Origin of ferromagnetism in Cs2_2AgF4_4: importance of Ag - F covalency

The magnetic nature of Cs$_{2}$AgF$_{4}$, an isoelectronic and isostructural analogue of La$_2$CuO$_4$, is analyzed using density functional calculations. The ground state is found to be ferromagnetic and nearly half metallic. We find strong hybridization of Ag-$d$ and F-$p$ states. Substantial moments reside on the F atoms, which is unusual for the halides and reflects the chemistry of the Ag(II) ions in this compound. This provides the mechanism for ferromagnetism, which we find to be itinerant in character, a result of a Stoner instability enhanced by Hund's coupling on the F

Fermi Velocity Spectrum and Incipient Magnetism in TiBe2

We address the origin of the incipient magnetism in TiBe$_2$ through precise first principles calculations, which overestimate the ferromagnetic tendency and therefore require correction to account for spin fluctuations. TiBe$_2$ has sharp fine structure in its electronic density of states, with a van Hove singularity only 3 meV above the Fermi level. Similarly to the isovalent weak ferromagnet ZrZn$_2$, it is flat bands along the K-W-U lines of hexagonal face of the fcc Brillouin zone make the system prone to magnetism, and more so if electrons are added. We find that the Moriya $B$ coefficient (multiplying $\frac{\omega}{q}$ in the fluctuation susceptibility $\Delta \chi(q,\omega)$) is divergent when the velocity vanishes at a point on the Fermi surface, which is very close (3 meV) to occurring in TiBe$_2$. In exploring how the FM instability (the $q$=0 Stoner enhancement is $S\approx 60$) might be suppressed by fluctuations in TiBe$_2$, we calculate that the Moriya A coefficient (of $q^2$) is negative, so $q$=0 is not the primary instability. Explicit calculation of $\chi_o(q)$ shows that its maximum occurs at the X point $(1,0,0)\frac{2\pi}{a}$; TiBe$_2$ is thus an incipient {\it anti}ferromagnet rather than ferromagnet as has been supposed. We further show that simple temperature smearing of the peak accounts for most of the temperature dependence of the susceptibility, which previously had been attributed to local moments (via a Curie-Weiss fit), and that energy dependence of the density of states also strongly affects the magnetic field variation of $\chi$

A primary breast cancer with distinct foci of estrogen receptor-alpha positive and negative cells derived from the same clonal origin as revealed by whole exome sequencing

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Background/purpose: Tumor heterogeneity is a now well-recognized phenomenon that can affect the classification, prognosis and treatment of human cancers. Heterogeneity is often described in primary breast cancers based upon histologic subtypes, hormone- and HER2-receptor status, and immunolabeling for various markers, which can be seen within a single tumor as mixed cellular populations, or as separate discrete foci. Experimental design/methods: Here, we present a case report of a patient’s primary breast cancer that had two separate but adjacent histologic components, one that was estrogen receptor (ER) positive, and the other ER negative. Each component was subjected to whole exome sequencing and compared for gene identity to determine clonal origin. Results: Using prior bioinformatic tools, we demonstrated that both the ER positive and negative components shared many variants, including passenger and driver alterations. Copy number variations also supported the two components were derived from a single common clone. Conclusions: These analyses strongly suggest that the two ER components of this patient’s breast cancer were derived from the same clonal origin. Our results have implications for the evolution of breast cancers with mixed histologies, and how they might be best managed for optimal therapy

Divergent controls of soil organic carbon between observations and process-based models

The storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationships a priori. While we observe qualitatively similar relationships between SOC and covariates in observations and models, the magnitude and degree of non-linearity vary substantially among the models and observations. Models appear to overemphasize the importance of temperature and primary productivity (especially in forests and herbaceous biomes, respectively), while observations suggest a greater relative importance of soil minerals. This mismatch is also evident globally. However, we observe agreement between observations and model outputs in select individual biomes – namely, temperate deciduous forests and grasslands, which both show stronger relationships of SOC stocks with temperature and productivity, respectively. This approach highlights biomes with the largest uncertainty and mismatch with observations for targeted model improvements. Understanding the role of dominant SOC controls, and the discrepancies between models and observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for projections under novel future conditions

Divergent controls of soil organic carbon between observations and process-based models

The storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationships a priori. While we observe qualitatively similar relationships between SOC and covariates in observations and models, the magnitude and degree of non-linearity vary substantially among the models and observations. Models appear to overemphasize the importance of temperature and primary productivity (especially in forests and herbaceous biomes, respectively), while observations suggest a greater relative importance of soil minerals. This mismatch is also evident globally. However, we observe agreement between observations and model outputs in select individual biomes – namely, temperate deciduous forests and grasslands, which both show stronger relationships of SOC stocks with temperature and productivity, respectively. This approach highlights biomes with the largest uncertainty and mismatch with observations for targeted model improvements. Understanding the role of dominant SOC controls, and the discrepancies between models and observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for projections under novel future conditions

Pairing competition in a quasi-one-dimensional model of organic superconductors (TMTSF)2X_{2}X in magnetic field

We microscopically study the effect of the magnetic field (Zeeman splitting) on the superconducting state in a model for quasi-one-dimensional organic superconductors (TMTSF)$_{2}X$. We investigate the competition between spin singlet and spin triplet pairings and the Fulde-Ferrell-Larkin-Ovchinnikov(FFLO) state by random phase approximation. While we studied the competition by comparison with the eigenvalue of the gap equation at a fixed temperature in our previous study (Phys. Rev. Lett. \textbf{102} (2009) 016403), here we obtain both the $T_c$ for each pairing state and a phase diagram in the $T$(temperature)-$h_z$(field)-$V_y$(strength of the charge fluctuation) space. The phase diagram shows that consecutive transitions from singlet pairing to the FFLO state and further to $S_z=1$ triplet pairing can occur upon increasing the magnetic field when $2k_{F}$ charge fluctuations coexist with $2k_{F}$ spin fluctuations. In the FFLO state, the singlet d-wave and $S_{z}=0$ triplet $f$-wave components are strongly mixed especially when the charge fluctuations are strong.Comment: 11 pages, 9 figure

1963: Abilene Christian College Bible Lectures - Full Text

THE CHRISTIAN AND MORALITY” Being the Abilene Christian College Annual Bible Lectures 1963 Price: $3.95 Published by ABILENE CHRISTIAN COLLEGE STUDENTS EXCHANGE ACC Station Abilene, Texa

Microbial carbon use efficiency: accounting for population, community, and ecosystem-scale controls over the fate of metabolized organic matter

Microbial carbon use efficiency (CUE) is a critical regulator of soil organic matter dynamics and terrestrial carbon fluxes, with strong implications for soil biogeochemistry models. While ecologists increasingly appreciate the importance of CUE, its core concepts remain ambiguous: terminology is inconsistent and confusing, methods capture variable temporal and spatial scales, and the significance of many fundamental drivers remains inconclusive. Here we outline the processes underlying microbial efficiency and propose a conceptual framework that structures the definition of CUE according to increasingly broad temporal and spatial drivers where (1) CUEP reflects population-scale carbon use efficiency of microbes governed by species-specific metabolic and thermodynamic constraints, (2) CUEC defines community-scale microbial efficiency as gross biomass production per unit substrate taken up over short time scales, largely excluding recycling of microbial necromass and exudates, and (3) CUEE reflects the ecosystem-scale efficiency of net microbial biomass production (growth) per unit substrate taken up as iterative breakdown and recycling of microbial products occurs. CUEE integrates all internal and extracellular constraints on CUE and hence embodies an ecosystem perspective that fully captures all drivers of microbial biomass synthesis and decay. These three definitions are distinct yet complementary, capturing the capacity for carbon storage in microbial biomass across different ecological scales. By unifying the existing concepts and terminology underlying microbial efficiency, our framework enhances data interpretation and theoretical advances