dataesr/scientific-paragraphs-categorization huggingface dataset:
The kinetics of caspase-3/7 activity was determined in a continuous manner by recording the data every 5 min for 60 min. The ROS activity was performed by Total ROS/superoxide detection Kit (Enzo Life Sciences, Villeurbanne, France). Briefly, 50 µL of the DG suspension was mixed with 50 µL of 1 mM solution of 2′,7′-dichlorfluorescein-diacetate (DCFH-DA) then drop in black flat bottom 96-well plate. This molecule will penetrate through the cell membrane and the presence of intracellular ROS will cleave to release the fluorophore molecule DCF that can be detected by measuring the fluorescence at excitation/emission = 480 nm/530 nm. To assess specific activity of caspase-3/7 and ROS production, total proteins contents was determined by the BCA Protein assay (Bio-Rad, Marnes-la-Coquette, France) according to the manufacturer protocol.
Acknowledgments We thank  Marc Leroy  for technical assistance. We thank also  Tim Green  for his help in the writing of this paper. This work was supported by the  IFREMER  and the  APOTOX  project funded by the  French National program   EC2CO  ( INSU CNRS, Ecosphè re Continentale et Côtiè re ) and by  AVERROES program  for post-doc financial support.
Colony formation assay (Soft agar cell culture) A mixture of 2 ml of pre-warmed (37 1C) RPMI1640 medium supplemented with 10% FBS and 2 ml pre-warmed (42 1C) 0. 66% Bacto Agar Select/plate (Sigma) (0. 33% final agar) was mixed with a 2 Â 10 5 cell suspension and seeded over a 0. 66% agar/full medium pre-layer (7 ml) in a 60-mm dish. The cells were allowed to grow in a humidified 37 1C incubator with 5% CO 2 for 3-4 weeks. Colony formation assays were assessed in the presence or absence of 5 mM LiCl, 10 mm GSK3 inhibitor VIII or 5 mm SB216763 (Sigma). Plates were scanned and the colony numbers were quantified using the ImageJ software.
ACKNOWLEDGEMENTS We thank  Christophe Alberti  and  Elodie Belloir  for technical assistance. We are also grateful to  Drs Dominique Dusmenil ,  Isabelle Dusanter  and  Evelyne Lauret  for the transfer of various technologies and helpful advice. This work was funded by the  Fondation ARC  (grant number # 5089 ) and Ligue contre le cancer (grant numbers  CV/CB/055-10  and  NM/CP/M16649 ). NH received fellowships from  Fondation ARC and Institut Curie.
These four genes harbor mutations predicted to result in amino-acid substitutions potentially affecting the activity of the encoded protein. All non-synonymous SNPs were analyzed by SIFT (Sorting Intolerant From Tolerant). This program is a tool based on sequence homology for predicting the probability that a mutation might be deleterious and affects the protein function  [46]. Phylogenetic analysis (Additional file 4: Figure  S4 ) showed that the two allelic forms of the different genes were relatively similar. Mds3p displays four nonsynonymous mutations with respect to the two parental strains, and two mutations are positioned in N-terminus protein. According to SIFT analysis, these mutations should not affect the protein function in a deleterious manner. Phylogenetic analysis suggested that two of the variants present in the 2029-C5 parental strain were ancestral whereas the other two were ancestral in the 1782-B1 strain (Figure  5B ).
This fermentation monitoring method was validated in a previous study  [60]. The rate of CO 2 production was calculated by polynomial smoothing of the last 10 measurements of fermenter weight loss. The many acquisitions of data for the mass and the precision of weighing (0. 1 to 0. 01 g) made it possible to calculate the rate of CO 2 production with a high level of precision  [58]. The high frequency of online measurements of CO 2 production (one measurement every 20 minutes) made it possible to calculate the rates of CO 2 production by sliding-window second-order polynomial fitting in a custom-developed Labview application. This measurement made it possible to determine the overall rate of fermentation during stationary phase and to compare segregants at the same stage of fermentation.
Reciprocal hemizygosity analysis This technique was used for the identification of allelespecific contributions to the phenotype  [61]. We used the same primers for each candidate gene and the same transformation strategy as for the two parental strains. We inactivated one copy of the candidate gene with the hygromycin selection cassette. We then carried out a specific PCR to check that the gene was indeed inactivated. The primers used for cassette integration are indicated in the supplementary data (Additional file 5: Figure  S5 ). The contribution of the alleles to the phenotype was analyzed by comparing fermentation kinetics in SM100 (1. 2-liter fermenters).
Microarray design For the detection of genetic variation in the segregant population, we designed a DNA microarray with isothermal-melting probes, as described by Gresham et al. [62]. The genome sequences of Saccharomyces cerevisiae RM11 and Saccharomyces cerevisiae P3-D5 (related to MTF1782 and MTF2029, respectively) were used to construct a set of 6,318 pairs of isothermal probes. Biallelic positions at least 100 bp apart were chosen. We also included 300 replicates and 1000 invariant control probes in the array. The primers were designed with primer3  [63] , to ensure hybridization at 50°C. The array design is available on GEO under the accession number GPL18217.
Statistical analysis Statistical analysis was performed with R software, version 2. 15. 2  [64]. The phenotype heritability H 2 was calculated as previously described  [65] , i. e. H 2 = ((Var seg -Var env )/ Var seg ) x100, where Var env is the pooled variance among parental measurement and Var seg is the variance among phenotype values for the segregants. Transgressive segregation was defined as in  [65, 66]  by the number of segregants whose phenotype level lay at least 2σ higher than the mean phenotype level of the higher parent or 2σ lower than the mean phenotype level of the lower parent; σ is the pooled standard deviation of the parents. The Agilent 8x15K array was imported into R software with the limma package  [67]. For each probe of each block, the log2 (green signal/red signal) ratio was calculated and centered.
Sequence analysis For each candidate gene, we compared gene and protein sequences, to identify nonsynonymous mutations distinguishing between the two parental alleles. The polymorphic change was analyzed by SIFT (Sorting Intolerant From Tolerant)  [46]. For each allelic form, we analyzed its distribution in the genomes of strains available from the Saccharomyces Genome Database (SGD, http://www. yeastgenome. org) and strains from the Saccharomyces Genome Resequencing Project (SGRP, 42 strains)  [69, 70]. Phylogenies were inferred with MEGA  [71] , by the maximum likelihood method, based on the Kimura two-parameter model  [72]. The trees with the highest log likelihood are shown. The trees are drawn to scale, with branch lengths proportional to the number of substitutions per site.
ESI was used with a dry gas flow rate of 12 L min -1 , a dry gas temperature of 365 • C and nebuliser gas pressure of 70 psi (oxygen-free nitrogen, OFN, BOC, UK). The MS was operated in alternating polarity mode, scanning from m/z (mass-to-charge ratio) 50 to 600. Tandem MS was achieved through the automated MS 2 function within the Esquire software (Bruker Daltonics, software version 5. 2).
5 s with a source accumulation time of 0. 002 s. The collision radio frequency (RF) and ion cooler time was set to favour lower masses at 1300 Vpp (volts peak-to-peak) and 0. 010 s, respectively. An approximate resolution of 38 000 at m/z 400 was obtained for both ionisation modes. The spectral analysis was performed using DataAnalysis 4. 0 software (Bruker Daltonics, Bremen, Germany). Monoisotopic elemental formulae were calculated using the following restrictions: unlimited C, H and O were allowed and up to 3 N atoms, O : C < 3, H : C > 0. 5, DBE (double bond equivalent) < 20, and in positive mode, Na and K adducts were also allowed. The accuracy of the molecular formulae (elemental composition) assignment in shown by the error; where the error equals the difference between the exact and measured mass for the assigned molecular formula.
The HPLC-QTOFMS was externally calibrated using an ESI-L low-concentration tuning mix (Agilent Technologies, UK). ESI was used and the parameters were set to a dry gas flow rate of 10 L min -1 , dry gas temperature of 350 • C and a nebuliser pressure of 4 bar (nitrogen, BOC, UK). Tandem mass spectra were acquired for a mass range of m/z 50-800 using the auto MS 2 function within the Compass 1. 3 micrOTOF-SR3 software, control version 3. 0 (Bruker Daltonics, UK). The three most abundant precursor ions per spectrum were automatically selected by the software and subjected to collision induced dissociation (CID). The collision energy for CID fragmentation was set at 8. 0 eV at a collision RF of 800. 0 Vpp. The spectral analysis was performed using DataAnalysis 4. 0 software (Bruker Daltonics, Bremen, Germany).
Acknowledgements. The assistance of scientists at EUPHORE and the  York Centre of Excellence in Mass Spectrometry  is gratefully acknowledged. This work was supported by  EUROCHAMP-2  ( TA  project  E2-2011-04-19-0059 ). A. R. Rickard acknowledges the support of the  National Centre for Atmospheric Science. The  York Centre of Excellence in Mass Spectrometry  was created thanks to a major capital investment through  Science City York , supported by  Yorkshire Forward  with funds from the  Northern Way Initiative. K. L. Pereira  acknowledges support of a  NERC  PhD studentship ( NE106026057 ). Edited by:  Y. Rudich.
ACKNOWLEDGMENTS We thank the  Lenne group  for discussion and critical reading of the manuscript, as well as members of the  interdisciplinary network LABEX INFORM "INformation Flow and ORganization at the Membrane " on the  Marseille-Luminy Campus  for discussion. This work was supported by the  CNRS , the  Fondation de la Recherche Médicale (FRM) , and the  Agence Nationale de la Recherche (ANR Blanc  " MorFor " and  ANR-10-INBS-04-01   France Bio Imaging). Binh-An Truong-Quang  was supported by a PhD fellowship by the  Région PACA-Andor Technology and Association pour la Recherche sur le Cancer (ARC).
Experimental details Ingots with nominal compositions Fe 80 Zr 10 Cr 10 (purity > 99. 9 wt%) were prepared by arc melting in a titanium-gettered argon atmosphere. The ingots were remelted several times in order to achieve a homogeneous master alloy. From these ingots, cylindrical bulk samples with different diameters (Φ = 1, 2, 3 and 4 mm) and 80 mm length were prepared by copper mold casting. According to a previous study  [16] , for nominal composition Fe 80 Zr 10 Cr 10, the as-cast material is composed of essentially two phases: ferrite and hexagonal C14/C36 Laves phases; C15 Laves phase can be present at a minor level. Phase analysis was carried out by x-ray diffraction (XRD) to check for the presence of the expected phases using a STOE Stadi P diffractometer (Mo-K α1 radiation). The Rietveld method was applied for the structure refinement using the WinPlotR software package  [21].
This paper aims to characterize for the first time the dust emissivity in the frequency range 100-353 GHz of the diffuse emission in the Galactic plane. For this purpose, we remove the free-free emission contribution using the RRL data (Sect. 3. 1). We start by describing the Planck and ancillary data used in this work in Sects. 2 and 3. In Sect. 4 we present the data analysis techniques, followed by the main results of the paper in Sect. 5. These are discussed further and interpreted in Sect. 6, followed by the conclusions in Sect. 7.
2010). In the present work we use data from the Planck 2013 data release which can be obtained from the Planck Legacy Archive 2. We use the HFI data acquired between 13 August 2009 and 27 November 2010. These are converted to intensity units of MJy sr -1 following the IRAS SED convention  (Beichman et al. 1988) , which assumes a spectral index α = -1. Colour corrections based on the observed emission spectrum and on the spectral response of the receiver, are applied to derive the specific intensity at the effective frequency of each band (Planck Collaboration IX 2014). The Planck CMB map is derived from the SMICA component separation method and presented in Planck Collaboration XII  (2014). Close to the plane of the Galaxy, it is not possible to correctly separate the CMB fluctuations from the much brighter Galactic emission.
2013)  bolometer solution  (Planck Collaboration XIII 2014). These are converted from line integrated units to intensity units as  described in Planck Collaboration XIII (2014). The calibration uncertainties on these maps are of 10, 2, and 5 % at 100, 217, and 353 GHz, respectively. The 100 GHz MILCA map has been compared with ground-based data, in particular the  Dame et al. (2001)  12 CO J=1→0 survey along the Galactic plane, for which there is an overall agreement of 16 % (Planck Collaboration XIII 2014). However, in the Galactic plane region of the present study, both datasets agree within 25 %. This discrepancy can be explained by the shifting of the CO line frequency due to Doppler effects, that is to say, the rotation of the Galactic disk  (Planck Collaboration XIII 2014). The overall calibration uncertainties for the Planck HFI maps are 10 % at 857 and 545 GHz, 1.
15   e  (Gordon & Sorochenko 2009). Alves et al. used an average value of T e = 6000 K; an increase of this value by 500 K (1000 K) would increase the brightness temperature by 10 % (19 %). The RRL free-free data are similarly used in the work of Planck Collaboration Int. XXII (in prep. ) to separate the differ- 3 Following the definition of brightness temperature by  Spitzer (1978) , in the Rayleigh-Jeans regime. ent emission components in the Galactic plane and to determine the contribution of the AME. In that work, the free-free map estimated from the radio data are compared to two other free-free solutions, given by the Planck fastMEM (Planck Collaboration Int. XXII in prep. ) and WMAP MEM  (Bennett et al. 2013 ) component separation methods. The fastMEM and WMAP results agree within 2 % but they are about 20 % higher than the RRL estimation.
For this purpose, we use the free-free map estimated from the RRLs (Sect. 3. 1), as this is currently the only direct measure of this emission in the Galactic plane, in particular in the 24 • × 8 • region centred on (l,b) = (32 • ,0 • ). This region is shown in Fig. 1 , in the Planck 353 GHz channel. The free-free continuum, estimated from the RRL data at 1. 4 GHz, is extrapolated to the HFI frequencies using a frequency dependent Gaunt factor (Eq. 10. 9 of Draine 2011) and an electron temperature of 7000 K. We used the IDL MPFIT routine to fit the final SEDs pixel-bypixel in the 24 • ×8 • region. This routine performs weighted leastsquares fitting of the data  (Markwardt 2009) , taking into account the noise (both statistical noise and systematic uncertainties) for each spectral band. We also include a noise term from the CMB fluctuations, typically 80 µK, which will be dominant outside the Galactic plane and at the lowest frequencies.
95 across the map. However, such a strong anti-correlation is not observed in Fig. 5 (a), nor in Fig. 5(b) , where β FIR -T d is plotted for the thin Galactic disk, |b| < ∼ 1 •. Therefore, there is a real variation of β FIR across this region which decreases the anti-correlation trend generated by the data noise. The distribution of β mm as a function of T d , for the thin Galactic disk, where τ 353 ≥ 4 × 10 -4 , is shown in Fig. 5(c ). For this region, the correlation coefficient between the uncertainties on β mm and T d varies between -0. 06 and -0. 03. A value of -0. 5 is reached outside the Galactic disk, where the signal-to-noise ratio decreases due to the CMB noise term included in the data uncertainties. Thus, Fig. 5 (c) indicates that there is no evident trend of β mm with T d (as it is also seen by comparing the corresponding maps in Fig.
Such a correction steepens the dust spectrum, as we can see from the results of Fig. 7(b). A linear fit to the points gives β mm = 1. 53 + 22 × τ 353. This is not, however, capable of reproducing the much steeper slope of β mm with the dust optical depth. For that to be the case, both the CO and RRL maps would have to be systematically underestimated by 30 %. We thus conclude that neither the uncertainty in the CO and free-free correction of the maps nor omitting the CMB in the  -4 , compared to that estimated in Sect. 5 and given by the red line. The colour scale is logarithmic and it represents the density of points. The three contours show the densities for a cumulated fraction of 5, 30, and 56 % of the data points, from red to yellow. spectral fits is responsible for the correlation of β mm with τ 353.
Grain coagulation is a possible interpretation of the change in dust opacity. Coagulation models indicate that dust aggregation produces an overall increase of the dust specific opacity in molecular clouds, without significantly changing the apparent β mm  (Köhler et al. 2012). Further, there is evidence of variations in the dust opacity within the local atomic ISM  (Planck Collaboration XXIV 2011; Planck Collaboration Int. XVII 2014) , where grain coagulation is unlikely to occur. Further studies are needed to explain the correlation with the molecular material observed here, which remains phenomenological and whose origin does not rely on a physical model. In particular, Planck polarization data will be a first test of the nature of the dust SED flattening.
Acknowledgements. We acknowledge the use of the HEALPix (Górski et al. 2005) package and IRAS data. The Planck Collaboration acknowledges the support of:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN  and  JA (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  DEISA (EU). A detailed description of the Planck Collaboration  and a list of its members can be found at http://www. rssd. esa. int/index. php?project=PLANCK& page=Planck_Collaboration The research leading to these results has received funding from the  European Research Council  under the  European Unions Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement n • 267934.
Anti-5-methylcytosine antibody validation The specificity of 5-methyl cytosine enrichment immuneprecipitation assay was validated by simultaneously immunoprecipitating gDNA isolated from the mammary gland of an adult rat, using three anti-5-methylcytosine (5meC) mouse monoclonal antibodies: MAb-5Mecyt-100 (Diagenode, Belgium), Bi-MeCy-0100 (Eurogentec, USA) and 33D3 (Abcam, USA). A monoclonal antibody raised against an unrelated protein (CITED1)  [36]  was used as the negative control. The 5meC enrichment maps obtained after hybridization of immune-precipitation products with input gDNA were compared with regard to the specificity of all four antibodies (Figure  S1 A ). Microarray data analysis showed that the three anti-5meC antibodies had similar methylated gDNA enrichment profiles, as depicted by the methylation peaks, thus confirming the specificity of the assay.
Genomic DNA analysis In order to obtain sufficient material for microarray hybridization, 10 ng of IP and input gDNA from each sample were amplified using the GenomePlex Complete Whole Genome Amplification 2 (WGA2) kit (Sigma, St. Louis, MO). The amplification product was purified using the QIAquick PCR Purification kit (Qiagen) and 50 ng of WGA2-amplified gDNA were re-amplified using the WGA3 kit (Sigma, USA) in order to generate. 10 mg gDNA for each sample. The WGA3-amplified IP and input gDNA was purified again using the QIAquick kit, quantified and subsequently labeled with the Cy5 and Cy3 dyes respectively. The labeled samples were hybridized to the ChiPchip RefSeq Promoter array for rat (RN34 RefSeq promoter, NimbleGen, Reykjavik, Iceland). Signal intensity data was extracted by scanning images of each array using NimbleScan and visualized using the software Signal Map (NimbleGen).
Gene expression microarrays Total RNA from the mammary glands of animals treated with BPA or vehicle was isolated using the RNeasy Lipid Tissue kit (Qiagen Inc. , Valencia CA), which uses a combination of Qiazol followed by column extraction. Next, 50 ng of the isolated RNA was used to generate amplified cDNA using the WT-Ovation Pico RNA Amplification System (NuGEN, San Carlos, CA). Five mg of amplified cDNA from each sample were labeled with biotin using the Encore Biotin Module kit (NuGEN) and subsequently hybridized to the Rat Genome 230 2. 0 Chip Arrays (Affymetrix). Eight microarray data sets, which represented 4 BPA-treated and 4 control animals, were obtained for each time point (24 microarrays in total). Raw data were generated by the GeneChip Scanner 3000 using the appropriate software.
Acknowledgments The authors wish to thank  Antonia Calafat  for her critical review of this manuscript and  Cheryl Schaeberle  for her help in editing the manuscript. We thank  A. Calafat ,  X. Ye ,  X. Zhou ,  J. Kramer , and  L. Jia  at the  Centers for Disease Control and Prevention (Atlanta, GA, USA)  for the BPA measurements.
This paper compares two versions of the ORCHIDEE land surface model over the Amazon basin, focusing on the hydrological and to phenological impacts. These two versions do not differs only by the use of two different soil hydrology, as the title would have us believe, but also in the parameterization of the river routing module (page 83, line 25-27). It is therefore difficult to clearly attribute the very very slight difference between the two versions only to soil module. In addition, this paper is very long and very descriptive. This article would be clearer if the sections 3 and 4 were reduced or if only the ORCHIDEE 11LAY was used. Finaly, I regret that there is no direct comparison with time-series of observed discharge. Principally for all these reasons, I propose that this article should be in major review.
C23 Major comments: As already mentioned, the two versions of ORCHIDEE do not differs only by the use of two different soil hydrology, but also in the parameterization of the river routing module. It is very surprising that the river routing module depends on the soil hydrology and then requires a specific tuning for the both versions. Why that ? Is it physical ? I don't think, these two module should be independents. Please clarify this fact and use the same river routing module (and the same tuning) to compare your simulation. If not, then you can change the title and the history of your paper because to focus only on soil module difference will not be justify, especially for seasonal TWS results. This remark also leads me to wonder if this comparison between two soil modules in ORCHIDEE is not vein.
It is now well know that multi-layer schemes are superior to old bucket schemes. In addition, because this paper is very long and generally very descriptive, it looks more like a report than a scientific article with a clear message. This reprot is certainly very interesting for your colleagues in your laboratory, but is it the case for the entire community ? Perhaps your work would benefit to focus only on the ORCHIDEE 11LAY. This would be an effective way to shorten this work. Whatever your choice, this article would be clearer if the sections 3 and 4 were reduced. Another major comment is that there is no comparison between observed and simulated river discharges while daily observations exist over this bassin in the HYBAM database  (Gimberteau et al. 2012). For me, annual comparison is not sufficient and some skill scores, like nash criterion and/or deseasonalized root mean square error, should be used as in  Gimberteau et al.