Addition of Risk-enhancing Factors Improves Risk Assessment of Atherosclerotic Cardiovascular Disease in Middle-aged and Older Chinese Adults: Findings from the Chinese Multi-provincial Cohort Study

Objective: This study aimed to examine whether integrating risk-enhancing factors into the Chinese Society of Cardiology-recommended clinical risk assessment tool (i.e., the CSC model) for atherosclerotic cardiovascular disease (ASCVD) might improve 10-year ASCVD risk stratification in Chinese adults. Methods: A total of 4910 Chinese participants who were 50–79 years of age and free of cardiovascular disease in the 2007–2008 Survey from the Chinese Multi-provincial Cohort Study were included. We assessed the updated model’s clinical utility (i.e., Harrell’s C-index and net reclassification improvement [NRI]) by adding risk-enhancing factors individually or the number of risk-enhancing factors to the CSC model, for all individuals or those at intermediate risk. Risk-enhancing factors, including a family history of CVD, triglycerides ≥2.3 mmol/L, high-sensitivity C-reactive protein ≥2 mg/L, Lipoprotein (a) ≥50 mg/dL, non-high-density lipoprotein cholesterol ≥4.9 mmol/L, overweight/obesity, and central obesity, were evaluated. ASCVD events were defined as a composite endpoint comprising ischemic stroke and acute coronary heart disease events (including nonfatal acute myocardial infarction and all coronary deaths). Results: During a median 10-year follow-up, 449 (9.1%) ASCVD events were recorded. Addition of ≥2 risk-enhancing factors to the CSC model yielded a significant improvement in the C-index (1.0%, 95% confidence interval [CI]: 0.2–1.7%) and a modest improvement in the NRI (2.0%, 95% CI: −1.2–5.4%) in the total population. For intermediate-risk individuals, particularly individuals at high risk of developing ASCVD, significant improvements in NRI were observed after adding ≥2 risk-enhancing factors (17.4%, 95% CI: 5.6–28.5%) to the CSC model. Conclusions: Addition of ≥2 risk-enhancing factors refined 10-year ASCVD risk stratification, particularly for intermediate-risk individuals, supporting their potential in helping tailor targeted interventions in clinical practice

Oral microbiome and risk of malignant esophageal lesions in a high-risk area of China: A nested case-control study.

OBJECTIVE: We aimed to prospectively evaluate the association of oral microbiome with malignant esophageal lesions and its predictive potential as a biomarker of risk. METHODS: We conducted a case-control study nested within a population-based cohort with up to 8 visits of oral swab collection for each subject over an 11-year period in a high-risk area for esophageal cancer in China. The oral microbiome was evaluated with 16S ribosomal RNA (rRNA) gene sequencing in 428 pre-diagnostic oral specimens from 84 cases with esophageal lesions of severe squamous dysplasia and above (SDA) and 168 matched healthy controls. DESeq analysis was performed to identify taxa of differential abundance. Differential oral species together with subject characteristics were evaluated for their potential in predicting SDA risk by constructing conditional logistic regression models. RESULTS: A total of 125 taxa including 37 named species showed significantly different abundance between SDA cases and controls (all P0.84. CONCLUSIONS: The oral microbiome may play an etiological and predictive role in esophageal cancer, and it holds promise as a non-invasive early warning biomarker for risk stratification for esophageal cancer screening programs

Action-taking gods: animal spirit shamanism in Liaoning, China

This thesis explores animal spirit shamanism (chuma xian) as it occurs in Liaoning, China. Aspects of this form of shamanism to be discussed and analysed include its origins, development, and practices; its relationships with Manchu shamanism and the Han Chinese cult of the fox; its medical implications and its involvement with Buddhism, Daoism, and other local cults. The history and characteristics of the chuma xian practice are closely tied to questions of power, and reflect Foucault's theory of power pluralism. This thesis argues that chuma xian practice is a particular product of local history and ethnography; it is also a means for expressing and exercising local religious beliefs of the people in Liaoning, especially within under-privileged groups (socio-economic status, etc.) within society.Ce mémoire explore le shamanisme de l'esprit animal (chuma xian) tel qu'il existe en tant que phénomène dans la province de Liaoning dans la République Populaire de Chine. Les aspects de cette forme de shamanisme qui sont discutés et analysés incluent ses origines, son développement et ses pratiques spécifiques, ses liens avec le shamanisme Manchu et le culte Han du renard ainsi que ses implications avec le Bouddhisme, le Daoisme et d'autres cultes mineurs. L'histoire et les caractéristiques de la pratique chuma xian sont étroitement reliés aux questions de pouvoir et reflètent certains aspects de la théorie du pluralisme du pouvoir de Michel Foucault. Cette thèse soutient que la pratique chuma xian est le produit de particularités historiques et ethnographiques locales et qu'elle est un moyen, pour la population de Liaoning et spécialement au sein de groupes socio économiquement défavorisés, d'exercer des croyances religieuses

A Code Reviewer Recommendation Approach Based on Attentive Neighbor Embedding Propagation

Code review as an effective software quality assurance practice has been widely applied in many open-source software communities. However, finding a suitable reviewer for certain codes can be very challenging in open-source communities due to the difficulty of learning the characteristics of reviewers and the code-reviewer interaction sparsity in open-source software communities. To tackle this problem, most previous approaches focus on learning developers’ capabilities and experiences and recommending suitable developers based on their historical interactions. However, such approaches usually suffer from data-sparsity and noise problems, which may reduce the recommendation accuracy. In this paper, we propose an attentive neighbor embedding propagation enhanced code reviewer recommendation framework (termed ANEP). In ANEP, we first construct the reviewer–code interaction graph and learn the semantic representations of the reviewer and code based on the transformer model. Then, we explicitly explore the attentive high-order embedding propagation of reviewers and code and refine the representations along their neighbors. Finally, to evaluate the effectiveness of ANEP, we conduct extensive experiments on four real-world datasets. The experimental results show that ANEP outperforms other state-of-the-art approaches significantly

HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems.

Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application

Identification and Localisation Algorithm for Sugarcane Stem Nodes by Combining YOLOv3 and Traditional Methods of Computer Vision

Sugarcane stem node identification is the core technology required for the intelligence and mechanization of the sugarcane industry. However, detecting stem nodes quickly and accurately is still a significant challenge. In this paper, in order to solve this problem, a new algorithm combining YOLOv3 and traditional methods of computer vision is proposed, which can improve the identification rate during automated cutting. First, the input image is preprocessed, during which affine transformation is used to correct the posture of the sugarcane and a rotation matrix is established to obtain the region of interest of the sugarcane. Then, a dataset is built to train the YOLOv3 network model and the position of the stem nodes is initially determined using the YOLOv3 model. Finally, the position of the stem nodes is further located accurately. In this step, a new gradient operator is proposed to extract the edge of the image after YOLOv3 recognition. Then, a local threshold determination method is proposed, which is used to binarize the image after edge extraction. Finally, a localization algorithm for stem nodes is designed to accurately determine the number and location of the stem nodes. The experimental results show that the precision rate, recall rate, and harmonic mean of the stem node recognition algorithm in this paper are 99.68%, 100%, and 99.84%, respectively. Compared to the YOLOv3 network, the precision rate and the harmonic mean are improved by 2.28% and 1.13%, respectively. Compared to other methods introduced in this paper, this algorithm has the highest recognition rate