1,453 research outputs found

    Decision-Making and Depressive Symptomatology

    Get PDF
    Difficulty making decisions is a core symptom of depressive illness, but the nature of these difficulties has not been well characterized. The two studies presented herein use the same hypothetical scenarios that call for a decision. In Study 1, participants were asked to make and explain their decisions in a free-response format, as well as to describe their prior experiences with similar situations. The results suggest that those with more depressive symptoms make decisions that are less likely to further their interests. We also identified several interesting associations between features of decision-making and the presence of depressive symptoms. In Study 2, participants were guided through their decisions with simple decision tools to investigate whether the association between depressive symptoms and poor decisions is better accounted for by failure to use of good decision-making strategies, or by other factors, such as differences in priorities or goals. With this minimal intervention the quality of decisions no longer declined significantly as a function of depressive symptom severity. Moreover, few associations between depressive symptom severity and decision-related goals and priorities were evident, suggesting that the previously-exposed difficulties of depressed individuals with decision-making were largely the result of their failure to use effective decision-making techniques

    Current Challenges in Detecting Food Allergens by Shotgun and Targeted Proteomic Approaches: A Case Study on Traces of Peanut Allergens in Baked Cookies

    Get PDF
    There is a need for selective and sensitive methods to detect the presence of food allergens at trace levels in highly processed food products. In this work, a combination of non-targeted and targeted proteomics approaches are used to illustrate the difficulties encountered in the detection of the major peanut allergens Ara h 1, Ara h 2 and Ara h 3 from a representative processed food matrix. Shotgun proteomics was employed for selection of the proteotypic peptides for targeted approaches via selective reaction monitoring. Peanut presence through detection of the proteotypic Ara h 3/4 peptides AHVQVVDSNGNR (m/z 432.5, 3+) and SPDIYNPQAGSLK (m/z 695.4, 2+) was confirmed and the developed method was able to detect peanut presence at trace levels (≥10 μg peanut g−1 matrix) in baked cookies

    Micro-proteomics with iterative data analysis:proteome analysis in <i>C.elegans</i> at the single worm level

    Get PDF
    Proteomics studies typically analyze proteins at a population level, using extracts prepared from tens of thousands to millions of cells. The resulting measurements correspond to average values across the cell population and can mask considerable variation in protein expression and function between individual cells or organisms. Here, we report the development of micro-proteomics for the analysis of Caenorhabditis elegans, a eukaryote composed of 959 somatic cells and approximate to 1500 germ cells, measuring the worm proteome at a single organism level to a depth of approximate to 3000 proteins. This includes detection of proteins across a wide dynamic range of expression levels (&gt;6 orders of magnitude), including many chromatin-associated factors involved in chromosome structure and gene regulation. We apply the micro-proteomics workflow to measure the global proteome response to heat-shock in individual nematodes. This shows variation between individual animals in the magnitude of proteome response following heat-shock, including variable induction of heat-shock proteins. The micro-proteomics pipeline thus facilitates the investigation of stochastic variation in protein expression between individuals within an isogenic population of C. elegans. All data described in this study are available online via the Encyclopedia of Proteome Dynamics (), an open access, searchable database resource

    Causal Network Accounts Of Ill-being: Depression & Digital Well-being

    Get PDF
    Depression is a common and devastating instance of ill-being which deserves an account. Moreover, the ill-being of depression is impacted by digital technology: some uses of digital technology increase such ill-being while other uses of digital technology increase well-being. So a good account of ill-being would explicate the antecedents of depressive symptoms and their relief, digitally and otherwise. This paper borrows a causal network account of well-being and applies it to ill-being, particularly depression. Causal networks are found to provide a principled, coherent, intuitively plausible, and empirically adequate account of cases of depression in every-day and digital contexts. Causal network accounts of ill-being also offer philosophical, scientific, and practical utility. Insofar as other accounts of ill-being cannot offer these advantages, we should prefer causal network accounts of ill-being
    corecore