A rough granular computing in discovery of process models from data and domain knowledge

The rapid expansion of the Internet has resulted not only in the ever growing amount of data therein stored, but also in the burgeoning complexity of the concepts and phenomena pertaining to those data. This issue has been vividly compared by the renowned statistician, prof. Friedman of Stanford University, to the advances in human mobility from the period of walking afoot to the era of jet travel. These essential changes in data have brought new challenges to the development of new data mining methods, especially that the treatment of these data increasingly involves complex processes that elude classic modeling paradigms. “Hot” datasets like biomedical, financial or net user behavior data are just a few examples. Mining such temporal or stream data is on the agenda of many research centers and companies worldwide. In the data mining community, there is a rapidly growing interest in developing methods for process mining, e.g〖DK〗., for discovery of structures of temporal processes from data. Works on process mining have recently been undertaken by many renowned centers worldwide. This research is also related to functional data analysis , cognitive networks , and dynamical system modeling, e.g., in biology. In the lecture, we outline an approach to discovery of processes from data and domain knowledge which is based on the roughgranular computing.     

A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25

Lung cancer is the most common cause of cancer death worldwide, with over one million cases annually. To identify genetic factors that modify disease risk, we conducted a genome-wide association study by analysing 317,139 single-nucleotide polymorphisms in 1,989 lung cancer cases and 2,625 controls from six central European countries. We identified a locus in chromosome region 15q25 that was strongly associated with lung cancer (P = 9 x 10(-10)). This locus was replicated in five separate lung cancer studies comprising an additional 2,513 lung cancer cases and 4,752 controls (P = 5 x 10(-20) overall), and it was found to account for 14% (attributable risk) of lung cancer cases. Statistically similar risks were observed irrespective of smoking status or propensity to smoke tobacco. The association region contains several genes, including three that encode nicotinic acetylcholine receptor subunits (CHRNA5, CHRNA3 and CHRNB4). Such subunits are expressed in neurons and other tissues, in particular alveolar epithelial cells, pulmonary neuroendocrine cells and lung cancer cell lines, and they bind to N'-nitrosonornicotine and potential lung carcinogens. A non-synonymous variant of CHRNA5 that induces an amino acid substitution (D398N) at a highly conserved site in the second intracellular loop of the protein is among the markers with the strongest disease associations. Our results provide compelling evidence of a locus at 15q25 predisposing to lung cancer, and reinforce interest in nicotinic acetylcholine receptors as potential disease candidates and chemopreventative targets.     

A TWO-PHASE APPROACH TO FUZZY SYSTEM IDENTIFICATION

A two-phase approach to fuzzy system identification is proposed. The first phase produces a baseline design to identify a prototype fuzzy system for a target system from a collection of input-output data pairs. It uses two easily implemented clustering techniques: the subtractive clustering method and the fuzzy c-means (FCM) clustering algorithm. The second phase (fine tuning) is executed to adjust the parameters identified in the baseline design. This phase uses the steepest descent and recursive least-squares estimation methods. The proposed approach is validated by applying it to both a function approximation type of problem and a classification type of problem. An analysis of the learning behavior of the proposed approach for the two test problems is conducted for further confirmation.     

Improving the Timeliness of Turning Signals for Business Cycles with Monthly Data

The conventional growth rate measures (such as month‐on‐month, year‐on‐year growth rates and 6‐month smoothed annualized rate adopted by the US Bureau of Labor Statistics and Economic Cycle Research Institute) are popular and can be easily obtained by computing the growth rate for monthly data based on a fixed comparison benchmark, although they do not make good use of the information underlying the economic series. By focusing on the monthly data, this paper proposes the k‐month kernel‐weighted annualized rate (k‐MKAR), which includes most existing growth rate measures as special cases. The proposed k‐MKAR measure involves the selection of smoothing parameters that are associated with the accuracy and timeliness for detecting the change in business turning points. That is, the comparison base is flexible and is likely to vary for different series under consideration. A data‐driven procedure depending upon the stepwise multiple reality check test for choosing the smoothing parameters is also suggested in this paper. The simple numerical evaluation and Monte Carlo experiment are conducted to confirm that our measures (in particular the two‐parameter k‐MKAR) improve the timeliness subject to a certain degree of accuracy. The business cycle signals issued by the Council for Economic Planning and Development over the period from 1998 to 2009 in Taiwan are taken as an example to illustrate the empirical application of our method. The empirical results show that the k‐MKAR‐based score lights are more capable of reflecting turning points earlier than the conventional year‐on‐year measure without sacrificing accuracy. Copyright © 2016 John Wiley & Sons, Ltd.     

A Robust Test for Threshold‐Type Nonlinearity in Multivariate Time Series Analysis

There is growing interest in exploring potential forecast gains from the nonlinear structure of multivariate threshold autoregressive (MTAR) models. A least squares‐based statistical test has been proposed in the literature. However, previous studies on univariate time series analysis show that classical nonlinearity tests are often not robust to additive outliers. The outlier problem is expected to pose similar difficulties for multivariate nonlinearity tests. In this paper, we propose a new and robust MTAR‐type nonlinearity test, and derive the asymptotic null distribution of the test statistic. A Monte Carlo experiment is carried out to compare the power of the proposed test with that of the least squares‐based test under the influence of additive time series outliers. The results indicate that the proposed method is preferable to the classical test when observations are contaminated by outliers. Finally, we provide illustrative examples by applying the statistical tests to two real datasets. Copyright © 2015 John Wiley & Sons, Ltd.     

Genome-wide association study identifies a susceptibility locus for thyrotoxic periodic paralysis at 17q24.3

Thyrotoxic periodic paralysis (TPP) is a potentially life-threatening complication of thyrotoxicosis. We conducted a genome-wide association study (GWAS) and a replication study with a total of 123 southern Chinese with TPP (cases) and 1,170 healthy controls and identified a susceptibility locus on chromosome 17q24.3 near KCNJ2 (rs312691: odds ratio (OR) = 3.3; P(meta-analysis) = 1.8 × 10(-14)). All subjects with TPP also had Graves' disease, and subsequent TPP versus Graves' disease comparison confirmed that the association at 17q24.3 was specific to TPP. The area under the curve (AUC) of rs312691 genotype for risk prediction of TPP in subjects with Graves' disease was 0.73. Expression quantitative trait locus (eQTL) analysis identified SNPs in the region flanking rs312691 (±10 kb) that could potentially affect KCNJ2 expression (P = 0.0001). Our study has identified a susceptibility locus associated with TPP and provides insight into the causes of TPP.     

Mitochondrial dysfunction-induced amphiregulin upregulation mediates chemo-resistance and cell migration in HepG2 cells

The aim of this study was to investigate the contribution of mitochondrial dysfunction to chemoresistance and migration of hepatoma cells. We found that inhibition of mitochondrial respiration and mitochondrial DNA (mtDNA) depletion resulted in induction of amphiregulin (AR) expression in HepG2 cells. Upon oligomycin treatment of HepG2 cells, the cytosolic Ca²⁺ was significantly raised after 30 min, and the intracellular level of reactive oxygen species (ROS) was elevated 2.2-fold after 4 h. Moreover, the condition medium of oligomycin-treated HepG2 cells was found to stimulate the migration of SK-Hep-1 cells. On the other hand, oligomycin-induced cisplatin-resistance and cell migration of HepG2 cells were attenuated by AR-specific RNA interference (#L-017435, Dharmacon) and a neutralizing antibody (MAB262, R&D Systems), respectively. Together, these findings suggest that mitochondrial dysfunction induced Ca²⁺ mobilization, and ROS overproduction, which modulated the chemo-resistance and migration of hepatoma cells through the induction and activation of AR. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. Y.-H. Wei, H.-C. Lee: These authors contribute equally to this work. Received 02 December 2008; received after revision 16 March 2009; accepted 17 March 2009     

海洋环境中溶解有机磷的生物活性初探

在实验室条件下，用三种溶解有机磷化合物（ＤＯＰ）对浮游植物进行一次性培养，结果表明：三磷酸腺苷、甘油磷酸钠、核糖－５－磷酸均可被浮游植物吸收利用，而且ＤＯＰ与溶解无机磷（ＤＩＰ）具有相近的营养性；ＤＯＰ和ＤＩＰ共存时，浮游植物先吸收ＤＩＰ．ＤＯＰ的利用受到抑制，当ＤＩＰ耗尽后，ＤＯＰ亦被吸收；介质中ＤＯＰ浓度较低时，吸收速率更快，显示了ＤＯＰ潜在的环境效应．