dataesr/scientific-paragraphs-categorization huggingface dataset:
TAXONOMIC COMPOSITION OF BACTERIAL COMMUNITIES At the division or sub-division level, 10 taxa included altogether more than 96% of all sequences present in Dataset_3 (Figure  2 ; Table  S2 ). Proteobacteria was the most abundant division in this dataset and alone represented above 85% of the total number of reads (Figure  2 ; Table  S2 ). Within them three taxa (Gamma-, Delta-, and Alphaproteobacteria) corresponded to 77% of the total number of amplicons (Table  S2 ). Gammaproteobacteria were remarkably over-represented in short-term samples (MAR-A/W-IV; EM-W-II and MAR-A-V, Figure  2 ; Table  S2 ). Epsilonproteobacteria were also highly abundant in short-term samples (MAR-A/W-IV and MAR-A/W-V), while not exceeding 2% of total sequence numbers in long term samples. They were particularly dominant (98%) in the shortterm wood sample from M-G on MAR.
While almost absent from short-term samples, Delta-and Alphaproteobacteria represented a significant fraction of reads in all long-term samples. Other taxa represented below 30% of reads per sample. For further community analyses at the OTU level, we focused on the most abundant OTUs, which represented above 1% of the total number of sequences in the dataset (>3000 amplicons; Table  S3 ). 15 OTUs within the Proteobacteria and 1 within the Bacteroidetes matched this criterion, representing 131712 sequences (almost 43% of the total sequences) (Figure  3 ; Table  S3 ). All were related to marine bacteria. Four major OTUs (OTU_00201, OTU_11277, OTU_00411 and OTU_00023) were abundant in almost all long-term samples and constituted altogether 38 to 86% of the total number of sequences in each sample (Table  S3 ).
IDENTIFICATION AND DISTRIBUTION OF BACTERIA RELATED TO CHEMOSYNTHETIC SYMBIONTS A total of 30607 reads within 34 OTUs (∼9. 2 % of the Dataset_2) have been identified as potential symbionts (>97% of identity with sequences in local database of symbionts) while 21612 reads, distributed in 16 OTUs, had the "symbiont" keyword in at least one of their ten best blast hit definitions (∼6. 5    S3. Refer to Table  1  for sample ID. % of the Dataset_2). Putatively related symbionts were distributed within 9 potential metazoan host families. Above 22000 belonged to relatives of symbionts associated with Rimicaris shrimps, Idas mussels and Lyrodus shipworms. A phylogenetic tree was computed using representative sequences of potential symbiont OTU, one best blast hit from the local database of symbionts and four best blast hits from public databases (Figure  S3 ).
Only the OTUs displaying the keyword "symbiont" within at least one out of ten best blast hits (public database) and above 97% sequence identity with a symbiont sequence were retained as potential candidates for free-living forms of symbionts (Table  S5 ). We obtained 21612 sequences representing 16 OTUs related to symbionts (Table  S5 ). OTUs related to Rimicaris-associated Epsilonproteobacteria (97. 7-99% identity) represented 9693 reads, followed by Idas-associated Symbiont G and Bacteroidetes (6888 and 2558 reads with above 97. 4 and 99% identities, respectively), and Lyrodus symbionts-related OTUs (1392, above 97. 4% identity). Symbionts of Tubificoides benedii (589 sequences with 99. 7% of identity), relatives of bacteria associated with Siboglinidae polychaetes (487, 97. 6-99. 7% identity) and Thyasiridae bivalves (5 sequences only with 97.
COLONIZATION OF SUBSTRATES BY SYMBIONT-RELATED BACTERIA The sequence dataset was screened for sequences related to chemosynthetic symbionts of metazoans. Based on percentage similarity (97%) criterion with documented symbionts, we retained sequences closely related to documented symbiotic bacteria as reasonably supported candidates. Despite that the short length of reads hampers the quality of phylogenetic reconstructions, the combination of sequence similarity and phylogenetic relatedness appears a rather conservative approach. Almost 70% of symbiont-related sequences were found on pine wood (Table  S1 ). The higher diversity within alfalfa samples may indeed "dilute" symbiont-related sequences in higher numbers of other bacterial OTUs, making their discovery less likely. Putative freeliving forms of symbionts or symbiont-related bacteria from five host metazoan families were identified.
Finally, the analyzed hypervariable region of 16S rRNA seems to be too short to be a good target for highly specific probes. Standardized devices are currently being deployed in other geographical regions and in other habitats. There is a high potential to improve the protocol we applied in our study with the increasing length of pyrosequencing reads, thus making the identification of microbes more reliable. For better understanding of the influence of environment on the microbial communities, the chemistry of water should be monitored precisely in the proximity of our colonization devices. Table  S3  | total number of V5-V6 sequences at OTU identity threshold of 97% in the most abundant OTUs, represented by more than 1% of the total number of sequences in the dataset (>3000 amplicons). OTUs were assigned to different bacterial phylotypes by Blast on SILVA 115 database with OTU identity threshold at 97%.
The relative percentage was normalized by the total number of the most abundant bacterial sequences per site. Refer to Table  1  for sample ID. Table  S4  | Estimation of the influence of each variable and factor and their respective significance in redundancy analysis (RDA), assessed using ANOVA permutation tests in R (R Development Core Team, 2013). Sample IDs are described in Table  1. Table  S5  | The total number of sequences of symbiont-related OTUs identified by the "symbio" key word in at least one of their four best BLAST hit definitions or high similarity with a sequence from the symbiont database and confirmed by the phylogenetic proximity. Only OTUs displaying a symbiont sequence as their closest relative on the phylogenetic tree were retained. Sample IDs are described in Table  1.
ACKNOWLEDGMENTS We thank captains, crew and chief scientists of RV's, Pourquoi pas?, Meteor, Atalante, Polarstern,  James Cook ,  Pelagia and Belgica  and teams operating ROVs Victor 6000 (Ifremer, France),  Quest 4000  ( MARUM, Bremen, Germany ) and submersible Nautile (Ifremer, France) and also people on board from all cruises participating in the deployment and recovery of CHEMECOLIs. We thank  Françoise Gaill  who initiated the  CHEMECO EuroDEEP ESF program. This research was supported by  UPMC ,  ANR DeepOases, CHEMECO EuroDEEP ESF, GDR DIWOOD, ITN Symbiomics  and in Portugal by European funds (COMPETE) and by national funds through the  Portuguese Science Foundation  ( FCT-EURODEEP/0001/2007  and  FCT-PEst-C/MAR/LA0017/2013 ). Kamil Szafranski  was funded through a  Ph. D. grant from the Marie Curie Actions Initial Training Network  (ITN) SYMBIOMICS (contract number  264774 ).
Sequence Data Processing and Taxonomic Classification Read-pairs from 16S rRNA amplicons were quality-filtered and overlapped using usearch (options fastq_truncqual = 10, fastq_maxdiff = 5, fastq_maxee = 0. 5) and truncated to a length of 253 nt representing the expected amplicon length between primers  (Edgar, 2013). ITS read-pairs were trimmed to remove the reverse primer using cutadapt  (Martin, 2011)  before being overlapped using the same parameters, without truncation, since large numbers of "staggered" pairs were encountered and amplicon lengths found to vary considerably. All qualityfiltered overlapped sequences from 16S rRNA and ITS amplicons were merged across datasets and clustered into OTUs at 97% sequence similarity using vsearch  (Rognes et al. , 2015). Clustering included de-replication, sorting by abundance (descending and not retaining singletons), then clustering into OTUs at 97% sequence similarity and finally chimera filtered using the uchime reference-based method using the ChimeraSlayer reference database, followed by de novo filtering.
Representative prokaryotic and fungal OTU sequences were aligned to the SilvaMod v106 and UNITE reference databases, respectively, using blastn (v. 2. 2. 25+ task megablast) and taxonomically classified using CREST with default parameters  (Lanzén et al. , 2012). The resulting taxon distribution was studied at order rank as determined by CREST for the fungal ITS datasets, and at family rank for prokaryotic taxa of the 16S rRNA datasets. Taxa of lower ranks detected that lacked child nodes at order or family level were manually included into the respective dataset. Relative taxon abundances derived by CREST were used in further analysis (the sum of read abundances mapped to OTUs classified to a particular taxon or its child nodes in a particular sample classified, divided by total sample reads) and plotted using the R package ggplot2  (Wickham, 2009).
Structural Equation Modeling (SEM) and Statistical Analyses Multivariate statistics, calculation of diversity indices and visualization was performed using the R vegan package  (Oksanen et al. , 2015). Rarefied richness values estimating the expected richness at the lowest sample-specific sequencing depth were used to compensate for the considerable variation in read numbers. OTU distribution across samples was transformed into relative abundances using the function decostand. Community composition between samples was thereafter compared using Bray-Curtis dissimilarities. Non-metric multidimensional scaling (NMDS) was carried out using metaMDS. Correlation analyses between continuous parameters or taxon abundances; with factors such as treatment (Q5), land-use (Q5), bedrock and elevation zone; were determined using group-wise ANOVA and Tukey's Range Test (Honest Significant Difference).
Nutrient concentrations were log-transformed and compaction measured standardized prior to model fitting. To verify and extend the results from the SEM analysis, targeting effects of abiotic parameters on taxon composition rather than diversity (Q1), correlations between explanatory continuous variables and taxon abundances were determined using Kendall's rank correlation. Bonferroni correction and group-wise confirmation in the M and V subsets was also carried out for these analyses as described above. Further, Pearson correlations were determined between diversity estimates of different groups (prokaryotes, fungi and macrofauna; Q4). Co-occurrence network analysis (Q4) based on all taxa (merging ITS and 16S rRNA datasets) with average relative abundance above 0. 01% was performed with the R package igraph  (Csardi and Nepusz, 2006)  using Kendall rank correlation.
Influence of Environmental Parameters on Individual Taxa The SEM and NMDS analyses described above could only take into account correlations to overall diversity and community   1 ) and corresponding questions targeted. Latent variables are illustrated as ellipses, exogenous variables are as boxes and theoretical constructs as boxes with dashed lines. Causal links indicated as significant in the total or group-wise (M only) best-fitting maximum likelihood solution to the model (see Table  S4 ) are indicated in black while others are indicated as gray. Positive or negative correlations are indicated by ± signs and when differing between the fit for the total dataset and M both are indicated. structure, respectively. In order to study impacts on individual taxa of parameters determined to influence or be influenced by the belowground community, we carried out individual rank correlations to relative taxon abundances.
These were not possible to model and fit with the data considered here. An alternative interpretation explaining the negative correlation between production and belowground activity is that highly productive sites were exposed to more intensive grazing, thus limiting litter decomposition. Grazing pressure was not measured or included in the model. To do so is challenging, as animals were free to move between mountain sites. However, grazing would also explain why production appeared positively influenced by soil compaction, which otherwise is counter-intuitive.
1. 12 1. 12 Digital traceability has transformed the context of the collaboration between social and natural scientists ant its agenda should change accordingly. The problem is not anymore to simulate data that would be too expensive to collect. The problem is to handle an avalanche of traces whose magnitude and diversity is unprecedented for the social sciences For the first time in their history, social scientists have continuous information about their objects and the last thing they need are models that break them in micro/macro oppositions. 1. 13 1. 13 This does not mean, of course, that the modeling tradition of natural sciences ceases to be relevant for the study of collective life. Quite the contrary! Such experience is crucial to develop the new methods necessary to handle larger and more diverse datasets.
Obinutuzumab serum concentrations with 1600/800 mg were compared with a 1000 mg fixed-dose regimen (D1, D8 and D15, C1; D1, C2-C8) using pharmacokinetic modeling simulations. Factors related to CD20-antigenic mass were more influential on obinutuzumab pharmacokinetics with 400/400 versus 1600/800 mg. Higher serum concentrations were observed with 1600/800 versus 400/400 mg, irrespective of CD20-antigenic mass. Tumor shrinkage was greater with 1600/800 versus 400/400 mg; there was no significant increase in adverse events. Fixed dose 1000 mg with an additional C1 infusion resulted in similar serum concentrations to 1600/800 mg in model-based analyses. The obinutuzumab 1000 mg fixed-dose regimen identified in this exploratory analysis was confirmed in a full covariate analysis of a larger dataset, and is undergoing phase III evaluation.
Patients and study designs Pharmacokinetic data for analyzing optimal obinutuzumab dose and schedule were derived from iNHL and aggressive NHL (aNHL) patients participating in the phase I/II GAUGUIN and phase Ib GAUDI studies. In GAUGUIN phase I (dose escalation), 21 heavily pre-treated patients with relapsed/refractory CD20positive iNHL received obinutuzumab monotherapy at a dose of 50/100, 100/200, 200/400, 400/800, 800/1200, 1200/2000, or 1600/800 mg (8 x 21-day cycles; n=3 per cohort). The former dose of each regimen was administered at first infusion (D1, C1) and the latter was infused for the remainder of the regimen (D8, C1; D1, C2-C8). 26 he dose selection for GAUGUIN phase II was based on the safety, efficacy and PK results obtained during the phase I dose escalation. During phase II, iNHL (FL) or aNHL [DLBCL, mantle cell lymphoma (MCL)] patients were randomized to obinutuzumab 400/400 mg or 1600/800 mg (former dose of each regimen administered on D1 and D8, C1; latter dose administered on D1, C2-C8; 21-day cycles).
Pharmacokinetic predictions to select an optimal dosing regimen Based on PK data collected during phases I and II of GAUGUIN, the 1600/800 mg dose of obinutuzumab was identified as likely to produce the highest serum concentrations of obinutuzumab in NHL patients. This decision was also supported by the stronger response in iNHL patients with the 1600/800 mg dose (55%) than with the 400/400 mg dose (17%), coupled with more marked decreases in tumor size at the higher dose and at higher obinutuzumab concentrations (Figures  2B  and 5 ). However, a single dose for all infusions would be significantly more convenient and easier to administer than a bodyweight-or size-adjusted dose. A dose of 1000 mg with three infusions during cycle 1 was then proposed. Under such a regimen, patients would receive a cumulative obinutuzumab dose of 3000 mg in cycle 1, similar to the 3200 mg received during cycle 1 by patients randomized to the 1600/800 mg dose arm of GAUGUIN phase II.
Database of genes associated with ESSENCE Several genetic databases provide clinical and functional annotation of genes associated with early onset neurodevelopmental disorders. The Online mendelian Inheritance in man (OmIm) database catalogues more than 5000 human genetic diseases (http://www. omim. org/). Decipher (http://decipher. sanger. ac. uk/) and the Database of genomic Variants (http://dgv. tcag. ca/dgv/app/home) are interactive Web-based databases which incorporate a suite of tools designed to aid the interpretation of genomic variants. Two databases of genes associated with aSD are updated regularly: autismKB (http://autismkb. cbi. pku. edu. cn) and SFarI gene (https://gene. sfari. org) (36). a total of 197 genes are included in both databases, and 481 are additionally included in either one or the other (255 in autismKB and 226 in SFarI gene).
The main difference between the two databases concerns the excitatory and inhibitory synapses (62-64). In addition, genes associated with epilepsy, such as SCN1A, which encodes a voltage-gated sodium channel, were also found mutated in patients with aSD (7). While different, these pathways most likely affect neuronal homeostasis at the end point  (17, 65). Some suggest potential drug targets; indeed some early clinical trials are ongoing to determine whether targeting some such proteins could improve the symptoms of patients (see  (66, 67)  for reviews).
Acknowledgements-I want to thank Steve hyman and roberto Toro for their helpful reading of the manuscript. This work was funded by the  Institut Pasteur , the  Bettencourt-Schueller Foundation ,  Centre National de la recherche Scientifique, university Paris Diderot, agence Nationale de la recherche  (aNr-13-Sama-0006; SynDivautism), the  Conny-maeva Charitable Foundation , the  Cognacq Jay Foundation , the  Orange Foundation , and the  Fondation Fondamental.
MATERIALS AND METHODS The survey was conducted from April 2013 to September 2013 as an online study. Data were gathered in eight European countries, and after quality control, 3097 interviews remained for analysis with most respondents being citizens of the country in which the survey was carried out (Germany n = 652, France n = 200, Spain n = 298, Portugal n = 838, Romania n = 83, Serbia n = 800, Montenegro n = 199, and Belgium n = 27; Romania and Belgium are not considered in the detailed analysis per country due to the small sample sizes). The mean age of the participants was 33. 7 years, with 60% male and 40% female. The majority of the respondents are well educated, with a mean of 16. 7 of education years. Most respondents consider themselves as middle class people (mean 5. 32 on a 10point scale from bottom to top of society).
SUBJECTIVE EXPOSURE IMPACT KNOWLEDGE AND EMF RISK PERCEPTION The third research question refers to the issue how general EMF RP is actually affected by the knowledge about the various exposure characteristics. Therefore, a regression analysis was conducted using the exposure characteristics as independent variables and general EMF RP as dependent variable. We computed all calculations with the linear regression model (enter method) as specified in SPSS®, V20. The analysis demonstrates that the distance to the exposure source is not a significant predictor of general EMF RP (β = 0. 014, p = 0. 613). Significant predictors are the number of the exposure sources (β = 0. 148, p = 0. 000), the frequency of exposure (β = 0. 129, p = 0. 000) as well as the time of the day of exposure (β = 0. 136, p = 0.
miRNA Microarray Processing and Analysis Global miRNA expression was assessed in 12 of 26 PRL tumors (8 non-aggressive and 4 aggressive) (Table  1 , tumors in bold) using Human miRNA Microarray v3, 8 × 15k from Agilent Technologies. Microarrays were processed as described in the miRNA Microarray System with miRNA Complete Labeling and Hyb Kit version 2. 4 September 2011 (available at http://www. genomics. agilent. com). Microarrays were scanned on an Agilent Scanner Type C. Data were extracted using Agilent feature extraction software 10. 7. Data files (. txt) from Feature Extraction and then were normalized using spikes in Genespring Software 7. 0 (Agilent Technologies). Statistical analysis was performed using Genespring software 7. 0 to isolate differentially expressed miRNAs in aggressive-invasive vs.
TaqMan Low Density Array Analysis TaqMan Low Density Array (Applied Biosystems, Carlsbad, CA, USA) allowed testing of a large subset of miRNAs by RT-qPCR in a single assay. Briefly, 500 ng of total RNA was used for cDNA synthesis with the TaqMan microRNA Reverse Transcription kit according to the manufacturer's protocol. Then, miRNA expression analysis was performed by Real-Time PCR using the Taq-Man MicroRNA Array (Applied Biosystems) according to the manufacturer's protocol and run on a 7900HT Fast Real-Time PCR System (Applied Biosystems). Threshold cycle (Ct) values provided an index of miRNA level. The level of miRNA RNU6 was used as an internal standard. Values were obtained using RQ Manager Software for analysis (Applied Biosystems) and the detection limit was 32Ct. Comparison between samples was made using Genespring software 7.
mRNA levels were analyzed by qPCR using TaqMan small RNA assays at 1× final concentration and TaqMan Universal PCR Master Mix II No UNG at 0. 5× final concentration, run on a 7900HT Fast Real-Time PCR System with the following cycle: standard mode, 10 min at 95°C then 40 cycles of denaturation at 95°C for 15 s and hybridization and elongation at 60°C for 60 s. Relative miRNA expression was measured using hsa-let-7g (shown to be constant among tumors by microarray) and RNU6B as a reference with RQ Manager Software (Applied Biosystems).
Gene Expression Analysis Five hundred nanograms of total RNA were reverse-transcribed using the iScript cDNA synthesis kit (Bio-Rad, Hercules, CA, USA) following the manufacturer's protocol. The cDNA synthesized was measured by RT-qPCR using FAST SYBR Green Master Mix (Applied Biosystems) following the manufacturer's recommendations. The PCR program was as follows: 95°C for 20 s then 40 cycles of denaturation at 95°C for 1 s and hybridization and elongation at 60°C for 20 s on a 7900HT Fast Real-Time PCR System. Relative expression analysis was performed using RQ Manager Software (Applied Biosystems) with the RPL4 gene as a reference. Primers were designed using the NCBI Primer-Blast algorithm. All primers are listed in Table  S2  in Supplementary Material.
Proliferation and Cell Cycle Assay Growth curves were performed by daily counting of viable pre-miR-183 transfected and scramble transfected cells expressing GFP protein using an LSRII FACS flow cytometer (BD Biosciences Europe, Erembodegem, Belgium). For cell cycle distributions, pre-miR-183 transfected and scramble transfected cells were collected and incubated 1 h at 4°C with propidium iodide (0. 05 mg/ml) solution containing Non-idet-P40 (0. 05%) for proliferation and cell cycle assays. Cells were analyzed using a FACSCalibur flow cytometer (BD Biosciences Europe, Erembodegem, Belgium) and cell cycle distribution was determined using Modfit LT 2. 0 ™ software (Veritysoftware Inc. , Topsham, ME, USA). BrdU incorporation assays were performed using the APC-BrdU flow kit (BD Pharmingen), following the manufacturer's protocol.
Results miRNAs are Globally Deregulated in Aggressive vs. Non-Aggressive PRL Tumors In 12 PRL tumors classified according to the new classification  (7)  and split into two groups (9): aggressive tumors (n = 4) and non-aggressive tumors (n = 8) (Table  1 , tumors in bold), miRNA expression levels were assessed using Human miRNA Microarray V3 (Agilent Technologies) and analyzed with GeneSpring Software 7. 0. Signature comparison revealed a down-regulation of 11 miRNAs in aggressive compared to non-aggressive tumors (Table  2 ). A technical validation of microarrays was performed using TaqMan Low Density Assay (TLDA, Applied Biosystems) on the same samples and 9 of the 11 miRNAs interrogated by the TLDA assays presented the same changes in expression (Table  2 ).