Parquet approach to nonlocal vertex functions and electrical conductivity of disordered electrons

A diagrammatic technique for two-particle vertex functions is used to describe systematically the influence of spatial quantum coherence and backscattering effects on transport properties of noninteracting electrons in a random potential. In analogy with many-body theory we construct parquet equations for topologically distinct {\em nonlocal} irreducible vertex functions into which the {\em local} one-particle propagator and two-particle vertex of the coherent-potential approximation (CPA) enter as input. To complete the two-particle parquet equations we use an integral form of the Ward identity and determine the one-particle self-energy from the known irreducible vertex. In this way a conserving approximation with (Herglotz) analytic averaged Green functions is obtained. We use the limit of high spatial dimensions to demonstrate how nonlocal corrections to the $d=\infty$ (CPA) solution emerge. The general parquet construction is applied to the calculation of vertex corrections to the electrical conductivity. With the aid of the high-dimensional asymptotics of the nonlocal irreducible vertex in the electron-hole scattering channel we derive a mean-field approximation for the conductivity with vertex corrections. The impact of vertex corrections onto the electronic transport is assessed quantitatively within the proposed mean-field description on a binary alloy.Comment: REVTeX 19 pages, 9 EPS diagrams, 6 PS figure

Disordered Correlated Kondo-lattice model

We propose a self-consistent approximate solution of the disordered Kondo-lattice model (KLM) to get the interconnected electronic and magnetic properties of 'local-moment' systems like diluted ferromagnetic semiconductors. Aiming at $(A_{1-x}M_x)$ compounds, where magnetic (M) and non-magnetic (A) atoms distributed randomly over a crystal lattice, we present a theory which treats the subsystems of itinerant charge carriers and localized magnetic moments in a homologous manner. The coupling between the localized moments due to the itinerant electrons (holes) is treated by a modified RKKY-theory which maps the KLM onto an effective Heisenberg model. The exchange integrals turn out to be functionals of the electronic selfenergy guaranteeing selfconsistency of our theory. The disordered electronic and magnetic moment systems are both treated by CPA-type methods. We discuss in detail the dependencies of the key-terms such as the long range and oscillating effectice exchange integrals, 'the local-moment' magnetization, the electron spin polarization, the Curie temperature as well as the electronic and magnonic quasiparticle densities of states on the concentration $x$ of magnetic ions, the carrier concentration $n$, the exchange coupling $J$, and the temperature. The shape and the effective range of the exchange integrals turn out to be strongly $x$-dependent. The disorder causes anomalies in the spin spectrum especially in the low-dilution regime, which are not observed in the mean field approximation.Comment: Accepted by JMM

Dynamical Mean-Field Theory

The dynamical mean-field theory (DMFT) is a widely applicable approximation scheme for the investigation of correlated quantum many-particle systems on a lattice, e.g., electrons in solids and cold atoms in optical lattices. In particular, the combination of the DMFT with conventional methods for the calculation of electronic band structures has led to a powerful numerical approach which allows one to explore the properties of correlated materials. In this introductory article we discuss the foundations of the DMFT, derive the underlying self-consistency equations, and present several applications which have provided important insights into the properties of correlated matter.Comment: Chapter in "Theoretical Methods for Strongly Correlated Systems", edited by A. Avella and F. Mancini, Springer (2011), 31 pages, 5 figure

The National Lung Matrix Trial: translating the biology of stratification in advanced non-small-cell lung cancer

© The Author 2015.Background: The management of NSCLC has been transformed by stratified medicine. The National Lung Matrix Trial (NLMT) is a UK-wide study exploring the activity of rationally selected biomarker/targeted therapy combinations. Patients and methods: The Cancer Research UK (CRUK) Stratified Medicine Programme 2 is undertaking the large volume national molecular pre-screening which integrates with the NLMT. At study initiation, there are eight drugs being used to target 18 molecular cohorts. The aim is to determine whether there is sufficient signal of activity in any drug-biomarker combination to warrant further investigation. A Bayesian adaptive design that gives a more realistic approach to decision making and flexibility to make conclusions without fixing the sample size was chosen. The screening platform is an adaptable 28-gene Nextera next-generation sequencing platform designed by Illumina, covering the range of molecular abnormalities being targeted. The adaptive design allows new biomarker-drug combination cohorts to be incorporated by substantial amendment. The pre-clinical justification for each biomarker-drug combination has been rigorously assessed creating molecular exclusion rules and a trumping strategy in patients harbouring concomitant actionable genetic abnormalities. Discrete routes of pathway activation or inactivation determined by cancer genome aberrations are treated as separate cohorts. Key translational analyses include the deep genomic analysis of pre- and post-treatment biopsies, the establishment of patient-derived xenograft models and longitudinal ctDNA collection, in order to define predictive biomarkers, mechanisms of resistance and early markers of response and relapse. Conclusion: The SMP2 platform will provide large scale genetic screening to inform entry into the NLMT, a trial explicitly aimed at discovering novel actionable cohorts in NSCLC

Genomic analysis of diet composition finds novel loci and associations with health and lifestyle

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10−8), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10−5) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (rg ≈ 0.15–0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|rg| ≈ 0.1–0.3) and positive genetic correlations with physical activity (rg ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (rg ≈−0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction

Biology, Fishery, Conservation and Management of Indian Ocean Tuna Fisheries

The focus of the study is to explore the recent trend of the world tuna fishery with special reference to the Indian Ocean tuna fisheries and its conservation and sustainable management. In the Indian Ocean, tuna catches have increased rapidly from about 179959 t in 1980 to about 832246 t in 1995. They have continued to increase up to 2005; the catch that year was 1201465 t, forming about 26% of the world catch. Since 2006 onwards there has been a decline in the volume of catches and in 2008 the catch was only 913625 t. The Principal species caught in the Indian Ocean are skipjack and yellowfin. Western Indian Ocean contributed 78.2% and eastern Indian Ocean 21.8% of the total tuna production from the Indian Ocean. The Indian Ocean stock is currently overfished and IOTC has made some recommendations for management regulations aimed at sustaining the tuna stock. Fishing operations can cause ecological impacts of different types: by catches, damage of the habitat, mortalities caused by lost or discarded gear, pollution, generation of marine debris, etc. Periodic reassessment of the tuna potential is also required with adequate inputs from exploratory surveys as well as commercial landings and this may prevent any unsustainable trends in the development of the tuna fishing industry in the Indian Ocean

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

A guide to using the Theoretical Domains Framework of behaviour change to investigate implementation problems

Background: Implementing new practices requires changes in the behaviour of relevant actors, and this is facilitated by understanding of the determinants of current and desired behaviours. The Theoretical Domains Framework (TDF) was developed by a collaboration of behavioural scientists and implementation researchers who identified theories relevant to implementation and grouped constructs from these theories into domains. The collaboration aimed to provide a comprehensive, theory-informed approach to identify determinants of behaviour. The first version was published in 2005, and a subsequent version following a validation exercise was published in 2012. This guide offers practical guidance for those who wish to apply the TDF to assess implementation problems and support intervention design. It presents a brief rationale for using a theoretical approach to investigate and address implementation problems, summarises the TDF and its development, and describes how to apply the TDF to achieve implementation objectives. Examples from the implementation research literature are presented to illustrate relevant methods and practical considerations. Methods: Researchers from Canada, the UK and Australia attended a 3-day meeting in December 2012 to build an international collaboration among researchers and decision-makers interested in the advancing use of the TDF. The participants were experienced in using the TDF to assess implementation problems, design interventions, and/or understand change processes. This guide is an output of the meeting and also draws on the a uthors' collective experience. Examples from the implementation research literature judged by authors to be representative of specific applications of the TDF are included in this guide. Results: We explain and illustrate methods, with a focus on qualitative approaches, for selecting and specifying target behaviours key to implementation, selecting the study design, deciding the sampling strategy, developing study materials, collecting and analysing data, and reporting findings of TDF-based studies. Areas for development include methods for triangulating data, e.g. from interviews, questionnaires and observation and methods for designing interventions based on TDF-based problem analysis. Conclusions: We offer this guide to the implementation community to assist in the application of the TDF to achieve implementation objectives. Benefits of using the TDF include the provision of a theoretical basis for implementation studies, good coverage of potential reasons for slow diffusion of evidence into practice and a method for progressing from theory-based investigation to intervention

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57