5,202 research outputs found
Decius S. Wade\u27s The Common Law
Decius S. Wade\u27s The Common La
Thermodynamically stable lithium silicides and germanides from density-functional theory calculations
Density-functional-theory (DFT) calculations have been performed on the Li-Si
and Li-Ge systems. Lithiated Si and Ge, including their metastable phases, play
an important technological r\^ole as Li-ion battery (LIB) anodes. The
calculations comprise structural optimisations on crystal structures obtained
by swapping atomic species to Li-Si and Li-Ge from the X-Y structures in the
International Crystal Structure Database, where X={Li,Na,K,Rb,Cs} and
Y={Si,Ge,Sn,Pb}. To complement this at various Li-Si and Li-Ge stoichiometries,
ab initio random structure searching (AIRSS) was also performed. Between the
ground-state stoichiometries, including the recently found LiSi
phase, the average voltages were calculated, indicating that germanium may be a
safer alternative to silicon anodes in LIB, due to its higher lithium insertion
voltage. Calculations predict high-density LiSi and LiGe
layered phases which become the ground state above 2.5 and 5 GPa
respectively and reveal silicon and germanium's propensity to form dumbbells in
the LiSi, stoichiometry range. DFT predicts the stability of
the LiGe , LiGe and LiGe
phases and several new Li-Ge compounds, with stoichiometries LiGe,
LiGe, LiGe and LiGe.Comment: 10 pages, 5 figure
Ultracold atoms at unitarity within quantum Monte Carlo
Variational and diffusion quantum Monte Carlo (VMC and DMC) calculations of
the properties of the zero-temperature fermionic gas at unitarity are reported.
The ratio of the energy of the interacting to the non-interacting gas for a
system of 128 particles is calculated to be 0.4517(3) in VMC and 0.4339(1) in
the more accurate DMC method. The spherically-averaged pair-correlation
functions, momentum densities, and one-body density matrices are very similar
in VMC and DMC, but the two-body density matrices and condensate fractions show
some differences. Our best estimate of the condensate fraction of 0.51 is a
little smaller than values from other quantum Monte Carlo calculations
Energetics of hydrogen/lithium complexes in silicon analyzed using the Maxwell construction
We have studied hydrogen/lithium complexes in crystalline silicon using
density-functional-theory methods and the ab initio random structure searching
(AIRSS) method for predicting structures. A method based on the Maxwell
construction and convex hull diagrams is introduced which gives a graphical
representation of the relative stabilities of point defects in a crystal and
enables visualization of the changes in stability when the chemical potentials
are altered. We have used this approach to study lithium and hydrogen
impurities in silicon, which models aspects of the anode material in the
recently-suggested lithium-ion batteries. We show that hydrogen may play a role
in these anodes, finding that hydrogen atoms bind to three-atom lithium
clusters in silicon, forming stable {H,3Li} and {2H,3Li} complexes, while the
{H,2Li} complex is almost stable.Comment: (5 pages, 4 figures
Linkage disequilibrium mapping via cladistic analysis of phase-unknown genotypes and inferred haplotypes in the Genetic Analysis Workshop 14 simulated data
We recently described a method for linkage disequilibrium (LD) mapping, using cladistic analysis of phased single-nucleotide polymorphism (SNP) haplotypes in a logistic regression framework. However, haplotypes are often not available and cannot be deduced with certainty from the unphased genotypes. One possible two-stage approach is to infer the phase of multilocus genotype data and analyze the resulting haplotypes as if known. Here, haplotypes are inferred using the expectation-maximization (EM) algorithm and the best-guess phase assignment for each individual analyzed. However, inferring haplotypes from phase-unknown data is prone to error and this should be taken into account in the subsequent analysis. An alternative approach is to analyze the phase-unknown multilocus genotypes themselves. Here we present a generalization of the method for phase-known haplotype data to the case of unphased SNP genotypes. Our approach is designed for high-density SNP data, so we opted to analyze the simulated dataset. The marker spacing in the initial screen was too large for our method to be effective, so we used the answers provided to request further data in regions around the disease loci and in null regions. Power to detect the disease loci, accuracy in localizing the true site of the locus, and false-positive error rates are reported for the inferred-haplotype and unphased genotype methods. For this data, analyzing inferred haplotypes outperforms analysis of genotypes. As expected, our results suggest that when there is little or no LD between a disease locus and the flanking region, there will be no chance of detecting it unless the disease variant itself is genotyped
An Evaluation of Statistical Approaches to Rare Variant Analysis in Genetic Association Studies
Genome-wide association (GWA) studies have proved to be extremely successful in identifying novel common polymorphisms contributing effects to the genetic component underlying complex traits. Nevertheless, one source of, as yet, undiscovered genetic determinants of complex traits are those mediated through the effects of rare variants. With the increasing availability of large-scale re-sequencing data for rare variant discovery, we have developed a novel statistical method for the detection of complex trait associations with these loci, based on searching for accumulations of minor alleles within the same functional unit. We have undertaken simulations to evaluate strategies for the identification of rare variant associations in population-based genetic studies when data are available from re-sequencing discovery efforts or from commercially available GWA chips. Our results demonstrate that methods based on accumulations of rare variants discovered through re-sequencing offer substantially greater power than conventional analysis of GWA data, and thus provide an exciting opportunity for future discovery of genetic determinants of complex traits. Genet. Epidemiol. 34: 188–193, 2010. © 2009 Wiley-Liss, Inc
A Comparison of Sample Size and Power in Case-Only Association Studies of Gene-Environment Interaction
Assuming continuous, normally distributed environmental and categorical genotype variables, the authors compare 6 case-only designs for tests of association in gene-environment interaction. Novel tests modeling the environmental variable as either the response or the predictor and allowing a genetic variable with multiallelic variants are included. The authors show that tests imposing the same genotypic pattern of inheritance perform similarly regardless of whether genotype is the response variable or the predictor variable. The novel tests using the genetic variable as the response variable are advantageous because they are robust to non-normally distributed environmental exposures. Dominance deviance—deviation from additivity in the main or interaction effects—is key to test performance: When it is zero or modest, tests searching for a trend with increasing risk alleles are optimal; when it is large, tests for genotypic effects are optimal. However, the authors show that dominance deviance is attenuated when it is observed at a proxy locus, which is common in genome-wide association studies, so large dominance deviance is likely to be rare. The authors conclude that the trend test is the appropriate tool for large-scale association scans where the true gene-environment interaction model is unknown. The common practice of assuming a dominant pattern of inheritance can cause serious losses of power in the presence of any recessive, or modest dominant, effects
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