Superpixel segmentation integrated feature subset selection for wetland classification over Yellow River Delta.

Wetlands are one of the world's most significant and vulnerable ecosystems. The wetlands of the Yellow River Delta are subject to multiple influences of ocean tidal action and the massive sediment deposits of the Yellow River, resulting in a more complex and unstable composition of land cover types. To better distinguish the wetlands in the region, we conducted the classification using an object-oriented combined with feature preference machine learning approach. To alleviate the pretzel phenomenon in pixel-based classification, a superpixel segmentation method using the watershed algorithm with H-minima labeling was used to segment the images at the optimal scale. The best feature subset for classification was filtered using the recursive feature elimination cross-validation approach, which extracts multiple spectral indices from the images. A random forest classifier combining superpixel segmentation and feature selection methods was proposed for the wetland classification. The model improves the classification accuracy of wetlands compared to three classical pixel-based machine learning classification methods. And the overall accuracy was 91.74% and the kappa coefficient was 0.9078, both of which were improved by about 4.53% and 0.0506, respectively, compared with the best-performing random forest classifier in pixel-oriented. The results showed that this method can effectively improve the classification accuracy of the Yellow River Delta wetlands compared with the previous studies.

Evaluating global ecosystem water use efficiency response to drought based on multi-model analysis.

Drought has serious consequences on terrestrial ecosystems, particularly for their carbon and water processes. As an important indicator to examine the balance of ecosystem water and carbon cycles, ecosystem water use efficiency (WUE) has been widely used to investigate ecosystem responses to drought. However, the response of WUE to drought and the role of different ecosystem processes in controlling the response of WUE to drought are not well studied. In this paper, we used four WUE datasets from different remote sensing-driven (RS-driven) models and three drought indices (Standardized Precipitation Evapotranspiration Index, soil moisture anomaly index and water storage anomaly-based drought index) to comprehensively investigate the response of WUE to drought and its dominant ecosystem processes during the period of 2001-2018. The results showed the WUE datasets from four different RS-driven models had discrepancies in WUE temporal trends, particularly in tropical and subtropical forest and semi-arid regions. The Spearman correlation analysis revealed that the positive correlations between WUE and drought accounted for more than half of global vegetated lands, while negative relationship mainly occurred in the high latitude regions. We further explored the dominant ecosystem processes (represented by GPP and ET) in controlling WUE response to drought, and found ET controlled WUE-drought relationship in the high latitude areas and semi-arid/sub-humid regions, while GPP dominated it in tropical forest regions. Additionally, the effects of GPP and ET on controlling WUE response to drought were examined to change with different drought indices, especially in the semi-arid regions. Our study suggests multi-model analysis tend to reduce uncertainties in analyzing WUE response to drought caused by a single WUE data. Moreover, our results highlight the different role of ecosystem processes in controlling WUE response to drought and provide new information for the underlying mechanism of drought impacts on ecosystem water and carbon cycles.

Spatiotemporal variations of global terrestrial vegetation climate potential productivity under climate change.

Evaluating the climate potential productivity (CPP) of terrestrial vegetation is crucial to ascertain the threshold of vegetation productivity, to maximize the utilization of regional climate resources, and to fully display the productivity application level. In this study, the maximum net primary productivity (NPPmax) representing the highest possible productivity of vegetation was calculated using the FLUXNET maximum gross primary productivity (GPPmax) from 177 flux towers. The relationships between NPPmax and a set of climate variables were established using the classification and regression tree (CART) modeling framework. The CART algorithm was used to upscale the CPP to the global scale under the current climate baseline (1980-2018) and future climate scenarios. The spatiotemporal variations in CPP over the globe were analyzed and the impacts of climate factors on it were assessed. The results indicate that global CPPs range from 0 to 2000 g C/m2. The tropical rainforest area is the region with the highest CPP, whereas the lowest CPP occurs in arid/semiarid areas. These two regions were identified as the areas with the largest CPP reductions in the future. The findings reveal that CPP shows signs of productivity saturation and that future climate is not conducive to the increases in vegetation productivity in these regions. The increases in average annual temperature, minimum temperature, and solar radiation are beneficial to CPP increase in most parts of the globe under climate change. However, the negative contribution of maximum temperature increase and precipitation reduction to CPP is higher than the positive contribution of the above three rising factors to CPP in tropical and arid/semiarid areas. Our study is important to aid in creating targeted policies for future sustainable development, resource allocation, and vegetation management.

Functional and Genetic Analysis of Viral Receptor ACE2 Orthologs Reveals Broad Potential Host Range of SARS-CoV-2

The pandemic of Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a major global health threat. Epidemiological studies suggest that bats are the natural zoonotic reservoir for SARS-CoV-2. However, the host range of SARS-CoV-2 and intermediate hosts that facilitate its transmission to humans remain unknown. The interaction of coronavirus with its host receptor is a key genetic determinant of host range and cross-species transmission. SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) as the receptor to enter host cells in a species-dependent manner. It has been shown that human, palm civet, pig and bat ACE2 can support virus entry, while the murine ortholog cannot. In this study, we characterized the ability of ACE2 from diverse species to support viral entry. We found that ACE2 is expressed in a wide range of species, with especially high conservation in mammals. By analyzing amino acid residues of ACE2 critical for virus entry, based on structure of SARS-CoV spike protein interaction with human, bat, palm civet, pig and ferret ACE2, we identified approximately eighty ACE2 proteins from mammals that could potentially mediate SARS-CoV-2 entry. We chose 48 representative ACE2 orthologs among eighty orthologs for functional analysis and it showed that 44 of these mammalian ACE2 orthologs, including those of domestic animals, pets, livestock, and animals commonly found in zoos and aquaria, could bind SARS-CoV-2 spike protein and support viral entry. In contrast, New World monkey ACE2 orthologs could not bind SARS-CoV-2 spike protein and support viral entry. We further identified the genetic determinant of New World monkey ACE2 that restricts viral entry using genetic and functional analyses. In summary, our study demonstrates that ACE2 from a remarkably broad range of species can facilitate SARS-CoV-2 entry. These findings highlight a potentially broad host tropism of SARS-CoV-2 and suggest that SARS-CoV-2 might be distributed much more widely than previously recognized, underscoring the necessity to monitor susceptible hosts to prevent future outbreaks.

Dietary patterns derived by factor analysis are associated with cognitive function among a middle-aged and elder Chinese population.

The aim of this study was to assess the association of empirically derived dietary patterns with cognitive function among a middle-aged and elder Chinese population. This study comprised 1676 Chinese adults ≥45 years of age, who participated in a Health Survey and completed semi-quantitative food frequency questionnaire (FFQ) and cognitive screening test in the city of Linyi.We performed factor analysis using the principal component analysis method to identify the major dietary patterns. Binary logistic regression models were used to estimate odds ratio(OR) and 95% confidence interval(CI) for cognitive impairment according to quartiles of each dietary pattern score. Three dietary patterns were identified, namely traditional Chinese, Western-style and grains-fruits-vegetable patterns. A total of 362 participants (101 men and 261 women) were classified as cognitive impaired. After controlling for potential confounders, participants in the highest quartile of the Western-style pattern had a greater OR for incident cognitive impairment, compared to participants in the lowest quartile. Compared with the lowest quartile of grains-fruits-vegetable pattern, the highest quartile had a lower OR for incident cognitive impairment. Conclusions: Our findings demonstrate that the Western-style pattern is associated with an elevated risk and the grains-fruits-vegetables pattern is associated with a decreased risk of cognitive impairment.

An observational study on the relationship between serum uric acid and hypertension in a Northern Chinese population aged 45 to 59 years.

Little is known about the relationship serum uric acid (SUA) and hypertension in Chinese population. Therefore, the aim of this study was to determine the association between SUA and hypertension in a northern Chinese population. The participants were a group of 1730 Chinese adults aged 45 to 59 years in Shandong Province, who were recruited from the Linyi Nutrition and Health Survey (2015-2016). Hypertension was defined as systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg. Hyperuricemia was defined as SUA >420 µmol/L (7.0 mg/dL) for men and >360 µmol/L (6.0 mg/dL) for women. All anthropometric measurements and biochemical data were collected following standard protocols. Multivariate logistic regression analysis was used to examine the association between SUA and hypertension with adjustment of confounding variables. Body mass index, waist circumference, waist-to-hip ratio, systolic blood pressure, diastolic blood pressure, fasting blood glucose, triglycerides, alanine aminotransferase, aspartate aminotransferase, SUA, and the prevalence of hypertension and hyperuricemia were significantly higher in males than in females (P < .001). The females had significantly higher levels of total cholesterol and high-density lipoprotein cholesterol. Besides, after adjustment for confounding variables, hyperuricemia was associated with an increased risk of hypertension in both male and female patients, with odds ratios of 2.152 (95% confidence interval 1.324-3.498) and 2.133(95% confidence interval 1.409-3.229), respectively.Hyperuricemia was significantly associated with the risk of hypertension. Further longitudinal studies and trails are needed to confirm our findings.

[Identification of Pummelo Cultivars Based on Hyperspectral Imaging Technology].

Existing methods for the identification of pummelo cultivars are usually time-consuming and costly, and are therefore inconvenient to be used in cases that a rapid identification is needed. This research was aimed at identifying different pummelo cultivars by hyperspectral imaging technology which can achieve a rapid and highly sensitive measurement. A total of 240 leaf samples, 60 for each of the four cultivars were investigated. Samples were divided into two groups such as calibration set (48 samples of each cultivar) and validation set (12 samples of each cultivar) by a Kennard-Stone-based algorithm. Hyperspectral images of both adaxial and abaxial surfaces of each leaf were obtained, and were segmented into a region of interest (ROI) using a simple threshold. Spectra of leaf samples were extracted from ROI. To remove the absolute noises of the spectra, only the date of spectral range 400~1000 nm was used for analysis. Multiplicative scatter correction (MSC) and standard normal variable (SNV) were utilized for data preprocessing. Principal component analysis (PCA) was used to extract the best principal components, and successive projections algorithm (SPA) was used to extract the effective wavelengths. Least squares support vector machine (LS-SVM) was used to obtain the discrimination model of the four different pummelo cultivars. To find out the optimal values of σ2 and γ which were important parameters in LS-SVM modeling, Grid-search technique and Cross-Validation were applied. The first 10 and 11 principal components were extracted by PCA for the hyperspectral data of adaxial surface and abaxial surface, respectively. There were 31 and 21 effective wavelengths selected by SPA based on the hyperspectral data of adaxial surface and abaxial surface, respectively. The best principal components and the effective wavelengths were used as inputs of LS-SVM models, and then the PCA-LS-SVM model and the SPA-LS-SVM model were built. The results showed that 99.46% and 98.44% of identification accuracy was achieved in the calibration set for the PCA-LS-SVM model and the SPA-LS-SVM model, respectively, and a 95.83% of identification accuracy was achieved in the validation set for both the PCA-LS-SVM and the SPA- LS-SVM models, which were built based on the hyperspectral data of adaxial surface. Comparatively, the results of the PCA-LS-SVM and the SPA-LS-SVM models built based on the hyperspectral data of abaxial surface both achieved identification accuracies of 100% for both calibration set and validation set. The overall results demonstrated that use of hyperspectral data of adaxial and abaxial leaf surfaces coupled with the use of PCA-LS-SVM and the SPA-LS-SVM could achieve an accurate identification of pummelo cultivars. It was feasible to use hyperspectral imaging technology to identify different pummelo cultivars, and hyperspectral imaging technology provided an alternate way of rapid identification of pummelo cultivars. Moreover, the results in this paper demonstrated that the data from the abaxial surface of leaf was more sensitive in identifying pummelo cultivars. This study provided a new method for to the fast discrimination of pummelo cultivars.