Measuring 14 elemental abundances with R=1,800 LAMOST spectra

The LAMOST survey has acquired low-resolution spectra (R=1,800) for 5 million stars across the Milky Way, far more than any current stellar survey at a corresponding or higher spectral resolution. It is often assumed that only very few elemental abundances can be measured from such low-resolution spectra, limiting their utility for Galactic archaeology studies. However, Ting et al. (2017) used ab initio models to argue that low-resolution spectra should enable precision measurements of many elemental abundances, at least in theory. Here we verify this claim in practice by measuring the relative abundances of 14 elements from LAMOST spectra with a precision of $\lesssim$ 0.1 dex for objects with ${\rm S/N}_{\rm LAMOST}$ > 30 (per pixel). We employ a spectral modeling method in which a data-driven model is combined with priors that the model gradient spectra should resemble ab initio spectral models. This approach assures that the data-driven abundance determinations draw on physically sensible features in the spectrum in their predictions and do not just exploit astrophysical correlations among abundances. Our analysis is constrained to the number of elemental abundances measured in the APOGEE survey, which is the source of the training labels. Obtaining high quality/resolution spectra for a subset of LAMOST stars to measure more elemental abundances as training labels and then applying this method to the full LAMOST catalog will provide a sample with more than 20 elemental abundances that is an order of magnitude larger than current high-resolution surveys, substantially increasing the sample size for Galactic archaeology.Comment: 6 pages, 3 figures, ApJ (Accepted for publication- 2017 October 9

Enhanced mechanical, thermal and flame retardant properties by combining graphene nanosheets and metal hydroxide nanorods for Acrylonitrile–Butadiene–Styrene copolymer composite

Three metal hydroxide nanorods (MHR) with uniform diameters were synthesized, and then combined with graphene nanosheets (GNS) to prepare acrylonitrile–butadiene–styrene (ABS) copolymer composites. An excellent dispersion of exfoliated two-dimensional (2-D) GNS and 1-D MHR in the ABS matrix was achieved. The effects of combined GNS and MHR on the mechanical, thermal and flame retardant properties of the ABS composites were investigated. With the addition of 2 wt% GNS and 4 wt% Co(OH)2, the tensile strength, bending strength and storage modulus of the ABS composites were increased by 45.1%, 40.5% and 42.3% respectively. The ABS/GNS/Co(OH)2 ternary composite shows the lowest maximum weight loss rate and highest residue yield. Noticeable reduction in the flammability was achieved with the addition of GNS and Co(OH)2, due to the formation of more continuous and compact charred layers that retarded the mass and heat transfer between the flame and the polymer matrix

Signatures of unresolved binaries in stellar spectra: implications for spectral fitting

The observable spectrum of an unresolved binary star system is a superposition of two single-star spectra. Even without a detectable velocity offset between the two stellar components, the combined spectrum of a binary system is in general different from that of either component, and fitting it with single-star models may yield inaccurate stellar parameters and abundances. We perform simple experiments with synthetic spectra to investigate the effect of unresolved main-sequence binaries on spectral fitting, modeling spectra similar to those collected by the APOGEE, GALAH, and LAMOST surveys. We find that fitting unresolved binaries with single-star models introduces systematic biases in the derived stellar parameters and abundances that are modest but certainly not negligible, with typical systematic errors of $300\,\rm K$ in $T_{\rm eff}$, 0.1 dex in $\log g$, and 0.1 dex in $[\rm Fe/H]$ for APOGEE-like spectra of solar-type stars. These biases are smaller for spectra at optical wavelengths than in the near-infrared. We show that biases can be corrected by fitting spectra with a binary model, which adds only two labels to the fit and includes single-star models as a special case. Our model provides a promising new method to constrain the Galactic binary population, including systems with single-epoch spectra and no detectable velocity offset between the two stars.Comment: Accept to MNRAS with minor revisions since v1. 7 pages, 5 figure

A novel ensemble learning approach to unsupervised record linkage

© 2017 Record linkage is a process of identifying records that refer to the same real-world entity. Many existing approaches to record linkage apply supervised machine learning techniques to generate a classification model that classifies a pair of records as either match or non-match. The main requirement of such an approach is a labelled training dataset. In many real-world applications no labelled dataset is available hence manual labelling is required to create a sufficiently sized training dataset for a supervised machine learning algorithm. Semi-supervised machine learning techniques, such as self-learning or active learning, which require only a small manually labelled training dataset have been applied to record linkage. These techniques reduce the requirement on the manual labelling of the training dataset. However, they have yet to achieve a level of accuracy similar to that of supervised learning techniques. In this paper we propose a new approach to unsupervised record linkage based on a combination of ensemble learning and enhanced automatic self-learning. In the proposed approach an ensemble of automatic self-learning models is generated with different similarity measure schemes. In order to further improve the automatic self-learning process we incorporate field weighting into the automatic seed selection for each of the self-learning models. We propose an unsupervised diversity measure to ensure that there is high diversity among the selected self-learning models. Finally, we propose to use the contribution ratios of self-learning models to remove those with poor accuracy from the ensemble. We have evaluated our approach on 4 publicly available datasets which are commonly used in the record linkage community. Our experimental results show that our proposed approach has advantages over the state-of-the-art semi-supervised and unsupervised record linkage techniques. In 3 out of 4 datasets it also achieves comparable results to those of the supervised approaches

Investigation of medication-­related osteonecrosis of the jaw by real-­time in vitro assays, histologic examination, and radiographic evaluation

Although medication-related osteonecrosis of the jaw (MRONJ) was first reported over fifteen years ago, to date the pathogenesis of the disease remains unclear. As a result, there is no consensus on a unified treatment or prevention protocol. Treatment of MRONJ is difficult and costly, and disease sequela can include pain, infection, inability to eat, extraoral fistula, and pathologic fracture, all of which significantly impact the quality of life for patients. This project aimed to provide a more profound picture of how bisphosphonates and denosumab could influence the expression patterns of human gingival fibroblasts and participating immune cells and mediators using real-time in vitro assays. The composition of the bone in the MRONJ disease process was also examined in additional histologic and radiographic examinations to correlate the altered bone structure with different disease variants and degree of antiresorptive exposure. Our results lead us to suggest that the etiology of MRONJ could be attributed to a multifactorial process involving soft tissue toxicity, mechanical damage and surgical trauma, local inflammation, immune suppression and dysfunction, infection and biofilm alteration, dysfunctional bone resorption, over-­ossification and a limited osteocyte network, and impaired wound healing leading to necrotic bone exposur

A novel pattern recognition algorithm: Combining ART network with SVM to reconstruct a multi-class classifier

AbstractBased on the principle of one-against-one support vector machines (SVMs) multi-class classification algorithm, this paper proposes an extended SVMs method which couples adaptive resonance theory (ART) network to reconstruct a multi-class classifier. Different coupling strategies to reconstruct a multi-class classifier from binary SVM classifiers are compared with application to fault diagnosis of transmission line. Majority voting, a mixture matrix and self-organizing map (SOM) network are compared in reconstructing the global classification decision. In order to evaluate the method’s efficiency, one-against-all, decision directed acyclic graph (DDAG) and decision-tree (DT) algorithm based SVM are compared too. The comparison is done with simulations and the best method is validated with experimental data

Basement membrane collagens and disease mechanisms

Basement membranes (BMs) are specialised extracellular matrix (ECM) structures and collagens are a key component required for BM function. While collagen IV is the major BM collagen, collagens VI, VII, XV, XVII and XVIII are also present. Mutations in these collagens cause rare multi-systemic diseases but these collagens have also been associated with major common diseases including stroke. Developing treatments for these conditions will require a collective effort to increase our fundamental understanding of the biology of these collagens and the mechanisms by which mutations therein cause disease. Novel insights into pathomolecular disease mechanisms and cellular responses to these mutations has been exploited to develop proof-of-concept treatment strategies in animal models. Combined, these studies have also highlighted the complexity of the disease mechanisms and the need to obtain a more complete understanding of these mechanisms. The identification of pathomolecular mechanisms of collagen mutations shared between different disorders represent an attractive prospect for treatments that may be effective across phenotypically distinct disorders