Chuan He

This paper lays out the parameters of the term “ethical” itself, distinguishing the ethical from the moral. I then construe ethical experience by reflecting upon interviews with workers and student at a Beijing high school. Ethical life is the degree of recognition of the life these subjects gain through participating in a sociocultural life with other free and equal selves. Such recognition fails in the ideal, engendering the ethical experience of performative contradictions. I attend closely to the interviewees’ rational transitions—their ways of reasoning and understanding experiences—not as they follow logical ideals, but as material inferences arising from their lived experience. As they manifest wishes for a better world, I reflect on their cognition of a participatory life inside and outside of China, which limits a conceptual scheme implying forms of curricular practice that acknowledge, protect, and foster the integrity of injurable selves in an environment of injurable others.

ObjectiveDespite its prevalence, insomnia disorder remains poorly understood. In this study, we used machine learning to analyze the functional connectivity disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimulation and pharmacotherapy.Materials and methods51 adult patients with chronic insomnia and 42 healthy age and education matched controls underwent baseline anatomical T1 magnetic resonance imaging, resting-stage functional MRI, and diffusion weighted imaging. Imaging was repeated for 24 ID patients following four weeks of treatment with pharmacotherapy, with or without rTMS. A recently developed machine learning technique, Hollow Tree Super was used to classify subjects into ID and control groups based on their FC, and derive network and parcel-based FC features contributing to each model. The number of FC anomalies within each network was also compared between responders and non-responders using median absolute deviation at baseline and follow-up.ResultsSubjects were classified into ID and control with an area under the receiver operating characteristic curve of 0.828. Baseline FC anomaly counts were higher in responders than non-responders. Response as measured by the Insomnia Severity Index was associated with a decrease in anomaly counts across all networks, while all networks showed an increase in anomaly counts when response was measured using the Pittsburgh Sleep Quality Index. Overall, responders also showed greater change in all networks, with the Default Mode Network demonstrating the greatest change.ConclusionMachine learning analysis into the functional connectome in ID may provide useful insight into diagnostic and therapeutic targets.