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      • Record: found
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      Clustering Consumers Based on Trust, Confidence and Giving Behaviour: Data-Driven Model Building for Charitable Involvement in the Australian Not-For-Profit Sector

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      PLoS ONE
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          Abstract

          Organisations in the Not-for-Profit and charity sector face increasing competition to win time, money and efforts from a common donor base. Consequently, these organisations need to be more proactive than ever. The increased level of communications between individuals and organisations today, heightens the need for investigating the drivers of charitable giving and understanding the various consumer groups, or donor segments, within a population. It is contended that `trust' is the cornerstone of the not-for-profit sector's survival, making it an inevitable topic for research in this context. It has become imperative for charities and not-for-profit organisations to adopt for-profit's research, marketing and targeting strategies. This study provides the not-for-profit sector with an easily-interpretable segmentation method based on a novel unsupervised clustering technique (MST-kNN) followed by a feature saliency method (the CM1 score). A sample of 1,562 respondents from a survey conducted by the Australian Charities and Not-for-profits Commission is analysed to reveal donor segments. Each cluster's most salient features are identified using the CM1 score. Furthermore, symbolic regression modelling is employed to find cluster-specific models to predict `low' or `high' involvement in clusters. The MST-kNN method found seven clusters. Based on their salient features they were labelled as: the `non-institutionalist charities supporters', the `resource allocation critics', the `information-seeking financial sceptics', the `non-questioning charity supporters', the `non-trusting sceptics', the `charity management believers' and the `institutionalist charity believers'. Each cluster exhibits their own characteristics as well as different drivers of `involvement'. The method in this study provides the not-for-profit sector with a guideline for clustering, segmenting, understanding and potentially targeting their donor base better. If charities and not-for-profit organisations adopt these strategies, they will be more successful in today's competitive environment.

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          Most cited references7

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          Is Open Access

          Genome-wide analysis of long noncoding RNA stability

          Transcriptomic analyses have identified tens of thousands of intergenic, intronic, and cis -antisense long noncoding RNAs (lncRNAs) that are expressed from mammalian genomes. Despite progress in functional characterization, little is known about the post-transcriptional regulation of lncRNAs and their half-lives. Although many are easily detectable by a variety of techniques, it has been assumed that lncRNAs are generally unstable, but this has not been examined genome-wide. Utilizing a custom noncoding RNA array, we determined the half-lives of ∼800 lncRNAs and ∼12,000 mRNAs in the mouse Neuro-2a cell line. We find only a minority of lncRNAs are unstable. LncRNA half-lives vary over a wide range, comparable to, although on average less than, that of mRNAs, suggestive of complex metabolism and widespread functionality. Combining half-lives with comprehensive lncRNA annotations identified hundreds of unstable (half-life < 2 h) intergenic, cis -antisense, and intronic lncRNAs, as well as lncRNAs showing extreme stability (half-life > 16 h). Analysis of lncRNA features revealed that intergenic and cis -antisense RNAs are more stable than those derived from introns, as are spliced lncRNAs compared to unspliced (single exon) transcripts. Subcellular localization of lncRNAs indicated widespread trafficking to different cellular locations, with nuclear-localized lncRNAs more likely to be unstable. Surprisingly, one of the least stable lncRNAs is the well-characterized paraspeckle RNA Neat1 , suggesting Neat1 instability contributes to the dynamic nature of this subnuclear domain. We have created an online interactive resource ( http://stability.matticklab.com ) that allows easy navigation of lncRNA and mRNA stability profiles and provides a comprehensive annotation of ∼7200 mouse lncRNAs.
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            Behavioral economics. Avoiding overhead aversion in charity.

            Donors tend to avoid charities that dedicate a high percentage of expenses to administrative and fundraising costs, limiting the ability of nonprofits to be effective. We propose a solution to this problem: Use donations from major philanthropists to cover overhead expenses and offer potential donors an overhead-free donation opportunity. A laboratory experiment testing this solution confirms that donations decrease when overhead increases, but only when donors pay for overhead themselves. In a field experiment with 40,000 potential donors, we compared the overhead-free solution with other common uses of initial donations. Consistent with prior research, informing donors that seed money has already been raised increases donations, as does a $1:$1 matching campaign. Our main result, however, clearly shows that informing potential donors that overhead costs are covered by an initial donation significantly increases the donation rate by 80% (or 94%) and total donations by 75% (or 89%) compared with the seed (or matching) approach.
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              'Neuroinflammation' differs categorically from inflammation: transcriptomes of Alzheimer's disease, Parkinson's disease, schizophrenia and inflammatory diseases compared.

              'Neuroinflammation' has become a widely applied term in the basic and clinical neurosciences but there is no generally accepted neuropathological tissue correlate. Inflammation, which is characterized by the presence of perivascular infiltrates of cells of the adaptive immune system, is indeed seen in the central nervous system (CNS) under certain conditions. Authors who refer to microglial activation as neuroinflammation confuse this issue because autoimmune neuroinflammation serves as a synonym for multiple sclerosis, the prototypical inflammatory disease of the CNS. We have asked the question whether a data-driven, unbiased in silico approach may help to clarify the nomenclatorial confusion. Specifically, we have examined whether unsupervised analysis of microarray data obtained from human cerebral cortex of Alzheimer's, Parkinson's and schizophrenia patients would reveal a degree of relatedness between these diseases and recognized inflammatory conditions including multiple sclerosis. Our results using two different data analysis methods provide strong evidence against this hypothesis demonstrating that very different sets of genes are involved. Consequently, the designations inflammation and neuroinflammation are not interchangeable. They represent different categories not only at the histophenotypic but also at the transcriptomic level. Therefore, non-autoimmune neuroinflammation remains a term in need of definition.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2015
                7 April 2015
                : 10
                : 4
                : e0122133
                Affiliations
                [1 ]Centre for Bioinformatics, Biomarker Discovery & Information-Based Medicine, The University of Newcastle, Callaghan, New South Wales, Australia
                [2 ]Faculdade de Medicina de Ribeirao Preto, Universidade de São Paulo, São Paulo, Brazil
                East China University of Science and Technology, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: PM NJdV. Performed the experiments: RR NJdV. Analyzed the data: NJdV PM RR. Contributed reagents/materials/analysis tools: PM RR. Wrote the paper: NJdV RR PM. Data pre-processing: RR. Cluster analysis: NJdV PM.

                Article
                PONE-D-14-53731
                10.1371/journal.pone.0122133
                4388642
                25849547
                b9edcb3d-e122-4b36-859c-57ec747f8e74
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 1 December 2014
                : 11 February 2015
                Page count
                Figures: 9, Tables: 4, Pages: 28
                Funding
                The authors have no support or funding to report.
                Categories
                Research Article
                Custom metadata
                All data are publicly available online through the ACNC website ( https://www.acnc.gov.au/ACNC/Pblctns/Rpts/ACNC/Publications/Reports/Trust_con.aspx).

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