Background Although treatment approaches have been explored in internet gaming disorder (IGD), however, little is known about the specific neural activity that is associated with treatment outcomes. This study aims to establish a treatment-sensitive network that reliably predicts treatment outcomes across different interventions for IGD.Methods Resting-state fMRI data were collected from 90 individuals with IGD who received one of three different intervention protocols (mindfulness meditation, approach bias modification, or retrieval-aversive conditioning training). Connectome-based predictive modelling (CPM) was used to build a treatment-sensitive network to predict individual symptom reduction, then validated them on the external dataset.Results The negative (r = 0.401, p <.001) and combined network (r = 0.480, p <.001) can significantly predict a reduction of addiction severity (baseline minus post-test). Specifically, the negative network was primarily centred on the motor, subcortical, and salience networks. The positive network primarily involved cerebellar-related connections. Incorporating these into a combined model significantly improved predictive accuracy, suggesting that cerebellar-related connectivity may contribute to the prediction of intervention-related clinical improvement. Finally, the treatment-sensitive network was validated in a separate data.Conclusions This study established a treatment-sensitive network based on the whole-brain connectome. These findings provide valuable empirical evidence for identifying biomarkers for IGD treatment and advance personalised therapeutic strategies.Clinical Trials Registration The trial protocol was pre-registered with the Chinese Clinical Trial Registry (www.chictr.org.cn; ChiCTR2300077831(MM), ChiCTR2300075869(ApBm), ChiCTR2500095444(R-AC), ChiCTR2400090834(SAE)).
China’s expanding industrial transfer to the five Lancang–Mekong countries has intensified the tension between development demand and the region’s limited resource–environmental carrying capacity. Achieving a sustainable and spatially rational industrial layout requires a land-use oriented approach that explicitly integrates suitability conditions, ecological constraints, and alternative development pathways. This study constructs a spatial decision-support framework by coupling an ANN with a Partitioned Asynchronous CA, using multi-source geospatial data for 2019 and 2024 to derive industrial transition probabilities and simulate cross-border industrial land allocation. Five policy-oriented scenarios—infrastructure-led, economic-led, ecological conservation, coordinated development, and business-as-usual pathways—were developed to evaluate trade-offs between development intensity and environmental constraints. Results indicate that the model achieves moderate agreement performance (overall accuracy= 0.81; Kappa = 0.62), effectively capturing industrial clustering and diffusion processes. High-suitability industrial zones concentrate in Ho Chi Minh City and Hanoi (Vietnam), Phnom Penh (Cambodia), Vientiane (Laos), the Bangkok–Chonburi corridor (Thailand), and Naypyidaw (Myanmar). Scenario-based optimization reveals a three-tier spatial hierarchy: Vietnam and Thailand as multi-factor advantageous areas, Cambodia and Myanmar as cost-dependent expansion zones, and Laos as a policy-driven development region constrained by topography and ecological sensitivity. The differentiated planning recommendations suggest: technological upgrading in Thailand, transition beyond low-end manufacturing in Vietnam, special economic zone–guided development in Myanmar, green industry upgrading in Cambodia, and light-capital, corridor-oriented development along the China–Laos Railway. The proposed framework demonstrates a replicable land-use optimization tool for cross-border industrial planning, offering evidence to support sustainable spatial governance in the Lancang–Mekong region and the broader Green Belt and Road Initiative.
The accumulation of volatile fatty acids (VFAs) presents a major challenge in thermophilic anaerobic digestion (TAD), potentially hindering the efficiency of biogas production. In this study, mesophilic anaerobic digestion (MAD) was used as a benchmark to investigate the key factors and mechanisms by which an acidified thermophilic anaerobic digestion system, specifically treating food waste, improves gas production. The results indicate that, to alleviate acidification, thermophilic conditions following secondary inoculation increased acetate kinase concentrations, induced expression of cytochrome c-associated enzymes, and raised the fluorescence intensity proportion of humic-acid-like substances, thereby promoting the recovery of gas production. Thermodynamic analysis shows that thermophilic conditions reduced the energetic barriers for butyrate degradation (-5.17% to 22.58%) and hydrogenotrophic methanogenesis (HM) (-7.06% to 57.06%), lowered hydrogen partial pressure, and thus favored the hydrogenotrophic methanogenic pathway. In addition, thermophilic conditions likely triggered microbial community succession: after gas production recovered, syntrophic bacteria such as norank_c_D8A-2 and Syntrophomonas, together with the hydrogenotrophic methanogen Methanoculleus, were enriched while the relative abundance of Methanosarcina declined, indicating a shift in methanogenesis from an acetoclastic to HMP. Functional prediction analysis suggests that thermophilic conditions may have enhanced acetoclastic syntrophic oxidation (SAO), butyrate degradation, methanogenic metabolism, and ATP biosynthesis, enabling concurrent consumption of excess VFAs and methane production. This study delineated the important factors driving gas recovery in TAD acidification systems and provided mechanistic guidance for mitigating VFAs accumulation and increasing methane yield under thermophilic conditions.
While regional climate establishes the hydrothermic baseline for decomposition, inconsistent grazing effects across ecosystems suggest that macroclimatic frameworks alone cannot predict ecosystem sensitivity to contemporary disturbances. We hypothesized that such context-dependent responses are associated with site-specific legacies, defined as integrated functional states shaped by past environmental and management conditions. We conducted a multisite experiment across four temperate grasslands in northern China differing in hydrothermic regimes and grazing histories. Within each site, three-year contemporary grazing treatments were paired with ungrazed controls, and the widespread Leymus chinensis was used as a standardized substrate to isolate decomposition-environment effects. During the 130-day peak decomposition period, decay in ungrazed plots was positively associated with climatic humidity, whereas grazing effects varied among sites: strong inhibition at the historically ungrazed site, promotion at the moderately grazed site, and weak inhibition at heavily grazed and overgrazed sites. Path analysis suggested that regional humidity and grazing history were jointly associated with baseline vegetation and soil properties linked to the decomposition microhabitat. We propose that plant-soil ecological memory provides a useful conceptual filter for interpreting ecosystem responses to contemporary grazing, suggesting that restoration planning should account for historical grazing legacies and prioritize soil-functional recovery in degraded grasslands.
Graphitic carbon nitride (g-C3N4) has an appropriate band structure, excellent chemical stability and low-cost acquisition, thereby has extensive potential in the field of photocatalysis. The atomic structure, morphological characteristics, photocatalytic properties, and preparation methods of g-C3N4 are firstly summarized. The effective strategies to enhance g-C3N4 photocatalytic efficiency and stability include elemental doping, defect engineering, and heterojunction construction. These strategies not only introduce impurity levels to narrow the bandgap, broaden the light absorption range, and suppress the recombination of photo-generated electron-hole pairs, but also leverage bandgap matching and synergistic effects between different materials to provide more reaction sites. Precisely tunable microstructure of g-C3N4 is also helpful for improving the photocatalytic performance and stability. With employing g-C3N4 photocatalysis, significant progress has been achieved in CO2 reduction, water splitting for hydrogen production, organic degradation, and heavy metal ion reduction. The hydrogen production has reached 56,660 mu mol g-1 h-1 . For organic pollutant degradation, 99.36% removal for RhB, with 92% activity retention after four cycles has been obtained. These results can provide references for future g-C3N4 photocatalytic applications.
