Parkinson's disease (PD) is a progressive neurodegenerative disorder commonly accompanied by cognitive decline, yet the mechanisms linking disrupted brain homeostasis to progressive cognitive impairment remain unclear. Emerging evidence suggests that the choroid plexus (ChP) and the locus coeruleus (LC) are involved in cerebrospinal fluid dynamics and norepinephrine regulation, respectively, but their longitudinal alterations and interrelationships in PD have not been systematically examined. We conducted a two-year longitudinal study including 90 PD patients and 51 healthy controls (HCs) undergoing multimodal MRI. ChP volume (ChP-V) was derived from T1-weighted structural imaging, ChP blood flow (ChP-BF) was assessed using pseudo-continuous arterial spin labeling, and LC integrity was ascertained with the contrast-to-noise ratio of the LC (LC-CNR) in neuromelanin-sensitive MRI. Group differences, longitudinal alterations, and associations with neuropsychological performance were examined. Over two years, PD patients showed progressive increases in ChP-V (F = 12.45, p < 0.001), reductions in ChP-BF (F = 18.98, p < 0.001), and declines in LC-CNR (F = 16.80, p < 0.001). Baseline LC-CNR was already reduced in PD compared with HCs (t = 3.023, p = 0.003). The longitudinal changes were more pronounced in male patients. ChP-BF was positively correlated with LC-CNR at baseline (r = 0.293, p = 0.006). Moreover, reductions in ChP-BF and LC-CNR were associated with worsening cognitive performance. While LC dysfunction was evident early in the disease course, progressive ChP alterations, particularly in ChP perfusion, provided additional information on longitudinal disease progression, supporting their combined value and highlighting the importance of gender-specific longitudinal monitoring.
H. Li, J. Jia, J. Wang et al.· medRxiv· 0 citations
Carbon footprint accounting (CFA) is critical for decarbonization efforts but remains constrained by static databases, fragmented data sources, and labor-intensive expert workflows. Conventional life cycle assessment (LCA) methods struggle to adapt to dynamic production changes, policy updates, and enterprise-specific data privacy requirements. While large language models (LLMs) offer promising automation capabilities, no practical frameworks currently exist for fully automated CFA; directly applying LLMs introduces limitations such as weak factual grounding, poor responsiveness, high inference costs, and insufficient handling of confidential data. To address these gaps, this paper proposes LLMs-RAG-CFA, a unified framework that combines large language models with retrieval-augmented generation (RAG) to deliver real-time, reliable, cost-efficient, and privacy-preserving CFA. The system incorporates semantic segmentation, top-k domain-specific fragment retrieval, uncertainty-aware and input-length-aware prompt construction strategies to optimize real-time professional coverage, reduce uncertainty and reduce token consumption. Interval-based uncertainty metrics are designed to quantify retrievaland accounting-stage uncertainty, supporting more interpretable and trustworthy carbon assessments. Extensive experiments across five carbon-intensive industries (primary aluminum, lithium batteries, photovoltaics, new energy vehicles, and transformers) demonstrate that LLMs-RAG-CFA consistently outperforms baseline CFA workflows by achieving higher retrieval completeness, lower information deviation, and lower accounting deviation. A complete set of analysis covering real-time adaptability, cost trade-offs, and privacy handling further supports its practical viability. This framework offers a scalable, practical pathway for real-time carbon emission monitoring and supports improved sustainability practices.
H. Wang, M. Zhang, Z. Chen et al.· Journal of Environmental Inf...· 0 citations