Qiang Wang

Sep 27, 2024 | nature.com | Qiang Wang |Xinhua Wang |Rongrong Li

This study examines the impact of geopolitical risk on energy transition, focusing on the moderating roles of environmental regulations and green innovation within OECD countries. By employing a multivariate linear and nonlinear regression model, we identify a substantial positive effect of geopolitical risk on energy transition. Our analysis indicates that stronger environmental regulations and advancements in green innovation significantly amplify this effect. Through threshold effect bootstrap sampling tests, we detect a nonlinear relationship between geopolitical risk and energy transition at varying levels of environmental regulation and green innovation. We also explore lag effects, revealing that the influence of geopolitical risk on energy transition grows stronger over time. The inclusion of interaction terms in our analysis further clarifies the moderating influences of environmental regulation and green innovation. Utilizing a range of geopolitical risk indicators and regression methods, our findings are robust, consistently highlighting the proactive role of geopolitical risk in fostering energy transition. These insights highlight the importance of integrated strategies that harness environmental regulations and technological innovation to facilitate a resilient and efficient energy transition in the face of challenges posed by geopolitical uncertainties.

Aug 14, 2024 | nature.com | Qiang Wang |Rongrong Li |Yuanfan Li

This study examines the multifaceted impact of artificial intelligence (AI) on environmental sustainability, specifically targeting ecological footprints, carbon emissions, and energy transitions. Utilizing panel data from 67 countries, we employ System Generalized Method of Moments (SYS-GMM) and Dynamic Panel Threshold Models (DPTM) to analyze the complex interactions between AI development and key environmental metrics. The estimated coefficients of the benchmark model show that AI significantly reduces ecological footprints and carbon emissions while promoting energy transitions, with the most substantial impact observed in energy transitions, followed by ecological footprint reduction and carbon emissions reduction. Nonlinear analysis indicates several key insights: (i) a higher proportion of the industrial sector diminishes the inhibitory effect of AI on ecological footprints and carbon emissions but enhances its positive impact on energy transitions; (ii) increased trade openness significantly amplifies AI’s ability to reduce carbon emissions and promote energy transitions; (iii) the environmental benefits of AI are more pronounced at higher levels of AI development, enhancing its ability to reduce ecological footprints and carbon emissions and promote energy transitions; (iv) as the energy transition process deepens, AI’s effectiveness in reducing ecological footprints and carbon emissions increases, while its role in promoting further energy transitions decreases. This study enriches the existing literature by providing a nuanced understanding of AI’s environmental impact and offers a robust scientific foundation for global policymakers to develop sustainable AI management frameworks.

Aug 9, 2024 | nature.com | Qiang Wang |taehyeung Kim |Marta Martínez-Bonet |Sangwan Sim |Jing Cui |Jeffrey A Sparks | +9 more

Genome-wide association studies implicate multiple loci in risk for systemic lupus erythematosus (SLE), but few contain exonic variants, rendering systematic identification of non-coding variants essential to decoding SLE genetics. We utilized SNP-seq and bioinformatic enrichment to interrogate 2180 single-nucleotide polymorphisms (SNPs) from 87 SLE risk loci for potential binding of transcription factors and related proteins from B cells. 52 SNPs that passed initial screening were tested by electrophoretic mobility shift and luciferase reporter assays. To validate the approach, we studied rs2297550 in detail, finding that the risk allele enhanced binding to the transcription factor Ikaros (encoded by IKZF1), thereby modulating expression of IKBKE. Correspondingly, primary cells from genotyped healthy donors bearing the risk allele expressed higher levels of the interferon / NF-κB regulator IKKε. Together, these findings define a set of likely functional non-coding lupus risk variants and identify a regulatory pathway involving rs2297550, Ikaros, and IKKε implicated by human genetics in risk for SLE. Here, the authors use SNP-seq to screen 87 lupus risk loci for functional non-coding variants. Validation at one locus identified a risk variant through which enhanced Ikaros binding amplifies expression of the interferon / NFκB regulator IKKε.

Mar 11, 2024 | nature.com | Jiang Gu |Yaqian Peng |Chao Luo |Qiang Wang |Anyang Wei |Xiaolan Qi | +1 more

Erectile dysfunction (ED) is a common and difficult to treat disease, and has a high incidence rate worldwide. As a marker of vascular disease, ED usually occurs in cardiovascular disease, 2–5 years prior to cardiovascular disease events. The extracellular matrix (ECM) network plays a crucial role in maintaining cardiac homeostasis, not only by providing structural support, but also by promoting force transmission, and by transducing key signals to intracardiac cells. However, the relationship between ECM and ED remains unclear. To help fill this gap, we profiled single-cell RNA-seq (scRNA-seq) to obtain transcriptome maps of 82,554 cavernous single cells from ED and non-ED samples. Cellular composition of cavernous tissues was explored by uniform manifold approximation and projection. Pseudo-time cell trajectory combined with gene enrichment analysis were performed to unveil the molecular pathways of cell fate determination. The relationship between cavernous cells and the ECM, and the changes in related genes were elucidated. The CellChat identified ligand-receptor pairs (e.g., PTN-SDC2, PTN-NCL, and MDK-SDC2) among the major cell types in the cavernous tissue microenvironment. Differential analysis revealed that the cell type-specific transcriptomic changes in ED are related to ECM and extracellular structure organization, external encapsulating structure organization, and regulation of vasculature development. Trajectory analysis predicted the underlying target genes to modulate ECM (e.g., COL3A1, MDK, MMP2, and POSTN). Together, this study highlights potential cell–cell interactions and the main regulatory factors of ECM, and reveals that genes may represent potential marker features of ED progression.