An optimized chiral nucleon-nucleon interaction at next-to-next-to-leading order

We optimize the nucleon-nucleon interaction from chiral effective field theory at next-to-next- to-leading order. The resulting new chiral force NNLOopt yields \chi^2 \approx 1 per degree of freedom for laboratory energies below approximately 125 MeV. In the A = 3, 4 nucleon systems, the contributions of three-nucleon forces are smaller than for previous parametrizations of chiral interactions. We use NNLOopt to study properties of key nuclei and neutron matter, and demonstrate that many aspects of nuclear structure can be understood in terms of this nucleon-nucleon interaction, without explicitly invoking three-nucleon forces.Comment: 6 pages, 4 figure

Modularity revisited: A novel dynamics-based concept for decomposing complex networks

Finding modules (or clusters) in large, complex networks is a challenging task, in particular if one is not interested in a full decomposition of the whole network into modules. We consider modular networks that also contain nodes that do not belong to one of modules but to several or to none at all. A new method for analyzing such networks is presented. It is based on spectral analysis of random walks on modular networks. In contrast to other spectral clustering approaches, we use different transition rules of the random walk. This leads to much more prominent gaps in the spectrum of the adapted random walk and allows for easy identification of the network's modular structure, and also identifying the nodes belonging to these modules. We also give a characterization of that set of nodes that do not belong to any module, which we call transition region. Finally, by analyzing the transition region, we describe an algorithm that identifies so called hub-nodes inside the transition region that are important connections between modules or between a module and the rest of the network. The resulting algorithms scale linearly with network size (if the network connectivity is sparse) and thus can also be applied to very large networks

Instabilities in the Nuclear Energy Density Functional

In the field of Energy Density Functionals (EDF) used in nuclear structure and dynamics, one of the unsolved issues is the stability of the functional. Numerical issues aside, some EDFs are unstable with respect to particular perturbations of the nuclear ground-state density. The aim of this contribution is to raise questions about the origin and nature of these instabilities, the techniques used to diagnose and prevent them, and the domain of density functions in which one should expect a nuclear EDF to be stable.Comment: Special issue "Open Problems in Nuclear Structure Theory" of Jour.Phys.G - accepted. 7 pages, 2 figure

Genetic and Environmental Influences in Delinquent Peer Affiliation: From the Peer Network Approach

Mainstream criminologists have long maintained that delinquent peer group formation is largely a function of family-environmental variables, and have ignored self-selection into peer groups because of genetic proclivities. A small number of recent studies, however, suggest that genes are implicated in delinquent peer affiliation. Given the potentially far-reaching implication of such research findings, the authors replicate Beaver, Wright, & DeLisi\u27s (2008) study, among others, using a direct measure of peer delinquency. That is, the authors analyze the Add Health genetic data employing a measure of peer delinquency which is based on the delinquency counts reported by peers themselves rather than respondents‘ self-reports. Even employing this alternative measure, their results clearly support the original study, providing further evidence of genetic underpinnings of delinquent peer group formation

Development and potential role of type-2 sodium-glucose transporter inhibitors for management of type 2 diabetes

There is a recognized need for new treatment options for type 2 diabetes mellitus (T2DM). Recovery of glucose from the glomerular filtrate represents an important mechanism in maintaining glucose homeostasis and represents a novel target for the management of T2DM. Recovery of glucose from the glomerular filtrate is executed principally by the type 2 sodium-glucose cotransporter (SGLT2). Inhibition of SGLT2 promotes glucose excretion and normalizes glycemia in animal models. First reports of specifically designed SGLT2 inhibitors began to appear in the second half of the 1990s. Several candidate SGLT2 inhibitors are currently under development, with four in the later stages of clinical testing. The safety profile of SGLT2 inhibitors is expected to be good, as their target is a highly specific membrane transporter expressed almost exclusively within the renal tubules. One safety concern is that of glycosuria, which could predispose patients to increased urinary tract infections. So far the reported safety profile of SGLT2 inhibitors in clinical studies appears to confirm that the class is well tolerated. Where SGLT2 inhibitors will fit in the current cascade of treatments for T2DM has yet to be established. The expected favorable safety profile and insulin-independent mechanism of action appear to support their use in combination with other antidiabetic drugs. Promotion of glucose excretion introduces the opportunity to clear calories (80–90 g [300–400 calories] of glucose per day) in patients that are generally overweight, and is expected to work synergistically with weight reduction programs. Experience will most likely lead to better understanding of which patients are likely to respond best to SGLT2 inhibitors, and under what circumstances

Variational Approach to Molecular Kinetics

The eigenvalues and eigenvectors of the molecular dynamics propagator (or transfer operator) contain the essential information about the molecular thermodynamics and kinetics. This includes the stationary distribution, the metastable states, and state-to-state transition rates. Here, we present a variational approach for computing these dominant eigenvalues and eigenvectors. This approach is analogous the variational approach used for computing stationary states in quantum mechanics. A corresponding method of linear variation is formulated. It is shown that the matrices needed for the linear variation method are correlation matrices that can be estimated from simple MD simulations for a given basis set. The method proposed here is thus to first define a basis set able to capture the relevant conformational transitions, then compute the respective correlation matrices, and then to compute their dominant eigenvalues and eigenvectors, thus obtaining the key ingredients of the slow kinetics