Attitudes and Behavioral Response Toward Key Tobacco Control Measures from the FCTC among Chinese Urban Residents

BACKGROUND. The Chinese National People's Congress ratified the WHO Framework Convention on Tobacco Control (FCTC) on 27 August 2005, signaling China's commitment to implement tobacco control policies and legislation consistent with the treaty. This study was designed to examine attitudes towards four WHO FCTC measures among Chinese urban residents. METHODS. In a cross-sectional design study, survey data were collected from two Chinese urban cities involving a sample of 3,003 residents aged 15 years or older. Through a face-to-face interview, respondents were asked about attitudes toward four tobacco control measures developed by the WHO FCTC. Data on the four dependent measures were analyzed using multivariate logistic regression analyses. Using descriptive statistics, potential change in smoking behavior that smokers might make in response to increasing cigarette prices is also reported. RESULTS. 81.8% of the respondents in the study sample supported banning smoking in public places, 68.8% favored increasing the cigarette tax, 85.1% supported health warnings on cigarette packages, and 85.7% favored banning tobacco advertising. The likelihood to support these measures was associated with gender, educational level, and personal income. Smokers were less likely to support these measures than non-smokers, with decreased support expressed by daily smokers compared to occasional smokers, and heavy smokers compared to light smokers. The proportion of switching to cheaper cigarette brands, decreasing smoking, and quitting smoking altogether with increased cigarette prices were 29.1%, 30.90% and 40.0% for occasional smokers, respectively; and 30.8%, 32.7% and 36.5% for daily smokers, respectively. CONCLUSION. Results from this study indicate strong public support in key WHO FCTC measures and that increases in cigarette price may reduce tobacco consumption among Chinese urban residents. Findings from this study have implications with respect to policymaking and legislation for tobacco control in China

High Expression of DNMT1 was Correlated with beta-catenin Accumulation and Malignant Phynotype of Lung Squamous Cell Carcinoma and Adenocarcinoma

Background and objective DNA methyltransferase 1 (DNMT1) is one of the important molecules regulating DNA methylation. The abnormal expression of DNMT1 was associated with the methylation and inactivation of tumor suppressor gene and tumorigenesis. The aim of this study is to clarify the difference of DNMT1 expression between lung cancer tissues and corresponding normal lung tissues, to analyze the relationships between DNMT1 expression and clinicopathologic characteristics of lung squamous cell carcinoma and adenocarcinoma, and to investigate the correlation between the expressions of DNMT1 and β-catenin. Methods The expressions of DNMT1 and β-catenin were examined in 84 lung squamous cell carcinoma and adenocarcinoma tissues and corresponding normal lung tissues using tissue microarray and immunohistochemistry. Results The average positive rate of DNMT1 was significantly higher in 84 lung cancer tissues [(58.04±35.07)%] than that in corresponding normal lung tissues [(6.88±10.26)%](t=12.835, P < 0.001). The high expression of DNMT1 was positively correlated with adenocarcinoma histological type (r=0.365, P=0.001), poor differentiation (r=0.253, P=0.021) and lymph node metastasis (r=0.246, P=0.024) in lung cancer. The expression of DNMT1 was significantly correlated with the cytoplasmic expression of β-catenin (r=0.571, P < 0.001). Conclusion The high expression of DNMT1 was a common phenomenon in lung squamous cell carcinoma and adenocarcinoma. The high expression of DNMT1 was correlated with the malignant phynotype of lung cancer. DNMT1 may express coordinately with β-catenin in lung cancer

Supervised Knowledge May Hurt Novel Class Discovery Performance

Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset by leveraging prior knowledge of a labeled set comprising disjoint but related classes. Given that most existing literature focuses primarily on utilizing supervised knowledge from a labeled set at the methodology level, this paper considers the question: Is supervised knowledge always helpful at different levels of semantic relevance? To proceed, we first establish a novel metric, so-called transfer flow, to measure the semantic similarity between labeled/unlabeled datasets. To show the validity of the proposed metric, we build up a large-scale benchmark with various degrees of semantic similarities between labeled/unlabeled datasets on ImageNet by leveraging its hierarchical class structure. The results based on the proposed benchmark show that the proposed transfer flow is in line with the hierarchical class structure; and that NCD performance is consistent with the semantic similarities (measured by the proposed metric). Next, by using the proposed transfer flow, we conduct various empirical experiments with different levels of semantic similarity, yielding that supervised knowledge may hurt NCD performance. Specifically, using supervised information from a low-similarity labeled set may lead to a suboptimal result as compared to using pure self-supervised knowledge. These results reveal the inadequacy of the existing NCD literature which usually assumes that supervised knowledge is beneficial. Finally, we develop a pseudo-version of the transfer flow as a practical reference to decide if supervised knowledge should be used in NCD. Its effectiveness is supported by our empirical studies, which show that the pseudo transfer flow (with or without supervised knowledge) is consistent with the corresponding accuracy based on various datasets. Code is released at https://github.com/J-L-O/SK-Hurt-NCDComment: TMLR 2023 accepted paper. arXiv admin note: substantial text overlap with arXiv:2209.0912

Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumes

Atrial Fibrillation (AF) is characterized by rapid, irregular heartbeats, and can lead to fatal complications such as heart failure. The disease is divided into two sub-types based on severity, which can be automatically classified through CT volumes for disease screening of severe cases. However, existing classification approaches rely on generic radiomic features that may not be optimal for the task, whilst deep learning methods tend to over-fit to the high-dimensional volume inputs. In this work, we propose a novel radiomics-informed deep-learning method, RIDL, that combines the advantages of deep learning and radiomic approaches to improve AF sub-type classification. Unlike existing hybrid techniques that mostly rely on na\"ive feature concatenation, we observe that radiomic feature selection methods can serve as an information prior, and propose supplementing low-level deep neural network (DNN) features with locally computed radiomic features. This reduces DNN over-fitting and allows local variations between radiomic features to be better captured. Furthermore, we ensure complementary information is learned by deep and radiomic features by designing a novel feature de-correlation loss. Combined, our method addresses the limitations of deep learning and radiomic approaches and outperforms state-of-the-art radiomic, deep learning, and hybrid approaches, achieving 86.9% AUC for the AF sub-type classification task. Code is available at https://github.com/xmed-lab/RIDL.Comment: Accepted by MICCAI2

Correction: A novel enantioselective synthesis of 6H-dibenzopyran derivatives by combined palladium/norbornene and cinchona alkaloid catalysis

Correction for 'A novel enantioselective synthesis of 6H-dibenzopyran derivatives by combined palladium/norbornene and cinchona alkaloid catalysis' by Di Xu et al., Org. Biomol. Chem., 2015, DOI: 10.1039/c4ob02551b

Strongly-coupled nanotube electromechanical resonators

Coupling an electromechanical resonator with carbon-nanotube quantum dots is a significant method to control both the electronic charge and the spin quantum states. By exploiting a novel micro-transfer technique, we fabricate two strongly-coupled and electrically-tunable mechanical resonators on a single carbon nanotube for the first time. The frequency of the two resonators can be individually tuned by the bottom gates, and strong coupling is observed between the electron charge and phonon modes of each resonator. Furthermore, the conductance of either resonator can be nonlocally modulated by the phonon modes in the other resonator. Strong coupling is observed between the phonon modes of the two resonators, which provides an effective long distance electron-electron interaction. The generation of phonon-mediated-spin entanglement is also theoretically analyzed for the two resonators. This strongly-coupled nanotube electromechanical resonator array provides an experimental platform for future studies of the coherent electron-phonon interaction, the phonon mediated long-distance electron interaction, and entanglement state generation