Fuzzy-Granular Based Data Mining for Effective Decision Support in Biomedical Applications

Due to complexity of biomedical problems, adaptive and intelligent knowledge discovery and data mining systems are highly needed to help humans to understand the inherent mechanism of diseases. For biomedical classification problems, typically it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). In this dissertation, a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, is proposed to build such a DSS for binary classification problems in the biomedical domain. Empirical studies show that FARM-DS is competitive to state-of-the-art classifiers in terms of prediction accuracy. More importantly, FARs can provide strong decision support on disease diagnoses due to their easy interpretability. This dissertation also proposes a fuzzy-granular method to select informative and discriminative genes from huge microarray gene expression data. With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. Empirical studies show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies

S-adenosyl-L-methionine improves ventricular remodeling after myocardial infarction by regulating angiogenesis and fibrosis

Purpose: To investigate the effect of S-adenosyl-L-methionine (SAM) on angiogenesis and fibrosis in the heart of rats with myocardial infarction (MI), and to determine the mechanism of action.Methods: Sprague Dawley rats with MI received SAM treatment (15 mg/kg) intraperitoneally. The cumulative survival (%) of rats was recorded to determine their rate of survival. Hematoxylin-eosin staining, echocardiography, and hemodynamics were also performed. In addition, the effects of SAM vascular regeneration in the rats were analyzed by determining the expression of vascular endothelial growth factor (VEGF), basic fibroblast growth factor (bFGF) and hypoxia-inducible factor 1-α (HIF1-α) in rats.Results: The 8-week survival rate of the MI group was significantly lower than that of the sham group, while SAM significantly improved the survival rate of the rats. In addition, SAM improved the contractile and diastolic heart function in the rats and also increased the ventricular pressure change. Furthermore, SAM elevated the expressions of VEGF, bFGF and HIF1-α in rat myocardium and serum. In myocardial tissues of SAM-treated rats, the expressions of collagen I, collagen III and α-sma were reduced, indicating that SAM inhibited myocardial fibrosis. In addition, SAM promoted cardiac angiogenesis by activating Jagged1/Notch1 signaling pathway.Conclusion: SAM promotes angiogenesis of the myocardium by activating Jagged1/Notch1 signaling pathway and inhibiting fibrosis in rat myocardium. Therefore, SAM effectively inhibits ventricular remodeling in rats after MI, thereby improving the rats’ heart structure and function. The results may provide new targets for the treatment of myocardial infarction

FUZZY-GRANULAR BASED DATA MINING FOR EFFECTIVE DECISION

Due to complexity of biomedical problems, adaptive and intelligent knowledge discovery and data mining systems are highly needed to help humans to understand the inherent mechanism of diseases. For biomedical classification problems, typically it is impossible to build a perfect classifier with 100 % prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). In this dissertation, a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, is proposed to build such a DSS for binary classification problems in the biomedical domain. Empirical studies show that FARM-DS is competitive to state-of-the-art classifiers in terms of prediction accuracy. More importantly, FARs can provide strong decision support on disease diagnoses due to their easy interpretability. This dissertation also proposes a fuzzy-granular method to select informative and discriminative genes from huge microarray gene expression data. With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers ca

A Business Process Oriented Dynamic Cyber Threat Intelligence Model

The Publisher's final version can be found by following the DOI link.Cyber threat intelligence (CTI) is a method for strengthening information security. CTI provides information on threats and the countermeasures. Businesses can benefit from the defensive knowledge if the relevant CTI is found. However, business environments involve miscellaneous dynamics of the business processes that can dynamically change the contexts. Correspondingly, threats associated with the contextual risk factors can change dynamically at the same time. Every time the contextual changes take place, CTI-based defensive strategies for businesses may not be useful and effective any more. However, the existing connection strategies between CTI and business risk contexts are still somewhat static. This paper proposes a business process oriented dynamic CTI model. The model can observe and capture the dynamics from the business environments. Every time the dynamics are captured, the model will then trigger adjustments of the connection strategies within the model. We use a case study to illustrate the use of the model and present how the model adjusts the connection strategies according to the dynamics. We then conclude the paper with future directions of the research

Analysis of Vulnerability on Weighted Power Networks under Line Breakdowns

Vulnerability is a major concern for power networks. Malicious attacks have the potential to trigger cascading failures and large blackouts. The robustness of power networks against line failure has been of interest in the past several years. However, this scenario cannot cover weighted situations in the real world. This paper investigates the vulnerability of weighted power networks. Firstly, we propose a more practical capacity model to investigate the cascading failure of weighted power networks under different attack strategies. Results show that the smaller threshold of the capacity parameter can enhance the vulnerability of weighted power networks. Furthermore, a weighted electrical cyber-physical interdependent network is developed to study the vulnerability and failure dynamics of the entire power network. We perform simulations in the IEEE 118 Bus case to evaluate the vulnerability under various coupling schemes and different attack strategies. Simulation results show that heavier loads increase the likelihood of blackouts and that different coupling strategies play a crucial role in the cascading failure performance

An Energy and SLA-Aware Resource Management Strategy in Cloud Data Centers

Reducing energy consumption of data centers is an important way for cloud providers to improve their investment yield, but they must also ensure that the services delivered meet the various requirements of consumers. In this paper, we propose a resource management strategy to reduce both energy consumption and Service Level Agreement (SLA) violations in cloud data centers. It contains three improved methods for subproblems in dynamic virtual machine (VM) consolidation. For making hosts detection more effective and improving the VM selection results, first, the overloaded hosts detecting method sets a dynamic independent saturation threshold for each host, respectively, which takes the CPU utilization trend into consideration; second, the underutilized hosts detecting method uses multiple factors besides CPU utilization and the Naive Bayesian classifier to calculate the combined weights of hosts in prioritization step; and third, the VM selection method considers both current CPU usage and future growth space of CPU demand of VMs. To evaluate the performance of the proposed strategy, it is simulated in CloudSim and compared with five existing energy–saving strategies using real-world workload traces. The experimental results show that our strategy outperforms others with minimum energy consumption and SLA violation

Subcutaneous adipose tissue is associated with acute kidney injury after abdominal trauma based on the generalized propensity score approach: A retrospective cohort study

Introduction: Obesity is associated with an increased risk of acute kidney injury (AKI) after trauma. However, the associations between different adipose tissue depots and AKI remain unknown. Our study aims to quantify the effect of abdominal adiposity on AKI in trauma patients. Methods: We performed a retrospective cohort study of abdominal trauma patients who were admitted into our hospital from January 2010 to March 2020. Abdominal VAT (visceral adipose tissue) and SAT (subcutaneous adipose tissue) were measured at the level of the third lumbar vertebra using computed tomography. Causal modeling based on the generalized propensity score was used to quantify the effects of BMI (body mass index), VAT and SAT on AKI. Results: Among 324 abdominal trauma patients, 67 (20.68%) patients developed AKI. Patients with AKI had higher BMI (22.46 kg/m2 vs. 22.04 kg/m2, P = 0.014), higher SAT areas (89.06 cm2 vs. 83.39 cm2, P = 0.151) and VAT areas (140.02 cm2 vs. 91.48 cm2, P = 0.001) than those without AKI. By using causal modeling, we found that the risk of developing AKI increased by 8.3% (P = 0.001) and 4.8% (P = 0.022) with one unit increase in BMI (per 1 kg/m2), and ten units increase in SAT (per 10 cm2), respectively. However, VAT did not show a significant association with AKI (P = 0.327). Conclusion: SAT, but not VAT, increased the risk of AKI among abdominal trauma patients. Measurement of SAT might help to identify patients at higher risk of AKI

High Fat-to-Muscle Ratio Was Associated with Increased Clinical Severity in Patients with Abdominal Trauma

Overweight and moderate obesity confer a survival benefit in chronic diseases such as coronary artery disease and chronic kidney disease, which has been termed the “obesity paradox”. However, whether this phenomenon exists in trauma patients remains controversial. We performed a retrospective cohort study in abdominal trauma patients admitted to a Level I trauma center in Nanjing, China between 2010 and 2020. In addition to the traditional body mass index (BMI) based measures, we further examined the association between body composition-based indices with clinical severity in trauma populations. Body composition indices including skeletal muscle index (SMI), fat tissue index (FTI), and total fat-to-muscle ratio (FTI/SMI) were measured using computed tomography. Our study found that overweight was associated with a four-fold risk of mortality (OR, 4.47 [95% CI, 1.40–14.97], p = 0.012) and obesity was associated with a seven-fold risk of mortality (OR, 6.56 [95% CI, 1.07–36.57], p = 0.032) compared to normal weight. Patients with high FTI/SMI had a three-fold risk of mortality (OR, 3.06 [95% CI, 1.08–10.16], p = 0.046) and double the risk of an intensive care unit length of stay ≥ 5 d (OR, 1.75 [95% CI, 1.06–2.91], p = 0.031) compared to patients with low FTI/SMI. The obesity paradox was not observed in abdominal trauma patients, and high FTI/SMI ratio was independently associated with increased clinical severity