dataesr/scientific-paragraphs-categorization huggingface dataset:
This regulon is involved in the protection against superoxide and nitric oxide-induced stress. Finally, we used the lacZ gene promoter as a control. The advantage of this control reporter is that it can be fully induced with IPTG. All tested promoters were fused to the gene coding for GFP protein  23. The cells were grown with and without ampicillin in 96-well microplates, and optical density and GFP fluorescence were measured for 3 hours (Table  1 ). We broadly classified the gene promoters that showed significant induction when compared to the lacZ control, in three functional categories according to the gene functions described in the EcoCyc E. coli database (http://ecocyc. org/). (i) Genes coding for protection against ROS and reactive nitrogen species (RNS); soxR, sensor and transcriptional regulator of the SoxRS regulon; fpr, a member of the SoxRS regulon, which is involved in the protection against oxidative agents such as methyl viologen and paraquat; nfnB, reduction of nitroaromatic compounds including the antibiotics nitrofurazone and nitrofurantoin; norV/norW, nitric oxide reductase.
Flavin Mono Nucleotide (FMN), Flavin Adenine Dinucleotide (FAD) and Riboflavin were purchased from Sigma-Aldrich. Flow cytometry and cell sorting. We used a Gallios (Beckman Coulter, Brea, USA) analyzer 10 color, 4 laser system to analyze samples. FL1 channel (excitation at 488 nm with bandpass filters of 525/20 nm) was used for autofluorescence analysis. FL6 channel (excitation at 638 nm with bandpass filters of 660/20 nm) was used for AF633H and TOPRO ® -3 analysis. Samples were run at a rate between 1,000 and 2,000 events/s; 20,000 to 50,000 events were collected depending on sample type (prokaryotic or eukaryotic cells). Data acquisition was performed using the Kaluza software. For ImageStream cytometry, samples were acquired on the ImageStreamX MarkII system (Amnis Corporation, Seattle, USA) using a 488 nm laser (Bandpass filter 505-560 nm).
20,000 events per condition were collected. The IDEAS software (Amnis) was used to analyze the measurements. Image acquisition and image analysis were performed using the IMAG'IC Facility (Institut Cochin, Paris, France). For cell sorting, we used a S3e cell sorter (Bio-Rad, Hercules, USA). FL1 channel (excitation at 488 nm emission at 525/30 nm). Samples were run at an event rate of 1,200 events/s and 200 cells per condition were sorted in 200 µL 1x PBS with the "single cell" sort mode. Data acquisition was performed using the ProSort TM software (Bio-Rad).
Acknowledgements We thank  Claudine Navas ,  William Michael Dunne  and  Arnaud Gutierrez  for critical reading of the manuscript. We thank  Pierre Bourdoncle ,  IMAG'IC Facility, Institut Cochin, Paris, France , for technical assistance with the Imagestream X Mark II. HeLa cells were kindly provided by  Dr. Benoît Miotto  ( Institut Cochin, INSERM U1016, Paris, France ). Jérémy Surre was supported by a fellowship, from the  French Association Nationale de la Recherche et de la Technologie (ANRT)  number  2014/0300. This work was supported by grants from:  Idex   ANR-11-IDEX-0005-01/ANR-11-LABX-0071  and  FRM  Grant  DBF20160635736.
Data analysis. Flow cytometry data analysis and fluorescence normalization were performed using the Flowjo © V10. 2 software (Flowjo, Ashland, USA). Data from the microplate reader and Imagestream X were processed using Magellan 7. 2 (Tecan) and Excel © 2010 (Microsoft ® , Redmond, USA). Statistical analyses were per- formed using Prism V7. 0 (GraphPad TM , La Jolla, USA). FCS Express V4 (De novo software, Los Angeles, USA) was used to export raw data from the flow cytometer for correlation analysis.
Foods available: NO FEASIBLE SOLUTION The literature strategy used to compile the 67 relevant original publications using mathematical diet optimization is presented in Supplemental Methods 1. Studies using mathematical optimization without a whole-diet approach were excluded from this review. A description of each of the 67 studies reviewed can be found in the Supplemental Tables 1-3, which detail the population studied, type of modeling (individual or population-based), objective function, constraints for each dimension of diet sustainability (health/nutrition, culture, economy, environment), and the software programs used. Not all software programs can address all dimensions of diet sustainability. Preconceived software programs such as Optifood  (35)  can take into account metrics related to nutrition, cost, and food habits.
The FBRs were created in several steps: 1) using diet optimization to design a nutritionally optimal diet that respects population food-habit constraints and then using this optimized diet as a basis to create draft FBRs, re-expressed as frequency of consumption of typical serving sizes; 2) testing whether fulfilling the draft FBRs ensures nutritional adequacy; and 3) adjusting the FBRs on the basis of expert feedback  (30, 40). On the basis of this approach, a pre-set software program called "Optifood" was created specifically for designing FBRs, taking into account subpopulation specificities (e. g. , age, sex, food habits, and food supply) in low-income countries  (35). Optifood's modules are further detailed in Supplemental Methods 2. The Optifood software program has been extensively used for developing FBRs for children aged <2 y [e.
Sometimes it can be impossible to reach nutritional adequacy, either because of incompatibility between RVs imposed in constraints or a problem with food availability, especially when background poverty limits physical or financial access to nutrient-rich foods. Nevertheless, one way to improve nutritional quality is to seek the "nutritionally best diet" by maximizing nutrient coverage itself while respecting food-habit constraints  (30, 42). Nutrients for which RVs are not fulfilled in the "nutritionally best diets" are defined as "problem nutrients"  (42). For example, this approach integrated in the Optifood software program (Supplemental Methods 2) helped to highlight iron as a "problem nutrient" because the nutritionally best diets only reached 21-66% of the iron RV depending on age-district subgroup in Bogota (Colombia)  (42).
When fulfilling the set of RVs is too difficult, a goal-programming approach can be used to find the best achievable (or "nearoptimal") diet, accepting that it is not fully nutritionally adequate  (30). Indeed, when the food supply lacks nutrientrich foods, it is preferable to maximize nutritional quality rather than trying to obtain diets that respect a set of hard nutritional constraints at the price of impracticability or even infeasibility. This is the method applied in the Optifood software program as described in Supplemental Methods 2  (35). Further advances have been made to more accurately estimate the health dimension. Some studies have improved the assessment of nutritional adequacy by accounting for nutrient bioavailability  (36, 39, 82)  or exposure to food contaminants  (106, 107).
Quantified food intakes are a prerequisite in any attempt to ensure cultural acceptability, whether by identifying the list of foods usually consumed in the population, taking into account food consumption distribution in the constraints, or deriving the average observed diet if the aim is to stay as close as possible to current food habits. Complete and accurate nutrient composition data, environmental food data, or cost data are crucial in all models to avoid boosting or deleting certain foods in the optimized diets due to possible over-or underestimated values. The main challenge for modeling sustainable diets now is probably to combine several databases containing metrics of each dimension of diet sustainability. A methodology was recently published that describes how to combine different data sources on foodconsumption habits, nutrient composition, food prices, and environmental impact of foods  (111).
Finally, the applicability of the results of a diet optimization study, which remain very theoretical, can also be improved, either upstream by adapting the parameters of the model (variables, objective function, constraints) or downstream by refining the expression of the results or adjusting them after testing their practicability in real life. For instance, food contents of the optimized diet are generally expressed in real numbers, in grams per day or per week, which are probably not meaningful for individuals when food changes amount to only a few grams. More pragmatic dietary changes could be obtained by conducting optimization with integer numbers (number of portion sizes), called "integer linear programming"  (29) , but the process is computationally intensive. In the Optifood software program, the decisionvariable values, which are the number of portions for each food (expressed in real numbers), are expressed a posteriori in integer numbers of portions per week or per day, by rounding the optimized values  (35).
S1 gene sequencing The amplified cDNA was purified using the NucleoSpin ® (MACHEREY-NAGEL GmbH, Germany) Gel and PCR Clean-up kit according to the manufacturer's instructions and sent for sequencing to x-OVO Limited (x-OVO Limited Laboratories, Thomson Cooper, United Kingdom). The nucleotide and amino acid sequences of the S1 gene of the IBV isolates were assembled, aligned, and compared with reference IBV strains using BASIC BLAST program in DNAStar. Phylogenetic analysis of the S1 amino acid sequences was performed with the neighbor-joining method using the DNAStar software  [18]. The partial S1 nucleotide sequences from the IBV isolates have been submitted to the GenBank database and assigned the accession number MG252734. The nucleotide and amino acid sequences of the S1 gene of IBV strains used for the comparison are available in GenBank under the following accession numbers: CR 88 (KM067900), 4/91 (JN600614), Qx (AF193423), Ma5 (AY561713), H120 (M21970), M41 (GQ219712), D274 (KR106999), Italy 02 (AJ457137), and QX D388 (DQ674739).
Acknowledgments The authors gratefully acknowledge the  MSD International Laboratory, AMMAN, Jordan , and  MSD, Algeria , for providing the facilitation and equipment throughout the study. The authors are also thankful to Entisar and  Husam Bakri  for technical assistance and help with the HI test analysis. The authors had used the facility of  MSD International Laboratory , Jordan but no person from MSD was involved at any stage of the experiment or publication. Also, authors have not received any fund from  MSD Algeria.
These results demonstrate the power of the meta-analysis conducted here to directly identify a small number of variants as putative causative mutations. While GWAS analysis with imputed sequence data has identified causal mutations in cattle, imperfect imputation (Supplementary Figs. 1 2 3 4 5 ) may result in the causal mutation not being identified as the most highly associated variant, especially if the variant is rare. Here the accuracy of imputation was > 0. 9 for variants with a minor allele frequency (MAF) > 0. 10 and for most of the variants in the PLAG1 region (Supplementary Table  5 ; note that the rs109815800 variant was among those genotyped on the 630K array in some populations)  10. To investigate what type of variants affect stature in cattle, genome annotation, eQTL and ChIP-seq data were used.
All sequenced animals were used as a reference when imputing whole-genome sequence genotypes in each population. Ancestral allele determination. To determine the ancestral allele, the following genome assemblies were used: (i) the cattle UMD3. Pairwise alignments of the bovine genome sequence to the yak, water buffalo, bison and sheep genome sequences were carried out using the LASTZ sequence alignment program  46  (see URLs for LASTZ documentation). LASTZ was run with the parameters --nogapped (skip gapped extension when doing alignment); --notransition (do not allow any match positions in seeds to be satisfied by transitions); --step = 20 (offset between the starting positions of successive target words considered for potential seeds); --format = maf (specifies the output format (the maf format in our study)).
A custom Python script was subsequently used to predict the yak, water buffalo, bison and sheep putative ancestral allelic state of the 164 SNPs  47. The script is available from the authors upon request. White blood cell eQTLs. 360 Holstein cows from the 'Novel Strategies to Breed Dairy Cattle for Adaptation and Reduced Methane Emissions' Australian project were sampled during a 3-year project, 120 cows per year, in three batches of 40 cows. Whole-blood cell samples were taken from all cows at the DEDJTR Ellinbank research facility at weeks 2 and 4 of the trial period, with approval from the DEDJTR Animal Ethics Committee (2013-14), as follows. Blood was collected by venipuncture of the coccygeal vein after routine morning milking and was processed according to the blood fractionation and WBC stabilization procedure in the protocol for the RiboPure Blood kit (Ambion by Life Technologies).
Adaptor sequences and bases with a quality score less than 20 were trimmed from the ends of reads. Reads were discarded with mean quality scores less than 20, more than three no calls (Ns), greater than three consecutive bases having a quality score less than 15 or a final length less than 50 bases. Only paired reads were retained for alignment. Paired RNA reads for each sample were aligned to the UMD3. 1 bovine genome assembly using TopHat2, allowing for two mismatches  48, 49. Computer scripts were used to assess sequencing performance, library quality and alignment quality; these scripts are available from the authors upon request. Alignment files (. bam) for WBC libraries with > 12. 5 million read pairs (after quality control filtering) and also having a mapping rate > 80% were retained for gene count matrix generation.
Gene counts for the aforementioned alignment files were created using the Python package HTSeq  50. Counts were combined to form a gene-by-sample count matrix. This count matrix was then normalized to take into account library size using the R software package, DESeq  51. Statistical analysis. Meta-analysis. Subsequently, GWAS was performed within each population on the imputed whole-genome sequence variants (SNPs and short insertions and deletions) using mixed linear models that included each population's genomic relationship matrix (GRM), which were constructed with at least 630,000 SNPs (BovineHD chip) to account for population stratification and familial relationships. Association was tested by linear regression of phenotypic measures on the number of copies of the alternate allele, assuming additive effects.
NaTuRe GeNeTICS In the third validation approach, the average height of seven breeds was predicted from their whole-genome sequences and compared to height reported in three experiments measuring the height of these breeds  [38] [39] [40]. There were two Dexter sequences, 33 Charolais sequences, 10 Belgian Blue sequences, 59 Brown Swiss sequences, 34 Gelbvieh sequences, 31 Limousin sequences and 5 Piedmontese sequences. Allele frequencies for each breed calculated from these sequences were used in the prediction equation β ∑ =  p 2 i i i 1 163 with the terms defined as above. Proportion of variation accounted for by 17 previously identified loci within village dogs. We reanalyzed the village dog dataset from Hayward et al. 3. The dataset we analyzed included 330 village dogs measured for body weight.
Bootstrap sampling was performed to contribute evidence that the lead variants could be causative mutations. We recorded the proportion of bootstrap samples in which the lead variant from the original meta-analysis remained the lead variant in the bootstrap sample. Bootstrap sampling was performed by sampling 17 populations with replacement from the 17 populations used in the meta-analysis. Once the 17 populations were sampled, the metaanalysis was rerun for the 25. 4 million variants using METAL 37 as described above. There were 100 bootstrap samples. eQTL analysis. Whole-genome sequence data were imputed into 630,000 (imputed from 43,000) genotypes for the cows using the bull whole-genome sequences in Run4 of the 1000 Bull Genomes project. After removing variants that had a MAF less than 0.
FLK and hapFLK were calculated with hapFLK software (see URLs), using ancestral allele information to root the population tree. P values were estimated for each test using procedures documented with the software. q values were calculated using the qvalue R package, and SNPs corresponding to a false discovery rate of 5% were called significant. Enrichment analysis. An enrichment analysis among GWAS hits was performed based on a stratified FDR approach  53. FLK P values for all SNPs were divided into two sets: a set of GWAS hits and the set of non-GWAS hits. Within each set, the proportion of true positives (1 -π 0 ) was estimated with the qvalue R package. The enrichment in the GWAS set as compared to the non-GWAS set was calculated as the ratio of the GWAS hits value to the non-GWAS hits values.
The analysis is presented in Pausch et al. 10. However, figures demonstrating that imputed sequence data could discover known causative mutations were not presented in that the manuscript and are presented here. 214 Brown Swiss and 345 Holstein animals were genotyped using the Illumina BovineHD BeadChip that comprises 777,962 SNPs. All other animals were genotyped using the Illumina BovineSNP50 BeadChip that comprises 54,609 SNPs. The Brown Swiss, Holstein and Fleckvieh animals were imputed to higher density using FImpute  45  (Brown Swiss) and Minimac  44  (Holstein and Fleckvieh). The final dataset included 573,650 and 564,374 autosomal SNPs. Sequence variant genotypes were imputed in 6,777 Fleckvieh, 5,204 Holstein and 1,646 Brown Swiss animals using the 1000 Bull Genomes Run4 multi-breed reference population with Minimac  44.
Life Sciences Reporting Summary Nature Research wishes to improve the reproducibility of the work that we publish. This form is intended for publication with all accepted life science papers and provides structure for consistency and transparency in reporting. Every life science submission will use this form; some list items might not apply to an individual manuscript, but all fields must be completed for clarity. For further information on the points included in this form, see Reporting Life Sciences Research. For further information on Nature Research policies, including our data availability policy, see Authors & Referees and the Editorial Policy Checklist. Please do not complete any field with "not applicable" or n/a. Refer to the help text for what text to use if an item is not relevant to your study.
n/a Confirmed The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement (animals, litters, cultures, etc. ) A description of how samples were collected, noting whether measurements were taken from distinct samples or whether the same sample was measured repeatedly A statement indicating how many times each experiment was replicated The statistical test(s) used and whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section. Existing software was used, including Fimpute, Beagle3, Minimac, METAL, EMMAX, ASREML. For manuscripts utilizing custom algorithms or software that are central to the paper but not yet described in the published literature, software must be made available to editors and reviewers upon request.
Materials availability Indicate whether there are restrictions on availability of unique materials or if these materials are only available for distribution by a third party. Materials and Methods. Sequence for miniature cattle can be found at Bioproject PRJNA238491 (1000 bull genomes project), Biosample accession numbers are: SAMN05803879, BTAUGLWAUSF000000AG0134, SAMN05803880 BTAUGLWAUSF000000AG0135, SAMN05803881, BTAUHERAUSF000000AG0137, SAMN05803882 BTAUHERAUSF000000AG0139, SAMN05803883 BTAUDXTAUSM000000AG0144, SAMN05803884 BTAUAANAUSF000000AG0149, SAMN05803885, BTAUAANAUSF000000AG0150, SAMN05803886 BTAUAANAUSM000000AG0152, SAMN05803887 BTAUAANAUSM000000AG0154, SAMN05803888, BTAUDXTAUSF000000AG0156. RNA Sequence for the eQTL experiment can be found at Bioproject PRJNA305942, SRP067373, SAMPLE 210004817-W2-Blood-RNA, SRS1206435, SAMPLE 210004817-W2-Milk-RNA, SRS1206437, SAMPLE Y10ST0027-W2-Blood-RNA, SRS1206444, SAMPLE Y10ST0027-W2-Milk-RNA, SRS1206446, SAMPLE Y10ST0106-W2-Blood-RNA, SRS1206447, SAMPLE Y10ST0106-W2-Milk-RNA SRS1206629.
Eukaryotic cell lines a. State the source of each eukaryotic cell line used. Not applicable b. Describe the method of cell line authentication used. Not applicable c. Report whether the cell lines were tested for mycoplasma contamination. Not applicable d. If any of the cell lines used are listed in the database of commonly misidentified cell lines maintained by ICLAC, provide a scientific rationale for their use. Not applicable Animals and human research participants Policy information about studies involving animals; when reporting animal research, follow the ARRIVE guidelines 11. Description of research animals Provide all relevant details on animals and/or animal-derived materials used in the study. The eQTL experiment described in the paper was approved by the Department of Economic Development, Jobs, Transport and Resources Animal Ethics Committee (2013-14).
Bos mutus isolate yakQH1 genome, https://www. ncbi. nlm. nih. gov/nuccore/AGSK01000000; water buffalo reference genome UMD_CASPUR_ WB_2. 0, https://www. ncbi. nlm. nih. gov/assembly/GCA_000471725. 1/#/st; LASTZ, http://www. bx. psu. edu/miller_lab/dist/README. lastz-1. 02. 00/README. lastz-1. 02. 00a. html; QualityTrim, https://bitbucket. org/arobinson/qualitytrim; hapFLK software, https://forge-dga. jouy. inra. fr/projects/hapflk; human GWAS catalog, https://www. ebi. ac. uk/gwas/.
Acknowledgements H. D. D. , A. J. C. , P. J. B. and B. J. H. would like to acknowledge the  Dairy Futures Cooperative Research Centre  for funding. H. P. and R. F. acknowledge funding from the  German Federal Ministry of Education and Research (BMBF)  within the AgroClustEr ' Synbreed-Synergistic Plant and Animal Breeding ' (grant  0315527B ). H. P. , R. F. , R. E. and K. -U. G. acknowledge the  Arbeitsgemeinschaft Süddeutscher Rinderzüchter , the  Arbeitsgemeinschaft Österreichischer Fleckviehzüchter  and  ZuchtData EDV Dienstleistungen  for providing genotype data. A. Bagnato acknowledges the  European Union (EU) Collaborative Project LowInputBreeds  (grant agreement  222623 ) for providing  Brown Swiss genotypes. Braunvieh Schweiz  is acknowledged for providing Brown Swiss phenotypes. H.
Not applicable 10. 10 Eukaryotic cell lines a. State the source of each eukaryotic cell line used. Not applicable b. Describe the method of cell line authentication used. Not applicable c. Report whether the cell lines were tested for mycoplasma contamination. Not applicable d. If any of the cell lines used are listed in the database of commonly misidentified cell lines maintained by ICLAC, provide a scientific rationale for their use. Not applicable Animals and human research participants Policy information about studies involving animals; when reporting animal research, follow the ARRIVE guidelines 11. Description of research animals Provide all relevant details on animals and/or animal-derived materials used in the study. The eQTL experiment described in the paper was approved by the Department of Economic Development, Jobs, Transport and Resources Animal Ethics Committee (2013-14).