Improved Fast Randomized Iteration Approach to Full Configuration Interaction

We present three modifications to our recently introduced fast randomized iteration method for full configuration interaction (FCI-FRI) and investigate their effects on the method's performance for Ne, H$_2$O, and N$_2$. The initiator approximation, originally developed for full configuration interaction quantum Monte Carlo, significantly reduces statistical error in FCI-FRI when few samples are used in compression operations, enabling its application to larger chemical systems. The semi-stochastic extension, which involves exactly preserving a fixed subset of elements in each compression, improves statistical efficiency in some cases but reduces it in others. We also developed a new approach to sampling excitations that yields consistent improvements in statistical efficiency and reductions in computational cost. We discuss possible strategies based on our findings for improving the performance of stochastic quantum chemistry methods more generally.Comment: 13 pages, 5 figure

Approximating matrix eigenvalues by subspace iteration with repeated random sparsification

Traditional numerical methods for calculating matrix eigenvalues are prohibitively expensive for high-dimensional problems. Iterative random sparsification methods allow for the estimation of a single dominant eigenvalue at reduced cost by leveraging repeated random sampling and averaging. We present a general approach to extending such methods for the estimation of multiple eigenvalues and demonstrate its performance for several benchmark problems in quantum chemistry.Comment: 31 pages, 7 figure

Inhibition of Toll-Like Receptor 2-Mediated Interleukin-8 Production in Cystic Fibrosis Airway Epithelial Cells via the α7-Nicotinic Acetylcholine Receptor

Cystic Fibrosis (CF) is an inherited disorder characterised by chronic inflammation of the airways. The lung manifestations of CF include colonization with Pseudomonas aeruginosa and Staphylococcus aureus leading to neutrophil-dominated airway inflammation and tissue damage. Inflammation in the CF lung is initiated by microbial components which activate the innate immune response via Toll-like receptors (TLRs), increasing airway epithelial cell production of proinflammatory mediators such as the neutrophil chemokine interleukin-8 (IL-8). Thus modulation of TLR function represents a therapeutic approach for CF. Nicotine is a naturally occurring plant alkaloid. Although it is negatively associated with cigarette smoking and cardiovascular damage, nicotine also has anti-inflammatory properties. Here we investigate the inhibitory capacity of nicotine against TLR2- and TLR4-induced IL-8 production by CFTE29o- airway epithelial cells, determine the role of α7-nAChR (nicotinic acetylcholine receptor) in these events, and provide data to support the potential use of safe nicotine analogues as anti-inflammatories for CF

The Host Galaxy and Central Engine of the Dwarf AGN POX 52

We present new multi-wavelength observations of the dwarf Seyfert 1 galaxy POX 52 in order to investigate the properties of the host galaxy and the active nucleus, and to examine the mass of its black hole, previously estimated to be ~ 10^5 M_sun. Hubble Space Telescope ACS/HRC images show that the host galaxy has a dwarf elliptical morphology (M_I = -18.4 mag, Sersic index n = 4.3) with no detected disk component or spiral structure, confirming previous results from ground-based imaging. X-ray observations from both Chandra and XMM show strong (factor of 2) variability over timescales as short as 500 s, as well as a dramatic decrease in the absorbing column density over a 9 month period. We attribute this change to a partial covering absorber, with a 94% covering fraction and N_H = 58^{+8.4}_{-9.2} * 10^21 cm^-2, that moved out of the line of sight in between the XMM and Chandra observations. Combining these data with observations from the VLA, Spitzer, and archival data from 2MASS and GALEX, we examine the spectral energy distribution (SED) of the active nucleus. Its shape is broadly similar to typical radio-quiet quasar SEDs, despite the very low bolometric luminosity of L_bol = 1.3 * 10^43 ergs/s. Finally, we compare black hole mass estimators including methods based on X-ray variability, and optical scaling relations using the broad H-beta line width and AGN continuum luminosity, finding a range of black hole mass from all methods to be M_bh = (2.2-4.2) * 10^5 M_sun, with an Eddington ratio of L_bol/L_edd = 0.2-0.5.Comment: 19 pages, 16 figures, accepted for publication in Ap

A Machine Learning Classifier Trained on Cancer Transcriptomes Detects NF1 Inactivation Signal in Glioblastoma

We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules. We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM)

Integrative analysis identifies candidate tumor microenvironment and intracellular signaling pathways that define tumor heterogeneity in NF1

Neurofibromatosis type 1 (NF1) is a monogenic syndrome that gives rise to numerous symptoms including cognitive impairment, skeletal abnormalities, and growth of benign nerve sheath tumors. Nearly all NF1 patients develop cutaneous neurofibromas (cNFs), which occur on the skin surface, whereas 40-60% of patients develop plexiform neurofibromas (pNFs), which are deeply embedded in the peripheral nerves. Patients with pNFs have a ~10% lifetime chance of these tumors becoming malignant peripheral nerve sheath tumors (MPNSTs). These tumors have a severe prognosis and few treatment options other than surgery. Given the lack of therapeutic options available to patients with these tumors, identification of druggable pathways or other key molecular features could aid ongoing therapeutic discovery studies. In this work, we used statistical and machine learning methods to analyze 77 NF1 tumors with genomic data to characterize key signaling pathways that distinguish these tumors and identify candidates for drug development. We identified subsets of latent gene expression variables that may be important in the identification and etiology of cNFs, pNFs, other neurofibromas, and MPNSTs. Furthermore, we characterized the association between these latent variables and genetic variants, immune deconvolution predictions, and protein activity predictions

Tunable Landau-Zener transitions in a spin-orbit-coupled Bose-Einstein condensate

The Landau-Zener (LZ) transition is one of the most fundamental phenomena in quantum dynamics. It describes nonadiabatic transitions between quantum states near an avoided crossing that can occur in diverse physical systems. Here we report experimental measurements and tuning of LZ transitions between the dressed eigenlevels of a Bose-Einstein condensate (BEC) that is synthetically spin-orbit (SO) coupled. We measure the transition probability as the BEC is accelerated through the SO avoided crossing and study its dependence on the coupling between the diabatic (bare) states, eigenlevel slope, and eigenstate velocity-the three parameters of the LZ model that are independently controlled in our experiments. Furthermore, we performed time-resolved measurements to demonstrate the breaking down of the spin-momentum locking of the spin-orbit-coupled BEC in the nonadiabatic regime, and we determined the diabatic switching time of the LZ transitions. Our observations show quantitative agreement with the LZ model and numerical simulations of the quantum dynamics in the quasimomentum space. The tunable LZ transition may be exploited to enable a spin-dependent atomtronic transistor