Determination of spin and orbital magnetization in the ferromagnetic superconductor UCoGe

International audienceThe magnetism in the ferromagnetic superconductor UCoGe has been studied using a combination of magnetic Compton scattering, bulk magnetization, X-ray magnetic circular dichroism and electronic structure calculations, in order to determine the spin and orbital moments. The experimentally observed total spin moment, Ms, was found to be-0.24 ± 0.05 µB at 5 T. By comparison with the total moment of 0.16 ± 0.01 µB, the orbital moment, M l , was determined to be 0.40 ± 0.05 µB. The U and Co spin moments were determined to be antiparallel. We find that the U 5f electrons carry a spin moment of Us ≈-0.30 µB and that there is a Co spin moment of Cos ≈ 0.06 µB induced via hybridization. The ratio U l /Us, of −1.3 ± 0.3, shows the U moment to be itinerant. In order to ensure an accurate description of the properties of 5f systems, and to provide a critical test of the theoretical approaches, it is clearly necessary to obtain experimental data for both the spin and orbital moments, rather than just the total magnetic moment. This can be achieved simply by measuring the spin moment with magnetic Compton scattering and comparing this to the total moment from bulk magnetizatio

Signatures of Spin and Charge Energy Scales in the Local Moment and Specific Heat of the Two-Dimensional Hubbard Model

Local moment formation driven by the on--site repulsion $U$ is one of the most fundamental features in the Hubbard model. At the simplest level, the temperature dependence of the local moment is expected to have a single structure at $T \sim U$, reflecting the suppression of the double occupancy. In this paper we show new low temperature Quantum Monte Carlo data which emphasize that the local moment also has a signature at a lower energy scale which previously had been thought to characterize only the temperatures below which moments on {\it different} sites begin to correlate locally. We discuss implications of these results for the structure of the specific heat, and connections to quasiparticle resonance and pseudogap formation in the density of states.Comment: 13 pages, 19 figure

Semi-empirical catalog of early-type galaxy-halo systems: dark matter density profiles, halo contraction and dark matter annihilation strength

With SDSS galaxy data and halo data from up-to-date N-body simulations we construct a semi-empirical catalog (SEC) of early-type systems by making a self-consistent bivariate statistical match of stellar mass (M_star) and velocity dispersion (sigma) with halo virial mass (M_vir). We then assign stellar mass profile and velocity dispersion profile parameters to each system in the SEC using their observed correlations with M_star and sigma. Simultaneously, we solve for dark matter density profile of each halo using the spherical Jeans equation. The resulting dark matter density profiles deviate in general from the dissipationless profile of NFW or Einasto and their mean inner density slope and concentration vary systematically with M_vir. Statistical tests of the distribution of profiles at fixed M_vir rule out the null hypothesis that it follows the distribution predicted by N-body simulations for M_vir ~< 10^{13.5-14.5} M_solar. These dark matter profiles imply that dark matter density is, on average, enhanced significantly in the inner region of halos with M_vir ~< 10^{13.5-14.5} M_solar supporting halo contraction. The main characteristics of halo contraction are: (1) the mean dark matter density within the effective radius has increased by a factor varying systematically up to ~ 3-4 at M_vir = 10^{12} M_solar, and (2) the inner density slope has a mean of ~ 1.3 with rho(r) ~ r^{-alpha} and a halo-to-halo rms scatter of rms(alpha) ~ 0.4-0.5 for 10^{12} M_solar ~< M_vir ~< 10^{13-14} M_solar steeper than the NFW profile (alpha=1). Based on our results we predict that halos of nearby elliptical and lenticular galaxies can, in principle, be promising targets for gamma-ray emission from dark matter annihilation.Comment: 43 pages, 20 figures, JCAP, revised and accepted versio

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dysregulated in tumors, but only a handful are known to play pathophysiological roles in cancer. We inferred lncRNAs that dysregulate cancer pathways, oncogenes, and tumor suppressors (cancer genes) by modeling their effects on the activity of transcription factors, RNA-binding proteins, and microRNAs in 5,185 TCGA tumors and 1,019 ENCODE assays. Our predictions included hundreds of candidate onco- and tumor-suppressor lncRNAs (cancer lncRNAs) whose somatic alterations account for the dysregulation of dozens of cancer genes and pathways in each of 14 tumor contexts. To demonstrate proof of concept, we showed that perturbations targeting OIP5-AS1 (an inferred tumor suppressor) and TUG1 and WT1-AS (inferred onco-lncRNAs) dysregulated cancer genes and altered proliferation of breast and gynecologic cancer cells. Our analysis indicates that, although most lncRNAs are dysregulated in a tumor-specific manner, some, including OIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergistically dysregulate cancer pathways in multiple tumor contexts. Chiu et al. present a pan-cancer analysis of lncRNA regulatory interactions. They suggest that the dysregulation of hundreds of lncRNAs target and alter the expression of cancer genes and pathways in each tumor context. This implies that hundreds of lncRNAs can alter tumor phenotypes in each tumor context

A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers

We analyzed molecular data on 2,579 tumors from The Cancer Genome Atlas (TCGA) of four gynecological types plus breast. Our aims were to identify shared and unique molecular features, clinically significant subtypes, and potential therapeutic targets. We found 61 somatic copy-number alterations (SCNAs) and 46 significantly mutated genes (SMGs). Eleven SCNAs and 11 SMGs had not been identified in previous TCGA studies of the individual tumor types. We found functionally significant estrogen receptor-regulated long non-coding RNAs (lncRNAs) and gene/lncRNA interaction networks. Pathway analysis identified subtypes with high leukocyte infiltration, raising potential implications for immunotherapy. Using 16 key molecular features, we identified five prognostic subtypes and developed a decision tree that classified patients into the subtypes based on just six features that are assessable in clinical laboratories. By performing molecular analyses of 2,579 TCGA gynecological (OV, UCEC, CESC, and UCS) and breast tumors, Berger et al. identify five prognostic subtypes using 16 key molecular features and propose a decision tree based on six clinically assessable features that classifies patients into the subtypes