Occurrence and Fate of five representative neonicotinoid insecticides across different wastewater treatment plants and the impact on receiving water bodies.

Neonicotinoids (NEOs), despite their widespread use as insecticides, exhibit a notable knowledge deficit in regards to their presence in wastewater treatment plants (WWTPs) and their surrounding environments. This study delves into the presence and disposition of 5 NEOs: Thiamethoxam (THM), Clothianidin (CLO), Imidacloprid (IMD), Acetamiprid (ACE), and Thiacloprid (THA) across 3 domestic WWTPs and their receiving waters. Notably, THM, CLO, and ACE were consistently detected in all water and sludge samples, with THM emerging as the most abundant compound in both influent and effluent. Among the 3 WWTPs, WWTP 2, employing a fine bubble oxidation process, achieved the highest removal efficiency, surpassing 68%, in contrast to WWTP 1 (CAST) at 37% and WWTP 3 (A/A/O) at 7%. Biodegradation played a pivotal role in NEO removal, accounting for 36.7% and 68.2% of the total removal in WWTP 1 and WWTP 2, respectively. Surprisingly, in WWTP 3, biotransformation process inadvertently increased ACE and CLO concentrations by approximately 4.1% and 4.5%, respectively. The total NEO concentration in the receiving surface waters ranged from 72.7 to 155.5 ng/L, while sediment concentrations were significantly lower, spanning between 0.10 and 1.53 ng/g. WWTPs serve as both a removal and concentration point for NEOs, thereby significantly influencing their transportation. Additionally, the concentration of most NEOs in the receiving waters progressively increased from upstream to downstream, highlighting the substantial impact of WWTP discharges on natural water environments. This research offers valuable insights into NEO pollution surrounding WWTPs in the Pearl River Delta, ultimately aiding in pollution control and environmental protection decisions.

Identification of prognostic biomarkers in neuroblastoma using WGCNA and multi-omics analysis.

BACKGROUND: Neuroblastoma (NB) is one of the most frequent parenchymal tumors among children, with a high degree of heterogeneity and wide variation in clinical presentation. Despite significant therapeutic advances in recent years, long-term survival in high-risk patients remains low, emphasizing the urgent need to find new biomarkers and construct reliable prognostic models. METHODS: In this study, data from neuroblastoma samples in the ArrayExpress database were utilized to identify key gene modules and pivotal genes associated with NB prognosis by weighted gene co-expression network analysis (WGCNA). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis was performed using the DAVID database. Based on these hub genes, survival prognosis models were constructed and validated on an independent validation set in the Gene Expression Omnibus (GEO) database. Differences in biological functions and immune microenvironments and the sensitivity to pharmacological and immunotherapeutic treatments of patients in the high- and low-risk groups were examined by gene set enrichment analysis (GSEA) and immune infiltration analysis. RESULTS: WGCNA identified 14 gene modules and screened the module with the highest relevance to the International Neuroblastoma Staging System (INSS), containing 60 pivotal genes. GO and KEGG analyses demonstrated that these pivotal genes were mainly implicated in biological processes and signaling pathways including DNA replication, cell division, mitotic cell cycle, and cell cycle. Based on Lasso regression and COX regression analysis, a prognostic model containing DHFR, GMPS and E2F3 was constructed, and the RiskScore was significantly correlated with the 1-, 3- and 5-year survival of the patients. GSEA and immune infiltration analyses revealed significant differences in the levels of cell cycle-related pathways and immune cell infiltration between the high and low RiskScore groups. In particular, patients in the high-risk group are less likely to benefit from immunotherapy and may be better suited for treatment with drugs such as Oxaliplatin and Alpelisib. CONCLUSION: This research systematically identified biomarkers related to NB prognosis and developed a reliable prognostic model applying WGCNA and multiple bioinformatics methods. The model has important application value in predicting patients' prognosis, evaluating drug sensitivity and immunotherapy effect, and provides new ideas and directions for precise treatment of neuroblastoma.

Construction of a methodology framework to characterize dynamic full-sector land-use carbon emissions embodied in trade.

The globally massive land-use changes associated with unprecedented urbanization rate are leading to prodigious quantities of carbon emissions. Nonetheless, the dynamics of land-use carbon emissions, particularly driven by supply-chain activities across all relevant industrial sectors, remain largely unexplored, especially in non-agricultural sectors. Here, we constructed a novel methodological framework to quantify full-sector land-use carbon emissions in Shenzhen, China, an international megacity grappling with acute land resource scarcity. Then, we integrated this framework with multiregional input-output analysis to uncover the multi-scale embodied land-use emissions propelled by Shenzhen's supply-chain activities. Our results indicate a marked increase in Shenzhen's embodied carbon emissions, approximately two orders of magnitude greater than its physical emissions, tripling during 2005-2018. Remarkably, non-agriculture sectors contributed 81.3-90.5 % of physical and 46.6-58.4 % of embodied land-use emissions. The land-use changes occurred outside Shenzhen accounted for 6.5-13.3 % of Shenzhen's total embodied land-use emissions. The sectoral analysis revealed a transition from traditional manufacturing (e.g., metallurgy, chemical products, textiles, wood products) in 2010-2015 to high-tech sectors (e.g., electronic equipment and other manufacture) in 2015-2018. This shift was primarily attributed to concurrent industry transfer actions, leading to aggressive changes in land-use emission intensity discrepancies within and outside Shenzhen. This study provides a scientific basis for designing effective strategies to mitigate land-use carbon emissions associated with supply-chain activities.

The carbon emissions related to the land-use changes from 2000 to 2015 in Shenzhen, China: Implication for exploring low-carbon development in megacities.

Megacities exploit enormous amounts of lands from outside of the city boundary. However, there is a large knowledge gap in the impact of socioeconomic activities associated land-use changes on carbon emissions of megacities during the urbanization. In the current work, we combined the material-flow analysis, environmental extended input-output model, and land matrix data to construct a hybrid network framework. Such a framework was used to estimate the carbon emissions driving from trade between sectors and associated land use changes during 2000-2015 in Shenzhen, China. Results indicated that the total carbon emissions of Shenzhen had a growth rate of 262.7% from 2000 to 2010 and a declining rate of 17.6% from 2010 to 2015. This pattern is associated with large declining rates in the overall energy and carbon intensities by 53.8% and 63.2% during the period of 2000-2015. Meanwhile, embodied carbon emissions of Shenzhen kept rising by approximately twofold, accompanied by the increasing trends in the land-use related carbon emissions both inside and outside of city boundary. The land uses per unit GDP showed a dramatical decline by 85.7% and with a large contribution of the transportation and industrial land, and this caused a gradual increase in overall land-use related emissions with average growth rate of 7.1%. In addition, the land-use change related carbon emissions of the transportation and industrial land had a cumulative growth of 85%. As for the embodied land-use related carbon emissions, the dominated contributor was the Agriculture sector which drove an average of 0.13 MtC yr-1 emissions via importing agricultural products from outside of Shenzhen. This study provides a scientific foundation for corporately mitigate carbon emissions between megacities and their surrounding regions.