Quasi-particle Lifetimes in a d_{x^2-y^2} Superconductor

We consider the lifetime of quasi-particles in a d-wave superconductor due to scattering from antiferromagnetic spin-fluctuations, and explicitly separate the contribution from Umklapp processes which determines the electrical conductivity. Results for the temperature dependence of the total scattering rate and the Umklapp scattering rate are compared with relaxation rates obtained from thermal and microwave conductivity measurements, respectively.Comment: 14 pages, 4 figure

Optical symmetries and anisotropic transport in high-Tc superconductors

A simple symmetry analysis of in-plane and out-of-plane transport in a family of high temperature superconductors is presented. It is shown that generalized scaling relations exist between the low frequency electronic Raman response and the low frequency in-plane and out-of-plane conductivities in both the normal and superconducting states of the cuprates. Specifically, for both the normal and superconducting state, the temperature dependence of the low frequency $B_{1g}$ Raman slope scales with the $c-$axis conductivity, while the $B_{2g}$ Raman slope scales with the in-plane conductivity. Comparison with experiments in the normal state of Bi-2212 and Y-123 imply that the nodal transport is largely doping independent and metallic, while transport near the BZ axes is governed by a quantum critical point near doping $p\sim 0.22$ holes per CuO$_{2}$ plaquette. Important differences for La-214 are discussed. It is also shown that the $c-$ axis conductivity rise for $T\ll T_{c}$ is a consequence of partial conservation of in-plane momentum for out-of-plane transport.Comment: 16 pages, 8 Figures (3 pages added, new discussion on pseudogap and charge ordering in La214

N-body simulations of gravitational dynamics

We describe the astrophysical and numerical basis of N-body simulations, both of collisional stellar systems (dense star clusters and galactic centres) and collisionless stellar dynamics (galaxies and large-scale structure). We explain and discuss the state-of-the-art algorithms used for these quite different regimes, attempt to give a fair critique, and point out possible directions of future improvement and development. We briefly touch upon the history of N-body simulations and their most important results.Comment: invited review (28 pages), to appear in European Physics Journal Plu

The genetic architecture of the human cerebral cortex

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function

Mapping and characterization of structural variation in 17,795 human genomes

A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline1 to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0–11.2% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing

Boosting the Performance of Nearest Neighbour Methods with Feature Selection

This paper describes a Nearest Neighbour procedure for variable selection in function approximation, pattern classification, and time series prediction. Given a training set of input/output vector pairs the procedure identifies a subset of input vector components that effectively capture the input-output relationship implicit in the training set. The utility of this procedure is demonstrated with numerous data sets from the UCI repository of machine learning databases and the Mackey-Glass time series prediction. A comprehensive set of benchmark problems is used to demonstrate comparable performance to that of much more complex boosted C4.5 decision trees