Vacancies, disorder-induced smearing of the electronic structure, and its implications for the superconductivity of anti-perovskite MgC0.93_{0.93}Ni2.85_{2.85}

The anti-perovskite superconductor MgC$_{0.93}$Ni$_{2.85}$ was studied using high-resolution x-ray Compton scattering combined with electronic structure calculations. Compton scattering measurements were used to determine experimentally a Fermi surface that showed good agreement with that of our supercell calculations, establishing the presence of the predicted hole and electron Fermi surface sheets. Our calculations indicate that the Fermi surface is smeared by the disorder due to the presence of vacancies on the C and Ni sites, but does not drastically change shape. The 20\% reduction in the Fermi level density-of-states would lead to a significant ($\sim 70\%$) suppression of the superconducting $T_c$ for pair-forming electron-phonon coupling. However, we ascribe the observed much smaller $T_c$ reduction at our composition (compared to the stoichiometric compound) to the suppression of pair-breaking spin fluctuations.Comment: 11 pages, 3 figure

Magnetic frustration, short-range correlations and the role of the paramagnetic Fermi surface of PdCrO₂

Frustrated interactions exist throughout nature, with examples ranging from protein folding through to frustrated magnetic interactions. Whilst magnetic frustration is observed in numerous electrically insulating systems, in metals it is a rare phenomenon. The interplay of itinerant conduction electrons mediating interactions between localised magnetic moments with strong spin-orbit coupling is likely fundamental to these systems. Therefore, knowledge of the precise shape and topology of the Fermi surface is important in any explanation of the magnetic behaviour. PdCrO2, a frustrated metallic magnet, offers the opportunity to examine the relationship between magnetic frustration, short-range magnetic order and Fermi surface topology. By mapping the short-range order in reciprocal space and experimentally determining the electronic structure, we have identified the dual role played by the Cr electrons in which the itinerant ones on the nested paramagnetic Fermi surface mediate the frustrated magnetic interactions between local moments

Spin-resolved fermi surface of the localized ferromagnetic Heusler compound Cu2MnAl measured with spin-polarized positron annihilation

We determined the bulk electronic structure in the prototypical Heusler compound Cu$_2$MnAl by measuring the Angular Correlation of Annihilation Radiation (2D-ACAR) using spin-polarized positrons. To this end, a new algorithm for reconstructing 3D densities from projections is introduced that allows us to corroborate the excellent agreement between our electronic structure calculations and the experimental data. The contribution of each individual Fermi surface sheet to the magnetization was identified, and summed to a total spin magnetic moment of $3.6\,\pm\,0.5\,\mu_B/\mathrm{f.u.}$

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible

Transethnic Genome-Wide Association Study Provides Insights in the Genetic Architecture and Heritability of Long QT Syndrome

BACKGROUND: Long QT syndrome (LQTS) is a rare genetic disorder and a major preventable cause of sudden cardiac death in the young. A causal rare genetic variant with large effect size is identified in up to 80% of probands (genotype positive) and cascade family screening shows incomplete penetrance of genetic variants. Furthermore, a proportion of cases meeting diagnostic criteria for LQTS remain genetically elusive despite genetic testing of established genes (genotype negative). These observations raise the possibility that common genetic variants with small effect size contribute to the clinical picture of LQTS. This study aimed to characterize and quantify the contribution of common genetic variation to LQTS disease susceptibility. METHODS: We conducted genome-wide association studies followed by transethnic meta-analysis in 1656 unrelated patients with LQTS of European or Japanese ancestry and 9890 controls to identify susceptibility single nucleotide polymorphisms. We estimated the common variant heritability of LQTS and tested the genetic correlation between LQTS susceptibility and other cardiac traits. Furthermore, we tested the aggregate effect of the 68 single nucleotide polymorphisms previously associated with the QT-interval in the general population using a polygenic risk score. RESULTS: Genome-wide association analysis identified 3 loci associated with LQTS at genome-wide statistical significance (P&lt;5×10-8) near NOS1AP, KCNQ1, and KLF12, and 1 missense variant in KCNE1(p.Asp85Asn) at the suggestive threshold (P&lt;10-6). Heritability analyses showed that ≈15% of variance in overall LQTS susceptibility was attributable to common genetic variation (h2SNP 0.148; standard error 0.019). LQTS susceptibility showed a strong genome-wide genetic correlation with the QT-interval in the general population (rg=0.40; P=3.2×10-3). The polygenic risk score comprising common variants previously associated with the QT-interval in the general population was greater in LQTS cases compared with controls (P&lt;10-13), and it is notable that, among patients with LQTS, this polygenic risk score was greater in patients who were genotype negative compared with those who were genotype positive (P&lt;0.005). CONCLUSIONS: This work establishes an important role for common genetic variation in susceptibility to LQTS. We demonstrate overlap between genetic control of the QT-interval in the general population and genetic factors contributing to LQTS susceptibility. Using polygenic risk score analyses aggregating common genetic variants that modulate the QT-interval in the general population, we provide evidence for a polygenic architecture in genotype negative LQTS.</p

Cu2MnAl (positron)

Measurement of the spin-dependent Fermi surface of Cu2MnA

Cu2MnAl (positron)

Measurement of the spin-dependent Fermi surface of Cu2MnA

ZrZn2_DMFT

Calculations and data to support our DFT+DMFT study of the electronic structure of ZrZn