The Einstein-de Haas effect in an Fe15cluster.

Classical models of spin-lattice coupling are at present unable to accurately reproduce results for numerous properties of ferromagnetic materials, such as heat transport coefficients or the sudden collapse of the magnetic moment in hcp-Fe under pressure. This inability has been attributed to the absence of a proper treatment of effects that are inherently quantum mechanical in nature, notably spin-orbit coupling (SOC). This paper introduces a time-dependent, non-collinear tight binding model, complete with SOC and vector Stoner exchange terms, that is capable of simulating the Einstein-de Haas (EdH) effect in a ferromagnetic Fe15cluster. The tight binding model is used to investigate the adiabaticity timescales that determine the response of the orbital and spin angular momenta to a rotating, externally appliedBfield, and we show that the qualitative behaviors of our simulations can be extrapolated to realistic timescales by use of the adiabatic theorem. An analysis of the trends in the torque contributions with respect to the field strength demonstrates that SOC is necessary to observe a transfer of angular momentum from the electrons to the nuclei at experimentally realisticBfields. The simulations presented in this paper demonstrate the EdH effect from first principles using a Fe cluster.

Ab initio quantum chemistry with neural-network wavefunctions.

Deep learning methods outperform human capabilities in pattern recognition and data processing problems and now have an increasingly important role in scientific discovery. A key application of machine learning in molecular science is to learn potential energy surfaces or force fields from ab initio solutions of the electronic Schrödinger equation using data sets obtained with density functional theory, coupled cluster or other quantum chemistry (QC) methods. In this Review, we discuss a complementary approach using machine learning to aid the direct solution of QC problems from first principles. Specifically, we focus on quantum Monte Carlo methods that use neural-network ansatzes to solve the electronic Schrödinger equation, in first and second quantization, computing ground and excited states and generalizing over multiple nuclear configurations. Although still at their infancy, these methods can already generate virtually exact solutions of the electronic Schrödinger equation for small systems and rival advanced conventional QC methods for systems with up to a few dozen electrons.

Discovering Quantum Phase Transitions with Fermionic Neural Networks.

Deep neural networks have been very successful as highly accurate wave function Ansätze for variational Monte Carlo calculations of molecular ground states. We present an extension of one such Ansatz, FermiNet, to calculations of the ground states of periodic Hamiltonians, and study the homogeneous electron gas. FermiNet calculations of the ground-state energies of small electron gas systems are in excellent agreement with previous initiator full configuration interaction quantum Monte Carlo and diffusion Monte Carlo calculations. We investigate the spin-polarized homogeneous electron gas and demonstrate that the same neural network architecture is capable of accurately representing both the delocalized Fermi liquid state and the localized Wigner crystal state. The network converges on the translationally invariant ground state at high density and spontaneously breaks the symmetry to produce the crystalline ground state at low density, despite being given no a priori knowledge that a phase transition exists.

Estimating anisotropy directly via neural timeseries.

An isotropic dynamical system is one that looks the same in every direction, i.e., if we imagine standing somewhere within an isotropic system, we would not be able to differentiate between different lines of sight. Conversely, anisotropy is a measure of the extent to which a system deviates from perfect isotropy, with larger values indicating greater discrepancies between the structure of the system along its axes. Here, we derive the form of a generalised scalable (mechanically similar) discretized field theoretic Lagrangian that allows for levels of anisotropy to be directly estimated via timeseries of arbitrary dimensionality. We generate synthetic data for both isotropic and anisotropic systems and, by using Bayesian model inversion and reduction, show that we can discriminate between the two datasets - thereby demonstrating proof of principle. We then apply this methodology to murine calcium imaging data collected in rest and task states, showing that anisotropy can be estimated directly from different brain states and cortical regions in an empirical in vivo biological setting. We hope that this theoretical foundation, together with the methodology and publicly available MATLAB code, will provide an accessible way for researchers to obtain new insight into the structural organization of neural systems in terms of how scalable neural regions grow - both ontogenetically during the development of an individual organism, as well as phylogenetically across species.

Quasiparticle Effective Mass of the Three-Dimensional Fermi Liquid by Quantum Monte Carlo.

According to Landau's Fermi liquid theory, the main properties of the quasiparticle excitations of an electron gas are embodied in the effective mass m^{*}, which determines the energy of a single quasiparticle, and the Landau interaction function, which indicates how the energy of a quasiparticle is modified by the presence of other quasiparticles. This simple paradigm underlies most of our current understanding of the physical and chemical behavior of metallic systems. The quasiparticle effective mass of the three-dimensional homogeneous electron gas has been the subject of theoretical controversy, and there is a lack of experimental data. In this Letter, we deploy diffusion Monte Carlo (DMC) methods to calculate m^{*} as a function of density for paramagnetic and ferromagnetic three-dimensional homogeneous electron gases. The DMC results indicate that m^{*} decreases when the density is reduced, especially in the ferromagnetic case. The DMC quasiparticle energy bands exclude the possibility of a reduction in the occupied bandwidth relative to that of the free-electron model at density parameter r_{s}=4, which corresponds to Na metal.

Rendering neuronal state equations compatible with the principle of stationary action.

The principle of stationary action is a cornerstone of modern physics, providing a powerful framework for investigating dynamical systems found in classical mechanics through to quantum field theory. However, computational neuroscience, despite its heavy reliance on concepts in physics, is anomalous in this regard as its main equations of motion are not compatible with a Lagrangian formulation and hence with the principle of stationary action. Taking the Dynamic Causal Modelling (DCM) neuronal state equation as an instructive archetype of the first-order linear differential equations commonly found in computational neuroscience, we show that it is possible to make certain modifications to this equation to render it compatible with the principle of stationary action. Specifically, we show that a Lagrangian formulation of the DCM neuronal state equation is facilitated using a complex dependent variable, an oscillatory solution, and a Hermitian intrinsic connectivity matrix. We first demonstrate proof of principle by using Bayesian model inversion to show that both the original and modified models can be correctly identified via in silico data generated directly from their respective equations of motion. We then provide motivation for adopting the modified models in neuroscience by using three different types of publicly available in vivo neuroimaging datasets, together with open source MATLAB code, to show that the modified (oscillatory) model provides a more parsimonious explanation for some of these empirical timeseries. It is our hope that this work will, in combination with existing techniques, allow people to explore the symmetries and associated conservation laws within neural systems - and to exploit the computational expediency facilitated by direct variational techniques.

Neural Systems Under Change of Scale.

We derive a theoretical construct that allows for the characterisation of both scalable and scale free systems within the dynamic causal modelling (DCM) framework. We define a dynamical system to be "scalable" if the same equation of motion continues to apply as the system changes in size. As an example of such a system, we simulate planetary orbits varying in size and show that our proposed methodology can be used to recover Kepler's third law from the timeseries. In contrast, a "scale free" system is one in which there is no characteristic length scale, meaning that images of such a system are statistically unchanged at different levels of magnification. As an example of such a system, we use calcium imaging collected in murine cortex and show that the dynamical critical exponent, as defined in renormalization group theory, can be estimated in an empirical biological setting. We find that a task-relevant region of the cortex is associated with higher dynamical critical exponents in task vs. spontaneous states and vice versa for a task-irrelevant region.

Conservation laws by virtue of scale symmetries in neural systems.

In contrast to the symmetries of translation in space, rotation in space, and translation in time, the known laws of physics are not universally invariant under transformation of scale. However, a special case exists in which the action is scale invariant if it satisfies the following two constraints: 1) it must depend upon a scale-free Lagrangian, and 2) the Lagrangian must change under scale in the same way as the inverse time, [Formula: see text]. Our contribution lies in the derivation of a generalised Lagrangian, in the form of a power series expansion, that satisfies these constraints. This generalised Lagrangian furnishes a normal form for dynamic causal models-state space models based upon differential equations-that can be used to distinguish scale symmetry from scale freeness in empirical data. We establish face validity with an analysis of simulated data, in which we show how scale symmetry can be identified and how the associated conserved quantities can be estimated in neuronal time series.

Survey of primary care physicians' views about breast and ovarian cancer screening for true BRCA1/2 non-carriers.

Despite some controversy, true BRCA1/2 non-carriers are generally considered to be at an average risk for breast and ovarian cancer. Primary care physicians are then expected to encourage their non-carrier patients to adopt cancer screening practices appropriate to women of the same age in the general population. This study aimed to describe breast and ovarian cancer screening recommendations that primary care physicians would consider advisable for young true BRCA1/2 non-carriers. One hundred thirty-four family physicians and 123 gynecologists (response rate 45%) completed a cross-sectional mailed survey administered in the Province of Quebec, Canada. The survey included questions about basic genetic knowledge and screening recommendations for two fictitious cases (< 40 years), one carrier and one non-carrier, from a BRCA1/2 mutation-positive family. Screening exams considered advisable did not differ significantly between family physicians and gynecologists. More than 75% of physicians considered the cancer risks of true non-carriers to be comparable with that of the general population and 14% to be a little higher. Still, 53% would prescribe a biennial and or even an annual (27%) mammography to a non-carrier woman before the recommended starting age. Physician considerations of non-carriers' expectations or requests for screening were associated with more screening prescriptions. More than half of primary care physicians would recommend more mammography screenings than expected for a young true BRCA1/2 non-carrier. Personalized cancer risk assessment may help primary care physicians tailor screening of women from BRCA1/2 mutation-positive families and allow these women to make more informed choices regarding cancer risk management options.

The microscopic Einstein-de Haas effect.

The Einstein-de Haas (EdH) effect, where the spin angular momentum of electrons is transferred to the mechanical angular momentum of atoms, was established experimentally in 1915. While a semiclassical explanation of the effect exists, modern electronic structure methods have not yet been applied to model the phenomenon. In this paper, we investigate its microscopic origins by means of a noncollinear tight-binding model of an O2 dimer, which includes the effects of spin-orbit coupling, coupling to an external magnetic field, and vector Stoner exchange. By varying an external magnetic field in the presence of spin-orbit coupling, a torque can be generated on the dimer, validating the presence of the EdH effect. The avoided energy level crossings and the rate of change of magnetic field determine the evolution of the spin. We also find that the torque exerted on the nuclei by the electrons in a time-varying B field is not only due to the EdH effect. The other contributions arise from field-induced changes in the electronic orbital angular momentum and from the direct action of the Faraday electric field associated with the time-varying magnetic field.

The HANDE-QMC Project: Open-Source Stochastic Quantum Chemistry from the Ground State Up.

Building on the success of Quantum Monte Carlo techniques such as diffusion Monte Carlo, alternative stochastic approaches to solve electronic structure problems have emerged over the past decade. The full configuration interaction quantum Monte Carlo (FCIQMC) method allows one to systematically approach the exact solution of such problems, for cases where very high accuracy is desired. The introduction of FCIQMC has subsequently led to the development of coupled cluster Monte Carlo (CCMC) and density matrix quantum Monte Carlo (DMQMC), allowing stochastic sampling of the coupled cluster wave function and the exact thermal density matrix, respectively. In this Article, we describe the HANDE-QMC code, an open-source implementation of FCIQMC, CCMC and DMQMC, including initiator and semistochastic adaptations. We describe our code and demonstrate its use on three example systems; a molecule (nitric oxide), a model solid (the uniform electron gas), and a real solid (diamond). An illustrative tutorial is also included.

A two-phase Hessian approach improves the DFT relaxation of slabs.

A two-phase Hessian approach to DFT slab relaxation of slabs has been implemented and tested. It addresses weaknesses in the modified Broyden and Pfrommer BFGS algorithms specific to relaxing slabs. Complete Hessian and then inverse Hessian matrices with no strain/stress components are first constructed at high force signal-to-noise ratios with no accompanying relaxation. In a second phase the static inverse Hessian is used to relax the slab down to a low force tolerance.

Breast cancer in systemic lupus erythematosus (SLE): receptor status and treatment.

Objective There is a decreased risk of breast cancer in systemic lupus erythematosus (SLE) versus the general population; little is known regarding the receptor status of breast cancers in SLE, or treatment. Methods Breast cancer cases occurring after SLE diagnosis were ascertained through linkage with tumor registries. We determined breast cancer positivity for estrogen receptors (ER), progesterone receptors (PR), and/or Human Epidermal Growth Factor Receptor 2 (HER2), as well as cancer treatment. Results We obtained information on ER, PR, and/or HER2 status for 63 SLE patients with breast cancer. Fifty-three had information on ER and/or PR status; 36 of these (69%) were ER positive. Thirty-six of the 63 had information on HER2 status; of these, 26 had complete information on all three receptors. Twenty-one of these 26 (81%) were HER2 negative; seven of 26(27%) were triple negative. All but one patient underwent surgery; 11.5% received both non-tamoxifen chemotherapy and radiotherapy, 16.4% radiotherapy without non-tamoxifen chemotherapy, and 14.7% received non-tamoxifen chemotherapy without radiotherapy. Conclusion ER positivity was similar to historical general population figures, with a trend toward a higher proportion of triple-negative breast cancers in SLE (possibly reflecting the relatively young age of our SLE patients).

The risk of breast cancer in BRCA1 and BRCA2 mutation carriers without a first-degree relative with breast cancer.

The objective of this study was to estimate the lifetime risk of breast cancer in women with a BRCA1 or BRCA2 mutation with and without at least 1 first-degree relative with breast cancer. A total of 2835 women with a BRCA1 or BRCA2 mutation were followed. Age- and gene-specific breast cancer rates were calculated. The relative risks of breast cancer for subjects with a family history of breast cancer, compared to no family history were calculated. The mean age at baseline was 41.1 years, and they were followed for a mean of 6.0 years. The estimated penetrance of breast cancer to age 80 years was 60.8% for BRCA1 and 63.1% for BRCA2. For all BRCA carriers, the penetrance of breast cancer to age 80 for those with no first-degree relative with breast cancer was 60.4% and 63.3% for those with at least 1 first-degree relative with breast cancer. The risk of breast cancer for BRCA carriers with no first-degree relative with breast cancer is substantial, and as a result, clinical management for these women should be the same as those for women with an affected relative.

No evidence of excessive cancer screening in female noncarriers from BRCA1/2 mutation-positive families.

BACKGROUND: In families with a proven BRCA1/2 mutation, women not carrying the familial mutation should follow the cancer screening recommendations applying to women in the general population. In the present study, we evaluated the cancer screening practices of unaffected noncarriers from families with a proven BRCA mutation, and we assessed the role of family history in their screening practices. METHODS: Self-report data were provided retrospectively by 220 unaffected female noncarriers for periods of up to 10 years (mean: 4.3 years) since disclosure of their BRCA1/2 genetic test result. A ratio for the annual frequency of breast and ovarian cancer screening exams (mammography, breast ultrasonography, breast magnetic resonance imaging, transvaginal or pelvic ultrasound, cancer antigen 125 testing) was calculated as number of screening exams divided by the number of years in the individual observation period. RESULTS: The annual average for mammography exams was 0.15, 0.4, 0.56, and 0.71 in women 30-39, 40-49, 50-59, and 60-69 years of age respectively. The uptake of other breast and ovarian cancer screening exams was very low. Mammography and breast ultrasonography and magnetic resonance imaging were generally more frequent among participants with at least 1 first-degree relative affected by breast cancer. CONCLUSIONS: In most noncarriers, screening practices are consistent with the guidelines concerning women in the general population. When noncarriers adopt screening behaviours that are different from those that would be expected for average-risk women, those behaviours are influenced by their familial cancer history. IMPACT: Decision tools might help female noncarriers to be involved in their follow-up in accordance with their genetic status and their family history, while taking into account the benefits and disadvantages of cancer screening.

Compromised BRCA1-PALB2 interaction is associated with breast cancer risk.

The major breast cancer suppressor proteins BRCA1 and BRCA2 play essential roles in homologous recombination (HR)-mediated DNA repair, which is thought to be critical for tumor suppression. The two BRCA proteins are linked by a third tumor suppressor, PALB2, in the HR pathway. While truncating mutations in these genes are generally pathogenic, interpretation of missense variants remains a challenge. To date, patient-derived missense variants that disrupt PALB2 binding have been identified in BRCA1 and BRCA2; however, there has not been sufficient evidence to prove their pathogenicity in humans, and no variants in PALB2 that disrupt either its BRCA1 or BRCA2 binding have been reported. Here we report on the identification of a novel PALB2 variant, c.104T>C (p.L35P), that segregates in a family with a strong history of breast cancer. Functional analyses showed that L35P abrogates the PALB2-BRCA1 interaction and completely disables its abilities to promote HR and confer resistance to platinum salts and PARP inhibitors. Whole-exome sequencing of a breast cancer from a c.104T>C carrier revealed a second, somatic, truncating mutation affecting PALB2, and the tumor displays hallmark genomic features of tumors with BRCA mutations and HR defects, cementing the pathogenicity of L35P. Parallel analyses of other germline variants in the PALB2 N-terminal BRCA1-binding domain identified multiple variants that affect HR function to varying degrees, suggesting their possible contribution to cancer development. Our findings establish L35P as the first pathogenic missense mutation in PALB2 and directly demonstrate the requirement of the PALB2-BRCA1 interaction for breast cancer suppression.

Oncogenic role of PDK4 in human colon cancer cells.

BACKGROUND: Cancer cells maintain high rates of glycolysis. Pyruvate dehydrogenase kinases (PDK) contribute to this phenomenon, which favours apoptosis resistance and cellular transformation. We previously reported upregulation of PDK4 in normal mucosa of colorectal cancer (CRC) patients compared with controls and in preneoplastic intestine of our mouse model. Decreased methylation of four consecutive PDK4 CpGs was observed in normal mucosa of patients. Although other members of the PDK family have been investigated for transformation potential, PDK4 has not been extensively studied. METHODS: PDK4 methylation in blood of CRC patients and controls was evaluated by pyrosequencing. PDK4 expression in human colon carcinoma cells was down-regulated by RNAi. Cellular migration and invasion, apoptosis and qRT-PCR of key genes were assessed. RESULTS: Pyrosequencing revealed decreased methylation of the same four consecutive CpGs in the blood of patients compared with controls. Cellular migration and invasion were reduced and apoptosis was increased following transient or stable inhibition of PDK4. Expression of vimentin, HIF-1 and VEGFA was reduced. CONCLUSIONS: These studies demonstrate the involvement of PDK4 in transformation. Methylation assessment of PDK4 in the blood may be useful for non-invasive CRC detection. PDK4 should be considered as a target for development of anticancer strategies and therapies.

Breast cancer in systemic lupus.

Objective There is a decreased breast cancer risk in systemic lupus erythematosus (SLE) versus the general population. We assessed a large sample of SLE patients, evaluating demographic and clinical characteristics and breast cancer risk. Methods We performed case-cohort analyses within a multi-center international SLE sample. We calculated the breast cancer hazard ratio (HR) in female SLE patients, relative to demographics, reproductive history, family history of breast cancer, and time-dependent measures of anti-dsDNA positivity, cumulative disease activity, and drugs, adjusted for SLE duration. Results There were 86 SLE breast cancers and 4498 female SLE cancer-free controls. Patients were followed on average for 7.6 years. Versus controls, SLE breast cancer cases tended to be white and older. Breast cancer cases were similar to controls regarding anti-dsDNA positivity, disease activity, and most drug exposures over time. In univariate and multivariate models, the principal factor associated with breast cancers was older age at cohort entry. Conclusions There was little evidence that breast cancer risk in this SLE sample was strongly driven by any of the clinical factors that we studied. Further search for factors that determine the lower risk of breast cancer in SLE may be warranted.

Ab initio Exchange-Correlation Free Energy of the Uniform Electron Gas at Warm Dense Matter Conditions.

In a recent Letter [T. Dornheim et al., Phys. Rev. Lett. 117, 156403 (2016)PRLTAO0031-900710.1103/PhysRevLett.117.156403], we presented the first quantum Monte Carlo (QMC) results for the warm dense electron gas in the thermodynamic limit. However, a complete parametrization of the exchange-correlation free energy with respect to density, temperature, and spin polarization remained out of reach due to the absence of (i) accurate QMC results below θ=k_{B}T/E_{F}=0.5 and (ii) QMC results for spin polarizations different from the paramagnetic case. Here we overcome both remaining limitations. By closing the gap to the ground state and by performing extensive QMC simulations for different spin polarizations, we are able to obtain the first completely ab initio exchange-correlation free energy functional; the accuracy achieved is an unprecedented ∼0.3%. This also allows us to quantify the accuracy and systematic errors of various previous approximate functionals.