dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of endocytic sites For the different superresolution experiments at least two biological replicates were performed, in each of which typically images of 1,000-10,000 cells were acquired. In the individual experiments single fields of view were excluded as described in Quality control of automated superresolution imaging or entire experiments were repeated if quality standards were not met. To statistically analyze the differences between the outer radii of two datasets, the null hypothesis of equal medians was tested using a Mann-Whitney U test (Wilcoxon rank-sum test, MATLAB function ranksum), which is non-parametric and does not rely on the data following any specific distribution. The definition of the different levels of significance is stated in the figure legends. n denotes the number of analyzed individual endocytic sites and can be found in the figures, the figure legends, the main text or Table  S1.
cerevisiae MK0100 Kaksonen lab N/A S. cerevisiae MK0102 Kaksonen lab N/A A full list of strains is presented in Table S3 This paper N/A Recombinant DNA pFA6a-EGFP-HIS3MX6 Janke et al. , 2004 N/A pJR58 pFA6a-mMaple-HIS3MX6 This paper N/A pMM02 pFA6a-mMaple-hphNT1 This paper N/A pJR40 pFA6a-SNAPf-HIS3MX6 This paper N/A pMaM173 Khmelinskii et al. , 2011 N/A pMaM173-mMaple This paper N/A Software and Algorithms Blender 2. 78 Blender Foundation https://www. blender. org/ Cytosim Nedelec and Foethke, 2007 https://github. com/nedelec/cytosim Fiji (ImageJ) Schindelin et al. , 2012 http://fiji. sc/ MATLAB MathWorks https://www. mathworks. com/products/matlab. html Running Z-Projector Fiji plugin Nico Stuurman https://valelab4. ucsf. edu/$nstuurman/IJplugins/ Running_ZProjector. html (Continued on next page) d METHOD DETAILS B Yeast strain creation B Sample preparation for imaging B Superresolution imaging B Data analysis B Analysis of endocytic structures B Simulation of actin polymerization in Cytosim d QUANTIFICATION AND STATISTICAL ANALYSIS B Analysis of endocytic sites B Analysis of Cytosim simulations d DATA AND SOFTWARE AVAILABILITY.
Observations To date, the Cosmic Evolution Survey (COSMOS) field ([α J2000 , δ J2000 ] = [10:00:28. 6,+02:12:21. 0]) is arguably the most wellstudied patch of the entire sky. It has been observed at wavelengths which span nearly the entirety of the electromagnetic spectrum, a large fraction of which we utilize in this paper. In Appendix A we summarize the various datasets available in the COSMOS field.
3), at least at the current level of implementation, to be more suspect for both fields at the redshift of this study (z > ∼ 4. 5), meaning we cannot robustly discriminate between low-and moderate-density environments at these redshifts. For further discussion of the survey design, observations, reduction, redshift determination, and the properties of the full VUDS sample, see Le  Fèvre et al. (2015). See also  (Tasca et al. 2016)  for the first VUDS data release, which is available through the Centre de donnéeS Astrophysiques de Marseille (CeSAM) database 3.
Estimation of physical parameters For galaxies with secure spectroscopic redshifts, we used three different methods to derive associated parameters. The first was to use the package Le Phare in a method identical to the one described in  Lemaux et al. (2014a). For this fitting we drew upon v2. 0 of the PSF-matched photometry given in  Capak et al. (2007)  using all bands blueward and including Spitzer/IRAC  [4. 5]. The Spitzer/IRAC cryogenic bands  ([5. 8 ]/[8. 0]) were ex-A&A proofs: manuscript no. AA/2017/30870 cluded from the fitting due to relatively large (∼ 1 -2. 5 mag) offsets seen in these bands in ∼30% of all VUDS-COSMOS galaxies with respect to the best-fit model estimated without the use of these bands. We note that this issue appears limited to the  Capak et al. (2007)  catalog and does not apply to fitting performed with the COSMOS2015 photometry.
The process of PSF-homogenizing all UV/optical/ground-based NIR images, source detection, the inclusion of the Spitzer/IRAC data, and the conversion of all magnitudes to "pseudo-total" magnitudes is described in detail in  Ilbert et al. (2013). Details of this fitting including the input parameters and the effect of various assumptions made for this fitting are given in  Lemaux et al. (2014a). For some analyses, a similar fitting was performed on VUDS rest-frame near-ultraviolet (NUV) spectra in conjunction with identical photometry as in the Le Phare analysis using GOS-SIP+, a modified version of the Galaxy Observed-Simulated SED Interactive Program (GOSSIP;  Franzetti et al. 2008). The details of the modifications made for GOSSIP+ as well as the fitting process as it pertains to VUDS spectra and the advantages of the fitting over traditional photometric SED fitting, particularly in regards to estimating galaxy ages, are given in  Thomas et al.
(2017a,b). Both methodologies, Le Phare and GOSSIP+, use some form of a χ 2 ν minimization in order to recover the bestfit model from which the physical parameters are estimated. In addition, both methodologies employ a nearly identical set of input parameters including  Bruzual & Charlot (2003)  (hereafter BC03) stellar templates, models generated from exponentially declining and delayed star formation histories (SFHs), dust extinctions, and a  Chabrier (2003)  initial mass function (IMF). For a full listing of the parameters adopted for the fitting see  Lemaux et al. (2014a)  and  Thomas et al. (2017a). The final method employs the three-component SED-fitting code SED3FIT 6  (Berta et al. 2013) , which combines the emission from stars, dust heated by star formation, and a possible AGN emission component.
The fiducial package of galaxy templates is taken from the Multi-wavelength Analysis of Galaxy PHYSical properties (MAGPHYS; da  Cunha et al. 2008 ) code, which relies on an energy balance between the dust-absorbed stellar continuum and the reprocessed emission in the mid-to far-IR. Stellar templates are taken from the BC03 library, whose output SEDs are modulated by the effects of dust attenuation and the stellar heating of dust  (Charlot & Fall 2000; da Cunha et al. 2008). To this fiducial set of models, the SED3FIT code implements a set of libraries which include both an accretion disc component and a warm dust component surrounding the AGN in a smooth toroidal structure  (Feltre et al. 2012 ; see also  Fritz et al. 2006)  following the method of  (Berta et al. 2013). For each galaxy in our sample, we run both the SED3FIT and the MAG-PHYS codes to the full COSMOS2015 photometry  (Laigle et al.
g. , A&A proofs: manuscript no. AA/2017/30870 COSMOS PCl J1001+0220 <SFR> PCl =38. 6 +35. 1 -5. 9 M O • yr -1 <SFR> Field =49. 8 +9. 8 -11. 9 M O • yr -1 log( M * /M O •) > 9. 7 JVLA 3 GHz Mean Stack (PCl) JVLA 3 GHz Mean Stack (Field) 1. 0 1. 5 2. 0 2. 5 3. 0 log(SFR M O • -1 yr) 11. 5 11. 0 10. 5 10. 0 9. 5 log(M * /M O • ) Fig. 11. Distribution of the PCl J1001+0220 spec-z members (red diamonds and histogram) and coeval field galaxies (navy points and histogram) in the SFR-M * plane as estimated by Le Phare. Histograms are normalized as in other figures such that the area is identical for both samples. The upper limit to the radio-derived SFR of spec-z member and coeval field galaxies coming from stacked 3 GHz JVLA data is shown as the green and light blue lines/arrows, respectively. The lack of detection for both proto-cluster members and coeval field galaxies suggests that prodigious dusty star formation (IRX ∼ < 10) is no more common in the proto-cluster than in the overall field at these redshifts and is not particularly common in either environment.
• A variety of methods were used to attempt to quantify the level of AGN activity amongst the various member samples using some of the deepest multiwavelength data ever compiled. In contrast with studies of many other high-redshift proto-clusters, no indication was found for the presence of any type of AGN within the proto-cluster. • No evidence was found for either suppression or enhancement of star formation activity within the proto-cluster bounds through comparison of dust-corrected rest-frame UV star-formation indicators. Through cross-matching and stacking newly obtained deep JVLA 3 GHz imaging of the COSMOS field for both the proto-cluster member and coeval field samples it was demonstrated that neither environment contained a large number of dusty starbursting galaxies. Limits on the stacking analysis and the distribution of measured β slopes precluded the possibility of copious amounts of dust in the average galaxy in both populations.
Acknowledgements. This work was supported by funding from the  European Research Council Advanced  Grant  ERC-2010-AdG-268107-EARLY  and by  INAF  Grants  PRIN 2010 ,  PRIN 2012  and  PICS 2013. This work was additionally supported by the  National Science Foundation  under Grant No. 1411943  and  NASA  Grant Number  NNX15AK92G. VS, ID and MN acknowledge the  European Union 's  Seventh Framework programme  under grant agreement  337595  ( ERC Starting Grant , "CoSMass"). We thank  Thibaud Moutard ,  Iary Davidzon ,  Lori M. Lubin , and  Adam R. Tomczak  for their insight relevant to the various analyses presented in this paper and their willingness to impart it through conversation. We also thank  Conor Mancone  and  Anthony Gonzalez  for their support with EZGal under circumstances that made such support difficult and  Clotilde Laigle  for generously providing access to the COSMOS2015 catalog at an early stage of this work, a gesture which immensely helped its development.
Lifetime measurements were recorded with a Datastation HUB-B with a nanoLED controller and software DAS6. The lifetime data were fitted using the Jobin-Yvon software package. MALDI-TOF mass spectrometry was carried out in Bruker Autoflex instrument. Microwave reactions were carried out using a CEM Focused Microwave Synthesis System, Model Discover instrument. Polarized Light Optical Microscopy was performed with a Leica DMRB microscope equipped with a hot stage Mettler Toledo FP 90. Differential Scanning Calorimetry was performed with a Perkin Elmer DSC-7 instrument calibrated with Indium as reference (mp: 156. 6 ˚C, ΔH=28. 4 J/g) and water, the thermograms were recorded at 5°/min. Microbalance Perkin Elmer was used to weight the samples. Temperature-variable small-angle X-ray patterns were obtained using a linear focalized monochromatic Cu-Kα1 beam (λ = 1.
C-NEXAFS spectroscopy at the "bulk" scale The 'bulk' carbon speciation of clay subfractions was investigated by NEXAFS spectroscopy using the CLS beamline 11-ID-1 Spherical Grating Monochromator (SGM)  (Regier et al. , 2007). See Supplementary Material for details about the method. Each spectrum reported in the present study corresponds to the average of about 50 measurements. Of note, only the first 250 nanometers of the sample surface are probed using the SGM setup. Spectra were averaged, background subtracted and normalized using the Igor Pro software.
STXM-based NEXAFS spectroscopy STXM-based NEXAFS data were collected using the CLS beamline 10ID-1 (SM beamline  (Kaznatcheev et al. , 2007)  that works in the soft X-ray energy range (130-2500 eV) using an elliptically polarized undulator. See Supplementary Material for details about the method. All clay subfractions were analysed for each sampling time, i. e. 15 samples were analysed. We first analyzed each sample at the millimetric scale to identify regions of a few square micrometers on which to collect full STXMbased NEXAFS data over the 250-450 eV energy range covering the C K edge (280-295 eV), the K L2,3 edges (295-305 eV), the Ca L2,3 edges (345-355 eV) and the N K edge (395-405 eV). For each sample, we selected one or two regions which contained carbon-rich particles and presented a diversity of absorptions allowing us to qualitatively investigate the organomineral interactions present in each fraction at each date.
The C-NEXAFS spectra shown here correspond to homogeneous areas of several hundreds of square nanometers. Extensive databases of reference C-NEXAFS spectra are available in the literature  (Solomon et al. , 2009). The compositional maps presented in this study are derived from the analysis of the selected micrometric regions, they are the visual representation of the assignment of each pixel of the image to the category of particle it belongs to, according to its associated NEXAFS spectra.
The mineral composition of each investigated clay subfraction did not evolve during the 79 years of bare fallow as indicated by XRD (data not shown). The soils studied do not contain significant amounts of iron (Fe) and aluminium (Al) minerals as evidenced by Fernández-Ugalde et al. , (2013) who analysed soils from an adjacent field in the same experimental site of Versailles. Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2018-343 Manuscript under review for journal Biogeosciences Discussion started: 18 September 2018 c Author(s) 2018. CC BY 4. 0 License.
Acknowledgments The  INSU EC2CO program  is acknowledged for financial support. NEXAFS data were acquired at the beamline11ID-1 at the  CLS , which is supported by the  NSERC , the  CIHR , the  NRC  and the  University of Saskatchewan. Special thanks go to  Jian Wang  and  Jay Dynes  for their expert support of the STXM at the CLS and to  Tom Regier  and  Adam Gillespie  for their expert support on the SGM-beamline at CLS.
[37]. See Supplementary Table  1  and Supplementary Information for details. Amplicon sequencing data (Illumina MiSeq pair-end 300 bp) were preprocessed using FastQC  [38]  and the illumitag pipeline as described in Sinclair et al. [37]. Chimeric sequences were removed and reads were grouped into Operational Taxonomic Units (OTU) clustering at 97 % sequence identity. The taxonomical annotation of the OTUs was subsequently performed by CREST using the SILVA database  [37]. Data were not rarefied to avoid overlooking rare members of the communities that may be more sensitive to the environmental gradients of interest  [39]. Sequence data have been deposited in the European Nucleotide Archive (http://www. ebi. ac. uk/ena) under the accession number PRJEB20838.
Amplicons were sequenced using the Illumina MiSeq instrument and pair-end 300 bp mode. Due to the length of the PCR product, only the forward read sequence was used for downstream dataanalysis. Bad quality reads were trimmed with SICKLE  [40] , and left-over adapters and primers were removed with CUTADAPT  [41]. Reads were truncated, dereplicated and singletons removed with USEARCH version 8. 0  [42]. The obtained set of reads was then clustered using cd-hit-est with a 60 % similarity threshold  [43]. The original qualitycontrolled reads were finally mapped to the representative sequences of the obtained clusters to generate a count table using the USEARCH software. The database used for annotation of the sequences is based on the sequences used by Podar et al. [19] , additionally a Hidden Markov Model (HMM) based on these sequences was made using HMMER  [44]  and subsequently used to mine δ-Proteobacteria from the IMG database of JGI.
Bacterial community composition: 16S rRNA gene The composition and diversity of the combined bacterial community was investigated by sequencing of 16S rRNA amplicons. Close to 524'000 sequences (between 13'133 and 59'205 per sample) were clustered into OTUs at three different identity levels; 80 % roughly corresponding to the phylum level, 95 % corresponding to genus-level, and   4. The overall coverage of the sediment community is reflected in the combined richness detected for random subsets of analyzed samples, where the logarithmic shape indicated that most of the species richness occurring in the sediments was covered in the combined dataset (Fig. 2 ). Overall the bacterial community composition was quite uniform across replicates and samples (Fig. 3 , Supplementary Fig. 4 ), and showed only a slight change in diversity with depth at the OTU level (Supplementary Fig.
As for 16S rRNA, community rarefaction indicated that most of the Hg methylator diversity targeted by these primers was captured (Fig. 2 ). Analogous to 16S rRNA, Hgmethylating community composition was also quite uniform across replicates and samples with only minor changes with depth (Fig. 4 , Supplementary Fig. 5 ). The hgcA gene data set consisted of altogether 741'890 reads ranging from 22'522 to 62'442 reads per sample. A total of 356 OTUs were identified at 88 % identity level, 325 of which were affiliated with Bacteria, 25 with Archaea, while the affiliation of 6 OTUs could not be resolved. Among the bacterial hgcA OTU's, 264 were annotated as Proteobacteria, 41 as Firmicutes while 20 could not be linked to any specific phylum. In terms of their relative abundance, Bacteria represented 99 % of the reads, while Archaea represented 0.
Acknowledgements We thank  Philippe Arpagaus  and  Alexander Grosse-Honebrink  for their help during field-work. Camille Thomas  and  Daniel Ariztegui  are acknowledged for helping with the CHN analyses. This work was supported by the  State Secretariat for Education and Research (SER)  through grants to C. C. ( COST-FA0906  contract  no C11. 0117 ), the  Swiss National Science Foundation  through grants to  C. C. (project no  205321_138254  and  200020_157173 ) and the  Swedish Research Council (VR)  through grants to  A. G. B. (project no  2011-7192 ) and  S. B. (project no  2012-3892  and  2013-6978 ).
Phylogenetic 2 Number of OTU's detected as function of the number of collected sediment samples from Vidy Bay. Plots were made with the specaccum function of the Vegan package in R. The OTU clustering was done at 97 and at 88 % similarity levels for 16S rRNA gene and hgcA gene sequences, respectively. A % 0 5 10 15 20 25 30 35 B 2. 3% 8. 6% 11. 5% Acidobacteria 5. 0% Actinobacteria Bacteroidetes 18. 4% Chlorobi Chloroflexi Division OD1 28. 5% Division OP3 6. 6% Firmicutes Lentisphaerae Nitrospirae 13. 1% 6. 0% Planctomycetes ε Bdellovibrionales Desulfarculales Proteobacteria α β γ δ Desulfobacterales Desulfuromonadales Others Verrucomicrobia others Myxococcales Syntrophobacterales Unknown Nitrospinaceae Syntrophorhabdaceae Fig. 3 distribution of bacterial 16S rRNA gene sequences in the combined Vidy Bay sediment dataset: A.
4,15-Diacetoxyscirpenol (DAS) is a mycotoxin primarily produced by Fusarium fungi and occurring predominantly in cereal grains. As requested by the European Commission, the EFSA Panel on Contaminants in the Food Chain (CONTAM) assessed the risk of DAS to human and animal health related to its presence in food and feed. Very limited information was available on toxicity and on toxicokinetics in experimental and farm animals. Due to the limitations in the available data set, human acute and chronic health-based guidance values (HBGV) were established based on data obtained in clinical trials of DAS as an anticancer agent (anguidine) after intravenous administration to cancer patients. The CONTAM Panel considered these data as informative for the hazard characterisation of DAS after oral exposure.
Data on combined effects of DAS and other mycotoxins in vivo were available mainly in poultry, and only one experiment was performed in pigs. Although limited, the effects observed for DAS in combination with T-2 toxin, aflatoxin B1, ochratoxin A and fusaric acid were generally more marked than when DAS was administered alone. The CONTAM Panel noted that because of the lack of dose-response data, it was difficult to draw definitive conclusions concerning the nature of the combined effects and interaction with other Fusarium toxins, including type A trichothecenes. Based on the conclusions on the genotoxicity and the mode of action (MoA) of DAS, the CONTAM Panel decided to establish health-based guidance values (HBGV) for both the acute and chronic exposure of DAS to humans. The Panel concluded that the database from the oral studies on rats and guinea pigs and the i.
For poultry, oral lesions were observed at the lowest level of exposure in the following species: chickens, laying hens, turkeys and ducks with LOAELs of 0. 010, 0. 054, 0. 012 and 0. 022 mg DAS/kg bw per day, respectively. At equal or higher doses, reduction of body weight gain, decreased eggs production and decreased fertility were other adverse effects observed in various studies in different poultry species. No data were available on the oral exposure of dogs. Emesis and haematotoxicity were observed following i. v. administration. Assuming equivalent bioavailability and systemic toxicity of DAS following i. v. and oral administration, the CONTAM Panel identified NOAELs of 0. 031 and 0. 016 mg/kg bw per day for acute and chronic toxicity, respectively. In addition to ruminants, no toxicity data suitable for hazard characterisation of DAS were identified for farmed rabbits, farmed fish, farmed mink, horses and cats.
In addition, studies of the toxicokinetics of DAS after oral and i. v. exposure in experimental animals are required, in addition to in vivo studies on the genotoxicity of DAS. More data would also be needed on the cellular and molecular MoA, in particular for a better understanding of cytotoxicity, DNA and protein synthesis inhibition, stimulation of apoptosis and effects on haematopoietic progenitors and bone marrow. Well-designed dietary studies on adverse effects (including the investigations for oral and gastrointestinal lesions) of DAS in farm animals other than poultry are also recommended. More occurrence data on DAS in food and feed obtained with state-of-the-art validated analytical methods with adequate sensitivity, such as LC-MS/MS, are needed to also reduce the uncertainty in the exposure assessment for humans and farm and companion animals.
Previous risk and exposure assessments on DAS Only one scientific risk assessment on DAS in food and/or feed performed by national agencies or national and international independent expert advisory committees was identified by the CONTAM Panel; the recent assessment of the Joint FAO/WHO Expert Committee on Food Additives (JECFA) in November 2016 (FAO/WHO technical report 1002, 2017). For exposure assessment, a total of 11,842 available records in the GEMS/Food contaminants database were analysed by the JECFA. In Europe, the prevalence of DAS in cereals and cereal based food was found to be 1. 5%. Low prevalence and low concentration of DAS in European countries were also identified in published literature. JECFA estimated dietary exposure of DAS using the concentration data from the GEMS/Food contaminants database and the consumption data from the GEMS/Food cluster diets.
JECFA concluded that there was sufficient evidence to support the establishment of a group provisional maximum tolerable daily intake (PMTDI) for T-2/HT-2 toxins and DAS. The group PMTDI of 60 ng/kg bw per day established for T-2/ HT-2 toxins at the fifty-sixth meeting of JECFA  (2001) , was considered to be sufficiently conservative to include DAS because of the observation that T-2 toxin was consistently more potent than DAS when comparing similar in vitro and in vivo endpoints. However, it was recommended to update the JECFA evaluation from 2001 since new toxicity data on T-2/HT-2 toxin became available. In the 2001 JECFA evaluation, the total LB mean dietary exposure of T-2/HT-2 toxins from the European population was estimated as 16. 3 ng/kg bw per day, but no UB estimate was available at that time.