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One avian H3N2 influenza virus, providing its PB1 and HA segments, reassorted with one human H2N2 virus and caused a pandemic outbreak in 1968, killing over 1 million people. After its introduction to humanity, the pandemic H3N2 virus continued adapting to humans and has resulted in epidemic outbreaks every influenza season. To understand the functional roles of the originally avian PB1 gene in the circulating strains of human H3N2 influenza viruses, we analyzed the evolution of the PB1 gene in all human H3N2 isolates from 1968 to 2019. We found several specific residues dramatically changed around 2002-2009 and remained stable through to 2019. Then, we verified the functions of these PB1 mutations in the genetic background of the early pandemic virus, A/Hong Kong/1/1968(HK/68), as well as a recent seasonal strain, A/Jiangsu/34/2016 (JS/16). The PB1 V709I or PB1 V113A/K586R/D619N/V709I induced higher polymerase activity of HK/68 in human cells. And the four mutations acted cooperatively that had an increased replication capacity in vitro and in vivo at an early stage of infection. In contrast, the backward mutant, A113V/R586K/N619D/I709V, reduced polymerase activity in human cells. The PB1 I709V decreased viral replication in vitro, but this mutant only showed less effect on mice infection experiment, which suggested influenza A virus evolved in human host was not always consisted with highly replication efficiency and pathogenicity in other mammalian host. Overall, our results demonstrated that the identified PB1 mutations contributed to the viral evolution of human influenza A (H3N2) viruses.

Background Influenza virus remains a continuous and severe threat to public health worldwide, and its prevention and treatment have always been a major international issue. Because of its ability to evade immune surveillance through rapid antigenic drift and antigenic shift, broad-spectrum vaccines seem increasingly important. Methods A mAb named 3C12 from an immortalized hybrid cell was generated via immunizing mice with HA2 protein from A/chicken/Anhui/BRI99/2016 (AH/BRI99/16, H9N2) generated by prokaryotic expression. Then, its broad-spectrum activity was analyzed by WB and IFA. Next, the minimal linear epitope was identified via analyzing the reaction of a series of HA truncations with 3C12. Finally, the protective effects of 3C12 were evaluated in vitro and in vivo infection experiments. Results The mAb could react with the viruses of subtypes H1, H2, H5, H8, H9, H12, H13, H16, and HA protein of H18 in group 1, but failed to react with viruses in group 2. The minimal linear epitope targeted by the mAb was 433NAELLVL439 in full length of HA and localized in the C-helix region of HA2 (residue 95-101, HA2 numbering). What’s more, the mAb 3C12 inhibited H1, H2, H5, H8, H9, H12, H13 and H16 virus-replication in vitro and also has shown effectiveness in preventing and treating disease in mice challenged with lethal dose of AH/BRI99/16 (H9N2) virus in vivo. These results suggested that the broadly reactive anti-HA stem mAb 3C12 exhibited prophylactic and therapeutic efficacy. Conclusions Here, we have demonstrated that the linear epitope identified in this study could be a novel target for developing broad-spectrum influenza diagnostics or vaccine design, and the HA2-based monoclonal antibody is indeed a promising strategy for broad-spectrum protection against seasonal and pandemic influenza viruses.

MicroRNAs (miRNAs) are important regulators involved in the antiviral response to influenza virus infection, however, an analytical comparison of miRNA and mRNA expression changes induced by several H7N9 host-adapting PB2 mutants remains undone. Here, miRNA microarray and transcriptome sequencing of BALB/c mouse lungs infected with A/Anhui/1/2013 (H7N9) [hereafter referred to as H7N9/AH1-PB2-627K(WT)] and mutant variants with PB2 amino acid substitutions (avian-like H7N9/AH1-PB2-627E and mammalian-adapted H7N9/AH1-PB2-627E/701N) were directly compared. The results showed that influenza virus infection induced dysregulation of numerous host cell processes. In a miRNA-mRNA network associated with immunity, changes in the expression of 38 miRNAs and 58 mRNAs were detected following influenza virus infection. Notably, the miRNAs of mmu-miR-188-5p, mmu-miR-511-5p, mmu-miR-483-5p, and mmu-miR-690 were specifically associated with the replication of the avian-like virus H7N9/AH1-PB2-627E. Likewise, the miRNAs of mmu-miR-691, mmu-miR-329-3p, and mmu-miR-144-3p were specifically associated with the mammalian-adapted virus H7N9/AH1-PB2-627E/701N. Finally, the miRNAs of mmu-miR-98-5p, mmu-miR-103-3p, mmu-miR-199a-5p, and mmu-miR-378a-3p were specifically associated with H7N9/AH1-PB2-627K(WT) virus replication. This is the first report of comparative integration analysis of miRNA-mRNA expression of these three H7N9 influenza viruses with different host-adapting PB2 mutations. Our results highlight potential miRNAs of importance in influenza virus pathogenesis.

To meet the high frame rate requirements of correct point correspondences with a sub-pixel precision, this paper first proposes a Field Programmable Gate Array (FPGA) architecture that consists of corner detection, corner matching, outlier rejection, and sub-pixel precision localisation. In the architecture, a combined Features from Accelerated Segment Test (FAST)+ Binary Robust Independent Elementary Features (BRIEF) algorithm is adopted for detection and matching with pixel precision, a combined algorithm of Slope-based Rejection (SR) and Correlation-Coefficient-based Rejection (CCR) is used to reject the outliers, and a gradient centroid-based algorithm is used for sub-pixel precision localisation. The whole FPGA architecture is implemented on a single FPGA platform (Xilinx XC72K325T). Five image datasets with different spatial resolutions, textures, lights, rotate angles, and viewpoints are used to evaluate the performance of the FPGA-based implementation. The experimental results show that (1) the SR and CCR algorithms are effective for outlier rejection; (2) a higher correct matching rate is achieved for the image pairs that cover artificial textures than for those that cover natural textures; (3) the proposed architecture is also suitable for image pairs with small change of lights, rotate angles, and viewpoints; (4) the speed of the FPGA-based implementation can reach 280 Frames Per Second (FPS), which is 35 times faster than the Personal Computer (PC)-based implementation; and (5) the usage of FPGA resources is acceptable for the selected FPGA platform. The speed and usage of the FPGA resources can be improved when the whole FPGA-based implementation is further optimised.

Orthophoto imagery has been widely used in different fields, such as for preventing disasters and planning public facility construction. Orthophoto acquisition has become an urgent issue. However, ...

For real-time monitoring of natural disasters, such as fire, volcano, flood, landslide, and coastal inundation, highly-accurate georeferenced remotely sensed imagery is needed. Georeferenced imagery can be fused with geographic spatial data sets to provide geographic coordinates and positing for regions of interest. This paper proposes an on-board georeferencing method for remotely sensed imagery, which contains five modules: input data, coordinate transformation, bilinear interpolation, and output data. The experimental results demonstrate multiple benefits of the proposed method: (1) the computation speed using the proposed algorithm is 8 times faster than that using PC computer; (2) the resources of the field programmable gate array (FPGA) can meet the requirements of design. In the coordinate transformation scheme, 250,656 LUTs, 499,268 registers, and 388 DSP48s are used. Furthermore, 27,218 LUTs, 45,823 registers, 456 RAM/FIFO, and 267 DSP48s are used in the bilinear interpolation module; (3) the values of root mean square errors (RMSEs) are less than one pixel, and the other statistics, such as maximum error, minimum error, and mean error are less than one pixel; (4) the gray values of the georeferenced image when implemented using FPGA have the same accuracy as those implemented using MATLAB and Visual studio (C++), and have a very close accuracy implemented using ENVI software; and (5) the on-chip power consumption is 0.659 W. Therefore, it can be concluded that the proposed georeferencing method implemented using FPGA with second-order polynomial model and bilinear interpolation algorithm can achieve real-time geographic referencing for remotely sensed imagery.

Conventional rational polynomial coefficients (RPC)-based orthorectification methods are unable to satisfy the demands of timely responses to terrorist attacks and disaster rescue. To accelerate the orthorectification processing speed, we propose an on-board orthorectification method, i.e., a field-programmable gate array (FPGA)-based fixed-point (FP)-RPC orthorectification method. The proposed RPC algorithm is first modified using fixed-point arithmetic. Then, the FP-RPC algorithm is implemented using an FPGA chip. The proposed method is divided into three main modules: a reading parameters module, a coordinate transformation module, and an interpolation module. Two datasets are applied to validate the processing speed and accuracy that are achievable. Compared to the RPC method implemented using Matlab on a personal computer, the throughputs from the proposed method and the Matlab-based RPC method are 675.67 Mpixels/s and 61,070.24 pixels/s, respectively. This means that the proposed method is approximately 11,000 times faster than the Matlab-based RPC method to process the same satellite images. Moreover, the root-mean-square errors (RMSEs) of the row coordinate (ΔI), column coordinate (ΔJ), and the distance ΔS are 0.35 pixels, 0.30 pixels, and 0.46 pixels, respectively, for the first study area, and, for the second study area, they are 0.27 pixels, 0.36 pixels, and 0.44 pixels, respectively, which satisfies the correction accuracy requirements in practice.

In a hybrid AC/DC microgrid, the DC transformer (DCT) is more competitive than the conventional line frequency AC transformer (ACT) to connect the AC and DC bus operating with bidirectional interlinking converter (BIC) due to the high frequency, high power density and adjustable characteristics of the DCT. However, the hybrid AC/DC microgrid application often poses a series of specific challenges to the DCT design, such as the system energy management requirement, efficiency, volume, galvanic isolation and power balance challenges, etc. In this paper, a symmetrical CLLC resonant dual active bridge (DAB) converter has been employed to realize the DCT topology. Meanwhile, a systematic design method of the DCT including transmission power analysis, power control and operation mode analysis have also been presented with the consideration of the hybrid AC/DC microgrid application. Finally, all of the theoretical analysis has been experimentally verified in a 6-kVA hybrid AC/DC microgrid.

A DC distribution network (DDN) with multi-level bus voltages is proposed to satisfy various DC load requirements. In the proposed DDN, a special multi-port DC transformer (MDCT) with the symmetrical CLLC resonant structure is employed for load-side voltage matching and galvanic isolation of different bus voltages. The MDCT is actually a three-port integration power converter which can construct three different bus voltages via only one power converter, and hence effectively reducing the multiple reverse conversions and improving the distribution efficiency. In addition, the power dispatch scheme of the energy management system (EMS) of the proposed DDN is also designed for transmitting the power within the battery, Photovoltaic (PV) energy and DDN bidirectionally. Finally, a SiC-based experimental prototype of the proposed DDN is established. Both the proposed DDN and EMS have been verified via experimental results.

With increasing demands in real-time or near real-time remotely sensed imagery applications in such as military deployments, quick response to terrorist attacks and disaster rescue, the on-board geometric calibration problem has attracted the attention of many scientists in recent years. This paper presents an on-board geometric calibration method for linear CCD sensor arrays using FPGA chips. The proposed method mainly consists of four modules&mdash, Input Data, Coefficient Calculation, Adjustment Computation and Comparison&mdash, in which the parallel computations for building the observation equations and least squares adjustment, are implemented using FPGA chips, for which a decomposed matrix inversion method is presented. A Xilinx Virtex-7 FPGA VC707 chip is selected and the MOMS-2P data used for inflight geometric calibration from DLR (K&ouml, ln, Germany), are employed for validation and analysis. The experimental results demonstrated that: (1) When the widths of floating-point data from 44-bit to 64-bit are adopted, the FPGA resources, including the utilizations of FF, LUT, memory LUT, I/O and DSP48, are consumed at a fast increasing rate, thus, a 50-bit data width is recommended for FPGA-based geometric calibration. (2) Increasing number of ground control points (GCPs) does not significantly consume the FPGA resources, six GCPs is therefore recommended for geometric calibration. (3) The FPGA-based geometric calibration can reach approximately 24 times faster speed than the PC-based one does. (4) The accuracy from the proposed FPGA-based method is almost similar to the one from the inflight calibration if the calibration model and GCPs number are the same.

Studies conducted over several decades have shown that solubility trapping in karst areas is an important component of the “missing carbon” in the global carbon cycle. Chinese researchers have taken advantage of their natural geography that includes numerous karst areas, representing approximately 1/7 of the global karst area, to conduct studies of solubility trapping. This paper reviews over 30 years of research progress on solubility trapping by China's researchers and summarizes the following five findings: (a) water plays an important role in the karst solubility trapping, regardless of its role as reactant or carrier; (b) the CO2 concentration gradient in the soil environment, which contains biological activity, organic matter content, pH, and temperature, affects the karst solubility trapping; (c) transfer rules for CO2 in the karst system are influenced by different land cover/land use; (d) relationships between biological metabolism and karst solubility trappings are analyzed; and (e) multiple models have been established to estimate the regional and global karst solubility trapping. This review concludes with proposals for future solubility trapping research.

To evaluate the wave energy conditions along the coastal waters of Beibu Gulf, China, the SWAN (Simulated WAve Nearshore) model is used to estimate the wave field using 20 years of wind field data. The results show that (1) the wave field conditions are promising, for example, in the coastal waters off Guangxi, where the annually averaged significant wave height and peak wave period are approximately 0.45 m and 3.0 s, respectively, and especially along the coast of Hainan Island, where the annually averaged significant wave height and peak wave period are approximately 0.60 m and 3.7 s, respectively; (2) The major direction of wave energy is west or southwest of Guangxi and northeast or northeast by north of Hainan Island; and (3) The wave energy conditions show clear seasonal variations. The maximum storages are approximately 4.48 × 10 3 kWh/m off Guangxi and approximately 8.02 × 10 3 kWh/m off Hainan Island. In addition, the wave energy is more stable in the coastal waters off Guangxi than off Hainan Island. Finally, Weizhou Island and the southern coast of Hainan Island are suggested as potential wave power sites.

This paper develops the temporal-spatial distribution of wave energy at the Beibu Gulf at a monthly, seasonal, and annual base using the Simulated WAves Nearshore (SWAN) model, which is called third generation wave prediction model. The model is forced with the ECMWF ERA Interim wind field data covering 34 years at a time interval of 6 h. Wave parameters, including significant wave height (Hs), peak wave period (Tp), and wave direction (θ) are simulated by SWAN model. Simulated data are validated against the altimeter data using Bias, MSE, and R, and the validation results show that the simulated data can effectively be used to calculate the wave energy in Beibu Gulf. This paper highlights the temporal-spatial distributions of monthly, seasonal, and annual of wave energy. The results reveal that (1) the wave energy propagations in inner waters are dominantly along northeast and west-northwest; (2) the high-energy areas are located at the middle and the southern Beibu Gulf; (3) the biggest monthly wave energy is from January through March with approximately 2.5 kW/m, and the biggest seasonal wave energy is from winter through spring with approximately 1.7 kW/m, and the biggest annual wave energy is about 1.5 kW/m. Additionally, the quantitative analysis, in which the total storage of wave energy vs. the percentage of wave energy greater than 2 kW/m is mapped at a 0.5° × 0.5° grid, identifies those grids used for wave energy convertor deployment. Finally some suggestions are drawn up for decision-makers of the local government.

The traditional ortho-rectification technique for remotely sensed imagery, which is performed on the basis of ground image processing platform, has been unable to meet the timeliness requirements. To solve this problem, this paper presents the research on ortho-rectification technique based on field programmable gate array (FPGA) platform, which can be implemented onboard and spaceborne for a real-time processing. Through comparing the correction accuracy and the consuming time of traditional ortho-rectification method with the proposed method based on FPGA, it is demonstrated that the proposed FPGA-based onboard orthorectification has great advantage over improving the image ortho-rectification speed and can reach the requirements of correction accuracy.

Since conventional decision tree (DT) induction methods cannot efficiently take advantage of geospatial knowledge in the classification of remotely sensed imagery, a co-location decision tree (CL-DT) method, combining the co-location technique with the conventional DT method, has been proposed by several researchers. However, the CL-DT method only considers the Euclidean distance of neighborhood events, which cannot truly reflect the co-location relationship between instances for which there is a nonlinear distribution in a high-dimensional space. For this reason, this paper develops the theory and method for a maximum variance unfolding (MVU)-based CL-DT method (known as MVU-based CL-DT). The presented method has been validated by classifying remote sensing image and is compared with CL-DT method and random forest (RF) method. The experimental results show that the proposed method can better construct decision tree and reach a high classification accuracy.

At present, APD (Avalanche Photo Diode) arrays LIDAR (Light Detection and Ranging) has been broadly accepted as an important means to obtain 3D (Three Dimensional) data. A new method of a scalable adaptive N × N channel communication system is introduced in the paper, which is aimed at improving the transmission rate of APD array LIDAR data. This paper presents the research on multi-channel communication system based on FPGA (Field Programmable Gate Arrays) configuration, which can be realized multi-channel parallel data independent receiving and transmitting. The multi-channel transmission system not only improves the data bandwidth, but also improve the load capacity of the system. The feasibility of multi-channel communication is verified sufficiently in the paper.

In this paper a new way of timing discrimination to measure the timing point of echo signal in pulsed laser rangefinder is presented. This method is an improvement program of high-pass filter scheme. With multiple differential processing of pulsed laser echo signal through high-pass filter circuit, the timing point of laser echo signal will multiple forward approximately. The final identification time and the echo signal which rise along the time interval of initial point will be reduced to the minimum degree, and improve the overall accuracy of the results for a pulsed laser rangefinder, which will eventually minimize walk error between the timing point of the echo signal with the actual beginning timing point of the echo signal and improve precision of the results overall pulse laser radar rangefinding. In addition, the laser echo signal with a small distortion can be still measured and obtained the starting point of the leading edge. Finally, the experimental results show that the proposed new method can reduce the walk error caused by the time delay of the echo pulse timing discrimination, and can also deal with the distorted signal effectively.

Due to the lack of ground control points (GCPs) and parameters of satellite orbits, as well as the interior and exterior orientation parameters of cameras in historical declassified intelligence satellite photography (DISP) imagery, a second order polynomial equation-based block adjustment model is proposed for orthorectification of DISP imagery. With the proposed model, 355 DISP images from four missions and five orbits are orthorectified, with an approximate accuracy of 2.0–3.0 m. The 355 orthorectified images are assembled into a seamless, full-coverage mosaic image map of the karst area of Guangxi, China. The accuracy of the mosaicked image map is within 2.0–4.0 m when compared to 78 checkpoints measured by Real–Time Kinematic (RTK) GPS surveys. The assembled image map will be delivered to the Guangxi Geological Library and released to the public domain and the research community.

This paper presents retrieving the ocean wave parameters in shallow water area of the South China Sea from ERS-2 imagery. Based on the MPI (Max-Planck Institute) algorithm, the proposed method uses the TMA (Texel-Marsen-Arsloe) model, which used for description of the ocean wave spectra in shallow water, as the first-guess spectra to estimate the ocean wave parameters. The model is extended and developed through adding the factor of water depth. The ERS-2 images in South China Sea are selected as the experimental data. The data of ECMWF is used to verify the peak wavelength and SWH (significant wave height) retrieved by the proposed method. The results show the RMSE (Root Mean Square Error) of peak wavelength and SWH are 14.19 m and 0.13 m, respectively; the mean bias of peak wavelength and SWH are 11.30 m and 0.02 m, respectively. The analyzed results discover that the ocean wave spectrum parameters retrieved by the proposed method are consistent with the corresponding parameters from ECMWF in shallow water, and the developed model can effectively response the effects of coastal shoaling because of the varying bathymetry to the surface wave propagation.