Automating Method Naming with Context-Aware Prompt-Tuning

Method names are crucial to program comprehension and maintenance. Recently, many approaches have been proposed to automatically recommend method names and detect inconsistent names. Despite promising, their results are still sub-optimal considering the three following drawbacks: 1) These models are mostly trained from scratch, learning two different objectives simultaneously. The misalignment between two objectives will negatively affect training efficiency and model performance. 2) The enclosing class context is not fully exploited, making it difficult to learn the abstract function of the method. 3) Current method name consistency checking methods follow a generate-then-compare process, which restricts the accuracy as they highly rely on the quality of generated names and face difficulty measuring the semantic consistency. In this paper, we propose an approach named AUMENA to AUtomate MEthod NAming tasks with context-aware prompt-tuning. Unlike existing deep learning based approaches, our model first learns the contextualized representation(i.e., class attributes) of PL and NL through the pre-training model, then fully exploits the capacity and knowledge of large language model with prompt-tuning to precisely detect inconsistent method names and recommend more accurate names. To better identify semantically consistent names, we model the method name consistency checking task as a two-class classification problem, avoiding the limitation of previous similarity-based consistency checking approaches. The experimental results reflect that AUMENA scores 68.6%, 72.0%, 73.6%, 84.7% on four datasets of method name recommendation, surpassing the state-of-the-art baseline by 8.5%, 18.4%, 11.0%, 12.0%, respectively. And our approach scores 80.8% accuracy on method name consistency checking, reaching an 5.5% outperformance. All data and trained models are publicly available.Comment: Accepted by ICPC-202

A machine learning-based radiomics model for prediction of tumor mutation burden in gastric cancer

Purpose: To evaluate the potential of machine learning (ML)-based radiomics approach for predicting tumor mutation burden (TMB) in gastric cancer (GC).Methods: The contrast enhanced CT (CECT) images with corresponding clinical information of 256 GC patients were retrospectively collected. Patients were separated into training set (n = 180) and validation set (n = 76). A total of 3,390 radiomics features were extracted from three phases images of CECT. The least absolute shrinkage and selection operator (LASSO) model was used for feature screening. Seven machine learning (ML) algorithms were employed to find the optimal classifier. The predictive ability of radiomics model (RM) was evaluated with receiver operating characteristic. The correlation between RM and TMB values was evaluated using Spearman’s correlation coefficient. The explainability of RM was assessed by the Shapley Additive explanations (SHAP) method.Results: Logistic regression algorithm was chosen for model construction. The RM showed good predictive ability of TMB status with AUCs of 0.89 [95% confidence interval (CI): 0.85–0.94] and 0.86 (95% CI: 0.74–0.98) in the training and validation sets. The correlation analysis revealed a good correlation between RM and TMB levels (correlation coefficient: 0.62, p < 0.001). The RM also showed favorable and stable predictive accuracy within the cutoff value range 6–16 mut/Mb in both sets.Conclusion: The ML-based RM offered a promising image biomarker for predicting TMB status in GC patients

Effect of Dissolved Oxygen Concentration on the Microbiologically Influenced Corrosion of Q235 Carbon Steel by Halophilic Archaeon Natronorubrum tibetense

The influence of dissolved oxygen concentration (DOC) on the microbiologically influenced corrosion (MIC) of Q235 carbon steel in the culture medium of halophilic archaeon Natronorubrum tibetense was investigated. The increase of DOC from 0.0 to 3.0 ppm was found to strengthen the oxygen concentration cell by promoting cathodic reaction. Meanwhile, the increased DOC also promoted archaeal cell growth, which could consume more metallic iron as energy source and aggravated the localized corrosion. When the DOC further increased to 5.0 ppm, the uniform corrosion was dominant as the biofilms became uniformly presented on the steel surface. Combined with the stronger inhibition effect of oxygen diffusion by the increased biofilm coverage, the MIC of carbon steel in the 5.0 ppm medium was weaker than that in the 3.0 ppm medium. From weight loss and electrochemical tests, the results all demonstrated that the carbon steel in the 3.0 ppm medium had the largest corrosion rate

Wall shear stress and its role in atherosclerosis

Atherosclerosis (AS) is the major form of cardiovascular disease and the leading cause of morbidity and mortality in countries around the world. Atherosclerosis combines the interactions of systemic risk factors, haemodynamic factors, and biological factors, in which biomechanical and biochemical cues strongly regulate the process of atherosclerosis. The development of atherosclerosis is directly related to hemodynamic disorders and is the most important parameter in the biomechanics of atherosclerosis. The complex blood flow in arteries forms rich WSS vectorial features, including the newly proposed WSS topological skeleton to identify and classify the WSS fixed points and manifolds in complex vascular geometries. The onset of plaque usually occurs in the low WSS area, and the plaque development alters the local WSS topography. low WSS promotes atherosclerosis, while high WSS prevents atherosclerosis. Upon further progression of plaques, high WSS is associated with the formation of vulnerable plaque phenotype. Different types of shear stress can lead to focal differences in plaque composition and to spatial variations in the susceptibility to plaque rupture, atherosclerosis progression and thrombus formation. WSS can potentially gain insight into the initial lesions of AS and the vulnerable phenotype that gradually develops over time. The characteristics of WSS are studied through computational fluid dynamics (CFD) modeling. With the continuous improvement of computer performance-cost ratio, WSS as one of the effective parameters for early diagnosis of atherosclerosis has become a reality and will be worth actively promoting in clinical practice. The research on the pathogenesis of atherosclerosis based on WSS is gradually an academic consensus. This article will comprehensively review the systemic risk factors, hemodynamics and biological factors involved in the formation of atherosclerosis, and combine the application of CFD in hemodynamics, focusing on the mechanism of WSS and the complex interactions between WSS and plaque biological factors. It is expected to lay a foundation for revealing the pathophysiological mechanisms related to abnormal WSS in the progression and transformation of human atherosclerotic plaques

Multiple Independent Loci at Chromosome 15q25.1 Affect Smoking Quantity: a Meta-Analysis and Comparison with Lung Cancer and COPD

Recently, genetic association findings for nicotine dependence, smoking behavior, and smoking-related diseases converged to implicate the chromosome 15q25.1 region, which includes the CHRNA5-CHRNA3-CHRNB4 cholinergic nicotinic receptor subunit genes. In particular, association with the nonsynonymous CHRNA5 SNP rs16969968 and correlates has been replicated in several independent studies. Extensive genotyping of this region has suggested additional statistically distinct signals for nicotine dependence, tagged by rs578776 and rs588765. One goal of the Consortium for the Genetic Analysis of Smoking Phenotypes (CGASP) is to elucidate the associations among these markers and dichotomous smoking quantity (heavy versus light smoking), lung cancer, and chronic obstructive pulmonary disease (COPD). We performed a meta-analysis across 34 datasets of European-ancestry subjects, including 38,617 smokers who were assessed for cigarettes-per-day, 7,700 lung cancer cases and 5,914 lung-cancer-free controls (all smokers), and 2,614 COPD cases and 3,568 COPD-free controls (all smokers). We demonstrate statistically independent associations of rs16969968 and rs588765 with smoking (mutually adjusted p-values<10$^{−35}$ and <10$^{−8}$ respectively). Because the risk alleles at these loci are negatively correlated, their association with smoking is stronger in the joint model than when each SNP is analyzed alone. Rs578776 also demonstrates association with smoking after adjustment for rs16969968 (p<10$^{−6}$). In models adjusting for cigarettes-per-day, we confirm the association between rs16969968 and lung cancer (p<10$^{−20}$) and observe a nominally significant association with COPD (p = 0.01); the other loci are not significantly associated with either lung cancer or COPD after adjusting for rs16969968. This study provides strong evidence that multiple statistically distinct loci in this region affect smoking behavior. This study is also the first report of association between rs588765 (and correlates) and smoking that achieves genome-wide significance; these SNPs have previously been associated with mRNA levels of CHRNA5 in brain and lung tissue

Multiple Independent Loci at Chromosome 15q25.1 Affect Smoking Quantity: a Meta-Analysis and Comparison with Lung Cancer and COPD

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