dataesr/scientific-paragraphs-categorization huggingface dataset:
The presently released AA families are strongly biased towards the fungal kingdom. New kingdoms will be explored in the near future, especially in Bacteria where lignin breakdown may be more significant than previously thought  [73]. Indeed, there are a number of literature reports of soil bacteria or aromatic-degrading bacteria from the digestive microbiota of animals that are able to depolymerise lignin (e. g. Nocardia, Rhodococcus, Sphingobium; Streptomyces)  [74] [75] [76]. The precise enzymology of bacterial lignin conversion is still in infancy and enzyme families involved in lignin breakdown will be added to the AA category in due time. It is hoped that the common annotation procedures and the dissemination provided by the CAZy database framework will allow cross fertilization of the various components of lignocellulose research.
Family creation and propagation The new families were created based on sequence similarity criteria analogous to those used in the past to define CAZy families  [3]. Family creation starts with the identification of biochemically-characterized enzyme sequences that are decomposed into functional modules using available structural data whenever possible, followed by comparisons and manual curation. The modularity of these seeding examples is propagated to clearly identify homologous sequences using BLASTP  [77]  against GenBank sequences  [78]  in the initial stages of family definition. Families GH61 and CBM33 were just renamed (respectively AA9 and AA10) and proper information has been added to the headers of former families GH61 and CBM33 to indicate this fact. It is important to stress that inclusion of new sequences in the families is driven solely by sequence similarity and not by taxonomy or biochemistry considerations.
Crystallographic studies Crystals of the repressor and its complexes with the effector and operator molecules were obtained as described in Text S1. All diffraction datasets were collected from single crystals at 100 K using synchrotron radiation at beamlines ID14. 4 and ID29 (European Synchrotron Radiation Facility, Grenoble, France) or Proxima 1 (SOLEIL, Saint-Aubin, France). Data were processed with either XDS  [44]  or iMosflm  [45]  and scaled with XSCALE from the XDS package or SCALA from the CCP4 suite  [46]. The crystal structures of SaFapR alone and its complex with malonyl-CoA were solved by molecular replacement methods using the program AMoRe  [47]  and the effector-binding domain of B. subtilis FapR (PDB entry 2F41) as the search model. In all cases, the missing DNA-binding domains were manually traced from sigma A-weighted Fourier difference maps.
The structure of the repressor-operator complex was solved by a combination of molecular replacement and single-wavelength anomalous diffraction (SAD) techniques using SeMet-labeled SaFapR. The selenium substructure was determined with the program SHELXD  [48]  and further refined with the program SHARP  [49]. Structures were refined with REFMAC5  [50]  or BUSTER  [51]  (for the SeMet-labeled protein-DNA complex) using a TLS model and non-crystallographic symmetry restraints when present, alternated with manual rebuilding with COOT  [52]. Models were validated through the MolProbity server  [53]. Data collection and refinement statistics are reported in Table  1. Graphic figures were generated and rendered with programs Pymol  [54]  and 3DNA  [55].
This research was supported by grants from the  Agence Nationale de la Recherche  ( ANR ,  France , contract  ANR-06-PCVI-0009-01 );  Consejo Nacional de Investigaciones Cientı ´ficas y Tecnicas, Argentina; Agencia de Promocion Cientı ´fica y Tecnologica, Argentina ;  Institut Pasteur ;  CNRS (France)  and  ECOS France-Argentine  (Action Grant  A05B02 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
These tags are nonetheless far more expensive than geolocators and GPS recorders, which constraints mass-deployments across life stages of a population. Between August and October 2011, we deployed 24 PTTs (Microwave Telemetry, Columbia, USA) simultaneously on 12 juveniles, nine immatures and three breeding adult Scopoli's shearwaters from Riou, Frioul and Lavezzi islands (Table  1 ). Juveniles were equipped just before fledging in early October at both Riou (nine individuals) and Frioul (three individuals) islands (Table  1 ). Adults were equipped at Riou Is. in early October whereas, immatures were equipped from mid-August to mid-September 2011 at Riou and Lavezzi islands (Table  1 ). When captured on colonies, the identity of immature birds was checked in the demographic databases. Immature birds were equipped if they were banded as chick, younger than 7 years old and never observed as breeders in the colony.
The model was fitted to each individual bird with a one-hour time step for tracks reconstruction. Analyses were performed using the free software R  [52]  and JAGS using the package 'bsam 0. 42'  [51]. Wide, flat, prior probability distributions were assumed for model parameters. A total of 80,000 Monte Carlo Markov-Chains iterations were recovered from two chains (40,000 iterations per chain). The first 30,000 iterations of each chain were discarded as a burn-in, while one every 10 th value of the remaining 10,000 iterations was retained to reduce autocorrelation. The final sample size on which inferences were drawn was thus 2,000 iterations. We checked convergence of the error parameter estimates using the potential scale reduction factor,R; values close to one were consistent with convergence  [53].
14° N, 5. 35° W) was determined by visual inspection of the reconstructed tracks. Life stage comparisons of daily distance travelled were tested using analysis of variance (ANOVA) on log transformed data to meet normality. Sinuosity of migratory pathways after crossing Gibraltar was estimated for each life stage using rose diagram of turning angles calculated between two successive locations, when speed was > 10 km. h -1 , corresponding to migratory movements. Straightness of the migratory path was assessed by calculating turning angles distributions when travelling (Figure  2 ). We tested for life stage differences in mean direction and dispersion of turning angles using Rao's test for homogeneity of angular data  [55]. Life stage specific habitat use was compared by plotting twodimensional kernel density estimation of bathymetry and monthly chlorophyll-a concentration extracted along reconstructed trajectories.
Bathymetry was downloaded from the ETOPO2v2 database at a spatial resolution of 0. 033° and chlorophyll-a concentration (Aqua MODIS NPP 0. 05°, mg. m -3 ) was obtained from BloomWatch website (http:// coastwatch. pfel. noaa. gov/coastwatch/CWBrowserWW360. jsp). Both environmental datasets were interpolated on a regular 0. 1°×0. 1° grid before extraction of environmental variables along the trajectories.
Acknowledgements We are very grateful to all fieldworkers involved in this study:  Julien Nezan ,  Jean-Baptiste Pons ,  Aurélien Prudor ,  Jérémy Tornos ,  Pierrick Giraudet ,  Léo Martin ,  Nicolas Hanuise  and  Marie Pellé. We also thank the agents from  Marseille  and  Lavezzi islands  for providing support during the fieldwork. We are grateful to  Ian Jonsen  for sharing his code to perform statespace modelling,  Jiacopo Ceccere  for providing the locations of the Italian Scopoli's shearwater colonies and  Matthieu Authier  for valuable comments on the manuscript. We thank  Marguerite Platon  and  Lisa Defend  for their administrative support at CEFE CNRS and two anonymous referees for valuable comments on a previous version of the manuscript.
Related Work The related work described in this section is divided in two categories, work on uncertainty tackled using goal-based models for the case of SASs and decisionmaking using Bayesian theory. Researchers have tackled uncertainty in SASs in different ways. As discussed by  [3, 13] , there is a dearth of applicable techniques for handling uncertainty in this setting. Welsh et al  [23]  introduced REAssuRE to use goal models and Claims for driving self-adaptation. In contrast to REAssuRE, in our case and when using DDNs, preferences among softgoals are specified using both expected utilities with reward functions and also penalty functions. Uncertainty in adaptive systems has also been tackled by RELAX  [22] , a requirements language that explicitly addresses uncertainty inherent in adaptive systems.
0 and P(Observation|Redundant Prevents Network Partitions =false )= 1. 0. However, different from our earlier research  [23] , the DDN approach can take into account those uncertainties what we leave for future work. As in the case of DDNs, the approach presented in  [19]  can tackle the uncertainty related to lack of confidence of sensor's reports. Liaskos et al. [15]  present a framework for specifying both mandatory and optional requirements, along with quantitative preferences over the optional requirements, within the context of a goal model. The goal tree and the specified preferences are translated into the Hierarchical Task Network (HTN) and Planning Domain Description Language (PDDL) planning formalisms, respectively; the HTNPlan-P planning tool is then used to obtain the most preferred design.
Similar to our work,  [15]  focus on on modeling and reasoning about properties and alternative solutions and working on preferences-based exploration of alternatives requirements. However, they do not use probability theory. Bayesian networks have been used to enable reasoning over probabilistic causal model and to make predictions about partially satisfied affirmation  [6]. However the Bayesian paradigm does not provide any direct means for modelling dynamic systems  [21]. In contrast to our model in which we combine Bayesian networks and decision networks to achieve a sophisticated architecture that could be used as a powerful decision-making tool for solving complex or real-time decision problems and to model a system that is dynamically changing or evolving over time such as SASs. A number of interesting and related research approaches using DDNs can be found in the area of AI, Portinale and Raiteri  [17]  have proposed a formal model for FDIR(Fault Detection, Identification and Recovery) analysis in autonomous systems based on a formal Fault Tree modeling language able to express stochastic dependencies and multi-state components which is called Extended Dynamic Fault Tree (EDFT).
The optical density of P 450arom , IRS-1, -2 and -4 bands were measured using the Quantity One Software Program version 4. 4. 0 (Bio-Rad Laboratories). The specific density of the P 450arom , IRS-1, -2 and -4 for each sample was calculated by subtraction of the background density and divided by the specific density of corresponding b-actin band or quality control band.
Statistical analysis The statistical analyses were performed using the SPSS Statistical Software programme version 15. 0 (SPSS, Inc. ). All data are presented as the meansGS. E. M. Where data were not normally distributed, they were transformed to logarithms. Data for hormone concentrations in plasma and body weight were analysed using a repeated measures, mixed model ANOVA with time as the within subject (within sheep) repeated measure and treatments (diet and stage of the oestrous cycle) as between subject (between sheep) factors. Data on hormone concentrations in follicular fluid and the levels of P 450arom , IRS-1, -2 and -4 were analysed using a repeated measures, mixed model ANOVA with follicles within sheep as the within subject factor and treatments (diet and stage of the oestrous cycle) as between subject (between sheep) factors.
pyrosequencing and gene annotation The clone containing the multiresistant ampicillin fosmid was sent for sequencing. Briefly, the Amp R clone was grown overnight at 37°C and purified as mentioned above followed by an 1-hour incubation with Riboshredder RnaseA (Epicentre). The 454 pyrosequencing reaction was done by DNAvision (Gosselies, Belgium). The sequences were assembled using CAP3 version 3 (available at Mobyle Pasteur: http://mobyle. pasteur. fr/cgi-bin/portal. py#forms::cap3) and annotated by the free online program Softberry (http://li-nux1. softberry. com/berry. phtml?topic=fgenesb&group=pro-grams&subgroup=gfindb).
Global analysis of the Amp R clone To gain information regarding the gene functions associated with the detected b-lactamase, the complete clone sequence was obtained (Genbank accession number KF033132). After aligning the fragments, a single contig of 47,977 nucleotides was obtained. Thirty-eight complete ORFs were detected, plus an incomplete ORF39 (Table  S1  and Fig. S1 ). The first ORF encodes a phosphomannomutase (phosphoglucomutase), ORF2 encodes a peptidase, ORF3 a tryptophanyl-tRNA synthetase, ORF4 a methionine aminopeptidase, ORF5 an aminotransferase, and ORF6 the class A b-lactamase. ORFs 7-10 follow and are transcribed in the same forward orientation and they code for hypothetical proteins (Fig. S1 ). ORFs 11-14 are transcribed in the reverse orientation and correspond to the kdpAFCB genes which encode the components of a potassium transporting ATPase (Table  S1  and Fig.
For the transferred NOE experiments, samples were prepared by dissolving 0. 47 mg of AT and 300 g of the dodecasaccharide in phosphate buffer reaching a molar ratio of 10:1 AT/dodecasaccharide. Proton spectra were recorded with presaturation of the residual water signal with a recycle delay of 12 s and 64 scans. Bidimensional double quantum filter-COSY and two-dimensional TOCSY spectra were acquired using 32 scans per series of 2048 ϫ 512 data points with zero filling in F1 (4096 ϫ 2048), and a shifted (/3) squared cosine function was applied prior to Fourier transformation. Heteronuclear single quantum coherence (HSQC) spectra were obtained in phase sensitivity-enhanced pure absorption mode with decoupling in the acquisition period. The matrix size of 1024 ϫ 320 data points was zero-filled to 4096 ϫ 2048 by application of a squared cosine function prior to Fourier transformation.
The STD spectrum was obtained by phase cycling subtraction of the on-resonance and off-resonance data acquired in interleaved mode. The number of scans and dummy scans were 2048 and 16, respectively. Antithrombin Affinity-The interaction with antithrombin of the dodecasaccharide and of fondaparinux, as a reference compound, was studied by the fluorimetric titration method, according to the method previously described  (15). Anti-FXa Activity in Plasma or in a Buffer Medium-The anti-FXa activity of the dodecasaccharide was determined with the 10 ACL 7000 automated instrument for measuring coagulation (Instrumentation Laboratory), using the Heparin kit (Instrumentation Laboratory), which contains antithrombin, Factor Xa, and the chromogenic substrate S-2765. The measurements were carried out according to the manufacturer's recommendations.
0 bar; Dry gas 7 liters/min; dry temperature 200 °C; mass range 200 -2000 m/z). Model Complex Building-The molecular editing was done with the Maestro 9. 1 graphical interface. Two models of the 1:1 AT-dodecasaccharide complex, with the nonreducing and reducing AGA*IA framed by a nonsulfated IdoUA residue and terminating at the reducing end with a 4,5-unsaturated (⌬U) residue (structure b of Fig. 1 ) were built fitting the corresponding AT-binding site. The basic element of the complex between AT and AGA*IA pentasaccharide was isolated by molecular editing from the ternary complex AT-T-Hep (antithrombinthrombin-heparin) included in the Protein Data Bank code 1TB6. Both AT-dodecasaccharide complexes were created with the conformation of unsaturated uronate and IdoUA residues in 2 H 1 and 1 C 4, respectively, in accord with a previous study  (15).
For example, the enzymatic depolymerization procedure with heparinase I depolymerizes the pentasaccharide sequence at the level of A* and generates truncated and inactive sequences  (29) , which is likely the reason why such sequences were not isolated by this methodology  (21). The possible presence of more than one pentasaccharide sequence in a given heparin chain was supported by data obtained by fluorescence spectroscopy on the stoichiometry of the interaction of AT with active high molecular weight heparin fractions  (10). This finding was confirmed by quantification, by NMR spectroscopy, of the glucuronic acid residue linked to A* (G-A*). This disaccharide, contained only in the pentasaccharide sequence and considered the real marker of the AT-binding sequence, was found to be present in high affinity fractions of LMWHs (i.
Acknowledgments-We thank  Dr. Detlef Moskau  ( Bruker Biospin, Zurich, Switzerland ) for the access to the 800 MHz NMR spectrometer. The 900 MHz spectra were recorded with the support of  Dr. Hans Wienk , at the  SONNMR Large Scale Facility in Utrecht, The Netherlands , and supported by  Access  to Research Infrastructure Activity in the 7th Framework Program of the EC (Project Number  261863 ,  Bio-NMR ).
LTR-RT Prediction DNA sequences of Uwum family were downloaded from the maize TEs project (http://maizetedb. org/~maize/, last accessed April 10, 2013) and those of soybean from SoyTEdb database  (Du et al. 2010). The genomic sequences were downloaded from the Phytozome web site (http://www. phytozome. net/, last accessed April 10, 2013). De novo detection of LTR-RTs was performed using the LTRharvest software (http://www. zbh. uni-hamburg. de/?id=206, last accessed April 10, 2013). Default parameters were used except for -xdrop 37 -motif tgca -motifmis 1 -minlenltr 100 -maxlenltr 3000 -mintsd 2. A typical LTR-RT should harbor a TG. CA box, with TG at the 5 0 -end of 5 0 -LTR and CA at the 3 0 -end of 3 0 -LTR. However, some LTR-RTs families, such as Tos17 in rice, have a TG. GA motif. For this reason, we allowed one mismatch on the canonical feature TG.
Family Classification by Clustering Clustering was performed using the SILIX software package (http://lbbe. univ-lyon1. fr/SiLiX, last accessed April 10, 2013). This step first requires to perform a nucleotide Basic Local Alignment Search Tool (BLAST) search of LTR sequences16 (5 0 -LTR or 3 0 -LTR) generated by LTRharvest/LTRdigest software in an all against all comparison. The following parameters were used: -r 2 (reward for a nucleotide match. Note that this is a default parameter in blast2+ version), -F F (filter query sequence ¼ false), and -m 8 (alignment view options: tabular). The -r 2 options is more adapted for divergent sequences. Using these options will yield longer high score pairing (HSP) with less sequence identity and therefore facilitates the sequence clustering by SILIX software.
Two sequences are included in the same family if the HSPs in the BLAST tabular output cover at least 70% of the sequence length with an identity of at least 60% (see http://lbbe. univ-lyon1. fr/ Documentation,3012. html, last accessed April 10, 2013 for further documentation). All the family classification data of the eight plant genomes are freely available and can be accessed at the following link: http://gamay. univ-perp. fr/ ~moaine/, last accessed April 10, 2013.
Solo-LTRs and Truncated Elements Detection A PERL script was written for solo-LTRs detection (available on request). For each LTR-RT family (for which the exact border can be determined), a consensus LTR sequence was manually chosen based on a sample of paralogs. Then a BLAST search was performed to retrieve all the LTRs of each family and verify the presence/absence of the internal region around it followed by manual inspection using DOTTER software  (Sonnhammer and Durbin 1995). To evaluate the portion of genomes corresponding to fragmented and nested LTR-RTs that are not detected by structure-based methods (LTRharvest), the genomic sequence of each species was split into small fragments of 1 kb using splitter software from EMBOSS package (http:// emboss. bioinformatics. nl/cgi-bin/emboss/help/splitter, last accessed April 10, 2013).
This method is, however, far too time consuming because it requires a lot of manual checking during the alignment step. For all these reasons, we chose to build our data set solely on elements harboring both their LTRs. One could argue that this lead to a bias in our analysis, because only young insertions would thus be analyzed. However, the results shown in figures 5 and 6 clearly show that our data set allows to cover a time range of at least 2 Myr, which is enough to measure the deletion rate in all six genomes. Phylogeny Reconstruction LTR sequences of the two families Uwum and RLC_Gmr6/18 were aligned using MAFFT software  (Katoh et al. 2002). Highly variable regions were deleted, and alignments were modified by hand using Seaview  (Galtier et al. 1996 ; http:// pbil. univ-lyon1. fr/software/seaview.
The Removal Rate of LTR-RTs through Deletions Is Not Lineage Dependent The removal rate was estimated based on a data set of LTR-RT that harbors both their LTR to date their insertion (see Materials and Methods for technical discussion about removal rate estimation). Arabidopsis thaliana and Brachypodium were not included in this analysis because their genome does not harbor enough paralogs even for the most repeated families to conduct relevant statistical tests. For each family of the remaining six genomes, we separated the elements into four classes according to their insertion date: 0-0. 5 Myr, 0. 5-1 Myr, 1-1. 5 Myr, and >1. 5 Myr. Figure  4  shows the histograms of the average of DNA loss for these four classes in the six genomes. The first observation is that in most cases, as expected, DNA loss is higher for older elements.
Acknowledgments M. E. performed programming, conducted data analyses, and wrote the manuscript. O. P. designed the research, supervised this work, participated to data analyses, and finalized the manuscript. The authors thank  Cristian Chaparro  and  Vincent Miele  for their technical help and  Scott Jackson  and  Marie Mirouze  for their critical comments on the manuscript. This work was supported by a  CNRS/Region Languedoc Roussillon research grant  and by the  University of Perpignan Via Domitia.
Stimulus material A first set of 213 French words was selected from the online database LEXIQUE3. 55 (New et al. , 2001). This set included 93 neutral (e. g. motivation, giant, statue) and 120 disgust-related words (e. g. infection, vomit, vermin). Eighteen participants who did not participate in the present experiment rated all stimuli for emotional valence (from À3, very negative, to þ3, very positive), arousal (from 1, not stimulating, to 5, very stimulating) and imageability (from 1, very imageable, to 7, not imageable at all). From this list of 213 rated words, we then selected 35 neutral and 35 disgust-related words that were matched for arousal and standard word recognition variables (Table  1 ). All items along with their translations and item characteristics can be found in the supplementary material.
EEG acquisition Electrical activity was continuously recorded with a sampling rate of 256 Hz using the Biosemi ActiveTwo system (BioSemi, Amsterdam, Netherlands). Horizontal and vertical electrooculograms were recorded using two facial electrodes placed, respectively, above the left eye and to the outer canthi of the right eye. Scalp EEG was recorded from 64 electrodes attached to an electrode cap and positioned according to the extended 10-20 EEG system. All sites were re-referenced offline to the algebraic average of the left and right mastoids. The data were filtered using a high pass of 0. 1 Hz and a low pass of 30 Hz. Each trial lasted for 1250 ms including a 250 ms prestimulus time period. Data were baseline corrected using the first 250 ms of the epoch, which corresponded to the presentation of the fixation cross.