dataesr/scientific-paragraphs-categorization huggingface dataset:
To this end, they used classical fostering approaches, and investigated the effects of prenatal (embryo transfer) and postnatal (cross-fostering) environments in two strains of inbred mice with profound and reliable differences in behavior, namely C57Bl/6J and Balb/cJ mice. They found that C57Bl/6J mice that developed in a Balb/cJ uterus and were reared by a Balb/cJ mother displayed a Balb/cJ phenotype in most behavior tested (exploration, anxiety, spatial learning), while C57Bl/6J mice exposed only to a uterine or a postanatal Balb/cJ environment did not change their phenotype. For the first time, this dataset emphasized the crucial interaction of pre-and post-natal environments in shaping the development of selected behaviors, and further indicated that prenatal environment may prime the developing pup to respond to postnatal environment-most certainly cues delivered by maternal care-in a way that would allow a strainspecific behavior to develop independently of genotype  (Francis et al.
, 2013 ), and we refer readers to the MANGO database that lists the different genes involved in controlling non pathological neurogenesis  (Overall et al. , 2012). In regard to these data, the prevailing view is that adult neurogenesis is a complex phenomenon that is likely to be controlled by the interaction of several regulatory loci involving many genes, and not one master regulatory locus acting as a switch to turn neurogenesis ''on'' or ''off''. In parallel, many studies have shown that different life events were capable of regulating or controlling neurogenesis, and both adult experience  (Opendak and Gould, 2015)  and early life events (for review, see  Fenoglio et al. , 2006; Korosi et al. , 2012; Lucassen et al. , 2013; Hoeijmakers et al. , 2014)  largely contribute to its levels, the impact of early life events appearing more pervasive and permanent, certainly for they act during developmental periods.
The double-stranded oligonucleotides were obtained by annealing with the complementary oligomers. The DNA probe used in EMSA was 5 CGAA-CACCGGTACCCAGCTTTTGTTCCCTTTA, bearing or not a 3 biotin moiety for the ss probe, or the same oligomer hybridized with its complementary oligomer for the ds probe. The same ss oligomer biotinylated at 3 , 5 or both ends was used as competitor in the EMSA on radiolabeled DNA probe; the extent of biotinylation was identical for the 3 probes, as measured in a control EMSA in the presence or not of streptavidin (data not shown). The biotinylated 75 bp fragment used in pull-down experiments was amplified by PCR with Phusion Hot Start (Thermo Scientific) from pBluescript-KS-II(-) with 5 -biotinylated reverse primer 5 GCGTTATCCCCTGATTCTGTGG and forward primer 5 CGTTCGGCTGCGGCGAGCGGTATCAGC and purified by Gel and PCR Clean-Up System (Promega).
ACKNOWLEDGEMENTS We are indebted to  P. R. Hamann  ( Wyeth Research ) for the gift of calicheamicin ␥ 1, to  Pr Eric A. Hendrickson  ( University of Minnesota Medical School, Minneapolis, USA ) for the gift of HCT116 Lig4 -/-cell line, to  Dr Dik C. van Gent  ( Cancer Genomics Center, Rotterdam, The Netherlands ) for the gift of pDVG94 plasmid, to  Dr Muriel Golzio  ( IPBS, Toulouse, France ) for the gift of Cy5-LNA ssO and to  Rachael V. Walker  ( Babraham Institute, Cambridge, UK ) for helping with the generation of RPE-1 hTERT XRCC6 +/-and RPE-1 hTERT XRCC6 Venus/-. We dedicate this work to the memory of our colleague and friend Pr Christophe Cazaux who died accidentally in early August 2015. We would like to acknowledge his great contribution as professor in genetic and cell biology, investigator in genomic instability and coordinator of both cancer research funding and popularization in Toulouse.
Some studies are vague in their definition of a potential focus species as well as an ecological question, and often the focus species is then defined afterwards depending upon what species were observed during surveys. The overall objective of many studies is then often limited to predict where and when cetaceans are present (e. g. [49] ). However, some studies do attempt to explain this presence further by linking them to features of the physical and biological oceanographic properties of their environment  [48] ; these properties have either been assessed using remote sensing data (e. g. sea surface temperature, sea surface height), variables measured in situ (e. g. depth, mixing layer thickness) or even modeled environmental data such as prey densities  [50] , hence allow to cover a very wide range of spatial and temporal scales; see Table  4  for further examples.
It is also stressed that the use of technologies in the field may also be complemented by, baseline cetacean habitat information gathered through the application of pre-existing data sets, particularly those gathered long term. When complemented with oceanographic information, gathered through the use of technologies or in situ, these have the potential to be of benefit to pre-existing data sets consisting of cetacean sightings and distribution patterns  [79]   [80]. Additionally, pre-existing data sets of species specific distributions have the potential to provide much insight into distribution in the way of being used as predictor tools for distribution (e. g. [2]  [71]  [81] ), which ultimately can help focus a study for a specific species or location. Additionally, these can now be combined with freely accessed oceanographic datasets through ocean portals, therefore it is possible to conduct preliminary studies based on all pre-existing data.
GWAS analysis According to the recommendations made by Teyssedre et al. [6] , data were analyzed using a mixed model that takes pedigree kinship into account. For each SNP (1 to 284,643) and each trait (1 to 16), the following mixed model was applied: Z = 1µ + Xα + E, with V(E) = Aσ 2 g + Iσ 2 e , where Z is the vector of the sire performances; µ is the general mean; X is the incidence matrix of genotypes for the SNP evaluated; α is the allele substitution effect of the SNP for the trait  [7] ; E is the vector of residuals with variance-covariance matrix V(E); A is the pedigree kinship matrix; I is the identity matrix; σ 2 g is the genetic variance and σ 2 e is the environmental variance. This model was fitted using the BLUPF90 program  [8]. Chromosome-wide thresholds (P < 1 %) to test the H0 hypothesis, i.
QTL × environment interaction For each trait, the GWAS analysis was repeated separately, as above, for the four datasets for each condition (egg collections at 50 and 70 weeks of age, and HE and LE diets) in order to estimate the effect of the top SNP for each QTL in the two environments, α 1 and α 2 , i. e. egg collection at 50 vs. 70 weeks of age and HE vs. LE diet. QTL × age and QTL × diet interactions were tested by comparing the two corresponding estimates using a Z test statistic. Variances explained by the top SNPs for each condition were calculated, and the residual variances for the different conditions were compared with a Fisher test to verify their equality (which was always accepted). The Z test statistic was calculated as follows: where σ 2 is the average residual variance and n is the average number of sires over the two conditions.
Z followed a normal distribution because n was large. However, if the interaction effect is large, some QTL may not be detected by analyzing the whole dataset. Indeed, Z = |α 1 -α 2 | σ × √ n, additional QTL, i. e. QTL that were not detected when analyzing the whole dataset, were identified by analyzing the four within-condition datasets separately. In such cases, we also tested the QTL × environment interaction. The QTL were defined using the results of the withincondition GWAS analyses, and the estimated effects of the top SNP for each QTL were compared using the Z test statistic described previously.
GWAS analyses For egg production traits, the GWAS analysis on the whole dataset resulted in 1202 significant tests at the 1 % chromosome significance level and identified 861 SNPs with an effect on at least one egg production trait. For egg quality traits, it resulted in 8116 significant tests at the 1 % chromosome significance level and identified 5384 SNPs with an effect on at least one trait. Analyses of the four subsets estimated the effect of each SNP for each of the four conditions investigated (HE diet, LE diet, egg collections at 50 and 70 weeks of age). Correlations between estimates of SNP effects based on HE and LE diets ranged from 0. 16 to 0. 80 (Table  3 ). They were lower for egg production traits than for egg quality traits. Figure  1  shows an example of the correlations between estimates of SNP effect for the HE and LE diets for egg shell color and EPR1.
QTL detected To analyze the genetic architecture of each trait, all the data were analyzed together, whereas to determine which QTL were specific to performances for egg collection at 50 and 70 weeks of age or with HE and LE diets, their detection was carried out by using the data divided into four subsets. To calculate the number of QTL detected and to give them a name, we considered the QTL according to their chromosomal localization, regardless of which traits they influenced. For QTL that were identified by analyzing the whole dataset, 11 had a significant effect on at least one egg production trait and 58 QTL had an effect on at least one egg quality trait (see Additional file 1). When GWAS was applied separately to the four subsets, 19 additional QTL with a significant effect on at least one egg production trait and 46 additional QTL with an effect on at least one egg quality trait were detected (see Additional files 2, 3).
Egg production (EPR, EPR1, EPR2, EPR3) traits For egg production traits, analysis of the whole dataset identified 11 QTL that had an effect on at least one trait and separate analysis of the four subsets detected 21 additional QTL. Previously, several QTL for egg production have been reported  [11, 12]  that are located close to QTL65 and QTL67 identified here. For EPR1, 23 QTL were detected on 15 chromosomes, which indicates that it has a polygenic determinism. Together, these QTL explained 34 % of the variance of EPR1, regardless of environmental condition. For EPR and EPR2, nine and six QTL were detected, which explained 26. 8 and 24. 1 % of the variance, respectively. Although fewer QTL were detected for EPR and EPR2, they had a larger effect when the data are considered together. QTL7 had an effect on the four traits (EPR, EPR1, EPR2 and EPR3) but its effect was diet-dependent.
Egg weight (EW) and egg form (SLE and ESshape) traits Analysis of the whole dataset identified 13 QTL that had an effect on at least one of the following traits EW, SLE and ESshape and analysis of the four subsets separately detected 14 additional QTL. Together, these QTL explained 65. 4 % of the variance of ESshape, 55 % of the variance of EW but only 28. 7 % of the variance of SLE. QTL106 was common to all three egg weight and shape traits and was located on chromosome 26, between 1. 2 and 1. 3 Mb. Two QTL for EW have been reported on Three other QTL common to EW and SLE were detected (see Additional file 1), of which two, QTL121 and QTL122, were located on chromosome Z. This agrees with studies that have shown that chromosome Z plays a role in the regulation of EW  [14, 15].
Egg shell color (ESC, RSS, LSS and YSS) traits For egg shell color traits, analysis of the whole dataset identified 22 QTL that had an effect on at least one trait and analysis of the four subsets separately detected 17 additional QTL. Several QTL have been reported for egg shell color  [16, 17]  that are localized close to QTL73 and QTL10. Together, these QTL explained more than 100 % of the variance of ESC and LSS, which, although their effects may be overestimated, suggests the existence of other interactions such as dominance or epistasis, which were not considered in the model. The number of QTL detected and their distribution along the genome suggest that egg shell color has a polygenic determinism. Most of the QTL for ESC also had an effect on RSS and YSS or on YSS, but only a few had effects on RSS, LSS and YSS.
Egg shell solidity (ESSTIF and ESS) traits For egg shell solidity traits, analysis of the whole dataset identified seven QTL that had an effect on at least one trait, i. e. five for ESSTIF and two for ESS but no QTL was common to both traits. Analysis of the four subsets separately detected seven additional QTL that had a significant effect on at least one of these traits, i. e. two for ESSTIF and five for ESS. Overall, the same number of QTL was found for ESS and ESSTIF, but the total variance explained by these QTL was larger for ESSTIF than for ESS. QTL103 was common to both traits and had a pleiotropic effect.
Internal egg quality (YOLKIND, HU and EMTSP) traits For internal egg quality traits, analysis of the whole dataset identified 18 QTL and analysis of the four subsets separately detected 15 additional QTL that had a significant effect on YOLKIND and/or HU. One of these QTL (QTL123) was previously reported by  [18]. YOLKIND, for which 16 QTL were identified, is the trait that had the largest proportion of variance explained, followed by EMTSP for which nine QTL were detected. Among the 33 QTL detected for the three internal egg quality traits, QTL33 was common to HU and YOL-KIND and QTL100 was common to HU and EMTSP. For YOLKIND, QTL88 and QTL89 were localized on chromosome 20 between 11 and 12 Mb, nearby a QTL detected for EMTSP. Previously, a QTL for HU, on chromosome 20 at 11 Mb was reported by  [13].
This hypothesis was investigated using in silico approaches. Searching bibliographic data, we noted a DALI structure similarity search (26) that identified a P. aeruginosa protein, AmiC, which shows structural homologies with the binding domain of natriuretic peptides receptors  (27, 28). Using tools from the Protein Data Bank in Europe (http://www. ebi. ac. uk/pdbe/), we investigated in detail the structure of P. aeruginosa AmiC bound to its known ligand acetamide. We observed that it shares high structural similarity with the natriuretic peptide receptor subtype NPR-C bound to one of its agonists, the atrial natriuretic peptide (ANP) (data not shown). This supports the hypothesis that AmiC could act as a bacterial natriuretic peptide sensor. The three-dimensional (3D) structure of P. aeruginosa AmiC (Protein Data Bank [PDB] ID: 1PEA) (29) was compared to that of the human NPR-C receptor (hNPR-C) (PDB ID: 1JDP)  (30).
Using the STAMP software  (31) , we superimposed an AmiC monomer onto each protomer of the structure of an hNPR-C dimer in complex with CNP. Although the sequences of these proteins share only 19% identity and 26% similarity, these two proteins present an overall similar 3D structure in the homodimeric form (Fig. 3A ), particularly at the interface between monomers, which is considered the binding cleft of hNPR-C. The highest similarity was found in the helix of AmiC, corresponding to the region involved in the binding of CNP to hNPR-C. Next, we split the AmiC monomer into two parts, corresponding to the two lobes of the protein. The first lobe consists of amino acids 8 to 123 and 261 to 338, and the second lobe consists of amino acids 124 to 260 and 339 to 375 of AmiC. Then, we superimposed each lobe on the 3D structure of hNPR-C (Fig.
3B ). Each lobe of hNPR-C and AmiC showed a high level of secondary structure conservation with similar 3D positions, reinforcing the concept of structural homology between these two proteins. Using GASH structural superposition software  (32)  on each lobe of hNPR-C and AmiC, we observed that each of these two proteins has 254 residues estimated to be involved in equivalent secondary structures, covering 61. 7% of the AmiC sequence. The hinge between the two lobes was essentially formed with flexible loops. Once the structures were superimposed, we looked at the amino acids involved in the binding of CNP, supporting the hypothesis that AmiC and hNPR-C bind the peptide in a similar fashion. Table  S1  in the supplemental material lists AmiC amino acids that are potentially involved in CNP binding and the corresponding amino acids involved in the binding of hNPR-C to CNP, as described by He et al.
S3 ). We then tested the interaction of AmiC with natriuretic peptide receptor agonists and isatin using microscale thermophoresis (Fig. 5A ). AmiC bound to CNP tightly, showing a dissociation constant (K D ) of 2. 0 Ϯ 0. 3 M (Fig. 5 ). In contrast, we observed that BNP possessed no affinity for AmiC (Fig. 5A ). Isatin could also bind to AmiC, although in this case, the interaction was not so strong, showing a K D of 600 Ϯ 200 M (Fig. 5A  and B ). Additionally, we measured the interactions of other natriuretic peptide receptor agonists. sANP, a specific NPR-A agonist, showed a weak interaction with AmiC which had clearly not proceeded to completion at 900 M (Fig. 5B ), the highest concentration that we were able to assess. These data clearly predict that the K D is greater than 600 M. Conversely, our initial experiments on osteocrin (a specific NPR-C agonist) showed a very strong interaction with AmiC, apparently tighter than we were able to determine using the available instrument.
DISCUSSION We have shown that P. aeruginosa is sensitive to micromolar concentrations of natriuretic peptides (i. e. , BNP and CNP) and re- PA14 control (PA14), the ⌬amiC strain, and the amiC complemented strain (amiC Comp), not exposed to peptide or exposed to CNP (10 Ϫ7 M) under dynamic conditions, at 37°C for 24 h in LB broth. Biofilms were stained with SytoX and observed by confocal laser scanning microscopy. (B) COMSTAT analyses of biofilms of P. aeruginosa PA14 control, the ⌬amiC strain, the amiC complemented strain, and the AmiC ϩ strain. (C) COMSTAT analyses of biofilms of the P. aeruginosa PA14 control, the ⌬amiC strain, and the amiC complemented strain exposed to CNP (10 Ϫ7 M), to BNP (10 Ϫ7 M), or to neither of them. Data are the means for 27 samples from 11 independent experiments for the PA14 control, 11 samples from four independent experiments for PA14 exposed to CNP, nine samples from three independent experiments for PA14 exposed to BNP, 19 samples from seven independent experiments for the ⌬amiC control strain, 12 samples from four independent experiments for the ⌬amiC strain exposed to CNP, eight samples from three independent experiments for the amiC strain exposed to BNP, 24 samples from six independent experiments for the amiC Comp control strain, and 12 samples from four independent experiments for the amiC Comp strain exposed to CNP.
aeruginosa inner membrane. Indeed, AmiC belongs to the type I periplasmic binding fold protein family, and the Uniprot database claims that AmiC is located in the periplasmic space (http:// www. uniprot. org; reference P27017). One protein being located in multiple bacterial compartments to undertake unrelated functions is not unprecedented. Indeed, the EF-Tu protein, which acts in the bacterial cytoplasm as a ribosomal elongation factor, was shown to migrate upon stress to the plasma membrane and acquire new functions as a sensor molecule  (45). In P. aeruginosa, EF-Tu, after migration into bacteria membrane, also behaves as a plasminogen-binding protein  (46). To support this hypothesis, we observed, using AmiC antibodies, that while AmiC protein is mainly located in the bacterial cytosol, a proportion of AmiC is associated with the inner membrane compartment (see Fig.
Observations were made using a confocal laser scanning microscope (LSM 710 confocal laser-scanning microscope; Zeiss). The biofilm thicknesses and corresponding biovolumes were estimated by measuring field samples from at least 3 independent experiments using COMSTAT software  (62). Biofilm formation under static conditions. The static bacterial biofilm formation activity was determined after 24 h of development. Precultures of P. aeruginosa, containing the pSMC2. 1 plasmid harboring the green fluorescent protein (GFP) gene, were grown overnight at 37°C. An aliquot of preculture (100 l) was then added to 5 ml of LB medium supplemented with 400 g/ml kanamycin. Tested molecules or the corresponding excipient were added after 2 h of subculture. Three hours later, bacteria were collected by centrifugation (7,000 ϫ g, 10 min).
The supernatant was then removed, and the pellets were resuspended in 25 ml of physiological solution (0. 9% NaCl) and adjusted to an OD 600 of 0. 1. Sterile glass slides, previously cleaned with 4% TFD4 in water (Dutscher, Brumath, France), were layered in petri dishes and covered by the bacterial suspension. The slides were then incubated for 2 h at 37°C under static conditions. At the end of the attachment period, the physiological solution was replaced by LB medium and incubated for 24 h at 37°C in static condition. The slides were then rinsed twice in 25 ml sterile phosphatebuffered saline (PBS; 0. 1 M; pH 7. 4), and bacteria were heat fixed. Biofilms were observed using a confocal laser scanning microscope (LSM 710; Zeiss). Images were treated using a 3-median filter and segmented using Zen 2009 software (Zeiss).
The volumes and thicknesses of the biofilms were estimated as described elsewhere  (62). All 2D images are representative of 15 observations. In silico studies. STAMP software  (31)  was used within VMD (63) on a single computer. GASH structural superposition software  (32)  was used on the web server (http://sysimm. ifrec. osaka-u. ac. jp/gash/). Potential ligand/protein interactions were investigated in silico by the molecular docking technique using the amidase sensor protein of P. aeruginosa AmiC (PDB ID: 1QO0). Essential hydrogen atoms, Kollman united atom type charges, and solvation parameters were added with the aid of AutoDock tools  (34). Affinity (grid) maps of 20-by 20-by 20-Å grid points and 0. 375-Å spacing were generated using the Autogrid program  (34). AutoDock parameter set-and distance-dependent dielectric functions were used in the calculation of the van der Waals and electrostatic terms, respectively.
Instrument parameters were adjusted with 50% LED power and 20 to 40% MST power. Data from three independently pipetted measurements were analyzed (NT. Analysis software version 1. 5. 41; NanoTemper Technologies) using the signal from thermophoresis and temperature jump. Following data analysis, the thermophoresis signals were fitted to the formula for K D from the law of mass action: f(c) ϭ unbound ϩ (bound Ϫ unbound) ϫ ͑[AmiC] ϩ c ϩ K D Ϫ ͱ ([AmiC] ϩ c ϩ K D ) 2 Ϫ (4 ϫ [AmiC] ϫ c)͒ 2 ϫ [AmiC] where f(c) is the observed thermophoresis signal, "unbound" is the signal in the absence of ligand (normalized to 0), "bound" is the signal at infinite ligand concentration, c is the concentration of ligand in the same units as AmiC concentration, and K D is the dissociation constant. The data were fitted using GraphPad Prism version 5, and figures for thermophoresis were generated using this software.
For cANF 4-23 , incubation with AmiC resulted in a dose-dependent increase in AmiC fluorescence, which confounds the MST signal. This increase was therefore measured using the MST device, and the data were fitted to the above equation. To confirm that the fluorescence increase was related to binding, samples were heated to 95°C for 10 min with 2% (wt/vol) SDS and 20 mM dithiothreitol (DTT) and retested: addition of cANF 4-23 no longer increased fluorescence. Statistical analysis. The nonparametric Mann-Whitney test was used to compare the means within the same set of experiments.
ACKNOWLEDGMENTS We thank  Magalie Barreau  and  Olivier Maillot  for technical assistance. We thank  Christine Farmer  for linguistic insight for the manuscript. T. Rosay  is a recipient of a doctoral fellowship from the  French Ministry of Research (MRE). This work was supported by grants from the  Communauté d'Agglomération d'Evreux , the  Conseil Général de l'Eure, European Union  (FEDER no. 31970 ), the  French Association "Vaincre la Mucoviscidose " and the  InterReg IVA PeReNE  project.