dataesr/scientific-paragraphs-categorization huggingface dataset:
Input of LOMBARDE If G is the set of genes in a studied organism, then LOMBARDE requires the following inputs: 1. Co-expressed pairs: A set C ⊆ G × G of pairs of co-expressed genes, selected based on the values of their correlation or mutual information. An example of such a set would be the results of ARACNe  [2] , MRNET  [4] , or other mutual information based methods evaluated using expression profiles obtained under different environmental conditions. 2. Affinity pairs: A set A ⊆ G×G of gene pairs obtained based on TF/BS sequence affinity and the associated p-values. Specifically, a pair of genes (A, B) is in A if gene A codes for a TF that has high affinity with a BS in the promoter region of gene B. For instance, A could be the result of matches in the Prodoric database  [9]. We assume that pairs with high p-values would already have been discarded from A.
Results and discussion A large number of TF/BS associations have been validated experimentally for E. coli; therefore, we used public data for this bacteria to evaluate our model. The genome sequence and gene annotation of E. coli K12 [GenBank:NC_000913] were downloaded from the NCBI ftp site  [11]  (http://www. ncbi. nlm. nih. gov/ nuccore/NC_000913. 3). Differential expression profiles for 907 conditions were obtained from Ecoli_v4_Build_ Fig. 3  Comparison between LOMBARDE output and an alternative minimum global cost explanations. Left: Graph with minimal cost that explains all pairs of co-expressions. Note that the explanation proposed for (G,H) has cost 18, which is much higher than the optimal explanation of cost 6. Center: LOMBARDE output all optimal explanations for each pair. Right: Minimum cost subgraph containing an optimal explanation for each pair.
Input data Here, we describe briefly the sources and methods used to generate input data for LOMBARDE. As mentioned above, for bacteria we condense the information into operons to simplify computation. It is important to note that the methods we used to produce the input for LOMBARDE were merely a choice. Because data production was not our main concern, as far as possible, we used simple pipelines that are currently used by the community to build the putative TRNs. C: Pairs of co-expressed operons: A set of 61,506 pairs of co-expressed operons was generated by analyzing the E. coli differential expression profiles as follows. A matrix with the mutual information between all pairs of the 4297 E. coli genes was computed using a parametric Gaussian density estimator with the minet [13] library of the R statistical package  [14].
The matrix had over 18 million values, although most of them were either insignificant or redundant and were discarded using the MRNET  [4]  strategy. From the remaining pairs of genes we considered only the 100,000 that had the highest mutual information, i. e. about 5 % of the total pairs. This number was chosen to support the inclusion of the main co-expressions. Finally, two operons were considered to be co-expressed if each one contained a gene from a pair of co-expressed genes. After discarding redundant and trivial cases, we obtained a set of 61,506 pairs of co-expressed operons, involving 2492 different operons. A: Affinity network and its p-values: A set of 25,604 pairs of operons with high TF/BS affinity was produced as follows. Genes coding for TFs were identified by BLAST homology searches (E-value cutoff of 10 -10 ) between the gene product and a known instance of the TF in the Prodoric database  [9]  (http://www.
The gene purC was co-expressed with metF, metA, and metQ; metN was co-expressed with purE and pyrD; metQ was co-expressed with codA; clcB was co-expressed with ydfN; and agaS was co-expressed with ugpA of the Prodoric database and found that LOMBARDE produced smaller graphs containing a greater proportion of validated arcs (Fig. 7 , bar graph C). This is not surprising because Prodoric is based on several prokaryote organisms, while RegulonDB is based only on E. coli. However, the differences were not significant when validated regulations were included (Fig. 7 , bar graph D). The LOMBARDE results also were robust to the methods used to determine co-expressions. We evaluated LOM-BARDE using different sets of co-expressions as determined by ARACNe  [2] , C3NET, CLR  [3] , and MRNET  [4]. The LOMBARDE results were similar for two of the methods.
Conclusions Deciphering which regulatory interactions can provide a causal explanation for a set of observed co-expressions remains an important challenge in systems biology. We developed LOMBARDE, a modeling method that uses an optimization principle to determine a simple and confident set of regulations that serve as causal explanations for a given set of co-expressions. When the set of co-expressions involves genome-wide interactions, LOM-BARDE produces an explanatory putative TRN that has some basic topological characteristics close to observed TRNs and is biased to include regulations that have been independently validated experimentally. The LOMBARDE method was illustrated for a E. coli data set, where coexpressions were determined using mutual information under several environmental conditions.
Acknowledgements We thank  Alejandra Medina-Rivera ,  Heladia Salgado  and  Julio Collado-Vides  for useful discussions about the interpretation and use of the RegulonDB database. This work was supported by grants  Fondap 15090007 ,  Basal CMM ,  Fondecyt 1140631  and  ANR  Biotempo  ANR10-BLANC0218. We also thank the  international cooperation fellowships IntegrativeBioChile INRIA Associative Team and CIRIC-INRIA Chile.
S1 ). No cellulose cleavage was observed when AAO was tested together with its best substrate anisyl alcohol in the presence of PaLPMO9E under similar experimental conditions (Fig. S1 ). To verify the identity of the oxidized products generated from cellulose by PaLPMO9E in cooperation with AAQO1, mass spectrometry analysis of the product mixture was performed. It confirmed the presence of oxidized and non-oxidized cello-oligosaccharides. Oxidized species potentially corresponding to aldonic acid at m/z 705. 22 (Δ + 16 from the non-oxidized species) which is a [M + Na] + species and m/z 727. 21 (Δ + 22 from the oxidized species) which is a [M + 2Na-H] + species (Fig. 2A ). Both MS2 fragmentation patterns of m/z 705. 22 (Fig. 2B, C ) and m/z 727. 21 (data not shown) contain fragment ions, e.
Material and Methods Production of enzymes. AA9 LPMOs and CDH from Podospora anserina (PaLPMO9E Genbank ID CAP67740, PaLPMO9H Genbank ID CAP61476 and PaCDHB Genbank ID CAP61651) and AAO from Ustilago maydis (Genbank ID UM04044) were produced in Pichia pastoris in flasks or in bioreactors as described in 9 and 28. AAQO1 (scf185002. g8), AAQO2 (scf184746. g7) and GDH (scf184803. g17) from P. cinnabarinus were produced in A. niger as described in  27  and  29. All of the enzymes were purified to homogeneity and quantified as described previously. For AAQO, the enzyme concentration was determined using the molar absorbance of proteins at 456 nm. These extinction coefficients were determined based on the quantification of free FAD extracted from the protein samples by heat denaturation and using the equation: ε 456 = ε FAD × A456/A450, where A456 is the enzyme absorbance at 456 nm, A450 is the free-FAD absorbance at 450 nm, and ε FAD is the free-FAD extinction coefficient at 450 nm (11300 M -1 cm -1 ).
Acknowledgements The authors would like to thank  F. Piumi  and  S. Grisel  for technical assistance and  R. Ludwig  for insightful discussions. This study was funded by the  AMIDEX foundation (Funcopper project , grant number  A* M-AAP-EI-13-13-130115-15. 37  and  MicrobioE  project, grant number  ANR-11-IDEX-0001-02 ) and the  INDOX European project  ( FP7-KBBE-2013-7-613549 ). The authors are grateful to the  EPR  facilities available at the national  EPR network RENARD  ( IR CNRS 3443 ) and the  Aix-Marseille University  EPR center.
In our data set, the meal criterion could be estimated as 2208s (Figure  2 ), but only a very small proportion of small interval durations was observed (11% of 20,552 intervals). Consequently, based on this criterion, meals consisted on average of 1. 10 visits. As compared to previous studies  (Howie et al. , 2010) , and in particular  Basso et al. (2014) , who used similar feeders (but without doors closing behind the animals) and the same algorithm to compute the meal criterion, this distribution suggested that the visits to the feeders after their modification (addition of doors) are actually consistent with biological meals. Therefore, meals were considered as the recorded visits in our study. The raw data set contained 5 wk of records. Traits were first computed on a daily basis to obtain 35 daily measurements for each trait and each duck: daily feed intakes, daily feeding times, daily feeding rates (ratio between the daily feed intake and daily feeding time), daily interval between meals and number of meals per day.
To summarize, our data set was composed of 35 daily/5 weekly/one over the feeding test period measures for each of the following traits: daily feed intake, daily feeding time, daily feeding rate, number of meals per d, and daily interval between meals. For ADG and FCR, we computed 5 weekly/one over the feeding test period values. Each duck had one value of RFI for the feeding test period. Behavioral Tests During Growth. An open field (OF) test combined with a test measuring the response to human presence was performed to evaluate the response to a stressor of the 2 lines. Birds were tested individually, in the morning, on 3 successive d between age 62 d and 64 d (Figure  1 , approximately one-third of the birds were tested each d), in an OF measuring 3. 0 × 3. 0 m 2 subdivided into 9 equal zones of one m 2 each.
Data Analysis For traits measured on a weekly basis, the least square means (LSmeans) of the weekly effects on feeding behaviors and line effects were compared for each trait using model 1. For other traits measured over the entire test, line effects were compared using models 2 to 6, as applicable, using a Student t test (SAS, Inst. Inc. ,  Cary, NC, 2008). The phenotypic correlations between RFI, FCR, liver weight, and melting rate, and the feeding behavior traits and carcass traits were estimated for all traits recorded during the test period.
Mutational databases listing potential beneficial substitutions for phosphotriesterase activity were designed  (Chabriere et al. 2014; Chabriere et al. 2015). These databases combined with in silico analyses were used as starting point for engineering SsoPox toward OP based pesticide degradation (Fig. 3 ). Variants with up to 2000-fold increased activity against certain pesticides and enlarged substrate scope were obtained (unpublished results). Mutations R223H and Y97W were first shown to impact phosphotriesterase activity  (Elias et al. 2008). Residue W263 was also deeply investigated  (Hiblot et al. 2012b ). This residue is located in the active site and was shown to be involved in enzyme conformational flexibility, mediating substrate promiscuity. Saturation mutagenesis was performed at this position using NNS-degenerated primers.
Fifty serum samples were collected from five sheep (3-12 wpi) that were experimentally infected with Babesia sp. BQ1 (Lintan). In total, 141 sera and anticoagulated blood samples were collected from sheep in the Xinjiang, Henan and Gansu provinces of China. Genomic DNA was purified from blood samples and tested using specific PCR primers, and 141 genomic DNA samples were found to be specifically positive for Babesia sp. BQ1 (Lintan) in the sequencing analysis (data not shown). The sera samples (n = 141) that corresponded to these genomic DNA samples were selected as the positive serum samples. A total of 191 sera were used as positive samples to evaluate the sensitivity of the ELISA, and a mixture of these sera was used as the positive control serum. Sera were collected from 492 lambs that were purchased from a Babesia-free region in Jingtai country (Gansu province of China) from 2010 to 2015.
BQ1 (Lintan) RAP-1a61-1 (Gen-Bank accession number KC953701) and modified to include either Xba I and BamH I or Nde I and Hind III (New England BioLabs) enzyme restriction sites (underlined below) at their 5' end. The following primers were used: RAP-1a61-full-F: 5'-CGC TCT AGA -GT ACG CCA TTA CCA CCG TTC-3', RAP-1a61-full-R: 5'-GCC GGA TCC GAG CCA CGA ATC ATC GGA TTC-3', RAP-1a61/CT-F: 5'-CGC CAT ATG ACC ACG GTG ATG CCC GAG-3', and RAP-1a61/CT-R: 5'-GCC AAG CTT GAG CCA CGA ATC ATC GGA-3'. The PCR products were cloned into the pUC57 vector according to the manufacturer's instructions (Genscript). The inserts were sequenced using vector primers. Representative plasmids were selected and digested using either Xba I and BamH I or NdeI and Hin-dIII restriction enzymes and subsequently cloned into digested pET-30a expression vectors (Genscript) according to the manufacturer's instructions.
Cloning and expression of RAP-1a61-1 and C-terminaltruncated RAP-1a61-1 The genes encoding RAP-1a61-1 (aa 22-474) and Cterminal RAP-1a61-1/CT (aa 325-474) (aa sequence corresponding to GenBank accession number AGV15809) were successfully amplified using PCR and inserted into the cloning vector pUC57. The pET-30a-RAP-1a61-1 and pET-30a-RAP-1a61-1/CT recombinant plasmids were identified using enzyme restriction analysis (Sac I/Sal I and Mlu I/Hind III, respectively) and confirmed by sequencing using specific primers. The insert sequences were also verified (data not shown). After the expression of each recombinant protein was induced using IPTG, the E. coli lysates were analyzed using SDS-PAGE and Western blot analysis of an anti-His serum. The results of SDS-PAGE showed that rRAP-1a61-1 and rRAP-1a61-1/ CT were expressed as proteins that were approximately 52 kDa and 20 kDa in size, respectively (Fig.
In contrast, a gradual increase in the number of antibodies produced against RAP-1a was observed in the three French sheep (Nos. 3390, 3533 and 3446). Higher antibody levels were observed at 6-7 weeks post-infection (WPI6-7) in sheep Nos. 3390 and 3446 and at 9 weeks (WPI9) in sheep No. 3533. In general, antibodies were produced at 1 week post-inoculation, continued to increase through 6 weeks (WPI6), and remained stable until 9 weeks (WPI9), after which slight decreases were observed through 12 weeks (WPI9-12). High levels of antibodies against RAP-1a usually lasted 3 months (84 days p. i. ), despite the decrease in production that was observed after 6 weeks (42 days p. i. ) (Fig. 7 ). Evaluation of specificity, sensitivity and positive threshold values in ELISA to detect rRAP-1/CT MedCalc statistical software was used to evaluate the sensitivity, specificity, and positive threshold value in ELISAs performed to test 191 positive sera and 492 negative sera, as previously described  [40].
These results were consistent with the findings of a recent study, in which a RAP-1a peptide designed from the RAP-1a Nterminal of Babesia sp. BQ1 (Lintan) was used as an antigen to perform ELISA. Antibody production against this peptide displayed kinetics that were similar to those observed against rRAP-1a61-1 (data not shown). In contrast, the production of specific antibodies against Babesia sp. BQ1 (Lintan) in the three infected French sheep was increased at three weeks post-infection and peaked between six and nine weeks post-infection. On average, RAP-1a was expressed at sequentially higher levels from week 1 to week 7 post-infection before its levels subsequently decreased. The difference in the kinetics of antibody production between these five infected sheep might result from differences in sheep breeds or individual variation.
Acknowledgements This study was financially supported by the  NSFC  (Nos. 31502054 ,  31372432 ,  31201899 ,  31272556 ,  31402189 , and  31471967 ),  ASTIP, Creative Research Groups of Gansu Province  (No. 1210RJIA006 ),  NBCIS CARS-38 , the  Special Fund for Agro-scientific Research in Public Research  (Nos. 201303035 and 201303037),  MOA , the  973 Program  ( 2015CB150300 ),  Supporting Program  ( 2013BAD12B03 ,  2013BAD12B05 ),  MOST (China) , and the  Jiangsu Co-innovation Center Programme for the Prevention and Control of Important Animal Infectious Diseases and Zoonoses as a State Key Laboratory of Veterinary Etiological Biology  Project.
Study species, study sites and sample size The Zenaida dove is a granivorous columbid species characterized by long-term pair-bonding and yearround breeding and territoriality  (Wiley 1991, Quinard and Cézilly 2012). The species shows a slight sexual dimorphism, with males being on average 2-5% larger than females, although size distribution shows a large overlap between sexes  (Dechaume-Moncharmont et al. 2011). Juveniles can be distinguished from adults based on morphological characteristics (plumage development and bill morphology,  Wiley 1991 , del Hoyo et al. 1997 , Gibbs et al. 2001) , but age cannot be ascertained when birds are first captured as adults. The data presented here come from a long-term study of the Barbados (West Indies) population of Zenaida doves. There, individuals may either actively defend, alone or in pairs, a territory against conspecifics, or feed with very little aggression towards conspecifics in large aggregations at specific sites where food availability is particularly high  (Dolman et al.
2011 for details). Data were collected during both the high breeding season (from 1 March to 9 April 2012 and from 18 February to 19 March 2013) and the low breeding season (from 12 November to 2 December 2012). Data were obtained for a total of 122 individuals, 73 caught at the Sunset Crest and Folkestone areas and 49 caught at the Roberts site. Of these, 60 were females and 62 were males. Two birds were captured twice during two different sessions. For these individuals, we used the data recorded during the first session.
Although this parameter does not take into account the UV part of the spectrum to which birds are sensitive and is adapted to trichromatic vision only, hue was used in order to compare our data to previously reported variation in color as assessed from the human eye (see Introduction). Finally, ultraviolet, blue-green, and red peaks of the iridescent patch were described by their spectral position (spectral position of maximum reflectance e. g. Delhey and Kempenaers 2006)  and their chroma (wavelength of maximum reflectance, and relative proportion of reflectance in the ranges  [340, 400] ,  [390, 550]  and [550, 700] nm, respectively). Mean and s. d. values for each color parameter are given in Supplementary material Table  A1.
Avian color discrimination of sexual dichromatism and of variation in plumage colour among individuals of the same sex To quantify sexual dichromatism and to estimate whether individuals perceive variation in plumage color among potential mates from the avian visual perspective, we computed the chromatic contrast of the reflectance spectra (ΔS,  Vorobyev et al. 1998 ) both between sexes, and within each sex, using the mean spectrum for the three measures (or six, for the right and left iridescent patches and dark streaks) per individual. We used the pavo package  (Maia et al. 2013)  developed for the R software (v. 2. 101; R Core Team 2011) to run these analyses. We created a visual model (vismodel function) using the spectral sensitivity data from the Rock pigeon (Columba livia,  Bowmaker et al.
Acknowledgements We are particularly grateful to  Mr. Steve Devonish , Director of  Natural Heritage (division of Ministry of Environment and Drainage of Barbados)  and  Kim Downes Agard  for granting us the permission to capture, blood sample, and ring Zenaida doves. We thank the  CRBPO , and particularly  Olivier Dehorter , for kindly providing us with aluminium rings. We thank  Carla Daniel  for help in catching birds and  Cyril Bhola  for hospitality. The study was supported by the  Agence Nationale pour la Recherche  (programme blanc Monogamix), the  Conseil Régional de Bourgogne , the  CNRS , and the  Institut Universitaire de France. Finally, we would like to thank the Subject Editor,  Alexandre Roulin , and two reviewers for their detailed and helpful comments on previous versions of the manuscript.
FUNDING The research leading to these results has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  7th Framework Programme  ( FP7/2007-2013) under REA Grant Agreement 289217  and the the European Community's  Framework VII Program   FP7/2007-2013  to the  SMARTCELL  project  KBBE-2007-3-1-01. This review reflects only the authors' view and the  European Union  is not liable for any use that may be made of the information contained herein. Authors also acknowledge the support of the  European Fund for Regional Development in the program INTERREG IVA Broad Region EU  invests in your future,  COST (European Cooperation in Science and Technology ) of the Plant Engine Action ( FA1006 ), and of the  Agence Nationale pour le Recherche  to the  InteGrape  ( ANR-13-BSV6-0010 ) and  TerpFactory (LabCom)  projects.
Cytochrome oxidase I analysis Sequences produced from tissue samples of R. maderensis (n = 37), R. straeleni (n = 6) and a subset of 30 R. clavata were aligned alongside those from the closely related R. radula downloaded from BOLD database. A haplotype genealogy was constructed from trees using a ML approach. A HKY  (Hasegawa et al. 1985)  nucleotide substitution model determined by JModelTest (DAICc = 1. 42, Darriba et al. 2012 ) was implemented in PHYML 3. 0, and genealogy was visualised using Haploviewer  (Salzburger et al. 2011). To build a comparative phylogeny, sequences of 65 additional Atlantic skate species were downloaded from the Barcode of Life (BOLD) and Genbank databases. Only sequences originating from the following specialist publications were utilised to minimise inaccuracy with species identification:  Spies et al.
(2006 ), Serra-Pereira et al. (2011) ,  Coulson et al. (2011 ), Mabragan ˜a et al. (2011)  and  Lynghammar et al. (2014). Additional sequences from the starry ray (R. asterias, a Mediterranean endemic species) were also downloaded, as this species is considered closely related to R. clavata (summarised in Table  S3 ). Evolutionary divergence between pairs of species and genera was estimated using the TrN ? I ? C substitution model  (Tamura and Nei 1993). Standard error estimates were obtained by bootstrapping (1000 replicates) in Mega 5. The number of sequences was subsequently collapsed so only unique haplotypes were represented using online fasta toolbox software FaBox  (Villesen 2007). Phylogenetic relationships were then inferred using maximum-likelihood (ML) tree methods with the HKY ? I ? C model  (Hasegawa et al.
However, this work is the result of many years of focused sample collection, represents a substantial effort to collect data from remote and inaccessible areas of Europe and Africa. This difficulty is also reflected in the fact that no samples were collected from the coastal waters of Madeira, a key part of the described range of R. maderensis. The region remains difficult to trawl due to its rough topography, but samples were collected in near-by waters, approximately 100 nautical miles north east of the archipelago. Indeed, further sampling along the coast of north Africa is not only important for assessing the genetic distinctiveness of R. maderensis and establishing the distribution of R. clavata and R. straeleni, but also the elasmobranch fauna of the whole region generally remains rather poorly described  (Bonfil 1994; Compagno 1994 ;  Kroese and Sauer 1998).