In this paper, an extension of the statistical downscaling model (SDSM), namely data-mining downscaling model (DMDM), has been developed. DMDM has the same platform as the most cited statistical downscaling models, na...
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In this paper, an extension of the statistical downscaling model (SDSM), namely data-mining downscaling model (DMDM), has been developed. DMDM has the same platform as the most cited statistical downscaling models, namely SDSM and ASD. Multiple linear regression (MLR), ridge regression (RR), multivariate adaptive regression splines (MARS) and model tree (MT) constitute the mathematical core of DMDM. DMDM uses linear basis functions in MARS and linear regression rules in MT to keep the linear structure of SDSM;therefore, all of the SDSM assumptions are also valid in DMDM. These methods highlight the effect of data partitioning for meteorological predictors in the downscaling procedure. Inputs and output of the presented approaches are the same as SDSM and ASD. In the case study of this research, NCEP/NCAR databases have been used for calibration and validation. According to the inherent linearity of the methods, suitable predictor selection has been done with stepwise regression as a preprocessing stage. The results of DMDM have been compared with observed precipitation in 12 rain gauge stations that are scattered in different basins in Iran and represent different climate regimes. Comparison between the results of SDSM and DMDM has indicated that the presented approach can highly improve downscaling efficiency in terms of reproducing monthly standard deviation and skewness for both calibration and validation datasets. Among the proposed methods in DMDM, the results of the case study have shown that MT has provided better performances both in modelling occurrence and amount of precipitation. Also, MT is potentially recognized as a powerful diagnostic tool that could extract information in key atmospheric drivers affecting local weather. It also has fewer parameters during dry seasons, in which the number of historical precipitation events might not be enough for calibrating SDSM model in many arid and semi-arid regions.
This paper investigates the possibility of using various data-mining methods for network attack detection. A modular architecture of an intrusion detection system has been designed that enables classification of netwo...
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This paper investigates the possibility of using various data-mining methods for network attack detection. A modular architecture of an intrusion detection system has been designed that enables classification of network packets in several support vector machines.
Polydatin is one of the main bioactive constituents isolated from Polygonum cuspidatum Sieb. et Zucc., which possesses various pharmacological activities. In this study, we established an efficient strategy based on u...
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Polydatin is one of the main bioactive constituents isolated from Polygonum cuspidatum Sieb. et Zucc., which possesses various pharmacological activities. In this study, we established an efficient strategy based on ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry to uncover polydatin metabolites in rat urine and plasma. Firstly, multiple mass defect filters (MMDFs) combined with high-resolution extracted ion chromatograms (HREICs) was utilized to perform post-acquisition data-mining in ESI-MS1 stage. Secondly, metabolite candidates of polydatin were expounded systematically on the basis of diagnostic product ions (DPIs), chromatographic retention times, accurate mass, and neutral loss fragments (NLFs). Consequently, a total of 41 metabolites (polydatin included) were detected and identified tentatively in 12 min. These metabolites, including 40 in rat urine and 7 in rat plasma, were presumed to generate through glucuronidation, sulfation, deglucosylation, dehydrogenation, methylation, hydrogenation, hydroxylation and their composite reactions. Meanwhile, metabolite clusters, which were set in the form radially, were found in this study. In conclusion, our study expounded polydatin metabolites in rats and provided a reference for further researches on therapeutic material basis and mechanism of polydatin.
Puerarin, a bioactive natural C-glycoside isoflavonoid isolated from Pueraria lobata (Willd.) Ohwi, possesses many potential health benefits. However, the in vivo metabolic fates of puerarin have not been comprehensiv...
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Puerarin, a bioactive natural C-glycoside isoflavonoid isolated from Pueraria lobata (Willd.) Ohwi, possesses many potential health benefits. However, the in vivo metabolic fates of puerarin have not been comprehensively clarified yet. In this study, an efficient strategy based on UHPLC-LTQ-Orbitrap mass spectrometer in both positive and negative ion modes was developed to profile and characterize puerarin metabolites in rat urine and plasma. Meanwhile, post-acquisition data-mining methods including high-resolution extracted ion chromatogram (HREIC) and multiple mass defect filtering (MMDF) were utilized to screen potential puerarin metabolites from HR-ESI-MS1 stage. The mass fragmentation behaviors of five reference standards, including puerarin, daidzin, daidzein, genistin, and genistein, were comprehensively studied for construction of diagnostic product ions (DPIs), which could be employed to implement rapid identification of puerarin metabolites. Finally, a total of 66 metabolites (prototype compound included) were tentatively or positively identified based on standard substances, chromatographic retention"times, accurate mass measurement, and corresponding ClogP values. Our results demonstrated that puerarin underwent multiple in vivo metabolic reactions including methylation, hydroxylation, dehydroxylation, hydrogenation, deglycosylation, glycosylation, sulfonation, glucuronidation, and their complicated reactions. In conclusion, the newly discovered puerarin metabolites significantly expanded the understanding on its pharmacological effects and built the foundation for further toxicity and safety studies.
Liquiritin (LQ), a main component of liquorice, exerts various biological activities. However, insufficient attentions have been paid to the metabolism study on this natural compound until now. Our present study was c...
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Liquiritin (LQ), a main component of liquorice, exerts various biological activities. However, insufficient attentions have been paid to the metabolism study on this natural compound until now. Our present study was conducted to investigate the LQ metabolites in rats urine, faeces and plasma using UHPLC-LTQ-Orbitrap mass spectrometer in both positive and negative ion modes. Meanwhile, post-acquisition data-mining methods including high-resolution extracted ion chromatogram (HREIC), multiple mass defect filters (MMDFs), neutral loss fragments (NLFs) and diagnostic product ions (DPIs) were utilised to screen and identify LQ metabolites from HR-ESI-MS to ESI-MSn stage. As a result, a total of 49 metabolites were detected and characterised unambiguously or tentatively. These metabolites were presumed to generate through glucuronidation, sulfation, deglucosylation, dehydrogenation, methylation, hydrogenation, hydroxylation, ring cleavage and their composite reactions. Our results not only provided novel and useful data to better understand the biological activities of LQ, but also indicated that the proposed strategy was reliable for a rapid discovery and identification drug-related constituents in vivo.
We have proposed an adaptive model of a system for detecting intrusions in a distributed computer network. The basis of the detection system consists of various data-mining methods that make it possible to classify ne...
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We have proposed an adaptive model of a system for detecting intrusions in a distributed computer network. The basis of the detection system consists of various data-mining methods that make it possible to classify network interaction as normal or anomalous using many attributes extracted from network traffic.
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