dataesr/scientific-paragraphs-categorization huggingface dataset:
Heat flux data can be used to infer the microbial growth rate constant provided the existence of a correlation between isothermal microcalorimetry data and microbiological data (e. g. , cell counts) at early growth  [38]. During the exponential growth phase, microbial growth follows a first-order kinetics defined by the specific growth rate constant μ c (h -1 ). Analogously, the heat flux follows an exponential behaviour determined by the parameter μ c as described by  [37, 38]. dQ dt ¼ m c � Q ð1Þ The growth rate constant μ c can be determined by fitting the exponential part of the heat flux-time curve using the fitting function of the TAM Assistant Software. In our case study, careful selection of the exponential phase of heat flux dynamics was performed to provide a reliable estimation of the maximum growth rate constant from calorimetric data.
Hence, it follows that Y ¼ 1 10 � ð1 À f Þ ð9Þ If Y is known, the fraction f can be computed from Eq  (9). The yield factors of CO 2 and CH 4 can be expressed as functions of the the fraction f: Y CO 2 ¼ 1 4 � � � f þ 5 10 � � � ð1 À f Þ ð10Þ Y CH 4 ¼ 1 4 � � � f ð11Þ The model has two physicochemical parameters (k L a, K H;CO 2 ) and four biological parameters (μ max , K s , Y, k d ). The initial condition for s CO 2 is unknown and was also included in the parameter vector for estimation. The Henry's law coefficients are known values calculated at 39˚C using the equations provided by Batstone et al. [40]. An implementation of the model in the Open Source software Scilab (https://www. scilab. org/) is available at https://doi. org/10. 5281/zenodo. 3271611. Theoretical model to study interactions among methanogens.
Parameter identification Before performing the numerical estimation of the model parameters, we addressed the question of whether it was theoretically possible to determine uniquely the model parameters given the available measurements from the experimental setup. This question is referred to as structural identifiability  [44]. Structural identifiability analysis is of particular relevance for model whose parameters are biologically meaningful, since knowing the actual value of the parameter is useful for providing biological insight on the system under study  [45]. Moreover, in our case, we are interested in finding accurate estimates that can be further used as priors in an extended model describing the in vivo system. We used the freely available software DAISY  [46]  to assess the structural identifiability of our model.
By this, only the affinity constant for each strain and the initial condition of s CO 2 were left to be estimated. The parameter identification for each methanogen was performed with the IDEAS Matlab 1 toolbox  [48]  (freely available at http://genome. jouy. inra. fr/logiciels/IDEAS). The parameter identification was performed with the data of the in vitro growth experiments. The measured variables are the number of moles in the gas phase (H 2 , CH 4 , CO 2 ). The Lin's concordance correlation coefficient (CCC)  [49]  was computed to quantify the agreement between the observations and model predictions.
Acknowledgments We are grateful to  Dominique Graviou  ( UMRH, Inra ) for her skilled assistance on the in vitro growth experiments and qPCR assays. We thank the  Inra PHASE department and the Inra MEM  metaprogramme for financial support. RMT, MP and DPM acknowledge the support of  ERA -net gas co-fund for funding the  RumenPredict  project.
On November 1-2, 2015, a total of 5 mL of blood was collected from consenting residents of the compound. The blood was collected in a serum collection tube according to standard procedures and labeled with a coded identification number linked to the data collection forms. Transport of specimens within national borders complied with the applicable national regulations of Saudi Arabia. International transport of MERS-CoV specimens followed applicable international regulations  (20). Serologic assays used to detect and confirm seropositivity in the serum samples were MERS-CoV S1 IgG ELISA (EUROIMMUN EI 2604-9601G kit, https://www. euroimmun. com), MERS-CoV spike pseudoparticle neutralization test (ppNT), and 90% plaque-reduction neutralization test (PRNT 90 ). Serologic testing for MERS-CoV antibodies was conducted at the University of Hong Kong, as previously described  (21).
Statistical Analyses We entered all data for analysis in the entry form in Epi Info 3. 5. 4 (https://www. cdc. gov/epiinfo) and exported it to statistical software Stata 14 (https://www. stata. com). We estimated risk factors for infection among case-patients and non-case-patients (risk ratios [RRs] and 95% CIs) and within a nested case-control study (odds ratios [ORs] and 95% CIs) by restricting analyses to residents living in villas in which laboratory-confirmed cases had been identified.
Participants Patients were included from three critical care units affiliated with the University Teaching Hospital (Toulouse, France) between January 2013 and January 2015. We compared rs-fMRI data of 25 cardiac arrest survivors with severe anoxic-ischemic brain injury, who met the clinical definition of coma (Glasgow Coma Scale score  (Teasdale & Jennett, 1974)  at the admission to hospital < 8, with motor responses < 6; Mean=51y; SD=18y; age range=18-80y; 12M) to 22 agematched healthy controls (Mean=44y; SD=20y; age range=22-74y; 10M). Patients were scanned at least 2 days (4 +/-2 days) after complete withdrawal of sedation and under normothermic condition. The delay between primary brain injury (i. e. cardiac arrest) and MRI scan was of 6 days (Mean=6; SD=3). Standardized clinical examination was performed on the day of the scanning using the Glasgow Coma Scale and the Full Outline of Unresponsiveness  (Wijdicks et al.
Data acquisition In all participants, 11 min resting state fMRI was obtained using a 3T magnetic resonance scanner (Intera Achieva; Philips, Best, the Netherlands). Two hundred and fifty multislice T2*-weighted images were retrieved with a gradient echo-planar sequence using axial slice orientation (37 slices; voxel size: 2 x 2 x 3. 5 mm; TR = 2,600 ms; TE = 30 ms; flip angle = 90°; FOV = 240 mm). In addition, a 3D T1-weighted sequence (170 contiguous slices; TR= 8. 1 ms, TE = 3. 7 ms, FOV= 220|232|170mm, flip angle = 8°, resolution= 1mm3 isovoxel) was also acquired in the same session and later used for visual assessment of the structural integrity of regions of interest.
Network metrics The network analysis was done in R (v. 3. 3. 2; The R Project for Statistical Computing; http://www. R-project. org/) using the brainwaver (v. 1. 6) and iGraph (v. 1. 1. 2) package freely downloadable at http://cran. r-project. org. Following global metrics were calculated: clustering and global efficiency. The clustering (global average local efficiency -see description below) is a topological measure of segregated information transfer. The global efficiency (GE) is a metric for efficiency of integrative information transfer across the network. This measure is inversely related to the characteristic path length (average shortest path between nodes) but is adapted to fragmented that is disconnected graphs. To explore the local/nodal network metrics we employed the degree and the local efficiency.
Statistical analysis Global and nodal statistics (and functional connectivity) were compared between groups with the aid of the permutation test (10000 iterations) using the package perm (v. 1. 0-0. 0) implemented in R (v. 3. 3. 2; The R Project for Statistical Computing; http://www. R-project. org/). For each nodal metric, the significance level of p-values was adjusted to 0. 05/N (p=. 00018), where N represent the number of nodes included in the analysis. The results were visualized with the BrainNet Viewer  (Xia et al. , 2013).
Results Cohort descriptions. The Optifast900 cohort included both obese and severely obese subjects (mean BMI = 43. 2 kg/m2 ± 0. 3 standard error of the mean) and the DiOGenes cohort included overweight and obese participants (mean BMI = 34. 5 kg/m2 ± 0. 2). Clinical characteristics of the participants are available in Table  1. Upon a 5-week low calorie diet (LCD), participants lost on average 9. 3% (11. 3 kg) and 7. 5% (7. 5 kg) of initial body weight, respectively for the Optifast900 and DiOGenes participants. At baseline, Optifast900 participants were considered more insulin-resistant than DiOGenes subjects (HOMA-IR = 4. 16 ± 0. 14 vs. 3. 15 ± 0. 10), as expected given the more severe obesity. Gene-based association studies. We searched for SNPs associated with degree of weight loss using the largest cohort (Opti-fast900, n = 1166) as a discovery dataset.
Discussion Here we describe a genome-wide association for weight loss. We used two independent weight loss cohorts: a Canadian Opti-fast900 cohort (n = 1166) for discovery and the pan-European DiOGenes cohort (n = 789) for replication. Recent analysis  14  of the Look AHEAD and DPP cohorts only focused on 91 established obesity loci  9, 22  and found association of only one of the loci with weight loss and regain after three years,   Panels from top to bottom correspond to results from the meta-analysis, the Optifast900 cohort and the DiOGenes cohort. Source data are provided as a Source Data file namely the MTIF3-rs1885988 SNP. Analysis of this SNP in our two cohorts did not confirm this association (meta-analysis pvalue = 0. 52). This could be due to differences between the behavior-based and low-caloric dietary interventions as well as the length/type of the intervention in the different studies.
This SNP was not tested in our data as it did not achieve a good imputation metrics (r 2 < 80%). This SNP is known as an NKX6-3 cis-eQTL in human islets  52. Additionally, NKX6-3 transcripts encode transcription factors required for the development of alpha and beta cells in the pancreas  53  and have been show to influence insulin secretion  54, 55. Glycemic improvements following LCD were only measured in the DiOGenes cohort. Association between our three top NKX6. 3 variants and rs13262861 did not reveal any significant association with insulin sensitivity improvements (Matsuda index). Our prioritization analyses based on epigenomic annotation highlighted rs6981587 as the top regulatory variant. Since these annotation marks are derived from cell lines, deciphering the exact underlying mechanism may be premature and would require access to specific tissues for a subset of our GWA participants (e.
The biological importance of the genes near the weight-loss associated variants and the proximity or overlap with annotated regulatory marks, provides evidence for a functional role in metabolism within this locus. However, given the complex relationship between different organs (brain, adipose, pancreas, liver and muscle) as well as the interplay between metabolic pathways, further investigation would require access to different tissues and under different conditions (e. g. weight loss, induced obesity) to elucidate the potential contribution of each gene. In conclusion, we performed a weight loss GWA using data from a large clinical practice (the Canadian Optifast900 meal replacement program). Two loci (RBSG4 and MIR486/NKX6. 3) were successfully replicated with data from a controlled trial (DiOGenes).
Actin-Gal4 Actin-Gal4; Gal80ts Health Science, Switzerland). Program adherence criteria included: attendance to 75% of clinical sessions and strict adherence to the meal replacement (evaluated by the number of meal-replacement products that were consumed). Patients under medication known to affect rate of weight loss or glucose homeostasis or abnormal thyroid indices were excluded from the analyses. In total, 1436 Optifast900 subjects were eligible for genotyping and had available weight measurements. The DiOGenes study (NCT00390637) is an interventional, multi-center pan-European study  17, 18. Eight partners participated to the study: Bulgaria, Czech Republic, Denmark, Germany, Greece, the Netherlands, Spain and United Kingdom. Participants followed an 8-week LCD. The LCD provided 800 kcal per day with the use of a meal-replacement product (Modifast, Nutrition et Santé France).
Genotype data. Genotype data were generated using HumanCoreExome-12 v1. 1 with 264,909 tag SNP marker and 244,593 exome-focused markers. They were processed with the Illumina TM platform following Infinium® HD Assay Ultra, Manual according to manufacturer's instructions. Genotypes were called with the GenomeStudio Software (Illumina). Quality control excluded SNPs with call rate <95%, violating Hardy-Weinberg equilibrium (FDR < 20%), low minor allele frequency <1%. Subjects were excluded if they had low call rate (<95%), abnormally high autosomal heterozygosity (FDR <1%), an XXY karyotype, or gender inconsistencies between genotype data and clinical records. For subjects with high identity-by-state (IBS > 95%), only the one having the highest call rate was kept. Principal component analyses (PCA) were performed independently on each cohort to discard subjects that were outliers in term of genetic structure.
Subjects from both cohorts were all of European ancestry and the two cohorts had similar genetic structure (Supplementary Fig. 1 ). Upon all genetic QCs, 1166 Ottawa and 789 DiOGenes subjects were kept for subsequent analyses. Genotype imputation was then performed using SHAPEIT  62  and IMPUTE2 63 based on the European reference panel from the 1000 Genome project  64  (March 2012 release, phase 1 version 3). Imputation post-filtering removed SNPs with reference allele frequency less than 1% and INFO score <0. 8. Upon such filtering, data for 4. 9 M imputed SNPs were available for both datasets. Functional analyses in Fly. Fly strains: Fly stocks were maintained on standard diet with agar, sugar and yeast and were raised in 25 °C incubator at a 12/12 dark and night cycle. Actin-Gal4 and TubGal80ts was from Bloomington.
For Coomassie staining, the same amount of supernatant was put into the nitrocellulose membrane and the Coomassie blue staining was dropped into the protein-loaded areas for a few times until no more colour changed, and the membrane was washed once with TBST for imaging. Images were taken using ChemiDoc system (Bio-rad). Quantification was performed using Image J software. Statistical analyses. Single-SNP analyses were performed using BOLT-LMM  24  , a Bayesian linear mixed effect model to adjust for population structure and cryptic relatedness between individuals. The rate of weight loss was adjusted for sex, age and starting BMI. Results from the two cohorts, were subsequently meta-analyzed using Genome-Wide Association Meta Analysis (GWAMA) software  67  with random-effect modeling and a double genomic-control (GC) correction  68  (GC correction at study-level and also at meta-analysis level).
Software. General statistical analysis was performed using R statistical environment version 3. 3. 1. We used LocusZoom was used to plot regional association and LD with lead SNP using the 1000 genome CEU population data (hg19/1000 Genomes Mar 2012 EUR). Epigenomic plots were made using the Bioconductor Gviz package. Image J was used to quantified dot-blot results. URLs. For Bolt-LMM, see https://data. broadinstitute. org/alkesgroup/BOLT-LMM/; for GWAMA, see http://www. geenivaramu. ee/en/tools/gwama; for Impute2, see http://mathgen. stats. ox. ac. uk/impute/impute_v2. html; for GenABEL, see http:// www. genabel. org/; for Gviz, see https://bioconductor. org/packages/release/bioc/ html/Gviz. html; for Riviera-beta, see https://yueli-compbio. github. io/RiVIERAbeta/; for LocusZoom, see http://locuszoom.
Acknowledgements We are grateful to  Virginie Alexandre  for useful scientific discussions and help with project management. We are also grateful to  Olivier Simon ,  Radovan Chytracek ,  Richard Cote , and  Kitsa Paluku  for development of a clinical and omics data integration system. We thank  Dr. Jan Veenstra  for generous gifts of antibodies. The DiOGenes project was supported by the  European Commission  ( Food Quality and Safety Priority of the Sixth Framework Program :  FP6-2005-513946 ). Local sponsors made financial contributions to the shop centers, which also received a number of foods free of charge from food manufacturers. A full list of these sponsors can be seen at www. DiOGenes-eu. org/sponsors/. Functional analyses in fly were supported by the  Australian government NHMRC (National Health and Medical Research Council)  project grants  APP1028887  and  APP1124723.
(2011)  for the Amazon, RADAMBRASIL  (de Negreiros et al. , 2009)  for non-Amazonian areas of Brazil, and IGBP  (Tempel et al. , 1996)  for non-Amazonian areas elsewhere, and the meteorological forcing from the Princeton University Global Meteorological Forcing Dataset (PGMF,  Sheffield et al. , 2006)  for 1969 to 2008, which was recycled multiple times to simulate a period equivalent to 1500 through 2002. To estimate the model sensitivity to light and water availability across the region, we used the annual average downwelling shortwave irradiance from the Clouds and the Earth's Radiant Energy System's Energy Balanced And Filled product (CERES-EBAF;  Kato et al. , 2013)  between 2001 and 2017, and the average of annual precipitation from the Tropical Rainfall Measurement Mission's (TRMM) Multi-Satellite Precipitation Analysis Product (TMPA-3B43 V7; TRMM, 2011;  Liu et al.
Acknowledgements. The research was partially carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. We thank  Miriam Johnston  and  Luciana Alves  for suggestions that improved the manuscript;  Luciana Alves ,  Bruce Daube ,  David Fitzjarrald ,  Elaine Gottlieb ,  Elizabeth Pyle ,  Lucy Hutyra ,  Natalia Restrepo-Coupe ,  Raphael Tapajós ,  Scott Stark , and  Kenia Wiedemann  for the management and data processing; and  Valerio Avitabile ,  Alessandro Baccini , and  Sassan Saatchi  for providing remote-sensing estimates of biomass. The model simulations were carried out at the Odyssey cluster, supported by the  FAS Division of Science ,  Research Computing Group at Harvard University. M.
Code and data availability. The ED-2. 2 software and further developments are publicly available. The most up-to-date source code, postprocessing R scripts, and an open discussion forum are available on https://github. com/EDmodel/ED2. The code described in this manuscript, along with a wiki-based technical manual, is stored as a release at https://github. com/mpaiao/ED2/releases/tag/rev-85 and permanently stored at https://dx. doi. org/10. 5281/zenodo. 2579481. Eddy covariance data from GYF and TNF sites are available at https://fluxnet. fluxdata. org. Forest inventory are available upon request to the data PI: GYF -D. B. (damien. bonal@inra. fr); TNF -P. B. d. C (pcamargo@cena. usp. br). The PGMF meteorological data set can be requested at http://hydrology. princeton. edu/data. pgf. php.
The Oak Ridge National Laboratory Distributed Active Archive Center provides access to the RADAMBRASIL (https://dx. doi. org/10. 3334/ORNLDAAC/941), IGBP (http:// dx. doi. org/10. 3334/ORNLDAAC/565) and the photosynthetically active radiation (https://dx. doi. org/10. 3334/ORNLDAAC/899) data sets. Remote-sensing biomass maps were obtained by contacting the corresponding authors  (Saatchi et al. , 2011;  Baccini et al. , 2012), and from http://lucid. wur. nl (Avitabile et al. , 2016). CERES EBAF-Surface and TMPA-3B43 products are available at https://ceres. larc. nasa. gov and https://dx. doi. org/10. 5067/TRMM/TMPA/MONTH/7, respectively. MODIS Leaf area index product (MCD15A2H, Collection 6) is available at http://doi. org/10. 5067/MODIS/MCD15A2H. 006.
MRS processing and quantification Scan-to-scan phase correction was performed on metabolite signal before summing individual scans on Matlab, to correct for incoherent averaging leading to artefactual signal loss. Eddy current correction was achieved using water reference. Spectra were analyzed with LCModel  (Provencher, 1993) , using a basis set generated for each TM with home-made routines based on the density matrix formalism. Signal could be reliably quantified according to our quality standards (Cram er-Rao lower bounds CRLB<5%) for NAA, tCr, tCho, Glu, Ins, and Tau at all b-values and t d. Individual macromolecule spectra acquired in mice were summed with respect to their group and then included in LCModel basis sets associated to the CNTF and control groups. Metabolite concentrations were evaluated from spectra acquired at t d ¼ 53.
Statistical analysis of MRS data For metabolite levels measured in MRS experiments, statistical significance of the effect of the group (CNTF or control) on metabolites concentrations (only those quantified with Cram er-Rao lower bounds <5%) was assessed by an unpaired Student's t-test. To compensate for the type I error due to multiple comparisons, p-values were adjusted by a Bonferroni correction over the eight metabolites considered. In DW-MRS experiments, we wanted to evaluate the statistical significance of the effect of the group (CNTF or control) on the functional form of the diffusion, i. e. signal attenuation over b or on ADC over t d , as this functional form carries the information about the underlying microstructure. This was assessed by an analysis of variance (ANOVA) with one betweensubject factor (group CNTF or control) and one within-subject factor (bvalue or t d ).
DTI and DKI analysis Diffusion-weighted images were denoised using MP-PCA as described in  (Veraart et al. , 2016) , corrected for Gibbs ringing artifacts following  (Kellner et al. , 2016)  and for eddy-currents artifacts using FSL eddy  (Andersson and Sotiropoulos, 2016). No artifacts due to brain motion were detected. DTI  (Basser et al. , 1994; Basser and Pierpaoli, 1996)  and DKI  (Jensen et al. , 2005; Tabesh et al. , 2011)  analyses were performed using home-made script in python, harnessing the DIPY's  (Garyfallidis et al. , 2014)  DTI and DKI modules employing weighted least square minimization to voxel-wise estimate the 6 and 15 independent components of the diffusion (DT) and kurtosis (KT) tensors, respectively. From the DT, standard diffusion statistics such as mean diffusivity (MD) and fractional anisotropy (FA) were computed, as described in  Basser and Pierpaoli (1996).
Fitting was performed by using a nonlinear least-square regression, based on the trust-region-reflective algorithm implemented in MATLAB (The MathWorks, Natick, Massachusetts, USA). The error on estimated parameters was evaluated using a Monte Carlo (MC) approach (n ¼ 2500 draws). For each draw, random noise (whose standard deviation s. d. was estimated from the difference between the best fit and the experimental data) was generated and added to the best fit to generate a new data set, which could be analyzed using the model. Statistical difference between control and CNTF values was assessed by permutation analysis of the parameter distribution estimated from the 2500 MC draws using unpaired two-tailed t-test (with Bonferroni correction for the two structural parameters of interest R fiber and R soma ).
An efficient code was implemented in Matlab to parallelize this huge amount of computation on a GPU device (NVIDIA Tesla K20c). The whole procedure required about 3 min for each set of morphometric parameters, and about 400 h to build the full database by  Ligneul et al. NeuroImage 191 (2019)  457-469 A nm ¼ X ∞ n¼0 8 < : 1 1 þ δ n0 Â J ' n γg ? δ R fiber Ã 2 X ∞ m¼1 α 2 nm À α 2 nm À n 2 ÁÂ α 2 nm À À γg ? δ R fiber Á 2 Ã2 e À D intra α 2 nm t d R fiber 2 9 = ; B nm ¼ X ∞ n¼0 ( Â j ' n γgδ R soma Ã 2 X ∞ m¼1 ð2n þ 1Þα 2 nm À α 2 nm À n 2 À n ÁÂ α 2 nm À ðγgδ R soma Þ 2 Ã 2 e À D intra α 2 nm t d Rsoma 2 ) C. varying D intra , mean N branch and mean L segment. To learn the mapping between the substrate parameters and ADC time-dependency, a random forest regression was used  (Nedjati-Gilani et al.