Spatiotemporal distribution and prediction of chlorophyll-a in Ulansuhai lake from an arid area of China

Lake Ulansuhai, a typical shallow lake in an arid area that is economically and ecologically important along the Yellow River, is currently eutrophic. Long-term (2010–2020) data on chlorophyll-a, nutrient, and environmental factors were obtained from three Lake Ulansuhai monitoring stations. The temporal and spatial distribution characteristics of Chl-a were analyzed. Additionally, a hybrid evolutionary algorithm was established to simulate and predict Chl-a, and sensitivity analysis revealed the interaction between environmental factors and eutrophication. The results indicated that (1) the seasonal variation of eutrophication showed an obvious trend of spring > summer > autumn > winter, and the concentration of Chl-a in the inlet was significantly higher than that in the outlet; (2) The inlet, center, and outlet of Ulansuhai Lake are satisfactorily affected by HEA in the best suited method. The fitting coefficients (R2) of the optimal models were 0.58, 0.59, and 0.62 for the three monitoring stations, and the root mean square errors (RMSE) were 3.89, 3.21, and 3.56, respectively; (3) under certain range and threshold conditions, Chl-a increased with the increase of permanganate index, water temperature, dissolved oxygen concentration, and ammonia nitrogen concentration, but decreased with the increase of water depth, Secchi disk depth, pH, and fluoride concentration. The results indicate that the HEA can simulate and predict the dynamics of Chl-a, and identify and quantify the relationships between eutrophication and the threshold data. The research results provide theoretical basis and technical support for the prediction and have great significance for the improvement of water quality and environmental protection in arid and semi-arid inland lakes

Assessing Changes in the Landscape Pattern of Wetlands and Its Impact on the Value of Wetland Ecosystem Services in the Yellow River Basin, Inner Mongolia

The Yellow River Basin of Inner Mongolia has significant ecological advantages, and it is critical to research the landscape pattern of its watershed wetland ecosystem and the changes in its service value in order to protect the environment and develop the region in a high-quality manner. In this paper, we use the landscape index method, the equivalent factor method, and a field survey to investigate changes in wetland landscape patterns and the dynamics of wetland ecosystem service values in the Yellow River Basin of Inner Mongolia from 1990 to 2020, and then examine the impact of landscape pattern evolution on wetland ecosystem service values in the region. The study’s findings indicate that rivers, lakes, and herbaceous marshes are the most common types of wetland landscapes in Inner Mongolia’s Yellow River Basin. The landscape types in the research area are diverse, and landscape fragmentation is increasing. In the Yellow River Basin of Inner Mongolia, the overall value of wetland ecosystem benefits is negatively connected with Patch Density and the Shannon Diversity Index, and positively correlated with the Contagion Index

Geographical and environmental distance differ in shaping biogeographic patterns of microbe diversity and network stability in lakeshore wetlands

Soil microorganisms play a crucial role in wetland ecosystems, but the specific mechanisms underlying their diversity and network stability across lakeshore wetlands, as well as their importance in microbial ecology, are still poorly understood. Here, we analyzed the biogeographical pattern and network stability of lakeshore wetland microbial communities across a regional scale, and identified the primary factors that influence their composition. The results revealed that α-diversity of wetland bacteria decreased linearly with increasing longitude and latitude, whereas the α-diversity of fungal showed an increasing trend in lakeshore wetlands. Species richness was the principal way that the biogeographical pattern of α-diversity was expressed. The β-diversity of soil microbial in lakeshore wetlands has a significant geographical attenuation pattern. Fungal communities were influenced by stochastic spatial diffusion, while bacterial communities were influenced by deterministic environmental filtration as they transition from semi-arid to arid regions. Community network stability was specifically determined by bacterial and fungal diversity. Structural equation modelling revealed that spatial distance and climate indirectly affect bacterial diversity by influencing soil and plant diversity, which in turn affects feedback network stability. These findings show that the geographical pattern of soil microbial diversity in lakeshore wetlands influences network stability at the spatial scale, offering insights into the adaptation of wetland ecosystem diversity and stability at the regional level

Combined effects of genetic variants of the PTEN, AKT1, MDM2 and p53 genes on the risk of nasopharyngeal carcinoma.

Phosphatase and tensin homolog (PTEN), v-akt murine thymoma viral oncogene homolog 1 (AKT1), mouse double minute 2 (MDM2) and p53 play important roles in the development of cancer. We examined whether the single nucleotide polymorphisms (SNPs) in the PTEN, AKT1, MDM2 and p53 genes were related to the risk and severity of nasopharyngeal carcinoma (NPC) in the Chinese population. Seven SNPs [p53 rs1042522, PTEN rs11202592, AKT1 SNP1-5 (rs3803300, rs1130214, rs3730358, rs1130233 and rs2494732)] were genotyped in 593 NPC cases and 480 controls by PCR direct sequencing or PCR-RFLP analysis. Multivariate logistic regression analysis was used to calculate adjusted odds ratios (ORs) and 95% confidence intervals (CIs). None of the polymorphisms alone was associated with the risk or severity of NPC. However, haplotype analyses indicated that a two-SNP core haplotype (SNP4-5, AA) in AKT1 was associated with a significantly increased susceptibility to NPC risk (adjusted OR  =  3.87, 95% CI  =  1.96-7.65; P<0.001). Furthermore, there was a significantly increased risk of NPC associated with the combined risk genotypes (i.e., p53 rs1042522 Arg/Pro + Pro/Pro, MDM2 rs2279244 G/T + G/G, PTEN rs11202592 C/C, AKT1 rs1130233 A/A). Compared with the low-risk group (0-2 combined risk genotypes), the high-risk group (3-4 combined risk genotypes) was associated with a significantly increased susceptibility to NPC risk (adjusted OR  =  1.67, 95% CI  =  1.12-2.50; P = 0.012). Our results suggest that genetic variants in the PTEN, AKT1, MDM2 and p53 tumor suppressor-oncoprotein network may play roles in mediating the susceptibility to NPC in Chinese populations

Haplotypes of <i>AKT1</i> polymorphisms and the risk of nasopharyngeal carcinoma.

<p>(<i>a</i>) Genomic structure of the <i>AKT1</i> locus and the polymorphic sites used. Exons (boxes) and introns are not drawn to scale; open boxes represent noncoding sequences, and filled boxes represent coding sequences. The physical distance between SNPs is shown in nucleotides. (<i>b</i>) Linkage disequilibrium (LD) map of SNPs based on <i>D</i> ´. (<i>c</i>) LD map of SNPs based on <i>r</i>². (<i>d</i>) Global <i>P</i> values from single-locus and multi-locus (two to five) based association analysis. (<i>e</i>) Haplotypes showing significant genetic associations with the risk of nasopharyngeal carcinoma. The two-SNP core haplotype is highlighted in gray.</p

Stratification analysis of the combined genotypes of the <i>PTEN</i>, <i>AKT1</i>, <i>MDM2</i> and <i>p53</i> polymorphisms and risk of nasopharyngeal carcinoma.

<p>Abbreviations: OR, odds ratio; CI, confidence interval.</p>a<p>ORs and <i>P</i> values were calculated by multivariate logistic regression, adjusted for age, sex, smoking and drinking status, smoking level and nationality when appropriate within the strata.</p>b<p>For differences in ORs within each stratum.</p>c<p>Low-risk group, individuals carrying 0–2 risk genotypes; high-risk group, individuals carrying 3–4 risk genotypes.</p

Primers and restriction enzymes used to investigate polymorphisms in the <i>PTEN</i>, <i>AKT1</i> and <i>p53</i> genes.

a<p>Italicized lowercase letters indicate base mismatches.</p>b<p>Designated rs2498799 in previous literature.</p