Scanning Electrochemical Microscopy of DNA Monolayers Modified with Nile Blue

Scanning electrochemical microscopy (SECM) is used to probe long-range charge transport (CT) through DNA monolayers containing the redox-active Nile Blue (NB) intercalator covalently affixed at a specific location in the DNA film. At substrate potentials negative of the formal potential of covalently attached NB, the electrocatalytic reduction of Fe(CN)63− generated at the SECM tip is observed only when NB is located at the DNA/solution interface; for DNA films containing NB in close proximity to the DNA/electrode interface, the electrocatalytic effect is absent. This behavior is consistent with both rapid DNA-mediated CT between the NB intercalator and the gold electrode as well as a rate-limiting electron transfer between NB and the solution phase Fe(CN)63−. The DNA-mediated nature of the catalytic cycle is confirmed through sequence-specific and localized detection of attomoles of TATA-binding protein, a transcription factor that severely distorts DNA upon binding. Importantly, the strategy outlined here is general and allows for the local investigation of the surface characteristics of DNA monolayers both in the absence and in the presence of DNA binding proteins. These experiments highlight the utility of DNA-modified electrodes as versatile platforms for SECM detection schemes that take advantage of CT mediated by the DNA base pair stack

On Limitations of Using Rough Set Approach to Analyse Non-Trivial Medical Information Systems

The rough set theory has been used to analyse medical experience with urolithiasis patients treated by extracorporeal shock wave lithotripsy (ESWL). The aim of this analysis was to evaluate the significance of attributes for two classifications expressing the patients' condition after the ESWL treatment and to discover strong decision rules representing classification patterns interesting for practitioners. The ESWL information system is an example of non-trivial medical data set where the use of a simple rough set model gives a high number of possible reducts which are impossible to interpret. Two heuristic strategies based on the rough set theory are proposed. They lead to the selection of the most significant attributes having a good clinical interpretation. Inducing only discriminating decision rules do not give good interpretation results - rules are weak and too specific. Discovery of partly discriminating rules allows to extract strong classification patterns. 1 Introduction Me..

Rough set approach to multiple criteria classification with imprecise evaluations and assignments

Dominance-based Rough Set Approach (DRSA) has been introduced to deal with multiple criteria classification (also called multiple criteria sorting, or ordinal classification with monotonicity constraints), where assignments of objects may be inconsistent with respect to dominance principle. In this paper, we consider an extension of DRSA to the context of imprecise evaluations of objects on condition criteria and imprecise assignments of objects to decision classes. The imprecisions are given in the form of intervals of possible values. In order to solve the problem, we reformulate the dominance principle and introduce second-order rough approximations. The presented methodology preserves well-known properties of rough approximations, such as rough inclusion, complementarity, identity of boundaries and precisiation. Moreover, the meaning of the precisiation property is extended to the considered case. The paper presents also a way to reduce decision tables and to induce decision rules from rough approximations.Dominance-based rough set approach Multiple criteria decision analysis Multiple criteria classification Ordinal classification Monotonicity constraints Decision rules Imprecise information Interval order

Ant-based extraction of rules in simple decision systems over ontological graphs

In the paper, the problem of extraction of complex decision rules in simple decision systems over ontological graphs is considered. The extracted rules are consistent with the dominance principle similar to that applied in the dominancebased rough set approach (DRSA). In our study, we propose to use a heuristic algorithm, utilizing the ant-based clustering approach, searching the semantic spaces of concepts presented by means of ontological graphs. Concepts included in the semantic spaces are values of attributes describing objects in simple decision system