when the number of pilots is 2048 and the Signal-to-Noise Ratio (SNR) is 0 dB, the ranging accuracy can reach 0.8 m, and the pilot occupies only 50% of the communication subcarriers, thus improving the utilization of communication resources and meeting the public demand for communication and location services.

Phosphate rock has great potential value in the application of cut-off walls for heavy metal contaminated sites. The effects of initial concentration of Pb2+, contact time, temperature and pH on the adsorption performance of phosphate rock modified soil-bentonite (PSB) were analyzed by Batch tests, and the microstructure and physicochemical properties of the adsorbent were characterized and analyzed by scanning electron microscopy (SEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR). The results showed that the incorporation of phosphate rock improves the adsorption performance of the mixed material for Pb2+ in an acidic environment, and the maximum adsorption amount of PSB can be 154% greater than that of soil-bentonite (SB). Kinetic analysis showed that intraparticle diffusion and liquid film diffusion both participate in control of the adsorption process, and the Langmuir model fitted the isothermal adsorption data better. The adsorption process of PSB to Pb2+ is an exothermic reaction process, and a low temperature environment is beneficial to the adsorption of Pb2+. Studies on the microscopic mechanism show that PO43− reacts with Pb2+ in solution to form phosphate precipitation, and –OH and PO43− are on the surface or interlayer of the adsorbent participate in the adsorption process of Pb2+. HIGHLIGHTS Proposes a new modified material to be used for a cut-off wall.; The adsorption mechanism is investigated by kinetic, thermodynamic and microscopic analyses.;

In agricultural remote sensing monitoring, climate often affects the quality of optical remote sensing image data acquisition. The acquired satellite imagery results usually contain cloud information, leading to a lack of ground data information. Unlike thick clouds, the semi-transparent nature of thin clouds prevents thin clouds from completely obscuring the ground scene. In order to remove thin clouds in the cultivated land and restore the actual ground information as much as possible, we proposed a cloud removal method of spatial information fusion self-attention generative adversarial network (SI-SA GAN) based on multi-directional perceptual attention and self-attention mechanism. The proposed method identifies and focuses on cloud regions using spatial attention, channel attention, and self-attention mechanism, which can enhance image information. The modules of the discriminator utilize residual networks and self-attention non-local neural networks to guide image information output. The generative adversarial network (GAN) is applied to remove clouds and restore the corresponding irregular occlusion area according to the depth characteristics of the input information. A gradient penalty is applied to improve the robustness of the generative network. In this paper, we compared the evaluation indexes of other advanced models. The qualitative and quantitative results of Sentinel-2A and public RICE datasets confirmed that the proposed method could enhance image quality effectively after cloud removal. The model has excellent thin cloud removal performance with small-scale training data.

ABSTRACT Hundreds of sarbecoviruses have been found in bats, but only a fraction of them have the ability to infect cells using angiotensin-converting enzyme 2 (ACE2), the receptor for SARS-CoV and -2. To date, only ACE2-dependent sarbecoviruses have been isolated from field samples or grown in the laboratory. ACE2-independent sarbecoviruses, comprising the majority of the subgenus, have not been propagated in any type of cell culture, as the factors and conditions needed for their replication are completely unknown. Given the significant zoonotic threat posed by sarbecoviruses, cell culture models and in vitro tools are urgently needed to study the rest of this subgenus. We previously showed that the exogenous protease trypsin could facilitate cell entry of viral-like particles pseudotyped with spike protein from some of the ACE2-independent sarbecoviruses. Here, we tested if these conditions were sufficient to support bona fide viral replication using recombinant bat sarbecoviruses. In the presence of trypsin, some of the spike proteins from clade 2 viruses were capable of supporting bat sarbecovirus infection and replication in human and bat cells. Protease experiments showed a specific viral dependence on high levels of trypsin, as TMPRSS2 and furin had no effect on clade 2 virus entry. These results shed light on how sarbecoviruses transmit and coexist in their natural hosts, provide key insights for future efforts to isolate and grow these viruses from field samples, and further underscore the need for broadly protective, universal coronavirus vaccines. IMPORTANCE Our studies demonstrate that some unexplored sarbecoviruses are capable of replicating in human and bat cells in an ACE2-independent way but need a high trypsin environment. We found that trypsin is not compensated by other known proteases involved in some coronavirus entry. This work provides important information that the trypsin-dependent entry may be a widely employed mechanism for coronaviruses and will help for further understanding the biological features of the less-studied viruses.

Classical swine fever (CSF), caused by the classical swine fever virus (CSFV), is a highly contagious and fatal viral disease, posing a significant threat to the swine industry. Heat shock protein 90 kDa alpha class A member 1 (HSP90AA1) is a very conservative chaperone protein that plays an important role in signal transduction and viral proliferation. However, the role of HSP90AA1 in CSFV infection is unknown. In this study, we found that expression of HSP90AA1 could be promoted in PK-15 and 3D4/2 cells infected by CSFV. Over-expression of HSP90AA1 could inhibit CSFV replication and functional silencing of HSP90AA1 gene promotes CSFV replication. Further exploration revealed that HSP90AA1 interacted with CSFV NS5A protein and reduced the protein levels of NS5A. Since NS5A has an important role in CSFV replication and is closely related to type I IFN and NF-κB response, we further analyzed whether HSP90AA1 affects CSFV replication by regulating type I IFN and NF-κB pathway responses. Our research found HSP90AA1 positively regulated type I IFN response by promoting STAT1 phosphorylation and nuclear translocation processes and promoted the nuclear translocation processes of p-P65. However, CSFV infection antagonizes the activation of HSP90AA1 on JAK/STAT and NF-κB pathway. In conclusion, our study found that HSP90AA1 overexpression significantly inhibited CSFV replication and may inhibit CSFV replication by interacting with NS5A and activating JAK/STAT and NF-κB signaling pathways. These results provide new insights into the mechanism of action of HSP90AA1 in CSFV infection, which abundant the candidate library of anti-CSFV.

BackgroundIt has been reported that sacubitril/valsartan can improve cardiac function in acute myocardial infarction (AMI) patients complicated by heart failure (HF). However, a number of patients cannot be treated successfully; this phenomenon is called sacubitril/valsartan resistance (SVR), and the mechanisms remain unclear.MethodsIn our present research, the expression profiles of transfer RNA (tRNA)-derived small RNAs (tsRNAs) in SVR along with no sacubitril/valsartan resistance (NSVR) patients were determined by RNA sequencing. Through bioinformatics, quantitative real-time PCR (qRT-PCR), and cell-based experiments, we identified SVR-related tsRNAs and confirmed their diagnostic value, predicted their targeted genes, and explored the enriched signal pathways as well as regulatory roles of tsRNAs in SVR.ResultsOur research indicated that 36 tsRNAs were upregulated and that 21 tsRNAs were downregulated in SVR. Among these tsRNAs, the expression of tRF-59:76-Tyr-GTA-2-M3 and tRF-60:76-Val-AAC-1-M5 was upregulated, while the expression of tRF-1:29-Gly-GCC-1 was downregulated in the group of SVR. Receiver operating characteristic (ROC) curve analysis demonstrated that these three tsRNAs were potential biomarkers of the therapeutic heterogeneity of sacubitril/valsartan. Moreover, tRF-60:76-Val-AAC-1-M5 might target Tnfrsf10b and Bcl2l1 to influence the observed therapeutic heterogeneity through the lipid and atherosclerosis signaling pathways.ConclusionHence, tsRNA might play a vital role in SVR. These discoveries provide new insights for the mechanistic investigation of responsiveness to sacubitril/valsartan.

Akabane virus (AKAV) is a world wide epidemic arbovirus belonging to the Bunyavirales order that predominantly infects livestock and causes severe congenital malformations. The nucleocapsid (N) protein of AKAV possesses multiple important functions in the virus life cycle, and it is an ideal choice for AKAV detection. In this study, we successfully constructed two stable BHK-21 cell lines (C8H2 and F7E5) that constitutively express the AKAV N protein using a lentivirus system combined with puromycin selection. RT-PCR analysis confirmed that the AKAV N gene was integrated into the BHK-21 cell genome and consistently transcribed. Indirect immunofluorescence (IFA) and Western blot (WB) assays proved that both C8H2 and F7E5 cells could react with the AKAV N protein mAb specifically, indicating potential applications in AKAV detection. Furthermore, we analyzed the growth kinetics of AKAV in the C8H2 and F7E5 cell lines and observed temporary inhibition of viral replication at 12, 24 and 36 h postinfection (hpi) compared to BHK-21 cells. Subsequent investigations suggested that the reduced viral replication was linked to the down-regulation of the viral mRNAs (Gc and RdRp). In summary, we have established materials for detecting AKAV and gained new insights into the function of the AKAV N protein.

Abstract With the tight interconnection of multiple energy flows in the integrated energy system, the security problem owing to the interaction between the electric network and the heating network has gained wide attention. To facilitate the security assessment of the integrated energy system, this article initially proposes the definition for the secure operation region (SOR) of the heat‐electricity integrated energy system under the exact AC power flow model and non‐linear heating formulation. The SOR of the integrated energy system is defined as a set of operation points for which the operational constraints of the electric network and the coupling heating network are satisfied. Next, complete characterization for the SOR of the integrated energy system is achieved based on the non‐linear dynamical quotient gradient system. After that, the evaluation criteria based on the integrated energy system (IES) SOR is defined to screen flexibility providers. Flexibility improvement by utilizing plug‐in hybrid electrical vehicle, installing energy storage, installing a heat storage tank, installing electric boiler, and utilizing building thermal inertia in the heat network is simulated and measured by application of the completely characterized IES SOR. Finally, numerical studies are given to verify the effectiveness of the proposed scheme.

Complexity and harsh working conditions pose limitations for fabrication of patterned materials. Here the authors report a single-step method for in situ deposition of materials that is based on semiconductor nanoparticle assisted photon-induced chemical reduction and optical trapping.

The anaerobic ammonium oxidation (anammox) by autotrophic anaerobic ammonia-oxidizing bacteria (AnAOB) is a biological process used to remove reactive nitrogen from wastewater. It has been repeatedly reported that elevated nitrite concentrations can severely inhibit the growth of AnAOB, which renders the anammox process challenging for industrial-scale applications. Both denitrifying (DN) and dissimilatory nitrate reduction to ammonium (DNRA) bacteria can potentially consume excess nitrite in an anammox system to prevent its inhibitory effect on AnAOB. However, metabolic interactions among DN, DNRA, and AnAOB bacteria under elevated nitrite conditions remain to be elucidated at metabolic resolutions. In this study, a laboratory-scale anammox bioreactor was used to conduct an investigation of the microbial shift and functional interactions of AnAOB, DN, and DNRA bacteria during a long-term nitrite inhibition to eventual self-recovery episode. The relative abundance of AnAOB first decreased due to high nitrite concentration, which lowered the system’s nitrogen removal efficiency, but then recovered automatically without any external interference. Based on the relative abundance variations of genomes in the inhibition, adaptation, and recovery periods, we found that DN and DNRA bacteria could be divided into three niche groups: type I (types Ia and Ib) that includes mainly DN bacteria and type II and type III that include primarily DNRA bacteria. Type Ia and type II bacteria outcompeted other bacteria in the inhibition and adaptation periods, respectively. They were recognized as potential nitrite scavengers at high nitrite concentrations, contributing to stabilizing the nitrite concentration and the eventual recovery of the anammox system. These findings shed light on the potential engineering solutions to maintain a robust and efficient industrial-scale anammox process.

Objective To explore the role of miR-223 and miR-126 in predicting treatment responses to dual antiplatelet therapy (DAPT) in patients with ST-elevation myocardial infarction (STEMI). Methods Plasma miR-223 and miR-126 levels were measured before treatment. Treatment responses and 2-year survival were determined. In vitro experiments were performed to explore the mechanism of action. Results Patients with resistance to DAPT had a lower level of miR-223 and miR-126. Cardiac-event-free survival was shorter in patients with lower miR-223 or miR-126 levels. MiR-223 and miR-126 independently predicted DAPT resistance. Modulating miR-223 or miR-126 in platelets in vitro significantly changed the response to clopidogrel by regulating platelet aggregation. Conclusion MiR-223 and miR-126 play a role in DAPT resistance and may provide potential biomarkers in patients with STEMI.

Abstract Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by severe fever with thrombocytopenia syndrome virus (SFTSV). SFTSV has been found in humans, ticks and animals, and SFTS has high mortality and increasing prevalence in East Asia. In the study, the samples (heart, liver, lung, kidney, spleen, brain tissue and serum) were collected from 374 domestic animals and 241 wild animals in Pingqiao District and Xinxian County of Xinyang in Henan Province, China. 275 (44.72%, 275/615) animals were positive for anti-SFTSV antibodies, the anti-SFTSV antibodies positive ratios of domestic and wild animals were 43.58% (163/374) and 46.47% (112/241), respectively. There was no significant difference in domestic and wild animals, but significant differences were detected among different species of animals (χ 2 = 112.59, P

As the increasing number of people watching mobile phones while walking, sidewalk accidents occur more frequently. Using mobile camera instead of distracted pedestrians to monitor the road conditions ahead can effectively prevent pedestrian accidents. In order to develop this kind of applications, a wide-field sidewalk dataset becomes a necessity. In this paper, three major contributions are concluded. Firstly, a dataset quality evaluation model is proposed, which directs the establishment of a wide-view dataset named PESID for the sidewalk environment. PESID currently contains more than 1.9K labeled images which cover more than 5 districts, 10 communal facilities, 6 typical roads. Secondly, a criterion is presented to evaluate 9 up-to-date object detection algorithms in order to train a mobile feasible obstacle detection model. Finally, a reliable and low-cost framework is designed for obstacle detection based pedestrian safety application. The proposal is able to avoid 71.4% collisions on average by evaluating the mean average precision (MAP), the dangerous reminder omission rate and the false detection rate of the model.

The problem of ground moving target imaging and detection is difficult due to the large range cell migration (RCM) and time-varying Doppler frequency migration (DFM) in synthetic aperture radar (SAR) system. To finely refocus a moving target, motion parameters need be accurately estimated for compensating the RCM and DFM. In this work, an efficient ground moving target refocusing method based on pseudopolar fast Fourier transform (PPFFT) and coherently integrated cubic phase function (CICPF) is proposed without a priori knowledge of the motion parameters. First, based on the known radar platform motion parameters, the range curvature correction is completed, and then the range walk is corrected using the efficient PPFFT via estimating the slope of the range walk. In doing so, the energy of the moving target has been focused into the same range cell. Second, time-varying DFM parameters are obtained utilizing the proposed CICPF, and thus the well-focused result of the moving target is obtained. Compared with the existing methods, the proposed method has the advantages in computational complexity due to avoiding the searching and interpolation operation, and anti-noise performances thanks to fully exploiting the two-dimensional (2-D) coherently accumulation characteristics and the energy of all the scatterers in moving target for estimating motion parameters. Finally, both the simulated and real SAR data processing results are presented to validate the proposed method.

Antiplatelet therapy has become a cornerstone in the treatment of coronary heart disease (CHD). However, due to high-residual-platelet-reactivity, clopidogrel resistance (CR) is a common phenomenon, and it is rarely known about the relationship between CR and epigenetic changes. This study compared the whole genomic methylation patterns of blood samples from patients with CR (n = 6) and non-CR (n = 6) with the Human Methylation 850K BeadChip assay. We explored differentially methylated CpG sites, genes, and pathways using bioinformatics profiling. The CR and control groups showed significantly different DNA methylation at 7,098 sites, with 979 sites showing hypermethylation and 6,119 sites showing hypomethylation. The pyrosequencing method was used to validate four differentially methylated CpG loci (cg23371584, cg15971518, cg04481923, cg22507406), confirming that DNA methylation was associated with the risk of CR (30 CR vs. 30 non-CR). The relative mRNA expression of the four genes (BTG2, PRG2, VTRNA2-1, PER3) corresponding to the loci above was also associated with CR, suggesting that alterations in DNA methylation may affect the expression of these four genes, eventually resulting in CR. Additionally, differentially methylated sites are partially related to genes and pathways that play key roles in process of circadian entrainment, insulin secretion, and so on. Hence, the mechanism and biological regulation of CR might be reflected through these epigenetic alterations, but future research will need to address the causal relationships.

The generalized Pareto (GP) distribution model is often used to describe the amplitude statistical feature of sea clutter. Generally, the parameters of GP distribution are estimated by moments estimators. However, when the sea state is high, the appearance of sea spikes will increase the echo of the anomalous scattering units, which leads to a decrease in the parameter estimation accuracy and target detection performance. To improve the parameter estimation accuracy, this paper proposes a novel parameter estimation method based on variable intervals. Considering the local properties of sea clutter, we take a variable interval of the entire sea clutter series for parameter estimation, where the interval position is selected according to the sea state conditions. For contrast, the bipercentile parameter estimation and truncate moment estimation are also introduced. Finally, the experiment based on the real measured X-band sea clutter datasets indicates that the proposed method outperforms the state-of-the-art moments estimators.

Microwave remote sensing instruments such as synthetic aperture radar (SAR) play an important role in scientific research applications, while they suffer great measurement distortion with the presence of radio frequency interference (RFI). Existing methods either adopt model−based optimization or follow a data−driven black−box learning scheme, and both have specific limitations in terms of efficiency, accuracy, and interpretability. In this paper, we propose a hybrid model−constrained deep learning approach for RFI extraction and mitigation by fusing the classical model-based and advanced data-driven method. Considering the temporal-spatial correlation of target response, as well as the random sparsity property for time−varying interference, a joint low−rank and sparse optimization framework is established. Instead of applying the iterative optimization process with uncertain convergency, the proposed scheme approximates the iterative process with a stacked recurrent neural network. By adopting this hybrid model−constrained deep learning strategy, the original unsupervised decomposition problem is converted to a supervised learning problem. Experimental results show the validity of the proposed method under diverse RFI scenarios, which could avoid the manual tuning of model hyperparameters as well as speed up the efficiency.

Abstract A new technique is advanced to obtain tropospheric temperature profile using vibrational Raman (VR) and rotational Raman (RR) backscattering. The proposed technique can obtain tropospheric temperature and decrease retrieval errors caused by insufficient suppression of the Mie backscattering in the RR detection channels. The retrieval equations are obtained using VR and RR lidar equations. The feasibility of this technique is demonstrated by applying the algorithm to the Hampton University VR and RR lidar signals. Intercomparisons of these atmospheric temperature retrievals from the Hampton University lidar show good agreement with local radiosonde measurements. Moreover, the proposed technique is applied to a cloudy case and is further validated by showing the good agreements with radiosonde measurements of cloud temperature.

The reasonable sharing and effective integration of data is the premise of improving the comprehensive governance ability of society through data empowerment. It can be determined that the fine-grained splitting of microservice framework can promote the reasonable sharing and effective integration of data. However, how to determine the granularity of microservice splitting is a multiparameter and multiobjective decision-making problem, which is also a key basic problem to be solved urgently both in academic research and application. From the perspective of application, this paper puts forward the criteria and basic framework that can guide the microservice splitting, and for the first time, based on the perspective of maintainability, it gives the decision criteria and methods of microservice splitting granularity. After the theoretical research, this paper also takes the provincial microservice governance of food and drug regulation as an example: the example shows that the framework and methods proposed can effectively improve data sharing and system expandability. Through microservice governance, collaborative social governance featuring “one network to achieve management objectives, one network to realize comprehensive business processing, one network for comprehensive view of all information” can be achieved. It is an exemplary mode worth popularization.

Image registration is an important basis of image processing, which is of great significance in image mosaicking, target recognition, and change detection. Aiming at the automatic registration problem of multi-angle optical images for ground scenes, a registration method combining point features and line features to register images is proposed. Firstly, the LSD (Line Segment Detector) algorithm is used to extract line features of images. The obtained line segments whose length are less than a given threshold are eliminated by a visual significant algorithm. Then, an affine transform model obtained by estimating a Gaussian mixture model (GMM) is applied to the image to be matched. Lastly, Harris point features are utilized in fine matching to overcome shortages of methods based on line features. In experiments, the proposed algorithm is compared with popular feature-based registration algorithms. The results indicate that the proposed algorithm in this work has obvious advantages in terms of registration accuracy and reliability for optical images acquired at different angles.

Cylindrical rollers are important elements of bearings, and their machining accuracy and consistency affect the bearing quality. Using a GCr15 cylindrical roller of Ф11 × 12 as the processing object in this study, the effects of loading pressure, abrasive concentration, and speed combination on cylindrical roller machining precision were investigated using the orthogonal experimental design method on a double-side eccentric pendulum lapping and polishing machine. The machining parameters of the lapping stage were optimized, and the lapping optimal process parameters were determined by S/N response analysis and analysis of variance (ANOVA). The results show that when the experiment was optimized using loading pressure of 10 N/roller, abrasive concentration (3000#Al2O3) of 20.0 wt%, and rotational speed combination, the material removal rate (MRR) of cylindrical roller reached 0.0896 μm/min; the average roughness of the batch decreased from 0.056 μm to 0.027 μm, 51.8% lower than the original batch average roughness, and the deviation decreased from the initial 0.022 μm to 0.014 μm; the batch average roundness error decreased from 0.47 μm to 0.28 μm, 40.4% lower than the original batch average roundness error, and the deviation decreased from the initial 0.19 μm to 0.038 μm; and the batch average diameter variation decreased from 4.5 μm to about 3.6 μm, 20% lower than the original batch average diameter variation. The double-side eccentric lapping of cylinder rollers does not only lead to improvement in the surface quality and shape accuracy of rollers, but also improvement in the batch consistency. Keywords: Cylindrical roller, Eccentric pendulum type, Orthogonal experiment, Lapping, Batch consistency

Abstract A novel C17N Lycopodium alkaloid (LA), lycoplanine B (1), containing an unusual formyl group, along with two new LAs, lycoplanines C (2) and D (3), were isolated from the whole plant of Lycopodium complanatum. Their structures were elucidated by extensive NMR techniques, including 1D- and 2D-NMR experiments, as well as comparing their spectral data with those of the known analogues. A possible biogenetic pathway for 1 was also proposed. Graphical Abstract

Abstract Background Cardiovascular disease (CVD) has become the leading cause of death in China, and most of the cases can be prevented by controlling risk factors. The goal of this study was to build a corpus of CVD risk factor annotations based on Chinese electronic medical records (CEMRs). This corpus is intended to be used to develop a risk factor information extraction system that, in turn, can be applied as a foundation for the further study of the progress of risk factors and CVD. Results We designed a light annotation task to capture CVD risk factors with indicators, temporal attributes and assertions that were explicitly or implicitly displayed in the records. The task included: 1) preparing data; 2) creating guidelines for capturing annotations (these were created with the help of clinicians); 3) proposing an annotation method including building the guidelines draft, training the annotators and updating the guidelines, and corpus construction. Meanwhile, we proposed some creative annotation guidelines: (1) the under-threshold medical examination values were annotated for our purpose of studying the progress of risk factors and CVD; (2) possible and negative risk factors were concerned for the same reason, and we created assertions for annotations; (3) we added four temporal attributes to CVD risk factors in CEMRs for constructing long term variations. Then, a risk factor annotated corpus based on de-identified discharge summaries and progress notes from 600 patients was developed. Built with the help of clinicians, this corpus has an inter-annotator agreement (IAA) F1-measure of 0.968, indicating a high reliability. Conclusion To the best of our knowledge, this is the first annotated corpus concerning CVD risk factors in CEMRs and the guidelines for capturing CVD risk factor annotations from CEMRs were proposed. The obtained document-level annotations can be applied in future studies to monitor risk factors and CVD over the long term.

Abstract Three new lycodine-type Lycopodium alkaloids, namely 1-methyllycodine (1), 8α-hydroxy-15,16-dehydro-des-N-methyl-α-obscurine (2), N-methyl-16-hydroxyhuperzine B (3), and one new natural lycodine-type Lycopodium alkaloid, N-methylhuperzine A (4), along with 11 known analogues (5–15), were isolated from the whole plants of club moss Huperzia serrata. The structures of 1–4 were elucidated on the basis of NMR spectroscopic and mass spectrometry data. Among them, compound 1 was the first lycodine-type alkaloid possessing a methyl group at C-1. In addition, the structure of 5 was confirmed by the single-crystal X-ray crystallography data and its 13C NMR was reported for the first time in current study. Compounds 1–5 were tested their BACE1 inhibitory activity. Graphical Abstract

Abstract Background Marbofloxacin is a veterinary fluoroquinolone with high activity against Pasteurella multocida. We evaluated it’s in vivo activity against P. multocida based on in vivo time-kill data in swine using a tissue-cage model. A series of dosages ranging from 0.15 to 2.5 mg/kg were administered intramuscularly after challenge with P. multocida type B, serotype 2. Results The ratio of the 24 h area under the concentration-time curve divided by the minimum inhibitory concentration (AUC24TCF/MIC) was the best PK/PD index correlated with the in vivo antibacterial effectiveness of marbofloxacin (R2 = 0.9279). The AUC24TCF/MIC necessary to achieve a 1-log10 CFU/ml reduction and a 3-log10 CFU/ml (90% of the maximum response) reduction as calculated by an inhibitory sigmoid Emax model were 13.48 h and 57.70 h, respectively. Conclusions Marbofloxacin is adequate for the treatment of swine infected with P. multocida. The tissue-cage model played a significant role in achieving these PK/PD results.

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is a severe emerging disease caused by SFTS virus (SFTSV), and the geographical distribution of SFTS has been increasing throughout China in recent years. To assess SFTSV-specific antibody seroprevalence, a cross-sectional study was conducted for healthy people in high SFTS endemic areas of Henan province in 2016.MethodsThis study used a stratified random sampling method to select 14 natural villages as the investigation sites. From April to May 2016, participants completed a questionnaire survey and serum samples were collected. All serum samples were subjected to ELISA to detect SFTSV-specific IgM and IgG. All IgM-positive samples were further tested by real-time RT-PCR, and isolation of virus from serum was attempted. Any participant who was IgM-positive was followed up with a month later to confirm health status.ResultsIn total, 1463 healthy people participated in this study. The average seropositive rates for SFTSV-specific IgG and IgM were 10.46% (153/1463) and 0.82% (12/1463), respectively. IgM was detected in 12 individuals, and SFTSV RNA was detected in six of them. Virus was isolated from five of the six SFTSV RNA-positive individuals, and phylogenetic analyses revealed that all five isolates belonged to SFTSV group A. No IgM-positive participants exhibited any symptoms or other signs of illness at the one-month follow up.ConclusionsThis study identified a relatively high incidence of SFTSV-specific antibody seropositivity in healthy people in Xinyang city. Moreover, our data provide the first evidence for asymptomatic SFTSV infections, which may have significant implications for SFTS outbreak control.

Background/Aims: Rapamycin is a potential anti-cancer agent, which modulates the activity of mTOR, a key regulator of cell growth and proliferation. However, several types of cancer cells are resistant to the anti-proliferative effects of rapamycin. In this study, we report a MDM2/p53-mediated rapamycin resistance in human renal cancer cells. Methods: Trypan blue exclusion tests were used to determine the cell viability. Changes in mRNA and protein expression were measured using real-time PCR and western blot, respectively. Xenograft models were established to evaluate the in vivo effects of rapamycin combined with a MDM2 inhibitor. Results: Rapamycin treatment suppresses the expression of MDM2 and exogenous overexpression of MDM2 in A498 cells contributes to rapamycin resistance. By establishing a rapamycin resistant cell line, we observed that MDM2 was significantly upregulated in rapamycin resistant cells than that in rapamycin sensitive cells. Importantly, the rapamycin resistant cells demonstrated attenuated accumulation of p53 in the nucleus in response to rapamycin treatment. Moreover, the inhibition of MDM2 by siMDM2 sensitizes A498 cells to rapamycin through the activation of p53. In both in vitro and in vivo models, the combination of rapamycin with the MDM2 inhibitor, MI-319, demonstrated a synergistic inhibitory effect on rapamycin resistant cells. Conclusion: Our study reports a novel mechanism for rapamycin resistance in human renal cancer and provides a new perspective for the development of anti-cancer drugs.