Evaluation of Global Historical Cropland Datasets with Regional Historical Evidence and Remotely Sensed Satellite Data from the Xinjiang Area of China

Global land use/cover change (LUCC) datasets are essential for quantitatively assessing the impacts of LUCC on global change, but many uncertainties in existing global datasets seriously hamper climate modeling. Evaluating the reliability of existing global LUCC datasets is a precondition for improved data quality. In this study, based on the regional historical document-based reconstructions, satellite-based data, and historical reclamation evidence for the Xinjiang area of China, the accuracy and rationality of cropland data for this area in the HYDE 3.2 and SAGE datasets were evaluated by utilizing comparative analysis regarding three aspects, namely the change tendency of the cropland area, the area of cropland, and the differences in spatial pattern. This study concluded that the amount of cropland in the Xinjiang area in the global and regional datasets shows both disparate trends and large differences in absolute values. Spatially, historical reclamation evidence indicated that agricultural cultivation in the Xinjiang area underwent expansion from south to north and from east to west over the past 300 years; however, the global datasets revealed that the cropland spatial patterns in the Xinjiang area in the historical period are similar to those in the current period. These differences are attributable to the uncertainties of the basic assumptions, per capita cropland area estimates, and reconstruction methods in the global datasets. The findings of the study highlight the necessity of regional studies on historical LUCC in the Xinjiang area

Exploring Spatiotemporal Pattern of Grassland Cover in Western China from 1661 to 1996

Historical grassland cover change is vital for global and regional environmental change modeling; however, in China, estimates of this are rare, and therefore, we propose a method to reconstruct grassland cover over the past 300 years. By synthesizing remote sensing-derived Chinese land use and land cover change (LULCC) data (1980–2015) and potential natural vegetation data simulated by the relationship between vegetation and environment, we first determined the potential extent of natural grassland vegetation (PENG) in the absence of human activities. Then we reconstructed grassland cover across western China between 1661 and 1996 at 10 km resolution by overlaying the Chinese historical cropland dataset (CHCD) over the PENG. As this land cover type has been significantly influenced by anthropogenic factors, the data show that the proportion of grassland in western China continuously decreased from 304.84 × 106 ha in 1661 to 277.69 × 106 ha in 1996. This reduction can be divided into four phases, comprising a rapid decrease between 1661 and 1724, a slow decrease between 1724 and 1873, a sharp decrease between 1873 and 1980, and a gradual increase since 1980. These reductions correspond to annual loss rates of 7.32 × 104 ha, 2.90 × 104 ha, 17.04 × 104 ha, and −2.37 × 104 ha, respectively. The data reconstructed here show that the decrease in grassland area between 1661 and 1724 was mainly limited to the Gan-Ning region (Gansu and Ningxia) and was driven by the early agricultural development policies of the Qing Dynasty. Grassland was extensively cultivated in northeastern China (Heilongjiang, Jilin, and Liaoning) and in the Xinjiang region between 1724 and 1980, a process which resulted from an exponential increase in immigrants to these provinces. The reconstruction results enable provide crucial data that can be used for modeling long-term climate change and carbon emissions

Research on High-Resolution Face Image Inpainting Method Based on StyleGAN

In face image recognition and other related applications, incomplete facial imagery due to obscuring factors during acquisition represents an issue that requires solving. Aimed at tackling this issue, the research surrounding face image completion has become an important topic in the field of image processing. Face image completion methods require the capability of capturing the semantics of facial expression. A deep learning network has been widely shown to bear this ability. However, for high-resolution face image completion, the network training of high-resolution image inpainting is difficult to converge, thus rendering high-resolution face image completion a difficult problem. Based on the study of the deep learning model of high-resolution face image generation, this paper proposes a high-resolution face inpainting method. First, our method extracts the latent vector of the face image to be repaired through ResNet, then inputs the latent vector to the pre-trained StyleGAN model to generate the face image. Next, it calculates the loss between the known part of the face image to be repaired and the corresponding part of the generated face imagery. Afterward, the latent vector is cut to generate a new face image iteratively until the number of iterations is reached. Finally, the Poisson fusion method is employed to process the last generated face image and the face image to be repaired in order to eliminate the difference in boundary color information of the repaired image. Through the comparison and analysis between two classical face completion methods in recent years on the CelebA-HQ data set, we discovered our method can achieve better completion results of 256*256 resolution face image completion. For 1024*1024 resolution face image restoration, we have also conducted a large number of experiments, which prove the effectiveness of our method. Our method can obtain a variety of repair results by editing the latent vector. In addition, our method can be successfully applied to face image editing, face image watermark clearing and other applications without the network training process of different masks in these applications

Pulse Electrodeposited Super-Hydrophobic Ni-Co/WS2 Nanocomposite Coatings with Enhanced Corrosion-Resistance

The hydrophobicity and corrosion resistance of composite coatings can be effectively improved by changing the electrodeposition method and adding inorganic nanoparticles. In this work, the incorporation of WS2 nanoparticles significantly increased the surface roughness of Ni-Co coatings. The best hydrophobicity and corrosion resistance of the Ni-Co/WS2 nanocomposite coatings (water contact angle of 144.7°) were obtained in the direct current electrodeposition mode when the current density was 3 A/dm2 and the electrodeposition time was 50 min. Compared with direct current electrodeposition, the pulsed current electrodeposition method was more conducive to improving the electrodeposition performance of the nanocomposite coatings. Under the conditions of a current density of 3 A/dm2, pulse duty cycle of 70%, and pulse frequency of 1000 Hz, the nanocomposite coatings reached a superhydrophobic state (water contact angle of 153.8°). The nanocomposite coatings had a slower corrosion rate and larger impedance modulus in this state, and thus the corrosion resistance was superior. The wetting state of the Ni-Co/WS2 nanocomposite coating surface was closer to the Cassie–Baxter model. The protective air layer formed by the layered rough microstructures significantly reduced the actual contact area between the liquid and the substrate, achieving excellent hydrophobic and corrosion resistance properties

Multinomial machine learning identifies independent biomarkers by integrated metabolic analysis of acute coronary syndrome

Abstract A multi-class classification model for acute coronary syndrome (ACS) remains to be constructed based on multi-fluid metabolomics. Major confounders may exert spurious effects on the relationship between metabolism and ACS. The study aims to identify an independent biomarker panel for the multiclassification of HC, UA, and AMI by integrating serum and urinary metabolomics. We performed a liquid chromatography-tandem mass spectrometry (LC–MS/MS)-based metabolomics study on 300 serum and urine samples from 44 patients with unstable angina (UA), 77 with acute myocardial infarction (AMI), and 29 healthy controls (HC). Multinomial machine learning approaches, including multinomial adaptive least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), and assessment of the confounders were applied to integrate a multi-class classification biomarker panel for HC, UA and AMI. Different metabolic landscapes were portrayed during the transition from HC to UA and then to AMI. Glycerophospholipid metabolism and arginine biosynthesis were predominant during the progression from HC to UA and then to AMI. The multiclass metabolic diagnostic model (MDM) dependent on ACS, including 2-ketobutyric acid, LysoPC(18:2(9Z,12Z)), argininosuccinic acid, and cyclic GMP, demarcated HC, UA, and AMI, providing a C-index of 0.84 (HC vs. UA), 0.98 (HC vs. AMI), and 0.89 (UA vs. AMI). The diagnostic value of MDM largely derives from the contribution of 2-ketobutyric acid, and LysoPC(18:2(9Z,12Z)) in serum. Higher 2-ketobutyric acid and cyclic GMP levels were positively correlated with ACS risk and atherosclerosis plaque burden, while LysoPC(18:2(9Z,12Z)) and argininosuccinic acid showed the reverse relationship. An independent multiclass biomarker panel for HC, UA, and AMI was constructed using the multinomial machine learning methods based on serum and urinary metabolite signatures

Effects of different doses of ticagrelor on platelet aggregation and endothelial function in diabetic patients with stable coronary artery disease

We performed this study to observe the effects of different doses of ticagrelor and standard-dose clopidogrel on platelet reactivity and endothelial function in diabetic patients with stable coronary artery disease (CAD). Sixty type 2 diabetic patients were assigned to one-quarter standard-dose ticagrelor, half standard-dose ticagrelor, standard-dose ticagrelor and standard-dose clopidogrel groups. Light transmission aggregometry (LTA) and VerifyNow assay were used to measure platelet function. Endothelial function was assessed by measurement of flow-mediated vasodilation (FMD) and plasma von Willebrand factor (VWF) levels were detected. Enzyme-linked immunosorbent assay (ELISA) examined the Interleukin-8(IL-8) and IL-10. The results suggested that the one-quarter dose (34.0%± 14.7%), half-dose (26.9%± 11.6%) and standard-dose (17.3%± 10.3%) ticagrelor showed lower platelet aggregation rate than clopidogrel (52.8%± 18.3%; P ＜0.0001). PRU values in three ticagrelor groups were lower than that in clopidogrel group (102 (76–184)；75 (33–88)；38 (11–52) versus 194 (138–271) and；P ＜0.0001). FMD levels were higher in ticagrelor groups compared with baseline levels while lower in clopidogrel group after treatment. However, no significant differences were found in the percentage increase in the FMD between ticagrelor groups and clopidogrel group. The levels of VWF after treatment were lower than the baseline levels, but there was no statistically significant difference between ticagrelor group and clopidogrel group after treatment. The concentration of IL-8 and IL-10 were decreased in patients with half and standard-dose ticagrelor group. In conclusion, one-quarter standard-dose ticagrelor produced similar inhibitory effects on platelet aggregation as standard-dose clopidogrel in diabetic patients with stable CAD. The half standard-dose ticagrelor had a similar inhibitory effect on platelet inhibition as standard-dose ticagrelor, which was stronger than that of clopidogrel. Moreover, the half-dose ticagrelor had equal protection of endothelial function and inhibition of inflammatory factor as standard-dose ticagrelor

Effects of ticagrelor monotherapy vs. clopidogrel monotherapy on platelet reactivity: a randomized, crossover clinical study (SINGLE study)

Increasing clinical trials demonstrated that the discontinuation of aspirin while maintaining a P2Y12 inhibitor monotherapy could decrease the risk of bleeding without losing the antithrombotic effect. However, no data are available on the platelet reactivity of patients undergoing ticagrelor monotherapy vs. clopidogrel. Therefore, we performed this study to observe the efficacy of ticagrelor monotherapy vs. clopidogrel in Chinese patients with chronic coronary syndrome. This randomized, single-blinded, crossover trial enrolled 50 patients who were administered with ticagrelor (90 mg twice daily for 2 weeks) or clopidogrel (75 mg once daily for 2 weeks). Followed by a 2-week washout period, the two groups of patients underwent a crossover trial. Light transmission aggregometry (LTA) and thromboelastography (TEG) assays were used to test platelet reactivity. The platelet aggregation rate (PAgR) of ADP and AA was significantly lower with ticagrelor than clopidogrel (PAgR of ADP, 27.30% (7.30%-42.635%) vs. 35.55% (12.03%-69.25%), P = .0254; PAgR of AA, 77.80% (21.60%-86.43%) vs. 83.10% (67.13%-87.20%), P = .0400). There was no significant difference in PAgR of collagen and epinephrine between the two groups. The TEG assay showed that ADP and AA, which induced the inhibition of platelet aggregation, were significantly higher in the ticagrelor group than those in the clopidogrel group [ADP%, 69.00% (59.68%–88.95%) vs. 60.55% (35.98%–78.35%), P = .0020; AA%, 53.65% (22.75%–79.28%) vs. 15.15% (5.75%–70.25%), P = .0127]. High on-treatment platelet reactivity (HTPR) on ADP was 2.17% with ticagrelor and 19.57% with clopidogrel. HTPR on AA was 50.00% with ticagrelor and 69.57% with clopidogrel. Ticagrelor and clopidogrel caused the inhibition of ADP and AA-induced platelet aggregation. Moreover, ticagrelor monotherapy had a stronger inhibitory effect than clopidogrel monotherapy (except on collagen and epinephrine)