2,891 research outputs found

    Neo-Aristotelian Naturalism and the Evolutionary Objection: Rethinking the Relevance of Empirical Science

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    Neo-Aristotelian metaethical naturalism is a modern attempt at naturalizing ethics using ideas from Aristotle’s teleological metaphysics. Proponents of this view argue that moral virtue in human beings is an instance of natural goodness, a kind of goodness supposedly also found in the realm of non-human living things. Many critics question whether neo-Aristotelian naturalism is tenable in light of modern evolutionary biology. Two influential lines of objection have appealed to an evolutionary understanding of human nature and natural teleology to argue against this view. In this paper, I offer a reconstruction of these two seemingly different lines of objection as raising instances of the same dilemma, giving neo-Aristotelians a choice between contradicting our considered moral judgment and abandoning metaethical naturalism. I argue that resolving the dilemma requires showing a particular kind of continuity between the norms of moral virtue and norms that are necessary for understanding non-human living things. I also argue that in order to show such a continuity, neo-Aristotelians need to revise the relationship they adopt with empirical science and acknowledge that the latter is relevant to assessing their central commitments regarding living things. Finally, I argue that to move this debate forward, both neo-Aristotelians and their critics should pay attention to recent work on the concept of organism in evolutionary and developmental biology

    Robot rights? Towards a social-relational justification of moral consideration \ud

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    Should we grant rights to artificially intelligent robots? Most current and near-future robots do not meet the hard criteria set by deontological and utilitarian theory. Virtue ethics can avoid this problem with its indirect approach. However, both direct and indirect arguments for moral consideration rest on ontological features of entities, an approach which incurs several problems. In response to these difficulties, this paper taps into a different conceptual resource in order to be able to grant some degree of moral consideration to some intelligent social robots: it sketches a novel argument for moral consideration based on social relations. It is shown that to further develop this argument we need to revise our existing ontological and social-political frameworks. It is suggested that we need a social ecology, which may be developed by engaging with Western ecology and Eastern worldviews. Although this relational turn raises many difficult issues and requires more work, this paper provides a rough outline of an alternative approach to moral consideration that can assist us in shaping our relations to intelligent robots and, by extension, to all artificial and biological entities that appear to us as more than instruments for our human purpose

    AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis.

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    Funder: Horizon 2020 Framework Programme; doi: http://dx.doi.org/10.13039/100010661Funder: Medical University of ViennaPURPOSE: To assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI can discriminate between benign and malignant breast lesions. METHODS: A population of 102 patients with 120 breast lesions (101 malignant and 19 benign) detected on ultrasound and/or mammography was prospectively enrolled. All patients underwent hybrid 18F-FDG PET/MRI for diagnostic purposes. Quantitative parameters were extracted from DCE (MTT, VD, PF), DW (mean ADC of breast lesions and contralateral breast parenchyma), PET (SUVmax, SUVmean, and SUVminimum of breast lesions, as well as SUVmean of the contralateral breast parenchyma), and T2-weighted images. Radiomics features were extracted from DCE, T2-weighted, ADC, and PET images. Different diagnostic models were developed using a fine Gaussian support vector machine algorithm which explored different combinations of quantitative parameters and radiomics features to obtain the highest accuracy in discriminating between benign and malignant breast lesions using fivefold cross-validation. The performance of the best radiomics and ML model was compared with that of expert reader review using McNemar's test. RESULTS: Eight radiomics models were developed. The integrated model combining MTT and ADC with radiomics features extracted from PET and ADC images obtained the highest accuracy for breast cancer diagnosis (AUC 0.983), although its accuracy was not significantly higher than that of expert reader review (AUC 0.868) (p = 0.508). CONCLUSION: A radiomics and ML model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric 18F-FDG PET/MRI images can accurately discriminate between benign and malignant breast lesions

    Determination of Pericardial Adipose Tissue Increases the Prognostic Accuracy of Coronary Artery Calcification for Future Cardiovascular Events

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    Objectives: Pericardial adipose tissue (PAT) is associated with coronary artery plaque accumulation and the incidence of coronary heart disease. We evaluated the possible incremental prognostic value of PAT for future cardiovascular events. Methods: 145 patients (94 males, age 60 10 years) with stable coronary artery disease underwent coronary artery calcification (CAC) scanning in a multislice CT scanner, and the volume of pericardial fat was measured. Mean observation time was 5.4 years. Results: 34 patients experienced a severe cardiac event. They had a significantly higher CAC score (1,708 +/- 2,269 vs. 538 +/- 1,150, p 400, 3.5 (1.9-5.4; p = 0.007) for scores > 800 and 5.9 (3.7-7.8; p = 0.005) for scores > 1,600. When additionally a PAT volume > 200 cm(3) was determined, there was a significant increase in the event rate and relative risk. We calculated a relative risk of 2.9 (1.9-4.2; p = 0.01) for scores > 400, 4.0 (2.1-5.0; p = 0.006) for scores > 800 and 7.1 (4.1-10.2; p = 0.005) for scores > 1,600. Conclusions:The additional determination of PAT increases the predictive power of CAC for future cardiovascular events. PAT might therefore be used as a further parameter for risk stratification. Copyright (C) 2012 S. Karger AG, Base
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