Deregulation Using Stealth “Science” Strategies

In this Article, we explore the “stealth” use of science by the Executive Branch to advance deregulation and highlight the limited, existing legal and institutional constraints in place to discipline and discourage these practices. Political appointees have employed dozens of strategies over the years, in both Democratic and Republican administrations, to manipulate science in ends-oriented ways that advance the goal of deregulation. Despite this bald manipulation of science, however, the officials frequently present these strategies as necessary to bring “sound science” to bear on regulatory decisions. To begin to address this problem, it is important to reconceptualize how the administrative state addresses science-intensive decisions. Rather than allow agencies and the White House to operate as a cohesive unit, institutional bounds should be drawn around the scientific expertise lodged within the agencies. We propose that the background scientific work prepared by agency staff should be firewalled from the evaluative, policymaking input of the remaining officials, including politically appointed officials, in the agency

Quantum Moment Map and Invariant Integration Theory on Quantum Spaces

It is shown that, on the one hand, quantum moment maps give rise to examples for the operator-theoretic approach to invariant integration theory developed by K.-D. Kürsten and the second author, and that, on the other hand, the operator-theoretic approach to invariant integration theory is more general since it also applies to examples without a well-defined quantum moment map

A simple physical model for scaling in protein-protein interaction networks

It has recently been demonstrated that many biological networks exhibit a scale-free topology where the probability of observing a node with a certain number of edges (k) follows a power law: i.e. p(k) ~ k^-g. This observation has been reproduced by evolutionary models. Here we consider the network of protein-protein interactions and demonstrate that two published independent measurements of these interactions produce graphs that are only weakly correlated with one another despite their strikingly similar topology. We then propose a physical model based on the fundamental principle that (de)solvation is a major physical factor in protein-protein interactions. This model reproduces not only the scale-free nature of such graphs but also a number of higher-order correlations in these networks. A key support of the model is provided by the discovery of a significant correlation between number of interactions made by a protein and the fraction of hydrophobic residues on its surface. The model presented in this paper represents the first physical model for experimentally determined protein-protein interactions that comprehensively reproduces the topological features of interaction networks. These results have profound implications for understanding not only protein-protein interactions but also other types of scale-free networks.Comment: 50 pages, 17 figure

Non-universality of artificial frustrated spin systems

Magnetic frustration effects in artificial kagome arrays of nanomagnets with out-of-plane magnetization are investigated using Magnetic Force Microscopy and Monte Carlo simulations. Experimental and theoretical results are compared to those found for the artificial kagome spin ice, in which the nanomagnets have in-plane magnetization. In contrast with what has been recently reported, we demonstrate that long range (i.e. beyond nearest-neighbors) dipolar interactions between the nanomagnets cannot be neglected when describing the magnetic configurations observed after demagnetizing the arrays using a field protocol. As a consequence, there are clear limits to any universality in the behavior of these two artificial frustrated spin systems. We provide arguments to explain why these two systems show striking similarities at first sight in the development of pairwise spin correlations.Comment: 7 pages, 6 figure