dataesr/scientific-paragraphs-categorization huggingface dataset:
Since plasma cytokine/ chemokine levels are not necessarily representing the tumour microenvironment, we used the data of our transcriptomic study and real-time PCR to assess the expression levels of cytokines/chemokines and their receptors in primary tumours. We observed that the most downregulated (<-0. 8 Log2FC) cytokines/chemokines in JL primary tumours, including Ifng, Cxcl13, Tnfs18 and Cxcl11, are known to favour an anti-tumour immune response (Fig. 6a ; Supplementary Data 3). While the other hand, the most upregulated (>0. 8 Log2FC) ones, including Cxcl3, Cxcl5, Il10 and Il1b, are linked to immunosuppression or tumour progression (Fig. 6a , Supplementary Data 3; Supplementary Fig. 9b ). In BM mononucleated cells, transcriptomic analysis revealed a high degree of similarity between LD and JL conditions.
Respective log2FoldChange (log2FC) are listed in Supplementary Data 3. e Mammosphere-formation efficiency (MFE%) of MCF12A normal human mammary cells in different circadian phases (n = 3). Blue squares and grey diamonds represent peaks of BMAL1 HIGH /PER2 LOW and BMAL1 LOW /PER2 HIGH expression, respectively. Data are presented as a dot plot with dots indicating the median with interquartile range (error bars). P-value was calculated from a one-way ANOVA/Tukey's multiple comparisons test. f Tumour-initiation study based on orthotopic injection of primary tumour cells from LD (n = 6) and JL (n = 6) mice in the mammary fat pad (MFP) of host mice (n = 4 per each donor). Tumour-initiation potential was calculated as the percentage of host mice that formed tumours. We set a threshold based on MFP weight at 0.
Rest/activity and temperature recording. We used the DSI Implantable Telemetry system (Data Sciences International, St. Paul, MN) to assess locomotor activity and core body temperature of mice kept in either LD or JL conditions. A telemetric transmitter (PhysioTel TA-F10, DSI) was implanted in the peritoneal cavity of each mouse under isoflurane anaesthesia. Dataquest A. R. T. v4. 30 software was used for data collection. Data were recorded every 10 min throughout the experiment. We implanted transmitters in 6 mice at the age of 14 weeks (mice had reached the required weight of 20 g). Mice were kept 2 weeks in standard light conditions (LD) for recovery/synchronisation, then they were randomly assigned to either the LD or JL protocol for 4 weeks of recording. Actograms were created using Microsoft Excel following the method described by Oike et al.
90. Actogram data were analysed and visualised in ImageJ using the ActogramJ software package  91  and in GraphPad Prism v601. Data from D10-D20 (mice age of 15 to 16 weeks) and D32-D46 (mice age of 18 to 19 weeks) post-implantation were used for periodogram analysis. Period was determined in ActogramJ software package for activity and core body temperature patterns using Fourier and Lomb-Scargle methods, respectively. To confirm circadian rhythmicity of activity and temperature patterns we defined the coefficients of determination (R 2 ) by fitting a cosine algorithm in GraphPad Prism v601  29. Bioluminescence imaging. For in vivo imaging of tumours, mice were intraperitoneally (i. p. ) injected with 150 mg luciferin kg -1. Luciferin was resuspended in PBS at a concentration of 15 mg/ml and filter-sterilised with a 0.
22-µm filter. Following injection, mice were anaesthetised with isoflurane gas (2% during imaging period). Bioluminescence imaging was performed 15, 20, 25 and 30 mins after injection, as kinetic analysis determined that peak luciferase activity occurred 20-25 min post-injection (IVIS Spectrum imaging system, PerkinElmer). Images were analysed with Living Image software. To determine tumour burden, total flux (photons per second) was measured in a fixed region of interest (whole body). Sample collection. Mice were anesthetised with isoflurane inhalation. Blood was collected by cardiac puncture and placed in EDTA (10% 0. 5 M EDTA). Following cervical dislocation, we collected tumours/mammary glands, lungs, and both hindlimb bones and stored them in ice-cold PBS until further processing. LD mice were sampled at ZT3-ZT4; in the case of JL mice, ZT was not followed.
NRecon reconstruction and DataViewer software were used for 3D reconstruction. Analysis of structural parameters was performed using CTVox. After the micro-CT scan, hind limbs were processed to obtain paraffin sections for histological analysis as follows: Hind limbs were decalcified in 20% EDTA (pH 7. 5) at 4 °C with constant shaking for 15 days; the EDTA solution was replaced every 2-3 days. Decalcified bones were embedded in paraffin and sectioned at 5 μm using a Leica microtome. Sections were then stained with hematoxylin and eosin. To detect disseminated tumour cells, mononuclear cells from blood or bone marrow were, respectively, stained for CD45 (1:100) or CD45 (1:100), CD31 (1:500), TER-119 (1:200) and CD140A (1:100). Following cell-surface staining, cells were fixed with 1% PFA/PBS for 30 min then permeabilised with 0.
1% Triton X/ PBS for 20 min. Cells were blocked with 5% FBS/1% BSA/PBS for 30 min. Following a PBS wash, cells were incubated with anti-PyMT AF488 antibody (1:10) for 1 h. All incubations and staining were performed at 4 °C. To distinguish between live/dead cells, either propidium iodide (PI, 1:1000) or Zombie Violet (1:500) fixable viability stain was used. Isotype controls were used, respectively, at corresponding concentration. Detailed antibody list provided as Supplementary Table  4. FlowJo Software (version 10, Tree Star Inc) was used for flow cytometry data analysis. RNA isolation and RT-PCR. Total RNA was extracted from bulk of dissociated cells from primary tumours and from bulk of bone marrow mononuclear cells using the PureLink RNA kit (Invitrogen) according to the manufacturer's instructions.
Adapters were ligated to the ends of these 3′ adenylated cDNA fragments, and cDNA fragments were then amplified by PCR and purified with Ampure XP Beads (AGENCOURT). The size and quantity of libraries were validated on an Agilent 2100 Bioanalyzer. The double-stranded PCR products were heat-denaturated and circularised using a splint oligo sequence. The single-strand circular DNA (ssCirDNA) was formatted as the final library. Each library was amplified with phi29 to make a DNA nanoball (DNB) which contained more than 300 copies of the given molecule. The DNBs were loaded into the patterned nanoarray and single-end 50-base reads were generated on a BGISEQ-500 sequencing platform. For each library a minimum of 23 million single-end reads were produced. All raw sequencing reads were filtered using SOAPnuke software (https://github.
com/BGIflexlab/SOAPnuke) to remove reads with adaptors, reads made up of more than 10% unknown bases, and low-quality reads. The resulting clean reads were then stored in FASTQ format. Reads were mapped using Bowtie2 92 on the mouse reference genome mm10 (GRCm38); gene expression levels were calculated with RSEM  93  and normalised using the value of fragments per kilobase of exon per million fragments mapped (FPKM) in each sample. Normalised gene expression values were used to produce heatmaps using the ComplexHeatmap package of R  94  , using Euclidean distances and the agglomeration method Ward. D2 of the function hclust. Principal Components Analyses were performed using the pca function of the mixOmics package  95. Differentially expressed genes were detected using DESeq2, with un-normalised counts as input  96.
DEGs were then used to assess pathway functional enrichment with the KEGG annotation database, using the R package hypeR  97. DEGs were ranked in a list according to their p-values, and Gene Set Enrichment Analysis (GSEA) was performed using GSEA software from the Broad Institute  98. Immunohistochemistry. Primary tumours (fixed in 4% PFA) were washed in PBS and transferred to 70% EtOH. They were then embedded in paraffin wax according to standard histological protocols. Paraffin-embedded-sections (5-µm-thick) were mounted on adhesive slides (Klinipath-KP-PRINTER ADHESIVES), then deparaffinised and stained with HES (hematoxylin, eosin and saffron) for morphological observation of tumours. All slides were scanned with a Pannoramic Scan 150 (3D Histech) and analysed with a CaseCenter 2. 9 viewer (3D Histech).
Mammosphere formation was assessed in 2-4 replicates. Spheres were imaged after 20 days for primary tumour cells and 8 days for MCF12A. Measurements were performed in ImageJ software; the threshold was 50 μm for primary tumourspheres and 40 μm for normal human spheres. Mean values of replicates were used for data analysis. Mammosphere-formation efficiency (MFE) was calculated and presented as a percentage. Statistical analysis. GraphPad Prism v6. 01 (GraphPad Software, USA) was used to prepare all graphs and perform statistics, unless stated otherwise. Results were analysed using t-tests (unpaired, two-sided), binomial two-sided test or one-way ANOVA, as appropriate.
Acknowledgements We thank all members of  UMRS935  for their technical assistance and daily scientific exchanges. We are grateful to  Ibrahim Casals  and  Benoit Peuteman  from the  INSERM UMS33 Animal Facility  (SEIVIL). We appreciate the assistance of  Marie-France Champy ,  Aurélie Auburtin ,  Tania Sorg  and  Yann Herault  from  Phenomin  ( Institut Clinique  de la souris, 1 rue Laurent Fries, 67404 ILLKIRCH cedex 2; CNRS,  UMR7104 , Illkirch, France; INSERM,  U964 ,  Illkirch, France ;  Université de Strasbourg, France)  with blood analyses. We thank  Karine Bailly  from the  Cochin Cytometry and Immunobiology Facility  ( CYBIO, Institut Cochin, Paris ) for performing the Luminex assay. We also thank  Lindsay Higgins  for her English editing services. We are grateful to  Francis Levi  and  Angela Nieto  for their helpful comments on the manuscript.
This work was funded by  Inserm, University Paris Sud ,  INRA ,  Association Institut de Cancérologie et d'Immunogénétique (ICIG), Vaincre le Cancer-NRB ,  Fond Avenir MASFIP , and  GEFLUC -Les Entreprises  contre le cancer. The post-doctoral fellowship of E. H. was granted by  Vaincre le Cancer-NRB  and the  University Paris Saclay (Project BioTherAlliance ).
Data availability The RNA sequencing data are available at the Sequence Read Archive (https://www. ncbi. nlm. nih. gov/sra) under the study accession number PRJEB33802. KEGG annotation database is available online at https://www. genome. jp/kegg/annotation/. All other relevant data are available in the Article, Supplementary Information or from the corresponding authors upon reasonable request. The source data underlying Figs. 1b-f , 2a-d, 3b-f, 4a-d, f, 5a-g and 6a-c and e-i, and Supplementary Figs. 1, 2, 6, 8-10 are provided as a Source data file. A reporting summary for this article is available as a.
1  (Edgar, 2010)  with a maxEE value of 1. Only forward reads were analysed due to length variability in the ITS2 region producing potential biases for paired data. Primer sequences were removed and UCHIME was used to detect chimeras  (Edgar, Haas, Clemente, Quince, & Knight, 2011). Comparisons were made to the QIIME/ UNITE reference ITS database (v7. 2; Nilsson et al. , 2019)  and sequences were clustered at 97% using QIIME  (Caporaso et al. , 2010). Singletons were removed from the data as these are often sequencing errors  (Majaneva, Hyytiäinen, Varvio, Nagai, & Blomster, 2015). Sequences have been deposited in the European Nucleotide Archive under accession number PRJEB28082.
78896 Å) from 4. 5-85° 2θ run at 45 kV and 40 mA conditions. Crystalline mineral phases were identified using PANalytical HighScore Plus software coupled to the International Centre for Diffraction Data (ICDD) PDF-4+database. Crystalline mineral and amorphous material quantification was completed using the Rietveld refinement technique within the same HighScore Plus software package and using crystallographic information from the Inorganic Crystal Structure Database  (Hellenbrandt, 2004)  following the methodology in  Kemp et al. (2016). X-ray diffraction analysis indicated that the basalt contained a high percentage of amorphous material interpreted to be glass (~25%; Table  1 ). The elemental composition of the basaltic glass was determined with scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDX) to better constrain our reactive transport model (RTM) calculations (Figure  S9 ; Table  S2 ).
15 a n3 H + 1 -Ω p 3 q 3 , r + ,geo = A A exp - � E A RT � ⎛ ⎜ ⎜ ⎝ a 3 H + a Al 3+ ⎞ ⎟ ⎟ ⎠ 1∕3 , | 3663 accelerating voltage of 20 kV, spot size 5 and under high vacuum conditions (<1 × 10 -4 Torr) also at the BGS laboratories. Energy dispersive X-ray analysis was conducted using Oxford Instruments X-MAX large area (50 mm 2 ) silicon drift detector, running with Oxford Instruments INCA (v4) software. The EDX system was used to identify the point-elemental compositions from samples and is capable of detecting elements from atomic number 4 (B) to atomic number 92 (U) with a detection limit of the order of 0. 2-0. 5 weight % for most elements. Areas of the amorphous glassy phase were identified from backscattered electron images and relevant spectra and concentration determined from point analyses.
| Reactive transport model (1) f x = x -soil basalt -soil , Reactive surface area for each mineral was apportioned according to the mineral weight per cent of the basalt grains. Chemical dissolution rate laws and coefficients for basaltic glass were sourced from  Flaathen, Gislason, and Oelkers (2010)  and those for other minerals from the US Geological Survey  (Palandri & Kharaka, 2004). Solubility constants of the basaltic minerals were taken from the THERMODDEM database  (Blanc et al. , 2012)  and for the basaltic glass from  Aradóttir, Sonnenthal, and Jónsson (2012). Changes in reacting particle sizes due to element mass transfer to solution during dissolution were simulated using the shrinking particle model  (Rimstidt, 2014). Background weathering of soil minerals was represented by a porewater solution chemically identical to leachate from the basalt-free systems.
Figures  S13  and S14  provide schematic and systems diagrams, respectively, defining the process and interactions of the RTM. Modelled geochemical processes included equilibrium of dissolved inorganic carbon species with atmospheric CO 2 , and a profile of partial pressures of CO 2 in the soil profile representing autotrophic and heterotrophic respiration  (Nan, Yue, Li, Huang, & Shen, 2016). Apparent reactive surface area (RSA) and CEC were optimized within MATLAB (version 2019a; The MathWorks, Inc. ) using the 'fmincon' function. Separate optimizations were performed on individual treated columns with several candidate sinks for Al and Fe (amorphous Al(OH) 3 and Fe(OH) 3 , gibbsite, kaolinite, and goethite). Based on the smallest root-mean-square errors between model and observed leachate Al (Figures  S15  and S16 ) and 18 leachate and extractable elements (Figures  S17  and S18 ), our solid equilibrium phases are determined to be amorphous Al(OH) 3 and Fe(OH) 3 , along with calcite, amorphous silica and pyrophyllite (MnO 2 ).
Column reactors were conceptualized as a 250 mm deep soil column constituting five 50 mm deep cells, and a 50 mm deep wellmixed fluid reservoir in contact with the atmosphere at the base of the column. The RTM was constructed with the PHREEQC platform (Parkhurst & Apello, 2013)  using the T&H. dat geochemical reaction database (Appelo & Postma, 2005) , which includes surface complexation constants for particulate organic matter based on the WHAM model (Tipping, 1998; Tipping & Hurley, 1992). Speciation of oxidized and reduced forms of nitrogen was explicitly decoupled to allow for irreversible kinetic transformations of excess urea fertilizer to nitrate, as observed empirically. Flow of irrigation water through the column was simulated by a time series of irrigation events at the top of the soil that resulted in 1 D vertical flow of pore solution and advection of solutes through the layers producing the overall mass of leachate from the columns (4.
AbsTrACT Objective Patients with renal failure suffer from symptoms caused by uraemic toxins, possibly of gut microbial origin, as deduced from studies in animals. The aim of the study is to characterise relationships between the intestinal microbiome composition, uraemic toxins and renal failure symptoms in human end-stage renal disease (esrD). Design characterisation of gut microbiome, serum and faecal metabolome and human phenotypes in a cohort of 223 patients with esrD and 69 healthy controls. Multidimensional data integration to reveal links between these datasets and the use of chronic kidney disease (cKD) rodent models to test the effects of intestinal microbiome on toxin accumulation and disease severity. results a group of microbial species enriched in esrD correlates tightly to patient clinical variables and encode functions involved in toxin and secondary bile acids synthesis; the relative abundance of the microbial functions correlates with the serum or faecal concentrations of these metabolites.
Microbiota from patients transplanted to renal injured germ-free mice or antibiotic-treated rats induce higher production of serum uraemic toxins and aggravated renal fibrosis and oxidative stress more than microbiota from controls. Two of the species, Eggerthella lenta and Fusobacterium nucleatum, increase uraemic toxins production and promote renal disease development in a cKD rat model. a probiotic Bifidobacterium animalis decreases abundance of these species, reduces levels of toxins and the severity of the disease in rats. Conclusion aberrant gut microbiota in patients with esrD sculpts a detrimental metabolome aggravating clinical outcomes, suggesting that the gut microbiota will be a promising target for diminishing uraemic toxicity in those patients. Trial registration number This study was registered at clinicalTrials.
Such investigations may yield therapeutic insights, as revealed by a recent study in which the modulation of specific gut microbes was shown to regulate concentrations of a circulating uraemic toxin, indoxyl sulphate. 18 erein, we performed a comprehensive study integrating multidimensional datasets of gut microbiome, serum and faecal metabolomes, and host characteristics that were based on clinical and questionnaire-derived data (referred to as 'phenome'), from a large-scale cohort of 223 haemodialysis patients and 69 healthy controls matched by age stage, body weight and dietary pattern and an independent validation cohort of 24 individuals (online supplementary table  1 ). Study workflow is described in the online supplementary figure  1  and data production is reported in the online supplementary table 2.
Metabolome profiling of serum and faecal samples Serum metabolic profiling was analysed using ultra-highperformance liquid chromatograph (HPLC) (Waters, USA) coupled with a tripleTOF 5600 plus (Applied Biosystems, USA) mass spectrometer. A total of 6600 metabolite peaks were measured, and 180 out of them were structurally identified and annotated according to an in-house LC-MS/MS database. Among them, five metabolites were further quantified in sixty randomly chosen serum samples by external calibration curves using Shimadzu nexera x2 ultra HPLC (Shimadzu, Japan) coupled with triple quadrupole mass spectrometer (Shimadzu 8050, Japan). Faecal volatile organic compounds were analysed using a headspace solid phase microextraction-gas chromatography-MS (GC-MS) method. A total of 582 faecal metabolite peaks were measured based on the NIST database, and 255 out of them were annotated and left after further filtering by excluding compounds showing mass spectra similarity factor <700.
Funding This work was financially supported by Beijing Municipal commission of education co-constructed Program, the  Beijing science and Technology Project  ( Z181100009318005 ), the 111 Project from the  education Ministry of china  (no. B18053 ). Part of this work was also supported by the Metagenopolis grant anr-11-DPBs-0001 and national natural science Foundation of china (nsFc31570116). ZY is supported by the  national natural science Foundation  of china (no. 81772984 ,  81572614 ); the  Major Project for cultivation Technology  ( 2016ZX08008001 );  Basic research Program  (2015Qc0104, 2015Tc041, 2016sY001, 2016Qc086); sKlB  Open grant  ( 2020sKlaB6-18 ).
The shotgun sequencing data for all metagenomic samples have been deposited into the european Bioinformatic institute (eBi) database under the BioProject accession code PrJna449784. The serum and faecal metabolomes datasets reported in this article are available at the Metabolights database (http:// www. ebi. ac. uk/ metabolights/) with accession number MTBls700. The authors declare that all other data supporting the findings of the study are available in the paper and supplementary materials, or from the corresponding authors on request.
Data availability The shotgun sequencing data for all metagenomic samples have been deposited into the European Bioinformatic Institute database under the BioProject accession code PRJNA449784. The serum and faecal metabolomes datasets reported in this article are available at the MetaboLights database (http://www. ebi. ac. uk/ metabolights/) with accession number MTBLS700. The authors declare that all other data supporting the findings of the study are available in the paper and online supplementary materials, or from the corresponding authors on request.
2  and e. g. ,  Kuhn et al. , 2014). The by far best studied young star-forming region is the Orion Nebula (Messier 42 and NGC 1976) and its associated star cluster, the ONC. Although not very massive (4. 6 × 10 3 M ,  Hillenbrand and Hartmann, 1998) , its close proximity (∼ 440 pc, O  'Dell and Henney, 2008)  makes it a perfect target to study the stellar and gas content. The ONC revealed the first direct imaging of protoplanetary disks  (proplyds, McCaughrean and O'dell, 1996; O'Dell et al. , 2008). A recent 3D kinematic study by  Zari et al. (2019)  using Gaia DR2 data  (Gaia Collaboration et al. , 2016 , 2018)  showed that the ONC is highly sub-structured, with stellar ensembles of different ages in several kinematic groups, mixed in 3D space, which are overlapping in projection. Jerabkova et al.
8 kpc from the Sun) by  Kharchenko et al. (2016)  has to be handled with care. It also shows that with the further data releases of the Gaia mission and an introduction of new techniques in machine learning and neural networks using big data may reveal many more clusters in the near future. 3 Star cluster populations in the local Universe; a statistical view of their formation and evolution As we move away from the Local Group, we lose resolution on the single components of star clusters but gain a viewpoint into whole cluster populations forming into a much larger spectrum of galactic environments than offered by the Local Group. YSCs form in the densest regions of GMCs. Turbulent energy regulates the density structure and distribution of the cold gas. When gravitational fragmentation takes over, the densest regions in a cloud start to collapse.
(2018)  for a careful review of the field. In this section, we compare observational data compiled from the literature to two different sets of cosmological simulations, that use radically different approaches and numerical recipes. In Fig. 9  we show the Γ vs. Σ SFR space. We combine all the observational data (black dots) included in the two plots of Fig. 8. Chandar et al. (2017)  reports several Γ estimates using different age ranges for each galaxy in their sample. In this figure, we report Γ estimated at the youngest age interval for each galaxy to avoid contamination by disruption. We report in Table  1  the median and quartiles of the observed Γ values estimated in Σ SFR intervals. These values are also included in Figure  9  to facilitate the comparison with simulations. It is important to notice that in order to estimate the median trends of the observational results, we have combined heterogeneous datasets that does not uniformly sample the Σ SFR space and, therefore, these trends should not be over-interpreted.