dataesr/scientific-paragraphs-categorization huggingface dataset:
A priori determinations of carbohydrate quality ought to be cross-checked against existing CQI scores that were developed specifically for that purpose  (3). The present analyses used USDA food category codes to assign foods into grains, snacks and sweets, vegetables, legumes, fruits, and beverages  (8). Nutrient density of carbohydrate-rich foods was assessed using the publicly available Food and Nutrient Database for Dietary Studies (FNDDS 2017-18)  (14). The testing algorithms included four published CQI scores (3) and a newly developed carbohydrate food quality score (CFQS-4).
The What We Eat in America Coding Schemes Nutrient composition data for carbohydrate foods came from the USDA Food and Nutrient Database for Dietary Studies (FNDDS 2017-2018) that is available online  (14). The FNDDS 2017-18 database lists 7,083 items, aggregated using What We Eat in America (WWEIA) coding schemes  (8). Onedigit codes identify 9 major food groups: grains, snacks and sweets, beans and nuts, vegetables, fruits, milk, meat, eggs, and fats and oils. Two-digit WWEIA codes identify 53 smaller food subgroups. For example, foods in the grains group are separated into cooked grains, breads, quick breads, ready-to-eat cereals (RTE), and cooked cereals. Four-digit WWEIA codes identify 138 food categories. For example, sweet bakery products, coded under snacks and sweets are now separated into cakes and pies, cookies and brownies, and donuts, pastries and sweet rolls.
Selecting Carbohydrate Foods From Food and Nutrient Database for Dietary Studies 2017-18 Only foods with ≥40% energy from carbohydrate per 100 g dry weight were included in the present analyses. Food dry weight and carbohydrate dry weight were calculated as derived variables. Non-starchy vegetables, legumes and fruits were identified using WWEIA classification codes. Whole grain content of foods was obtained from the FPEDs database and converted to g/100 g  (15, 16). Following past work, whole grain CF were identified as those foods that contained >25% whole grain by dry weight  (15, 16). Current US regulations state that foods containing at least 25% whole grain by dry weight may make a front of pack claim. For foods to be called whole grain in the product name, at least 50% by dry weight is required.
Statistical Analyses The distribution of carbohydrate foods among food groups and food subgroups was shown using pie charts. Frequency and percentage of foods that met the CQI criteria were calculated for each food group. Kappa coefficients were calculated between all scores. Means of energy density and carbohydrate quality scores were calculated food groups and subgroups. Scatterplots were used to compare energy and carbohydrate quality scores across food groups. A p-value < 0. 05 was considered as significant. All statistical analysis were conducted with SPSS (Statistical Package for the Social Sciences, IBM).
Carbohydrate Foods in the Food and Nutrient Database for Dietary Studies 2017-18 The present analytical sample was 2423 foods from FNDDS 2017-18. These foods were selected using What We Eat in America   4-digit codes and included grains, snacks and sweets, sugar sweetened beverages, 100% fruit juices, vegetables, and fruit. Of these, 36% were pre-classified as "higher quality" and 64% as "lower quality" carbohydrates following published papers  (4, 5). Figure  1A  shows the distribution of FNNDS carbohydrate foods by WWEIA food subgroup. Figure  1B  shows the same distribution by WWEIA food category.
Differences in the NU-Exposed Group in Each Generation Data observed from spermatozoa extracts were analyzed using different PLS-DA models. The first model was calculated using the matrix containing control and NU-exposed samples from three generations using generation as a dummy matrix. It showed a significant difference between generations (CV-ANOVA; p = 2. 19 × 10 -27 ; R2Y = 96. 3; Q2Y = 88. 5) (Figure  3 ). Furthermore, NU exposure was evaluated independently in three generations. PLS-DA models were created with no or with different ways of selecting variables to find a model which was able to differentiate between exposed and non-exposed individuals (Figure  4 ). A PLS-DA for generation F0 containing 48 putatively-identified metabolites did not clearly differentiate between exposed and non-exposed individuals.
Differences in the NU-Exposed Group in Each Generation Data observed from spermatozoa extracts were analyzed using different PLS-DA models. The first model was calculated using the matrix containing control and NUexposed samples from three generations using generation as a dummy matrix. It showed a significant difference between generations (CV-ANOVA; p = 2. 19 × 10 -27 ; R2Y = 96. 3; Q2Y = 88. 5) (Figure  3 ). Furthermore, NU exposure was evaluated independently in three generations. PLS-DA models were created with no or with different ways of selecting variables to find a model which was able to differentiate between exposed and non-exposed individuals (Figure  4 ). A PLS-DA for generation F0 containing 48 putatively-identified metabolites did not clearly differentiate between exposed and non-exposed individuals.
Data Pre-Processing The ProteoWizard application was used to convert raw spectra into separated positive/negative mzXML files. Only positive ionization spectra were processed. The XCMS library under the R environment was used to extract data using the following parameters: peak detection method-centWave; peak width-2 to 15 s; S/N threshold-3; noise -10000; m/z tolerance between two consecutive scans-5 ppm. The prefilter for the peaks detected was set to four consecutive scans with intensities higher than 100,000. The peaks of samples were aligned and grouped using Obiwarp and density methods, respectively. Extracted data matrices were then filtered to eliminate the analytical background and correct for analytical drift. Analytical batch drift was corrected using the Van der Kloet algorithm.
Blank samples were used to filter data from instrumental noise, and pool samples to filter signals that varied by more than 30%. The final matrix contained 555 ions and was used for the untargeted metabolomic analysis. Uranium concentration results are expressed as mean ± standard deviation (SD). Kruskal-Wallis One-way ANOVA was performed, and Dunn's Method was used for all pairwise multiple comparisons. The Fisher exact test was used to compare pregnancy rate, number of pups per litter and the male/female ratio. Differences were considered statistically significant when p < 0. 05 (Sigmaplot Stat software, SPSS, Paris, France).
Variable Selection and Metabolite Identification Discriminant metabolites were selected according to their variable importance in projection (VIP) score SIMCA P algorithm. Normal probability plot (NNP) distribution was used to determine the appropriate threshold for significance. Variables were putativelyidentified using the laboratory data base, Metlin and MZedDB database browsers (Aberystwyth University, Aberystwyth, UK) freely available online  [77] , according to chemical formulas generated from mass measurements (error < 5 ppm). Full MS and MS/MS spectra were compared for standard chemical samples, biological samples, and spectral databases (mainly laboratory data base, HMDB, Metlin, and MassBank) to identify metabolites.
2a ). Those results were confirmed by qRT-PCR experiments showing a dramatic reduction of bfrG mRNA abundance in the presence of Cu (Fig. 2b ). In contrast, Fe had no effect and Zn moderately affected bfrG mRNA levels, suggesting limited cross regulation. We performed qRT-PCR experiments on each of the three genes of the operon and normalized the results against a housekeeping gene. We also generated chromosomal reporter fusions by inserting lacZ in frame with the first codons of each gene to assess the effect of copper on their expression. bfrG and bp2921 were expressed at moderate levels in copper-restricted medium (Fig. 1c  and d ). Addition of copper to the medium abolished their transcription and translation, consistent with RNAseq data  (24). Intriguingly, bp2923 was hardly translated, despite being the first gene of the operon, and it did not appear to be regulated by copper (Fig.
The asterisk indicates another protein just above BfrG which is also recognized by the antibodies. b, Ratios of transcription (in log 2 ) of the three genes in bacteria grown in medium supplemented as in (a) relative to bacteria grown in standard medium. Data represent the means of three biological replicates, and the error bars show the SD. c, Kinetics of bfrG regulation by copper. Aliquots of BPSM cultures were taken at the indicated times after addition of 100 mM CuSO 4 to the medium for qRT-PCR analyses on bfrG. Data were normalized using a housekeeping gene, and they are compared to the expression level of bfrG immediately before the addition of CuSO 4. The data represent the means of three biological replicates, and the error bars show the SD.
6b, part 6 ). In contrast, replacement of a semiconserved Cys in the DUF2946 family preserved the regulation of bfrG (mutant C 51 S; Fig. 6b, part 7 ). The CXXC and the RAPP motifs are separated from each other by 45 intervening residues. Genome mining identified more than 2000 DUF2946 protein sequences in databases, mostly in Proteobacteria (Table  S1 ), and their analysis showed that the spacing between the two motifs is conserved to within one or two residues in the family (Fig. 6c ). We therefore probed its importance for expression and regulation of bfrG by shortening or lengthening the spacing by one, two, or five residues in the Bp2923 protein (mutants bp2923-1, bp2923-2, bp2923 1 1, bp2923 1 2 and bp2923 1 5, respectively; Fig. 6a ). We designed those mutations to minimize their effects on the mRNA structure (Fig.
Pellets were flash-frozen in liquid nitrogen and kept at 280°C. For RT-PCR, RNA extraction was performed using Tri-Reagent (InVitrogen), followed by a DNAse I treatment (Sigma Aldrich) to remove remaining genomic DNA. Retro-transcription was performed with the Verso cDNA synthesis kit (ThermoFisher). qRT-PCR was performed in a Roche LightCycler 480 Instrument II using the Takyon LowROX SYBR kit (Eurogentec). All qRT-PCR experiments were conducted with 3 biological replicates and 3 technical replicates, and data were normalized using the housekeeping gene bp3416. This gene coding for the purine biosynthesis protein PurH has been repeatedly used for normalization of qRT-PCR, as its expression level is constant. In particular, bp3416 was expressed at the same levels irrespective of the presence of Cu in the medium  (24).
5'RACE experiments were conducted on total RNA extracted from cultures of BP2923-OCU supplemented with BCS, using the Generacer kit (Invitrogen) and specific RACE primers for bfrG and cruR according to the manufacturer's instructions. For cruR, after PCR amplification with a first primer annealing within the gene, a nested PCR was performed using a second primer annealing immediately before the cruR start codon to enhance specificity. The cDNA isolated in the RACE experiment was used to build a library using the Illumina TruSeq Stranded RNA LT library preparation kit, followed by sequencing on an Illumina NextSeq 500 benchtop sequencer. The GeneRacer adapter sequence was removed from the reads using Cutadapt (https://github. com/marcelm/cutadapt) and the reads were mapped using the CLC Genomics software (Qiagen).
b-galactosidase activity measurements. B. pertussis strains carrying chromosomal translational fusions with lacZ were cultured to an OD 600 of 1. 5 to 2 in the indicated conditions and harvested by centrifugation. Pellets were resuspended to an OD 600 of 5 and lysed using a Ribolyser at speed 6 for 50 s. b-galactosidase activity was measured as described  (68). Experiments were conducted with 4 biological replicates and 3 technical replicates. Statistics. Four biological replicates were used in b-galactosidase activity studies. Statistical analyses were performed with the GraphPad Prism software using the nonparametric Mann-Whitney test with a confidence level of 95%. No statistical analyses were performed for qRT-PCR experiments as they were carried out on three independent biological samples only.
Reaction pellets were resuspended in loading buffer (95% formamide; 5mM EDTA) and analyzed by denaturing 7% polyacrylamide gel electrophoresis and by phosphorimaging with a Typhoon FLA-9500 instrument and ImageQuant TL software (GE Healthcare). Potential Cu 21 scavenging-by-buffer effects (70) were ruled out in control transcription termination experiments where Tris-HCl was replaced by MOPS, pH 7. 9 (not shown).
c, Kinetics of bfrG regulation by copper. Aliquots of BPSM cultures were taken at the indicated times after addition of 100 mM CuSO 4 to the medium for qRT-PCR analyses on bfrG. Data were normalized using a housekeeping gene, and they are compared to the expression level of bfrG immediately before the addition of CuSO 4. The data represent the means of three biological replicates, and the error bars show the SD.
ACKNOWLEDGMENTS We thank  Qing Chen  ( FDA, USA ) for the kind gift of the pQC2123 plasmid, and  Axel Innis  ( IECB Bordeaux ) for discussions. G. R. acknowledges the support of a  doctoral fellowship  from the  University of Lille. Several figures in this article were drawn with BioRender. This work was funded by the  Institut National de la Santé et de la Recherche Médicale (INSERM)  and the  University of Lille. The funders had no role in study design, data collection, and interpretation or the decision to submit this work for publication.
To replicate the finding at 9p21. 3, we tested the association between rs10122939 and ITA o in an additional Chinese cohort from Taizhou (Taizhou longitudinal cohort, n ¼ 1,787) and in a Latin American cohort from Colombia (n ¼ 266). The effect of rs10122939 was successfully replicated at nominal significance, and the G allele also showed a skin-darkening effect in both replication cohorts (Taizhou longitudinal cohort: b ¼ e0. 09, P ¼ 5. 96 Â 10 À3 , Colombian: b ¼ e0. 26, P ¼ 3. 89 Â 10 À2 ). A meta-analysis of all samples further enhanced the significance level of this association (P ¼ 2. 13 Â 10 À13 ). To further investigate the potential functions of rs10122939, we conducted a function annotation analysis using the quantitative scoring system embedding the 3DSNP database  (Lu et al. , 2017) , which revealed significant evidence for rs10122939 serving as an enhancer of SLC24A2 (Figure  2c ).
Study populations The Jidong cohort. The Jidong cohort is a community-based, long-term observational cohort study to evaluate healthrelated risk factors. The baseline data were collected from 2013 to 2014 in the Staff Hospital, Jidong Oilfield Branch, China. Approval was obtained from the Ethics Committee of Kailuan General Hospital of Tangshan City and the Medical Ethics Committee, Staff Hospital, Jidong Oilfield Branch, China National Petroleum Corporation in July 2013 (approval number 2013 YILUNZI1). The approval had been renewed in 2018. To date, 9,078 individuals aged >18 years have been enrolled after excluding individuals who were unable or unwilling to participate. Written informed consent was obtained from all participants. This study included a total of 5,601 individuals (2,512 men and 3,089 women, aged 31e87 years) who paid the return visit in 2018.
Therefore, only 1,930 individuals were included in this study. The Taizhou longitudinal cohort. The Taizhou longitudinal cohort study is a long-term observational cohort study to explore the environmental and genetic risk factors for common and noncommunicable diseases. This research program was conducted with the approval of the Ethics Committee of Fudan University (Ethics Research Approval 85), Shanghai, China. The detailed characteristics have been described before  (Wang et al. , 2009). Our replication set included 1,787 healthy Han Chinese with portrait photos, aged 31e85 years. Colombian cohort. The Colombian cohort collected data from participants of several Latin American countries to study the genetics of normal variation in physical appearance. For the genetic analysis of individual typology angle (ITA o ), 266 participants were recruited in the city of Medellin, Colombia.
The face landmarking was achieved by the shape_predictor function from the Cþþ library Dlib on the basis of the face region detected by the get_frontal_face_detector. This predictor was created by training an ensemble of regression trees for face alignment on the iBUG 300-W face landmark dataset  (Sagonas et al. , 2016; Sagonas et al. , 2013a Sagonas et al. , , 2013b)). It takes an image of a human face as input and identifies the locations of 68 facial landmarks including, the corners of the mouth and eyes, the tip of the nose, and edges of cheeks (Figure  1a ). For each side of the face, the pipeline automatically outlines a rectangle to segment the cheek part according to preselected anatomical landmarks in such a way that the rectangle contains the cheek-surrounding landmarks with the minimal horizontal and vertical distances between the landmarks (Figure  1a ).
The chip genotype data were firstly phased using SHAPEIT3  (O'Connell et al. , 2016) , and IMPUTE2 was then used to impute genotypes  (Howie et al. , 2009). SNPs with an imputation quality score (INFO) <0. 6, minor allele frequency <0. 01, or a missing rate >0. 01 were eliminated from further analyses. Finally, 8,039,700 SNPs passed quality control and were used for further analyses. For Taizhou longitudinal cohort, blood samples were collected, and DNA was extracted. All samples were genotyped using the Illumina HumanOmniZhongHua-8 chip (Illumina), which interrogates 894,517 SNPs. After quality control with PLINK, version 1. 9, the genotype data were phased using SHAPEIT and were imputed using IMPUTE2 with the 1000 Genomes Project phase 3. Blood samples were collected from the participants in the Colombian cohort.
DNA was extracted and genotyped on the Illumina Infinium Global Screening Array. After quality control using PLINK, version 1. 9, 511,848 SNPs were retained. The genotype data were then phased using SHAPEIT2 and imputed using IMPUTE2, with the 1000 Genomes Project phase 3 as the reference panel. Genotype data for SNP rs10122939 were obtained from the imputed dataset.
Statistical analysis GWASs and meta-analysis. GWASs were separately conducted in the Jidong and NSPT cohorts on Z-transformed ITA o values using software package PLINK  (Chang et al. , 2015)  where additive allele effects were tested in linear models adjusted for covariates (age, sex, sampling locations, and the top four genomic principal components). GWAS outputs were combined using inverse variance fixed-effect meta-analysis as implemented in METAL  (Willer et al. , 2010). Pvalues 5 Â 10 e8 from the meta-analysis were considered genome-wide significant. The inflation factor was estimated close to 1. 0 (l ¼ 0. 995) and not further considered. GWAS results were visualized using Manhattan plots, Q-Q plots. Regional linkage disequilibrium plots and association plots were generated using  Haploview and LocusZoom (Barrett et al.
iHS is an integration of extended haplotype homozygosity statistics to highlight the recent positive selection mutations that have not yet reached a fixed level. Larger values indicated adaptation to selection pressure in the most recent stage of evolution  (Sabeti et al. , 2002; Voight et al. , 2006). First, We used the selscan software (Szpiech and Hernandez, 2014) to calculate the iHS scores of SNPs on the basis of genotype data from the five populations, including African, East Asian, European, American, and South Asian of the 1000 Genome Project. The absolute iHS scores of the top 1% and 5% of all SNPs in these 22 randomly selected regions for each population were used as the empirical significance cutoff of the corresponding population. Then, we calculated the iHS scores of SNPs within 200 kb upstream and downstream of the lead SNP we identified, and the iHS absolute score represents the selection intensity.
ACKNOWLEDGMENTS This work has received funding from the  Strategic Priority Research Program of the Chinese Academy of Sciences  ( XDB38020400  and  XDB38010400 ), the  National Key Research and Development Project  ( 2018YFC0910403 ),  Shanghai Municipal Science and Technology Major Project  ( 2017SHZDZX01 ), the  National Basic Research Program  ( 2015FY111700 ), the  CAS Interdisciplinary Innovation Team project, Beijing Advanced Discipline Fund , the  National Natural Science Foundation of China  ( 81930056 ,  91651507 ,  32070579 ,  31771393 , and  31601016 ), the  Open Project of Key Laboratory of Genomic and Precision Medicine of the CAS, State Key Laboratory of Genetic Resources and Evolution  grant ( GREKF20-13 ), the  Leverhulme Trust  ( F/07 134/DF ),  BBSRC  ( BB/ I021213/1 ), the  Excellence Initiative of Aix-Marseille University -A*MIDEX  (a  French Investissements d'Avenir programme ,  2RUIZLRE/RHRE/ ID18HRU201  and  20-07874 ), the  Scientific and Technology Committee of Shanghai Municipality  ( 18490750300 ),  Ministry of Science and Technology of China  ( 2020YFE0201600 ), and the 111 Project ( B13016 ),  Santander Research  and  Scholarship Award , and  Bogue Fellowship  from  University College London.
Del Bino S, Bernerd F. Variations in skin colour A G G 0. 30 e0. 09 0. 02 A C C 0. 15 0. 10 0. 02 A C C 0. 30 e0. 10 0. 02 C T T 0. 15 0. 10 0. 02 G A A 0. 31 e0. 09 0. 02 G A A 0. 14 0. 10 0. 02 G C C 0. 30 e0. 09 0. 02 T TG TG 0. 15 0. 09 0. 02 G A A 0. 33 e0. 08 0. 02 A T A 0. 49 e0. 06 0. 02 challenge: database and results. Image Vis 8. 63Ee08 0. 29 e0. 08 0. 03 2. 74Ee03 1. 03Ee05 0. 12 0. 10 0. 03 4. 22Ee03 Comput 2016;47:3e18. 5. 77Ee08 0. 29 e0. 08 0. 03 3. 78Ee03 1. 02Ee05 0. 11 0. 10 0. 03 3. 23Ee03 Simcoe M, Valdes A, Liu F, Furlotte NA, 6. 54Ee08 0. 30 e0. 08 0. 03 2. 87Ee03 1. 44Ee05 0. 10 0. 11 0. 04 1. 67Ee03 Evans DM, Hemani G, et al. Genome-wide 1. 56Ee07 0. 29 e0. 07 0. 03 5. 04Ee03 4. 51Ee05 0. 12 0. 10 0. 03 3. 15Ee03 association study in almost 195,000 individuals identifies 50 previously unidentified genetic 2.
Study design This proof-of-concept, randomized, double-blind, placebo-controlled trial was performed on two parallel groups. The trial was conducted at the medical ICU at the Raymond Poincaré University Hospital (Assistance Publique Hôpitaux de Paris, Garches, France). The trial protocol was approved by an independent ethics committee (Comité de Protection des Personnes d'Ile de France XI, Saint-Germain-en-Laye, France), was registered with ClinicalTrials. gov, number NCT02968836 (registered November 21, 2016) and conducted in accordance with French law and the Declaration of Helsinki.
Anterior quadriceps volume was measured semi-automatically using isometric T1 weighted 3D images. Data are presented as the mean value of both legs. Following analysis of the overall results, we corrected for peripheral (muscle) oedema by indexing the quadriceps muscle volume to the patient's weight, measured on the same day as the MRI was performed. Quadriceps force was measured using magnetic stimulation of the femoral nerve  [34]. Briefly, patients were studied while in the supine position with the knee flexed at 90°  [35]. The ankle was attached to the arm of a dynamometer by means of an inextensible ankle trap. Stimulation was performed using a 45-mm figure-ofeight coil (Magstim 200, The Magstim Limited Company, Whitland, UK)  [36]. The coil was oriented tangentially to the surface of the femoral triangle, lateral to the femoral artery.
Acknowledgements We would like to thank all the patients and caregivers who participated in this trial. We thank  Jacques Vuichoud  for conducting the amino acid analyses as well as  Laurence Goulet ,  Adrien Frézal  and  Jean-Philippe Godin  ( Nestlé Research, Switzerland ) for their assistance with the nutrient profiling analysis. We also thank  Hayette Tessa  for her exceptional technical support,  Amelie Goyer  for efficient clinical project management,  Marie Langlois  for product development and production and  Sophie Demay , our nutritionist.