dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability. All relevant data necessary for producing the samples, assigning the protein signals, and calculating the structures are available from the corresponding author upon reasonable request. The NMR data and protein structure are deposited in the BioMagResBank (BMRB) with ID 34088 and the Protein Data Bank (PDB) with ID 5MWV, respectively. The script is deposited in GitHub and can be downloaded under: https://github. com/jorenretel/ompg_restraint_generation.
INTRODUCTION We present a "Forward Global Calibration Method (FGCM)" for photometric calibration of ground-based wide-band optical/near-IR surveys such as the Dark Energy Survey (DES) (DES Collaboration 2016) and the survey that will be carried out with the Large Synoptic Survey Telescope (LSST:  LSST Science Collaboration et al. 2009). We have applied this method to the first three years of the DES campaign  (Diehl et al. 2016) , and achieve sub-percent reproducibility and uniformity in the multi-band photometry of this dataset. This method also provides sufficiently detailed knowledge of the shape of the passband of each survey exposure to account with similar precision for the dependence of the photometry on the Spectral Energy Distribution (SED) of the source. This "chromatic correction" is required for proper scientific interpretation of the observed wide-band optical flux.
The FGCM is a photometric model-based approach  (Stubbs & Tonry 2006; Burke et al. 2010)  to calibration of multi-band imaging surveys. The FGCM does not rely on previously established "standard stars" or other celestial targets. In-situ instrumentation is used to periodically measure the optical properties of the survey instrumental system, and additional dedicated equipment is used to continuously monitor atmospheric conditions during periods of survey operations. These auxiliary data are combined with the repeated observations of stars found in the survey data to "forward" compute the fraction of photons in the telescope beam at the top of the atmosphere that are predicted to be detected in the sensors of the camera. The FGCM iteratively solves for parameters of the photometric model that best fit the number of photons observed in the camera.
In the second step, the calibration stars are used to determine observing conditions for individual science exposures. This calibration step does not require the exposures to have been used in the first step nor taken in "photometric" conditions, provided they have sufficient overlap with the calibration stars. Features of the FGCM are: 1. The instrumental response and the make-up of the atmosphere can be characterized at any time by a relatively small set of parameters. 2. These parameters vary in time slowly compared to the rate at which survey exposures are acquired. 3. We are free to choose survey exposures that best determine the calibration parameters and magnitudes of the calibration stars. 4. Data taken in any band will contribute to the calibration of all bands taken in the same period of time.
The DES also acquires real-time data from CTIO site meteorology instrumentation, the SUOMINET GPS system 2 , and auxiliary "aTmCAM" instrumentation  (Li et al. 2014)  to track changes in conditions at the observatory and the make up of the atmosphere above the observatory. The all-sky infrared cloud camera RASICAM  (Reil et al. 2014 ) is used to guide observing operations. The DES observations at CTIO are made over a combination of full and half nights equivalent to 105 full nights from August through February. Initial "Science Verification (SV)" observations were made in the 2012-2013 season to commission the instrument and survey strategy, and an "SVA1 Gold" data release 3 is available for public use. These data were calibrated with a version of the earlier "ubercal" technique, and successfully met the DES design requirement of 2% or better photometric accuracy  (Tucker et al.
2007 ). The main survey began in August 2013, and the third of the planned five-year science campaigns was completed in February 2016. We report here on the FGCM calibration of this first threeyear "Y3A1" dataset. In Section 2 of this paper we first present the concept of broad-band photometry with chromatic corrections, and follow in Section 3 with the formulation of the FGCM calibration model. In Section 4, we next discuss the FGCM process and the execution of the calibration of the DES Y3A1 three-year data release. In Section 5 we define FGCM metrics and tests, and present results of the performance of the FGCM calibration of Y3A1. In Section 6 we define and discuss the FGCM output data products and their use. Finally, in Section 7 we discuss plans for further improvements of the FGCM procedure.
FORWARD GLOBAL CALIBRATION FORMULATION The DES data management (DESDM) software package  (Morganson, et al. 2017, in preparation)  processes single DECam exposures using a dedicated version of the well-known "Source Extractor" software  (Bertin & Arnouts 1996)  to produce "FINALCUT" catalogs (FITS databases) of instrumental data from individual observations of celestial objects. Corrections have been applied to these data for a number of instrumental effects including electronic bias and non-linearity, variation in pixel-to-pixel response, and variation in the observing point-spread-function (including dependence on source brightness). Sky backgrounds have been subtracted and images have been screened to remove those that exhibit a number of observing or instrumental effects  (Bernstein et al.
2017, in preparation). The FINALCUT catalogs are queried (c. f. Appendix C) and the observational data are processed with software that implements the FGCM photometric calibration. The FGCM formulation follows the sequence in time over which the DES survey data are acquired. The FGCM model parameters include some that are continuous functions of time, some that vary nightly, some that vary over periods of months, and others that change only when some "event" occurs such as instrumental maintenance. The FGCM model does not include any ad hoc parameters unique to a given exposure. Assuming the instrumental properties do not depend on the atmospheric conditions, the observational passband (Eqn. 1) can be separated into two functions, S obs b (λ) ≡ S b (x,y,alt,az,t,λ) = S inst b (x,y,t,λ) × S atm (alt,az,t,λ), ( 17 ) where S atm is the transmittance (dimensionless) of photons from the top of the atmosphere to the input pupil of the telescope, and S inst b is the response (CCD electrons/photon) of the instrumental system with optical filter b to photons that pass through the input pupil of the telescope.
Standard Passbands and Observational Look-Up Tables Standard passbands were defined for the Y3A1 campaign, and look-up tables (LUTs) were pre-computed to allow rapid evaluation of the passband integrals I obs 0 and I obs 1 over a wide range of model parameter vectors. The standard instrumental system responses were chosen to be the average responses of the CCDs in the focal plane shown in Figure  1. These were synthesized from DECal scans taken during the first two years of DECam operations. The standard atmospheric transmittance was computed with the MODTRAN IV code  (Berk et al. 1999)  with the parameters given in Table  1  chosen as typical of those encountered during the Y3A1 campaign. The transmission of the various components of the standard atmosphere are shown in Figure  3   (Fukugita et al.
1996)    and the I std 0 and I std 1 integrals and their ratio I std 10 for these passbands are given in Table  2. Look-up tables of the I obs 0 and I obs 1 integrals were computed at discrete points over a broad range of the atmospheric parameter vector P atm using the focal-plane averaged instrumental passbands in Figure  1   The FGCM includes several steps that are done once at the outset (c. f. Appendix C). This includes SQL queries of the "FINALCUT" catalogs produced by DESDM to obtain both a "demand" list of exposures that are to be calibrated and a catalog of data from all observations of objects that are candidates to be used as calibration stars. These observations are then cross-matched by location on the celestial sky to assign them to unique objects. Selection is then made of candidate calibration stars, and a catalog of all observations of each candidate is created that serves as the basis for the calibration fit and subsequent computation of calibration data products.
( 32 ) The statistical error on CCD gray is typically ∼ 1 -2 mmag, so structure on physical scales larger than the ∼ 0. 2 • size of a DECam sensor can be resolved. To take advantage of this, the average and variance of the residual extinctions of the CCD images on each exposure are computed as, EXP gray (i) = ccd CCD gray (i,ccd)σ phot (i,ccd) -2 ccd σ phot (i,ccd) -2 , ( 33 ) and VAR gray (i) = ccd CCD gray (i,ccd) 2 σ phot (i,ccd) -2 ccd σ phot (i,ccd) -2 -EXP gray (i) 2. ( 34 ) 5 http://github. com/scipy/scipy/blob/v0. 14. 0/scipy/optimize/ lbfgsb. py#L47 Both EXP gray and VAR gray are used in the selection of calibration exposures to use in the FGCM fit (c. f. Appendix D). A night will be "calibratable" if a sufficient number of such calibration exposures were taken anytime during that night.
FGCM Process Flags Assignment of values to FGCM data products follows one of several paths identified by the parameter FLAG FGCM summarized in Table  5 , and detailed in this section. Data acquired from griz calibration exposures are given FLAG FGCM = 1, while data from Y -band calibration exposures are given FLAG FGCM = 2. Data from exposures that are not deemed to have been taken in photometric conditions, but that were taken on calibratable nights, are in the FLAG FGCM = 4 category. These first three categories comprise the exposures taken on calibratable nights that are themselves calibratable; 90. 5% of the exposures on the Y3A1 demand list are in one of these categories. Objects observed on these exposures will have valid m std values with accurate chromatic correction. There are a few individual CCD images that have a large number of calibration stars, but were taken on nights with no calibration fit.
FGCM Zero Points and Linear Chromatic Correction With sufficient knowledge of the SED of the target source, it is possible to compute the full chromatic correction for observations taken on calibrated nights (FLAG FGCM = 1, 2 or 4) using the fit parameter vectors. However, the linear approximation to the chromatic correction can be easily implemented, and is sufficient for many applications. The FGCM provides calibration data products for both fully integrated and the linearized corrections that are discussed in this section. A calibrated measurement (on exposure number EXP and sensor CCD) of the m std b magnitude of a celestial object can be computed as (c. f. Eq. 16)   (39) m The passband integral I obs 0 (EXP,CCD) is computed as in Eqns. 17 and 18 I obs 0 (EXP,CCD) = S superstar b (CCD,epoch) × S optics ( P inst (EXP), MJD) × ∞ 0 S atm ( P atm (EXP),λ) × S DECal b (CCD,λ) × λ -1 dλ, (40) and the "gray" correction ZPT gray is described in the following subsection.
The FGCM achieves reproducible and stable photometric calibration of standard magnitudes m std b of stellar sources over the multi-year DES Y3A1 data sample with residual random calibration errors σ ≈ 5 -6 mmag per exposure (Table  4 ). The accuracy of the calibration is uniform across the 5000 square degree DES footprint to within σ = 7 mmag (Fig. 14 ). The systematic uncertainty of magnitudes in the standard system due to the spectrum of the source is less than 5 mmag for main sequence stellar spectra with 0. 5 < g -i < 3. 0 mag (Sec. 5. 4). A catalog of standard stars with well-known magnitudes in the DES standard system is created by the FGCM procedure, as well as atmosphere models for each exposure that allow computation of corrections of measured magnitudes to the standard system. Continued analysis of the calibration of the DES Y3A1 data set has pointed to several improvements that we anticipate installing in the FGCM in the future: 1.
5 or less, which corresponds to differences below 5 mmag in the chromatic correction. While the FGCM fit does not make direct use of the retrieved values, we note that it includes this information intrinsically as the χ 2 function (Eq. 27) is sensitive to the colors of the calibration stars. C. INITIAL PREPARATIONS C. 1. FINALCUT Queries The DESDM software package accepts raw DECam exposures and produces "FINALCUT" catalogs of observed quantities for each object detected on science exposures. A set of quality cuts are applied to eliminate exposures on which one or more of a number of recognizable hardware failures occurred or that were taken through easily detectable cloud cover. The exposures selected for Y3A1 include wide-field survey and supernova fields, but no standard-star observations.
A control log is then created that drives the calibration process. Query (SQL) of the DESDM FINALCUT catalogs is done to make an initial selection of observations that are candidates to be used in the FGCM calibration. This query requires (Source Extractor data products are indicated in capital letters): 1. SExtractor flag = 0 (objects that were not  deblended, saturated, or   D. SELECTIONS OF CALIBRATION STARS, CALIBRATION EXPOSURES, AND "CALIBRATABLE" NIGHTS On the initial FGCM fit cycle, estimates of the magnitude of each object j in each of the griz bands are made by computing the observed TOA magnitudes m obs b (i,j) (Eqn. 4) with the parameters for the standard atmosphere in Table  1. The average value m obs b (j) of all observations of that object that are within 0. 10 mag of the brightest is then computed.
Deformation energy analysis was used to measure the amount of local flexibility in the protein structure (i. e. atomic motion relative to neighbouring atoms)  27, 28. High deformation indicates flexible regions, and our data showed that residues with the highest deformation energy (red bar) were present throughout the primary structure (Fig. 1b ). In the TIMP-1 three-dimensional (3D) structure, these residues are located between the N-and C-terminal domains (Fig. 1c ). Cα atomic fluctuations provide amplitudes of the absolute atomic motion  28  , and our data (Fig. 1d ) showed that residues with high fluctuation (red bar) were dispersed in the TIMP-1 primary structure but located outside the 3D structure (Fig. 1d  and e ). Interestingly, regions with the highest deformation energy seemed to be localised in the regions exhibiting the lowest atomic fluctuation (Fig.
Methods Molecular Modelling. NMA of the TIMP-1 protein was performed using the PDB 1UEA crystallographic structure. After the addition of hydrogen atoms, the structure was successively minimised using a combination of the Steepest Descent (1000 steps) and ABNR (50000 steps) methods. NMA was carried out using the Bio3D package within the Rstudio environment  27. We used the calphax force field to account for stronger beta bridges and helix 1-4 interactions, specifying the six disulfide bridges (1-70, 3-99, 132-137, 145-166, 127-174, 13-124) when building the force constants. Graphs were generated with Rstudio. A 100-ns-long molecular dynamics simulation in explicit solvent (TIP3P) was performed on the TIMP-1 minimised structure used for NMA with the NAMD software, along with the CHARMM27 force field, in the NPT ensemble at 293 K -1 atm, with a cut-off of 11 Å for long-range interactions.
Trajectory analysis and graphs were produced using the Bio3D package within the Rstudio environment  28. Pictures were generated with PyMol software. Plasmid construction, production and purification of recombinant proteins. The pCMV6-XL5 vector containing TIMP-1 cDNA was obtained from Origene (Origene, Rockville, USA). Mutated TIMP-1 forms were generated at the F12 and K47 positions using the QuickChange II Site-directed Mutagenesis kit (Stratagene, Agilent Technologies, Les Ulis, France) with the following primers: F12A (forward): 5′-CCACCACAGACGGCCGCCTGCAATTCCGACCTCG-3′; (reverse): 5′-CGAGGT CGGAATTGCAGGCGGCCGTCTGTGGGTGG-3′, K47A (forward): 5′-GATGACCAAGATGTATGCAGGGT TCCAAGCCTTAGGG-3′; (reverse):5′-CCCTAAGGCTTGGAACCCTGCATACATCTTGGTCATC-3′. TIMP-1 and mutated cDNA were cloned into the p3XFLAG-CMV14 vector (Sigma-Aldrich, Saint Quentin Fallavier, France) to produce TIMP-1 with fusion FLAG-tag in C-terminal using the following primers: 5′-TACAGAATTCCACCATGGCCCC-3′ (forward) and 5′-GAGAGCTAGCGGCTATCTGGGACC-3′ (reverse).
MMP inhibition assays. The inhibitory activities of T1-WT, T1-F12A and T1-K47A against MMP-1, -2, -3 and -9 were assayed as follows. All MMPs and substrates were obtained from Merck Biosciences. MMP-1: proMMP-1 was activated by incubation in activation buffer (25 mM Hepes, 5 mM CaCl 2 , 20% (v/v) glycerol, 0. 01% (v/v) Brij 35 and 4. 5 mM APMA) for 2 h at 37 °C. More than 80% of the proMMP-1 was activated in these conditions (data not shown). The activated MMP-1 (2 nM) was then incubated for 1 h at 27 °C in Tris-test buffer (50 mM Tris-HCl pH 7. 5 containing 150 mM NaCl and 5 mM CaCl 2 ) containing 0. 92 to 6. 44 nM TIMP-1 or its mutated forms. The assay was initiated by adding MMP-1/-9 fluorogenic substrate DNP-Pro-Cha-Gly-Cys( Me)-His-Ala-Lys(N-Me-Abz)-NH 2 (λ exc = 365 nm/λ em = 450 nm) at a final concentration of 5 µM.
The rate of each substrate cleavage was measured in triplicate for each concentration examined, using an Infinite F200 PRO spectrofluorimeter (Tecan, Lyon, France) with one measure per minute for 20 min. Non-linear regression analysis with Graphpad software (La Jolla, USA) allowed us to calculate the K i using the Morrison equation  43. Surface plasmon resonance analysis. Surface plasmon resonance experiments were performed using Biacore T200 (GE Healthcare Life Sciences, Velizy-Villacoublay, France) as previously described  6. Injections were performed with HBS-N buffer (50 mM Hepes, 150 mM NaCl, Tween 0. 005% (v/v), pH 7. 4). Recombinant LRP-1 mini-domains DII and DIV (R&D) with an Fc-tag were immobilised on a CMD500m sensor chip (Xantech) functionalised with anti-Fc antibody. Different concentrations of TIMP-1 and of its mutated forms (5-160 nM) were injected in multi-cycle kinetics to allow determination of the association (k on ) and dissociation (k off ) constants.
The neurons were rinsed with PBS and fixed in 4% (v/v) paraformaldehyde for 10 min. Immunofluorescence studies with anti-βIII-tubulin antibodies (1/100, clone SDL. 3D10, Sigma-Aldrich) were performed as previously described  30. The cells were observed using an Olympus BH2-RFCA fluorescent microscope with a 40x objective lens and photographed with a U-MPTV Camera using the DP Controller software (all from Olympus). In each experiment, fluorescence microphotographs were taken from at least 5 randomly selected fields per slide, with 4 slides per experimental condition (total 20 fields). The neurite length corresponded to the average length of the neurites of each cell and was obtained by dividing the total length of the neurites in a field by the number of neurite segments, as previously described  6.
After rinsing with PBS, the slides were mounted in fluorescence mounting medium. Acquisitions were made with a confocal microscope LSM710 with a 63x objective and the Zeiss operating system associated with the ZEN software program (all from Carl Zeiss MicroImaging GmbH, Germany). Acquisitions were performed by exciting Alexa Fluor 488 and Alexa Fluor 568 with an argon laser, a helium-neon laser and a chameleon infrared laser tuned at 730 nm. Emitted fluorescence was detected through the appropriate wavelength window. In each experiment, fluorescence microphotographs were taken from at least 10 randomly selected fields per slide, with 3 slides per experimental condition (total 30 fields). In each field, 20 images were captured with a 0. 3 μm z-step. Maximal intensity projection representations were realised using the AMIRA software program (v5.
4. 2, Visualization Sciences Group, Burlington, MA, USA). For the colocalisation studies, a multichannel field module was used, followed by a correlation plot treatment (subrange values of 15 to 255; gamma, 0. 5). Then, an isosurface representation of the correlation plot was realised using the same threshold for each treatment. The TIMP-1 average intensity of fluorescence per µm 2 for each cell was performed using the particle analysis function of the ImageJ software. Quantification of the TIMP-1/EEA1 colocalisation was performed using the AMIRA software program.
Statistical analysis. Statistical analysis was performed using a Student's t-test or one-way ANOVA with a Newman Keuls post-test (GraphPad Prism version 6. 01 for Windows, GraphPad Software, San Diego California USA, www. graphpad. com). The values represent the mean ± SD of at least three independent experiments. P-values of <0. 05, <0. 01, <0. 001 and <0. 0001 are identified with *, **, *** and ****, respectively. NS represents a value that is not statistically significant.
Acknowledgements The authors thank  Dr. Nicolas Floquet  for preliminary studies in this project,  Olivier Boquet  for his technical assistance in generating mutants,  Jean-Luc Breda  for animal care,  Christine Terryn  for technical support with confocal imaging and  Sebastien Almagro  for the ImageJ software plugins. They are grateful to  Dr. D. K. Strickland  ( Department of Surgery, University of Maryland School of Medicine, Baltimore, MD, USA ) for providing R2629 antibodies. They thank the  HPC Regional Center ROMEO  and the  PICT (Cell and Tissue Imaging Platform)  from the  University of Reims Champagne-Ardenne (France)  for providing time and support. This work was supported by the  Université de Reims Champagne-Ardenne , the  Region Champagne-Ardenne  and the  Centre National de la Recherche Scientifique (CNRS) ,  France.
Peaks indicated by an arrow were attributed to the oxidized species (-2, +16, +32 Da relative to the non-modified species) and were further characterized by tandem MS (full arrow data presented in the following sections; empty arrows data not shown). Labels of the oxidized species were proposed based on tandem MS results. For cellulose: "diol C4+ diol Cx" indicates a double oxidation (diols): one has been localized at C4 of the non-reducing end, while the second one could not be precisely localized and is either localized at the reducing end or non-reducing end or three (DP4) adjacent β-(1 → 4) linkages are predominant, while longer cellodextrin sequences contributed to less than 10% of the total polysaccharide chain in the starchy endosperm  [23]. If one assumes that DP4 structures of different shapes have comparable ionization efficiency in MS, the peak area in Fig.
Funding This study was funded by the  AMIDEX foundation (Funcopper project , Grant Number  A*M-AAP-EI-13-13-130115-15. 37  and  MicrobioE  project, Grant Number  ANR-11-IDEX-0001-02 ). Work in Vancouver was additionally supported by  NSERC  ( Discovery Grant ), the  Canada Foundation for Innovation  and the  British Columbia Knowledge Development Fund. Received: 2 November 2016 Accepted: 2 March 2017.
) with 4 Â 1000 W halogen lamps installed as infrared radiation sources by using the rod of the above prepared mixture. The temperature of the molten zone focused by mirrors was precisely controlled by adjusting the power of the lamps. During the growth, the molten zone was moved upwards at a rate of 5 mm h À1 , with the seed rod (lower sha) and the feed rod (upper sha) counter rotating at 30 rpm in Ar gas ow by 4 L min À1. A single crystal of Mn 4 Nb 2 O 9 of about 8 mm in diameter and 30 mm in length with black shining surface was obtained, as shown in Fig. 1a. The room temperature crystal structure was characterized by XRD (XRD, D/max2200) using Cu Ka radiation. Low temperature XRD data were collected with a high accuracy home-made Bragg-Brentano diffractometer equipped with a copper source monochromatic radiation l ¼ 1.