Brain data:Scanning, scraping and sculpting the plastic learning brain through neurotechnology

Neurotechnology is an advancing field of research and development with significant implications for education. As 'postdigital' hybrids of biological and informational codes, novel neurotechnologies combine neuroscience insights into the human brain with advanced technical development in brain imaging, brain-computer interfaces, neurofeedback platforms, brain stimulation and other neuroenhancement applications. Merging neurobiological knowledge about human life with computational technologies, neurotechnology exemplifies how postdigital science will play a significant role in societies and education in decades to come. As neurotechnology developments are being extended to education, they present potential for businesses and governments to enact new techniques of 'neurogovernance' by 'scanning' the brain, 'scraping' it for data and then 'sculpting' the brain toward particular capacities. The aim of this article is to critically review neurotechnology developments and implications for education. It examines the purposes to which neurotechnology development is being put in education, interrogating the commercial and governmental objectives associated with it and the neuroscientific concepts and expertise that underpin it. Finally, the article raises significant ethical and governance issues related to neurotechnology development and postdigital science that require concerted attention from education researchers

A double bind: youth activism, climate change, and education

The effects of climate change are becoming ever clearer. Young people’s participation in movements demanding action on climate change has grown and achieved new visibility. Yet the relations between climate change and education remain under-theorised. Such a theorisation should, we argue, take account of the current disconnection between climate change education and action, and the exclusion of the complex social, cultural, aesthetic and political effects of climate change from curricula. Further, it should consider a changed relation to young people as political subjects, that takes climate action as the moment and means of a more imaginative, interdisciplinary climate change education. Finally, we must confront the contradiction of such a climate change education to fundamental aspects of formal schooling including its governmental function. This special issue draws participants and contributions from across four continents and includes papers that take global or transnational perspectives and foreground the perspectives of Indigenous peoples. Its contributions engage with the problematic of climate change education by exploring the relationship between youth climate activism and education in terms of both education’s responsibility to foster generative encounters with young people whose futures will be conditioned by climate change, and the role of young people’s climate activism in disrupting and changing educational systems. Collectively the contributions pursue three broad lines of inquiry: (i) the dramatisation and visualisation of climate change for and by young people, (ii) the need for culturally responsive frameworks for climate change education, (iii) alternative pedagogical approaches that bring climate activism and education together

NEMHESYS-European Perspective on the Implementation of Next-generation Sequencing Into Clinical Diagnostics

© 2021 the Author(s).NGS Establishment in Multidisciplinary Healthcare (NEMHESYS) is an Erasmus+ programme with the purpose of providing qualified staff with the essential technical and bioinformatic knowledge and skills on next-generation sequencing (NGS) to be able to carry out NGS studies and perform some of the most common types of analyses. The clinical application of NGS has become easier with advancements in technologies.1 However, the investment needed to bring NGS into medical practice remains significant, with the scale of knowledge required being unprecedented at most hospitals. In addition, these novel technologies bring new challenges in translating NGS to clinical practice, at both technical and regulatory level, in terms of data management, interpretation of the results, and genetic counseling.2,3 All these aspects justify the consideration of what will be the precise role of NGS in diagnosis, risk assessment, response prediction, and treatment monitoring, today and tomorrow. Thus, to evaluate the implementation of NGS in European healthcare/research centers, a mapping survey was carried out, based on previous NGS mapping studies.This work was supported by Erasmus+ programme (European Commission, Call EAC/A03/2018 NEMHESYS_612639-EPP-1-2019-ES-EPPKA2-KA)

Automated facial expression classification and affect interpretation using infrared measurement of facial skin temperature

Machines would require the ability to perceive and adapt to affects for achieving artificial sociability. Most autonomous systems use Automated Facial Expression Classification (AFEC) and Automated Affect Interpretation (AAI) to achieve sociability. Varying lighting conditions, occlusion, and control over physiognomy can influence the real life performance of vision-based AFEC systems. Physiological signals provide complementary information for AFEC and AAI. We employed transient facial thermal features for AFEC and AAI. Infrared thermal images with participants’ normal expression and intentional expressions of happiness, sadness, disgust, and fear were captured. Facial points that undergo significant thermal changes with a change in expression termed as Facial Thermal Feature Points (FTFPs) were identified. Discriminant analysis was invoked on principal components derived from the Thermal Intensity Values (TIVs) recorded at the FTFPs. The crossvalidation and person-independent classification respectively resulted in 66.28% and 56.0% success rates. Classification significance tests suggest that (1) like other physiological cues, facial skin temperature also provides useful information about affective states and their facial expression; (2) patterns of facial skin temperature variation can complement other cues for AFEC and AAI; and (3) infrared thermal imaging may help achieve artificial sociability in robots and autonomous systems