dataesr/scientific-paragraphs-categorization huggingface dataset:
766 kgCO 2 e/kWh  [13] , due to different energy mixes to generate electricity. In order to choose an emission factor as close as possible to reality, we take into account the distribution of data center locations across the world, as the data will travel around the globe to reach the various GRAND institutes. We assume that the data centers are located in the same country as the data transmission network equipment. The countries hosting the largest number of data centers are the United States (38% of the centers), along with five other countries which host together 55% of the data centers in the world (see Appendix B). The emission factor is calculated as the weighted average of the emission factors of these countries, based on the proportion of data centers hosted. Note that this calculation does not account for the size of the data centers, which can vary from one country to another.
The emission factor obtained yields an emission of 0,518 kgCO 2 e/kWh. Finally, we calculate that the total electricity consumption needed for the raw data transmission for GRANDProto300 emits 470 tCO 2 e. This amount assumes that the data collected is only transferred once. If we assume that 10 institutes will transfer the data from the central data center in Beijing, emissions amount to 4700 tCO 2 e. Interestingly, for the primary scientific scope of the experiment, i. e. , radio-detection of astroparticles, the relevant data could be efficiently reduced on-site down to 100 GB per day, leading to 150 TB of data transmitted during the whole experiment. The transmission of this limited volume of data would imply 2 orders of magnitude less emissions, leading to a total of 4. 7 tCO 2 e emitted via data transfer at the GRANDProto300 stage.
Data storage The data collected will be stored in cloud servers. Cloud servers allow all the collaborators to easily have access to the data and to process them with virtual machines. The collaboration envisions that the data be stored at 3 different locations to provide back-up. The peak consumption of cloud storage is around 11. 3W/TB of data  [27]. For one TB of data for a year, this leads to 99 kWh of electricity consumption. Assuming the same emission factor of 0. 518 kgCO 2 e/kWh as above, it leads to an estimate of 51. 2 kgCO2e for one TB of data stored for a year. Each year, GRANDProto300 stores 3,000 TB of data, which leads to around 153. 6 tCO2e/yr for GRANDProto300.
Hardware equipment In the GRAND project, the hardware equipment will be dominated by the radio detection units. The units will be deployed in large numbers (200 000), hence their environmental impact cannot be neglected. A complete life cycle assessment of the hardware equipment is out of the scope of this work, and will be evaluated in a subsequent study. Still a rough assessment of the carbon footprint of the detection units can already initiate discussions about the impact of the hardware on the carbon footprint of the collaboration. Each detection unit consists in a radio antenna with a mechanical support setup (made primarily of 75 kg of stainless steel), data acquisition electronics (the material of the Print Circuit Board is MEGTRON  [28] ), several cables of 6-meter length in total, a solar panel (polycristalline module) and a battery (see Fig.
We calculated the emissions related to three unavoidable sources: travel, digital technologies and hardware equipment. We find that these emission sources have a different impact depending on the stages of the experiment. Digital technologies, and to a lesser extent travel, prevail for the small-scale prototyping phase (GRAND-Proto300), whereas hardware equipment (material production and transportation) and data transfer/storage largely outweigh the other emission sources in the largescale phase (GRAND200k). In the mid-scale phase (GRAND10k), the three sources contribute equally. We caution that the numbers brought in this study are order-of-magnitude estimates which serve to balance the choices and priorities of the GRAND collaboration to reduce its emissions. The estimates are subject to large uncertainties, and the projections presented are expected to vary widely, due to unforeseen experimental, technological and also human, societal changes.
As for the emissions from simulations and data analysis, the challenge is to reduce the millions of CPU hours projected to be spent yearly. Incentives to weigh the cost/benefit of the simulations runs may contribute to lower the carbon footprint in the years to come. Mitigating the emissions from manufacturing and hauling the hardware equipment will be a top priority for the design of the GRAND200k phase, as these emissions are projected to weigh most in the carbon footprint of GRAND200k phase. It is about balancing the environmental cost of the materials used for the antennas, the solar panels and the batteries, establishing a recycling plan, and monitoring the transportation from the production sites to the array-sites. The GRAND collaboration is planning to take several actions in response to this study.
Acknowledgements We are grateful to  Françoise Berthoud ,  Anna Risch , and to many GRAND collaborators (in particular  Rafael Alves Batista ,  Aurélien Benoit-Lévy ,  Simon Chiche ,  Valentin Decoene ,  Claire Guépin ,  Eric Hivon ,  Olivier Martineau ,  Valentin Niess ,  Simon Prunet ,  Markus Roth ,  Charles Timmermans ,  Matias Tueros ) for very fruitful discussions and ideas. This work was kindly supported by the  direction of IAP  and the GReCO team. This work is supported by the  APACHE grant  ( ANR-16-CE31-0001 ) of the  French Agence Nationale de la Recherche.
7, 8 In contrast, from HCs who were included in a database, maternal carrier status was established in 94% of pregnancies before conception, which underscores the importance of WGBD recognition and registration. 9 BD remain under-treated, with current approaches to peripartum management failing to reduce post-partum haemorrhage (PPH) risks. 0] [11] [12] [13]  HCs report more spontaneous bleeding and are at higher risk of prolonged bleeding post-surgery, tooth extractions and tonsillectomy, all suggesting inadequate preventive measures. 14, 15 HMB is a major issue, consistently reported by WGBD across different inher-ited BDs, interfering with normal life. [18]  Next to physical health, BD may negatively influence social activities and sexual relationships. BD diagnosis may severely impact WGBDs' decisions to have children.
PoC 10 involvement of WGBD in registries, clinical research and innovation Data on WBGD is limited. Standardized inclusion of WGBD in (inter-)national registries is lacking and clinical research on impact and management of BDs remains focused mainly on males with haemophilia. 83  improve our knowledge of WGBD, consistent and harmonized inclusion of WGBD in national/international BD registries is required. Uniform, internationally-defined patient relevant (female-specific) outcome measures should be used to enable better understanding of the impact of BD in women and which treatments effectively reduce female-specific bleeding and its consequences. It is insufficiently acknowledged that sex-based differences impact pharmacokinetics, hampering therapeutic optimization. 83 Increased attention should be paid to sex-specific pharmacovigilance and drug development.
Study site and field selection We analysed the root microbiota in individual vine plants collected in a single 66-ha vineyard located in Margaux (protected designation of origin; "AOP" in French "Appellation d'Origine Protégée") FIGURE 1. Presentation of the experimental design. (A) Scheme of the vineyard and distribution of the 37 sampled fields. (B) Plan showing the location of the 8 plants sampled in each of the 37 fields. The α-scale corresponds to the individual scale (one vine plant); the γ-scale represents the group of individuals at the field scale. The bacterial and fungal microbiota in each sample were identified using mass sequencing of amplified SSU rRNA gene fragments. (C) Environmental factor data obtained from the vine-makers dataset to test their effect on the bacterial and fungal communities at both α-and γ-scales.
Molecular work All the 296 extracted DNAs were normalised at 10 ng/µl using TE 0. 1X to ensure the use of similar DNA concentrations for all the PCRs. The fungal community in vine roots was analysed by amplifying an 18S rRNA gene fragment with the specific fungal primers NS22b (5′-AATTAAGCAGACAAATCACT-3′) and SSU817 (5′-TTAGCATGGAATAATRRAATAGGA-3′). The bacterial community was analysed from PCR amplification of a 16S rRNA gene fragment (V5-V7 16S regions) using the bacterial primers 799F (5′-AACMGGATTAGATACCCKG-3′) and 1223R (5′-CCATTGTAGTACGTGTGTA-3′). These primers were chosen because they do not amplify plant DNA and allow, in silico, the best bacterial sequence recruitment from both Sylva and GreenGenes databases compared to the other commonly used primer sets (unpublished in silico benchmark analysis).
Sequence trimming and clustering The base-calling step was performed on the MiSeq instrument with CASAVA v1. 8 software (Illumina). Sequence data trimming consisted of removing primers and deleting reads containing unidentified bases using Cutadapt software. As we mixed fungal and bacterial amplicons, we processed the fastq files to separate the two types of reads based on their specific DNA sequence (homemade script-EcogenO platform) and then analysed them separately. These goodquality sequences were then processed with the FROGS pipeline  (Escudié et al. , 2018)  using the standard protocol for bacterial reads and after a particular pre-processing step for fungi  (Kozich et al. , 2013). The FROGS pipeline notably includes SWARM as a clustering strategy, which enables analysis of reads without using the standard identity threshold of classical OTUs (97 %), and rather to produce what we call herein 'sequence-clusters'.
Finally, the taxonomic affiliations were determined using Silva132 16S rRNA for bacteria  (Quast et al. , 2012)  and Phymyco-DB databases for fungi  (Mahé et al. , 2012). The stringent parameters applied in the FROGS pipeline resulted in the production of data matrices containing 2,778,222 reads for bacteria and 1,549,109 reads for fungi. Rarefaction curves were generated using R (version 3. 6. 1) with the function 'rrarefy' in the vegan 2. 5-6 package. To perform subsequent statistical analysis, we normalised the number of reads for each bacterial and fungal dataset. Based on graphical observations (rarefaction curves), we normalised to the same number of reads for bacteria and fungi to 4264 and 1447 reads per sample respectively (Figure  S1 ). Samples with fewer reads were removed from the dataset resulting in 34 fields for bacterial datasets and 36 fields for fungal datasets (i.
e. , a field being removed when more than 50 % of the samples did not satisfy the sequencing depth). The final matrices containing 1051 bacterial and 158 fungal sequence-clusters were finally used for the statistical analyses. We calculated sequence-cluster richness and the diversity index (Simpson's evenness) using the R 'vegan' package (Table  S2 ) at two spatial scales: the vineyard level (γ-diversity scale) and the within-field level (α-diversity scale). Measures at the γ-scale were calculated using the total number of sequence-clusters and the mean number of reads. For all the statistical analyses, we decided to work at the phylum level because a large part of the sequence-clusters observed were unknown at the species level and a large proportion of them are unknown at higher taxonomic levels.
Plant genotypes (i. e. , rootstock genotype and scion genotype) sampled within a field were identical, but rootstock genotype varied among fields. However, the rootstock genotype was unknown for some of the parcels independently of the age of the parcel. Therefore, we did not include rootstock genotype in the analyses. Furthermore, we tested the rootstock effect on the partial data set and found no detectable effect on the microbiota (i. e. , p > 0. 05). Rootstock variations were thus considered herein as a background noise expected of the same range across age categories. Bacterial and fungal sequence-cluster composition of the fields was assessed using the normalised number of reads for sequence-clusters. The significance of the independent variables in the CCA structure was tested using permutational multivariate analysis of variance (PERMANOVA) with 999 permutations.
univ-rennes1. fr/?[en]) for help with DNA extraction and amplicon sequencing, respectively. We are also grateful to the Genotoul platform Toulouse Midi-Pyrénées (https://www. genotoul. fr/) for access to their bioinformatics pipeline and storage facilities. We would like to acknowledge all the trainees and people at Château Palmer for their help during the sampling campaign, especially Sylvain Fries who helped design the experiment and select the vine plants. We are also grateful to Sophie Michon-Coudouel, Romain Causse-Verdines and July Hémon for molecular analysis and to Léa Uroy and Lucie Lecoq for helpful advice on statistics. Data Accessibility: cological data are available on Figshare on 10. 6084/m9. figshare. 12453602. Sequence data are available at European Nucleotide Archive (ENA) under accession number PRJEB39327.
Data The data used are from the Census of the French population carried out by the National Institute of Statistics and Economic Administration (INSEE) from 2009 to 2013. Each year, the census collects information on a subsample of all French households. The French population is divided into five groups, with data on each group being collected from a representative sample of household every 5 years. The inclusion of 5 years of data therefore covers the whole French population. The final sample used in our analysis includes 19. 6 million observations representing 31% of the total French population living in private households. Each observation is weighted in order to make the sample representative of the total population in the median year of data collection  (2011). The only selection criteria we used to construct our sample was whether individuals lived in private households.
The extraction qualities, defined in Eqs. (  2 ) -(  6 ), are evaluated for only acceptable sources, cf. Eq. (  1 ), with errors in measurements within a factor of 2 1/2. The numbers of model sources are N T = 459 in Benchmark A and N T = 919 in Benchmark B. Source measurements in the image of derived surface densities are known to be inaccurate (e. g. , Appendix A of Paper I), hence the data are not presented. 2 ) -(  6 ), are evaluated for only acceptable sources, cf. Eq. (  1 ), with errors in measurements within a factor of 2 1/2. The number of model sources is N T = 919. Source measurements in the image of derived surface densities are known to be inaccurate (e. g. , Appendix A of Paper I), hence the data are not presented. Bench A 2 λ N Dλ N Gλ N Bλ N S C λ R λ G λ Q Pλ Q Tλ Q Eλ Q Dλ Q CRλ Q PTEλ Q λ getsf λ N Dλ N Gλ N Bλ N S C λ R λ G λ Q Pλ Q Tλ Q Eλ Q Dλ Q CRλ Q PTEλ Q λ getsf λ N Dλ N Gλ N Bλ N S C λ R λ G λ Q Pλ Q Tλ Q Eλ Q Dλ Q CRλ Q PTEλ Q λ getsf 75 λ N Dλ N Gλ N Bλ N S C λ R λ G λ Q Pλ Q Tλ Q Eλ Q Dλ Q CRλ Q PTEλ Q λ getsf 70 Bench B 4 λ N Dλ N Gλ N Bλ N S C λ R λ G λ Q Pλ Q Tλ Q Eλ Q Dλ Q CRλ Q PTEλ Q λ getsf.
Acknowledgements. This study used the cfitsio library (Pence 1999), developed at  HEASARC NASA (USA ), saoimage ds9 (Joye & Mandel 2003) and wcstools (Mink 2002), developed at the  Smithsonian Astrophysical Observatory (USA) , and the stilts software (Taylor 2006), developed at  Bristol University (UK). The plot utility and ps12d library, used in this work to draw figures directly in the PostScript language, were written by the author using the psplot library (by  Kevin E. Kohler ), developed at  Nova Southeastern University Oceanographic Center (USA) , and the plotting subroutines from the MHD code azeus (Ramsey et al. 2012), developed by  David Clarke  and the author at  Saint Mary's University (Canada ). HGBS and HOBYS are the Herschel Key Projects jointly carried out by  SPIRE Specialist Astronomy Group 3  ( SAG3 ), scientists of several institutes in the  PACS Consortium  (e.
The mass spectrometer conditions and data processing were the same as described in Chougui, et al. [55]. Experimental exact masses and MS 2 fragmentation data were compared to metabolomics data banks (Pubchem Compound: http://pubchem. ncbi. nlm. nih. gov/, Re-Spect: http://spectra. psc. riken. jp/, Mass Bank: http://www. massbank. jp) and available literature in order to identify the nature of the phenolic compounds. The phenolic quantification was realized on a U-HPLC system (Shimadzu, Japan) consisting of a binary solvent delivery pump connected to a diode array detector. Three to five microliters of the extracts were separated on a C18 Kinetex (100 mm×2. 1 mm) column (Phenomenex, USA) by using a gradient elution from 10 to 45% MetOH for 13 min, then 99% MetOH for 3 min with a flow rate of 300 µL min.
Analyses were performed on protein and transcript levels. Protein-protein associations within CD 178 were queried using the STRING platform  37. 176 CD 178 genes matched with proteins in the STRING database (Fig. 4a ). Notably, STRING network analysis separated 3 highly connected k-Means clusters within CD 178 : (A) with 10 proteins (A 10 ); (B) with 8 proteins (B 8 ); and (C) with 7 proteins (C 7 ; total = ABC 25 ) (Fig. 4a ). To interpret the identified protein interactions we performed unguided as well as cluster-guided gene set enrichment analysis (GSEA) using a FDR < 0. 05  38. Unguided GSEA correlated previously defined hallmark gene sets to CD 178 ; with Epithelial Mesenchymal Transition (EMT) being the most significantly correlated hallmark gene set (FDR = 1. 79 × 10 -11 ), supporting the previously described role of EMT in ADPKD cystogenesis (Fig.
4b )  16, 39. Given the smaller size of ABC 25 and, hence, smaller possibility space, cluster-guided GSEA was complemented with gene ontology (GO) analysis, transcription factor target analysis and evaluation of the Kyoto Encyclopedia of Genes and Genomes (KEGG) (Fig. 4c )  [40] [41] [42]. Interestingly, cluster A 10 correlated mainly with EMT; and also with factors related to the Endoplasmic Reticulum Lumen as well as Post Translational Protein Modification. Cluster B 8 instead comprised genes related to Epithelial Cell Differentiation, Animal Organ Morphogenesis and Sequence Specific DNA Binding, suggesting a role in tissue formation. And cluster C 7 seemed exclusively related to phosphorylation-dependent cellular signaling (Fig. 4c ). For full GSEA results, see Table  S6. The transcriptional regulation of CD 178 was probed by transcription factor enrichment analysis using the ShinyGO and GeneSetDB meta-databases  41, 42.
Ca 2+ -regulation and phosphorylation-dependence, for example, have been described for the zinc-finger protein GLI1 and the cellular tumor antigen p53  44, 45. TF 7 transcription factors may, hence, provide a mechanistic link from PC-1/TRPP2 channel activity to down-stream regulation of cellular gene expression. Another important contributor to both protein-protein associations and control of gene expression levels is alternative splicing of pre-mRNAs  46. To date, alternative splicing has not been reported in ADPKD. Analysis of differential exon usage in our wild-type, Pkd1 -/-and Pkd2 -/-RNA-seq data identified 176 Pkd-responsive exons in 107 distinct genes (Fig. 5a, b ). Within CD 178 , three genes (Pik3r1, Bex4 and Slc16a3) showed significant differences in exon usage (Fig. 5c ). Similarly, TF 7 Rbpj exon usage seemed to be Pkd-dependent (Fig.
4e ). Complementary mathematical analysis of Pkd1-and Pkd2-responsive transcripts identified a core ADPKD data set of 178 consistently regulated genes (CD 178 ). To identify genes involved in ADPKD pathogenesis, two independent mathematical approaches were used: (1) statistical analysis of differential gene expression (DGE); and (2) unbiased data simplification by principal component analysis (PCA). (a) and (b) Applying RNA-seq on Pkd1-and Pkd2-deficient mIMCD3 cells revealed a total of 791 differentially expressed genes in Pkd1 -/-and 1250 in Pkd2 -/-cells (large, dark-yellow dots: FDR < 0. 05, log 2 fold change ≥|1|). In Pkd1 -/-cells, > 54% (429 genes) and in Pkd2 -/-cells, > 63% (790 genes) were downregulated. (c) Filtering DGE of Pkd1 -/-and Pkd2 -/-for concordant changes provided 254 genes (DGE 254 ), of which 38 were up-and 216 downregulated genes (orange dots).
05). ABC 25 genes were subjected to an extended analysis, comprising hallmark gene sets, gene ontology (GO) analysis, transcription factor target analysis and evaluation of the Kyoto Encyclopedia of Genes and Genomes (KEGG)  42, [96] [97] [98]. See Table  S7  for full results. (d) To elucidate CD 178 transcriptional regulation, ShinyGO and GeneSetDB meta-databases were queried (FDR < 0. 05)  41, 42. This revealed a total of 17 CD 178 genes regulated by 7 trancriptional factors (TF 7 ). (e) 6 of 7 TF 7 were shown to form a k-means based cluster. Also, ABC 25 identified several regulatory cascades: 5 ABC 25 genes (Cd34, Spp1, Cdh2, C3 and Mmp2) were linked to 6 of the TF 7 (Ar, Esr1, Ets, Gli1, Rbpj, Tp53). Spp1 and Cdh2 were regulated by more than one TF 7 , whereas Ets2, Rbpj and Tp53 regulated more than one ABC 25   Combining published and newly generated data on PKD1 and PKD2 deficiency generated a cross-species transcriptional profile of ADPKD and identified 9 novel targets for future development.
The genetic contrast of wild-type and specifically engineered mutant renal tubular epithelial cells provided the opportunity to assess the intricacies of Pkd-dependent transcriptional regulation in simplified cellular isolation. To weigh the relative importance of newly identified CD 178 pathways in the complex ADPKD in vivo pathophysiology, we next expanded our investigation to the in vitro in vivo correlation of CD 178 -derived pathways. Renal expression of CD 178 genes was analyzed by cell type  47. As expected by the mIMCD3 data source, all CD 178 genes were consistently expressed along the nephron without particular cortical or juxtamedullary enrichment (Figure  S4a-c ). Within the nephron, parietal cells (> 60%), collecting duct (> 40%) and medullary pelvis epithelium (> 50%) show the highest expression of CD 178 genes (Figure  S4a-c ).
To evaluate the relevance of the newly identified CD 178 genes for ADPKD pathophysiology we performed a comprehensive cross-species meta-analysis of PKD-dependent RNA expression. At the time of analysis, data were available from six studies evaluating PKD1-dependent transcription and one evaluating loss of Pkd2: one in renal tubular cells; five in mutant mouse renal tissue; and one in cystic human kidneys (Table  S8 )  [12] [13] [14] [15] [16] 18. Dat  from non-orthologous models of ADPKD were not included in the analysis. For internal validation, we furthermore performed RNA-seq of whole kidneys from littermate control and adult-onset conditional Pkd1 -/-mice (n = 7; Pkd1 tm2Ggg /Pax8 rtTA ; induction from post-natal day 28 to 42; RNA isolation in week 12) (Figure  S5a-c ). Pooled analysis of aggregated data showed that expression of 117 CD 178 genes had been described previously as PKD-dependent (Figure  S6a ).
Here, we applied a genetics-guided data science approach to address these constraints and to reveal core genes and transcriptional pathways that have a high likelihood to promote ADPKD pathology. Methodologically, the kidney epithelium-specific transcriptome of ADPKD was profiled by comparing wild-type mIMCD3 cells with one clonal Pkd1 -/-and one Pkd2 -/-mIMCD3 cell line. Unbiased differential gene expression (DGE) and principal component analysis (PCA) were used to minimize the probability of erroneous inference and provide a robust ADPKD reference profile for more complex disease models. Data simplification by PCA is designed to reduce data dimensions and facilitate approximation of data. Data dimensionality in PCA is mathematically reduced by transforming a number of correlated variables into uncorrelated principal components (PCs).