Quantifying China's iron and steel industry's CO2 emissions and environmental health burdens: A pathway to sustainable transformation.

Assessing the iron and steel industry's (ISI) impact on climate change and environmental health is vital, particularly in China, where this sector significantly influences air quality and CO2 emissions. There is a lack of comprehensive analyses that consider the environmental and health burdens of manufacturing processes for ISI enterprises. Here, we present an integrated emission inventory that encompasses air pollutants and CO2 emissions from 811 ISI enterprises and five key manufacturing processes in 2020. Our analysis shows that sintering is the primary source of air pollution in the ISI. It contributes 71% of SO2, 73% of NO x , and 54% of PM2.5 emissions. On the other hand, 81% of total CO2 emissions come from blast furnaces. Significantly, the contributions of ISI have resulted in an increase of 3.6 µg m-3 in national population-weighted PM2.5 concentration, causing approximately 59,035 premature deaths in 2020. Emissions from Hebei, Jiangsu, Shandong, Shanxi, and Inner Mongolia provinces contributed to 48% of PM2.5-related deaths in China. Moreover, the transportation of air pollutants across provincial borders highlights a concerning trend of environmental health inequality. Based on the research findings, it is crucial for ISI manufacturers to prioritize the removal of outdated production capacities and adopt energy-efficient and advanced techniques, along with ultra-low emission technologies. This is particularly important for those manufacturers with substantial environmental footprints. These transformative actions are essential in mitigating the environmental and health impacts in the affected regions.

Proteomic insights into adventitious root formation in Larix kaempferi.

The adventitious root formaton (ARF) in excised plant parts is essential for the survival of isolated plant fragments. In this study, we explored the complex mechanisms of ARF in Larix kaempferi by conducting a comprehensive proteomic analysis across three distinct stages: the induction of adventitious root primordia (C1, 0-25 d), the formation of adventitious root primordia (C2, 25-35 d), and the elongation of adventitious roots (C3, 35-45 d). We identified 1976 proteins, with 263 and 156 proteins exhibiting increased abundance in the C2/C1 and C3/C2 transitions, respectively. In contrast, a decrease in the abundance of 106 and 132 proteins suggests a significant demand for metabolic processes during the C2/C1 phase. The abundance of IAA-amino acid hydrolase and S-adenosylmethionine synthase were increased in the C2/C1 phase, underscoring the role of auxin in adventitious root induction. The decrease in abundance of photosynthesis-related proteins during the C2/C1 phase highlights the significance of initial light conditions in adventitious root induction. Moreover, variation in cell wall synthesis and metabolic proteins in the C2/C1 and C3/C2 stages suggests that cell wall metabolism is integral to adventitious root regeneration. Gene Ontology enrichment analysis revealed pathways related to protein modification enzymes, including deubiquitinases and kinases, which are crucial for modulating protein modifications to promote ARF. Furthermore, the increased abundance of antioxidant enzymes, such as peroxidases and glutathione peroxidases, indicates a potential approach for enhancing ARF by supplementing the culture medium with antioxidants. Our study provides insights into metabolic changes during ARF in L. kaempferi, offering strategies to enhance adventitious root regeneration. These findings have the potential to refine plant propagation techniques and expedite breeding processes. SIGNFICANCE: The main challenge in the asexual reproduction technology of Larix kaempferi lies in adventitious root formation (ARF). While numerous studies have concentrated on the efficiency of ARF, proteomic data are currently scarce. In this study, we collected samples from three stages of ARF in L. kaempferi and subsequently performed proteomic analysis. The data generated not only reveal changes in protein abundance but also elucidate key metabolic processes involved in ARF. These insights offer a novel perspective on addressing the challenge of adventurous root regeneration.

Metabolite selection for machine learning in childhood brain tumour classification.

MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi-class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi-site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi-class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave-one-out and k-fold cross-validation. Metabolites identified as crucial in tumour classification include myo-inositol (P < 0.05, AUC=0.81±0.01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC=0.78±0.01 ) and total creatine (P < 0.05, AUC=0.77±0.01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC=0.79±0.01 ), total N-acetylaspartate (P < 0.05, AUC=0.79±0.01 ) and total choline (P < 0.05, AUC=0.75±0.01 ) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave-one-out cross-validation was 85% for 1.5 T 1 H-MRS through support vector machine and 75% for 3 T 1 H-MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours.