dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We firstly want to thank  I. Ruvalcaba Baroni  and two anonymous referees for greatly improving the paper. We are deeply indebted to  Pierrick Penven ,  Xavier Capet  and  Elodie Gutknecht  for methodological discussions. We also wish to thank  Tristan Le Toullec  and computer engineers from the  Institut Universitaire Européen de la Mer  ( IUEM, Brest, France ), www. biogeosciences. net /13/6419/2016/Biogeosciences, 13 ,  6419-6440 , 2016.
Schedule for CPF treatment After the acclimation period, female rats were mated with males (two females per male). Once a positive smear was observed, dams received daily doses of the respective treatment from days 0 to 21 (D21, the day of weaning) by oral gavage. Three exposure groups were studied: the rats in the CPF0 (control) group received 1 mL/kg bodyweight (BW) of rapeseed oil; the rats in the CPF1 group received 1 mg/kg BW/day of CPF in rapeseed oil, and the rats in the CPF5 group received 5 mg/kg BW/day of CPF in rapeseed oil. The rats in the litters were studied at two time points: firstly at weaning (after having been exposed to CPF in the dam's milk (D21: CPF0, n = 7; CPF1, n = 8, and CPF5, n = 6) and secondly in young adulthood (after the young rats had been gavaged with CPF individually from weaning to the age of 60 days (D60: CPF0, n = 5; CPF1, n = 7, and CPF5, n = 8).
Bacterial identification by MALDI Biotyper A MALDI-TOF-MS system (Autoflex III, Bruker Daltonics, Billerica, MA, USA) was used to examine each unique colony of microorganisms isolated by culture, according to a previously described procedure  [17] [18] [19]. In brief, samples were prepared with absolute ethanol, and 1 μL of matrix solution (2,5-dihydroxybenzoic acid 50 mg/mL, 30 % acetonitrile, 0. 1 % trifluoroacetic acid) was added. The analytical data were processed with Bruker Biotyper software (version 2. 0, Bruker Daltonics), as described previously  [20]. Identifications were performed in duplicate, according to the manufacturer's instructions.
5 μg/mL of ethidium bromide. Smart-Ladders ® (200-10 kb; Eurogentec, Seraing, Belgium) were used as molecular weight markers. The gel profiles were photographed with an ultraviolet light transilluminator (ChemiDoc Touch Imaging System, Bio-Rad Laboratories, Marnes-la-Coquette, France) and processed with Image Lab software (version 5. 2. 1, Bio-Rad).
Bacterial translocation Translocation was observed in 7 of the 31 CPF-exposed strains (22. 6 %, with a 95 % CI of [7-36 %]; Table  2 ). Five were detected at D21 and 2 were detected at D60; one belonged to the CPF0 group, 3 (42. 9 %) belonged to the CPF1 group and the remaining 3 (42. 9 %) belonged to the CPF5 group (Fig. 4a, b ). Of the 24 BT-free rats, 8 (33. 4 %) belonged to the CPF0 group, 10 (41. 6 %) belonged to the CPF1 group, and 6 (25 %) belonged to the CPF5 group; 16 (66. 6 %) were detected at D21 and 8 (33. 4 %) were detected at D60 (Table  3 ). Translocation increase with CPF exposure (OR [95 % CI] = 1. 25 [0. 22-7. 15]), although this was not statistically significant (p = 0. 7981). Furthermore, translocation was more likely on D21 than on D60 (OR [95 % CI] = 1. 25 [0. 22-7. 15]); again, the OR was not statistically significant (p = 0.
First, we quantified by fluorescence spectroscopy the interaction with tubulin of both D1 and A-C2 using acrylodan-labeled cysteine variants. The stepwise addition of tubulin to labeled DARPins led to an increase of the acrylodan fluorescence signal (Fig. 2a ). Fitting the data with a quadratic equation (Equation  1 , see "Methods") yielded the value of K D and indicated a change by two orders of magnitude of this value to less than 1 nM in the case of A-C2, down from ~125 nM in the case of D1 (Table  1 ), the latter value being in excellent agreement with the one determined previously by fluorescence anisotropy  9. We also determined the association (k on ) and dissociation (k off ) rate constants of the tubulin-DARPin complexes. A chase of labeled DARPin from tubulin by the unlabeled counterpart yielded the k off value (Fig.
The data were fitted according to Equation 3. The variation of k obs as a function of tubulin concentration is shown in inset, from which the k on (slope of the curve) is derived. (d) The tubulin-DARPin interaction monitored by SPR. D1 (left) and TM-3 (right) were immobilized through their His-tag on the sensor chip. Tubulin at the indicated concentration was applied at time zero for 60 s (D1) or 180 s (TM-3), followed by a washing buffer flow. The black curves are the fit of the experimental data using the Langmuir analysis, from which the k on and k off are extracted. In the case of D1, plotting the values at the plateau as a function of the tubulin concentration provided an estimate of the K D (Supplementary Fig. 5 ) that is very similar to the k off /k on ratio. "ready-to-interact" conformation, the one adopted in the complex.
Detection was performed as described above. Equilibrium and kinetic analysis of tubulin-DARPin interaction by fluorescence. The dissociation constant (K D ) at equilibrium was determined using a fixed concentration of acrylodan-labeled DARPin (either 100 nM (D1) or 15 nM (high affinity DARPins)) titrated against increasing concentration of tubulin in 20 mM K-phosphate buffer pH 7. 2, 100 mM KCl, 1 mM MgCl 2 , 0. 5 mM EGTA and 10 μ M GDP at 20 °C. The increase in fluorescence (λ ex , 290 nm; λ em , 510 nm) was monitored in a FluoroMax spectrofluorometer (Jobin Yvon, HORIBA). The data were fitted to a 1:1 binding isotherm using Equation  1 , ∆ = × + + - + + -× × × ⁎ ⁎ ⁎ ⁎ Fluo Fluo D T K D T K D T D [ ] [ ] ([ ] [ ] ) 4 [ ] [ ] 2 [ ] (1) Max D D 2 where Δ Fluo is the variation of the fluorescence signal, Fluo Max is the fluorescence at saturating concentration of tubulin, [T] and [D*] are the concentrations of tubulin and labeled DARPin, respectively, and K D is the dissociation equilibrium constant.
Min k t obs where Fluo Min is the fluorescence at time 0. k on was extracted from the variation of the apparent rate constant k obs as a function of the tubulin concentration. Kinetic analysis of tubulin-DARPin interaction by SPR. Surface plasmon resonance was measured using a Proteon XPR36 instrument (Bio-Rad). The measurements were performed at 20 °C in the same buffer as the one used in the fluorescence experiments, but with a lower (20 μ M) EGTA concentration and adding 0. 005% Tween 20. HTG sensor chips (Bio-Rad) were used to immobilize the DARPins through their His-tag. For the determination of kinetic data, tubulin was injected at five different concentrations centered around the expected K D value. The kinetic data were fitted according to the Langmuir isotherm using the Proteon Manager software.
The structure was solved by molecular replacement with Phaser 36 taking the structure of a 3-variable ankyrin repeat DARPin (PDB code 2XEE  37  ) as a search model. The structure was refined in Phenix  38  , alternating refinement with manual model building in Coot  39. Uncomplexed TM-3 was crystallized in 100 mM Na-acetate pH 4. 6 and 2 M Na-formate. The crystallization buffer was supplemented with 25% glycerol as a cryo protectant and data were collected at 100 K at the ESRF ID30A-3 beamline. Data were processed with XDS  34  and Aimless  40  and the structure was solved with Phaser taking a C-cap truncated model of D1. The Buster program was used for refinement  41. To crystallize the tubulin-A-C2 complex, the C-terminal tails of tubulin were first cleaved with subtilisin  42. A-C2 was added to digested tubulin in 1.
5:1 A-C2:tubulin molar ratio. The complex was purified on a Superdex 200 10/300 GL column (GE Healthcare), equilibrated with 20 mM Hepes pH 7. 2, 100 mM KCl, 1 mM MgCl 2 and 0. 5 mM EGTA. The GDP concentration was adjusted to 0. 2 mM, then the complex was concentrated to 200 μ M and stored in liquid N 2 until use. Crystals grew spontaneously within 24 h in 25% (w/v) polyethylene glycol 4000 (PEG 4000), 0. 1 M tri-sodium citrate pH 5. 6 and 0. 2 M ammonium sulfate. These crystals were used for microseeding of drops having a lower (16%) PEG 4000 concentration. Large, separate crystals were obtained within 7 days. They were frozen in the crystallization buffer supplemented with 20% glycerol as cryo protectant. Two diffraction data sets were collected on the same crystal at 100 K at the SOLEIL Proxima 1 beamline and processed, scaled and merged with XDS and XSCALE.
The structure was solved by molecular replacement with Phaser using tubulin-D1 (PDB code 4DRX  9  ) as a search model. The structure refinement was performed using Buster. Data collection and refinement statistics are reported in Supplementary Table  1. Coordinates and structure factors have been deposited with the Protein Data Bank with accession codes 4DUI (D1), 5EYL (TM-3) and 5EYP (tubulin-A-C2). Figures of structural models were generated with PyMOL  43. The electrostatic potential at the tubulin-D1 interface was calculated using APBS  44  and rendered in PyMOL.
Post cleavage buffer was exchanged to 50 mM ammonium acetate and the sample was analyzed by electrospray ionisation mass spectrometry (ESI-MS). Circular dichroism. Far-UV circular dichroism spectra of 20 μ M purified DARPins (5 μ M in the case of TM-3 1-149) in 50 mM Na-phosphate pH 7. 2, 150 mM NaCl were recorded from 200 to 260 nm in a 1 mm path length cuvette using a J-810 Spectropolarimeter equipped with a Peltier-controlled cuvette holder (Jasco). Data were acquired with 3 accumulations at a scan speed of 100 nm/min with 0. 2 nm pitch and 2 s integration time. The bandwidth was 2 nm. All spectra were buffer-subtracted and normalized and are represented as molar ellipticity. Thermal unfolding of DARPins was performed by heating 10 μ M of protein from 20 to 90 °C at a rate of 0. 5 °C per minute while recording ellipticity at 222 nm.
Acknowledgements Diffraction data were collected at the following synchrotron beam lines: ID23-1, ID30A-1 and ID30A-3 at the  European Synchrotron Radiation Facility (ESRF) (Grenoble, France) , and Proxima 1 and Proxima 2 at SOLEIL synchrotron (Saint-Aubin, France). We are most grateful to the machine and beam line groups for making these experiments possible. We thank  S. Cantos-Fernandes  for excellent assistance,  D. Mauchand  ( Unité Commune d'Expérimentation Animale, Institut National de la Recherche Agronomique ) for providing us with the material from which tubulin was purified, and  D. Cornu  and  L. Sago  ( SiCaps, I2BC, Gif-sur-Yvette ) for the mass spectrometry analysis. This work has benefited from the facilities and expertise of the I2BC crystallization and biophysics platforms.
S. A. was supported by a  Fondation pour la Recherche Médicale post-doctoral fellowship  ( SPF20130526829 ). This work was supported by grants from the  Fondation ARC pour la Recherche sur le Cancer  (to B. G. ), by  Agence Nationale de la Recherche  Grant  ANR-12-BSV8-0002-01  (to B. G. ), by  CNRS , and by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01.
The minimum viable population targets decreased asymptotically with increasing body mass and were on the same order of magnitude as minimum viable population estimates from species-and context-specific studies. Our approach provides a compromise between pragmatic, nonspecific population targets and detailed contextspecific estimates of population viability for which only limited data are available. It enables a first estimation of species-specific population targets based on a readily available trait and thus allows setting equitable targets for population persistence in large-scale and multispecies conservation assessments and planning.
The MVP is typically quantified on the basis of population viability analyses (PVAs), which are applied to estimate extinction probabilities based on long-term population data on wildlife demographic rates  (Akcakaya & Sjögren-Gulve 2000). The MVP estimated through PVA has a certain probability to persist given its current growth rate, which ultimately depends on species' life-history characteristics and environmental conditions (e. g. , habitat, resources, and external pressures). Hence, these MVPs have a short temporal validity, are highly context dependent, and are thus typically inapplicable to other conspecific populations. The applicability of an MVP derived via PVA for setting conservation targets has been questioned because of the limited availability of high-quality, populationspecific input data.
We aimed to derive a method to obtain equitable, life-history-informed population targets by developing and implementing allometric relationships for intrinsic growth rate and stochastic effects in models of population dynamics. We applied our method to obtain MVP targets for mammals for which sufficient data were available to derive allometric relationships for demographic parameters. We thus sought to fill the gap between contextspecific estimates of population viability and fixed, nonspecific population targets for conservation application. Our targets differed fundamentally from MVP estimates based on PVAs because they were context independent in that they were based solely on intrinsic characteristics of the species. As such, as in the original definition of the MVP concept  (Shaffer 1981; Shoemaker et al.
Application Our approach provides a coherent way to quantify population targets based on limited data. Our model does not provide an alternative to MVP estimates when sufficient population-specific data are available; rather, it allows one to obtain a first approximation of equitable persistence targets that can be applied to individual populations in multispecies conservation analyses. Our approach thus fills the gap between context-specific estimates of population viability and fixed, nonspecific population targets for conservation application. Given the scarcity of long-term population monitoring data required to obtain population-specific population parameters, we suggest our approach be used to establish tentative population targets for species conservation and recovery programs. In contrast to previously proposed targets for conservation (e.
Abstract. In this study, various ice particle habits are investigated in conjunction with inferring the optical properties of ice clouds for use in the Global Change Observation Mission-Climate (GCOM-C) satellite programme. We develop a database of the single-scattering properties of five ice habit models: plates, columns, droxtals, bullet rosettes, and Voronoi. The database is based on the specification of the Second Generation Global Imager (SGLI) sensor on board the GCOM-C satellite, which is scheduled to be launched in 2017 by the Japan Aerospace Exploration Agency. A combination of the finite-difference time-domain method, the geometric optics integral equation technique, and the geometric optics method is applied to compute the single-scattering properties of the selected ice particle habits at 36 wavelengths, from the visible to the infrared spectral regions.
This covers the SGLI channels for the size parameter, which is defined as a single-particle radius of an equivalent volume sphere, ranging between 6 and 9000 µm. The database includes the extinction efficiency, absorption efficiency, average geometrical cross section, single-scattering albedo, asymmetry factor, size parameter of a volume-equivalent sphere, maximum distance from the centre of mass, particle volume, and six nonzero elements of the scattering phase matrix. The characteristics of calculated extinction efficiency, single-scattering albedo, and asymmetry factor of the five ice particle habits are compared. Furthermore, sizeintegrated bulk scattering properties for the five ice particle habit models are calculated from the single-scattering database and microphysical data. Using the five ice particle habit models, the optical thickness and spherical albedo of ice clouds are retrieved from the Polarization and Directionality of the Earth's Reflectances-3 (POLDER-3) measurements, recorded on board the Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite.
Quantifying optical properties of these ice particle models is computationally expensive, and thus for application purposes it is useful to establish a number of libraries that pre-calculate the single-scattering properties of various ice particle habits. Using hexagonal plates and columns with a random orientation,  Hess and Wiegner (1994)  developed a single-scattering property database at wavelengths between 0. 35 and 3. 7 µm. Additionally,  Yang et al. (2000)  provided a database at wavelengths between 0. 2 and 5 µm for six ice particle habits (plates, solid and hollow columns, planar bullet rosettes, spatial bullet rosettes, and aggregates);  Yang et al. (2005)  further included two more ice habits and extended the database to wavelengths between 3 and 100 µm. Recently,  Yang et al. (2013)  released a database of a full set of scattering, absorption, and polarisation properties, assuming random orientation for a set of 11 habits at a number of wavelengths, ranging between 0.
2 and 100 µm. This database involved the addition of roughness to the particle surfaces. This library is based on the Amsterdam discrete dipole approximation, T-matrix, and IGOM methods. Using this updated library,  Baum et al. (2014)  developed a new set of bulk scattering and absorption models, with habit mixtures for radiative transfer calculations and remote sensing retrievals of ice clouds. There is increasing evidence that the ice particle model should contain some degree of surface roughness  (Foot, 1988; Baran et al. , 2001 Baran et al. , , 2003;; Ottaviani et al. , 2012; van Diedenhoven et al. , 2012 van Diedenhoven et al. , , 2013 van Diedenhoven et al. , , 2014;; Cole et al. , 2013 Cole et al. , , 2014;; Holz et al. , 2016). In particular, using an ensemble ice particle model,  Baran and C.
, 2010). As shown in Table  1 , SGLI has 19 channels, including two polarisation channels at visible and near-infrared wavelengths. A detailed description of the SGLI is reported by  Imaoka et al. (2010) ,  Nakajima et al. (2011), and Letu et al. (2012). Four of the ice particle habits (hexagonal columns, plates, bullet rosettes, and droxtals) employed in this study were chosen by referring to the MODIS Collection 5 ice particle model  (Baum et al. , 2005)  and ice cloud in situ measurement data. The habits shown in Fig. 1  are defined with the same parameters (semi-width, length, aspect ratio, and maximum dimension) as were employed in the scattering properties database by  Yang et al. (2000 Yang et al. ( , 2005)). The Voronoi habit was numerically determined by extraction of Wigner-Seitz cells from a 3-D mosaic image of the ice cloud microphysical data  (Ishimoto et al.
, 2012b). This habit is different from the aggregate model used in the scattering database reported by  Yang and Liou (1998b)  and  Yang et al. (2013). Spatial Poisson-Voronoi tessellations were used to determine the complex structure of the ice particles for the 3-D mosaic image. The geometry of each cell in the Voronoi tessellation was defined and based on the method by  Ohser and Mücklich (2000). A combination of the FDTD, GOIE, and GOM methods was employed to calculate the single-scattering properties of Voronoi ice habits for a wide range of size parameters (SZP) and is given by SZP = 2π • re/λ. (1) The refractive index of ice, published by  Warren and Brandt (2008) , is used in the computations. As shown in Table  2 , the FDTD method is used to calculate the singlescattering properties of ice particles with small size parameters (SZP < 50).
The GOIE and GOM methods are employed for calculating the scattering properties of ice particles with medium and large parameters, respectively. The wavelength selected for detailed calculations is determined by optimising the results of the scattering database for the SGLI channels  (Letu et al. , 2012). Calculations are performed at 27 spectral wavelengths (λ) from the visible to the infrared spectral region in the SGLI channels shown in Table  2. The volume-equivalent radius (re) ranges from 0. 7 to 533 µm and is defined as a single-particle radius of an equivalent volume sphere. The SZP ranges from 0. 35 to 6098. Consideration of the edge effect  (Bi et al. , 2010; Bi and Yang, 2014a ) is important for calculating the extinction efficiency (Q ext ) and absorption efficiency (Q abs ) by the GOIE method when the size parameter is less than 1000.
Microphysical data and bulk scattering properties of the ice particle model In this study, microphysical data obtained during 11 field campaigns were used to generate the particle size distributions (PSDs) of ice crystals using Eq. (3). To ensure the PSDs are unambiguously those of ice, microphysical data were filtered by limiting the cloud temperature to T ≤ -40 • C. More than 14 000 individual PSDs were selected to build bulk scattering properties of the ice particle models  (Heymsfield et al. , 2013). The microphysical data were obtained from the Space Science and Engineering Center, University of Wisconsin-Madison (http://www. ssec. wisc. edu/ice_models/ microphysical_data. html), and the PSDs are described by the following equation: n (D) = N 0 D µ e -λD , (3) where D is the particle maximum dimension, n(D) is the particle concentration per unit volume, N 0 is the intercept, λ is the slope, and µ is the dispersion.
Furthermore, spectral bulk scattering properties were calculated from SGLI single-scattering database, and the derived PSDs were based on the method described in  Baum et al. (2011). The main steps for calculating the bulk scattering properties are as follows: 1. Extract the total projected area, total volume, maximum dimension, scattering cross section, and scattering phase function parameters at a specific wavelength for five ice particle models from the SGLI single-scattering property database. 2. Calculate the effective diameter (D eff ) for 5 ice particle models based on Eq. (4). 3. Calculate the bulk-averaged single-scattering albedo (A s ), asymmetry factor (g), extinction efficiency (Q e ) and scattering phase function (P ) for five ice particle models based on Eqs. (  5 )-(  8 ). 4.
Data and methods Since 1996, three POLDER instruments have been flown to study clouds and aerosols using multiple angles and polarisation capabilities. The POLDER-1 and POLDER-2 instruments aboard JAXA's ADEOS satellite were operated from November 1996 to June 1997 and December 2002 to October 2003, respectively. Both of the POLDER instruments observed intensity from 14 viewing directions, with scattering angles ranging from 60 to 180 •. The spatial resolution of the product derived from POLDER-2 observation data is approximately 20 km, which is composed of 3 × 3 single pixels. POLDER-2 measured the upwelling total and polarised radiances from eight observing channels centred at wavelengths of 0. 443, 0. 490, 0. 565, 0. 670, 0. 763, 0. 765, 0. 865, and 0. 910 µm  (Baran and C. -Labonnote, 2006).
C. -Labonnote et al. (2000)  and  Baran and C. -Labonnote (2006)  proposed the SAD method for testing the phase function of the various ice particle models, using POLDER observational data with multiple viewing angles. For investigating the phase functions (P 11 ) of the different ice crystal models for retrieving the cloud microphysical properties, the cloud spherical albedo as a function of scattering angle is required. For calculating the cloud spherical albedo, bidirectional reflection is first determined by R cld (µ, µ 0 , φ -φ 0 ) = π L obs (µµ 0 φ -φ 0 )/µ 0 F 0 , ( 9 ) where u and u 0 are cosines of the satellite and solar zenith angles, φ -φ 0 is the relative azimuth angle between the satellite and the sun, L obs is the reflected solar radiance observed by the satellite, and F 0 is the solar flux density.