dataesr/scientific-paragraphs-categorization huggingface dataset:
( 2021 )  ( grey filled region ), and the constant brightness profile ( flat dashed line ). The colour of the data markers reflects their redshift ( blue to red equalling low to high redshift ). Each panel indicates a different CO-transition in the denominator of the luminosity ratio, while the x-axis refers to the CO transition in the numerator. We manually shift the points between J a -0. 5 and J a + 0. 5 so as not to o v erlap. The constant brightness profile assumes equal excitation among all CO transitions, and thus has a uniform value of 1. 0 for all CO transitions. The lines detected for each source depends strongly on their spectroscopic redshift, and is reflected in the clustering of colours at specific CO line transitions. For non-thermalized CO lines, the points lie abo v e 1 at x -v alues lo wer than J b , and below 1 at x -values larger than J b.
( 2013 ) , which correct both for the dust emission in contrast to the CMB, as well as additional dust heating by the CMB. MNRAS 521, 5508-5535 (2023)  Yang et al. 2017 ) and high-redshift (Walter et al. 2011 ; Alaghband-Zadeh et al. 2013 ; Bothwell et al. 2017 ; Yang et al. 2017 ; Valentino et al. 2020a ; Harrington et al. 2021 )  galaxies. In order to estimate the scaling relations accounting for errors in both the line and infrared luminosity, we use a linear regression fitting technique of SCIPY  (Virtanen et al. 2020  ) ODR (orthogonal distance regression) package, which is an implementation of the FORTRAN ODRPACK package  (Boggs et al. 1992  ). The best-fitting parameters in the scaling relations are reported in Table  B1  according to the perscription log 10 L line K km s -1 pc 2 = a log 10 ( L IR  [ L ]  ) + b.
(3) The line luminosity scalings of BEARS galaxies are consistent with the reference samples, spanning o v er four orders of magnitude for all lines. The best-fitting results of the CO and [C I] lines (except CO (2-1) and perhaps CO (3-2)) agree with a linear scaling relation across our sample. While the line fits are well constrained, the source-to-source variation across the reference samples and our data are of the order of ±0. 5 dex. This means that we can convert the luminosities of these emission lines into SFR estimates.
AC K N OW L E D G E M E N T S We w ould lik e to thank the anonymous referee for their insightful comments and suggested additions. This work was supported by the  National Astronomical Observatory of Japan (NAOJ)   ALMA Scientific Research  Grant nos. 2018-09B  and  Japan Society for the Promotion of Science (JSPS) KAKENHI  nos. 17H06130 ,  22H04939 , and  22J21948. SS was partly supported by the  ESCAPE  project;  ESCAPE-The European Science Cluster of Astronomy and Particle Physics ESFRI Research Infrastructures  has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 824064. SS also thanks the  Science and Technology Facilities Council  for financial support under grant  ST/P000584/1. SU would like to thank the  Open University School of Physical Sciences  for supporting this work.
HU acknowledges support from  JSPS KAKENHI  Grant Number  20H01953. CY acknowledges support from  European Research Council (ERC) Adv anced Grant  789410. HD ackno wledges financial support from the  Agencia Estatal de Investigaci ón del Ministerio de Ciencia e Innovaci ón (AEI-MCINN)  under grant ( La evoluci ón de los c íumulos de   galaxias desde el amanecer hasta el  mediod ía c ósmico) with reference ( PID2019-105776GB-I00/DOI:10. 13039/501100011033 ) and acknowledge support from the  Agencia Canaria de Investigaci ón ,  Innovaci ón y Sociedad de la Informaci ón (ACIISI) ,  Consejer ía de Econom ía, Conocimiento y Empleo del Gobierno de Canarias , and the  European Regional Development Fund (ERDF)  under grant with reference  PROID2020010107.
In silico analysis Ffars genes and related protein sequences were identified in the Genomicus software program, version 106. 01, 2021-08-15 (https:// www. genom icus. bio. ens. psl. eu/ genom icus-106. 01/ cgi-bin/ search. pl) and collected from Ensembl (http:// www. ensem bl. org, Ensembl Release 102; November 2020, RT genome available). The genoscope database (http:// www. genos cope. cns. fr/ trout) was used to identify ffar related genes in the rainbow trout genome using BLAST analysis. Sequences are available under the accession numbers reported in Table  3. Ensembl database (http:// www. ensem bl. org/ index. html) was also used to collect amino acids deduced sequences of ffar for all species studied. Protein alignment and the percentage Identity Matrix established with amino acids deduced sequences were performed using MUSCLE software (https:// www.
There were a total of 530 positions in the final dataset. Evolutionary analyses were conducted in MEGA11  [27]. Gene synteny analysis was carried out for all ffar genes between salmonid and other relevant genomes using Genomicus software. To determine the genomic neighbourhood around candidate genes and the conservation of gene order across species, genes were visually examined in NCBI's genomic region browser and Ensembl Gene Summary databases. The phylogenetic analysis showed that all the gar and teleosts sequences rooted together with FFAR2 tetrapods sequences and were divided in 2 distinct sub-trees that we respectively named ffar2a and ffar2b.
mRNA levels measurement by real-time quantitative PCR Total RNA was extracted from the oocytes and embryos (N = 3 pools), proximal gut and brain, (N = 6 per fish) using the TRIzol reagent method (Invitrogen, Carlsbad, CA) with Precellys ® 24 (Bertin technologies, Montigny le Bretonneux, France) following Trizol manufacturer's instructions. Luciferase control RNA (Promega), 10 pg per 1. 9 mg of embryo/alevin or oocyte, was added to each sample for ontogenesis analysis to allow for data normalization during early development as previously described  [22, 28]. Total RNA (2 µg) was used for cDNA synthesis. RNA purity was tested by optical density (OD) absorption ratio (OD 260 nm/280 nm) using a NanoDrop 2000c (Thermo, Vantaa, Finland), and only samples with an OD 260 nm/280 nm ratio > 1. 8 were used for analysis.
Cycle thresholds values superior than 35 cycles were not considered. Melting curves were monitored systematically (temperature gradient 0. 11 °C per second from 65 to 97 °C) at the end of the last amplification cycle to confirm the specificity of the amplification reaction. Duplicate wells were used for each sample and negative controls were included in all reactions, consisting in wells containing RNA samples and water instead of cDNA. Efficiency of all qPCR reactions was 96-100% and R2 was 0. 95-1. For ontogenic analysis, data were subsequently normalized to the exogenous luciferase transcript as previously described  [22]. The different PCR products were initially checked by sequencing to confirm the nature of the amplicon. For this, primers were tested on a pool of cDNA and amplified PCR products were sequenced.
Primer sequences and accession numbers are presented in Table  2. For nutritional analysis, data from mRNA gut and brain were extrapolated from standard curves and normalized to the housekeeping gene after validation; Table  3  Ingredients and composition of the experimental diets aMineral premix: (g or mg kg -1 diet): calcium carbonate (40% Ca), 2. 15 g; magnesium oxide (60% Mg), 1. 24 g; ferric citrate, 0. 2 g; potassium iodide (75% I), 0. 4 mg; zinc sulphate (36% Zn), 0. 4 g; copper sulphate (25% Cu), 0. 3 g; manganese sulphate (33% Mn), 0. 3 g; dibasic calcium phosphate (20% Ca, 18% P), 5 g; cobalt sulphate, 2 mg; sodium selenite (30% Se), 3 mg; KCl, 0. 9 g; NaCl, 0. elongation factor 1a gene (eef1a). Relative expression of the target genes was determined by the ΔΔCT method  [29]. mRNA sequences of trout found and used in this study are available in NCBI and Ensembl genome browser.
Phylogenetic and syntenic analysis of ffar2 We first collected annotated Ffar2 amino acids sequences in selected tetrapod species in Ensembl. We found only one annotated sequence for each selected species (i. e. mouse, rat, human, pig, chicken). Using the Blast function in Ensembl and Genomicus software V. 106. 01, we collected tetrapod-Ffar2 related sequences in selected teleosts species including 3 salmonids (i. e. huchen, atlantic salmon, rainbow trout), species selected to cover the major orders of teleosts. We identified three amino acids sequences related to tetrapods Ffar2 in the spotted gar genome, and between three and nine sequences in teleosts (4 for the zebrafish (Danio rerio), 6 for the Mexican tetra (Astyanax mexicanus), 4 for the Nile tilapia (Oreochromis niloticus), 3 for the Japanese medaka (Oryzias latipes), 3 for the fugu (Takifugu), 9 for the huchen (hucho hucho), 8 for the atlantic salmon (salmo salar) and 7 for rainbow trout).
In addition, the unique trout ffar2a1a sequence grouped together in the phylogenetic tree with 2 sequences in salmon and 2 in huchen (ENSSAG00000068149, ENSSAG00000093514 and ENSH-HUG00000032561, ENSHHUG00000014051, respectively) which were included in the same synthenic group (hamp-usf2-etfb-lim2-ffar2a-xrcc1-ethe1) found duplicated on 2 distincts chromosomes in these 2 species (ssa02 and ssa05 in salmon and QNTS01001724. 1 and QNTS01000300. 1 in huchen; data not shown). The sub-tree that we named ffar2b, was in turn divided into 2 main subtrees respectively called ffar2b1 and ffar2b2 containing respectively 2 (accession number ENSOMYG000000041393 and ENSOMYG000000041387) and 3 (accessions numbers ENSOMYG00000030493, ENSOMYG00000030500, ENSOMYG00000005604) rainbow trout sequences. In the ffar2b1 subtree, we named these two sequences ffar2b1.
1 and ffar2b1. 2, respectively. These two sequences for zebrafish (ENS-DARG000000063088 and ENSDARG000000058535) and mexican tetra (ENSAMXG000000042863 and ENSAMXG000000024912) grouped together. The sequence ENSOMYG000000041393 (ffar2b1. 1) from the rainbow trout grouped together only with salmonids sequences (i. e. sequences ENSSAG00000069128 in (See figure on next page. ) Fig. 1  Phylogenetic tree of ffar1 in vertebrates. FFAR1 protein sequences were aligned using the Maximum Likelihood Method (with Poisson model). Following alignment, the phylogenetic tree was constructed using the neighbour-joining method in Molecular Evolutionary Genetics Analysis (MEGA) software version 7. 0  (Tamura 2013 ). The branch support values were gained by non-parametric bootstrapping (500 replicates). The scale bar represents the calculated evolutionary distance.
Genbank accession numbers (from Ensembl or Genoscope databases) are specifed for each species. Mammalian and teleost FFAR2 protein sequences were used to root the tree salmon and ENSHHUG00000038647 in huchen). The sequence ENSOMYG000000041387 (ffar2b1. 2) from the rainbow trout grouped together with others teleost sequences (i. e. sequences ENSONIG00000043224 in nile tilapia, ENSTRUG00000012836 in fugu, ENSSAG00000069124 in salmon and ENSH-HUG00000038645 in huchen). This tree configuration is in a favour of a ffar2b1 tandem duplication ffar2b1 (into b1. 1, b1. 2) resulting of the duplication in salmonids. The syntenic analysis (Fig. 3 ) seemed to confirm this hypothesis showing that this two sequences ffar2b1. 1 and ffar2b1. 2 were included in the same synthenic group (ago3-olah-tekt2-ffar2b1-usf2-lim2.
0  (Tamura 2013). The branch support values were gained by non-parametric bootstrapping (500 replicates). The scale bar represents the calculated evolutionary distance. Genbank accession numbers (from Ensembl or Genoscope databases) are specifed for each species. Mammalian and teleost FFAR2 protein sequences were used to root the tree (See figure on next page. ) carangaria (greater amberjack, yellowtail amberjack and barramundi perch), cichlidae (nile tilapia), and one for pomacentridae (spiny chromis, clow anemonefish and orange clownfish). No gene was found for salmonids fish. The Phylogenetic analysis grouped the different Ffar4 protein sequences of teleosts closest to mammalian FFAR4.
Following alignment, the phylogenetic tree was constructed using the neighbour-joining method in Molecular Evolutionary Genetics Analysis (MEGA) software version 7. 0 (Tamura 2013). The branch support values were gained by non-parametric bootstrapping (500 replicates). The scale bar represents the calculated evolutionary distance. Genbank accession numbers (from Ensembl or Genoscope databases) are specifed for each species (See figure on next page. ) Fig. 5  Phylogenetic tree of ffar4 in vertebrates. FFAR4 protein sequences were aligned using the Maximum Likelihood Method (with Poisson model). Following alignment, the phylogenetic tree was constructed using the neighbour-joining method in Molecular Evolutionary Genetics Analysis (MEGA) software version 7. 0  (Tamura 2013 ). The branch support values were gained by non-parametric bootstrapping (500 replicates).
The scale bar represents the calculated evolutionary distance. Genbank accession numbers (from Ensembl or Genoscope databases) are specifed for each species The mRNA levels of ffar2b2a, ffar2b2b1 and ffar2b2b2 were significantly affected by the interaction between the diet and the time after the meal (with diet effect for ffa-r2b2b1) with an increase of their mRNA level for fish fed with commercial-like vs plant-based diet 20 min after the meal. The mRNA levels of ffar2b1. 2 gene remained stable in time after the meal with commercial-like or plant-based diet.
5%, 4%, and 3. 4%, respectively, independently explained variance (Fig. 2B ). Other variable categories displayed negligible contribution to prediction suggesting considerable information overlap with the metabolomic, metagenomic, biological and dietary datasets. Using feature attribution analysis (SHapley Additive exPlanations; SHAP  23, 28  ), we assessed how individual variables drive TMAOpredicting models. SHAP analysis identified 24 variables that contributed more than 4% of the regularized TMAO standard deviation (SD) to model outcomes. Of those, age affected predictions the most, followed by eGFR, urinary betaine, percentage of visceral body fat and serum butyryl-carnitine (Fig. 2C ). Besides eGFR, additional variables associated with kidney function including plasma urea  29  , the uremic toxin p-cresol  30  or markers of kidney function decline (i.
As in BMIS, the "top SHAP"   3  for n numbers and xgboost parameters), trained exclusively on variables from each feature category (Supplemental Data 1 for variables included in each group), or the full model (all variables), after 100 iterations. B Averaged independent predictive contribution of each feature category to full model predictions of plasma TMAO, trained as in (A), calculated as the average reduction of EV achieved in relation to the full model (set to 100%) after removing all variables belonging to each feature group after 100 iterations. C Swarm plots of impact on model output (SHAP values) for each BMIS individual with complete phenotypic data (N = 582) for all variables contributing more than 4% to model predictions of regularized TMAO standard deviation, as determined by xgboost algorithms trained on each feature category.
B Swarm plots of SHAP values (impact on model outcome) for each T2D MetaCardis participant with complete phenotypic data (N = 387); represented by individual dots, for all variables contributing more than 4% to model predictions of regularized TMAO standard deviation, computed from xgboost algorithms trained on each feature category. Numbers denote mean absolute SHAP values from all T2D participants (in descending order) next to their corresponding variable. Dots are colored by the inverse-normalized value of their corresponding variable. C Boxplots depicting Explained Variance (EV; R 2 ) of circulating TMAO in T2D individuals calculated by algorithms trained on clinical risk factors  31  , the full model containing all variables or all the variables contributing more than 4% of regularized TMAO standard deviation to T2D model predictions, as determined by SHAP analysis, after 100 iterations.
Study design, recruitment and exclusion criteria has been extensively described  26, 31, 41, 63. The overarching goal of the trial was to investigate the impact of qualitative and quantitative changes in the gut microbiota on the pathogenesis of cardiometabolic diseases (CMDs) and their associated co-morbidities (ClinicalTrials. gov Identifier: NCT02059538). For the present study, patients were subclassified in three groups: BMI-spectrum patients (BMIS  26  ; N = 837), encompassing MetaCardis participants presenting with metabolic syndromerelated risk factors or conditions (hypertension, as defined by the American Heart Association  64  ; obesity, as defined by the World Health Organization  65  and metabolic syndrome, as defined by the International Diabetes Federation 66 ) and patients diagnosed with type-2 diabetes (T2D, as defined by the American Diabetes Association 67 ; N = 561) or ischemic heart disease (IHD; N = 356).
The chromatographic column was an Agilent ZORBAX DB5-MS (30 m × 250 µm × 0. 25 µm + 10 m Duragard). The temperature gradient was 37. 5 min long and the mass analyzer was operated in full-scan mode between 50 and 600 m/z. Peaks were annotated with the use of the Fiehn library (Agilent G1676AA Fiehn GC/MS Metabolomics RTL Library, User Guide, Agilent Technologies, https://www. agilent. com/ cs/library/usermanuals/Public/G1676-90001_Fiehn. pdf). Metabolic features with low reproducibility or linearity were removed from the dataset, resulting in 102 annotated metabolic features. Ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). UPLC-MS/MS performed on a Waters Acquity UPLC-Xevo TQ-S UPLC-MS/MS system equipped with an Acquity BEH HILIC (2. 1 × 100 mm, 1. 7 µm) chromatographic column was employed to determine TMA, TMAO, choline, betaine, γ-butyrobetaine, betaine aldehyde, butyryl-carnitine, isovaleryl-carnitine, OH-isovaleryl-carnitine, stearoyl-carnitine, oleoyl-carnitine, linoleoyl-carnitine, myristoylcarnitine, lauroyl-carnitine, and decanoyl-carnitine as described previously  31, 41.
Machine learning (ML) analysis Variable groups. Patient phenotypic variables were separated into ten groups (Supplementary Data 1). Specifically, biological parameters included biochemical and clinical serum laboratory tests, including lipids, glycated hemoglobin, creatinine, and eGFR. Clinical parameters consisted of clinical history, BMI, systolic and diastolic blood pressure, anthropometric variables, and stool frequency and type. Demographic information comprised age, physical activity, educational and income levels, smoking status, ethnicity and country of recruitment. Drug variables included intake of common medication as described  41  , number of antibiotic courses in the last 5years and number of antihypertensive, anti-diabetic and lipid-lowering treatments. Dietary parameters included habitual consumption of 37 food items, daily nutrients intake derived from these food items calculated as in  35  , alternative Healthy Eating 70 (aHEI), Dietary Approaches to Stop Hypertension 71 (DASH) and Dietary Diversity 72 (DDS) scores.
0. 0) package. Least absolute shrinkage and selection operator (LASSO). We used the LASSO algorithm as implemented in the glmnet (v4. 1) package to predict regularized TMAO values in BMIS (N = 582) participants using all available variables as input (full model). Initially, we used fivefold cross-validation to determine the best regularization parameter lambda (from 100 values ranging from 10 -3 to 10 5 ) for predicting circulating TMAO in the left-out group. We next trained five LASSO regression models in 80% of BMIS participants excluding the corresponding left-out group with the optimal lambda parameter. These models were then used to predict regularized TMAO in the left-out groups and subsequently to calculate the coefficient of determination using the rsq function from yardstick (v0. 0.
5. 7). Mediation analysis. We performed mediation analysis  74  to assess the putative impact of eGFR on plasma TMAO increase with age and, conversely, the impact of TMAO on eGFR decline with age. We used the Preacher and Hayes bootstrapping method, as implemented in the mediation (v4. 5. 0) R package  75  , using general linear models with sex and country of recruitment as covariates. Confidence intervals and Bayesian P values were computed after 999 simulations. Propensity-score matching. MetaCardis participants with T2D were propensity-score matched using the R package MatchIt 76 (v. 4. 1. 0) with age, sex, disease group, and hypertension status as covariates using nearest neighbor matching determined by generalized linear models. All covariates were given equal weight.
Cytosolic [Ca 2+ ] measurements. Fibroblasts were serum-starved overnight in low-glucose DMEM and subsequently loaded with 5 µM fura-2-AM (Molecular Probes) in pH 7. 4 Hanks Balanced Salt Solution (HBSS; Sigma) containing Ca 2+ and Mg 2+. Measurements were obtained on an epifluorescence inverted microscope equipped with a ×20 fluorite objective. Single-cell intracellular Ca 2+ ([Ca 2+ ] i ) was monitored using excitation at 340 and 380 nm, through a monochromator (Cairn Research). Emitted light was reflected through a 515 nm filter to a QImaging Retiga CCD camera (Cairn Research) and digitized to 12-bit resolution. All imaging data were collected and analyzed using software from Andor. Animal procedures. All animal experiments were conducted in accordance with the United Kingdom Home Office Animals 1986 Scientific Procedures, with local ethical committee approval (Project License 70/8356).
Acknowledgements This work was supported by  European Union 's  Seventh Framework Program for research, technological development , and demonstration under grant agreement  HEALTH-F4-2012-305312  ( METACARDIS ). Assistance Publique-Hôpitaux de Paris (AP-HP ) is the promoter of  clinical investigation (MetaCardis). Partial funding supports to K. C. and J. A. -W. were also obtained from  Leducq Foundation  ( TransAtlantic grant ), SFN ( Société Française de Nutrition) , F -CRIN-FORCE  network for support,  INSERM  via  ITMO  and  JPI-Microdiet  study and  Novo Nordisk foundation  (Jacobaus prize). S. K. F. received support from  Deutsche Forschungsgesellschaft   SFB1365  (" RENOPROTECTION ") and  SFB1470 : "HFpEF". P. A. and K. Ch. are recipients of a  Wellcome   ISSF Fellowship  ( ISSF204834/Z/16/Z ).
M. -E. D. is supported by the  NIHR Imperial Biomedical Research Centre ,  GutsUK ,  Diabetes UK  and by grants from the  French National Research Agency  ( ANR-10-LABX-46 ,  European Genomics Institute for Diabetes) , from the  National Center for Diabetes Precision Medicine-PreciDIAB , which is jointly supported by the  French National Agency for Research  ( ANR-18-IBHU-0001 ), by the  European Union (FEDER) , by the  Hauts-de-France Regional Council  (Agreement  20001891/NP0025517 ) and by the  European Metropolis of Lille  (MEL, Agreement  2019_ESR_11 ) and by  Isite ULNE  ( R-002-20- TALENT-DUMAS ), also jointly funded by  ANR  ( ANR16-IDEX-0004-ULNE ), the  Hauts-de-France Regional Council  ( 20002845 ) and by the  European Metropolis of Lille (MEL). This research was conducted within the context of the  CNRS-Imperial International Research Project METABO-LIC.
Data availability The Raw shotgun sequencing data that support the findings of this study have been deposited in the European Nucleotide Archive under accession codes "PRJEB37249", "PRJEB38742", "PRJEB41311", and "PRJEB46098". The Serum NMR and urine NMR metabolome data have been uploaded to Metabolights with accession number "MTBLS3429". The Serum GC-MS and isotopically quantified serum metabolites (UPLC-MS/MS) that have been used in this study are deposited in MassIVE with accession numbers "MSV000088042 [https://doi. org/10. 25345/C5CV76]" and "MSV000088043 [https://doi. org/10. 25345/ C58246]", respectively. In adherence to EU and national privacy laws, unrestricted access to patient phenotypic data cannot be provided for this study. Interested researchers, wishing to access individual phenotypic data would need to submit argued applications to the relevant National Data Protection Agencies.