dataesr/scientific-paragraphs-categorization huggingface dataset:
Data We obtained Herschel  (Pilbratt et al. 2010 ) observations of the GRB 980425 host (project no. OT2 jmcastro 3, PI: J. M. Castro Cerón) using the Photodetector Array Camera and Spectrometer (PACS;  Poglitsch et al. 2010 ) with a total integration time of 4097 s, on 20 Apr 2013 1. The data were taken in the line spectroscopy mode with medium chopping/nodding to off source (and off-galaxy Data reduction for PACS was performed in the Herschel Imaging Processing Environment (Hipe; Ott 2010) v12. 1. 0 with version 65. 0 of the PACS calibration tree and uses the Ipipe Background Normalization script for Chop/Nod Range Scan data, which is optimised for faint sources by using off-source positions in the background subtraction and flux calibration. To avoid introducing correlated noise we set the upsample factor to 1 during flatfielding, and to minimise signal losses we also masked the spectral regions where spectral lines are expected during this process.
The final spectra are binned to be Nyquist sampled at the native PACS resolution. We also performed CO(2-1) observations of the GRB 980425 host on 29 Aug (precipitable water vapour [pwv] of 1. 7 mm), 12 Sep (pwv of 0. 75-0. 85 mm), 16 Sep (pwv of 1. 43-1. 57 mm), 31 Oct (pwv of 1. 22-1. 96 mm), and 01 Nov 2015 (pwv of 0. 66-0. 85 mm) using the Swedish Heterodyne Facility Instrument (SHeFI;  Vassilev et al. 2008 ) mounted at the Atacama Pathfinder Experiment (APEX;  Güsten et al. 2006 ) (project no. 096. D-0280 and 096. F-9302, PI: M. Micha lowski). A total of 4. 92 hr of onsource data were obtained. The APEX-1 single-sideband (SSB) was tuned to the observed frequency of the CO(2-1) line of 228. 6 GHz. At this frequency the APEX beam 1 OBSIDs: 1342270641 size is 27 ′′ (∼ 4. 8 kpc at the distance of the GRB).
All observations were completed in the on-off pattern and the position-switching mode. The fluxes were corrected using the main beam efficiency of 0. 75. We reduced and analysed the data using the Continuum and Line Analysis Single Dish Software (Class) package within the Grenoble Image and Line Data Analysis Software 2  (Gildas;  Pety 2005). We performed Band 3 ALMA observations on 1 Sep 2012 (project no. 2011. 0. 00046. S, PI: M. Micha lowski). A total of 67. 4 min of on-source data were obtained. Four 1. 875 GHz spectral windows were centred at 100. 6, 102. 4, 112. 5, and 114. 3  GHz. Twenty three antennas and baselines ranging between 24 and 384 m were available. Neptune, J1733-130, and J1945-552 were used as flux, bandpass, and phase calibrators, respectively. The amount of precipitable water vapour ranged between 1.
Acknowledgements. We thank  Joanna Baradziej  for help with improving this paper;  Per Bergman ,  Carlos De Breuck  and  Katharina Immer  for help with the APEX observations;  Tom Muxlow , and  Eelco van Kampen  with the ALMA observations; and our referee for useful comments. M. J. M. acknowledges the support of the  UK Science and Technology Facilities Council. The  Dark Cosmology Centre  is funded by the  Danish National Research Foundation. J. L. W. is supported by a  European Union   COFUND/Durham Junior Research Fellowship  under  EU  grant agreement number  267209 , and acknowledges additional support from  STFC  ( ST/L00075X/1 ). A. K. acknowledges support from the  Foundation for Polish Science (FNP)  and the  Polish National Science Center  grant  2013/11/N/ST9/00400. A. J. C. T. acknowledges support from the  Spanish Ministry  Project  AYA2015-71718-R.
Based on observations collected at the  European Organisation for Astronomical Research in the Southern Hemisphere  under  ESO  programmes  096. D-0280  and  096. F-9302 This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2011. 0. 00046. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada)  and  NSC and ASIAA (Taiwan) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. This research has made use of the GHostS database (http://www. grbhosts. org),which is partly funded by  Spitzer/NASA  grant  RSA  Agreement No. 1287913 ; the  NASA/IPAC  Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration ; SAOImage  DS9 , developed by  Smithsonian Astrophysical Observatory  (Joye & Mandel 2003); and the  NASA's Astrophysics Data System Bibliographic Services.
Regeneration was quantified in a subplot located at the centre of each main plot, whose size varied according to number of juveniles found for each species: (i) 4 Â 4 m (16 subplots of 1 Â 1 m were taken when o10 individuals for each species were found within the 16 subplots) and reduced to (ii) 2 Â 2 m (4 subplots of 1 Â 1 m when 410 individuals for each species were found in these subplots). In each regeneration plot, we identified juveniles to species (individuals of at least 1 year old till 1. 60 m high) of all focal tree species in each forest and recorded the abundance for each one. To standardize the sampling size to get a meaningful comparison in the abundance regeneration data among plots, we extrapolated the number of juveniles from 2 Â 2 to 4 Â 4 m, when necessary. Analyses were performed using a log10 transformation of the abundance data.
Detectors were calibrated to pick up all bat calls and were programmed to record from 1 h before sunset to 1 h after sunrise. Recordings were carried out only during nights without rain, with low wind speeds (o30 km h À 1 ) and with an ambient temperature 410 °C. Bats were identified to the finest possible taxonomic level on the basis of their calls by the same trained operator using Sonochiro v3. 2. 3 and Batsound 4. 1 softwares and updated species lists for each country. Identification to species level was achieved for about 80% of the calls, but two pairs of Myotis spp (Myotis mystacinus and M. brandtii; M. myotis and M. blythii) could not be fully separated. Bat species diversity was defined as the total number of species (or species pairs) of bats recorded per forest plot. Bat sampling was carried out in April-June 2012 in Finland, Germany and Italy and in May-July 2013 in Poland, Romania and Spain.
Understorey plant diversity. Each plot was subdivided in nine 100 m 2 subplots of which the southwest, central and northeast subplots were used for the understorey surveys from May to August 2012. In each of these three subplots, the percentage cover of each vascular plant species was estimated in a vegetation quadrat of 5 Â 5 m. These data were used to calculate Shannon-Wiener diversity of understorey plants. Plot-level understorey diversity was calculated as the average Shannon-Wiener diversity of the associated subplots. Earthworm biomass. Earthworm sampling was carried out in spring 2012 in Italy, Germany and Finland, and in autumn 2012 in Poland, Romania and Spain. Plots were divided in nine 10 Â 10 m subplots. 1 sample per plot was taken in the centre subplot. Sampling close to tree stems was avoided and whenever possible performed, in between multiple, different tree species.
Image analysis and processing Image analysis was done with the Fiji/ImageJ software (National Institute of Health)  [59]. The number of GFP-Atg8a dots and the size of the GFP-LAMP1 dots were determined using a semi-automated macro that allows for the identification, numbering and measuring of the dots whilst excluding the potential nuclear staining  [60]. The quantitative analysis of the colocalization between the green and red dots using the GFP-mCherry-Atg8a construct was done with the JACoP plugin and represented by the Pearson's correlation coefficient (PCC)  [61]. Image processing was done with Photoshop CC 2014 (Adobe). All the pictures shown are representative of the whole tissue and of the observations made from different animals.
Acknowledgements We thank  Drs G. Emery ,  T. Neufeld  and  I. Nezis  for providing transgenic flies,  Dr G. Juhasz  for the Ref(2)P antibody,  Dr D. Grunwald  for assistance with confocal microscopy and  C. Bama  for fly food preparation. The  Bloomington Drosophila Stock Center and Vienna Drosophila Resource Center contribute  to this work by providing transgenic fly strains. This work was supported by the  Fondation ARC  ( ARC n°DOC20130606618  to ACJ), the  AGIR program of University Grenoble Alpes (UGA)  and  Labex GRAL  ( ANR-10-LABX-49-01 ).
0 package  (Tamura et al. , 2013). The distance matrix was calculated as described by  Jukes and Cantor (1969)  and the tree was inferred by neighbor-joining  (Saitou and Nei, 1987). The numbers at the nodes indicate the levels of bootstrap support  (Felsenstein, 2005)  based on neighbor-joining analyses of 1000 resampled data sets (only values N50% are shown). Bar, 0. 002 nt substitution per nt position. Streptomyces bullii C2 T was used as outgroup. Sequences used in this analysis were those previously obtained by  Toumatia et al. (2015)  for strain IA and those available in the public sequence databases EzTaxon-e server (http://eztaxon-e. ezbiocloud. net/; a web-based tool for the identification of prokaryotes based on 16S rRNA gene sequences from type strains  (Kim et al. , 2012) ), for the related Streptomyces type-species.
Samples were then rinsed with distilled water before air drying for at least one day in the dark. This method was used for root surface colonization assay. In parallel to the root colonization study, the possibility that strain IA1 could be endophytic was evaluated by cutting longitudinally or transversally, using razor blades and after dehydration steps, roots, caryopses and leaves of young wheat seedlings. Hybridization, post hybridization processes, rinse steps were the same as described before. For microscopic examination, samples on slides were covered with coverslips before observation under a confocal microscope (Olympus Fluoview FV1000 with multiline laser FV5-LAMAR-2 HeNe(G)laser FV10-LAHEG230-2). X, Y, Z pictures of samples were taken at 405, 488, 633 nm at 10 ×, 20 × or 40 × objectives and then merged (RGB) using Image J software.
Plant growth promoting ability by of strain IA1 Using seedling assays, results showed that strain IA1 did not exhibit any significant improving of plant establishment (Table  1 ). However, shoot length of seedlings was increased in case of seeds pre-treated with strain IA1 by more than 27% (Table  1 ). To further determine mechanisms involved in plant growth promotion, IAA and GA3 productions by strain IA1 were evaluated. Data showed that strain IA1 produced both IAA and GA3 under controlled conditions of fermentation with amounts rating of 74. 39 ± 1. 46 and 94. 24 ± 23. 66 μg ml -1 , respectively (Table  2 ).
Identification of YvmB as repressor of yvmC-cypX and yvmBA Pairwise alignment of the amino acid sequence of YvmB with proteins from databases, reveals similarity in the secondary structures between YvmB and other characterized MarR regulators (Additional file 1: Figure  S1 ). The yvmB gene is divergently transcribed from the yvmC-cypX operon (Fig. 1 )  [33]. We observed that cells carrying a yvmB deletion overproduced a red pigment after entry into the stationary growth phase, in the ironrich MS medium (Fig. 2a ). To test whether YvmB controls yvmC-cypX expression, a transcriptional fusion between cypX and the lacZ reporter gene was constructed and studied in the wild-type and ΔyvmB cells (Table  1 ). In the wild-type, measurement of β-galactosidase activity revealed a slight increased of cypX expression during entry into stationary phase (Fig.
The nucleotides in bold belong to an inverted repeat nucleic sequence  expressed mainly during the transition phase between exponential and stationary phase, its induction is dependent upon the presence of Spo0A  [47]. By our proteomic analysis, two proteins of the Spo0A regulon are induced in a yvmB mutant. These are represented by the RocD ornithine aminotransferase and the Tkt transketolase, which might be due to a difference in phosphorylated/unphosphorylated form of the Spo0A regulator in a yvmB mutant. Therefore, YvmB appears an additional regulatory element into the cell's decision to grow or sporulate. A database search revealed that yvmBA, yvmC-cypX and yisI genes are present in B. licheniformis and Bacillus thuringiensis, suggesting the functional conservation of the role of YvmB in pulcherriminic acid synthesis and in sporulation in other Bacilli.
5 kV applied to an uncoated capillary probe (PicoTip EMITER 10-μm tip inner diameter; New Objective). Peptides ions were automatically analyzed in positive mode by the data-dependent method as follows: full MS scan (m/z 400 to 1400, resolution 70,000 at m/z 400) and MS/MS on the 12 most abundant precursors (resolution 17,500 at m/z 400). In the present study only +2 and +3 charged peptides were subjected to MS/ MS experiments with an exclusion window of 40 sec, in HCD fragmentation mode with a normalized collision energy fixed to 26 %. The lock mass option was activated on m/z dimethylcyclosiloxan 445. 12003. The raw data produced on Q-Exactive mass spectrometer were first converted in mzXML file with msconvert (http://proteowizard. sourceforge. net, version 3. 0. 3706), and protein identification was performed with X!Tandem software (X! Tandem Sledgehammer 2013.
09. 01. 1; http:// www. thegpm. org) against a protein database of B. subtilis 168 (downloaded to ftp://ftp. ebi. ac. uk/pub/databases/ integr8/last_release/fasta/proteomes contain 4253 proteins associated to a classical proteomic contaminant database). The X!Tandem search parameters were as follows: trypsin specificity with two missed cleavage, fixed alkylation of cysteine (+57. 0215), and variable oxidation of methionine (+15. 9949). The mass tolerance was fixed to 10 ppm for precursor ions and 0. 02 Da for fragment ions. For all proteins identified with a protein E-value of < 0. 01 in the first step, we searched for additional peptides to reinforce identification using similar parameters except that semitryptic peptides and protein N-terminal acetylations were accepted. All results for each analysis were merged with an homemade program written in java: (X!tandempipeline version 3.
Relative quantification of peptides and proteins Control quality of data, normalization, filtration and statistical analysis were performed by using MassChroqR (http://pappso. inra. fr/bioinfo/masschroqr/). The peaks showing a width greater than 100 s or instability in retention time (RT standard deviation greater than 20 s) were filtered out. Only proteins quantified with at least two specific and repeatable peptides were analyzed. Unspecific peptides were discarded. Repeatable peptides were those which were presents in at least 7 of 8 samples. Missing values were imputed by linear regression according to the values of the other peptides of the same protein. Data were normalized to compensate for global variations between LC-MS: for each LC-MS the normalization factor was the median value of the ratios of peptide intensities to their intensity in a reference LC-MS.
After incubation of the reaction mixture for 20 min at 30 °C, the shift reaction was loaded onto a 6 or 10 % native polyacrylamide gel (depending on the fragment size) and run for 1 h 45 min. at 150 V. The migration of the DNA bands was visualized by a Bio-Rad Chemidoc imager with Image Lab software (Cy5-based blot protocol).
Feasibility Of 149 invited, 19 GPs expressed an interest and were eligible to participate. Of these 19 practices, we selected 16 by stratified random sampling and 15 of those GPs completed patient recruitment. One hundred and twenty nine patients were recruited, from which we obtained baseline data by reviewing charts (n = 129) and interviewing most of the patients (n = 106). At follow up, we collected data from 13 GPs. We also collected data by reviewing charts (n = 115) and by interviewing 81 of those patients (see Fig. 1 ).
Funding  Health Research Board of Ireland funded the study  (Grant ID:  HRA-HSR-2012-14 ). Dr Klimas work was also part funded by  ELEVATE: Irish Research Council   International Career Development Fellowship (Marie Cure Actions   ELEVA-TEPD/2014/6 );  Dr Lambert 's work on the project was part funded by ' Hepcare Europe' (European Commission 3rd Health Programme ).
RDS-II (or VH) estimator The RDS-II estimator for P A is given by P d d A i s i i s i A = ∈ ∈ ∑ ∑ 1 1 Using simulations that compare the mean square errors of the two estimators, it was shown that the performance of RDS-II was almost always superior to that of RDS-I  (Gile and Handcock, 2010). The RDS-II estimator is currently the estimator used in RDSAT (respondent-driven sampling analysis tool), a free program for analyzing data from RDS surveys. Its variance is estimated using a bootstrap method  (Salganik, 2006). It has been shown that the RDS-II estimator is asymptotically unbiased under the following assumptions  (Volz and Heckathorn, 2008) : 1. The sample is selected with replacement. 2. The sampling fraction is small. The estimator's lack of bias is therefore based on a priori assumptions, and it is legitimate to ask how this estimator will behave if one or more of those assumptions are violated.
That being said, this estimator, like others, can induce biases; these are presented in a summary table in an article by Tomas and  Gile (2011). Recently, these three RDS estimators have been implemented in the R (R Core Team, free statistical software) packages RDS  (Handcock et al. , 2009)  and RDS Analyst  (Handcock et al. , 2013). However, the great majority of researchers used a publicly available software application (RDSAT) in which only RDS-II estimator is implemented. We will therefore come back to the latter's performance. Gile and Handcock (2010)  measured the performance of RDS-II, in particular the procedure for selecting seeds (noninfected, random and infected seeds), the behavior of respondents (whether homophily is weak or strong (individuals recruit more in their own group)), the sampling fraction, and the mean degree according to disease status (infected or non-infected).
, 2013; Salganik, 2012; Wylie and Jolly, 2013). However, some questions remain unanswered for anyone wanting to carry out RDS. As regards estimators, the question arises as to whether to continue using the RDS-II estimator and its bootstrap variance or whether the RDS-SS estimator should be used instead, knowing that it has been recently implemented only in the statistical software R. From a more practical standpoint, this raises further questions for the survey investigator: Are the conditions right (i. e. the assumptions are true) to use this sampling method? Should preliminary studies be carried out to determine the characteristics of the social network? Should this type of survey be ruled out in some cases? Salganik (2012)  calls for the data from RDSs to be made available so that the evaluation and development of this sampling method can be completed.
Quantitative Image Quality -Size of the Interpretable Area The above clarity score does not take into account several problems that may affect parts of fundus images only: overexposure and underexposure in particular. So, additionally, for each selected image, experts determined the largest circle solely including interpretable parts of the image. 27, 28 That circle was determined using the ImageJ software (National Institutes of Health [NIH], Bethesda, MD), version 1. 49. Its diameter (in pixels), divided by the camera's field of view (also in pixels), was used as quantitative quality score.
Characteristics of the Retinographsdid not have access to the patients' medical records: besides images themselves, they only had access to the patients' age and sex. Two reading sessions were organized. To minimize biases due to reading order, the patient dataset was divided into two subsets, denoted by D1 and D2, each containing data from one-half of the patients, and reading sessions were organized as follows. During the first session, expert E1 interpreted images acquired by retinograph R1 in D1 and images acquired by retinograph R2 in D2; expert E2 interpreted images acquired by retinograph R2 in D1 and images acquired by retinograph R1 in D2. During the second session, expert E1 interpreted images acquired by retinograph R2 in D1 and images acquired by retinograph R1 in D2; expert E2 interpreted images acquired by retinograph R1 in D1 and images acquired by retinograph R2 in D2.
State Dependence From Palaeo Data Some new (re-)interpretation of existing palaeo data has emerged since the review of the PALAEOSENS project in 2012. Previous observation-based studies  [21, 47]  had indicated that climate sensitivity might be state dependent, but we  ' re not yet able to quantify this dependence. Progress was made when it was inferred from data through the entire interval covered by ice cores (last 800 kyr) that S may have been 30-40 % lower during full glacial conditions than during intermediate glaciations  [20]  (see yellow dots in Fig. 1 ). This was found to apply to both the simplest version of the specific climate sensitivity parameter, S [CO 2 ,LI ] , in which CO 2 radiative forcing was corrected only for the land-ice (LI) albedo feedback, and for the most complex version that also accounted for radiative forcing by all main greenhouse gases (GHGs), including CH 4 and N 2 O, and including corrections for the slow feedbacks of albedo variation caused by vegetation (VE) and aerosols (AEs) (i.
g. based on sea-level changes  [49] ). In particular, Rohling et al. [27]  estimated zonally averaged changes in the landice albedo feedback from a combination of sea-level change, annual mean incoming solar radiation, and area-weighted scaling factors accounting for the latitudinal distribution of ice sheets but nevertheless detected no significant state dependence. A new dataset of proxy-based CO 2 from the Pliocene  [44]  has been used to state that S [CO 2 ,LI ] was similar in the Only studies published after the PALAEOSENS review paper  [21]  are considered. For comparison, the state-independent values from PALAEOSENS, and from the IPCC report  [3] , and the CMIP5 multi-model mean for present day  [41]  are also shown. All values of S [CO 2 ,LI] were given as mean (or most likely) ±1σ , apart from IPCC, which is the 90 % confidence (CF) range.
Acknowledgments This work arose from a workshop on Mathematical approaches to palaeo-climate sensitivity held in Utrecht in November 2015. We thank the  Netherlands Earth System Science Centre (NESSC) , financially supported by the  Ministry of Education, Culture and Science (OCW) , grant no. 024. 002. 001 , for financial support to the workshop. MG's work is partially supported by grant  N00014-16-1-2073  from the  Multidisciplinary University Research Initiative (MURI) of the Office of Naval Research  and by the  US National Science Foundation  grant  OCE 1243175. A. M. H. acknowledges that this review was completed in receipt of funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no. 278636. GL received funding by  Helmholtz  through  PACES and REKLIM.
Differential scanning calorimetry (DSC) experiments were performed on a Q100 analyzer from TA Instruments (New Castle, DE, USA) with masses from 10 to 50 mg and heating rates from 0. 2 up to 2 K min -1 were used. The measurements were carried out on samples in hermetically sealed high-pressure stainless steel pans from Perkin-Elmer to resist the vapor pressures of the solvents. Thermogravimetry (TG) experiments were conducted at a 2 K min -1 rate under a nitrogen flow with a Q50 thermobalance from TA Instruments (New Castle, DE, USA). The initial sample masses ranged between 2 and 10 mg. High-resolution X-ray powder diffraction data were recorded by a horizontally mounted INEL cylindrical position-sensitive detector (CPS-120) using Debye-Scherrer geometry (angular step ca. 0. 029 o -2θ over a 2θ-range from 2 to 115 o ) and monochromatic Cu Kα 1 (λ =1.