arXiv subjects

Lili Xia

Publications and source records attributed to Lili Xia.

3 recordsLinked to original sources

arXiv2026

Impacts of Nuclear War on Human Health from Changed Surface Ultraviolet Radiation

Climate model simulations indicate that surface ultraviolet (UV) radiation would change after soot injection into the stratosphere during a nuclear war, due to the competing effects of ozone depletion and aerosol attenuation. Using climate model simulations, we evaluate UV impacts under two scenarios: a regional India-Pakistan conflict producing 5 Tg of soot and a global U.S.-Russia war producing 150 Tg. UV enhancements due to ozone depletion substantially shorten safe outdoor exposure time, particularly for individuals with lighter skin types. Applying UV dose-response relationships to the year 2000 population data, without accounting for direct conflict mortality or famine-related population loss, the 5 Tg scenarios result in approximately 5,300-9,800 additional skin cancer deaths within 10-15 years and up to 75,000 cumulative excess deaths over the following century. In contrast, strong aerosol attenuation under the 150 Tg scenario initially suppresses surface UV, resulting in about 8,500 fewer skin cancer deaths within 15 years and a maximum cumulative reduction of approximately 17,000 deaths over the following century. These findings demonstrate that nuclear war-induced changes in surface UV radiation represent a persistent but previously understudied health impact of nuclear war. While skin cancer would not dominate overall mortality following a nuclear war, enhanced cumulative surface UV radiation represents an additional, long-lasting threat to human health that compounds other global impacts such as climate disruption and food insecurity. The excess UV may also pose negative impacts on animals and plants, including those used for agriculture, which remain to be quantified.

physics.ao-ph↗

arXiv2026

Impacts of Sulfate Injection Geoengineering on Particulate Matter with Diameter less than 2.5 {\mu}m

Particulate matter with aerodynamic diameter less than 2.5 {\mu}m (PM2.5) is of great concern for human health. Here, for the first time, we examine the impact of sulfate aerosol geoengineering on PM2.5, using the output from the Geoengineering Large Ensemble (GLENS) project. GLENS is an ensemble of climate model simulations injecting SO2 into the stratosphere to balance RCP8.5 forcing using the Community Earth System Model, version 1. GLENS geoengineering reduces global averaged surface PM2.5 mass concentrations compared with RCP8.5 and also changes PM2.5 composition with more percentages of organic carbon and sulfate. The total reduction of PM2.5 is a result of less dust and sea salt concentrations. Dust emission is declined under GLENS geoengineering because of increased soil moisture and leaf area index over desert regions, and less emission of sea salt is due to slower wind speeds when geoengineering is applied. Excluding dust and sea salt, there is more global averaged PM2.5 under GLENS geoengineering relative to RCP8.5, predominantly due to more aerosol phase secondary organic aerosol (SOA). Since gas precursors of SOA are prescribed in the simulations, a cooler environment with geoengineering tends to transfer more gas phase SOA to aerosol phase. Changes in PM2.5 concentration and composition with applied geoengineering may have potential human health impact. Another new finding of this study is that the large amount of injected SO2 does not increase surface sulfate aerosol as a component of PM2.5, as the majority of sulfate aerosol reaching the boundary layer is in the coarse mode, which represents a very small fraction of the PM2.5. But the difference of deposition spatial distribution between geoengineering and RCP8.5 may have potential impacts on ecosystem.

physics.ao-ph↗

arXiv2023

Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training

Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on the amount and quality of the annotated data. However, it is time-consuming and expensive for professional pathologists to provide accurate pixel-level ground truth, while it is much easier to get coarse labels such as point annotations. In this paper, we propose a weakly-supervised learning method for nuclei segmentation that only requires point annotations for training. First, coarse pixel-level labels are derived from the point annotations based on the Voronoi diagram and the k-means clustering method to avoid overfitting. Second, a co-training strategy with an exponential moving average method is designed to refine the incomplete supervision of the coarse labels. Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm. We comprehensively evaluate the proposed method using two public datasets. Both visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods, and its competitive performance compared to the fully-supervised methods. Code: https://github.com/hust-linyi/SC-Net

eess.IV↗