Fermi surface, possible unconventional fermions, and unusually robust resistive critical fields in the chiral-structured superconductor AuBe

The noncentrosymmetric superconductor (NCS) AuBe is investigated using a variety of thermodynamic and resistive probes in magnetic fields of up to 65~T and temperatures down to 0.3~K. Despite the polycrystalline nature of the samples, the observation of a complex series of de Haas-van Alphen (dHvA) oscillations has allowed the calculated bandstructure for AuBe to be validated. This permits a variety of BCS parameters describing the superconductivity to be estimated, despite the complexity of the measured Fermi surface. In addition, AuBe displays a nonstandard field dependence of the phase of dHvA oscillations associated with a band thought to host unconventional fermions in this chiral lattice. This result demonstrates the power of the dHvA effect to establish the properties of a single band despite the presence of other electronic bands with a larger density of states, even in polycrystalline samples. In common with several other NCSs, we find that the resistive upper critical field exceeds that measured by heat capacity and magnetization by a considerable factor. We suggest that our data exclude mechanisms for such an effect associated with disorder, implying that topologically protected superconducting surface states may be involved

Controlling magnetic anisotropy in a zero-dimensional S = 1 magnet using isotropic cation substitution

The [Zn1–xNix(HF2)(pyz)2]SbF6 (x = 0.2; pyz = pyrazine) solid solution exhibits a zero-field splitting (D) that is 22% larger [D = 16.2(2) K (11.3(2) cm–1)] than that observed in the x = 1 material [D = 13.3(1) K (9.2(1) cm–1)]. The substantial change in D is accomplished by an anisotropic lattice expansion in the MN4 (M = Zn or Ni) plane, wherein the increased concentration of isotropic Zn(II) ions induces a nonlinear variation in M-F and M-N bond lengths. In this, we exploit the relative donor atom hardness, where M-F and M-N form strong ionic and weak coordinate covalent bonds, respectively, the latter being more sensitive to substitution of Ni by the slightly larger Zn(II) ion. In this way, we are able to tune the single-ion anisotropy of a magnetic lattice site by Zn-substitution on nearby sites. This effect has possible applications in the field of single-ion magnets and the design of other molecule-based magnetic systems

Enhancing easy-plane anisotropy in bespoke Ni(II) quantum magnets

We examine the crystal structures and magnetic properties of several S = 1 Ni(II) coordination compounds, molecules and polymers, that include the bridging ligands HF2-, AF62- (A = Ti, Zr) and pyrazine or non-bridging ligands F-, SiF62-, glycine, H2O, 1-vinylimidazole, 4-methylpyrazole and 3-hydroxypyridine. Pseudo-octahedral NiN4F2, NiN4O2 or NiN4OF cores consist of equatorial Ni-N bonds that are equal to or slightly longer than the axial Ni-Lax bonds. By design, the zero-field splitting (D) is large in these systems and, in the presence of substantial exchange interactions (J), can be difficult to discriminate from magnetometry measurements on powder samples. Thus, we relied on pulsed-field magnetization in those cases and employed electron-spin resonance (ESR) to confirm D when J 0) and range from ≈ 8-25 K. This work reveals a linear correlation between the ratio d(Ni-Lax)/d(Ni-Neq) and D although the ligand spectrochemical properties may also be important. We assert that this relationship allows us to predict the type of magnetocrystalline anisotropy in tailored Ni(II) quantum magnets

A whole-genome sequence and transcriptome perspective on HER2-positive breast cancers.

HER2-positive breast cancer has long proven to be a clinically distinct class of breast cancers for which several targeted therapies are now available. However, resistance to the treatment associated with specific gene expressions or mutations has been observed, revealing the underlying diversity of these cancers. Therefore, understanding the full extent of the HER2-positive disease heterogeneity still remains challenging. Here we carry out an in-depth genomic characterization of 64 HER2-positive breast tumour genomes that exhibit four subgroups, based on the expression data, with distinctive genomic features in terms of somatic mutations, copy-number changes or structural variations. The results suggest that, despite being clinically defined by a specific gene amplification, HER2-positive tumours melt into the whole luminal-basal breast cancer spectrum rather than standing apart. The results also lead to a refined ERBB2 amplicon of 106 kb and show that several cases of amplifications are compatible with a breakage-fusion-bridge mechanism

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Data for Magneto-structural correlations in Ni^{2+}-halide ··· halide-Ni^{2+} chains

We present the magnetic properties of a new family of S = 1 molecule-based magnets, NiF2(3,5-lut)4·2H2O and NiX2(3,5-lut)4, where X = HF2, Cl, Br, or I (lut = lutidine C7H9N). Upon creation of isolated Ni–X···X–Ni and Ni–F–H–F···F–H–F–Ni chains separated by bulky and nonbridging lutidine ligands, the effect that halogen substitution has on the magnetic properties of transition-metal-ion complexes can be investigated directly and in isolation from competing processes such as Jahn–Teller distortions. We find that substitution of the larger halide ions turns on increasingly strong antiferromagnetic interactions between adjacent Ni2+ ions via a novel through-space two-halide exchange. In this process, the X···X bond lengths in the Br and I materials are more than double the van der Waals radius of X yet can still mediate significant magnetic interactions. We also find that a simple model based on elongation/compression of the Ni2+ octahedra cannot explain the observed single-ion anisotropy in mixed-ligand compounds. We offer an alternative that takes into account the difference in the electronegativity of axial and equatorial ligands

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Near-ideal molecule-based Haldane spin chain

The molecular coordination complex NiI2 (3, 5-lut)4 [where (3,5-lut) = (3,5-lutidine) = (C7H9N)] has been synthesized and characterized by several techniques including synchrotron x-ray diffraction, electron-spin resonance, superconducting quantum interference device magnetometry, pulsed-field magnetization, inelastic neutron scattering, and muon spin relaxation. Templated by the configuration of 3,5-lut ligands the molecules pack in-registry with the Ni–I ··· I–Ni chains aligned along the c axis. This arrangement leads to an uncommon through-space I ··· I magnetic coupling which is directly measured in this work. The net result is a near-ideal realization of the S = 1 Haldane chain with J = 17.5 K and energy gaps of = 5.3 K ⊥ = 7.7 K, split by the easy-axis single-ion anisotropy D = −1.2 K. The ratio D/J = −0.07 affords one of the most isotropic Haldane systems yet discovered, while the ratio 0/J = 0.40(1) (where 0 is the average gap size) is close to its ideal theoretical value, suggesting a very high degree of magnetic isolation of the spin chains in this material. The Haldane gap is closed by orientation-dependent critical fields μ0H c = 5.3 T and μ0H⊥ c = 4.3 T, which are readily accessible experimentally and permit investigations across the entirety of the Haldane phase, with the fully polarized state occurring at μ0H s = 46.0 T and μ0H⊥ s = 50.7 T. The results are explicable within the so-called fermion model, in contrast to other reported easy-axis Haldane systems. Zero-field magnetic order is absent down to 20 mK and emergent end-chain effects are observed in the gapped state, as evidenced by detailed low-temperature measurements