Rim Pathway-Mediated Alterations in the Fungal Cell Wall Influence Immune Recognition and Inflammation

ACKNOWLEDGMENTS We acknowledge Jennifer Lodge, Woei Lam, and Rajendra Upadhya for developing and sharing the chitin and chitosan MTBH assay. We thank Todd Brennan of Duke University for providing MyD88-deficient mice. We acknowledge Neil Gow for providing access to the Dionex HPAEC-PAD instrumentation. We also acknowledge Connie Nichols for critical reading of the manuscript. These experiments were supported by an NIH grant to J.A.A. and F.L.W., Jr. (R01 AI074677). C.M.L.W. was supported by a fellowship provided through the Army Research Office of the Department of Defense (no. W911NF-11-1-0136 f) (F.L.W., Jr.). J.W., L.W., and C.M. were supported by the Wellcome Trust Strategic Award in Medical Mycology and Fungal Immunology (097377) and the MRC, Centre for Medical Mycology (MR/N006364/1). FUNDING INFORMATION MRC Centre for Medical MycologyMR/N006364/1 Carol A. Munro HHS | NIH | National Institute of Allergy and Infectious Diseases (NIAID) https://doi.org/10.13039/100000060R01 AI074677J. Andrew Alspaugh Wellcome https://doi.org/10.13039/100010269097377 Carol A. Munro DOD | United States Army | RDECOM | Army Research Office (ARO) https://doi.org/10.13039/100000183W911NF-11-1-0136 f Chrissy M. Leopold WagerPeer reviewedPublisher PD

An amorphous oxide semiconductor thin-film transistor route to oxide electronics

Amorphous oxide semiconductor (AOS) thin-film transistors (TFTs) invented only one decade ago are now being commercialized for active-matrix liquid crystal display (AMLCD) backplane applications. They also appear to be well positioned for other flat-panel display applications such as active-matrix organic light-emitting diode (AMOLED) applications, electrophoretic displays, and transparent displays. The objectives of this contribution are to overview AOS materials design; assess indium gallium zinc oxide (IGZO) TFTs for AMLCD and AMOLED applications; identify several technical topics meriting future scrutiny before they can be confidently relied upon as providing a solid scientific foundation for underpinning AOS TFT technology; and briefly speculate on the future of AOS TFTs for display and non-display applications

Handling manuscript rejection: Insights from evidence and experience

The purpose of this article is to provide authors with insights gained from evidence and experience on how to handle rejected manuscripts

What Should Be Done To Tackle Ghostwriting in the Medical Literature?

Background to the debate: Ghostwriting occurs when someone makes substantial contributions to a manuscript without attribution or disclosure. It is considered bad publication practice in the medical sciences, and some argue it is scientific misconduct. At its extreme, medical ghostwriting involves pharmaceutical companies hiring professional writers to produce papers promoting their products but hiding those contributions and instead naming academic physicians or scientists as the authors. To improve transparency, many editors' associations and journals allow professional medical writers to contribute to the writing of papers without being listed as authors provided their role is acknowledged. This debate examines how best to tackle ghostwriting in the medical literature from the perspectives of a researcher, an editor, and the professional medical writer

Recent Advances in Understanding the Structure and Properties of Amorphous Oxide Semiconductors

Amorphous oxide semiconductors (AOSs)--ternary or quaternary oxides of post-transition metals such as In-Sn-O, Zn-Sn-O, or In-Ga-Zn-O–have been known for a decade and have attracted a great deal of attention as they possess several technological advantages, including low-temperature large-area deposition, mechanical flexibility, smooth surfaces, and high carrier mobility that is an order of magnitude larger than that of amorphous silicon (a-Si:H). Compared to their crystalline counterparts, the structure of AOSs is extremely sensitive to deposition conditions, stoichiometry, and composition, giving rise to a wide range of tunable optical and electrical properties. The large parameter space and the resulting complex deposition--structure--property relationships in AOSs make the currently available theoretical and experimental research data rather scattered and the design of new materials difficult. In this work, the key properties of several In-based AOSs are studied as a function of cooling rates, oxygen stoichiometry, cation composition, or lattice strain. Based on a thorough comparison of the results of ab initio modeling, comprehensive structural analysis, accurate property calculations, and systematic experimental measurements, a four-dimensional parameter space for AOSs is derived, serving as a solid foundation for property optimization in known AOSs and for design of next-generation transparent amorphous semiconductors

Drawing inferences for high‐dimensional linear models: A selection‐assisted partial regression and smoothing approach

Drawing inferences for high‐dimensional models is challenging as regular asymptotic theories are not applicable. This article proposes a new framework of simultaneous estimation and inferences for high‐dimensional linear models. By smoothing over partial regression estimates based on a given variable selection scheme, we reduce the problem to low‐dimensional least squares estimations. The procedure, termed as Selection‐assisted Partial Regression and Smoothing (SPARES), utilizes data splitting along with variable selection and partial regression. We show that the SPARES estimator is asymptotically unbiased and normal, and derive its variance via a nonparametric delta method. The utility of the procedure is evaluated under various simulation scenarios and via comparisons with the de‐biased LASSO estimators, a major competitor. We apply the method to analyze two genomic datasets and obtain biologically meaningful results.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151307/1/biom13013.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151307/2/biom13013-sup-0001-SuppData.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151307/3/biom13013_am.pd

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art