Weak Localization Effect in Superconductors by Radiation Damage

Large reductions of the superconducting transition temperature $T_{c}$ and the accompanying loss of the thermal electrical resistivity (electron-phonon interaction) due to radiation damage have been observed for several A15 compounds, Chevrel phase and Ternary superconductors, and $\rm{NbSe_{2}}$ in the high fluence regime. We examine these behaviors based on the recent theory of weak localization effect in superconductors. We find a good fitting to the experimental data. In particular, weak localization correction to the phonon-mediated interaction is derived from the density correlation function. It is shown that weak localization has a strong influence on both the phonon-mediated interaction and the electron-phonon interaction, which leads to the universal correlation of $T_{c}$ and resistance ratio.Comment: 16 pages plus 3 figures, revtex, 76 references, For more information, Plesse see http://www.fen.bilkent.edu.tr/~yjki

Species abundance dynamics under neutral assumptions: a Bayesian approach to the controversy

1. Hubbell's 'Unified Neutral Theory of Biodiversity and Biogeography' (UNTB) has generated much controversy about both the realism of its assumptions and how well it describes the species abundance dynamics in real communities. 2. We fit a discrete-time version of Hubbell's neutral model to long-term macro-moth (Lepidoptera) community data from the Rothamsted Insect Survey (RIS) light-traps network in the United Kingdom. 3. We relax the assumption of constant community size and use a hierarchical Bayesian approach to show that the model does not fit the data well as it would need parameter values that are impossible. 4. This is because the ecological communities fluctuate more than expected under neutrality. 5. The model, as presented here, can be extended to include environmental stochasticity, density-dependence, or changes in population sizes that are correlated between different species

Design and selection of high entropy alloys for hardmetal matrix applications using a coupled machine learning and CALPHAD methodology

This study aims to utilize a combined machine learning (ML) and CALculation of PHAse Diagrams (CALPHAD) methodology to design hardmetal matrix phases for metal-forming applications that can serve as the basis for carbide reinforcement. The vast compositional space that high entropy alloys (HEAs) occupy offers a promising avenue to satisfy the application design criteria of wear resistance and ductility. To efficiently explore this space, random forest ML models are constructed and trained from publicly available experimental HEA databases to make phase constitution and hardness predictions. Interrogation of the ML models constructed reveals accuracies >78.7% and a mean absolute error of 66.1 HV for phase and hardness predictions respectively. Six promising alloy compositions, extracted from the ML predictions and CALPHAD calculations, are experimentally fabricated and tested. The hardness predictions are found to be systematically under- and overpredicted depending on the alloy microstructure. In parallel, the phase classification models are found to lack sensitivity toward additional intermetallic phase formation. Despite the discrepancies identified between ML and experimental results, the fabricated compositions show promise for further experimental evaluation. These discrepancies are believed to be directly associated with the available databases but, importantly, have highlighted several avenues for both ML and database development

Transverse spin dynamics in a spin-polarized Fermi liquid

The linear equations for transverse spin dynamics in weakly polarised degenerate Fermi liquid with arbitrary relationship between temperature and polarization are derived from Landau-Silin phenomenological kinetic equation with general form of two-particle collision integral. The temperature and polarization dependence of the spin current relaxation time is established. It is found in particular that at finite polarization transverse spin wave damping has a finite value at T=0. The analogy between temperature dependences of spin waves attenuation and ultrasound absorption in degenerate Fermi liquid at arbitrary temperature is presented. We also discuss spin-polarized Fermi liquid in the general context of the Fermi-liquid theory and compare it with "Fermi liquid" with spontaneous magnetization.Comment: 10 page

Establishment and optimization of genomic selection to accelerate the domestication and improvement of intermediate wheatgrass

Citation: Zhang, X., Sallam, A., Gao, L., Kantarski, T., Poland, J., DeHaan, L. R., . . . Anderson, J. A. (2016). Establishment and optimization of genomic selection to accelerate the domestication and improvement of intermediate wheatgrass. Plant Genome, 9(1). doi:10.3835/plantgenome2015.07.0059Intermediate wheatgrass (IWG) is a perennial species and has edible and nutritious grain and desirable agronomic traits, including large seed size, high grain yield, and biomass. It also has the potential to provide ecosystem services and an economic return to farmers. However, because of its allohexaploidy and self-incompatibility, developing molecular markers for genetic analysis and molecular breeding has been challenging. In the present study, using genotyping-by-sequencing (GBS) technology, 3436 genomewide markers discovered in a biparental population with 178 genets, were mapped to 21 linkage groups (LG) corresponding to 21 chromosomes of IWG. Genomic prediction models were developed using 3883 markers discovered in a breeding population containing 1126 representative genets from 58 half-sib families. High predictive ability was observed for seven agronomic traits using cross-validation, ranging from 0.46 for biomass to 0.67 for seed weight. Optimization results indicated that 8 to 10 genets from each half-sib family can form a good training population to predict the breeding value of their siblings, and 1600 genomewide markers are adequate to capture the genetic variation in the current breeding population for genomic selection. Thus, with the advances in sequencing-based marker technologies, it was practical to perform molecular genetic analysis and molecular breeding on a new and challenging species like IWG, and genomic selection could increase the efficiency of recurrent selection and accelerate the domestication and improvement of IWG.A. © Crop Science Society of America

Whole exome sequence analysis reveals a homozygous mutation in PNPLA2 as the cause of severe dilated cardiomyopathy secondary to neutral lipid storage disease.

Accepted manuscript 12 month embargo, pre-print immediately

Crises and collective socio-economic phenomena: simple models and challenges

Financial and economic history is strewn with bubbles and crashes, booms and busts, crises and upheavals of all sorts. Understanding the origin of these events is arguably one of the most important problems in economic theory. In this paper, we review recent efforts to include heterogeneities and interactions in models of decision. We argue that the Random Field Ising model (RFIM) indeed provides a unifying framework to account for many collective socio-economic phenomena that lead to sudden ruptures and crises. We discuss different models that can capture potentially destabilising self-referential feedback loops, induced either by herding, i.e. reference to peers, or trending, i.e. reference to the past, and account for some of the phenomenology missing in the standard models. We discuss some empirically testable predictions of these models, for example robust signatures of RFIM-like herding effects, or the logarithmic decay of spatial correlations of voting patterns. One of the most striking result, inspired by statistical physics methods, is that Adam Smith's invisible hand can badly fail at solving simple coordination problems. We also insist on the issue of time-scales, that can be extremely long in some cases, and prevent socially optimal equilibria to be reached. As a theoretical challenge, the study of so-called "detailed-balance" violating decision rules is needed to decide whether conclusions based on current models (that all assume detailed-balance) are indeed robust and generic.Comment: Review paper accepted for a special issue of J Stat Phys; several minor improvements along reviewers' comment

A Zebrafish Compound Screen Reveals Modulation of Neutrophil Reverse Migration as an Anti-Inflammatory Mechanism

Diseases of failed inflammation resolution are common and largely incurable. Therapeutic induction of inflammation resolution is an attractive strategy to bring about healing without increasing susceptibility to infection. However, therapeutic targeting of inflammation resolution has been hampered by a lack of understanding of the underlying molecular controls. To address this drug development challenge, we developed an in vivo screen for proresolution therapeutics in a transgenic zebrafish model. Inflammation induced by sterile tissue injury was assessed for accelerated resolution in the presence of a library of known compounds. Of the molecules with proresolution activity, tanshinone IIA, derived from a Chinese medicinal herb, potently induced inflammation resolution in vivo both by induction of neutrophil apoptosis and by promoting reverse migration of neutrophils. Tanshinone IIA blocked proinflammatory signals in vivo, and its effects are conserved in human neutrophils, supporting a potential role in treating human inflammation and providing compelling evidence of the translational potential of this screening strategy