dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1997; Kozubowski et al. , 2003)  and the SUMO E3 ligases Siz1 and Siz2  (Johnson and Blobel, 1999; Johnson and Gupta, 2001). Deletion of PKC1 or 2009), Syp1-eGFP was recruited to the bud neck in late G1, before bud emergence. Its levels then decreased at the bud neck around the time of anaphase entry, marked by the increase in binucleate cells (Figure  5B ). Time-lapse video microscopy of cells expressing at the same time Syp1-eGFP and Shs1-mCherry revealed that Syp1 was recruited to the bud neck concomitant to which could interfere with Syp1 interaction with membranes. Consistently, in pkc1Δ mutant cells Syp1 localization at the bud neck increased compared with wild-type cells at the cost of patches normally observed at the rest of the cortex (Figure  5A ). These data suggest that Pkc1 inactivation stabilizes Syp1 at the bud neck.
Our data suggest that the upstream part of the CWI pathway is involved in septin ring stabilization at least partly through Syp1 phosphorylation. Of importance, the CWI pathway is induced not only under stress conditions, but also during the normal cell cycle, concomitant with polarized growth and bud emergence  (Zarzov et al. , 1996) , that is, when cells undergo the most pronounced membrane/cell wall remodeling and are most vulnerable to lysis. Of note, this is also the cell cycle phase when the septin ring undergoes the transition from fluid to frozen state. Thus it would make perfect sense that the same Rho1/Pkc1 pathway couples new cell wall synthesis with septin ring stability. Intriguingly, Syp1 interacts with the mechanosensor Mid2  (Reider et al. , 2009) , suggesting that it might itself relay mechanical stress signals, acting as a mechanotransducer and/or participating in a Rho1-controlling feedback For time-lapse video microscopy in Figure  7A , cells were mounted in synthetic medium on Fluorodishes and filmed at 30°C with 63× oil immersion objective mounted on a Zeiss LSM780 confocal microscope controlled by the Zen2010 software.
Individual Zstacks contained eight planes with a step size of 0. 7 μm and were acquired every 1 min. Fluorescence intensities of GFP-Cdc12 were measured with ImageJ on maximum projected images. Nuclear division was scored on cells stained with propidium iodide. Visualization of septin or Syp1-HA3 rings was performed on formaldehyde-fixed cells using anti-Cdc11 polyclonal antibodies (Santa Cruz Biotechnology, Dallas, TX) or anti-HA monoclonal antibody (12CA5) as previously described  (Merlini et al. , 2012). Still digital images were taken with an oil 63×, 1. 4-0. 6 HCX Plan-Apochromat objective (Zeiss) with a CoolSNAP HQ2-1 CCD camera (Photometrics, Tucson, AZ) mounted on a Zeiss AxioimagerZ1/Apotome fluorescence microscope controlled by the MetaMorph imaging system software (Molecular Devices, Silicon Valley, CA).
FRAP experiments in Figure  6B  were performed on a Zeiss LSM780 confocal microscope equipped with a Definite Focus module and controlled by the Zen2010 software (Zeiss). Logarithmically growing cells expressing Syp1-eGFP were grown overnight in synthetic complete medium at 30°C and mounted on Fluorodishes (World Precision Instruments, Sarasota, FL). Half of the Syp1 ring was bleached in small-budded cells with a sequence of five iterations at 100% of laser intensity. Fluorescence recovery was recorded every 0. 5 s at 30°C, and fluorescence intensities were analyzed with ImageJ. Background staining in each cell was subtracted. Data were fitted to monoexponential decay with Prism5 (GraphPad Software, San Diego, CA) to calculate the mobile fraction and the half recovery time. For time-lapse video microscopy in Figure  3F , cells were imaged at 30°C on 2% agar pads of complete synthetic medium.
Movies were recorded with a DeltaVision microscope and a 100× oil immersion objective using SoftWoRx software (GE Healthcare, Velizy-Villacoublay, France). Individual Z-stacks contained 10 planes with a step size of 0. 6 μm and were binned symmetrically by two. After image acquisition, the movies were projected as maximum intensity projections. For time-lapse video microscopy in Supplemental Figures  S3  and 5C , cells were mounted in synthetic medium on Fluorodishes and filmed at room temperature (∼22°C) with a DeltaVision OMX (GE Healthcare) using a 63× oil immersion objective using SoftWoRx software. Individual Z-stacks containing 31 planes were acquired every 1 min with a step size of 0. 2 μm and were binned symmetrically by one. Z-stacks were maximum projected and deconvolved with Huygens (Scientific Volume Imaging, Hilversum, Netherlands).
For time-lapse video microscopy in Figure  3H , cells were mounted in synthetic medium on Fluorodishes and filmed at 30°C with a 100× oil immersion objective mounted on a confocal spinning disk CSU-X1 Andor-Nikon microscope (Nikon, Tokyo, Japan) equipped with an electron-multiplying charge-coupled device (CCD) iXon Ultra camera and controlled by Andor iQ3 software. Individual Z-stacks contained eight planes with a step size of 0. 7 μm and were acquired every 1 min. Fluorescence intensities of GFP-Cdc12 were measured with ImageJ on maximum projected images. tions were fixed carbamidomethyl on cysteines and variable phosphorylation on serines/threonines and tyrosines. Mass tolerances were 7 ppm for precursor ions and 0. 5 Da for fragment ions. MS2 spectra corresponding to phosphopeptides were manually inspected for phosphosite assignment.
Ion signals corresponding to phosphorylated peptides were quantified from their ion chromatograms manually extracted using Qual browser, version 2. 1 (Thermo Fisher Scientific), with a tolerance of 5 ppm for mass deviation and normalized to signals of their nonphosphorylated counterparts. For interactome analysis, all MS/MS spectra were searched against the S. cerevisiae database (64,506 sequences, version 2013_03, uniprot. org/) by using Proteome Discoverer software, version 1. 4 (Thermo Scientific) with Mascot, version 2. 4, and phos-phoRS, version 3. 0 nodes  (Taus et al. , 2011). Modification options were fixed carbamidomethyl on cysteines, variable oxidation on methionines, and variable phosphorylation on serines/threonines and tyrosines. Management and validation of mass spectrometry data were performed using Proteome Discoverer software (Thermo Scientific; Mascot significance threshold p < 0.
ACKNOWLEDGMENTS We are grateful to  E. Bi ,  G. Braus ,  G. De Bettignies ,  M. Hall ,  T. Hoefken ,  B. Lapeyre ,  K. Lee ,  D. Levin ,  Y. Ohya ,  D. Pellman ,  M. Stark ,  D. Stillman ,  R. Tisi , and  B. Wendland  for sharing reagents;  R. Fraschini  and  M. Venturetti  for help with the initial phase of the genetic screen;  V. Georget  and  J. Mateos Langerak  for invaluable help with FRAP and video microscopy; the Montpellier RIO Imaging platform for image acquisition and FACS analysis; and  D. Liakopoulos ,  S. Martin , and  E. Schwob  for critical reading of the manuscript. This work was supported by Grant  ANR-09-BLAN-0125-01  from the  Agence Nationale pour la Recherche and  Grant  PJA 20141201926  from the  Fondation ARC  to S. P. M. A. J. was supported by the  Fondation pour la Recherche Médicale.
Data Over 6 million phenotypes were available for BW and WW, almost 3. 4 million for PWG, and over 1. 3 million for CE. Whereas BW, WW, and PWG are continuous traits, CE is a categorical trait with 5 calving scores, where 5 is abnormal delivery and is excluded. Because few animals had scores 3 and 4, these scores were combined into category 2, which resulted in 93% of animals with score 1 and 7% with score 2. The number of animals in the pedigree for evaluation of growth traits was 8,236,425, and for CE was 8,025,676. For evaluation of growth traits, 81,878 animals were genotyped for 54,609 SNP from the BovineSNP50k v2 BeadChip (Illumina Inc. , San Diego, CA). Currently, no genotyping strategy is applied by AAA; therefore, the members can choose which animals are being genotyped, and most of them are young.
A total of 29,995 genotyped animals were young without phenotypes for any of the 3 traits, which caused them to have genotypes excluded from this study. If the number of genotyped animals is relatively large, young genotyped animals without phenotypes in the dataset give very small contribution to their relatives' evaluation  (Misztal et al. , 2014). After removing SNP with unknown position or located on sex chromosomes and running a general quality control analysis, genotypes on 38,528 SNP markers were available for 32,465 males and 19,418 females born from 1977 to 2013; therefore, the maximum number of genotyped animals used in all analyses on growth traits was 51,883. For CE evaluation, a genotyping set with 72,069 animals was available, but only genotypes on 40,546 animals born from 1977 to 2013 (26,074 males and 14,472 females) were used for the same reason above.
The number of SNP that passed the general quality control for this dataset was 38,568. For this study, the animals were then split into training and validation populations according to year of birth. Thus, all 18,721 (13,166) genotyped animals born in 2013 were chosen to be in the validation population for growth (CE) traits and had their phenotypes removed from the evaluations. The pedigree relationship between training and validation populations ranged from 0 to 0. 82, with an average relationship of 0. 09. Traditional and genomic evaluations were performed for growth traits and CE. A multivariate linear animal model was used for growth traits as: y t = Xb + Z 1 u + Z 2 m + Z 3 p + e [1] where t is for each one of BW, WW, PWG; y, b, u, m, p, and e are vectors of phenotypes, fixed effect of contemporary group, additive direct genetic effect, additive maternal genetic effect, maternal permanent environmental effect, and random residuals, respectively; X, Z 1 , Z 2 , and Z 3 are incidence matrices for b, u, m, and p, respectively.
All random effects were present for WW, but only u, m, and e for BW, and u and e for PWG. A bivariate threshold-linear animal model was used to model CE jointly with BW: Y c = Xb + Z 1 u + Z 2 m + e [2] where for growth traits. Heritabilities for all traits were calculated by AAA using the same models as in [1] for BW,  WW, and PWG; and in [2]  for CE. For our study, the values were then provided by AAA and ranged from 0. 12 to 0. 41 (Table  1 ). Three different genomic analyses were performed using ssGBLUP  (Aguilar et al. , 2010; Christensen and Lund, 2010)  as implemented in BLUP90IOD program (http://nce. ads. uga. edu/wiki/BLUPmanual). Compared to BLUP, in ssGBLUP the inverse of the numerator relationship matrix A -1 is replaced by matrix H -1 defined as follows: H -1 =A -1 + ቈ 0 0 0 G -1 -A 22 -1 , where G is the genomic relationship matrix.
The computations used default options in BLUP90IOD. In all analyses the validation population was defined as genotyped animals born in 2013 with phenotypes excluded. First analysis: ssGBLUP with different reference populations. Different reference populations were defined according to EBV accuracy calculated with the ACCF90 program (http://nce. ads. uga. edu/wiki/BLUPmanual), which uses the concept of prediction error variance and reflects the standard error of EBV for each individual. The objective was to investigate the influence of different groups of reference animals on genomic predictions, and possibly to guide genotyping strategy. The current trend in livestock genomics is to genotype young animals; however, more important animals give more information to the evaluations. For growth traits (CE), the first reference population was composed of 1,628 (1,541) top bulls with EBV accuracy for BW ≥ 0.
Correlations between GEBV from full ssGBLUP and DGV y or GEBV y from indirect predictions are shown in Table  3. On average, correlations with DGV y were 0. 73, 0. 89, and 0. 96 for ref_2k, ref_8k, and ref_33k, respectively. Higher correlations were observed between GEBV and GEBV y , with values for the three reference sets being 0. 89, 0. 95, and 0. 97, respectively. Those results endorse the use of a reference population of size close to 33,000 animals for this American Angus dataset. By doing that, indirect predictions are as accurate as predictions including genotypes for young animals in the evaluation (full ssGBLUP). For young animals, GEBV = w 1 PA + w 4 DGV -w 5 PP, with all weights summing to 1. 0  (VanRaden and Wiggans, 1991; VanRaden et al. , 2009; Aguilar et al. , 2010). When the number of genotyped animals is small, w 4 is small and ignoring PA reduces predictivity.
To evaluate the antioxidant activity of raw materials several methods were investigated. These methods go through two steps. The first one is the extraction of the antioxidant compounds from the matrix of raw material and the second one consists in the determination of the antioxidant activity. For each step several alternatives are described in the bibliography. The data obtained showed a large variability depending on the method used. This renders the choice of an appropriate method a very sensitive task. The objective of this chapter is to make a critical study of the various methods of evaluation of the antioxidant activity of food raw materials. For this, in the first part, we will underline the different sources of variability from several examples of food raw materials. Then in the second part the different methods used for the extraction of antioxidants will be detailed.
Discussion -Conclusion Many results on the determination of the antioxidant activity of purified molecules and / or food raw material have been published over the last few years. However, the obtained data present a broad variability even for a given method or a given molecule. To overcome these problems, some authors have proposed other alternatives by developing new methods, or new ways to process data and express the results. [34]  proposed the « quencher method », where the antioxidant activity is directly measured from the solid sample without the extraction step. Free radicals are mixed with the food sample and a spectrophotometric method (ABTS, DPPH) was used. [35]  developed the global antioxidant response (GAR) method which uses an in vitro approach with enzymatic digestion, designed to mimic digestion through the gastrointestinal tract aimed at releasing antioxidants in foods.
-Shared successes: every network team needs early success, the more-established groups can mentor lessexperienced groups. -Shared resources and facilities: engineering designs, protocols, databases, analytical facilities, technical coordination, and protocols for meta-analyses. Networks need multidisciplinary research teams, consisting of scientists as well as engineers, technicians, and data managers. The complex interactions among ecosystem components require the involvement of researchers from many different disciplines. Modeling is important within the network for planning, experimental design, and data management. Modeling conducted before the experiments are implemented can evaluate and improve the sensitivity of the experiments to detect ecosystem changes, including changes www. soil-journal.
net/1/575/2015/ SOIL, 1, 575-582, 2015 in replication and duration  (Luo et al. , 2011). Furthermore, model predictions can generate hypotheses to be tested by the network experiments and hence identify needed measurements. Network observations and findings should lead to improvements in model structure and parameters. Technical support is critical to achieving the high scientific potential of an experimental network and to attend to the design, building, day-to-day operation, and maintenance of experiments. A network coordinator ensures that network projects use resources efficiently, avoid duplication of efforts yet make essential measurements, and share data and information. Funding for resources that would be shared internationally, like coordination and database management, can be difficult to sustain but is essential for long-term success.
7 The international soil experiment network for deep soil warming As one example of how such a network might operate: we are establishing a new network of soil experiments called iSEN (international Soil Experiment Network; Fig. 1 ), guided by the question: what are the effects of global warming on whole soil profile ecosystem services? The structure of iSEN is similar to a franchised business. The network develops the framework of core measurements and manipulations, provides the "recipes" -the protocols for experimental manipulations, basic measurements, and data formats -and the structure for shared resources such as databases. The principal investigator (PI) for each site obtains their own funding and may add experimental manipulations and measurements onto the core framework. The proposed network will define a minimum standard for the protocols and treatments needed to qualify to participate in the network, while allowing individual sites to add treatments reflecting their context.
Acknowledgements. For initiating the international Soil Experiment Network , we acknowledge the support of the  US Department of Energy, Office of Science, Office of Biological and Environmental Research Terrestrial Ecosystem Science Program  under contract number  DE-AC02-05CH11231. I. A. Janssens  acknowledges support from the  European Research Council Synergy  grant  610028  ( IMBALANCE-P). A. Chabbi  acknowledges support from the  European Commission  Grant Agreement no. 312690  and  ANR-11-INBS-0001. Edited by:  D. Dunkerley.
Data Analysis For the purpose of the experiments presented here, the physiological signal was digitally recorded at 100 Hz. Sniffs were pre-processed by removing baseline offsets, and aligned in time by setting the point where the sniff entered the inspiratory phase as time zero. Maximum sniff flow rate, sniff duration and volume were calculated for the first sniff of each trial. The endpoint for volume and duration calculation was the point where the sniff returned to zero flow (end of the inspiration phase). The relationship between hedonic ratings and sniffing behavior was analyzed with linear and degree-two polynomial regressions, with pleasantness as predictive variable and sniff characteristics as dependent variables. Where necessary, similar analyses were conducted between the other ratings (intensity, familiarity, edibility) and sniffing characteristics, and Pearson correlations were computed between pleasantness and the other ratings (intensity, familiarity, edibility).
RNA from spores and appressoria was extracted using the hot acid phenol protocol  [16]. RNA from infected rice leaves was extracted by the phenol-LiCl method  [16]. Reverse transcriptase-polymerase chain reaction (RT-PCR) was performed using 5 μg of total RNA as starting material and the Thermoscript RT-PCR system kit (Invitrogen, France) according to the manufacturer's instructions. Real time PCR was performed in 96-well plates using an ABI-7900 (Applied Biosystem, France), with water as negative control and cDNAs from mycelium grown on complete medium as positive control. Primer pairs were designed using Primer Express Software (Applied Biosystem, France). Primer efficiency was determined with different concentrations of forward and reverse primers (50 to 900 nM) using genomic DNA or mycelium derived cDNA as templates.
Characterization of the M. oryzae MET6 gene encoding a cobalamineindependent methionine synthase The protein sequence of Aspergillus nidulans methionine synthase METH  [26]  was used as query for BlastP searches of the M. oryzae protein database at the Broad Institute. A single protein sequence encoded by the gene MGG_06712. 7 was identified with 80% amino acid identity and 87% similarity to A. nidulans METH protein sequence. This gene was named MET6 following the nomenclature used for the S. cerevisiae cobalamine-independent methionine synthase  [13, 26]. Aligment of M. oryzae Met6 protein sequence with N. crassa (AF4040820_1), A. nidulans (AF275676_1), S. cerevisiae (YER091c) and cytoplasmic A. thaliana Met6 sequences (S3 Fig) revealed identities ranging respectively from 76, 80, 58 and 48%.
The predicted structure of MET6 including start codon, stop codon, intron positions and splicing sites was confirmed by alignment of its genomic sequence to ESTs from public databases. This comparison highlighted a partial 78-bp 5'UTR and a full length 531-bp 3'UTR (S1 Fig). Met6 protein sequence Interpro analysis identified a C-terminal domain (PFAM PF01717) and an N-terminal domain (PFAM PF08267) typical of methyltransferases. These two domains are characteristic of cobalamine-independent methionine synthases. In particular, these C-terminal domains contain conserved amino-acids involved in zinc binding, interaction with homocysteine and methyl-triglutamatetetrahydrofolate  [27]. Met6 is likely a cytoplasmic enzyme since it has no signal peptide at its N-terminus region. Using BlastP search, we identified a single gene encoding a cobalamine-independent methionine synthase in almost all fungal genomes.
Fungal cobalamine-independent methionine synthases were searched in available fungal protein databases using BlastP and Aspergillus nidulans METH (AAF82115) or Saccharomyces cerevisiae Met6p (YER091c) protein sequences as queries. Alignment was performed using Clustal W and blosum 62 matrix. The phylogenetic tree was constructed using the Minimum Evolution Method with pair wise deletion for gaps, missing data Poisson correction, and 2000 replicates for bootstraping. Accession numbers are listed in Table  2. doi:10. 1371/journal. pone. 0111108. g002 MET6 was expressed at high levels in mycelium grown on MM and in appressoria, and was down-regulated in mycelium grown on MM + Met and CM (2-fold) and during infection (2-fold). A strong down-regulation was observed in dormant spores (10-fold). RNA was extracted from wild type P1.
In support of this hypothesis, we identified by BlastP search of M. oryzae protein database, a gene (MGG_07195. 7) closely related to A. nidulans Hcs1 that encodes a protein with all the domains characteristic of homocysteine synthase. Further genetic analysis is needed to test if deletion of this gene in a met1-or a str3-background leads to double mutants that are as auxotrophic as our Δmet6 mutants. Alternative metabolic pathways leading to methionine biosynthesis from homocysteine independent of Met6 have been described in fungi. In A. nidulans, betaine and choline are metabolized into methionine by a betaine-homocysteine methyltransferase  [28]. In S. cerevisiae, SAM and SMM are metabolized into methionine by two specific SAM-and SMM-homocysteine methyltransferases encoded by SAM4 and MHT1, respectively  [31, 32].
Betaine and choline were unable to restore the growth of M. oryzae Δmet6 mutants on MM, suggesting a defect in betaine-homocysteine methyltransferases or an inability to import betaine/choline compared to A. nidulans. SAM and SMM only partially restored the growth of Δmet6 mutants on MM, suggesting the occurence of SAM-and SMM-homocysteine methyltransferases in M. oryzae. BlastP search of M. oryzae protein database using S. cerevisiae Sam4p and Mht1p protein sequences as queries, revealed a single hit (MGG_04215. 7) with respectively 39% and 42% sequence similarity. This M. oryzae protein is good candidate for a SAM/SMM homocysteine methyltransferase. Further genetic and biochemical work are needed to test if this gene encodes a homocysteine methyltransferases involved in the conversion of SAM/SMM into methionine.
No specific SAM or SMM transporters such as those described in yeast (SAM3 and MMP1,  [31] ) were identified in M. oryzae protein database. Sulfur amino-acids metabolism in M. oryzae: methionine homeostasis, homocysteine catabolism and SAM methyl cycle Quantification of sulfur metabolites (Table  3 ) in wild type M. oryzae grown on different media showed that the intracellular concentration of methionine was maintained at a constant level of 1. 6 μM (1. 3-1. 5 nmoles. mg -  dry weight mycelium, Table  3 ), even on MM supplemented with excess exogenous methionine (1 mM). M. oryzae Δmet6 mutant grown on MM supplemented with methionine also displays the same level of methionine as wild type (1. 6 μM, Table  3 ). This methionine homeostasis is a physiological phenomenom not previously described in fungi.