Imbalance in photosynthetic electron transport caused by environmental stress impairs carbon assimilation, triggering CO₂ release through processes like photorespiration. This exacerbates net carbon emissions and severely undermines microalgae's carbon sink function. As an energy allocation mechanism responding to environmental stress, cyclic electron transport (CET) compensates for the energy supply ratio of linear electron transport (LET) by independently regulating ATP synthesis, thereby safeguarding the photosynthetic system and maintaining its functionality. This review elucidates the activation mechanisms and photoprotective roles of CET in microalgae under various stress conditions, including high light, nutrient deficiency, CO₂ limitation, and toxic environments such as heavy metals, organic pollutants, and emerging contaminants. It discusses how CET synergizes photoprotection with carbon fixation by dynamically elevating the ATP/NADPH ratio, thereby regulating Calvin cycle efficiency and directing carbon metabolic flux. Finally, the paper prospects future research directions for enhancing microalgal carbon sequestration under environmental stresses through CET regulation, aiming to provide references for improving microalgal carbon sink capacity and advancing environmental engineering practices like carbon-neutral water remediation.

In this study, the pentachlorothiophenol (PCTP) and Chinese rare minnow (Gobiocypris rarus) transthyretin were selected as the objects of study. We determined the potential binding affinity data of pentachlorothiophenol on Chinese rare minnow transthyretin, revealed the underlying molecular recognition mechanism between pentachlorothiophenol and the protein, and derived the binary classification models for distinguishing whether a given substance was a potential Chinese rare minnow transthyretin disruptor or not by using the Chinese rare minnow transthyretin disrupting data measured in this study and previous studies. The experimental results indicated that the observed water solubility value of pentachlorothiophenol was (0.636 ± 0.0871) mg/L (25℃). The relative competing potency of pentachlorothiophenol with 3,3′,5,5′-Tetraiodo-L-thyronine (T₄) binding to Chinese rare minnow transthyretin (logRP) was (0.0200 ± 0.0533), implying that pentachlorothiophenol was a high potency Chinese rare minnow transthyretin disruptor. The molecular simulation results documented that the binding interaction between pentachlorothiophenol and Chinese rare minnow transthyretin was controlled by the halogen hydrogen bond, halogen bond, and hydrophobic interactions. In the modelling, three optimum binary classification models (i.e. k nearest neighbour model, random forest model, decision tree model) were successfully developed. The statistical parameters (e.g. predictive accuracy (Q)) values of those optimum models were 1, highlighting that those optimum models had great classification performance. Finally, the tool named "TTR Profiler" that used to identify potential transthyretin disruptors was updated by integrating the newly developed optimum models, which means that the application domain of the related models in the new tool covered the thiophenols.

This study focused on the protective mechanism of astaxanthin (AST) against arsenic-induced mitochondrial-dependent apoptosis in mouse hippocampal neuronal cells (HT-22). An in vitro sodium arsenite (NaAsO₂) exposure model was established in HT-22cells, including control, NaAsO₂-exposed (4, 6, 8, 10μmol/L), AST-treated (2μmol/L), and AST-intervened (2μmol/L AST +10μmol/L NaAsO₂) groups, so as to evaluate the effects of AST intervention on arsenic-induced neural lesions. DCFH-DA was used to detect the level of reactive oxygen species (ROS). An ATP kit was used to assess the energy metabolism of mitochondria via ATP production. The levels of protein, including PGAM5 and signaling pathways of mitochondrial dynamics (fission/fusion), autophagy, and apoptosis, was analyzed by Western blot. Results showed that NaAsO₂ exposure significantly induced increased generation of ROS (P<0.05), decreased synthesis of ATP (P<0.05), activation of PGAM5, up-regulation of p-Drp1-Ser616, and down-regulation of p-Drp1-Ser637 and mitochondrial fusion proteins (OPA1, MFN1), leading to excessive mitochondrial fission. Intervention by AST effectively reversed these effects (P<0.05), which were demonstrated by the inhibition of PGAM5-mediated mitochondrial fission, alleviation of oxidative stress, and reversion of energy metabolism. It is supposed that AST plays a role in AMPK-mTOR-dependent autophagy and mitochondrial apoptosis. On one hand, AST inhibited AMPK phosphorylation which subsequently activated mTOR and down-regulated LC3II/LC3I ratio (autophagy marker); on the other hand, AST down-regulated pro-apoptotic proteins (Bax, cleaved-caspase-3), and upregulated anti-apoptotic Bcl-2 (P<0.05), thereby regulating AMPK-mTOR-dependent autophagy and mitochondrial apoptosis. This study suggests that AST protects against NaAsO₂-induced neurotoxicity by targeting PGAM5through the recovery of mitochondrial dynamics, improvement of energy metabolism, and regulation of AMPK-mTOR-autophagy-apoptosis axis, providing highlights on potential therapeutic strategies for arsenic-related neuronal damage.

The oxidation potential of common bacterial and fungal aerosols in the atmosphere as well as their key influential factors was evaluated using the dithiothreitol (DTT) method. The results showed that bioaerosols induced reactive oxygen species (ROS) production, varying along the microorganism type, species, and concentration. The oxidative potential of fungi was approximately 2 to 3 times higher than that of bacteria. When the test bioaerosl concentrations were set highest (10⁵CFU/mL), Staphylococcus aureus [(0.41 ± 0.06) nmol/(min·mL)] and Penicillium [(1.32 ± 0.11) nmol/(min·mL)] exhibited the strongest oxidative potential, averaging about 1.5times higher than other bacteria and fungi. The oxidation potential of bioaerosols was also significantly associated with their activity and chemical components. Microbial activity was a key factor regulating oxidative potential, with inactivation treatment reducing it by up to 40%. The consumption rate of DTT when biological components (Staphylococcus aureus/Penicillium) and chemical components (Cu/1, 4-NQ) were simultaneously present in the system was lower than that of the single component. It indicated that there was an interaction between the two components that inhibited the DTT consumption reaction.

A bibliometric and visualization analysis using CiteSpace was performed on publications concerning emerging contaminants (ECs) and non-communicable diseases (NCDs) retrieved from the Web of Science Core Collection for the period 2005~2025, with the aim of mapping the field's evolution and identifying research frontiers. Publication output increased substantially over the study period, with accelerated growth after 2014, coinciding with intensified policy initiatives to regulate newly listed pollutants under the Stockholm Convention on Persistent Organic Pollutants. In addition, the University of California, the U.S. National Institutes of Health, and the Chinese Academy of Sciences were among the most productive institutions, and relevant studies were predominantly published in international journals, including Environmental Health Perspectives and Environmental Science & Technology. The disciplinary landscape was dominated by Environmental Sciences, Toxicology, and Public Environmental Occupational Health, with public health serving as a critical bridge between environmental exposure assessment and disease-related mechanisms. Earlier work largely examined links between endocrine disrupting chemicals (EDCs) and disinfection by products (DBPs) and cancer, whereas recent studies have increasingly emphasized metabolic disorders associated with per- and polyfluoroalkyl substances (PFAS) and nanoparticles, alongside mechanistic themes involving the gut microbiota and inflammation. Future research should prioritize elucidating toxicological pathways and constructing integrative multi-omics frameworks to better explain the pathogenesis of NCDs.

This review summarizes the applications and future prospects of metagenomics in studying antibiotic resistance pollution in marine environments. Results demonstrate that metagenomic analysis enables comprehensive characterization of genetic composition in marine microbial communities, particularly regarding the distribution, diversity, and spatiotemporal dynamics of antibiotic resistance genes (ARGs). The analysis reveals that ARGs are ubiquitously present in marine waters, sediments, and organisms, influenced by multiple factors including pollution sources, environmental conditions, and anthropogenic activities. Metagenomics not only elucidates ARG dissemination mechanisms but also provides scientific foundations for ecological risk assessment and pollution mitigation; however, limitations persist in functional validation and interpretation of environmental driving mechanisms. Future research should leverage multi-omics integration, third-generation sequencing technologies, and advanced bioinformatics tools to enhance the depth and precision of marine ARG pollution studies. Concurrently, integration with big data, machine learning, and artificial intelligence technologies will establish metagenomics as an irreplaceable tool in marine antibiotic resistance research, offering critical insights for combating the global antibiotic resistance crisis.

This study adopts a consumption-based perspective and, drawing on long time-series input-output data from 189economies around the world, calculates the scale embodied carbon transfers in China (referring specifically to China's mainland and excluding Hong Kong, Macao, and Taiwan) from 1997 to 2021. It identifies the characteristics of consumption-based carbon emissions of eight industries: agriculture; mining and quarrying; light industries; heavy industries; machinery and equipment manufacturing; electricity, gas, and water supply; other industries; and living and tertiary industries. Furthermore, it analyzes the key drivers of changes in consumption-based carbon emissions across different periods. The findings reveal that from 1997 to 2021, China consistently remained a net exporter of embodied carbon emissions, with light industries, heavy industries, and machinery and equipment manufacturing serving as the main sources of embodied carbon exports. The United States, Japan, and China's Hong Kong were the main destinations of these exports. In 2021, China's consumption-based carbon emissions totaled 10,156million tons, 1,289million tons lower than its production-based emissions, indicating that under the consumption-based perspective, China's carbon emission responsibility is 11.26% lower than that under the production-based perspective. In terms of driving factors, the increase in consumption scale was the main factor driving the growth of China's consumption-based carbon emissions from 1997 to 2021, with a contribution rate of 159.31%. Changes in population size, improvements in energy utilization efficiency, and adjustments in the energy structure contributed 11.04%, -63.40%, and -6.96%, respectively, to changes in consumption-based carbon emissions. Based on these results, the study discusses the unfairness of assigning carbon emission responsibility under the production-based perspective, highlights key areas of focus for China's carbon emission reduction efforts, and demonstrates the reliability of the findings.

The carbon lock-in effect represents a systematic challenge to achieve the goal of carbon peaking and carbon neutrality. Identifying its spatio-temporal evolution patterns and regional transmission mechanisms is of great significance for facilitating China's low-carbon transformation. Based on an improved evaluation system, the study measured the carbon lock-in levels of 30 provinces in China from 2002 to 2022. Spatio-temporal analysis method was employed to depict their spatial patterns and evolutionary laws, and further explore their spatial association mechanisms and evolutionary trends. The results show that: The national carbon lock-in level exhibits a three-stage trend overall: a slow decline, accelerated unlocking, and gentle fluctuation. It also demonstrates an evolutionary characteristic of "eastern regions taking the lead in unlocking, central regions undergoing rapid transformation, and western regions following suit continuously" in spatial patterns. Moreover, a comprehensive spatio-temporal analysis indicates that the degree of national carbon lock-in discretization has increased, regional disparities have expanded, and a multi-polarization tendency is evident. Carbon lock-in demonstrates a significant positive spatial spillover effect. The growth rate of fixed asset investment has a positive influence on carbon lock-in in adjacent areas, while the urbanization rate and the proportion of labor compensation have substantial negative impacts. The inter-provincial carbon lock-in network interaction shows a three-stage evolutionary trend bounded by the Hu Huanyong Line and transmission characteristics of "continuous deepening of inter-provincial connections, significant polarization of hub nodes, and explicit regional transmission paths". The differentiated evolution of functional interactions between northern and southern blocks bounded by the Qinling-Huaihe Line is obvious. The prediction results indicate that the carbon lock-in level is under pressure from diminishing marginal returns and faces risks of local rebound. The key to breaking the carbon lock-in network lies in the provinces with high betweenness centrality.

With the continuous increase in carbon emissions from road transportation and significant regional differences, understanding the spatio-temporal evolution patterns and influencing factors of carbon emissions from road transportation in China is crucial for energy conservation and carbon reduction in this sector. Based on the calculation of carbon emissions from road transportation in 31 provinces of China from 2003 to 2022, this study uses slope values, Theil index, and integrates XGBoost-SHAP to construct an interpretable machine learning model to analyze the temporal characteristics, spatial differences, and influencing factors of carbon emissions from road transportation in China and its eastern, central, and western regions. The results show that: The combined carbon emissions from small and medium-sized passenger cars and heavy-duty trucks account for over 80%, while the proportion of light-duty trucks first increases and then stabilizes, and that of medium-duty trucks continuously decreases. The proportions of medium and large-sized passenger cars, buses, and taxis are relatively low and show a downward trend. The carbon emissions from road transportation in China exhibit a phased characteristic of "rapid growth-decelerating growth". The eastern region had a relatively fast growth rate in the early stage, but it has been continuously decelerating in the later stage; the central region's growth rate surpassed that of the eastern region in the later stage; the western region has a low base but the fastest growth rate. The overall spatial pattern of carbon emissions from road transportation in China is "expansion in the east-increase in the central-stability in the west", with high-emission continuous belts formed successively in the eastern coastal areas and the central hinterland. The differences in carbon emissions from road transportation between regions are gradually decreasing, while the differences within regions are significantly increasing. The differences among provinces within the eastern and western regions are significant, while those in the central region are the smallest and remain stable. (4) At the national level, the turnover volume of road freight is the primary factor and has a significant driving effect on carbon emissions from road traffic, followed by the total population and the number of motor vehicles per 10,000people, moreover, each significant factor shows a synergistic effect of increasing emissions. At the regional level, the turnover volume of road freight remains the most significant factor in the eastern and western regions, but it is not significant in the central region. The contribution rate of motor vehicles per 10,000people and per capita GDP in the central region has significantly increased compared to the eastern and western regions.

To address the issue of volatility and nonlinearity in carbon emission data sequences from the transportation sector affecting prediction accuracy, this study proposes a carbon emission prediction model that integrates Multivariate Variational Mode Decomposition (MVMD), differential multi-head attention mechanism, improved iTransformer, and LSTM enhanced by Sparrow Search Algorithm(SSA). First, MVMD was introduced to decompose the transportation carbon emission feature data sequence into modal components of different frequencies. Sample entropy was then used to quantify the complexity of each component, which was categorized into high- and low-frequency data based on entropy values, thereby further mitigating the volatility and nonlinearity of the carbon emission data sequence. Subsequently, SSA-LSTM was employed to predict low-frequency data, capturing long-range dependencies, while LSTM combined with the improved iTransformer was applied to rapidly denoise and fit high-frequency data to avoid interference with the primary trend. Finally, the prediction results of high- and low-frequency data were combined using weights calculated based on the sizes of sample entropy values. The model was validated using carbon emission data from China's transportation sector from 2000 to 2022. The results showed that the proposed model achieved a root mean square error (RMSE), mean square error (MSE), and mean absolute percentage error (MAPE) of 10.31×10⁶t, 106.35×10⁶t, and 0.97%, respectively, outperforming other comparative models and confirming its effectiveness.