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Net ecosystem productivity ( NEP) represents the carbon sequestration capacity of a regional ecosystem. Based on the Google Earth Engine ( GEE) platform, this study analyzed the temporal and spatial variations in the NEP of the Three - River Headwaters Region ( TRHR) from 2001 to 2020 based on the Moderate Resolution Imaging Spectrometer ( MODIS) and meteorological data and revealed their relationships with climate factors. The results are as follows; 1 The TRHR had an important carbon sink function, with carbon sink areas accounting for 99. 89% ; The carbon source areas in the TRHR were primarily distributed in the northwest, accounting for only 0. 11%. The NEP of the TRHR decreased gradually from the southeast to the northwest and differed significantly among different ecological areas; 2 The NEP of the TRHR showed an upward trend overaU in the past 20 years, with an annual increasing rate of 1. 13 gC/(m ² • a), indicating huge carbon sequestration potential; 3 The area of zones whose NEP showed an upward trend accounted for 95. 05% of the total area. Ecological engineering construction significantly improved the NEP of vegetation. As a result, the carbon sink function gradually increased and was highly stable; 4 The TRHR had an annual average NEP of 120. 93 gC/( m ² • a), and the NEP was positively correlated with the annual precipitation but negatively correlated with average annual temperature and annual solar radiation. The warm, humid climate and the ecological engineering construction contributed to the carbon sink function of vegetation in the TRHR. This is of great significance for improving the carbon sink value of the terrestrial ecosystem and achieving the peak carbon dioxide emissions and carbon neutrality of China. [ABSTRACT FROM AUTHOR]

Objective: To investigate the characteristics of gut microbiota dysbosis in patients with severe pneumonia using 16SrDNA sequencing., Methods: A prospective observational research was conducted. The stool samples retained by natural defecation or enema within 2 days after hospital were collected from 16 patients with severe pneumonia admitted to department of intensive care unit (ICU) of General Hospital of Ningxia Medical University from June to December in 2018 and 10 persons for physical exam were enrolled as the healthy control group. The 16SrDNA sequencing technology was used to detect fecal flora and analyze biological information., Results: (1) 1 015 475 effective sequences were obtained from the stool samples from the severe pneumonia group and the healthy control group. Using 16SrDNA method, it was found that the average effective length of the sample sequence was 458.35 bp and the average sequence number of the total samples was 39 056.73. (2) Analysis of α diversity of gut microbiota showed that, compared with the healthy control group, the Ace index, Chao index and the Shannon index of gut microbiota diversity in the severe pneumonia group were significantly decreased [Ace index: 167.23 (143.14, 211.26) vs. 227.71 (214.53, 247.05), Chao index: 152.38 (138.09, 182.54) vs. 228.25 (215.49, 248.95), Shannon index: 2.37 (1.68, 2.89) vs. 3.39 (3.03, 3.63), all P < 0.01], and the Simpson index was significantly increased [0.21 (0.11, 0.33) vs. 0.07 (0.06, 0.12), P < 0.01], which indicated the gut microbiota diversity of the severe pneumonia group was decreased. (3) Analysis of β diversity of gut microbiota, principal coordinate analysis (PCoA) showed that gut microbiota structural with the healthy control group was similar, while that in the severe pneumonia group was different. Adonis analysis showed that the structural of the gut microflora revealing significant differences between the severe pneumonia group and the healthy control group (R 2 = 0.061, P = 0.05). (4) Analysis of phylum difference gut microflora showed that, compared with the healthy control group, the proportion of Firmicutes in severe pneumonia group was decreased [27.36 (18.12, 39.28)% vs. 52.25 (38.36, 63.82)%, P = 0.02], the proportions of Actinobacterias, Synergistetes and Fusobacterias were increased [2.30 (0.30, 4.80)% vs. 0.02 (0.00, 0.06)%, 0.36 (< 0.01, 0.57)% vs. < 0.01 (< 0.01, < 0.01)%, 0.01 (< 0.01, 0.08)% vs. < 0.01 (< 0.01, < 0.01)%, all P < 0.05]. (5) Analysis of genus difference gut microflora showed that, the proportions of Bifidobacterium, Ruminococcus, Pseudobutyrivibrio, Coprococcus, Lachnospira and Prevotella in the severe pneumonia group were significantly lower than those in healthy control group [0.18 (0.01, 0.25)% vs. 3.40 (0.46, 5.78)%, 0.01 (< 0.01, 0.29)% vs. 2.26 (0.84, 4.86)%, 0.01 (< 0.01, 0.02)% vs. 2.73 (1.87, 5.74)%, 0.02 (< 0.01, 0.07)% vs. 0.80 (0.50, 2.32)%, < 0.01 (< 0.01, < 0.01)% vs. 0.88 (0.33, 2.08)%, 0.02 (< 0.01, 0.31)% vs. 7.74 (0.07, 36.27)%, all P < 0.05]; the proportions of Escherichia and Enterococcus in the severe pneumonia group were higher than those in healthy control group, but there was no difference between the two groups [2.00 (0.57, 10.23)% vs. 1.16 (0.23, 2.68)%, 0.02 (< 0.01, 0.42)% vs. < 0.01 (< 0.01, 0.04)%, both P > 0.05]; the proportions of Fusobacterium and Staphylococcus in severe pneumonia group were significantly higher than those in healthy control group [0.01 (< 0.01, 0.08)% vs. < 0.01 (< 0.01, < 0.01)%, 0.01 (< 0.01, 0.02)% vs. < 0.01 (< 0.01, < 0.01)%, both P < 0.05]., Conclusions: Gut microbiota dysbiosis in patients with severe pneumonia shows that the abundance and diversity decrease, structure of intestinal flora changes, and beneficial symbiotic bacteria decrease and pathogenic bacteria increase, which may be associated with the occurrence and development of severe pneumonia.