In the past several years, Materials Genome Initiative (MGI) efforts have produced myriad examples of computationally designed materials in the fields of energy storage, catalysis, thermoelectrics, and hydrogen storage as well as large data resources that are used to screen for potentially transformative compounds. The bottleneck in high-throughput materials design has thus shifted to materials synthesis, which motivates our development of a methodology to automatically compile materials synthesis parameters across tens of thousands of scholarly publications using natural language processing techniques. To demonstrate our framework's capabilities, we examine the synthesis conditions for various metal oxides across more than 12 thousand manuscripts. We then apply machine learning methods to predict the critical parameters needed to synthesize titania nanotubes via hydrothermal methods and verify this result against known mechanisms. Finally, we demonstrate the capacity for transfer learning by using machine learning models to predict synthesis outcomes on materials systems not included in the training set and thereby outperform heuristic strategies.
Owing to their covalent modification by cholesterol and palmitate, Hedgehog (Hh) signaling proteins are localized predominantly to the plasma membrane of expressing cells. Yet Hh proteins are also capable of mobilizing to and eliciting direct responses from distant cells. The zebrafish you gene, identified genetically >15 years ago, was more recently shown to encode a secreted glycoprotein that acts cell-nonautonomously in the Hh signaling pathway by an unknown mechanism. We investigated the function of the protein encoded by murine Scube2, an ortholog of you, and found that it mediates release in soluble form of the mature, cholesterol- and palmitate-modified Sonic hedgehog protein signal (ShhNp) when added to cultured cells or purified detergent-resistant membrane microdomains containing ShhNp. The efficiency of Scube2-mediated release of ShhNp is enhanced by the palmitate adduct of ShhNp and by coexpression in ShhNp-producing cells of mDispatchedA (mDispA), a transporter-like protein with a previously defined role in the release of lipid-modified Hh signals. The structural determinants of Scube2 required for its activity in cultured cell assays match those required for rescue of you mutant zebrafish embryos, and we thus conclude that the role of Scube/You proteins in Hh signaling in vivo is to facilitate the release and mobilization of Hh proteins for distant action.
Predictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed. In this work, we present a collection of aggregated synthesis parameters computed using the text contained within over 640,000 journal articles using state-of-the-art natural language processing and machine learning techniques. We provide a dataset of synthesis parameters, compiled autonomously across 30 different oxide systems, in a format optimized for planning novel syntheses of materials.
The distribution and activities of morphogenic signaling proteins such as Hedgehog (Hh) and Wingless (Wg) depend on heparan sulfate proteoglycans (HSPGs). HSPGs consist of a core protein with covalently attached heparan sulfate glycosaminoglycan (GAG) chains. We report that the unmodified core protein of Dally-like (Dlp), an HSPG required for cell-autonomous Hh response in
Drosophila
embryos, alone suffices to rescue embryonic Hh signaling defects. Membrane tethering but not specifically the glycosylphosphatidylinositol linkage characteristic of glypicans is critical for this cell-autonomous activity. Our studies further suggest divergence of the two
Drosophila
and six mammalian glypicans into two functional families, an activating family that rescues cell-autonomous Dlp function in Hh response and a family that inhibits Hh response. Thus, in addition to the previously established requirement for HSPG GAG chains in Hh movement, these findings demonstrate a positive cell-autonomous role for a core protein in morphogen response in vivo and suggest the conservation of a network of antagonistic glypican activities in the regulation of Hh response.
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