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Purpose: The purpose of this study is to provide individualized concept structure analysis based on the comparisons with concept structure of expert. Fuzzy clustering can distinguish characteristics of concept structures on assessment of examination for registered nurse. Design/methodology/approach: This study adds a regulating factor of covariance matrix to each class in objective function and deletes the constraint of the determinant of covariance matrices. The fuzzy covariance matrices in the Mahalanobis distance with recursive process iteratively can be directly derived by minimizing the objective function and can overcome the drawback of Euclidean distance. Hence, we could extend the distance measure to alternative distance. Findings: To get a more stable partition clustering algorithm, this study replaces all the covariance matrices with the same common covariance matrix and solves the problem of the singular problem for the inverse covariance matrix. Each cluster of data can easily describe features of knowledge structures. Manage the knowledge structures of assessment of examination for registered nurse to construct the model of features in the pattern recognition completely. Practical implications: Provide the empirical data for concepts of Professional and Technical Examination for Registered Nurse from junior college students of learning deficiencies. The results show that students with different response patterns and total score own varied concept structures. Originality/value: Based on the findings and results, some suggestions and recommendations for future research are provided. Integrated algorithm could improve the assessment methodology of cognition diagnosis and manage the knowledge structures of Professional and Technical Examination for Registered Nurse's concepts easily. [ABSTRACT FROM AUTHOR]

Purpose: Fuzzy algorithms of Gath-Geva (GG) and Gustafson-Kessel (GK) based on Mahalanobis distance can improve those limitations of spherical structural clusters, but GG algorithm can only be used for the data with multivariate normal distribution. GK algorithm is limited by that it must know the distribution of data. An improved supervised clustering algorithm based on fuzzy c-means (FCM) has been proposed. Methodology: We take a new threshold value and a new convergent algorithm to improve those limitations of GG and GK algorithms, delete the constraint of the determinants of covariance matrices in the GK algorithm, and replace the covariance matrix with the correlation matrix which exists in the objective function. Findings: The experimental results of real data sets show that our proposed new algorithm can promote clustering accuracies and get better performance. Value: The popular FCM algorithm based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. Adding fuzzy covariance matrices in their distance measure was not directly derived from the objective function. But it is not stable enough when some of its covariance matrices are not equal. Hence, different initializations may lead to different results. [ABSTRACT FROM AUTHOR]

Nowadays, people pay increasing attention to health, and the integrity of medical records has been put into focus. Recently, medical data imputation has become a very active field because medical data usually have missing values. Many imputation methods have been proposed, but many model-based imputation methods such as expectation–maximization and regression-based imputation based on the variables data have a multivariate normal distribution, which assumption can lead to biased results. Sometimes, this becomes a bottleneck, such as computationally more complex than model-free methods. Furthermore, directly removing instances with missing values has several problems, and it is possible to lose the important data, produce ineffective research samples, and cause research deviations. Therefore, this study proposes a novel clustering-based purity and distance imputation method to improve the handling of missing values. In the experiment, we collected eight different medical datasets to compare the proposed imputation methods with the listed imputation methods with regard to the results of different situations. In imputation measures, the area under the curve (AUC) is used to evaluate the performance of the imbalanced class datasets in MAR and MCAR experiments, and accuracy is applied to measure its performance of the balanced class in MNAR experiment. Finally, the root-mean-square error (RMSE) is also used to compare the proposed and the listing imputation methods. In addition, this study utilized the elbow method and the average silhouette method to find the optimal number of clusters for all datasets. Results showed that the proposed imputation method could improve imputation performance in the accuracy, AUC, and RMSE of different missing degrees and missing types. [ABSTRACT FROM AUTHOR]

Decline in executive function (EF) occurs early in Alzheimer’s disease (AD) and can interfere with daily functioning. Unfortunately, little is known about the relative ability of traditional EF tests to detect these cognitive changes. Given that timely diagnosis and intervention are essential to improving functional outcome in this population, our aim was to identify the specific EF measures that best differentiated mild dementia from normal aging. Thirty-one patients with mild AD and 31 controls were administered 7 EF tests. Findings indicated significant between-group differences on all measures except Wisconsin Card Sorting Test. The remaining 6 tests displayed fair to good accuracy discriminating between AD cases and controls. Only category fluency and Tower of London test remained in the final regression model that yielded the highest AUC of 0.90, which was not statistically different from that of either test alone. Overall, most of the tests employed were valid for assessing mild EF disturbances. Specifically, the two measures can be used in isolation for quick screening or in combination to facilitate a more in-depth evaluation of EF performance. This study contributes to clinical field by testifying to the validity of various EF tests to identify AD-related compromises in this cognitive domain. [ABSTRACT FROM PUBLISHER]

In the entertainment industry, providing games to players is a main business activity with a long history. There are various kinds of game types such as the computer game, web game, pocket game, mobile game developed for play. Virtual reality is a new technology which can integrate into the computer or mobile for generating virtual reality games. The goal of this study is to develop a framework to evaluate and select the virtual reality games development project. In the proposed framework, Linguistic VIKOR is integrated with fuzzy cognitive maps to select the project. For readers to understand the proposed method, a case study has been introduced to evaluate the project based on the proposed method. Finally, the conclusion and future research are discussed as an ending. [ABSTRACT FROM AUTHOR]

Medical data usually have missing values; hence, imputation methods have become an important issue. In previous studies, many imputation methods based on variable data had a multivariate normal distribution, such as expectation-maximization and regression-based imputation. These assumptions may lead to deviations in the results, which sometimes create a bottleneck. In addition, directly deleting instances with missing values may have several problems, such as losing important data, producing invalid research samples, and leading to research deviations. Therefore, this study proposed a safe-region imputation method for handling medical data with missing values; we also built a medical prediction model and compared the removed missing values with imputation methods in terms of the generated rules, accuracy, and AUC. First, this study used the kNN imputation, multiple imputation, and the proposed imputation to impute the missing data and then applied four attribute selection methods to select the important attributes. Then, we used the decision tree (C4.5), random forest, REP tree, and LMT classifier to generate the rules, accuracy, and AUC for comparison. Because there were four datasets with imbalanced classes (asymmetric classes), the AUC was an important criterion. In the experiment, we collected four open medical datasets from UCI and one international stroke trial dataset. The results show that the proposed safe-region imputation is better than the listing imputation methods and after imputing offers better results than directly deleting instances with missing values in the number of rules, accuracy, and AUC. These results will provide a reference for medical stakeholders. [ABSTRACT FROM AUTHOR]

Aggressive tumor cells mainly rely on glycolysis, and further release vast amounts of lactate and protons by monocarboxylate transporter (MCT), which causes a higher intracellular pH (pHi) and acidic extracellular pH. Isoorientin, a principle flavonoid compound extracted from several plant species, shows various pharmacological activities. However, effects of isoorientin on anticancer and MCT await to explore in human lung cancer cells. Human lung cancer tissues were obtained from cancer patients undergoing surgery, while the human lung adenocarcinoma cells (A549) were bought commercially. Change of pHi was detected by microspectrofluorometry method with a pH-sensitive fluorescent dye, BCECF. MTT and wound-healing assay were used to detect the cell viability and migration, respectively. Western blot techniques and immunocytochemistry staining were used to detect the protein expression. Our results indicated that the expression of MCTs1/4 and CD147 were upregulated significantly in human lung tissues. In experiments of A549 cells, under HEPES-buffer, the resting pHi was 7.47, and isoorientin (1–300 μ M) inhibited functional activity of MCT concentration-dependently (up to − 4 2 %). Pretreatment with isoorientin (3–100 μ M) for 24 h, MCT activity and cell migration were significantly inhibited (− 2 5 % and − 4 0 %, respectively), while the cell viability was not affected. Moreover, the expression of MCTs1/4, CD147, and matrix metalloproteinase (MMP) 2/9 were significantly down regulated. In summary, MCTs1/4 and CD147 are significantly upregulated in human lung adenocarcinoma tissues, and isoorientin inhibits cells-migration by inhibiting activity/expression of MCTs1/4 and MMPs2/9 in human lung cancer cells. These novel findings suggest that isoorientin could be a promising pharmacological agent for lung cancer. [ABSTRACT FROM AUTHOR]

Endothelial progenitor cells (EPCs), widely existing in bone marrow and peripheral blood, are involved in the repair of injured vascular endothelium and angiogenesis which are important to diabetic mellitus (DM) patients with vascular complications. The number and the function of EPCs are related to the advanced glycation end products (AGEs) generated in DM patients. Lycopene (Lyc) is an identified natural antioxidant that protects EPCs under the microenvironment of AGEs from damage. However, the underlying mechanism remains unclear. To investigate the effect of Lyc on EPCs, we isolated EPCs from DM rat bone marrow and determined cell proliferation, cell cycle,apoptosis and autophagy of EPCs. The present study showed that 10 μg/mL Lyc improved cell proliferation and had low cytotoxicity in the presence of AGEs. In addition, Lyc rescued S phase of the cell cycle arrest, reduced apoptosis rate and decreased autophagic reaction including ROS and mitochondrial membrane potential (MMP) of EPCs. Moreover, Lyc combined use of autophagy inhibitors, 3-MA, had better protective effects. Taken together, our data suggests that Lyc promotes EPCs survival and protect EPCs from apoptosis and oxidative autophagy induced by AGEs, further remaining the number and function of EPCs. This study provides new insights into Lyc protective mechanism of AGEs-induced oxidative autophagy in EPCs from DM patients and offers a new therapy for DM vascular complications. [ABSTRACT FROM AUTHOR]

The sodium/glucose cotransporter 1 (SGLT1) is responsible for glucose uptake in intestinal epithelial cells. Its expression is decreased in individuals with intestinal inflammatory disorders and is correlated with the pathogenesis of disease. The aim of this study was to understand the regulatory mechanism of the SGLT1 gene. Using the trinitrobenzene sulfonic acid-induced mouse models of intestinal inflammation, we observed decreased SGLT1 expression in the inflamed intestine was positively correlated with the mucosal level of epidermal growth factor (EGF) and activated CREB. Overexpression of EGF demonstrated that the effect of EGF on intestinal glucose uptake was primarily due to the increased level of SGLT1. We identified an essential cAMP binding element (CRE) confers EGF inducibility in the human SGLT1 gene promoter. ChIP assay further demonstrated the increased binding of CREB and CBP to the SGLT1 gene promoter in EGF-treated cells. In addition, the EGFR- and PI3K-dependent CREB phosphorylations are involved in the EGF-mediated SGLT1 expression. This is the first report to demonstrate that CREB is involved in EGF-mediated transcription regulation of SGLT1 gene in the normal and inflamed intestine, which can provide potential therapeutic applications for intestinal inflammatory disorders. [ABSTRACT FROM AUTHOR]

This paper presents a strategy that efficiently allocates the limited resource and still maintains the coding performance for the spatial scalability of H.264/SVC. An integrated computation-scalable algorithm is proposed on the basis of priority-based mode decision (PBMD) and activitybased motion estimation (ABME). The rate-distortion cost (RD-cost) of base layer is utilized as the priority to decide the mode in the enhancement layer, and the motion vector difference (MVD) in the base layer is employed to determine the search range as well. Through the proposed approach, computation complexity of the enhancement layer could be reduced efficiently and well allocated. A fast algorithm with early termination mechanism is also proposed in this paper. Experimental results demonstrate that the various ratedistortion and time-saving performances with computation scalability are realized. [ABSTRACT FROM AUTHOR]