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A MACHINE-LEARNING METHOD TO INFER FUNDAMENTAL STELLAR PARAMETERS FROM PHOTOMETRIC LIGHT CURVES
Miller, A. A.,Bloom, J. S.,Richards, J. W.,Lee, Y. S.,Starr, D. L.,Butler, N. R.,Tokarz, S.,Smith, N.,Eisner, J. A. IOP Publishing 2015 The Astrophysical journal Vol.798 No.2
<P>A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are >10(9) photometrically cataloged sources, yet modern spectroscopic surveys are limited to similar to fewx10(6) targets. As we approach the Large Synoptic Survey Telescope era, new algorithmic solutions are required to cope with the data deluge. Here we report the development of a machine-learning framework capable of inferring fundamental stellar parameters (T-eff, log g, and [Fe/H]) using photometric-brightness variations and color alone. A training set is constructed from a systematic spectroscopic survey of variables with Hectospec/ Multi-Mirror Telescope. In sum, the training set includes similar to 9000 spectra, for which stellar parameters are measured using the SEGUE Stellar Parameters Pipeline (SSPP). We employed the random forest algorithm to perform a non-parametric regression that predicts Teff, log g, and [Fe/H] from photometric time-domain observations. Our final optimized model produces a cross-validated rms error (RMSE) of 165 K, 0.39 dex, and 0.33 dex for T-eff, log g, and [Fe/H], respectively. Examining the subset of sources for which the SSPP measurements are most reliable, the RMSE reduces to 125 K, 0.37 dex, and 0.27 dex, respectively, comparable to what is achievable via low-resolution spectroscopy. For variable stars this represents a approximate to 12%-20% improvement in RMSE relative to models trained with single-epoch photometric colors. As an application of our method, we estimate stellar parameters for similar to 54,000 known variables. We argue that this method may convert photometric time-domain surveys into pseudo-spectrographic engines, enabling the construction of extremely detailed maps of the Milky Way, its structure, and history.</P>
Nanobiocatalysis for protein digestion in proteomic analysis
Kim, Jungbae,Kim, Byoung Chan,Lopez-Ferrer, Daniel,Petritis, Konstantinos,Smith, Richard D. WILEY-VCH Verlag 2010 Proteomics Vol.10 No.4
<P>The process of protein digestion is a critical step for successful protein identification in bottom-up proteomic analyses. To substitute the present practice of in-solution protein digestion, which is long, tedious, and difficult to automate, many efforts have been dedicated for the development of a rapid, recyclable and automated digestion system. Recent advances of nanobiocatalytic approaches have improved the performance of protein digestion by using various nanomaterials such as nanoporous materials, magnetic nanoparticles, and polymer nanofibers. Especially, the unprecedented success of trypsin stabilization in the form of trypsin-coated nanofibers, showing no activity decrease under repeated uses for 1 year and retaining good resistance to proteolysis, has demonstrated its great potential to be employed in the development of automated, high-throughput, and on-line digestion systems. This review discusses recent developments of nanobiocatalytic approaches for the improved performance of protein digestion in speed, detection sensitivity, recyclability, and trypsin stability. In addition, we also introduce approaches for protein digestion under unconventional energy input for protein denaturation and the development of microfluidic enzyme reactors that can benefit from recent successes of these nanobiocatalytic approaches.</P>
Bio-Frontier Symposia : Proteomics ; Advanced strategies for protein profiling using LC-FTICR/MS
( Ljiljana Pasa Toli ),( Gordon A. Anderson ),( Mary S. Lipton ),( David G. Camp II ),( Yu Feng Shen ),( Christophe Masselon ),( Richard D. Smith ) 한국생화학분자생물학회 (구 한국생화학회) 2005 62회 KSBMB Annual Meeting in 2005 Vol.- No.-
Ahn, Hye‐,Kyung,Kim, Byoung Chan,Jun, Seung‐,Hyun,Chang, Mun Seock,Lopez‐,Ferrer, Daniel,Smith, Richard D.,Gu, Man Bock,Lee, Sang‐,Won,Kim, Beom Soo,Kim, Jungbae Wiley Subscription Services, Inc., A Wiley Company 2010 Biotechnology and bioengineering Vol.107 No.6
<P><B>Abstract</B></P><P>An efficient protein digestion in proteomic analysis requires the stabilization of proteases such as trypsin. In the present work, trypsin was stabilized in the form of enzyme coating on electrospun polymer nanofibers (EC‐TR), which crosslinks additional trypsin molecules onto covalently attached trypsin (CA‐TR). EC‐TR showed better stability than CA‐TR in rigorous conditions, such as at high temperatures of 40 and 50°C, in the presence of organic co‐solvents, and at various pH's. For example, the half‐lives of CA‐TR and EC‐TR were 1.42 and 231 h at 40°C, respectively. The improved stability of EC‐TR can be explained by covalent linkages on the surface of trypsin molecules, which effectively inhibits the denaturation, autolysis, and leaching of trypsin. The protein digestion was performed at 40°C by using both CA‐TR and EC‐TR in digesting a model protein, enolase. EC‐TR showed better performance and stability than CA‐TR by maintaining good performance of enolase digestion under recycled uses for a period of 1 week. In the same condition, CA‐TR showed poor performance from the beginning and could not be used for digestion at all after a few usages. The enzyme coating approach is anticipated to be successfully employed not only for protein digestion in proteomic analysis but also for various other fields where the poor enzyme stability presently hampers the practical applications of enzymes. Biotechnol. Bioeng. 2010;107: 917–923. © 2010 Wiley Periodicals, Inc.</P>
Madar, Inamul Hasan,Ko, Seung-Ik,Kim, Hokeun,Mun, Dong-Gi,Kim, Sangtae,Smith, Richard D.,Lee, Sang-Won American Chemical Society 2017 ANALYTICAL CHEMISTRY - Vol.89 No.2
<P>Mass spectrometry (MS)-based proteomics, which uses high-resolution hybrid mass spectrometers,such as the quadrupole-orbitrap mass spectrometer, can yield tens of thousands of tandem mass (MS/MS) spectra of high resolution dining a routine bottom-up experiment. Despite being a fundamental and key step in MS-based proteomics, the accurate determination and assignment of precursor monoisotopic masses to the MS/MS spectra remains difficult. The difficulties stem from imperfect isotopic envelopes of precursor ions, inaccurate charge states for precursor ions, and cofragmentation. We describe a composite method of utilizing MS data to assign accurate monoisotopic masses to MS/MS spectra, including those subject to cofragmentation. The method, 'multiplexed post-experiment monoisotopic mass refinement' (mPE-MMR), consists of the following: multiplexing of precursor masses to assign multiple monoisotopic masses of cofragmented peptides to the corresponding multiplexed MS/MS spectra, multiplexing of charge states to assign correct charges to the precursor ions of MS/MS spectra with no charge information, and mass correction for inaccurate monoisotopic peak picking. When combined with MS-GF+, a database search algorithm based on fragment mass difference, mPE-MMR effectively increases both sensitivity and accuracy in peptide identification from complex high-throughput proteomics data compared to conventional-methods.</P>