ABSTRACT
The accurate determination of the free nicotine content in cigarette smoke is crucial for assessing cigarette quality, studying harm and addiction, and reducing tar levels. Currently, the determination of free nicotine in tobacco products primarily relies on methods such as pH calculation, nuclear magnetic resonance (NMR) spectroscopy, headspace solid-phase microextraction (HS-SPME), and traditional solvent extraction. However, these methods have limitations that restrict their widespread application. In this study, the free nicotine in cigarette smoke was directly extracted by using cyclohexane according to the traditional solvent extraction method and detected via gas chromatography-mass spectrometry. Compared with the traditional two-phase solvent extraction, our experimental method is easy to execute and eliminates the influence of aqueous solutions on the original distribution of nicotine in cigarette smoke particulate matter. Furthermore, the presence of protonated nicotine in tobacco does not affect the determination. Compared with HS-SPME and NMR spectroscopy, our approach, which involves solvent extraction followed by chromatographic separation and instrumental detection, offers simplicity, improved precision, better detection limits, and reduced interference during the instrumental detection stage. The standard addition recoveries in the conducted experiment ranged from 96.2% to 102.5%. The limit of detection was 2.8 µg/cig, and the correlation coefficient (R2) for the quadratic regression of the standard curve exceeded 0.999. The relative standard deviation for parallel samples was between 1.7% and 3.4% (n = 5), fully meeting the requirements for the determination of free nicotine in cigarette smoke. Analysis of cigarette samples from 38 commercially available brands revealed that the content of free nicotine ranged from 0.376 to 0.716 mg/cig, with an average of 0.540 mg/cig, and free nicotine accounted for 39.1%-88.8% of the total nicotine content.
ABSTRACT
Deep Gradient Leakage (DGL) is a highly effective attack that recovers private training images from gradient vectors. This attack casts significant privacy challenges on distributed learning from clients with sensitive data, where clients are required to share gradients. Defending against such attacks requires but lacks an understanding of when and how privacy leakage happens, mostly because of the black-box nature of deep networks. In this paper, we propose a novel Inversion Influence Function (I2F) that establishes a closed-form connection between the recovered images and the private gradients by implicitly solving the DGL problem. Compared to directly solving DGL, I2F is scalable for analyzing deep networks, requiring only oracle access to gradients and Jacobian-vector products. We empirically demonstrate that I2F effectively approximated the DGL generally on different model architectures, datasets, modalities, attack implementations, and perturbation-based defenses. With this novel tool, we provide insights into effective gradient perturbation directions, the unfairness of privacy protection, and privacy-preferred model initialization. Our codes are provided in https://github.com/illidanlab/inversion-influence-function.
ABSTRACT
Woven coronary artery disease is a relatively rare congenital anomaly with unexplained etiology. Herein we presented the first unusual woven case involving all coronary arteries with chronic total occlusion lesion of the left anterior descending artery; describe intracoronary imagingâguided percutaneous coronary intervention for woven coronary artery disease with chronic total occlusion lesion of the left anterior descending artery; and discuss the complexities involved in intervening with such lesions.
