Reality and its Shadow, a brief yet powerful essay written in 1948, is the only text where Emmanuel Levinas deals solely with the ontology of art. Already in this early text, we can see how his understanding that ethics is the ground of philosophy drives his discussion. The nature of art is therefore treated in relation to what it does, ethically, to the subject, the maker, and the viewer. Art is the “inhumanity” and “inversion” of ethics. Only philosophical criticism reintegrates (...) its “inhumanity” in the ethical relation. The strength of Levinas’s philosophy issues from a pre-cognitive commitment to the “other”, epitomised in the “face to face” relation. Any philosophy emphasising the primacy of the subject over and above the “other” crumbles under his reading. Yet this same strength implies that those domains where the “face to face” relation is obscured lead to irresponsibility. One such domain is art. In this essay I argue that by applying his mature work to the criticism he advances in Reality and its Shadow we can find ethical value in art in virtue of its “inhumanity” and “inversion”. That is, we can agree with Levinas that art leads to irresponsibility, and yet ascribe to it positive ethical value in Levinas's own terms. This can help concretise the tension between the ethical and unethical aspects of art within a Levinasian framework. (shrink)

The concept of an individual, liberal data subject, who was traditionally at the centre of data protection efforts has recently come under scrutiny. At the same time, the particularly destructive effect of digital technology on Black people establishes the need for an analysis that not only considers but brings racial dimensions to the forefront. I argue that because Afrofuturism situates the Black struggle in persistent, yet continuously changing structural disparities and power relations, it offers a powerful departure point for re-imagining (...) data protection. Sketching an Afrofuturist data subject then centres on radical subjectivity, collectivity, and contextuality. (shrink)

Abstract: Retama raetam (RR) is a traditional medicinal plant belongs to fabaceae family which grows in North Africa and East Mediterranean region. Locally, RR is used in several diseases including diabetes mellitus and hypertension. Thus, this study aims to investigate certain behavioral and central effects of methanolic extract of RR seeds in experimental animals (male Albino adult mice of 20 – 35 gm). Three exploratory behavioral models are used in this study, open field, elevated plus maze and light-dark box models, (...) in addition, picrotoxin induced seizure model in mice. In elevated plus-maze test of anxiety, a dose of 25 mg/kg of RR induced a significant increase in number of open arms entries and time spent on open arms of the maze compared to the control. In light-dark model, time spent on light area and number of light-dark transitions are significantly increased after treatment with 25 mg/kg RR extract of seeds. Open field model also revealed a significant increase in general motor activity of the mouse after 25 mg/kg of RR. On the other hand, a dose of 50 mg/kg of RR significantly induced a profound central and peripheral analgesic responses. In summary, this study concludes that RR seeds have a profound anxiolytic-like effect and analgesic response with delay in latency time of seizure induced by picrotoxin in mice. (shrink)

Multinational companies commonly and increasingly undertake their research in low and middle-income countries through commercial clinical research organizations (CROs). The involvement of these scientific middle men complicates the application of the theories of justice. We examine those complexities, and conclude that while the difficulties are not immune to analysis in terms of these theories, the theories have to be deployed in new ways in order to be useful in the new commercial world.

This study employs a neural network to analyze the connection between smoking, drinking, and various health-related factors using a dataset of 5148 samples. Achieving an impressive 99.94% accuracy and an average training error of 0.0016, the model identifies influential factors such as serum aminotransferases, serum creatinine, sex, weight, and triglyceride levels. These findings enhance our understanding of lifestyle choices and their impact on health. This research underscores the potential of machine learning in studying complex health phenomena.