Mannose as a biomarker of coronary artery disease: Angiographic evidence and clinical significance

Background High mannose has previously associated with insulin resistance and cardiovascular disease (CVD). Our objective is to establish whether mannose is associated with anatomical evidence of coronary artery disease (CAD). Methods Plasma mannose concentrations were measured by liquid chromatography/tandem mass spectrometry in a discovery cohort (n = 513) and a validation cohort (n = 221) of carefully phenotyped individuals. In both cohorts CAD was quantitated using state-of-the-art imaging techniques (coronary computed coronary tomography angiography (CCTA), invasive coronary angiography and optical coherence tomography). Information on subsequent CVD events/death was collected. Associations of mannose with angiographic variables and biomarkers were tested using univariate and multivariate regression models. Survival analysis was performed using the Kaplan-Meier estimator. Results Mannose was related to indices of CAD and features of plaque vulnerability. In the discovery cohort, mannose was a marker of quantity and quality of CCTA-proven CAD and subjects with a mannose level in the top quartile had a significantly higher risk of CVD events/death (p = 3.6e-5). In the validation cohort, mannose was significantly associated with fibrous cap thickness < 65 \u3bcm (odds ratio = 1.32 per each 10 \u3bcmol/L mannose change [95% confidence interval, 1.05\u20131.65]) and was an independent predictor of death (hazard ratio for mannose 65vs < 84.6 \u3bcmol/L: 4.0(95%CI, 1.4\u201311.3), p = 0.006)

Structure Refinement for Vulnerability Estimation Models using Genetic Algorithm Based Model Generators

In this paper, a method for model structure refinement is proposed and applied in estimation of cumulative number of vulnerabilities according to time. Security as a quality characteristic is presented and defined. Vulnerabilities are defined and their importance is assessed. Existing models used for number of vulnerabilities estimation are enumerated, inspecting their structure. The principles of genetic model generators are inspected. Model structure refinement is defined in comparison with model refinement and a method for model structure refinement is proposed. A case study shows how the method is applied and the obtained results.model structure refinement, model generators, gene expression programming, software vulnerabilities, performance criteria, software metrics

Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs

Binary code analysis allows analyzing binary code without having access to the corresponding source code. A binary, after disassembly, is expressed in an assembly language. This inspires us to approach binary analysis by leveraging ideas and techniques from Natural Language Processing (NLP), a rich area focused on processing text of various natural languages. We notice that binary code analysis and NLP share a lot of analogical topics, such as semantics extraction, summarization, and classification. This work utilizes these ideas to address two important code similarity comparison problems. (I) Given a pair of basic blocks for different instruction set architectures (ISAs), determining whether their semantics is similar or not; and (II) given a piece of code of interest, determining if it is contained in another piece of assembly code for a different ISA. The solutions to these two problems have many applications, such as cross-architecture vulnerability discovery and code plagiarism detection. We implement a prototype system INNEREYE and perform a comprehensive evaluation. A comparison between our approach and existing approaches to Problem I shows that our system outperforms them in terms of accuracy, efficiency and scalability. And the case studies utilizing the system demonstrate that our solution to Problem II is effective. Moreover, this research showcases how to apply ideas and techniques from NLP to large-scale binary code analysis.Comment: Accepted by Network and Distributed Systems Security (NDSS) Symposium 201

Interaction between the MTHFR C677T polymorphism and traumatic childhood events predicts depression

Childhood trauma is associated with the onset and recurrence of major depressive disorder (MDD). The thermolabile T variant of the methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism (rs1801133) is associated with a limited (oxidative) stress defense. Therefore, C677T MTHFR could be a potential predictor for depressive symptomatology and MDD recurrence in the context of traumatic stress during early life. We investigated the interaction between the C677T MTHFR variant and exposure to traumatic childhood events (TCEs) on MDD recurrence during a 5.5-year follow-up in a discovery sample of 124 patients with recurrent MDD and, in an independent replication sample, on depressive syniptomatology in 665 healthy individuals from the general population. In the discovery sample, Cox regression analysis revealed a significant interaction between MTHFR genotype and TCEs on MOD recurrence (P = 0.017). Over the 5.5-year follow-up period, median time to recurrence was 191 days for T-allele carrying patients who experienced TCEs (T + and TCE +); 461 days for T - and TCE + patients; 773 days for T + and TCE - patients and 866 days for T - and TCE - patients. In the replication sample, a significant interaction was present between the MTHFR genotype and TCEs on depressive symptomatology (P = 0.002). Our results show that the effects of TCEs on the prospectively assessed recurrence of MOD and self-reported depressive symptoms in the general population depend on the MTHFR genotype. In conclusion, T-allele carriers may be at an increased risk for depressive symptoms or MOD recurrence after exposure to childhood trauma