BigFCM: Fast, Precise and Scalable FCM on Hadoop

Clustering plays an important role in mining big data both as a modeling technique and a preprocessing step in many data mining process implementations. Fuzzy clustering provides more flexibility than non-fuzzy methods by allowing each data record to belong to more than one cluster to some degree. However, a serious challenge in fuzzy clustering is the lack of scalability. Massive datasets in emerging fields such as geosciences, biology and networking do require parallel and distributed computations with high performance to solve real-world problems. Although some clustering methods are already improved to execute on big data platforms, but their execution time is highly increased for large datasets. In this paper, a scalable Fuzzy C-Means (FCM) clustering named BigFCM is proposed and designed for the Hadoop distributed data platform. Based on the map-reduce programming model, it exploits several mechanisms including an efficient caching design to achieve several orders of magnitude reduction in execution time. Extensive evaluation over multi-gigabyte datasets shows that BigFCM is scalable while it preserves the quality of clustering

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Ambiguity-driven fuzzy C-means clustering : how to detect uncertain clustered records

As a well-known clustering algorithm, Fuzzy C-Means (FCM) allows each input sample to belong to more than one cluster, providing more flexibility than non-fuzzy clustering methods. However, the accuracy of FCM is subject to false detections caused by noisy records, weak feature selection and low certainty of the algorithm in some cases. The false detections are very important in some decision-making application domains like network security and medical diagnosis, where weak decisions based on such false detections may lead to catastrophic outcomes. They mainly emerge from making decisions about a subset of records that do not provide sufficient evidence to make a good decision. In this paper, we propose a method for detecting such ambiguous records in FCM by introducing a certainty factor to decrease invalid detections. This approach enables us to send the detected ambiguous records to another discrimination method for a deeper investigation, thus increasing the accuracy by lowering the error rate. Most of the records are still processed quickly and with low error rate preventing performance loss which is common in similar hybrid methods. Experimental results of applying the proposed method on several datasets from different domains show a significant decrease in error rate as well as improved sensitivity of the algorithm

Supply chain quality management

In recent years, there are several methods introduced for the improvement of operational performances. Total quality management and supply chain management are two methods recommended for this purpose. These two approaches have been studied in most researches separately, while they have objectives in common, and this makes them a strategic means, which can be used, simultaneously. Total quality management and supply chain management play significant roles to increase the organizational competitiveness power. Moreover, they have only one purpose that is customer satisfaction, and they are different only on their approaches to reach their objectives. In this research, we aim to study both approaches of quality management and supply chain, their positive increasing effects that may be generated after their integration. For this purpose, the concept and definitions of each approach is studied, independently, their similarities and differences are recognized, and finally, the advantages of their integration are introduced

Immune Dysregulation in Children with Allergic asthma, a close Relationship between IL-17 but not IL-4 or IFN-g, and Disease Severity

Background : Allergic asthma is a chronic airway inflammatory disease often determined with degrees of inflammation, hypersensitivity, bronchial constriction, and airway changes. Th1, Th2, and Th17 cells are the main cells involved in asthma pathophysiology. To evaluate Th1, Th2, and Th17 functions by assessing INF-g, IL-4, and IL-17 gene and protein levels in asthma patients and healthy controls. Materials and methods: In total, 44 individuals of Iranian ethnicity including 24 patients with allergic asthma and 20 healthy controls were enrolled. Peripheral blood mononuclear cells of all participants were isolated and cDNA was synthesized following RNA extraction. Gene expression and protein levels of INF-g, IL-4, and IL-17 were evaluated by real-time polymerase chain reaction and sandwich ELISA, respectively. Results: The results of this study showed that the gene expression of IL-4 and IL-17 in patients was increased significantly compared to the control group (p = 0.046 and 0.03, respectively) whereas that of IFN-g was significantly decreased in the group of patients (p = 0.021). Compared to the healthy controls, serum levels of IL-17 and IL-4 were significantly increased in asthma patients (p = 0.015 and 0.03, respectively). Conclusion: Higher IL-17 and IL-4 mRNA expression and serum levels in asthma patients than healthy controls highlight the role of Th2 and Th17 cells in asthma pathogenesis and their potential as therapeutic targets

Association of Smoking With Semen Quality and µ-Calpain Level in Normospermia: A Case-Control Study

Objective: Calpains are a family of Ca2+ dependent proteases. There is some evidence that calpains involved in fusion process that occurs between spermatozoa and the oocyte. The current study aimed to investigate the association of smoking with semen quality and µ-calpain level. Materials and methods: This case-control study was conducted on 117 normospermia males  between June 2013 and march 2014 in Jahad Laboratory in ahvaz, Iran. The semen samples were collected from male smokers (n = 50) and non-smokers (n = 67). We divided these participants as light, moderate, or heavy smokers based on their cigarettes per day (CPD). ELISA assays were used to measure µ-calpain concentration. All semen samples were analyzed according to World Health Organization guidelines. Results: The analysis of semen showed the volume, concentration, motility and morphology of semen were significantly lower among the smoker men than the non-smoker men. Also this significant difference was observed based on the number (light, moderate and heavy smokers) and duration (short term and long term smoker) of smoking. Although, showed no significant difference between µ-calpain of smoker men and non-smoker men. CPD showed negatively correlation with semen volume, concentration, motility and morphology of sperm. Conclusion: Sperm quality was negatively correlated with CPD and duration of smoking. However, there is no significant correlation between smoking and µ-calpain concentration