Urban expansion can alter ecosystem services (ES), yet the effects of different expansion patterns and their socio-ecological drivers remain insufficiently understood. The study focused on the Pearl River Delta urban agglomeration (PRD) to understand how different geometric expansion types influence regional ES. The Landscape Expansion Index (LEI) was used to identify interior-filling, edge-spreading, and leap-frogging expansion, while the InVEST model assessed changes in habitat quality, carbon sequestration, soil retention, water yield, and food production. A Random Forest model was further employed to identify the relative importance of ten socio-ecological drivers under different expansion patterns. The results showed a clear spatial gradient, with interior-filling concentrated in the urban core, edge-spreading dominant in the middle belt, and leap-frogging more common in peripheral areas. Carbon sequestration, habitat quality, and food production declined over 2000–2020, with net changes of −11.86 × 108 gC, −21.67 × 104, and −17.12 × 105 t, respectively, while soil retention decreased by 7.73 × 106 t. Water yield increased by 7.24 × 107 mm despite declines during the early periods. Edge-spreading generated the largest absolute ES losses because of its extensive spatial footprint, whereas interior-filling produced the highest ES loss intensity per unit area. The driving mechanisms also differed among patterns: edge-spreading was more sensitive to topography and precipitation, while interior-filling was more strongly associated with socioeconomic factors. Leap-frogging had smaller regional effects but may increase local risks such as habitat fragmentation, soil erosion, and reduced ecological connectivity. These findings suggest that urban growth should be managed according to expansion patterns, with ecological land around urban fringes protected during edge-spreading, ecological safeguards strengthened during interior development, and green infrastructure improved in areas affected by leap-frogging expansion.
Co-based catalysts exhibit poorer activity and selectivity towards aldehyde in styrene hydroformylation comparing with aliphatic alkenes for the reason that styrene’s conjugated system over-stabilizes alkyl-cobalt intermediate, suppressing CO migratory insertion and promoting direct hydrogenolysis with H2. Here, we design a series of the interparticle porosity-dominated MFI-type zeolites (IPD-zeolites) with adjustable surface Lewis acidity to in-situ prepare uniform carbon-encapsulated Co nanoparticles (Co@C) for highly selective styrene hydroformylation towards aldehyde. Notably, Co@C nanoparticle supported on IPD-ZSM-5 exhibits exceptionally rapid styrene hydroformylation rate with 45% aldehyde selectivity, evidenced by its TOF of 11.3 h−1, more than twice that of acid-site-free analogues (IPD-S-1). This remarkable performance is mainly attributed to Al species with strong Lewis acidity and interparticle porosity in the support. Al sites acting as electron acceptors interact with the delocalized π bonds of styrene to generate electron-deficient aromatic intermediates, thereby enhancing styrene adsorption and boosting styrene hydroformylation rate. Meanwhile, Al sites promote electron transfer through an indirect pathway of support-carbon-Co to produce abundant electron-deficient Coδ+ centers to weaken electron back-donation into styrene π* orbital, destabilizing alkyl-cobalt intermediate and facilitating CO migratory insertion. Concurrently, such Coδ+ also reduces electron back-donation into H2 σ* orbital, suppressing H2 adsorption/dissociation. Both synergistically boost aldehyde chemoselectivity. Besides, the interparticle porosity formed by the ordered self-assembly of zeolite nanoparticles provides geometric confinement that enables uniform dispersion of Co@C nanoparticles and significantly improves their accessibility and stability. This work provides a valuable reference for designing non-noble metal hydroformylation catalysts via electron-deficient support effects and electronic metal-support interaction (EMSI).
Knowledge graph (KG)-based recommendation has emerged as an effective solution to data sparsity by incorporating structured semantic information into user and item representations. However, existing KG-based recommendation approaches still face two core challenges: (i) over-reliance on explicit relations while insufficiently capturing latent connections between items, and (ii) feature degradation during high-order propagation, which leads to the dilution of informative signals in learned representations. To this end, we propose PLRA-KG, a pattern-derived latent relation augmentation framework for KG-based recommendation. PLRA-KG uncovers implicit item associations by generating syn-relations and syn-entities from global user preference patterns and relational structures. Specifically, PLRA-KG first performs relation-aware clustering via self-training to group items with strong association patterns. It then introduces a discernment-aware relation-pair selection mechanism to identify relation combinations that significantly influence user decisions. Based on the selected relation-pairs, a pattern-derived syn-relation generation strategy is designed to construct latent relations by jointly modeling global user preferences and relational value interactions. These generated syn-relations and syn-entities are subsequently integrated into the original KG, resulting in an augmented graph that better captures hidden semantic connectivity. Finally, PLRA-KG is optimized in a unified framework that jointly considers recommendation learning, KG embedding, and clustering objectives, enabling seamless integration with various KG-based recommendation models. Extensive experiments on multiple benchmark datasets demonstrate that PLRA-KG consistently improves both recommendation accuracy and diversity, achieving average improvements of approximately 6.64% in Recall, 7.83% in AD, 5.56% in Coverage, and 6.20% reduction in ARP. The source code is accessible at https://github.com/ZZP-RS/PLRA-KG.
T cell receptor (TCR)-T cell therapy is effective for solid tumors, yet identifying potent, specific TCRs for tumor antigens is challenging. Conventional affinity maturation may cause fatal off-target toxicity. Catch bonds play a crucial role in mechanosensory receptor signaling, including the TCR, but their formation and potential to mitigate the challenges of TCR-T remain unclear. Here, we demonstrate that histidine scanning can identify TCR hotspots capable of forming additional catch bonds, which can be randomized to create TCR libraries for screening low-affinity, higher-potency variants. Mechanistically, histidine facilitates the formation of hydrogen bonds and salt bridges and fortifies the intracellular signaling cascade. Using this approach, we engineered different TCRs specific for various antigens, without off-target toxicity or on-target toxicity. Our findings introduce a universal method of engineering low-affinity, high-potency TCRs for safe TCR-T cell therapy, without requiring the structure for designing TCR libraries. Additionally, histidine scanning can be broadly applied to other mechanosensory ligand-recep tor systems.
Prior research has centered on mindfulness meditation-induced improvements in creative cognition, with scarce exploration of its effects on creative personality and the neural mechanisms involved. This randomized controlled trial (RCT) explored how mindfulness meditation affected creative personality and resting-state EEG (rs-EEG) microstates, seeking preliminary evidence for causal links among mindfulness, rs-EEG microstates, and creative personality. Fifty-eight Chinese undergraduates were randomized to a 4-week mindfulness meditation group or a waitlist control group. The Williams Creativity Aptitude Test and rs-EEG microstate parameters were assessed pre- and post-intervention. Post-test data indicated the mindfulness meditation group scored significantly higher than the control group in overall creative personality, risk-taking orientation, and challenge orientation. Additionally, the meditation group showed significant pretest-to-posttest improvement in total score and imagination dimension. Further analyses revealed the challenge dimension differences were jointly driven by the meditation group’s increased scores and control group’s decreased scores, while risk-taking orientation differences were solely attributable to the control group’s reduced scores. Rs-EEG microstate analysis showed the meditation group had significantly higher Microstate D occurrence but lower A → C and C → A transition probabilities than the control group. Post–pre adjusted correlation analysis revealed a significant positive correlation between ΔMicrostate D occurrence and Δimagination scores in the meditation group. Mindfulness meditation may positively influence the imagination trait of creative personality through modulating the occurrence of brain Microstate D. This study was not preregistered.
