Allergic fetal priming leads to developmental, behavioral and neurobiological changes in mice.

The state of the mother's immune system during pregnancy has an important role in fetal development and disruptions in the balance of this system are associated with a range of neurologic, neuropsychiatric and neurodevelopmental disorders. Epidemiological and clinical reports reveal various clues that suggest a possible association between developmental neuropsychiatric disorders and family history of immune system dysfunction. Over the past three decades, analogous increases have been reported in both the incidence of neurodevelopmental disorders and immune-related disorders, particularly allergy and asthma, raising the question of whether allergic asthma and characteristics of various neurodevelopmental disorders share common causal links. We used a mouse model of maternal allergic asthma to test this novel hypothesis that early fetal priming with an allergenic exposure during gestation produces behavioral deficits in offspring. Mothers were primed with an exposure to ovalbumin (OVA) before pregnancy, then exposed to either aerosolized OVA or vehicle during gestation. Both male and female mice born to mothers exposed to aerosolized OVA during gestation exhibited altered developmental trajectories in weight and length, decreased sociability and increased marble-burying behavior. Moreover, offspring of OVA-exposed mothers were observed to have increased serotonin transporter protein levels in the cortex. These data demonstrate that behavioral and neurobiological effects can be elicited following early fetal priming with maternal allergic asthma and provide support that maternal allergic asthma may, in some cases, be a contributing factor to neurodevelopmental disorders

Factor V Leiden and thrombosis in patients with systemic lupus erythematosus: a meta-analysis.

The aim of this study was to perform a meta-analysis of the association between the factor V Leiden polymorphism (FVL) and thrombosis among patients with systemic lupus erythematosus (SLE) and/or antiphospholipid antibody (aPL) positivity. Included studies recruited patients based on SLE or aPL-positive status, confirmed subjects' SLE diagnosis as defined by the American College of Rheumatology, and documented thrombotic events. Excluded studies were non-English or considered only arterial thrombosis. Individual patient data, available from 5 studies, together with unpublished data from 1210 European-American SLE patients from the UCSF Lupus Genetics Collection genotyped for FVL, were further analyzed. Seventeen studies (n=2090 subjects) were included in the initial meta-analysis. Unadjusted odds ratios (OR) were calculated to assess association of FVL with thrombosis. The OR for association of thrombosis with FVL was 2.88 (95% confidence interval (CI) 1.98-4.20). In the secondary analysis with our individual patient dataset (n=1447 European-derived individuals), SLE subjects with the FVL polymorphism still had more than two times the odds of thrombosis compared to subjects without this polymorphism, even when adjusting for covariates such as gender, age and aPL status. SLE and/or aPL-positive patients with the FVL variant have more than two times the odds of thrombosis compared to those without this polymorphism

Electrically controllable surface magnetism on the surface of topological insulator

We study theoretically the RKKY interaction between magnetic impurities on the surface of three-dimensional topological insulators, mediated by the helical Dirac electrons. Exact analytical expression shows that the RKKY interaction consists of the Heisenberg-like, Ising-like and DM-like terms. It provides us a new way to control surface magnetism electrically. The gap opened by doped magnetic ions can lead to a short-range Bloembergen-Rowland interaction. The competition among the Heisenberg, Ising and DM terms leads to rich spin configurations and anomalous Hall effect on different lattices.Comment: 5 pages, 3 figures, 1 tabl

Electrically-controllable RKKY interaction in semiconductor quantum wires

We demonstrate in theory that it is possible to all-electrically manipulate the RKKY interaction in a quasi-one-dimensional electron gas embedded in a semiconductor heterostructure, in the presence of Rashba and Dresselhaus spin-orbit interaction. In an undoped semiconductor quantum wire where intermediate excitations are gapped, the interaction becomes the short-ranged Bloembergen-Rowland super-exchange interaction. Owing to the interplay of different types of spin-orbit interaction, the interaction can be controlled to realize various spin models, e.g., isotropic and anisotropic Heisenberg-like models, Ising-like models with additional Dzyaloshinsky-Moriya terms, by tuning the external electric field and designing the crystallographic directions. Such controllable interaction forms a basis for quantum computing with localized spins and quantum matters in spin lattices.Comment: 5 pages, 1 figur

Spectral characteristics of propylitic alteration minerals as a vectoring tool for porphyry copper deposits

publisher: Elsevier articletitle: Spectral characteristics of propylitic alteration minerals as a vectoring tool for porphyry copper deposits journaltitle: Journal of Geochemical Exploration articlelink: http://dx.doi.org/10.1016/j.gexplo.2017.10.019 content_type: article copyright: © 2017 Elsevier B.V. All rights reserved.© 2017 Elsevier B.V. All rights reserved. The attached document is the authors’ final submitted version of the journal article. You are advised to consult the publisher’s version if you wish to cite from it

Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features

One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201

Sharp Global Bounds for the Hessian on Pseudo-Hermitian Manifolds

We find sharp bounds for the norm inequality on a Pseudo-hermitian manifold, where the L^2 norm of all second derivatives of the function involving horizontal derivatives is controlled by the L^2 norm of the sub-Laplacian. Perturbation allows us to get a-priori bounds for solutions to sub-elliptic PDE in non-divergence form with bounded measurable coefficients. The method of proof is through a Bochner technique. The Heisenberg group is seen to be en extremal manifold for our inequality in the class of manifolds whose Ricci curvature is non-negative.Comment: 13 page