dataesr/scientific-paragraphs-categorization huggingface dataset:
Limits on the X-ray luminosity of the AGN We place an upper limit on the X-ray flux of an AGN point source using the tool celldetect from the CIAO 4  software package. The events were restricted to the 0. 5-7. 0 keV energy range and the search was limited to fixed cell sizes of 1 pixel and 3 pixels near the cluster centre. The local cluster background was estimated (13. 67 ± 4. 54 counts pixels -2 ) from a 5 circle near the centre of the cluster. We did not find any >3σ detections of compact sources within 10 of the cluster centre. Using the CIAO aprates task we calculated a 3σ upper limit on the counts expected on an AGN point source. The algorithm 5  determines confidence intervals based on a Bayesian backgroundmarginalised posterior probability distribution function of possible source counts.
Acknowledgements. We thank the referee for the substantive comments that greatly improved the clarity and content of the work presented. This work is based on observations obtained at the  Southern Astrophysical Research (SOAR) telescope , which is a joint project of the  Ministério da Ciência, Tecnologia, e Inovação (MCTI) da República Federativa do Brasil , the  US National Optical Astronomy Observatory (NOAO) , the  University of North Carolina at Chapel Hill (UNC) , and  Michigan State University (MSU). This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2018. 1. 00940. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile.
The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. The  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc. This research made use of APLpy, an open-source plotting package for Python (Robitaille & Bressert 2012). Basic research in radio astronomy at the  Naval Research Laboratory  is funded by 6. 1 Base funding. Construction and installation of VLITE were supported by the  NRL Sustainment Restoration and Maintenance fund. L. D. M. is supported by the  ERC-StG "ClustersXCosmo " grant agreement  716762. G. W. P. acknowledges support from  CNES , the French space agency.
a Schematic representation of the Adriamycin-induced nephropathy model. BALB/c mice were injected with a single dose of 12 mg/kg of Adriamycin (ADR) or saline (NaCl 0. 9%) (day 0, D0), then euthanized 7 (D7), 14 (D14), or 32 days (D32) after injection. Proteinuria was monitored in the time course of the experiment for each individual mouse. b Kinetic analysis of kidney filtration function assessed by [albumin/creatinine ratio (ACR)] measurement in urine samples of ADR-injected female BALB/c mice (n = 10), before injection (D0), 11 (D11), and 32 (D32) days after injection. Data are represented for each animal in (mg/mg). c H&E (left panels), Masson Trichrome (middle panels), and Sirius Red (right panels) stained kidney sections of BALB/c mice injected with saline (NaCl) or collected 7 (D7), 14 (D14), and 32 (D32) days after ADR injection.
Scale bar = 200 µm. d Glomerular histology by PAS (Periodic Acid Schiff ) from saline (Normal) or ADR-injected (Sclerotic) BALB/c mice. Scale bar = 30 µm. Arrowheads: sclerotic area. e Quantification of glomeruli with abnormal morphology (such as displayed in (d)) in kidney sections of BALB/c mice injected with saline (NaCl) or collected 7 (D7), 14 (D14), and 32 (D32) days after ADR injection (n = 4 for each group). For each animal, all glomeruli (about 150) on the whole kidney section were analyzed. Data are shown for each animal and the mean value for each group is shown as a green line. **p = 0. 0035, and **p = 0. 0098 by t-test for ADR-D14 versus saline and for ADR-D32 versus ADR-D14, respectively. f Double immunostaining for the podocyte-restricted markers Synaptopodin (Syn, red) and Wilms tumor protein 1 (WT1, green), in kidney section from saline or ADR-injected mice, 7 (D7), 14 (D14), and 32 days (D32) after ADR injection showing effacement of podocytes starting from 7 days post-ADR treatment (arrowheads) followed by marked normalization of podocytes at day 32.
e. 18 days after ADR-injection. Comparison of TERT KO/KO and TERT WT/WT transcriptomes in saline-treated mice showed minimal changes, but suggested that TERT deficiency perturbed basal activation of several pathways including mTORC1, Wnt, and Notch signaling (Supplementary Figure  7 ). Further GSEA analysis of ADR-treated mice interestingly revealed that TERT deficiency precludes proper activation of signaling pathways, including Epithelial-to-Mesenchymal transition (EMT) and KRAS signaling, upon glomerular repair (Fig. 2f ). In addition, assessment of Gene Ontology term (GO term) using Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis highlighted impairment of extracellular matrix (ECM) remodeling upon deletion of TERT (Fig. 2g ). Examination of the status of metalloproteinases (MMPs) revealed that TERT deletion prevented the upregulation of MMP-24, MMP-12, MMP-10, and MMP-3 that is normally observed in TERT WT/WT mice upon glomerular repair following ADR injury (Supplementary Fig.
Tubular epithelial cells proliferate following a TERT ci pulse A common feature of regeneration in adult tissue or organ is the activation of quiescent adult stem cells, or differentiated cells, to govern the generation of progenitor cells that rapidly divide and give rise to terminally differentiated cells  26. Hence, we sought to Fig. 2  Endogenous TERT is required for glomerular repair following ADR-induced nephropathy. a TERT mRNA levels by RT-qPCR in whole kidneys from saline (NaCl, n = 12) and ADR-injected mice (ADR, n = 4 for each group) collected 7 (D7), 14 (D14), and 32 (D32) days after injection. Data are shown for each animal and the mean value for each group is shown as a green line. **p = 0. 006 by t-test for ADR-D7 versus saline. **p = 0. 005 by t-test for ADR-D14 versus saline.
For each animal, all glomeruli (about 150) on the whole kidney section were analyzed. Data are shown for each animal and mean value for each group is shown as a green line. **p = 0. 0071 by t-test for TERT knockout versus control mice at day 32. f Comparison of the top 20 enriched gene signatures found by Gene Set Enrichment Analysis (GSEA) Hallmark in TERT WT/WT and TERT KO/KO mice 18 days after ADR injection. Common gene signatures between TERT WT/WT and TERT KO/KO mice are highlighted in tan, gene signatures only enriched in TERT WT/WT mice are highlighted in blue, and gene signatures only enriched in TERT KO/KO mice are highlighted in red. NES stands for normalized enrichment score. Enrichment profiles of Epithelial-to-Mesenchymal transition and KRAS signaling UP are shown. g Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis showing the status of GO-term (Biological Process) related to the extracellular matrix (ECM) in TERT WT/WT (blue) and TERT KO/KO (red) mice 18 days after ADR injury.
b Kinetic analysis of kidney filtration function assessed by [albumin/creatinine ratio (ACR)] measurement from single transgenic actin-rtTA + (n = 3, black) and i-TERT ci (n = 3, pink) mice. Data are shown for each animal. c Schematic representation of the experimental design. Tamoxifen was injected 5 days prior to doxycycline treatment to induce permanent EGFP tagging of mature podocytes. Transient TERT ci overexpression (TERT ci ON) in i-TERT ci mice was then induced for 15 days (D15) by the mean of doxycycline treatment, then the mice were subsequently submitted to a reversal period (TERT ci OFF) for 20 days (R20) during which proteinuria gradually regressed. d Double immunostaining for Synaptopodin (SYN, red) and EGFP (green) in kidney sections from WT1 CreERt2 ;R26 mTmG ;actin-rtTA+ control (Upper panels) and WT1 CreERt2 ;R26 mTmG ;i-TERT ci experimental mice (lower panels) euthanized 20 days after the reversal period.
Scale bars = 20 µm. Arrow heads show differentiated podocytes (SYN + ) devoid of EGFP signal. e Quantification of data in (d). Percentage of SYN + area that do (EGFP + ) and do not (EGFP-) overlap with EGFP signal in glomeruli of WT1 CreERt2 ;R26 mTmG ;actin-rtTA+ control mice (n = 4) and WT1 CreERt2 ;R26 mTmG ;i-TERT ci experimental mice (n = 4) sacrificed 20 days after the stop of doxycycline treatment (R20). Data are shown for each animal and the mean value for each group is shown as a grey line. For each animal, all glomeruli (about 150) on the whole kidney section were analyzed. **p = 0. 0019 by t-test for WT1 CreERt2 ;R26 mTmG ;i-TERT ci vs. WT1 CreERt2 ;R26 mTmG ;actin-rtTA+ mice. f Double immunostaining for EGFP (green) and the nuclear podocyte-restricted marker Wilms' Tumor Protein 1 (WT1, magenta), in kidney sections from WT1 CreERt2 ;R26 mTmG ;actin-rtTA+ control (Upper panels), and WT1 CreERt2 ;R26 mTmG ;i-TERT ci experimental mice (Lower panels) sacrificed 20 days after the stop of doxycycline treatment.
Data are shown for each animal and the mean value for each group is represented as a green line. For each animal, the total number of glomeruli (about 60) per kidney mosaic tiles were quantified. ***p = 0. 0005 by t-test for i-TERT ci versus control mice. g Wholemount immunostaining for the nuclear marker of terminally differentiated podocyte WT1 (magenta) on semi-thick kidney sections from a UBC CreERt2 ;R26 Confetti ;i-TERT ci mouse with nuclear GFP clonal glomeruli showing co-localization of the GFP + cells with WT1 (arrowheads). of podocyte progenitor cells. Altogether, we consider that the i-TERT ci mouse is a key model to further investigate the nature of podocyte progenitors. Finally, a deficit of telomerase may prove to be involved in agerelated renal dysfunctions, end-stage renal disease (ESRD) being an increasingly common organ failure in the aging population.
At that time point, the doxycycline treatment was stopped (R0), and both i-TERT ci and actin-rtTA+ control mice entered a reversal period during which proteinuria gradually regressed in i-TERT ci mice. All mice were weighed twice a week in the time course of the experiments, and urine was collected for each individual mouse twice a week. Proteinuria values (mg/ml) in i-TERT ci (pink, n = 5) and i-TERT ci ;TERT KO/WT (orange, n = 8) mice during the reversal period. Data are shown for each animal and mean value for each group is shown as a green line. *p = 0. 028 and *p = 0. 023 by t-test for i-TERT ci ;TERT KO/WT versus i-TERT ci at R13 and R18, respectively. c Sirius Red staining on kidney sections from i-TERT ci and i-TERT ci ;TERT KO/WT mice collected 18 days after doxycycline withdrawal (R18).
Scale bar = 50 µm. d Quantification of glomeruli with abnormal morphology (such as displayed for i-TERT ci ; TERT KO/WT mice in (c)), in kidney sections from actin-rtTA+ (n = 5), i-TERT ci (n = 5), actin-rtTA+;TERT KO/WT (n = 5) and i-TERT ci ;TERT KO/WT (n = 8) mice at 18 days of reversal (R18). Data are shown for each animal and the mean value for each group is shown as a green line. For each animal, all glomeruli (about 150) on the whole kidney section were analyzed. *p = 0. 0306 and **p = 0. 002 by t-test for i-TERT ci versus actin-rtTA+ and for i-TERT ci ;TERT KO/WT versus actin-rtTA+;TERT KO/WT respectively. e Quantification of the mean number of Wilms tumor protein (WT1) positive cells per glomerulus. Data are shown for each animal and the mean value for each group is shown as a green line.
Image analysis using ImageJ software EGFP/Synaptopodin or EdU/nephron markers double-stained kidney sections were sequentially scanned for far-red and/or red and/or green signals allowing imaging of the entire section for each fluorophore. Sirius red-stained kidney sections were similarly scanned to acquire images of the entire section. For EGFP/Synaptopodin analysis, glomeruli areas were manually demarcated on entire kidney sections based on the synaptopodin positive structures. For Sirius red analysis, glomeruli areas were manually demarcated on entire sections based on histology. Demarcated glomeruli were defined as areas to be analyzed using ImageJ software. For EdU/nephron markers analysis, the total number of EdU positive nuclei on entire sections was determined by ImageJ. The signal inherent to nephron markers was then used to designate the areas to be subsequently analyzed.
Analysis of RNA-seq data Differentially expressed genes (DEGs) were identified using the Bioconductor package DESeq2. DEGs were considered statistically significant at an adjusted p-value (Padj) < 0. 05. DEGs were then sorted by log2 fold change (log2FC), and PCA and Heatmaps were plotted to check clustering and the homogeneity of the samples. Gene set enrichment analysis (GSEA) was performed using the hallmark C7 collections on GSEA software (version 3. 0) and MsigDB database and C2 (curated gene sets) database. The reported GSEA output was selected based on a normalized enrichment score (NES) > 1. 05. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to identify the enriched pathways using gene ontology (GO) terms. The g:Profiler tool set was used to identify the enriched pathways using the Reactome database.
ACKNOWLEDGEMENTS We thank  S. Artandi  at  Stanford University, USA , for i-TERT ci mice,  A. Schedl  at  Institut de Biologie Valrose (iBV), Nice, France , for  WT1 CreERt2  and  R26  mTmG mice, and  D. Bulavin  at  Institute for Research on Cancer and Aging, Nice (IRCAN), France , for R26 Confetti mice. We thank  S. Artandi  at  Stanford University , and  C. Féral  and  D. Bulavin  at  IRCAN  for helpful discussion and input on the manuscript. We thank  M. Mondin  at  Institut de Biologie Valrose (iBV ) for her help in calibrating the spectral confocal microscope. We thank other members of the  Shkreli lab  for helpful discussions and input on the manuscript. We acknowledge the  IRCAN Animal core facility  that is supported by "la  Région Provence Alpes-Côte d'Azur" and the "Inserm". We acknowledge the  IRCAN Molecular and Cellular Imaging (PICMI) core facility  that is supported by  "le Cancéropôle PACA", "la Région Provence Alpes-Côte d'Azur", "le Conseil Départemental 06", and the "Inserm".
We acknowledge the IRCAN Histology core facility that is supported by the " Université Côte d'Azur (UCA)" and the "Inserm". We acknowledge the  IRCAN Flow Cytometry core facility  (CytoMed) that is supported by  "le FEDER", "le Ministère de l'Enseignement Supérieur", "la Région Provence Alpes-Côte d'Azur", "le Conseil Départemental 06", "l'ITMO Cancer Aviesan (plan cancer)", "le Cancéropôle PACA", "le CNRS" and the "Inserm". We acknowledge "HISTALIM" that provided support for histological analysis. M. M. was supported by  "la Région Sud" and the "Inserm ". T. H. was supported by  "la Région Sud " and the  French Government (National Research Agency, ANR)  through the  "Investments for the Future" programs LABEX SIGNALIFE   ANR-11-LABX-0028-01  and  IDEX  UCAJedi  ANR-15-IDEX-01. L. Y. and L.
The untargeted processing of MS data from labelled material is also more complex. The extraction of isotopologues from the raw MS data is basically the same process as in unlabelled metabolomics so that the same toolssuch as XCMS  (Kessner et al. , 2008) , MS-Dial  (Tsugawa et al. , 2015) , MZmine 2  (Pluskal et al. , 2010) -can be used in both cases. However, the task of regrouping isotopologues into isotopic clusters is specific to isotopic studies. A number of dedicated software tools have been developed, such as X13CMS  (Patti et al. , 2014) , geoRge  (Capellades et al. , 2016) ,  MetExtractII (Buelschl et al. , 2017) , mzMatchIso  (Chokkathukalam et al. , 2012) , DynaMet  (Kiefer et al. , 2015)  and HiResTec  (Hoffmann et al. , 2018). Considering the wealth of information to be exploited in untargeted isotopic studies, the processing software needs to be robust and efficient in maximizing the number and quality of the extracted data.
Data were acquired with the following source parameters: the capillary temperature was 350 °C, the source heater temperature, 350 °C, the sheath gas flow rate, 50 a. u. (arbitrary units), the auxiliary gas flow rate, 10 a. u. , the S-Lens RF level, 65%, and the source voltage, 2. 75 kV. The data were acquired in a single analytical batch. As in untargeted metabolomics approaches, all the biological samples were randomized in the analytical run and the five-replicates of the reference sample were injected at regular intervals throughout the experiment. Raw LC/MS data were converted into the open "mzXML" format using the software Proteowizard  (Kessner et al. , 2008). The raw data were cut after 42 min to retain all essential information while avoiding artefacts from the cleaning step and reducing data size.
All these compounds were identified in the MS data with a confidence level 1  (Creek et al. , 2014) , including confirmation with authentic compounds. The isotopologues from these metabolites were extracted from the MS data collected on the reference material, and were assigned to molecular isotopic clusters in a targeted manner with the software Emzed  (Kiefer et al. , 2013)  using a mass tolerance of 0. 003 m/z. Carbon isotopologue distributions (CIDs) of the reference metabolites were then quantified from the corresponding mass fractions after correcting for the presence of all naturally occurring isotopes and the isotopic purity of the tracer (99%) using the software IsoCor, v2. 0. 4  (Millard et al. , 2019). The complete dataset (including the list of reference metabolites, the isotopologues, their analytical characterics, their abundances, the isotopic clusters and the metabolite CIDs) is detailed in Supplementary Information Table  S1  and was used as reference material to evaluate the optimization of data extraction.
Detection of LC/MS features using XCMS LC/MS features were extracted using the XCMS package  (Smith et al. , 2006)  in Rstudio. The isotopologue parameters optimization (IPO) tool  (Libiseller et al. , 2015)  was first used to optimize XCMS parameters, using unlabelled samples (E. coli) as required. The set of parameters selected using the IPO tool are given in SI Table  S2 , and were used as starting settings for subsequent data processing optimization. All raw datasets (i. e. from unlabelled and labelled PT samples and E. coli samples) were grouped and processed in a single batch with XCMS  (Smith et al. , 2006 ) so that peaks were identified and integrated using exactly the same processing parameters. This operation was iteratively repeated after changing the parameter settings to minimize the difference between the XCMS data and a reference dataset, as explained in the Results section.
Statistical analyses Principal Component Analysis (PCA) was applied to all the biological samples (unlabelled and labelled E. coli strains). PCA was performed using SIMCA  [REF, v 15. 0. 02. 5959]  to separate all the biological samples (unlabelled and labelled E. coli strains) into different classes. A Wilcoxon test (p value ≤ 0. 05) was used to identify the most discriminating isotopologues between the two E. coli strains. An in-house database with 47 metabolites was then used for metabolite identification based on exact masses and standard retention times (RTs). Metabolite identification was confirmed with authentic compounds. M k = n k * p k * (1 -p) n-k.
Definition of the reference dataset The reference dataset corresponds to analytical data manually extracted from the reference material for a list of selected metabolites (the reference metabolites) and used as reference data during the optimization process. As for the reference material, various sets of metabolites can be used. The reference metabolites should be sufficient in number to cover the metabolome. They should be known compounds so that their labelling patterns can be extracted in a targeted fashion and complete isotopic clusters should be reliably detected in the reference material to optimize isotopologue recovery and isotopologue grouping. Note that the reference metabolites do not necessarily have to occur in the biological samples for the data optimization process itself since this depends only on the data from the reference material.
However, they should be selected for their relevance to the objectives of the study. For this case study, we selected 25 metabolites that are representative of the central metabolism of E. coli and are known to be reliably detected with our analytical method. All of them are confirmed level 1 annotated metabolites  (Creek et al. , 2014). The complete list of the selected reference metabolites is given in the Material & Methods (Sect. 2. 3. 1). This set of metabolites was consistent with the metabolite content of the labelled reference material and the biological question for the E. coli strains considered in this work. According to the elemental formula of the 25 reference metabolites, the reference dataset should consist of 25 isotopic clusters containing 184 tracer (carbon) isotopologues in total.
By manually processing the MS data collected for the PT sample using the software Emzed  (Kiefer et al. , 2013) , all 25 isotopic clusters were found, along with 181 tracer isotopologues (Table  S1 ). The missing isotopologues corresponded to MS signals that were either undetected (CDP M0 and Mn) or with too low S/N ratio (G1P Mn). The reference dataset was further characterized for the mass accuracy and RT isotopic deviation of individual isotopologues. Compared to their theoretical values the mean mass error was 1. 42 ± 1. 1 ppm for the 181 isotopologues. For the 25 reference metabolites, the RT isotopic deviation ranged from zero to 3 s with a mean relative error of 0. 02% across the complete analytical run. These results indicate that the analytical characteristics (m/z, RT pairs) of the detected isotopologues are fully consistent with the values expected for the selected metabolites.
The experimental CIDs of the corresponding metabolites were calculated from the reference dataset to generate reference values (reference CIDs) (Table  S1 ), which were validated by comparing them with predicted values for the PT sample. The CIDs measured manually for all 25 metabolites deviated by less than 5% on average from the predicted values (Fig. S2 ). These results highlight one of the benefits of using a reference material such as the PT sample to optimize processing, namely that analytical problems-limited sensitivity in this case-can be identified and considered separately from processing issues. The 181 isotopologues in the reference dataset are referred to hereafter as the reference isotopologues.
Optimization of isotopologue extraction The proposed strategy for data processing optimization based on a reference material is illustrated in Fig. 2  and involves two steps (i) optimization of isotopologue extraction and (ii) optimization of isotopologue clustering, as Fig. 2  Proposed strategy for the two-step optimization of data processing in untargeted MS-based isotopic tracing studies described in detail below. Briefly, in the first step, the benchmark isotopologues of the reference metabolites are identified automatically using extraction software (XCMS in this work) and are compared to the reference isotopologues using three evaluation criteria (recovery rate, analytical characteristics, and isotopologue integrals) and the extraction parameters are then iteratively modified to minimize the difference between the two isotopologue datasets.