Epistemic policy networks in the European Union’s CBRN risk mitigation policy

This paper offers insights into an innovative and currently flagship approach of the European Union (EU) to the mitigation of chemical, biological, radiological, and nuclear (CBRN) risks. Building on its long-time experience in the CBRN field, the EU has incorporated methods familiar to the students of international security governance: it is establishing regional networks of experts and expertise. CBRN Centers of Excellence, as they are officially called, aim to contribute to the security and safety culture in different parts of Africa, the Middle East, South East Asia, and South East Europe, in the broadly construed CBRN area. These regional networks represent a modern form of security cooperation, which can be conceptualized as an epistemic policy networks approach. It offers flexibility to the participating states, which have different incentives to get involved. At the same, however, the paper identifies potential limitations and challenges of epistemic policy networks in this form

Separability criteria and entanglement witnesses for symmetric quantum states

We study the separability of symmetric bipartite quantum states and show that a single correlation measurement is sufficient to detect the entanglement of any bipartite symmetric state with a non-positive partial transpose. We also discuss entanglement conditions and entanglement witnesses for states with a positive partial transpose.Comment: 5 pages, no figures, LaTeX; v2: typos corrected, introduction extended; v3: small corrections, published version; for the proceedings of the DPG spring meeting, Hamburg, March 200

Analysis of shared heritability in common disorders of the brain

Paroxysmal Cerebral Disorder

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine