A hybrid metasurface (HMS) is proposed to form a low-profile wideband antenna array. The antenna element is an array of 4 × 4 square metal patches and fed by a 50 Ω microstrip line through an ...H-shaped coupling slot on the ground plane. Only are the edge patches of HMS antenna element grounded by shorting pins for the suppression of surface waves and crosspolarization levels as well as the enhancement of the gain. With the HMS antenna element, a compact 2 χ 2 array with an overall size of 1.58λ 0 × 1.58λ 0 ×0.068λ 0 (λ4 is the free-space wavelength at 5.0 GHz) is designed, where the adjacent elements share the edge patches of the elements. The measurement shows the impedance bandwidth of 28% (4.41-5.85 GHz) for |S 11 | ≤ -10 dB is obtained, and the boresight gain is greater than 8.4 dBi across the operating band, covering both fifth-generation (5G) sub 6 GHz and WiFi bands.
Detection of asymptomatic or subclinical novel human coronavirus SARS-CoV-2 infection is critical for understanding the overall prevalence and infection potential of COVID-19. To estimate the ...cumulative prevalence of SARS-CoV-2 infection in China, we evaluated the host serologic response, measured by the levels of immunoglobulins M and G in 17,368 individuals, in the city of Wuhan, the epicenter of the COVID-19 pandemic in China, and geographic regions in the country, during the period from 9 March 2020 to 10 April 2020. In our cohorts, the seropositivity in Wuhan varied between 3.2% and 3.8% in different subcohorts. Seroposivity progressively decreased in other cities as the distance to the epicenter increased. Patients who visited a hospital for maintenance hemodialysis and healthcare workers also had a higher seroprevalence of 3.3% (51 of 1,542, 2.5-4.3%, 95% confidence interval (CI)) and 1.8% (81 of 4,384, 1.5-2.3%, 95% CI), respectively. More studies are needed to determine whether these results are generalizable to other populations and geographic locations, as well as to determine at what rate seroprevalence is increasing with the progress of the COVID-19 pandemic. Serologic surveillance has the potential to provide a more faithful cumulative viral attack rate for the first season of this novel SARS-CoV-2 infection.
The effect of air pollution on the changing pattern of glomerulopathy has not been studied. We estimated the profile of and temporal change in glomerular diseases in an 11-year renal biopsy series ...including 71,151 native biopsies at 938 hospitals spanning 282 cities in China from 2004 to 2014, and examined the association of long-term exposure to fine particulate matter of <2.5 μm (PM
) with glomerulopathy. After age and region standardization, we identified IgA nephropathy as the leading type of glomerulopathy, with a frequency of 28.1%, followed by membranous nephropathy (MN), with a frequency of 23.4%. Notably, the adjusted odds for MN increased 13% annually over the 11-year study period, whereas the proportions of other major glomerulopathies remained stable. During the study period, 3-year average PM
exposure varied among the 282 cities, ranging from 6 to 114 μg/m
(mean, 52.6 μg/m
). Each 10 μg/m
increase in PM
concentration associated with 14% higher odds for MN (odds ratio, 1.14; 95% confidence interval, 1.10 to 1.18) in regions with PM
concentration >70 μg/m
We also found that higher 3-year average air quality index was associated with increased risk of MN. In conclusion, in this large renal biopsy series, the frequency of MN increased over the study period, and long-term exposure to high levels of PM
was associated with an increased risk of MN.
The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission employs a micro-pulse photon-counting LiDAR system for mapping and monitoring the biomass and carbon of terrestrial ecosystems over ...large areas. In preparation for ICESat-2 data processing and applications, this paper aimed to develop and validate an effective algorithm for better estimating ground elevation and vegetation height from photon-counting LiDAR data. Our new proposed algorithm consists of three key steps. Firstly, the noise photons were filtered out using a noise removal algorithm based on localized statistical analysis. Secondly, we classified the signal photons into canopy photons and ground photons by conducting a series of operations, including elevation frequency histogram building, empirical mode decomposition (EMD), and progressive densification. At the same time, we also identified the top of canopy (TOC) photons from canopy photons by percentile statistics method. Thereafter, the ground and TOC surfaces were generated from ground photons and TOC photons by cubic spline interpolation, respectively. Finally, the ground elevation and vegetation height were estimated by retrieved ground and TOC surfaces. The results indicate that the noise removal algorithm is effective in identifying background noise and preserving signal photons. The retrieved ground elevation is more accurate than the retrieved vegetation height, and the results of nighttime data are better than those of the corresponding daytime data. Specifically, the root-mean-square error (RMSE) values of ground elevation estimates range from 2.25 to 6.45 m for daytime data and 2.03 to 6.03 m for nighttime data. The RMSE values of vegetation height estimates range from 4.63 to 8.92 m for daytime data and 4.55 to 8.65 m for nighttime data. Our algorithm performs better than the previous algorithms in estimating ground elevation and vegetation height due to lower RMSE values. Additionally, the results also illuminate that the photon classification algorithm effectively reduces the negative effects of slope and vegetation coverage. Overall, our paper provides an effective solution for estimating ground elevation and vegetation height from micro-pulse photon-counting LiDAR data.
Objectives
To develop and test a new multifeature‐based computer‐aided diagnosis (CADx) scheme of lung cancer by fusing quantitative imaging (QI) features and serum biomarkers to improve CADx ...performance in classifying between malignant and benign pulmonary nodules.
Methods
First, a dataset involving 173 patients was retrospectively assembled, which includes computed tomography (CT) images and five serum biomarkers extracted from blood samples. Second, a CADx scheme using a four‐step–based semiautomatic segmentation method was applied to segment the targeted lung nodules, and compute 78 QI features from each segmented nodule from CT images. Third, two support vector machine (SVM) classifiers were built using QI features and serum biomarkers, respectively. SVM classifiers were trained and tested using the overall dataset with a Relief feature selection method, a synthetic minority oversampling technique and a leave‐one‐case‐out validation method. Finally, to further improve CADx performance, an information‐fusion method was used to combine the prediction scores generated by two SVM classifiers.
Results
Areas under receiver operating characteristic curves (AUC) generated by QI feature and serum biomarker‐based SVMs were 0.81 ± 0.03 and 0.69 ± 0.05, respectively. Using an optimal weighted fusion method to combine prediction scores generated by two SVMs, AUC value significantly increased to 0.85 ± 0.03 (P < 0.05).
Conclusions
This study demonstrates (a) higher CADx performance by using QI features than using the serum biomarkers and (b) feasibility of further improving CADx performance by fusion of QI features and serum biomarkers, which indicates that QI features and serum biomarkers contain the complementary classification information.
Coal and gas outburst is a frequent dynamic disaster during underground coal mining activities. After about 150 years of exploration, the mechanisms of outbursts remain unclear to date. Studies on ...outburst mechanisms worldwide focused on the physicochemical and mechanical properties of outburst-prone coal, laboratory-scale outburst experiments and numerical modeling, mine-site investigations, and doctrines of outburst mechanisms. Outburst mechanisms are divided into two categories: single-factor and multi-factor mechanisms. The multi-factor mechanism is widely accepted, but all statistical phenomena during a single outburst cannot be explained using present knowledge. Additional topics about outburst mechanisms are proposed by summarizing the phenomena that need precise explanation. The most appealing research is the microscopic process of the interaction between coal and gas. Modern physical-chemical methods can help characterize the natural properties of outburst-prone coal. Outburst experiments can compensate for the deficiency of first-hand observation at the scene. Restoring the original outburst scene by constructing a geomechanical model or numerical model and reproducing the entire outburst process based on mining environment conditions, including stratigraphic distribution, gas occurrence, and geological structure, are important. Future studies can explore outburst mechanisms at the microscale.
The accurate estimation of crop biomass during the growing season is very important for crop growth monitoring and yield estimation. The objective of this paper was to explore the potential of ...hyperspectral and light detection and ranging (LiDAR) data for better estimation of the biomass of maize. First, we investigated the relationship between field-observed biomass with each metric, including vegetation indices (VIs) derived from hyperspectral data and LiDAR-derived metrics. Second, the partial least squares (PLS) regression was used to estimate the biomass of maize using VIs (only) and LiDAR-derived metrics (only), respectively. Third, the fusion of hyperspectral and LiDAR data was evaluated in estimating the biomass of maize. Finally, the biomass estimates were validated by a leave-one-out cross-validation (LOOCV) method. Results indicated that all VIs showed weak correlation with field-observed biomass and the highest correlation occurred when using the red edge-modified simple ratio index (ReMSR). Among all LiDAR-derived metrics, the strongest relationship was observed between coefficient of variation (HCVof digital terrain model (DTM) normalized point elevations with field-observed biomass. The combination of VIs through PLS regression could not improve the biomass estimation accuracy of maize due to the high correlation between VIs. In contrast, the HCV combined with Hmean performed better than one LiDAR-derived metric alone in biomass estimation (R2 = 0.835, RMSE = 374.655 g/m2, RMSECV = 393.573 g/m2). Additionally, our findings indicated that the fusion of hyperspectral and LiDAR data can provide better biomass estimates of maize (R2 = 0.883, RMSE = 321.092 g/m2, RMSECV = 337.653 g/m2) compared with LiDAR or hyperspectral data alone.
The purpose of this article was to probe the mechanism underlying long noncoding RNA (lncRNA)-LINC00184 in cholangiocarcinoma development and to investigate the effects of LINC00184 on ...cholangiocarcinoma. We used bioinformatics to analyze the expression of LINC00184, microRNA (miR)-23b-3p and ANXA2 in cholangiocarcinoma tissues. The levels of LINC00184, miR-23b-3p, and ANXA2 were detected by qRT-PCR. Cell proliferation was tested by CCK8. Transwell assay was used to detect cell invasion and migration. The target connection between LINC00184, miR-23b-3p, or ANXA2 was probed by luciferase reporter assay. RNA pull-down method was employed to test the relationship among LINC00184/miR-23b-3p/ANXA2 in cholangiocarcinoma cells. The Pearson correlation coefficient analyzed was applied to analyze the correlation among LINC00184, miR-23b-3p, and ANXA2. LC-MS/M analysis was used to explore whether the changes of adenine metabolism was affected by LINC00184 in cholangiocarcinoma cells. We discovered that LINC00184 expression was heightened in cholangiocarcinoma patients and cells. Knockdown of LINC00184 repressed cell proliferation, invasion, migration and adenine metabolism in cholangiocarcinoma cells. miR-23b-3p was regarded as a target of LINC00184 and its depletion perversed the inhibitive influence of LINC00184 silencing on cholangiocarcinoma cells. ANXA2 was a target of miR-23b-3p and was negatively modulated by miR-23b-3p. Moreover, ANXA2 was positively modulated by LINC00184 via sponging miR-23b-3p. In short, silencing of LINC00184 suppressed cell proliferation, invasion and migration through over-expression of miR-23b-3p and reducing of ANXA2 in cholangiocarcinoma cells. These findings contribute to understanding the influences of LINC00184, miR-23b-3p, and ANXA2 on cholangiocarcinoma and provide basis for cholangiocarcinoma treatment.
Microhaplotypes are an emerging type of forensic genetic marker that are expected to support multiple forensic applications. Here, we developed a 124-plex panel for microhaplotype genotyping based on ...next-generation sequencing (NGS). The panel yielded intralocus and interlocus balanced sequencing data with a high percentage of effective reads. A full genotype was determined with as little as 0.1 ng of input DNA. Parallel mixture experiments and in-depth comparative analyses were performed with capillary-electrophoresis-based short tandem repeat (STR) and NGS-based microhaplotype genotyping, and demonstrated that microhaplotypes are far superior to STRs for mixture deconvolution. DNA from Han Chinese individuals (n = 256) was sequenced with the 124-plex panel. In total, 514 alleles were observed, and the forensic genetic parameters were calculated. A comparison of the forensic parameters for the 20 microhaplotypes with the top A
values in the 124-plex panel and 20 commonly used forensic STRs showed that these microhaplotypes were as effective as STRs in identifying individuals. A linkage disequilibrium analysis showed that 106 of the 124 microhaplotypes were independently hereditary, and the combined match probability for these 106 microhaplotypes was 5.23 × 10
. We conclude that this 124-plex microhaplotype panel is a powerful tool for forensic applications.
•Maize and soybean heights were successfully estimated using UAV-LiDAR data.•Method based on LiDAR variables yielded higher accuracy than CHM-based method.•Maize height prediction model outperformed ...soybean height prediction model.•Prediction results of crop height were poor when point density was below 1 point/m2.
Crop height is a key structure parameter for the modelling of crop growth, healthy status, yield forecasting and biomass estimation. Unmanned aerial vehicle (UAV) LiDAR systems can quickly and precisely acquire vegetation structure information at a low cost. UAV LiDAR data are increasingly used in vegetation parameters estimation. In this study, we estimated maize and soybean heights using two methods, i.e., based on LiDAR-derived CHM and based on LiDAR variables. The results show that UAV LiDAR data can successfully estimate maize and soybean heights. We found that the method based on LiDAR variables can produce more accurate estimates than CHM-based method. The estimation model of combined maize and soybean had a better prediction performance than those of the specific maize and soybean. Moreover, the soybean height estimation models derived from both methods yielded the lowest prediction precision. We studied the influence of LiDAR point density on crop height estimates through reduced point density (0.25–420 points/m2). When LiDAR point density was less than 1 point/m2, the estimation precision for the specific maize and soybean dropped rapidly with the decrease of point density. However, the point density had no significant influence on crop height estimation precision while LiDAR point density was greater than or equal to 1 point/m2. Moreover, the original point density did not generate the highest estimation precision in our study. Therefore, high LiDAR point density may be not required for estimating vegetation parameters, and a good balance between the point density and data acquisition cost should be found.
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