Phase fluctuations, dissipation and superfluid stiffness in d-wave superconductors

We study the effect of dissipation on quantum phase fluctuations in d-wave superconductors. Dissipation, arising from a nonzero low frequency optical conductivity which has been measured in experiments below $T_c$, has two effects: (1) a reduction of zero point phase fluctuations, and (2) a reduction of the temperature at which one crosses over to classical thermal fluctuations. For parameter values relevant to the cuprates, we show that the crossover temperature is still too large for classical phase fluctuations to play a significant role at low temperature. Quasiparticles are thus crucial in determining the linear temperature dependence of the in-plane superfluid stiffness. Thermal phase fluctuations become important at higher temperatures and play a role near $T_c$.Comment: Presentation improved, new references added (10 latex pages, 3 eps figures). submitted to PR

Dimers, Effective Interactions, and Pauli Blocking Effects in a Bilayer of Cold Fermionic Polar Molecules

We consider a bilayer setup with two parallel planes of cold fermionic polar molecules when the dipole moments are oriented perpendicular to the planes. The binding energy of two-body states with one polar molecule in each layer is determined and compared to various analytic approximation schemes in both coordinate- and momentum-space. The effective interaction of two bound dimers is obtained by integrating out the internal dimer bound state wave function and its robustness under analytical approximations is studied. Furthermore, we consider the effect of the background of other fermions on the dimer state through Pauli blocking, and discuss implications for the zero-temperature many-body phase diagram of this experimentally realizable system.Comment: 18 pages, 10 figures, accepted versio

Active Galactic Nuclei at the Crossroads of Astrophysics

Over the last five decades, AGN studies have produced a number of spectacular examples of synergies and multifaceted approaches in astrophysics. The field of AGN research now spans the entire spectral range and covers more than twelve orders of magnitude in the spatial and temporal domains. The next generation of astrophysical facilities will open up new possibilities for AGN studies, especially in the areas of high-resolution and high-fidelity imaging and spectroscopy of nuclear regions in the X-ray, optical, and radio bands. These studies will address in detail a number of critical issues in AGN research such as processes in the immediate vicinity of supermassive black holes, physical conditions of broad-line and narrow-line regions, formation and evolution of accretion disks and relativistic outflows, and the connection between nuclear activity and galaxy evolution.Comment: 16 pages, 5 figures; review contribution; "Exploring the Cosmic Frontier: Astrophysical Instruments for the 21st Century", ESO Astrophysical Symposia Serie

Layers of Cold Dipolar Molecules in the Harmonic Approximation

We consider the N-body problem in a layered geometry containing cold polar molecules with dipole moments that are polarized perpendicular to the layers. A harmonic approximation is used to simplify the hamiltonian and bound state properties of the two-body inter-layer dipolar potential are used to adjust this effective interaction. To model the intra-layer repulsion of the polar molecules, we introduce a repulsive inter-molecule potential that can be parametrically varied. Single chains containing one molecule in each layer, as well as multi-chain structures in many layers are discussed and their energies and radii determined. We extract the normal modes of the various systems as measures of their volatility and eventually of instability, and compare our findings to the excitations in crystals. We find modes that can be classified as either chains vibrating in phase or as layers vibrating against each other. The former correspond to acoustic and the latter to optical phonons. Instabilities can occur for large intra-layer repulsion and produce diverging amplitudes of molecules in the outer layers. Lastly, we consider experimentally relevant regimes to observe the structures.Comment: 17 pages, 20 figures, accepted versio

Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa

1000 Genomes-based metaanalysis identifies 10 novel loci for kidney function

HapMap imputed genome-wide association studies (GWAS) have revealed >50 loci at which common variants with minor allele frequency >5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-Analysis of kidney function based on the estimated glomerular filtration rate (EGFR) in 110,517 European ancestry participants using 1000 Genomes imputed data. We identified 10 novel loci with p-value < 5 × 10-8 previously missed by HapMap-based GWAS. Six of these loci (HOXD8, ARL15, PIK3R1, EYA4, ASTN2, and EPB41L3) are tagged by common SNPs unique to the 1000 Genomes reference panel. Using pathway analysis, we identified 39 significant (FDR < 0.05) genes and 127 significantly (FDR < 0.05) enriched gene sets, wh

Evaluating Activated Carbon-Water Sorption Coefficients of Organic Compounds Using a Linear Solvation Energy Relationship Approach and Sorbate Chemical Activities

A linear solvation energy relationship (LSER) approach was used to investigate the evolving contributions of intermolecular interactions controlling organic compound sorption by granular activated carbon (GAC) from water as a function of sorbate chemical activities. Using a particular GAC (20-40 mesh Darco), 14 sorption isotherms were measured using sorbates with diverse functional groups to represert the range of possible surface interactions, and the data for each sorbate were fit with the Freundlich equation. Using interpolated adsorption :coefficients, K(d) values (L/kg), LSERs for specific sorbate activities (0-1, 0.01, and 0.001 saturation) were deduced. These expressions revealed that the intermolecular interactions controlling sorption to our particular GAC from water evolved with sorbate activities, such that a global correlation dependent on sorbate activity was found: log K(d) (L/kg) = [(3.76 +/- 0.28) - (0.20 +/- 0.10) log a(i)]V + [(-0.80 +/- 0.14) - (0.48 +/- 0.05) log a(i)]S + [(-4.47 +/- 0.20) + (0.16 +/- 0.06) log a(i)]B + (0.73 +/- 0.28) - (0.24 +/- 0.09) log a(i) (N = 176, R(2) = 0.96), where log ai is the activity of sorbate i, V is McGowan's characteristic volume for the sorbate, S reflects the compound's polarity/polarizability, and B reflects the compound's electron-donation basicity. Hence, sorption was promoted by dispersive forces and was diminished for sorbates capable of proton acceptance/electron donation, although both of these became less important at higher sorbate activities. Other intermolecular interactions were only weakly contributing (e.g., the &quot;S&quot; term) or were not significant at all for this GAC (i.e., the &quot;R&quot; and &quot;A&quot; terms). This result implies the Freundlich coefficients, K(f), for sorbates are given by (3.76V - 0.80S - 4.47B + 0.73) + (0.20V + 0.48S - 0.16B + 0.24) log C(i,W)(satn), and their exponents, 1/n, are equal to -0.20V - 0.48S+ 0.16B + 0.76. The data set could also be used to deduce a sorbate concentration-dependent LSER which would be useful for estimating equilibrium sorption coefficients for new sorbates of interest: log K(d) (L/kg) = [(1.89 +/- 0.07) - (0.22 +/- 0.06) log C(iW)]V + [(0.90 +/- 0.05) - (0.48 +/- 0.03) log C(iw)]S + [(-2.36 +/- 0.07) + (0.30 +/- 0.05) log C(i,w)]B + (2.98 +/- 0.07) - (0.26 +/- 0.06) log C(iw) (N = 176, R(2) = 0.98), where log C(i,W) is the concentration in water of each sorbate (mg/L)