Ignoring improper data in decision support system for medical applications

A Decision Support System for Medical Applications was designed by applying the rough set theory to generate rules from the collected data. The data are kept in a table representing information system. There are some improper data in information systems and their removal can improve the quality of the retrieved information. By improper data we can understand such objects that disturb rules generation. They can be erroneous or corrupted or just exceptions. It is possible to find an algorithm of improper data removal to optimize the quality of information derived from decision tables. The improper data can be verified by checking whether some indicators of classification quality were improved after removal of the data. Some suggestions of identifying improper data are presented in the paper. In medical applications the improper data cannot be neglected

Elementary School Social Workers\u27 Perspectives on the Development of Resilience in Early Childhood

Researchers have stressed the importance of addressing the social/emotional needs of early childhood (EC) children, including the development of resilience; however, some U.S. school personnel focus more on academics than on these needs. When young children possess these skills, they can handle social/emotional challenges later in life. The purpose of this qualitative bounded case study was to explore school social workers\u27 (SWs) perspectives about resilience in EC settings. Research questions focused on knowledge of existing programs, participants\u27 perceptions of the successes and challenges of working with EC students, and their recommendations to improve EC students\u27 education. Bronfenbrenner\u27s ecological systems theory and O\u27Neill\u27s and Gopnik\u27s work on needs of young children informed this study. Five elementary school SWs with at least 6 years\u27 experience from 5 districts in the U.S. Midwest participated in 2 semistructured individual interviews. Interpretive phenomenological analysis, involving first-cycle, transition, and second cycle coding, was used to identify themes. SWs\u27 experiences indicated a need for a clear definition of resilience, and needs of young children, including EC programs that develop psychological resilience of children\u27s thoughts and an increase in adults to promote resilience. Additional research may expand and enhance educators\u27 and families\u27 understanding of resilience and help develop research-based preventive programs and strategies to foster psychological resilience in young children. These endeavors may enhance positive social change by adding components of psychological resilience to EC programs for school personnel and students and in parent/family workshops, which may result in sound mental health practices that enable them to become productive members of society

Deficits of knowledge versus executive control in semantic cognition: Insights from cued naming

Deficits of semantic cognition in semantic dementia and in aphasia consequent on CVA (stroke) are qualitatively different. Patients with semantic dementia are characterised by progressive degradation of central semantic representations, whereas multimodal semantic deficits in stroke aphasia reflect impairment of executive processes that help to direct and control semantic activation in a task-appropriate fashion [Jefferies, E., & Lambon Ralph, M. A. (2006). Semantic impairment in stroke aphasia vs. semantic dementia: A case-series comparison. Brain 129, 2132-2147]. We explored interactions between these two aspects of semantic cognition by examining the effects of cumulative phonemic cueing on picture naming in case series of these two types of patient. The stroke aphasic patients with multimodal semantic deficits cued very readily and demonstrated near-perfect name retrieval when cumulative phonemic cues reached or exceeded the target name's uniqueness point. Therefore, knowledge of the picture names was largely intact for the aphasic patients, but they were unable to retrieve this information without cues that helped to direct activation towards the target response. Equivalent phonemic cues engendered significant but much more limited benefit to the semantic dementia patients: their naming was still severely impaired even when most of the word had been provided. In contrast to the pattern in the stroke aphasia group, successful cueing was mainly confined to the more familiar un-named pictures. We propose that this limited cueing effect in semantic dementia follows from the fact that concepts deteriorate in a graded fashion [Rogers, T. T., Lambon Ralph, M. A., Garrard, P., Bozeat, S., McClelland, J. L., & Hodges, J. R., et al. (2004). The structure and deterioration of semantic memory: A neuropsychological and computational investigation. Psychological Review 111, 205-235]. For partially degraded items, the residual conceptual knowledge may be insufficient to drive speech production to completion but these items might reach threshold when they are bolstered by cues. (C) 2007 Elsevier Ltd. All rights reserved

Exploring mechanisms of insulin secretion regulators using C. elegans

Diabetes mellitus is a group of disorders characterized by disrupted glucose homeostasis. Diabetes is one of the most dangerous diseases worldwide since it affects currently more than 500 million people. The pathogenesis of the disease is associated with the insufficient production of insulin and is characterized by increased blood glucose levels. Insulin secretion takes place in pancreatic β-cells in the response to elevated glucose levels and is regulated by various factors. This thesis is aimed to understand the functions of three proteins and characterize their novel roles in the regulation of insulin signaling and secretion. The first study showed the role of ENPL-1 in the positive regulation of insulin secretion. Loss of enpl-1 resulted in reduced insulin signaling and inhibited insulin secretion. Furthermore, we identified proinsulin as a novel client protein of ENPL-1 and showed that ENPL-1 was required for its maturation. The next study was based on the previous findings showing that ASNA-1 is a positive regulator of insulin secretion. Our study showed that ASNA-1 is present in two redox states, oxidized and reduced and that the multiple functions of ASNA-1 are dependent on its redox states. Our analysis showed, that forcing ASNA-1 into the oxidized state, reduced its function of inserting tail-anchored proteins into the endoplasmic reticulum, without affecting the insulin secretion function. In the next study, we focused on the mutual role of both previously mentioned proteins. We identified the interaction of ASNA-1 and ENPL-1 and showed that proinsulin is required for this interaction to take place. Our study indicated that oxidized ASNA-1 rather than the reduced form was likely interacting with ENPL-1. In the last study, we focused on the role of a third protein, SMN-1, and its impact on the regulation of insulin secretion. Our analysis showed that loss of SMN-1 resulted in neuropeptide secretion defect and caused redistribution of insulin from its original place. In summary, we characterized the functions of three proteins and indicated their importance in the regulation of insulin secretion processes

Credibility Coefficients for Objects of Rough Sets

In this paper focus is set on data reliability. We propose a few methods, which calculate credibility coefficients for objects stored in decision tables. Credibility coefficient of object is a measure of its similarity with respect to the rest of the objects in the considered decision table. It can be very useful in detecting either corrupted data or abnormal and distinctive situations. It is assumed that the proper data appear in majority and can be separated from improper data by exploring mutual resemblance. The proposed methods take advantage of well known and widely used data mining technique - rough sets

Application of Credibility Coefficients Based on Decision Rules

Credibility coefficients reflect similarity of objects in respect to other ones in information systems. For decision tables we can use credibility coefficients based on decision rules. Knowledge discovery methods can extract rules from an information system. The knowledge represented by the rules may be not exact due to improper data. Calculation of credibility coefficients is based on an assumption that majority of data is correct and only a minor part may be improper. The main purpose of using credibility coefficients is to indicate to which group a particular object probably belongs. A main focus of the paper is set on an algorithm of calculating credibility coefficients and a presentation how credibility coefficients can be used. The algorithm of presented credibility coefficients is based on decision rules, which are generated using the rough set theory. Some remarks on practical results of identifying improper data by credibility coefficients are inserted in the paper as well