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The total electron content (TEC) of the ionosphere is an important parameter to describe the ionosphere, and it is a great significance to monitor and predict it accurately. In this paper, a hybrid ionospheric TEC prediction model based on the least squares support vector machine (LSSVM) and the Moth-Flame Optimization (MFO) algorithm is proposed. The parameters of the LSSVM model are optimized by the MFO algorithm. We use observation data of 15 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to extract ionospheric TEC from 2012 to 2019. The ionospheric TEC is forecasted using solar and geomagnetic activity indices in both the low solar activity year (2019) and the high solar activity year (2015). The results show that the prediction performance of the MFO_LSSVM model is significantly better than that of the IRI model, SVM model, and LSSVM model. Compared with the other three models, there are more stable prediction results in the low and high solar activity years. At the same time, the predicted value of the MFO_LSSVM model has a good correlation with the measured value, and it also has good prediction potential in areas with active geomagnetic activity. The comparison with the Long Short-Term Memory (LSTM) model shows that the MFO_LSSVM model has better performance than the single LSTM model. In conclusion, the MFO_LSSVM model can accurately predict ionospheric TEC in China, and has better accuracy than traditional long-term and short-term models. [ABSTRACT FROM AUTHOR]

Background Dietary diversity is essential for human health. The gut ecosystem provides a potential link between dietary diversity, host metabolism, and health, yet this mechanism is poorly understood. Objectives Here, we aimed to investigate the relation between dietary diversity and the gut environment as well as host metabolism from a multiomics perspective. Methods Two independent longitudinal Chinese cohorts (a discovery and a validation cohort) were included in the present study. Dietary diversity was evaluated with FFQs. In the discovery cohort (n  = 1916), we performed shotgun metagenomic and 16S ribosomal ribonucleic acid (rRNA) sequencing to profile the gut microbiome. We used targeted metabolomics to quantify fecal and serum metabolites. The associations between dietary diversity and the microbial composition were replicated in the validation cohort (n  = 1320). Results Dietary diversity was positively associated with α diversity of the gut microbiota. We identified dietary diversity–related gut environment features, including the microbial structure (β diversity), 68 microbial genera, 18 microbial species, 8 functional pathways, and 13 fecal metabolites. We further found 332 associations of dietary diversity and related gut environment features with circulating metabolites. Both the dietary diversity and diversity-related features were inversely correlated with 4 circulating secondary bile acids. Moreover, 16 mediation associations were observed among dietary diversity, diversity-related features, and the 4 secondary bile acids. Conclusions These results suggest that high dietary diversity is associated with the gut microbial environment. The identified key microbes and metabolites may serve as hypotheses to test for preventing metabolic diseases. [ABSTRACT FROM AUTHOR]

Smart energy plays an important role in alleviating energy poverty in China in the context of the new technological revolution and industrial change. This paper uses China's smart pilot policy as a quasi-natural experiment, employing China's provincial panel data from 2011 to 2022, measuring energy poverty indices based on a projection pursuit evaluation model, and conducting a quasi-natural experiment using a synthetic control method to assess the effect of smart energy on energy poverty and specific mechanisms of action. The study found that smart energy has a specific and significant impact on energy poverty, but the effect differs in different regions depending on the construction cycle and energy endowment. In the mechanism test, smart energy significantly alleviates energy poverty through the mechanisms of improving energy supply capacity, improving energy payment capacity, and improving energy cleanliness. The results of the study provide empirical references for regional solutions to energy poverty and the planning of smart energy construction. [ABSTRACT FROM AUTHOR]

Coal is the main source of energy for China's economic development, but coal gangue dumps are a major source of heavy metal pollution. Bacterial communities have a major effect on the bioremediation of heavy metals in coal gangue dumps. The effects of different concentrations of heavy metals on the composition of bacterial communities in coal gangue sites remain unclear. Soil bacterial communities from four gangue sites that vary in natural heavy metal concentrations were investigated using high-throughput sequencing in this study. Correlations among bacterial communities, heavy metal concentrations, physicochemical properties of the soil, and the composition of dissolved organic matter of soil in coal gangue dumps were also analyzed. Our results indicated that Actinobacteriota, Proteobacteria, Chloroflexi, Acidobacteriota, and Gemmatimonadota were the bacterial taxa most resistant to heavy metal stress at gangue sites. Heavy metal contamination may be the main cause of changes in bacterial communities. Heavy metal pollution can foster mutually beneficial symbioses between microbial species. Microbial-derived organic matter was the main source of soil organic matter in unvegetated mining areas, and this could affect the toxicity and transport of heavy metals in soil. Polar functional groups such as hydroxyl and ester groups (A 226-400) play an important role in the reaction of cadmium (Cd) and lead (Pb), and organic matter with low molecular weight (S R) tends to bind more to mercury (Hg). In addition to heavy metals, the content of nitrogen (N), phosphorus (P), and total organic carbon (TOC) also affected the composition of the bacterial communities; TOC had the strongest effect, followed by N, SOM, and P. Our findings have implications for the microbial remediation of heavy metal-contaminated soils in coal gangue sites and sustainable development. [Display omitted] • Natural differences of heavy metal concentration gradients at mine gangue sites. • Actinobacteriota, Proteobacteria were the main bacteria observed under heavy metal contamination. • KD4-96, Vicinamibacteraceae, Vicinamibacterales, Streptomyces may be resistant to Hg. • Actinomarinales, Pedomicrobium, and Alphaproteobacteria may be resistant to Cd and Pb. • Heavy metal pollution may promote mutually symbiosis between bacterial species. [ABSTRACT FROM AUTHOR]

Abstract: Objective: To investigate the relationship of liver enzymes with hyperglycemia in a large population in Shanghai and identify the association between liver enzymes and insulin resistance. Methods: A total of 3 756 participants were enrolled. Each participant underwent an oral glucose tolerance test and completed a questionnaire. Anthropometric indices were recorded and serum samples were collected for measurement. Results: Liver enzymes concentrations were independently associated with i-IGT, IFG+IGT, and diabetes. With the increase of ALT and GGT concentrations, ORs for i-IGT, IFG+IGT, and diabetes increased gradually. By comparing patients in the highest quartile of GGT concentrations or ALT concentrations with those in the lowest quartile (Q1), ORs for i-IGT, IFG+IGT, or diabetes was significant after adjustment. Both ALT and GGT concentrations were linearly correlated with HOMA-IR and independently associated with HOMA-IR [ALT OR (95% CI): 2.56 (1.51-4.34) P=0.00; GGT OR (95% CI): 2.66 (1.53-4.65) P=0.00]. Conclusion: Serum ALT and GGT concentrations were closely related to pre-diabetes and diabetes in the Shanghai population and positively associated with insulin resistance. [Copyright &y& Elsevier]

Reducing nonpoint-source pollution and improving soil quality by applying best management practices (BMPs) on sloping farmland are essential for ensuring water environmental safety and sustainable agricultural development. However, little is known about the accumulative effect of the long-term application of BMPs on reducing pollution and improving soil quality in major land use types of sloping farmland in watersheds. We conducted a nine-year field experiment for citrus orchard and cropland of sloping farmland in the Three Gorges Reservoir area of China. The treatment settings for the citrus experimental plots were as follows: (1) citrus intercropped with white clover (WC), (2) citrus orchard soil mulched with straw (SM), (3) citrus intercropped with Hemerocallis flava contour hedgerows (HF) and (4) conventional citrus planting pattern (C-CK). The treatment settings for the crop experimental plots were as follows: (1) wheat-peanut rotation intercropped with Toona sinensis contour hedgerows (TS), (2) wheat-peanut rotation intercropped with alfalfa contour hedgerows (AF), (3) ryegrass-sesame rotation (RS) and (4) conventional wheat-peanut rotation (W-CK). We found that compared with C-CK and W-CK, (1) runoff, sediment, total nitrogen and total phosphorus losses were reduced by 18.0 %, 66.1 %, 35.6 % and 40.2 %, respectively, in WC; by 22.4 %, 61.2 %, 43.9 % and 52.4 %, respectively, in SM; by 25.4%, 69.5 %, 52.0 % and 58.1 %, respectively, in HF; by 45.1 %, 73.7 %, 64.6 % and 66.0 %, respectively, in TS; by 41.8 %, 67.4 %, 59.3 % and 60.8 %, respectively, in AF; and by 24.1 %, 42.9 %, 39.3 % and 40.3 %, respectively, in RS; (2) the soil quality index values of the BMP treatments increased; and (3) the application of these BMPs did not significantly affect yield or product value. We suggest the application of these BMPs on sloping farmland to (1) efficiently reduce total runoff, total sediment, and soil total nitrogen and total phosphorus losses; (2) improve soil quality; and (3) have no significant effect on output, of which the BMP HF applied to citrus orchard and the BMP TS applied to cropland are the most effective. • Clarified accumulative effects of long-term application of best management practices for two major land use types. • Best management practices reduce the runoff, sediment, and soil nutrient losses of the land use types. • Best management practices improve the soil quality without reducing the output of the land use types. • The optimal best management practices for the two major land use types in the watershed are selected. • This study is essential for identifying the optimal best management practices for watershed sloping farmland use. [ABSTRACT FROM AUTHOR]

Rainfed agriculture is important to meet the increasing demands of food production. However, there is a lack of knowledges for the rainfed wheat systems in abundant rainfall areas, where the erratic rainfall may constrain the wheat yield and increases N losses from wheat fields with excessive fertilizer inputs. In the present study, a 6 consecutive years field experiments were conducted to investigate the wheat yield and N losses responses to different N fertilizer rates in the Taihu Lake region. Despite the fluctuations in the wheat yield, a 10–30% reduction of the district average N input sustained the current-level of wheat yield for the 6 consecutive years. The N rate of 168 kg ha−1 might be the threshold N application rate, because if it was exceeded, it was no longer the predominant factor determining the wheat yield, but significantly increased the N losses fluxes. The seasonal total N losses fluxes were 69.2 kg N ha−1, which accounted for 28.8% of district averaged N input. Runoff was the predominant pathway of N loss from the wheat field, followed by ammonia volatilization and leaching. Nitrate was the predominant form in the runoff and leakage water. Seasonal cumulative nitrate losses via runoff and ammonia volatilization averaged 25.2 and 20.1 kg ha−1 per wheat season, respectively, corresponding to 10.5% and 8.38% of the regional N application rates. However, the seasonal nitrate loss via leakage was the lowest, less than 8.98 kg ha−1. Reducing the fertilizer N rates significantly decreased the N losses via runoff and ammonia volatilization, but not by leaching. Our findings indicated that reducing N fertilizer was impressive for the sustainable development of agriculture in Eastern China and 168 kg N ha−1 was recommended for the rainfed wheat. [Display omitted] • Reducing N inputs (168–216 kg ha−1) sustained current-level of wheat yields for 6 years in rice-wheat rotation. • N loss (kg ha−1) was affected by, in order, N runoff (30.7) > ammonia volatilization (20.1) > nitrate leaching (11.0). • Precipitations, solar radiations and sunshine durations were the critical factors determining wheat yields. [ABSTRACT FROM AUTHOR]