Rice paddy fields are major sources of atmospheric methane (CH₄) and nitrous oxide (N₂O). Rice variety is an important factor affecting CH₄ and N₂O emissions. However, the interactive effects of rice metabolites and microorganisms on CH₄ and N₂O emissions in paddy fields are not clearly understood. In this study, a high greenhouse gas-emitting cultivar (YL 6) and a low greenhouse gas-emitting cultivar (YY 1540) were used as experimental materials. Metabolomics was used to examine the roots, root exudates, and bulk soil metabolites. High-throughput sequencing was used to determine the microbial community composition. YY 1540 had more secondary metabolites (flavonoids and isoflavonoids) in root exudates than YL 6. It was enriched with the uncultured members of the families Gemmatimonadanceae and Rhizobiales_Incertae_Sedis in bulk soil, and genera Burkholderia-Caballeronia-Paraburkholderia, Magnetospirillum, Aeromonas, and Anaeromyxobacter in roots, contributing to increased expression of pmoA and nosZ genes and reducing CH₄ and N₂O emissions. YL 6 roots and root exudates contained higher contents of carbohydrates [e.g., 6-O- acetylarbutin and 2-(3- hydroxyphenyl) ethanol 1′-glucoside] than those of YY 1540. They were enriched with genera RBG-16-58-14 in bulk soil and Exiguobacterium, and uncultured member of the Kineosporiaceae family in roots, which contributed to increased expression of mcrA, ammonia-oxidizing archaea, ammonia-oxidizing bacteria, nirS, and nirK genes and greenhouse gas emissions. In general, these results established a link between metabolites, microorganisms, microbial functional genes, and greenhouse gas emissions. The metabolites of root exudates and roots regulated CH₄ and N₂O emissions by influencing the microbial community composition in bulk soil and roots.

The growth and development patterns of crop plants are being seriously threatened by arsenic (As) contamination in the soil, and it also acts as a major hurdle in crop productivity. This study focuses on arsenate As(V) mediated toxicity in rice plants. Further, among the different type of NPs, iron oxide nanoparticles (FeO NPs) display a dose-dependent effect but their potential role in mitigating As(V) stress is still elusive. FeO NPs (500 μM) play a role in imparting cross-tolerance against As(V) induced toxicity in rice. Growth attributes, photosynthetic performance, nutrient contents and biochemical parameters were significantly altered by As(V). But FeO NPs rescued the negative consequences of As(V) by restricting its entry with the possible involvement of NO in rice roots. Moreover, results related with gene expression of NO(OsNoA1 and OsNIA1) and proline metabolism were greatly inhibited by As(V) toxicity. But, FeO NPs reversed the toxic effect of As(V) by improving proline metabolism and stimulating NO mediated up-regulation of antioxidant enzymes particularly glutathione-S-transferase which may be possible reasons for the reduction of As(V) toxicity in rice roots. Overall, it can be stated that FeO NPs may act as an As(V) barrier to restrict the As(V) uptake by roots and have the ability to confer cross tolerance by modulating various morphological, biochemical and molecular characteristics with possible intrinsic involvement of NO.

Cardiovascular disease (CVD) is a leading threat to global public health. Although associations between temperature and CVD hospitalization have been suggested for developed countries, limited evidence is available for developing countries or rural residents. Moreover, the effect of apparent temperature (AT) on the spectrum of cause-specific CVDs remains unknown. Based on 2,024,147 CVD hospitalizations for rural residents from eight regions in Fujian Province, China, during 2010–2016, a quasi-Poisson regression with distributed lag non-linear model was fitted to estimate the AT effect on daily CVD hospitalization for each region, and then pooled in a meta-regression that included regional indicators related to rural residents. Stratified analyses were performed according to the cause of hospitalization, sex and age groups. Finally, we calculated the fraction of CVD hospitalizations attributable to AT, as a reflection of the burden associated with AT. The heat effect appeared at lag 0–1 days, with 19% (95% CI, 11–26%) increased risk of CVD hospitalization, which was worse for ischemic heart disease, heart failure, arrhythmias and ischemic stroke. The decreased AT was associated with increase of hemorrhagic stroke at lag 0–28 days. People aged 65 and above suffered more from the heat effect on cardiovascular and cerebrovascular diseases. Regions with a lower gross value of agricultural production, rural residents’ per capita net income, number of air conditioners and water heaters were more susceptible. A large number of hospitalizations were attributable to heat for most subcategories. High AT level increased CVD hospitalization, and the subcategories had different susceptibilities. The effects were modified by individual and regional characteristics. These findings have important implications for the development of targeted interventions and for hospital service planning.

The theory-guided air quality model solves the mathematical equations of chemical and physical processes in pollution transportation numerically. While the data-driven model, as another scientific research paradigm with powerful extraction of complex high-level abstractions, has shown unique advantages in the PM₂.₅ prediction applications. In this paper, to combine the two advantages of strong interpretability and feature extraction capability, we integrated the partial differential equation of PM₂.₅ dispersion with deep learning methods based on the newly proposed DPGN model. We extended its ability to perform long-term multi-step prediction and used advection and diffusion effects as additional constraints for graph neural network training. We used hourly PM₂.₅ monitoring data to verify the validity of the proposed model, and the experimental results showed that our model achieved higher prediction accuracy than the baseline models. Besides, our model significantly improved the correct prediction rate of pollution exceedance days. Finally, we used the GNNExplainer model to explore the subgraph structure that is most relevant to the prediction to interpret the results. We found that the hybrid model is more biased in selecting stations with Granger causality when predicting.

Distillery sludge is a major source of aquatic pollution, but little is known about their microbial community and their association with the organic and metal pollutants. Sugarcane molasses-based distillery is an important industry in India, although the waste is usually treated prior to disposal, the treatment is often inadequate. The adverse effects of the organic and metal pollutants in sugarcane molasses-based distillery sludge on the microbial biodiversity and abundance in the disposal site have not been elucidated. This study aims to address this gap of knowledge. Samples were collected from the discharge point, 1 and 2 km downstream (D1, D2, and D3, respectively) of a sugarcane distillery in Uttar Pradesh, India, and their physico-chemical properties characterised. Using QIIME, taxonomic assignment for the V3 and V4 hypervariable regions of 16 S rRNA was performed. The phyla Proteobacteria (28–39%), Firmicutes (20–28%), Bacteriodetes (9–10%), Actinobacteria (5–10%), Tenericutes (1–9%) and Patescibacteria (2%) were the predominant bacteria in all three sites. Euryechaeota, were detected in sites D1 and D2 (1–2%) but absent in D3. Spirochaetes (5%), Sinergistetes (2%) and Cloacimonetes (1%) were only detected in samples from site D1. Shannon, Simpson, Chao1, and Observed-species indices indicated that site D1 (10.18, 0.0013, 36706.55 and 45653.84, respectively) has higher bacterial diversity and richness than D2 (6.66, 0.0001, 25987.71 and 49655.89, respectively) and D3 (8.31, 0.002, 30345.53 and 30654.88, respectively), suggesting the organic and metal pollutants provided the stressors to favour the survival of microbial community that can biodegrade and detoxify them in the distillery sludge. This study confirmed that the treatment of the distillery waste was not sufficiently effective and provided new metagenomic information on its impact on the surrounding microbial community. It also offered new insights into potential bioremediation candidates.

Phenanthrene (Phe) is a polycyclic aromatic hydrocarbon widely present in foods and drinking water. To explore the detrimental effects of Phe on body metabolism, female Kunming mice were treated with Phe in drinking water at concentrations of 0.05, 0.5 and 5 ng/mL. After exposure for 270 d, the animals exhibited dose-dependent reduced body weight and increased water consumption. The dose-dependent accumulation of Phe in the brain decreased hypothalamic neuron numbers, upregulated hypothalamic expression of anaplastic lymphoma kinase, elevated norepinephrine levels in white adipose tissue (WAT) and further activated lipolysis in WAT, leading to a reduction in fat mass. Brown adipose tissue formation was reduced, accompanied by the inhibition of the bone morphogenetic protein signaling pathway. A simultaneous reduced serum levels of antidiuretic hormone (arginine vasopressin) might be one of the reasons for increased water consumption. The present results indicate an environmental etiology and prevention way for the development of emaciation-thirst disease.

Silicon is absorbed as uncharged mono-silicic acid by plant roots through passive absorption of Lsi1, an influx transporter belonging to the aquaporin protein family. Lsi2 then actively effluxes silicon from root cells towards the xylem from where it is exported by Lsi6 for silicon distribution and accumulation to other parts. Recently, it was proposed that silicon nanoparticles (SiNPs) might share a similar route for their uptake and transport. SiNPs then initiate a cascade of morphophysiological adjustments that improve the plant physiology through regulating the expression of many photosynthetic genes and proteins along with photosystem I (PSI) and PSII assemblies. Subsequent improvement in photosynthetic performance and stomatal behaviour correspond to higher growth, development, and productivity. On many occasions, SiNPs have demonstrated a protective role during stressful environments by improving plant-water status, source-sink potential, reactive oxygen species (ROS) metabolism, and enzymatic profile. The present review comprehensively discusses the crop improvement potential of SiNPs stretching their role during optimal and abiotic stress conditions including salinity, drought, temperature, heavy metals, and ultraviolet (UV) radiation. Moreover, in the later section of this review, we offered the understanding that most of these upgrades can be explained by SiNPs intricate correspondence with phytohormones, antioxidants, and signalling molecules. SiNPs can modulate the endogenous phytohormones level such as abscisic acid (ABA), auxins (IAAs), cytokinins (CKs), ethylene (ET), gibberellins (GAs), and jasmonic acid (JA). Altered phytohormones level affects plant growth, development, and productivity at various organ and tissue levels. Similarly, SiNPs regulate the activities of catalase (CAT), ascorbate peroxidase (APX), superoxide dismutase (SOD), and ascorbate-glutathione (AsA-GSH) cycle leading to an upgraded defence system. At the cellular and subcellular levels, SiNPs crosstalk with various signalling molecules such as Ca²⁺, K⁺, Na⁺, nitric oxide (NO), ROS, soluble sugars, and transcription factors (TFs) was also explained.

Optimizing landscape pattern to reduce the risk of non-point source (NPS) pollution is an effective measure to improve river water quality. The “source-sink” landscape theory is a recent research tool for landscape pattern analysis that can effectively integrate landscape type, area, spatial location, and topographic features to depict the spatial heterogeneity of NPS pollution. Based on this theory, we quantitatively analyzed the influence of “source-sink” landscape pattern on the river water quality in one of the most intensive agricultural watersheds in Southeastern China. The results indicated that the proportion of “sink” landscape (68.59%) was greater than that of “source” landscape (31.41%) in the study area. In addition, when elevation and slope increased, the “source” landscape proportion decreased, and the “sink” landscape proportion increased. Nitrogen (N) and phosphorus (P) pollutants in rivers showed significant seasonal and spatial variations. Farmland was the primary source of nitrate nitrogen (NO₃⁻-N) and total nitrogen (TN) pollution, whereas residential land was the primary source of ammonium nitrogen (NH₄⁺-N) and total phosphorus (TP) pollution. Intensively cultivated areas and densely inhabited areas degraded water quality despite high proportions of forest land. The four “source-sink” landscape indices (LWLI, LWLI'e, LWLI's, LWLI'd) had significant positive correlations with NO₃⁻-N and TN and weak correlations with NH₄⁺-N and TP. The capacity of LWLI to quantify the NPS pollution was greater in agricultural areas than in residential areas. The “source-sink” landscape thresholds resulted in abrupt changes in water quality. When LWLI was ∼0.35, the probability of river water quality degradation increased sharply. The results suggest the importance of optimizing the “source-sink” landscape pattern for mitigating agricultural NPS pollution and provide policy makers with adequate new information on the agroecosystem-environmental interface in highly developed agricultural watersheds.

β-lactams are large group of antibiotics widely used to suppress the bacterial growth by inhibiting cell wall synthesis. Bacterial resistance against β-lactam antibiotics is primarily mediated through the production of Temoneria (TEM) β-lactamase (BLs), with almost 474 variants identified in Lactamase Engineering Database (LacED). The present study aims to develop a model to track the evolution of TEM BLs and their interactions with β-lactam and BLs inhibitors through data mining and computational approaches. Further, the model will be used to predict the effective combinations of β-lactam and BLs inhibitors to treat the bacterial infection harbouring emerging variants of β-lactamase. The molecular docking study results demonstrated that most TEM mutants recorded the least binding energy to penicillin and cephalosporin (I/II/III/IV/V generations) class of antibiotics. On the contrary, the same mutants recorded higher binding energy to carbapenem and Monobactam class of antibiotics. Among the BLs inhibitors, tazobactam recorded the least binding energy against most of the TEM mutants, indicating that it can lower the catalytic activity of TEM BLs, thereby potentiating antibiotic action. Similarly, data mining work has assisted us in creating a database of TEM mutants that has comprehensive data on mutations, bacterial diversity, Km, MIC, and IRT types. It has been noted that earlier released antibiotics like amoxicillin and ampicillin had lower Km and higher MIC values, which indicates the prevalence of bacterial resistance. By analysing the differential binding energy (ΔBE) of the selected TEM mutants against β-lactam and BLs inhibitors, the most effective combination of β-lactam (carbapenem and monobactam class of antibiotics) and BLs inhibitors (tazobactam) was identified, to cure bacterial diseases/infections and to prevent similar antibiotic resistance outbreaks. Therefore, our study opens a new avenue in developing strategies to manage antibiotic resistance in bacteria.

The spatial distribution, homologue patterns, and ecological risks of chlorinated paraffins (CPs) were investigated in sediments from sixteen mangrove wetlands along the South China Coast (SCS). The total concentrations of CPs in mangrove sediments from Guangdong, Fujian, Guangxi, and Hainan were in the range of 933–4760, 619–2300, 375–1550, and 271–658 ng/g dry weight, respectively. The contamination levels and spatial distribution of short-chain and medium-chain CPs (SCCPs and MCCPs, respectively) in mangrove sediments were mainly affected by local population scale and CP industries. The dominant CP patterns in sediments were C₁₀–₁₁Cl₆₋₈ and C₁₄Cl₇₋₉ for SCCPs and MCCPs, respectively. Redundancy analysis, based on CP levels and several potential influencing factors showed that MCCPs/SCCPs ratio was the main factor affecting the accumulation of CPs in mangrove sediments. Additionally, MCCP concentrations were significantly correlated with total organic carbon (TOC), indicating that TOC might affect MCCP accumulation in mangrove sediments. Risk assessments indicated that CPs would pose medium ecological risks to sediment dwelling organisms in nearly one-third of the sampling sites. This is the first comprehensive report of the sedimentary SCCPs and MCCPs in mangrove wetlands along the SCS and highlights the need for more sediment toxicity data for CPs.