Competing interactions in artificial spin chains

The low-energy magnetic configurations of artificial frustrated spin chains are investigated using magnetic force microscopy and micromagnetic simulations. Contrary to most studies on two-dimensional artificial spin systems where frustration arises from the lattice geometry, here magnetic frustration originates from competing interactions between neighboring spins. By tuning continuously the strength and sign of these interactions, we show that different magnetic phases can be stabilized. Comparison between our experimental findings and predictions from the one-dimensional Anisotropic Next-Nearest-Neighbor Ising (ANNNI) model reveals that artificial frustrated spin chains have a richer phase diagram than initially expected. Besides the observation of several magnetic orders and the potential extension of this work to highly-degenerated artificial spin chains, our results suggest that the micromagnetic nature of the individual magnetic elements allows observation of metastable spin configurations.Comment: 5 pages, 4 figure

Modeling reactivity to biological macromolecules with a deep multitask network

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural networkthe XenoSite reactivity modelusing literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model’s performances significantly outperformed reactivity indices derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be missed by standard reactivity screening experiments. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity

The SrTiO3_3 displacive transition revisited by Coherent X-ray Diffraction

We present a Coherent X-ray Diffraction study of the antiferrodistortive displacive transition of SrTiO$_3$, a prototypical example of a phase transition for which the critical fluctuations exhibit two length scales and two time scales. From the microbeam x-ray coherent diffraction patterns, we show that the broad (short-length scale) and the narrow (long-length scale) components can be spatially disentangled, due to 100 $\mu$m-scale spatial variations of the latter. Moreover, both components exhibit a speckle pattern, which is static on a $\sim$10 mn time-scale. This gives evidence that the narrow component corresponds to static ordered domains. We interpret the speckles in the broad component as due to a very slow dynamical process, corresponding to the well-known \emph{central} peak seen in inelastic neutron scattering.Comment: 4 pages, 3 figures, accepted in PR

Galaxy Selection and Clustering and Lyman alpha Absorber Identification

The effects of galaxy selection on our ability to constrain the nature of weak Ly\alpha absorbers at low redshift are explored. Current observations indicate the existence of a population of gas-rich, low surface brightness (LSB) galaxies, and these galaxies may have large cross sections for Ly\alpha absorption. Absorption arising in LSB galaxies may be attributed to HSB galaxies at larger impact parameters from quasar lines of sight, so that the observed absorption cross sections of galaxies may seem unreasonably large. Thus it is not possible to rule out scenarios where LSB galaxies make substantial contributions to Ly\alpha absorption using direct observations. Less direct tests, where observational selection effects are taken into account using simulations, should make it possible to determine the nature of Ly\alpha absorbers by observing a sample of ~100 galaxies around quasar lines of sight with well-defined selection criteria. Such tests, which involve comparing simulated and observed plots of the unidentified absorber fractions and absorbing galaxy fractions versus impact parameter, can distinguish between scenarios where absorbers arise in particular galaxies and those where absorbers arise in gas tracing the large scale galaxy distribution. Care must be taken to minimize selection effects even when using these tests. Results from such tests are likely to depend upon the limiting neutral hydrogen column density. While not enough data are currently available to make a strong conclusion about the nature of moderately weak absorbers, evidence is seen that such absorbers arise in gas that is around or between galaxies that are often not detected in surveys.Comment: 15 pages, 10 figures, accepted to the Astrophysical Journa