A System of Subroutines For Iteratively Reweighted Least Squares Computations

A description of a system of subroutines to compute solutions to the iteratively reweighted least squares problem is presented. The weights are determined from the data and linear fit and are computed as functions of the scaled residuals. Iteratively reweighted least squares is a part of robust statistics where "robustness" means relative insensitivity to moderate departures from assumptions. The software for iteratively reweighted least squares is cast as semi-portable Fortran code whose performance is unaffected (in the sense that performance will not be degraded) by the computer or operating-system environment in which it is used. An [ell sub1] start and an [ell sub2] start are provided. Eight weight functions, a numerical rank determination, convergence criterion, and a stem-and-leaf display are included.

Frustrated H-Induced Instability of Mo(110)

Using helium atom scattering Hulpke and L"udecke recently observed a giant phonon anomaly for the hydrogen covered W(110) and Mo(110) surfaces. An explanation which is able to account for this and other experiments is still lacking. Below we present density-functional theory calculations of the atomic and electronic structure of the clean and hydrogen-covered Mo(110) surfaces. For the full adsorbate monolayer the calculations provide evidence for a strong Fermi surface nesting instability. This explains the observed anomalies and resolves the apparent inconsistencies of different experiments.Comment: 4 pages, 2 figures, submitted to PR

Electron stimulated hydroxylation of a metal supported silicate film

Water adsorption on a double-layer silicate film was studied by using infrared reflection–absorption spectroscopy, thermal desorption spectroscopy and scanning tunneling microscopy. Under vacuum conditions, small amounts of silanols (Si–OH) could only be formed upon deposition of an ice-like (amorphous solid water, ASW) film and subsequent heating to room temperature. Silanol coverage is considerably enhanced by low-energy electron irradiation of an ASW pre-covered silicate film. The degree of hydroxylation can be tuned by the irradiation parameters (beam energy, exposure) and the ASW film thickness. The results are consistent with a generally accepted picture that hydroxylation occurs through hydrolysis of siloxane (Si–O–Si) bonds in the silica network. Calculations using density functional theory show that this may happen on Si–O–Si bonds, which are either parallel (i.e., in the topmost silicate layer) or vertical to the film surface (i.e., connecting two silicate layers). In the latter case, the mechanism may additionally involve the reaction with a metal support underneath. The observed vibrational spectra are dominated by terminal silanol groups (ν(OD) band at 2763 cm−1) formed by hydrolysis of vertical Si–O–Si linkages. Film dehydroxylation fully occurs only upon heating to very high temperatures (∼1200 K) and is accompanied by substantial film restructuring, and even film dewetting upon cycling hydroxylation/dehydroxylation treatment

Comparison of Neurite Orientation Dispersion and Density Imaging and Two-Compartment Spherical Mean Technique Parameter Maps in Multiple Sclerosis

BACKGROUND: Neurite orientation dispersion and density imaging (NODDI) and the spherical mean technique (SMT) are diffusion MRI methods providing metrics with sensitivity to similar characteristics of white matter microstructure. There has been limited comparison of changes in NODDI and SMT parameters due to multiple sclerosis (MS) pathology in clinical settings. PURPOSE: To compare group-wise differences between healthy controls and MS patients in NODDI and SMT metrics, investigating associations with disability and correlations with diffusion tensor imaging (DTI) metrics. METHODS: Sixty three relapsing-remitting MS patients were compared to 28 healthy controls. NODDI and SMT metrics corresponding to intracellular volume fraction (v_{in}), orientation dispersion (ODI and ODE), diffusivity (D) (SMT only) and isotropic volume fraction (v_{iso}) (NODDI only) were calculated from diffusion MRI data, alongside DTI metrics (fractional anisotropy, FA; axial/mean/radial diffusivity, AD/MD/RD). Correlations between all pairs of MRI metrics were calculated in normal-appearing white matter (NAWM). Associations with expanded disability status scale (EDSS), controlling for age and gender, were evaluated. Patient-control differences were assessed voxel-by-voxel in MNI space controlling for age and gender at the 5% significance level, correcting for multiple comparisons. Spatial overlap of areas showing significant differences were compared using Dice coefficients. RESULTS: NODDI and SMT show significant associations with EDSS (standardised beta coefficient −0.34 in NAWM and −0.37 in lesions for NODDI vin; 0.38 and −0.31 for SMT ODE and vin in lesions; p < 0.05). Significant correlations in NAWM are observed between DTI and NODDI/SMT metrics. NODDI vin and SMT vin strongly correlated (r = 0.72, p < 0.05), likewise NODDI ODI and SMT ODE (r = −0.80, p < 0.05). All DTI, NODDI and SMT metrics detect widespread differences between patients and controls in NAWM (12.57% and 11.90% of MNI brain mask for SMT and NODDI v_{in}, Dice overlap of 0.42). DATA CONCLUSION: SMT and NODDI detect significant differences in white matter microstructure between MS patients and controls, concurring on the direction of these changes, providing consistent descriptors of tissue microstructure that correlate with disability and show alterations beyond focal damage. Our study suggests that NODDI and SMT may play a role in monitoring MS in clinical trials and practice

Aromaticity in a Surface Deposited Cluster: Pd4_4 on TiO2_2 (110)

We report the presence of \sigma-aromaticity in a surface deposited cluster, Pd$_4$ on TiO$_2$ (110). In the gas phase, Pd$_4$ adopts a tetrahedral structure. However, surface binding promotes a flat, \sigma-aromatic cluster. This is the first time aromaticity is found in surface deposited clusters. Systems of this type emerge as a promising class of catalyst, and so realization of aromaticity in them may help to rationalize their reactivity and catalytic properties, as a function of cluster size and composition.Comment: 4 pages, 3 figure

SU(N) magnetism in chains of ultracold alkaline-earth-metal atoms: Mott transitions and quantum correlations

We investigate one dimensional SU$(N)$ Hubbard chains at zero temperature, which can be emulated with ultracold alkaline earth atoms, by using the density matrix renormalization group (DMRG), Bethe ansatz (BA), and bosonization. We compute experimental observables and use the DMRG to benchmark the accuracy of the Bethe ansatz for $N>2$ where the BA is only approximate. In the worst case, we find a relative error $\epsilon \lesssim 4$ in the BA ground state energy for $N \leq 4$ at filling 1/N, which is due to the fact that BA improperly treats the triply and higher occupied states. Using the DMRG for $N \leq 4$ and the BA for large $N$, we determine the regimes of validity of strong- and weak-coupling perturbation theory for all values of $N$ and in particular, the parameter range in which the system is well described by a SU$(N)$ Heisenberg model at filling 1/N. We find this depends only weakly on $N$. We investigate the Berezinskii-Kosterlitz-Thouless phase transition from a Luttinger liquid to a Mott-insulator by computing the fidelity susceptibility and the Luttinger parameter $K_\rho$ at 1/N filling. The numerical findings give strong evidence that the fidelity susceptibility develops a minimum at a critical interaction strength which is found to occur at a finite positive value for $N>2$.Comment: 19 pages, 13 figures and 2 tables; slightly revised version as published in Phys. Rev.

Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler

Probabilistic parameter estimation in model fitting runs the gamut from maximum likelihood or maximum a posteriori point estimates from optimization to Markov Chain Monte Carlo (MCMC) sampling. The latter, while more computationally intensive, generally provides a better characterization of the underlying parameter distribution than that of point estimates. However, in order to efficiently explore distributions, MCMC methods ideally require generating uncorrelated samples while also preserving reasonable acceptance probabilities; this becomes particularly important in problematic regions of parameter space. In this paper, we extend a recently proposed Hamiltonian MCMC sampler parametrized by neural networks (L2HMC) by modifying the loss function to jointly optimize the distance between samples and the acceptance probability such that it is stable and efficient. We apply this enhanced sampler to parameter estimation in a recently proposed MRI model, the multi-echo spherical mean technique. We show that it generally outperforms the state of the art Hamiltonian No-U-Turn (NUTS) sampler, L2HMC, and a least squares fitting in terms of accuracy and precision, also enabling the generation of more informative parameter posterior distributions. This illustrates the potential of machine learning enhanced samplers for improving probabilistic parameter estimation for medical imaging applications

RANKL Is a Downstream Mediator for Insulin-Induced Osteoblastic Differentiation of Vascular Smooth Muscle Cells

Several reports have shown that circulating insulin level is positively correlated with arterial calcification; however, the relationship between insulin and arterial calcification remains controversial and the mechanism involved is still unclear. We used calcifying vascular smooth muscle cells (CVSMCs), a specific subpopulation of vascular smooth muscle cells that could spontaneously express osteoblastic phenotype genes and form calcification nodules, to investigate the effect of insulin on osteoblastic differentiation of CVSMCs and the cell signals involved. Our experiments demonstrated that insulin could promote alkaline phosphatase (ALP) activity, osteocalcin expression and the formation of mineralized nodules in CVSMCs. Suppression of receptor activator of nuclear factor κB ligand (RANKL) with small interfering RNA (siRNA) abolished the insulin-induced ALP activity. Insulin induced the activation of extracellular signal-regulated kinase (ERK)1/2, mitogen-activated protein kinase (MAPK) and RAC-alpha serine/threonine-protein kinase (Akt). Furthermore, pretreatment of human osteoblasts with the ERK1/2 inhibitor PD98059, but not the phosphoinositide 3-kinase (PI3K) inhibitor, LY294002, or the Akt inhibitor, 1L-6-hydroxymethyl-chiro-inositol 2-(R)-2-O-methyl-3-O-octadecylcarbonate (HIMO), abolished the insulin-induced RANKL secretion and blocked the promoting effect of insulin on ALP activities of CVSMCs. Recombinant RANKL protein recovered the ALP activities decreased by RANKL siRNA in insulin-stimulated CVSMCs. These data demonstrated that insulin could promote osteoblastic differentiation of CVSMCs by increased RANKL expression through ERK1/2 activation, but not PI3K/Akt activation