dataesr/scientific-paragraphs-categorization huggingface dataset:
2. Dataset 2. Dataset 3. 223 Figure 3. Comparison of available anti-VAMP7 antibodies for exogenous and endogenous immunoprecipitation. (A) Cos cellsoverexpressing GFP-tagged mouse, rat or human VAMP7 constructs were lysed and VAMP7 was immunoprecipitated using indicated specific antibodies ("IP Antibody"). Normal isotype IgG ("IgGM" or "IgGR") and GFP antibody were used as negative and positive control, respectively. Supernatants after immunoprecipitation (SN) and immunoprecipitates (IP) were probed with indicated VAMP7 antibodies (WB primary Ab). (B) VAMP7 was immunoprecipitated from wild-type ("WT") and VAMP7 knock-out (KO) mouse cortex lysates using rabbit anti-VAMP7 TG50 antibody and detected with mouse anti-VAMP7 158. 2. Normal isotype IgG (IgGR) were used as negative control. Inputs (IN), supernatants after immunoprecipitation (SN) and immunoprecipitates (IP) were probed with mouse anti-VAMP7 158.
Raw images of immunoprecipitation experiments for Figure 28 shown in lower panel. Note that although signal intensity and background are different within these two replicates, the relative performance of the different tested antibodies remained the same. DOI: http:// doi. org/10. 5256/f1000research. 15707. d22136028. Dataset 2. Raw images of additional experimental replicates for Figure 2, immunofluorescence experiments. This dataset includes additional images from experimental replicates of the images presented in Figure 2. Immunofluorescence staining was performed as described for Figure 2. Images were taken at 40× objective. Bar, 15μm. DOI: http://dx. doi. org/10. 5256/ f1000research. 15707. d234810 29. Dataset 3.
Recent work on European bass  [14] , Atlantic cod  [18]  and three-spine stickleback  [19, 20]  have already reported a suite of single nucleotide polymorphism (SNP) within or closely located to genes involved in osmoregulation, altogether leading to a wide list of targeted salinity and osmoregulation genes known for teleost fishes (reviewed in Dennenmoser et al. [17] ). Detecting signals of selection linked to salinity variation may benefit the recent advances in the field. Indeed, adaptive genomics is still a fast evolving study field, with the recent development of numerous analytical methods  [8]. These methods are based on different statistical models and assumptions regarding neutral population structure, and then may give results that are not congruent  [21, 22]. The majority of the adaptive genomic studies in wild populations usually focuses on few of them  [17, [23] [24] [25] , and comparisons of various methods on empirical datasets are still lacking  [8] , whereas there were evidences of such need  [12, 26].
In the present study, we investigated the potential for local adaptation to salinity in an exploited marine fish species, the striped red mullet (Mullus surmuletus) along the Mediterranean Sea, using 1153 SNP markers on 47 locations. Due to its enclosed geography and high evaporation rate, the Eastern Mediterranean basin displays high levels of salinity compared to the Western basin and the Northeastern Atlantic  [27]  (Fig. 1 ). In this particular area, we tested the hypothesis that salinity may act as a selective agent for M. surmuletus populations. A previous seascape genetics study using the same extensive marine spatial database showed no genetic differentiation in separated populations, but a longitudinal pattern of isolation by distance  [28]. In this study, part of the wide neutral genetic variation was explained by the sea surface salinity variable, suggesting that M.
Genetic data and SNP calling Extraction of genomic DNA was undertaken using the DNeasy Blood & Tissue Kit (Qiagen) according to the manufacturer's protocol. DNA quality was assessed by running 3 μL of each DNA sample on 1% agarose gels. DNA concentration was determined using NanoDrop 8000. We individually genotyped 541 fish samples from these 47 sites using a genotyping by sequencing approach  [28]. Six 96-plex GBS libraries were constructed using restriction enzyme ApeKI (recognition site: GCWGC) following a protocol modified from Elshire et al. [29] , and sequenced at the Institute of Genomic Diversity at Cornell University using the Illumina HiSeq 2500 (100 bp, single-end reads). Each library was sequenced on a separate HiSeq flowcell lane. Raw read sequences were filtered according to quality base calling, removing sequences with average Phred quality below 25.
Trimming was performed to remove low quality bases at the extremities of the reads (Phred quality below 20). Sequences shorter than 60 bp were discarded from the dataset. For each sequenced library, the number of raw reads and filtered data are provided in Additional file 1: Table  S1  in Supporting Information. SNP calling was performed using the Tassel 3. 0 Universal Network Enabled Analysis Kit (UNEAK  [30] ). The GBS procedure produced individual sequences with a very low coverage, which lead to a high number of missing genotypes in the dataset at the individual level. The Stacks software  [31]  has also been used and showed similarly prohibitive missing data for individual genotype calling. To overcome this bias, we considered pooling together the individual samples belonging to the same site, in order to accurately estimate allele frequency.
Indeed, pooled and individually determined allele frequencies are expected to be similar  [32]. This 'pooling strategy' allowed us to produce a dataset of 47 'pools' (i. e. , sampling sites) containing between nine and eighteen individuals, and whose sequence coverage was >10X and showing a minor allele frequency > 0. 05 in all the dataset. The final dataset contained the allele frequencies of 47 pools at 1153 SNPs. The parameters used in both UNEAK and Stacks are detailed in Additional file 1: Appendix S1.
Population structure Genetic differentiation between the pairs of 47 sites was quantified by the Wright's pairwise F ST , calculated using the R package 'polysat'  [33]. To test for Isolation-by-Distance (IBD), we performed a Mantel test between pairwise F ST, computed from allele frequencies and marine geographical distances, computed as least-cost path distances with infinite resistance values assigned to landmasses. We also performed a Principal coordinates analysis (PCoA) of the 47 sites using the Nei genetic distance calculated from SNPs allele frequencies to analyse the population structure. In order to test for isolation by resistance, we used Mantel tests between genetic differentiation (pairwise F ST ) and environmental distances (maximum Sea Surface Temperature and Salinity). For both environmental variables separately, we calculated pairwise environmental distances as the difference in temperature/salinity between every pair of locations.
A set of 46 MEM axes were computed from the geographic coordinates of our 47 sampling sites using the R package 'PCNM' version 2. 1-4  [46]. The power spectrum of each SNP corresponds to the vector of squared correlations between its allele frequencies in each site and every MEM axes. Deviation of SNPs from the average power spectrum was measured using z-scored, and a list of candidates was obtained with a cut-off of 0. 05. At step 2, Moran Spectral Randomization (MSR) uses a randomization approach to build a null hypothesis of no correlation between SSS max and allele frequencies at each SNP detected as an outlier at step 1, given the power spectra of the candidate outliers. We performed MSR using the R package 'adepsatial' version 0. 0-7 with 199 permutations  [47]. The final set of candidate outliers was obtained as the SNPs significantly correlated to SSS max under a threshold p-value of 0.
Spatial generalized linear mixed models (gINLAnd) The software gINLAnd aims to quantify the correlation between allele counts at each site and the environmental variation using a logistic regression accounting for spatial autocorrelation between samples sites due to population history. For each SNP, gINLAnd fits a generalized linear spatial mixed model using an estimation of spatial covariance between sites as random factor, and the environmental variable as fixed explanatory variable. Spatial covariance is estimated according to the Integrated Nested Laplace Approximation (INLA) method proposed by Rue et al. [48]. For computational optimization, the parameters of the spatial covariance, τ and κ, were calculated on a random subset of 500 SNPs and then taking the average values. For each SNP, we then fitted a full model including the environmental (SSS max ) and the random (τ and κ) variables, and a reduced model including the random variables only.
05 to 0. 2. The number of latent factors (unobserved variables) has to be specified by the user for the analysis. Here, we used K = 1 and K = 2 to correct for population structure since multivariate analyses showed no clustering but isolation by distance (IBD; Additional file 1: Figure  S1 ). We used the implementation of LFMM in the R software using the 'LEA' package version 1. 6. 0 [51]  to conduct the analysis.
96). PCAdapt uses a PCA, a multivariate analysis able to detect population structure from the genetic data. The software constructs a set of axes from the previously generated individual genotypes, which are linear combination of the initial variables (Principal Components, PCs). The PCs are produced on a criterion of maximal variance. PCAdapt extends this analysis by calculating correlations between SNPs and a set of retained PCs  [52]. Candidate SNPs are detected as the ones that correlate significantly to this set of PCs under a specified False Discovery Rate (FDR). The analysis was performed using the version 3. 0. 4 of the R package 'PCAdapt'  [52]. We first assessed the optimal K value (i. e. , optimal number of genetic groups), from 1 to 20, using a screen plot of the proportion of variance explained by each PC using the PCAdapt function.
Blasting outlier sequences We assessed whether any of the 173 candidate SNPs identified by the six EA and PD approaches belong to known annotated genes in the NCBI's nr database  [54]. We blasted all candidate sequences of 80 bp length against the available NCBI genomic resources for teleost, cartilaginous and bony fishes. We retained only the sequences showing a minimal homology of at least 90% and a minimal alignment length of 15 bp, with a E-value of 10 -4. We then investigated the function of these genes according to the UNIPROT database (http:// www. uniprot. org). To ascertain whether a given mutation was synonymous or non-synonymous, the codon containing the SNP variants was translated into an amino acid according to the location of the start codon. From the subset of candidate genes found in the NCBI's nr database, we selected the genes that already known to have an influence in salinity adaptation in other organisms.
Using partial linear regression to correct for spatial genetic patterns in SNP candidates Longitude was previously suggested to have a wide influence on all the genomic variation of this species  [28] , and is strongly correlated to salinity. In order to test whether salinity was still significantly correlated to MAF variation independently of the effect of longitude, we performed a partial linear regression between the minor allele frequencies (MAF) at each location and the salinity gradient observed, for each candidate SNP retained by genome scans methods that do not correct for spatial structure (LM and PCAdapt). To perform this partial linear regression framework, we used the rda function available in the 'vegan' package and we tested the significance of the relationship using 1000 permutations.
SNP dataset Individual genotyping produced 626,867 SNPs. Filtering for an individual average minimal coverage of 5X and an average maximal coverage of 10× kept 1491 SNPs. Thirteen individuals with less than 3000 reads were discarded. Pooling individuals sampled in the same site allowed producing 1153 SNPs for 47 sites, with coverage of 10X and 100% of the sites genotyped at each marker (i. e. no missing data). On average over every loci, 11 individuals contribute to each pool, with a median of 10 and standard deviation of 2. 91. The distribution of the MAF per SNPs was computed to verify the validity of SNPs calling and filtering. This distribution is shown in Additional file 1: Figure  S2. The number of individuals for each site is shown in details in Additional file 1: Table  S3.
Gene ontology of candidate genes The BLAST search of these 173 outlier sequences against the NCBI'nr yielded to a set of 131 outlier sequences successfully identified as belonging to known genes in all NR database, using permissive criteria. Yet, only 30 outlier sequences were kept according to their homology, alignment length and their E-value (see criterion described in Methods). Among these 30 sequences, all were found in teleost and bony fishes, and 11 in cartilaginous fishes, as expected. From these 30 successfully annotated genes, four were already known to potentially play a role in adaptation to salinity. The sequences of the SNP TP106031, TP221669, TP346795 and TP61263 were located into these four genes (Table  2 ). Out of these four candidate SNPs, only the SNP TP106031, detected by PCAdapt, was nonsynonymous.
Indeed, as expected, methods that do not account for population or spatial structure (PCAdapt and LM) detected the largest sets of outliers, whereas gINLAnd, MSOD and RDA, which accounts for it, identified fewer candidates (between seven and eleven). Controlling for spatial structure or IBD is expected to decrease the number of false positive, but also to reduce the power to detect true adaptive SNPs, thus limiting the ability of the methods to detect genomic signals of selection  [75]. Such a control normally allows reducing the False Discovery Rate (FDR), by increasing the number of true positives relative to the total number of positives. This strategy is especially relevant when demographic patterns such as isolation by distance or allele surfing  [76]  may mimic genomic signals of selection along an environmental gradient, which is the case of our dataset since longitude is strongly correlated to salinity (i.
Limits of the study The relatively low number of markers (1153 SNPs) used in our study is expected to only represent a small fraction of the genome. Thus, our adaptive genomic study based on these SNPs most likely leaves out genes possibly involved in salinity adaptation but which do not contain -or are not linked to -any SNPs in this dataset  [77]. In addition, the union of six genome scans methods identified 15% of the SNPs as outliers, which is an extremely high proportion and surely contains a high number of false positives. However, the aim of this study is to provide a large set of outliers, and then test their relevance though the observation of their minor allele frequency distribution and using blast alignments. Hence, a high false discovery rate is not prohibitive here, since the candidate SNPs were not directly considered as adaptive and as we underlined the need to further investigate their impact at the phenotype level.
e. , reducing the FDR while maximizing the power of the analysis). Hence, this decision will strongly depend on the objectives of the studies, but also on the dataset (e. g. , poolseq or individual genotypes, number of genotyped markers) and a priori knowledge (i. e. , is there any population structure or IBD? is the environmental variable correlated to space or other variables?). In the current context of climate changes, adaptation of marine organisms to temperature is of greatest concern  [3]. Yet, salinity is an important variable to consider, since its level is also expected to increase in the future due to reduced precipitations and higher evaporation rates  [36, 79]. Identifying genes associated with key environmental factors, such as salinity and temperature, will help assessing the relative importance of evolutionary adaptation, and thus provide insight on species adaptive potential to the environmental modifications predicted to result from global change  [3].
Acknowledgements We thank  Frida Lasram ,  Jonathan Belmaker ,  Delphine Rocklin ,  Raquel Torres ,  Laure Velez ,  Jennifer Tournois ,  Eva Maire ,  Sebastien Villeger ,  Johanna Brun-Vitelli ,  Manuel Muntoni ,  Claire Seceh ,  Sofiène Ben Abdelhamid ,  Achraf Barbena ,  Itai Granot ,  Ori Frid  and  Shahar Malamud  for their participation to sampling, as well as all the fishermen that provided the fishes. We are grateful to the SMGE platform, and especially  Veronique Arnal  and  Marie-Pierre Dubois  for technical support during molecular analyses, and  Halim Maaroufi  for his expertise on the blast alignments. Finally, we thank  Renaud Vitalis  and  Marco Andrello  for the constructive discussions on the topic.
Funding This study was funded by the  'Total foundation'  through the  SEACONNECT  project, which supported every steps of the project. The  CNRS PICS-Canada  funded courses in data analyses, and the  ANR-10-IDEX_0001-02   PSL , project  RESMOD , covered the publication cost. Part the computations presented in this paper were performed using the Froggy platform of the CIMENT infrastructure (https://ciment. ujf-grenoble. fr), which is supported by the  Rhône-Alpes region  ( GRANT CPER07_13 CIRA ) and the  Equip@Meso project  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche.
While other studies reported negative association of interleukin with risk of B cell NHL  18, 23. The prevalence of these two cancers and the conflicting results regarding the role of inflammation underline that BC and B-cell NHL are particularly relevant to explore the role of inflammation in cancer initiation. Previous studies generally focus on the concentration levels of individual biomarkers, and since these biomarkers are systemic, associations between individual biomarkers and cancer may not be always apparent. The use of lower resolution summary scores may improve statistical power and reveal associations with cancer risk  27, 28. In the present work, we investigate inflammatory scores to ascertain their link with cancers onset. We used data from the EnviroGenoMarkers (EGM) project designed as two nested case-control studies including breast cancer (BC) and Β-cell Non-Hodgkin Lymphoma (B-cell NHL, including multiple myeloma) cases within two cohorts.
Specifically, we constructed our inflammatory score using a large panel of cytokines, chemokines and growth factors measured in prospectively collected peripheral blood samples in 268 participants from the Italian component of the European Prospective Investigation into Cancer and Nutrition (EPIC-Italy) and 492 participants from the Northern Sweden Health and Disease Study (NSHDS). Making the most of the EGM dataset, we examined participant's inflammatory status through the inflammatory score (and a corresponding unsupervised alternative) in relation to cancer onset as a common mechanism in disease initiation and progression. These associations were investigated for each type of cancer (breast cancer and B-cell NHL separately), and after stratification by time-to-diagnosis (TtD).
Results Study population. Key characteristics of the study population are reported in Table  1 , participants were predominantly from Sweden (60. 6%) with a mean age of 52. 8 years (SD = 7. 7). Participants from EPIC-Italy mostly women (74. 3%) tended to report a lower education level, more physical activity, and have a higher alcohol consumption compared to participants from NSHDS. Our study population includes 90 breast cancer (54. 4% from EPIC-Italy) and 248 B-cell NHL (33. 8% from EPIC-Italy) prospective cases which were diagnosed from 2 to 15. 5 years after inclusion. Pre-diagnostic inflammatory score and breast cancer. The breast cancer population includes a total of 167 women (56. 3% from EPIC-Italy) with a slightly higher proportion of cases in both cohorts after excluding individuals with missing data (Supplementary Table  S1 ).
Principal component analysis (PCA) is a mathematical algorithm that reduces the dimensionality of the data while retaining most of the variation in the data set  49. PCA of these 28 inflammatory markers concentrations shows that 19 PCs explained more than 90% of the total variation seen in the dataset (Supplementary Fig. S6 ). As a continuous and hypothesis-free alternative score, we used the scores of the first principal component (PC1, explaining 32. 6% of the total variance of the 28 proteins).
Sensitivity analyses. We fitted our models on data for each cohort separately (EPIC-Italy and NSHDS). To evaluate the impact of follow-up time and consider a potential effect of the preclinical phase of the disease, analyses were also stratified on the time to diagnosis as defined by the time elapsed between blood collection and clinical diagnosis: below or above the median time to diagnosis (6 years) across the entire population of cancer cases. We used the same time to diagnosis cut-off (below or above 6 years) for each type of cancer since the median time to diagnosis vary little across cancer type (5. 84 years in BC and 6. 09 years in B-cell NHL). In these analyses, cases were compared to all controls to preserve statistical power. Analyses were also performed by BC and B-cell NHL histological subtypes.
Acknowledgements The authors wish to thank all the centers that took part in the study and the additional members of the  EnviroGenoMarkers Consortium. For further information on the EnviroGenoMarkers Consortium please visit http://www. envirogenomarkers. net/. This work has been carried out as part of the  7th European Framework Programme  ( FP7) Envirogenomarkers  [ 226756  to S. K. ] project. This work was supported by the  Institut National du Cancer  [ SHSESP14-082  to R. C. ]. This work was funded by the  European Commission  as part of the  Lifepath  project [H2020 grant number  633666 ]. This work was supported by  La Ligue  nationale contre le cancer [Equipe Labellisée LIGUE 2017/CD]).
Specie-specific HRP conjugated IgG (1:10000; Vector Laboratories) were employed as secondary antibodies. The obtained chemiluminescent signals were visualized by ECL SuperSignal West Pico PLUS (Thermo scientific) and normalized to actin by using ImageJ software. Values are reported as relative units (R. U. ) or phosphoprotein/total protein ratio calculated as: (phosphoprotein/loading control)/(total protein/loading control).
Acknowledgments The authors are in debt with  Angela Maria Vergnano  for the helpful discussions and the critical revision of the manuscript. The authors thank  Carlo Corona  for helping with the gelatin zymography as well as  Annalisa Nespoli  and  Manuela Iezzi  for helping with brain cryosectioning. SLS is supported by research grants from the  Italian Department of Education  ( PRIN 2011 ;  2010M2JARJ_005 ) and the  Italian Department of Health  ( RF-2013-02358785  and  NET-2011-02346784-1 ).
This quantitative proxy for recognizing SP contents was introduced by  Worm (1999)  and improved by  Machac et al. (2007)  by introducing a set of intervals and field intensities. A close association between time and frequency domain percentage parameters was observed  (Machac et al. 2007 ) despite the fact that both approaches only provide semiquantitative estimates for mass or volume concentration of SP minerals. It can be explained because both time and fre-quency percentage parameters (frequency effect and magnetization decay) result from mixed contributions of mineral type, grain size and concentrations, therefore they are not simple indicators of concentration itself. These percentage proxies are partly conditioned by acquisition parameters, making the comparison of different datasets no simple matter.