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Generative AI impacts on intra-urban inequality and skill premium in Beijing

arXiv:2605.25505v1 Announce Type: cross Abstract: Generative artificial intelligence (GenAI) is the first automation wave to reach high-cognitive tasks at scale, yet its effects on intra-urban inequality remain largely unknown. Using 5 million job postings from Beijing (2018--2024), we construct a neighborhood-level GenAI Exposure Index by aggregating task-level assessments from five leading large language models. We examine the spatial, structural and causal mechanisms of this shock. We find that GenAI exposure is highly concentrated in the city's core districts, deepening the intra-urban AI divide. Since 2023, high-exposure neighborhoods have experienced wage stagnation even as they continue to attract high-skilled workers -- a "high-skill trap." This wage penalty is driven by task de-skilling and intensified labor-market crowding. A difference-in-differences design centered on ChatGPT's release supports a causal interpretation. These findings challenge the prevailing theory of skill-biased technological change and provide a basis for inclusive AI governance in global technology hubs.
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Equip Pre-ranking with Target Attention by Residual Quantization

arXiv:2509.16931v3 Announce Type: replace-cross Abstract: The pre-ranking stage in industrial recommendation systems faces a fundamental conflict between efficiency and effectiveness. While powerful models like Target Attention (TA) excel at capturing complex feature interactions in the ranking stage, their high computational cost makes them infeasible for pre-ranking, which often relies on simplistic vector-product models. This disparity creates a significant performance bottleneck for the entire system. To bridge this gap, we propose TARQ, a novel pre-ranking framework. Inspired by generative models, TARQ's key innovation is to equip pre-ranking with an architecture approximate to TA by Residual Quantization. This allows us to bring the modeling power of TA into the latency-critical pre-ranking stage for the first time, establishing a new state-of-the-art trade-off between accuracy and efficiency. Extensive offline experiments and large-scale online A/B tests at Taobao demonstrate TARQ's significant improvements in ranking performance. Consequently, our model has been fully deployed in production, serving tens of millions of daily active users and yielding substantial business improvements. The code and data are available at https://github.com/zyody/tarq_sigir2026.
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Multi-omics analysis of glutamine and fish collagen peptides in alleviating post-antibiotic Streptococcus pneumoniae injury in feline lung cells

Exp Ther Med. 2026 Mar 30;31(6):148. doi: 10.3892/etm.2026.13143. eCollection 2026 Jun.

ABSTRACT

Streptococcus pneumoniae (SP) infection often leads to persistent lung injury even after antibiotic treatment. Despite this phenomenon, the mechanisms underlying host cell recovery remain poorly understood. Upon breaching the epithelial barrier, SP primarily targets the pulmonary interstitial cells, which constitute the major mesenchymal component of the lung. These cells serve as essential effectors of tissue repair, extracellular matrix remodeling and epithelial restoration. Therefore, a feline pulmonary interstitial cell (FCA-L2) model of SP infection was established to investigate the protective effects of glutamine (GLU) and fish collagen peptides (FCP) through integrated transcriptomic and metabolomic analyses. Cells were infected with SP (0.05 McFarland units for 4 h) and then treated with doxycycline (7.5 Β΅g/ml for 18 h) followed by GLU (40 mM) or FCP (500 Β΅g/ml). Notably, SP infection increased lactate dehydrogenase (LDH) release by 3.5-fold, induced secretion of IL-1Ξ², TNF-Ξ± and IL-8, disrupted tight-junction proteins (claudin, ZO-1 and occludin) and caused oxidative imbalance and apoptosis despite antibiotic (doxycycline) treatment. However, treatment with GLU or FCP significantly reduced LDH release by ~40%, restored junctional proteins, suppressed inflammatory cytokines and enhanced antioxidant enzyme activities. Multi-omics analysis revealed that GLU promoted amino acid biosynthesis and energy metabolism and suppressed aminoacyl-tRNA synthetases and cell-cycle regulators, thereby enhancing metabolic adaptability. By contrast, FCP activated amino and nucleotide sugar metabolism, increased polyunsaturated fatty-acid synthesis and supported glycocalyx repair and membrane reconstruction. GLU and FCP provided complementary metabolic and structural protection, which mitigated post-infectious stress and promoted cellular recovery. The findings of the present study underscore the potential of bioactive food-derived compounds as adjunctive therapies that may accelerate lung tissue repair and enhance the efficacy of conventional antibiotics.

PMID:41988354 | PMC:PMC13077270 | DOI:10.3892/etm.2026.13143

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EBV strain interacts with host HLA to drive nasopharyngeal carcinoma risk

Nature, Published online: 15 April 2026; doi:10.1038/s41586-026-10416-8

A genome-to-genome association study identifies host and viral risk factors that interact to drive nasopharyngeal carcinoma endemicity in southern China.
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