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1

Simultaneous analysis of adenosine triphosphate and its metabolites in Saccharomyces Cerevisiae using RP-HPLC

Zhu P., Wang S., Wang J., Zhou L., Shi P.

Acta Chromatographica

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2016

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Vol. 28, no. 4

501--511

EN

In order to assess the contribution of adenosine triphosphate and its metabolites to the cellular metabolism process in Saccharomyces cerevisiae, it is very important to simultaneously determine the relative concentrations of ATP and its metabolites. In this study, a fast, simple reversed-phase high-performance liquid chromatography with high selectivity was developed to simultaneously measure adenosine triphosphate and its metabolites (adenosine diphosphate, adenosine monophosphate, and cyclic adenosine monophosphate) in yeast. The method was performed under the gradient grogram, and the detection was monitored at 254 nm. Analysis was achieved within 25 min. The four components can be detected with linear response over the concentration range from 1 to 100 mg L−1 with excellent correlation coefficients (r2) > 0.999. The recovery of the four analytes was 92.9%, 90.4%, 99.1%, and 105.1%, respectively. To demonstrate the good analysis of yeast samples, changes in the four adenine nucleotides levels caused by caloric restriction in yeast were determined. It is expected that the current method may contribute to further metabolomics and system biology investigations of yeast.

2

A Unified Definition of Consistent Functions

Zhu P., Xie H., Wen Q.

Fundamenta Informaticae

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2014

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Vol. 135, nr 3

331--340

EN

In recent years, homomorphisms have been exploited to compare the structures and properties of two generalized information systems. Some of these homomorphisms are based on consistent functions, which are a class of special mappings between universal sets. The purpose of this paper is to unify and extend the consistent functions in the literature into the framework of neighborhood systems. After introducing the notion of consistent functions with respect to neighborhood systems, we explore some important properties of the extended consistent functions such as preserving the inverse images and the intersections of neighborhoods. Our results provide a sound basis for further investigating neighborhood systems via homomorphisms.

3

An Improved Axiomatic Definition of Information Granulation

Zhu P.

Fundamenta Informaticae

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2012

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Vol. 120, nr 1

93-109

EN

To capture the uncertainty of information or knowledge in information systems, various information granulations, also known as knowledge granulations, have been proposed. Recently, several axiomatic definitions of information granulation have been introduced. In this paper, we try to improve these axiomatic definitions and give a universal construction of information granulation by relating information granulations with a class of functions of multiple variables. We show that the improved axiomatic definition has some concrete information granulations in the literature as instances.

4

An Axiomatic Approach to the Roughness Measure of Rough Sets

Zhu P.

Fundamenta Informaticae

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2011

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Vol. 109, nr 4

463-480

EN

In Pawlak's rough set theory, a set is approximated by a pair of lower and upper approximations. To measure numerically the roughness of an approximation, Pawlak introduced a quantitative measure of roughness by using the ratio of the cardinalities of the lower and upper approximations. Although the roughness measure is effective, it has the drawback of not being strictly monotonic with respect to the standard ordering on partitions. Recently, some improvements have been made by taking into account the granularity of partitions. In this paper, we approach the roughness measure in an axiomatic way. After axiomatically defining roughness measure and partition measure, we provide a unified construction of roughness measure, called strong Pawlak roughness measure, and then explore the properties of this measure. We show that the improved roughnessmeasures in the literature are special instances of our strong Pawlak roughness measure and introduce three more strong Pawlak roughness measures as well. The advantage of our axiomatic approach is that some properties of a roughness measure follow immediately as soon as the measure satisfies the relevant axiomatic definition.

5

Feature Selection via Maximizing Fuzzy Dependency

Hu Q., Zhu P., Liu J., Yang Y., Yu D.

Fundamenta Informaticae

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2010

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Vol. 98, nr 2/3

167-181

EN

Feature selection is an important preprocessing step in pattern analysis and machine learning. The key issue in feature selection is to evaluate quality of candidate features. In this work, we introduce a weighted distance learning algorithm for feature selection via maximizing fuzzy dependency. We maximize fuzzy dependency between features and decision by distance learning and then evaluate the quality of features with the learned weight vector. The features deriving great weights are considered to be useful for classification learning. We test the proposed technique with some classical methods and the experimental results show the proposed algorithm is effective.