dataesr/scientific-paragraphs-categorization huggingface dataset:
Fluid Cooling Simulations A simulation of the temperature distribution and of the thermomechanical stresses affecting the amplifier disks was carried out by means of Finite Element Analysis (FEA) software available in a commercial software package, i. e. , LAS-CAD (ver. 3. 6. 1) developed by LAS-CAD GmbH (www. las-cad. com), to calculate the spatial temperature distribution in the gain material, the resulting stress and deformation distribution induced by the thermal expansion, and finally the thermal aberrations computing the optical path difference (OPD) distribution across the crystal aperture due to both the variation of the refractive index with temperature and the variation in the crystal thickness due to thermal expansion and thermally induced stresses. Thermally induced birefringence  [20]  was not considered.
The fluidodynamics simulations were carried out using a commercial software package (COMSOL Multiphysics Version 5. 3a). An example is shown in Figure  6 , depicting the fluid flow in contact with the back (reflective) surface of the disk of Figure  2. The simulation considers a flow of water at 6 m/s entrance speed, in a cooling channel with a thickness of 5 mm. The fluid flow is heated from its bottom side by a constant heat flux of 25 W/cm 2 , corresponding to the heat input generated in the Ti:Sapphire disk under pumping at the P1 performance level. The length of the heated portion of the channel is 160 mm (corresponding to the diameter of the disk of AMP3). Figure  5  shows the calculated fluid velocity distribution. The temperature dependence of the water properties (viscosity, density) was taken into account; buoyancy effects were considered negligible.
The fluid-solid heat transfer process was studied by means of dedicated fluidodinamical simulations, in 2D geometry, in order to obtain reliable values for the heat transfer coefficient used in the full 3D simulation. The computational method was a numerical solution of the Navier-Stokes equations for mass and energy transport, using the so-called Low Reynolds k-ε method, which is capable of a quite accurate simulation of the behavior of the fluid-solid interface layer with a reasonable computational effort. The fluidodynamics simulations were carried out using a commercial software package (COMSOL Multiphysics Version 5. 3a). An example is shown in Figure  6 , depicting the fluid flow in contact with the back (reflective) surface of the disk of Figure  2. The simulation considers a flow of water at 6 m/s entrance speed, in a cooling channel with a thickness of 5 mm.
Acknowledgments: We gratefully acknowledge  P. Mason  and  K. Ertel  of the  Central Laser Facility ,  A. Bayramian ,  C. Siders , and  C. Haefner  of the  Lawrence Livermore National Laboratory , and  F. Falcoz  of  Amplitude Technologies  for fruitful discussion on the laser developments. Acknowledgments: We gratefully acknowledge  P. Mason  and  K. Ertel  of the  Central Laser Facility ,  A. Bayramian ,  C. Siders , and  C. Haefner  of the  Lawrence Livermore National Laboratory , and  F. Falcoz  of  Amplitude Technologies  for fruitful discussion on the laser developments.
Funding: The research leading to these results has received funding from the  European Union   Horizon 2020 Research and Innovation Program  under Grant Agreement No. 653782   EuPRAXIA. This project has received funding from the  CNR funded Italian research Network ELI-Italy  (D. M. n. 631 08. 08. 2016 ). Funding: The research leading to these results has received funding from the  European Union   Horizon 2020 Research and Innovation Program  under Grant Agreement No. 653782   EuPRAXIA. This project has received funding from the  CNR funded Italian research Network ELI-Italy  (D. M. n. 631 08. 08. 2016 ).
In silico analysis Published data were obtained from NCBI Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo) and analyzed using UCSC Genome Browser  [35;  http://genome. ucsc. edu]. DNAse I hypersensitive sites, were identified from GSM1003830 (DNAseDgf on mESC-CJ7), GSM1014154 (DNAseHS on mESC-E14), and GSM1014187 (DNAseHS on mESC-CJ7) datasets. Histone marks were identified from GSM769008 (H3K4me3 on mESC-Bruce4), GSM1000089 (H3K27me3 on mESC-Bruce4) and GSM1000124 (H3K4me3 on mESC-E14) datasets. ChIP-seq data were from GSM288345 (Nanog), GSM288346 (Oct4), GSM288347 (Sox2), GSM288349 (E2f1), GSM288350 (Tfcp2I1), GSM288353 (Stat3), GSM288354 (Klf4), GSM288355 (Esrrb), and GSM288356 (c-Myc) compendiums  [36] , and GSM470523 (Nr5a2)  [37]  and GSM1208217 (Klf4)  [38]. Several resources were used to predict the transcription factor binding site (TFBS)'s relative scores on the genomic sequence upstream of the Ccne1 gene, downloaded from the Ensembl database (genome assembly GRCm38/mm10, December 2011).
Quantitative real-time PCR (qRT-PCR) Total RNA was isolated from cell pellets using TRIzol (Ambion) according to the manufacturer's protocol and reverse-transcribed using a High-Capacity RNA-to-cDNA kit (Applied Biosystems). For microRNAs reverse-transcription, a stem-loop primer specific to each miRNA was used. Real-time PCR was performed using the StepOnePlus real-time PCR system (Applied Biosystems) and Fast SBYR Green Master Mix (Applied Biosystems) according to the manufacturer's instructions. The relative quantitation of gene expression was calculated using StepOne Software 2. 3 (Applied Biosystems). Expression of the target genes was normalized to those of the mouse β-actin gene (Actb) or to the mouse sno234 RNA for miRNA. Primers are listed in Table  S1.
ChIP-PCR ChIP for Esrrb, Klf4, and Tfcp2l1 was performed on E14Tg2a mESCs using previously described protocols  [46]. In brief, 10 7 cells were crosslinked with 1% formaldehyde for 15 min. Chromatin was sonicated to a length of less than 400 bp, and subsequently immunoprecipitated with 5 µg of anti-Esrrb (Perseus, pp-H6705-00), anti-Klf4 (Stemgent, 09-0021), and anti-Tfcp2l1 (AbCam, ab123354). DNA fragments encompassing binding sites for Esrrb, Klf4, and Tfcp2l1 in the P region of Cyclin E and the Nanog promoters were subsequently amplified by qPCR. A 3′ untranslated region of the Cyclin E gene lacking putative binding sites for Esrrb, Klf4, and Tfcp2l1 was used as negative control. Primers are listed in Table  S2. ChIP-qPCR data obtained for each specific antibody were normalized using the percent input method that normalizes according to the amount of chromatin input.
10-P+ PE+DE, respectively. For site-directed mutagenesis of Esrrb, Klf4, and Tfcp2l1 binding sites, the pGL4. 10-P plasmid was mutated by PCR using mutant primers and Q5 sitedirected mutagenesis kit (New England Biolabs, E0554) according to the manufacturer's instructions. Mutant primers were designed to modify 10-14 bp encompassing Esrrb, Klf4, and Tfcp2l1 binding sites into 10-14 bp sequences with low binding scores using JASPAR software (Table  S4 ). The shRNA sequences used to knock down Esrrb, Klf4, and Tfcp2l1 are described in Table  S5. An expression cassette containing the eGFP reporter was synthesized by GeneArt (Invitrogen) with MfeI restriction sites at either ends and subcloned into pMA to generate pMA-eGFP-miR30 plasmid as previously described  [47]. shRNA sequences for Esrrb, Klf4, Tfcp2l1, and a control sequence were subsequently introduced between XhoI and EcoRI restriction sites in pMA-eGFP-miR30 to generate pMA-eGFP-miR30-shEsrrb, pMA-eGFP-miR30-shKlf4, pMA-eGFP-miR30-shTfcp2l1, and pMA-eGFP-miR30-shControl, respectively.
Regions I, II, and III were thereafter called "promoter" (P), "Proximal Enhancer" (PE), and "Distal regulator Element" (DE), respectively. After transfection into a mouse fibroblast (STO) cell line, luciferase activity was dramatically reduced compared with that observed in mESCs, in line with the reduced transcript levels observed after mESC differentiation. Moreover, in contrast to the situation observed in mESCs, the PE region had no enhancer activity on transcription initiated from the P region. Using the JASPAR database  [39] , putative binding sites for the pluripotency regulators Oct4, Sox2, Esrrb, Tfcp2l1, Klf4, Klf5, Tcf3, and STAT3 were identified in the P and PE regions (Suppl. Figure  2 ). Binding sites for E2F1 and Nr5a2 were also identified in the P region as previously reported  [3, 4, 51].
05, **p < 0. 01, ***p < 0. 001, ****p < 0. 0001). (c) Binding of transcription factors to P (I), PE (II), and DE (III) regions of the Cyclin E 5′ flanking sequence identified from published previously ChIP-seq data [36,37] [GSM288349 (E2f1), GSM470523 (Nr5a2), GSM288355 (Esrrb), GSM288350 (Tfcp2l1), GSM288354 (Klf4), GSM288345 (Nanog), GSM288346 (Oct4), GSM288347 (Sox2), GSM288353 (Stat3), GSM288356 (c-Myc), and GSM1208217 (Klf4) datasets].
Acknowledgments This work was supported by the  Infrastructure Nationale en Biologie et Santé INGESTEM  ( ANR-11-0009 ), the  IHU-B CESAME  ( ANR-10-IBHU-003 ), the  LabEx REVIVE  ( ANR-10-LABX-0073 ), the  LabEx "DEVweCAN"  ( ANR-10-LABX -0061 ), the  LabEx " CORTEX " ( ANR-11-LABX-0042 ), the  University of Lyon ,  program 'Investissements d'Avenir'  ( ANR-11-IDEX-0007 ), and the  Fondation pour la Recherche Médicale  [DEQ20170336757] The authors would like to thank  ENAGO  (www. enago. com)for the English language review.
Funding This work was supported by the  Agence Nationale de la Recherche  [ ANR-10-LABX-0061 ];  Agence Nationale de la Recherche  [ ANR-11-LABX-0042 ]; Agence Nationale de la Recherche  [ ANR-10-IBHU-003 ];  Agence Nationale de la Recherche  [ ANR-11-INBS-0009 ];  Agence Nationale de la Recherche  [ ANR-11-IDEX-0007 ];  Fondation pour la Recherche Médicale  [DEQ20170336757].
05, *p < 0. 05, **p < 0. 01, ***p < 0. 001, ****p < 0. 0001). (c) Binding of transcription factors to P (I), PE (II), and DE (III) regions of the Cyclin E 5′ flanking sequence identified from published previously ChIP-seq data [36,37] [GSM288349 (E2f1), GSM470523 (Nr5a2), GSM288355 (Esrrb), GSM288350 (Tfcp2l1), GSM288354 (Klf4), GSM288345 (Nanog), GSM288346 (Oct4), GSM288347 (Sox2), GSM288353 (Stat3), GSM288356 (c-Myc), and GSM1208217 (Klf4) datasets].
SNP discovery and annotation We used the assembly ASM185804v2 (GenBank accession GCF_001858045. 1) of the O. niloticus as a reference genome sequence. This assembly consists of 1,010 Mb of total sequence comprising 2,990 contigs with a contig N50 of 3. 09 kb. Sequences from all samples were evaluated using FASTQC  (Wingett and Andrews 2018)  to assess base quality and primer adapter contamination. Burrows-Wheeler Aligner (BWA-MEM)  (Li and Durbin 2009)  was used to map the reads of each sample to the reference genome. Briefly, BWA-MEM starts a local alignment between a fragment of the read to the reference genome and extends it until the read is completely mapped, if the read cannot be fully mapped is soft clipped or eventually discarded. To avoid invalid flags in further analysis, reads without a pair were discarded from the output using SAMtools  (Li et al.
2009). In order to obtain a high quality BAM file all duplicated reads were masked as such using PICARD (http://broadinstitute. github. io/picard). For variant calling we used the standard protocol implemented in the Genome Analysis Tool (GATK) version 3. 5. 0. All high-quality BAM files for each sample obtained previously, were assessed at the SNP calling step and summarized into a single Genotyped Variant Calling Format file (VCF) containing all data. Each SNP was categorized as being either homozygous or heterozygous for the ALT allele (i. e. , the non-REF allele). To call a sample homozygous for an ALT allele at a given site, the most common ALT allele variant confidence divided by the total sample reads (QD) must be at least 10 (QD > 10). This normalized ALT alleles in zones with high density depth and poor-quality calls.
SNP filtering and validation Population genetics analyses and filtering described here, including Hardy-Weinberg Equilibrium (HWE), minor allele frequencies (MAF) and observed and expected heterozygosities (HO and HE, respectively), were carried out using VCFTools  (Danecek et al. 2011) and Plink (Purcell et al. 2007 ). An initial common quality control (QC) for the three populations was performed using VCF tools software. The genotypes were filtered to remove indels and sequence alterations, markers with genotypes quality (GQ<0. 15) and minimum quality (minQ<40). Furtherly, specific QC filters for each population were applied discarding SNPs with missing genotypes >0. 60, minor allele frequency (MAF) <0. 01, Hardy-Weinberg Equilibrium (HWE) p-value < 1e-06, Illumina score <0. 8 (Table  1 ).
SNPs were also filtered based on Mendelian error using genotypes from 8 trios (sire, dam and offspring) from POP B, in which markers with less than one Mendelian error were retained. In addition, SNP probes were aligned to the Nile tilapia reference genome to retain markers which have a unique position in the genome assembly (GenBank accession GCF_001858045. 1) generated by the University of Maryland and the University of Stirling  (Conte et al. 2017 ) using the following procedure: i) SNP probes of 121 bp were built using flanking SNPs sequences (60 bp upstream and 60 bp downstream of each SNP); and ii) each probe was aligned to the reference genome by means of BLASTN version 2. 3. 0+ (Madden 2002) , using the following parameters: word size of 11 (-w) and minimum e-value of e-40 (e); iii) all hits were evaluated tolerating only 2 mismatches, and no gaps were allowed; and iv) probes having a unique location in the genome were retained.
All this procedure was achieved using in-house Python scripts. Furtherly, SNPs with MAF > 0. 05 in the three commercial populations were prioritized. Finally, SNPs were selected so that they are as evenly distributed along the genome as possible. This was done by selecting SNPs from windows of equal size across various chromosomes of the genome using THIN < 9 kb command. When selecting SNPs from windows, higher preference was given to common SNPs between population POP B and POP C.
SNP distribution and annotation To determine the distribution of SNPs in the Nile tilapia genome, we identified their chromosome and position into the public GenBank accession assembly GCF_001858045. 1 produced by the University of Maryland and the University of Stirling  (Conte et al. , 2017). The SNPs cover 1. 01 Gb of the total assembly length and averaged one SNP every 9 kb. A total of 47,349 SNP (94. 70%) were located in chromosomes and 2,651 SNP were located into unplaced scaffolds. After SNP annotation, we found that most of the uniquely anchored SNPs were located in introns (57. 81%). Further a total of 12. 2%, 11. 97%, 7. 16% and 0. 63 were located downstream, upstream, intergenic and exon regions, respectively. The remaining SNPs were found in splice acceptor, splice donor, splice site, 3´UTR and 5´UTR regions.
Acknowledgments This study was partially funded through financial support from  CORFO  grant number  14EIAT-28667  from the  Government of Chile. This work was supported by  Basal  grant of the Center for Mathematical Modeling  AFB170001  ( UMI2807   UCHILE-CNRS ) and  Center For Genome Regulation Fondap  Grant  15090007. Powered@NLHPC This research was partially supported by the supercomputing infrastructure of the NLHPC ( ECM-02 ). We would like to acknowledge to  AquaAmerica and Aquacorporación Internacional  for kindly providing the samples used in this work. We would also like to acknowledge to  Gabriel Rizzato  and  Natalí Kunita  from  AquaAmerica  and  Diego Salas  and  José Soto  from  Aquacorporación International  and for their contribution of samples from Brazil and  Costa Rica , respectively.
Background: The mechanisms responsible for calorie restriction (CR)-induced improvement in insulin sensitivity (IS) have not been fully elucidated. Greater insight can be achieved through deep biological phenotyping of subjects undergoing CR, and integration of big data. Materials and Methods: An integrative approach was applied to investigate associations between change in IS and factors from host, microbiota, and lifestyle after a 6-week CR period in 27 overweight or obese adults (ClinicalTrials. gov: NCT01314690). Partial least squares regression was used to determine associations of change (week 6 -baseline) between IS markers and lifestyle factors (diet and physical activity), subcutaneous adipose tissue (sAT) gene expression, metabolomics of serum, urine and feces, and gut microbiota composition.
This diet consisted of habitually consumed foods plus meal replacement with four dietary products daily (60-75 kcal; designed by CEPRODI-KOT Laboratory). These supplements consisted of lyophilized powder enriched in protein and soluble fiber (mainly inulin) and composed of low glycemic index carbohydrates. This intervention has been previously described in detail  (Rizkalla et al. , 2012; Kong et al. , 2013). This study has been registered in ClinicalTrials. gov (NCT01314690) and approved by the Ethical Committee of Hôtel-Dieu Hospital in Paris, France in 2008 (under the number 0811792). All participants provided written informed consent. Data collection occurred in 2009 and 2010.
Assessment of Food and Drink Intake Diet was assessed with a 7-day unweighted food diary administered at baseline and week 6, as previously described in  Kong et al. (2014). The software PROFILE DOSSIER V3 was used for analysis of the diaries and consumed foods were grouped into 26 food groups according to French dietary surveys. Average intake of energy, nutrients, and food groups were calculated from intake over the 7 days. The PROFILE DOSSIER X029 computer software (Audit Conseil en Informatique Médicale, Bourges, France) was used to analyze all food diaries and generate nutrient intakes. This software has a food composition database consisting of 400 food items which are representative of the French diet  (Bouché et al. , 2002).
Change in Diet and Clinical Variables Changes in clinical and diet data were estimated using SAS 9. 4. Median and IQR are reported for all variables. Statistical significance in change in clinical outcomes was determined with Wilcoxon signed rank sum test. Correction for multiple testing per class of variable was done using BH with FDR at the 5% level, and adjusted P-values are reported. Post hoc statistical power calculations for the change in IS markers from baseline to week 6 of CR is presented in Supplementary Table  1.
0 Expression BeadChips (Illumina, Inc. ). Hybridized probes were detected with cyanin-3streptavidin (1 mg/mL; Amersham Biosciences, GE Health Care) and scanned by using an Illumina BeadArray Reader. Raw data were extracted with GenomeStudio 2011. 1 Software by using the default settings and quantile normalization. Relevant genes associated with changes in IS were annotated using the FunNet tool  (Prifti et al. , 2008) , using the whole human genome as the reference gene set.
Serum and Urine Metabolic Phenotyping by 1 H NMR Spectroscopy Urine (N = 21) and serum (N = 24) samples were randomized, prepared and measured on a NMR spectrometer (Bruker) operating at 600. 22 MHz 1 H frequency using previously published experimental parameters  (Dona et al. , 2014). The 1 H NMR spectra were then pre-processed and analyzed as previously reported  (Dumas et al. , 2006)  using Statistical Recoupling of Variables-algorithm  (Blaise et al. , 2009). Structural assignment was performed as reviewed in  Dona et al. (2016) , using in-house and publicly available databases.
Fecal Metabolomics Analysis of frozen fecal samples (N = 26) from patients was performed using an Agilent 7890A gas chromatography system coupled to an Agilent 5975C inert XL EI/CI MSD system (Agilent, Santa Clara, CA, United States). A HP-5MS fused-silica capillary column (30 m × 250 µm × 0. 25 µm; Agilent J & W Scientific, Folsom, CA, United States) was used. Sample preparation and GC/MS methods were performed as detailed in the paper of  Gao et al. (2009). Peaks obtained from fecal water samples were aligned, grouped and corrected according to the mass to charge ratio (m/z) and retention time using XCMS R tools  (Smith et al. , 2006). Resulting MS data was a data matrix in which 835 features were found and characterized by a retention time (RT), mass to charge ratio (m/z) and their corresponding intensities per patient.
Spectra were deconvoluted using Automated Mass Spectral Deconvolution and Identification System (AMDIS) before being compared with reference libraries (in-house and NIST05). Annotation and identification of features were based on standards proposed by  Sumner et al. (2007). Eighty-five features were found statistically relevant and identified based on the retention time, m/z and reference spectra of standards from a local database. Features were considered as putatively annotated when only the m/z and RT and main fragments were matched with the reference standards. The remaining features were putatively characterized (m/z and RT were verified with metabolites in our local database) or noted as unknown features.
Inputs From Host, Lifestyle, and Microbiota for Partial Least Squares Regression Analysis A large set of variables measured from elements in host, lifestyle patterns or microbiota have been included in the data integration analysis presented herein. Specifically, as shown in Figure  1A , the following numbers of variables have been included: 26 food groups and 34 nutrients derived from 7day food diaries, three physical activity variables obtained from the validated Baecke questionnaire, 741 MGS derived from metagenomic sequencing, 835 features from fecal metabolic features obtained using GC-MS, 562 urine and 180 serum metabolic features measured with NMR, 45 clinical parameters (including anthropometric measures, markers of IS, circulating concentrations of lipids, inflammatory markers, adipokines, creatine, and cystatin C, and adipocyte diameter measured from sAT biopsies).
Partial Least Squares Regression With Canonical Mode Multivariate methods such as PLSR are well-suited and often applied to big biological data, including situations where the number of variables (clinical parameters, metabolic features, genes, etc. ) is much larger than the number of observations (patients). In particular, PLSR is used to model relationships between two groups of variables  (Esposito Vinzi et al. , 2010). It maximizes the covariance between latent variables which are linear combinations of the observed pairs of variable groups. The results can be efficiently visualized as graphs that enable better understanding of underlying biological relations and processes. In our analysis, we have used the R "mixOmics" package (Omics Data Integration Project), and the function pls(), with the mode = "canonical, " which assumes no directional relationships between data sets.
Spectral Consensus Strategy for Accurate Reconstruction of Large Biological Networks Developing statistical methods to accurately reconstruct biological interaction networks is a central goal of integrative systems biology  (Herrgård et al. , 2008). In the networks that are of interest in our analysis, nodes represent variables and edges indicate statistical dependency. While many network reconstruction methods exhibit competitive results on various types of data, most state-of-the art methods are challenged to scale to very high dimension datasets where the number of variables might be several orders of magnitude larger than the number of observations. In our analysis, this is precisely the setting we encounter as the number of variables is 9,986 and the number of observations is 27. To accurately reconstruct such a network of interactions we have developed an original method  (Affeldt et al.
Analysis of Subcutaneous Adipose Tissue DDRGK1 in a Separate Obese Population To explore some of the novel links identified with PLSR and ScaleNet, we used existing data from an unpublished CR study and determined association between sAT DDRGK1 and IS. The methodologies for these variables were the same that in the present study. The validation study consisted of a 6-week very low calorie diet, followed by 6 weeks of weight stabilization. In this group of overweight/obese adults, only baseline and week 12 values (i. e. , after the weight regain and stabilization period) are presented.