dataesr/scientific-paragraphs-categorization huggingface dataset:
Briefly, Chip DNA was end-blunted and added with an "A" base so the adapters from Illumina with a "T" can ligate on the ends. Then 200-400 bp fragments are gel-isolated and purified. The library was amplified by 18 cycles of PCR. Primary analysis of ChIP-Seq data sets: the image analysis and base calling were performed by using Illumina's Genome Analysis pipeline. The sequencing reads were aligned to the mouse genome UCSC build by using BOWTIE32 alignment programs in two ways: only uniquely aligned reads were kept or both uniquely aligned reads and the sequencing reads that align to repetitive regions were kept for downstream analysis (if a read aligns to multiple genome locations, only one location is arbitrarily chosen). The multiple reads were collapsed in order to reduce the PCR biases.
The microarray experiments and data normalization were performed by the Cochin Institute transcriptomic core facility. Briefly, RNA profiling was performed using Affymetrix GeneChip Human Gene 2. 0 ST array, which interrogates 25,000 gene sequences. Raw data were normalized using the Robust Multichip Algorithm (RMA) in Bioconductor R software. Then, all quality controls and statistics were performed using Partek GS software. First, hierarchical clustering (Pearson's dissimilarity and average linkage) and principal composant analysis (PCA) were performed as unsupervised exploratory data analysis. Then, a classical analysis of variance (ANOVA) for each gene and pair wise Tukey's post-hoc tests between groups were conducted to find differentially expressed genes. Finally, p-values and fold changes were used to filter and select differentially expressed genes.
9) and then permeabilized for 2 min with 0. 1% Triton X-100 in Phem buffer. After three washes with Phem buffer, blocking solution (1% BSA in PBS, pH 7. 4) was applied for 30 min and primary antibodies against Flag and H3K9me2 were added in blocking buffer for 1 h at room temperature. After three washes with PBS/0. 1% Triton X-100, cells were incubated with DAPI and with secondary antibodies conjugated with fluorescent dyes for 1 h, washed again with PBS/0. 1% Triton X-100, and mounted in 20 mM Tris, pH 9/0. 1% p-phenylenediamine. Image intensities for each antibody were scaled identically. Human liver biopsies. We analyzed liver samples from white morbidly obese French patients who underwent abdominal surgery and were included in the Biological Atlas of Severe Obesity (ABOS) cohort realized at the "Centre Hospitalier Régional Universitaire de Lille", France (ClinicalGov NCT01129297)  66.
Type 2 diabetic patients were defined by fasting plasma glucose levels ≥07. 0 mmol/L; normoglycemic individuals were defined by glucose levels of <5. 6 mmol/L. HOmeostasis Model Assessment of Insulin Resistance (HOMA-IR) was assessed with the homeostasis model assessment and calculated according to Wallace et al  68. Needle liver biopsies were performed in the left hepatic lobe through laparoscopy, within 15 min from the beginning of the procedure. Samples were immediately frozen in liquid nitrogen and stored at -80 °C. Statistical analyses. Results are expressed as mean ± SEM, and were analyzed with analysis of variance using GraphPad Prism software. Sample sizes (n) were reported in the corresponding figure legend. All experiments were performed on at least three independent occasions. No statistical method was used to predetermine sample size.
Acknowledgements The authors would like to thank  Dr Anne-Françoise Burnol ,  Dr Sandra Guilmeau , and  Dr Françoise Levavasseur  ( Institut Cochin, Département d'Endocrinologie, Métabolisme et Diabète, Université Paris Descartes, Paris, France ) for helpful discussions and critical reading of the manuscript. The authors also thank  Catherine Esnous  for technical assistance,  Véronique Fauveau  for performing in vivo adenoviral injections,  Gilles Renault  and the small animal imagery core facility ( Institut Cochin, Inserm U1016, Paris ) for in vivo bioluminescence imaging,  Sébastien Jacques  and  Florent Dumont  from the genomic core facility ( Institut Cochin, Inserm U1016, Paris ) for microarray bioinformatic analysis, the  HistIM core facility  ( Institut Cochin, Paris, France ) for performing liver sections and immuno-staining and  Lorenne Robert  for managing the Nrf2-KO mice colony.
Mice used in this study were housed in an animal facility equipped with the help of the  Région Ile de France. The work was performed within the  Département Hospitalo-universitaire (DHU) AUToimmune and HORmonal diseaseS (AUTOHORS)  and within the  Recherche Hospitalo-Universitaire (RHU)  " QUID NASH. "This study was also supported by grants from the  agence nationale de la recherche  ( ANR-09-JCJC-0057-01 ,  R. D) , the  French "fondation pour la recherche médicale" (FRM , labélisation équipe, R. D and C. P), " La ville de Paris " (projets emergences, R. D), by the  European Commission  ( ERC starting grant ,  336629 , LIPIDOLIV; R. D), and by the  NIH  ( GM79465 ,  C. J. C ).
LjNBCL1 is required for nectary development, leaf patterning and for the control of the number of axillary meristem Database and L. japonicus genome analysis reveal that only one NBCL gene is present in this plant in contrast to other legume plants. LjNBCL1 gene expression analysis in the aerial part of wild-type L. japonicus shows that LjNBCL1 transcripts accumulate at low levels in leaves, internodes and in young pods. They were more abundant in nodes and highly abundant in flower from stage 10 to 13 according to the floral stages described in  Weng et al. , 2011 (Figure 5a ). These results are in agreement with LjNBCL1 expression data available from the Lotus japonicus Gene Expression Atlas (https://ljgea. noble. org/v2/; Figure  S1 ). In Ljnbcl1:LORE1 homozygous mutants, 13 % of the leaves had an additional leaflet relative to L.
DISCUSSION The numerous vegetative and reproductive developmental defects observed in Ljnbcl1 mutants and the absence of an LjNBCL2 sequence in databases suggest that there is only one NBCL (LjNBCL1) in the genome of L. japonicus ecotype GIFU in contrast to other legume plants studied, which have at least two distinct NBCL genes. In this work, we show that in the determinate nodules of L. japonicus, LjNBCL1 gene is constitutively expressed in roots, induced during nodulation and thus behaves as the MtNOOT1 gene  (Couzigou et al. , 2012). We demonstrate that, despite the differences existing in determinate nodule organogenesis and organization, LjNBCL1 gene function in maintenance of nodule identity through NVB root identity repression is conserved. A similar nodule to root conversion phenotype was described in Glycine max determinate nodules inoculated with Bradyrhizobium japonicum mutant strains  (ΔphyR, ΔecfG; Gourion et al.
Accepted Article This article is protected by copyright. All rights reserved. embedding moulds (Heraeus Kulzer). 5 µm sections were cut using a microtome (Leica, RM 2155) and tungsten disposable blade (Leica, TC-65). Sections were stained for 10 min with toluidine blue (0. 02 %), observed with a microscope (Olympus, BX53) and digital images were acquired using cellSens Standard software (Olympus life science). Acetylene reduction assay. Acetylene reduction assays (ARA) were performed on individual plants inoculated with M. loti at 35 dpi with a protocol modified from  Koch and Evans (1966). Basically, the nodulated root system of one plant was placed in 21 ml glass vial sealed with rubber septa in presence of 200 µl of water. Acetylene gas (500 µl) was injected into each vial and 2 h incubation was performed.
Plants were inoculated with M. loti strain NZP-2235 and 16 plants were used for each point. Gene expressions were normalized against the constitutively expressed LjSERINE/THREONINE-PROTEIN PHOSPHATASE 2A (LjPP2A) and LjUBIQUITIN-CONJUGATING ENZYME (LjUBC) genes and against non-inoculated primary root at 0 dpi. Y axis represents log10 relative gene expression values (log10 fold change). Results represent means ± SEM of three technical replicates and three biological replicates.
ACKNOWLEDGEMENTS This work was supported by the  CNRS  and by the grants  ANR-14-CE19-0003  (NOOT) from the  Agence National de la Recherche (ANR)  to PR. This work has benefited from the facilities and expertise of the  Servicio de Microscopía Electrónica Universitat politècnica de Valencia (Spain , http://www. upv. es/entidades/SME/) and of the  IMAGIF Cell Biology Unit of the Gif campus (France , www. imagif. cnrs. fr)which is supported by the  Conseil Général de l'Essonne. The authors thank  Dr. Mathias Brault  from the  Institute of Plant Sciences Paris-Saclay (France) , for providing pFRN:RNAi plasmid, A. rhizogenes ARqua1 strain and control GUS:RNAi construction, and  Dr. Simona Radutoiu  from the  University of Aarhus (Denmark) , for providing the Na-Borate/TRIzol RNA extraction protocol.
Results Identification of HOXC-AS3 by analyzing gastric cancer RNA-expression profiling data Raw microarray data was downloaded from GEO, including GSE50710 (n = 20) and GSE58828 (n = 6). To obtain differentially expressed lncRNAs, signal data were normalized and z-score-transformed (paired or group t-test according to the experimental design was used to validate statistical significance). The top 20 dysregulated lncRNAs in these GEO datasets were shown in Fig. 1a, b. For example, the lncRNA that showed clear upregulation was HOXA11-AS (the largest increase in GSE50710), and our previous study showed that HOXA11-AS could regulate cell proliferation and invasion of gastric cancer by scaffolding chromatin modification factors and patients with high HOXA11-AS expression had a shorter survival and poorer prognosis  [14].
The most obvious increase in GSE58828, LINC00473, could mediate tumor growth and elevated LINC00473 expression correlated with poor prognosis of lung cancer  [15]. Our previous study showed that FEZF1-AS1 could promote gastric cancer proliferation and higher expression of FEZF1-AS1 predicted poor prognosis  [16]. Qin et al. found that LINC01296 was upregulated in GC tissue and correlated with poor prognosis  [17]. CCAT1 is an oncogenic long noncoding RNA in human cancers including GC  [18]. Our previous work found that CCAT1 could regulate cell proliferation and migration in esophageal squamous cell carcinoma and higher expression of CCAT1 is correlated with poorer outcomes  [19]. In addition, CCAT1 could serve as important prognostic biomarkers in colorectal cancer  [20]. We focused on overexpressed lncRNAs because these lncRNAs may be more readily used as early diagnosis markers or therapeutic targets.
Therefore, although a small portion of lncRNAs have been functionally characterized, many members in the class remain uncharacterized. In our analysis of the results, HOXC-AS3 was the lncRNA clearly upregulated in both GSE50710 and GSE58828 (the rank of HOXC-AS3 is in the exceeding front position, both in GSE50710 and GSE58828), and the role in GC tumorigenesis including other cancer types remained unclear. This prompted us to explore the role of HOXC-AS3 in human GC. To further validate this result, we analyzed that RNA-Seq data (from The Cancer Genome Atlas (TCGA)) of lncRNAs of gastric cancer were from TANRIC  [21]  (http://ibl. mdanderson. org/tanric/_design/basic/ index. html). As shown in Fig. 2a , HOXC-AS3 was significantly upregulated in GC tissues from the TCGA data.
Gain of H3K4me3 and H3K27 acetylation-activated HOXC-AS3 is upregulated in human GC tissues and correlates with poor prognosis For further study, we first performed rapid amplification of cDNA ends (RACE) to identify the full sequence of HOXC-AS3 in BGC-823 cells according the sequence archived in the RefSeq database of NCBI (440 bp, with poly (A) tail, Fig. 2b ). To validate the expression results from high-throughput data, as shown in Fig. 2c , the HOXC-AS3 expression level in tumor tissues was significantly higher in 112 pairs of GC tumor tissues compared with adjacent normal tissues (P < 0. 001). One tumor tissue showed an upregulation of HOXC-AS3 greater than 521-fold relative to normal tissue. Next, we explored the correlation between HOXC-AS3 expression and the clinicopathological factors of patients with GC.
com/analysis/, detailed steps were described in Additional file 8: Supplementary Methods), which indicated that higher HOXC-AS3 expression correlated with worse OS, using publicly available chip data from 631 GC patients (Fig. 2f  ). Then, univariate and multivariate survival analyses (Cox proportional hazards regression model) were performed. Univariate analysis identified two prognostic factors: TNM stage and HOXC-AS3 expression. Multivariate analysis further revealed that HOXC-AS3 expression was an independent predictor for overall survival in patients with GC (P < 0. 001), as well as TNM stage (P = 0. 023) (Additional file 2: Table  S2 ). To explore the mechanism of high expression of HOXC-AS3 in GC, firstly, by using the UCSC Genome Bioinformatics Site (http://genome. ucsc. edu/), we found high enrichment and overlaps of H3K4me3 and H3K27Ac peaks at the promoter region of HOXC-AS3 (Fig.
HOXC-AS3 interacts with YBX1 To explore the mechanism of HOXC-AS3, firstly, we examined whether HOXC-AS3 acts in cis, affecting nearby gene expression (HOXC10; HOXC-AS3 was an antisense transcript of HOXC10). We found that knockdown of HOXC-AS3 did not affect expression of HOXC10 (data not shown), indicating it may act in trans. Then, subcellular fractionation location assays showed a considerable increase in HOXC-AS3 expression in the nucleus versus the cytosol (Fig. 5a ), thus suggesting that HOXC-AS3 may play a major regulatory function at the transcriptional level, indicating it might interact with nucleus molecules or proteins. Then, we performed an RNA pull-down assay followed by a proteomic analysis of the HOXC-AS3-associated protein complex in BGC-823 cells (Fig. 5b ). We incubated the in vitro transcribed HOXC-AS3 bound to beads with BGC-823 nuclear extracts to purify the HOXC-AS3 RNA-protein complex, and protein identity was revealed by mass spectrometry.
Microarray data analysis Microarray datasets from the GEO database were used to test HOXC-AS3 differential expression. Raw microarray data was downloaded from GEO including GSE50710 and GSE58828. Then, the raw microarray data were normalized and z-score-transformed using RMAExpress (http://www. rmaexpress. bmbolstad. com/). RNA-Seq data (from TCGA) of lncRNAs of gastric cancer were from TANRIC (http://ibl. mdanderson. org/tanric/_design/basic/index. html) [21].
Availability of data and materials Our RNA-seq data used in this study (RNA-seq after knockdown HOXC-AS3 and YBX1) have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO accession number GSE119021  [45]. The lncRNA expression profiles data were obtained from GEO, with accession numbers GSE50710  [46]  and GSE58828  [47].
Values of the reference wavelength were subtracted from absorbance values and data were corrected for background absorbance by subtracting the values from wells incubated without cells. Data were referred to solvent control which was set to 100%. Dose response curve fitting (four parameter logistic curve) was performed with SigmaPlot 13. 0 (Systat Software, Erkrath, Germany) and values for inhibitory concentration of 10% (IC10) were determined. At least two independent, biological replicates with five to six technical replicates per condition were performed.
Firefly luciferase activity was normalized to Renilla luciferase activity and expressed as relative activity referred to the positive control. Statistical analysis was performed with SigmaPlot 13. 0 (Systat Software, Erkrath, Germany) using the non-parametric Kruskal-Wallis test followed by Dunn's test. Statistical significance was assumed at p < 0. 05. An overview of the specific conditions for each reporter gene assay (plasmid, plasmid amount, cell line, positive control) is provided as a supplement (Table  S1 ).
The peptide mixture was further subjected to C18 purification using Sep-Pak Vac 1cc (50 mg) tC18 Cartridges (Waters, Eschborn, Germany) according to the manufacturer instructions. For the parallel reaction monitoring (PRM) LC-MS/MS method 38 proteins were selected corresponding to the steatosis-relevant gene transcripts examined at the mRNA expression level. PRM assays were generated in Skyline 39 (version 3. 5. 0. 9319) using a spectral library generated in-house by discoverybased LC-MS/MS analysis from the same samples and the background proteome from UniProtKB/Swiss-Prot (June 2014) specific to Homo Sapiens. At least 2 quantotypic peptides were selected for each protein and tested in PRM mode to select the most suitable peptides. Retention time windows for the PRM assays were defined by analyzing a pooled sample (aliquot of all conditions and biological replicates pooled together) in PRM mode.
High resolution MS scans were acquired in the Orbitrap (resolution 60,000, 50 s injection time) to monitor peptide ions across the mass range of 350-1400 m/z, followed by higher energy collision dissociation (HCD) MS/MS scans of the targeted peptides in the Orbitrap (resolution 15,000, 34 injection time, isolation window 1. 4 m/z). PRM data were analyzed in Skyline. Peak boundaries were individually inspected and manually set if necessary. Peaks with the lowest mass error value and best match to the library, relative fragment-ion intensities and peak shape similarity were selected for further quantitative analysis. Samples with more than 5 min retention time shift were excluded from further analysis (one of three biological replicates in the 0 µM condition and one of three biological replicates in the 200 µM condition were excluded).
BMC 50 calculation Dose-response modeling and benchmark concentration (BMC) analysis were performed using the Rbased software package PROAST (version 64. 14, RIVM, Bilthoven, Netherlands). Response data were provided as relative data normalized to the solvent control and submitted to PROAST as continuous, summary data containing mean, standard deviation and sample size in tab-delimited text files. Data were fitted to an exponential 3-parameter model (1) or to an exponential 4-parameter model (  2 ): y = a exp bx (1) y = a c -c -1 exp -bx (2) The model with the lowest Akaike information criterion (AIC) value was selected and the BMC and the corresponding two-sided 90% BMC confidence interval given by BMCL (lower bound of the BMC confidence interval) and BMCU (upper bound of the BMC confidence interval) were calculated for a benchmark response of 50% (BMR 50 ).
Funding Information The research leading to these results received funding from the European Union´s Horizon 2020 Dose-response modeling and benchmark dose (BMC) analysis were performed using the software package PROAST. The BMC and the respective two-sided 90% BMC confidence interval given by BMCL (lower bound of the BMC confidence interval) and BMCU (upper bound of the BMC confidence interval) were calculated for a benchmark response of 50% (BMR 50 ). Data were sorted for BMC 50. For high content cell imaging data after 24 h exposure no BMC 50 could be calculated due to the maximal response lying below a 50% change in response relative to the background.
research and innovation programme under Grant Agreement 633172 (European Test and Risk Assessment Strategies for Mixtures). This publication reflects only the author's views, and the Community is not liable for any use made of the information contained therein. C) leading to enhanced transcription and relocation of the transcriptional start site. Molecular pharmacology, 67, 1772-1782. (35) Quack, M. , Szafranski, K. , Rouvinen, J. , and Carlberg, C. (1998) The role of the T-box for the function of the vitamin D receptor on different types of response elements. Nucleic Acids BMC 50 values for in vitro assays after 24 h or 72 h of cyproconazole exposure a.
ACKNOWLEDGMENTS Funding for this work was provided to J. C. T. through the  Oak Ridge Associated Universities Ralph E. Powe Junior Faculty Enhancement Award  and the  Department of Biological Sciences of Louisiana State University. A portion of the funding for this work was provided by a  Planning Grant award  to O. U. M. from  Florida State University. Funding for the research vessel and collection of oceanographic data was provided by the  National Oceanic & Atmospheric Administration ,  Center for Sponsored Coastal Ocean   Research Award  ( NA09NOS4780204 ) to N. N. R. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Portions of this research were conducted with high-performance computing resources provided by  Louisiana State University  (http://www.
1% but not those with lower read abundance. Thus, our study focused only on microbes with read abundance of at least 1% in at least one sample. Three phyla (Proteobacteria, Firmicutes, and Actinobacteria) were detected in all 34 samples and accounted for 91% of the total ABFV reads (see Fig. S1  in the supplemental material). At the genus level, Streptococcus, Pseudomonas, Ralstonia, Escherichia, and Rothia were the most abundant; those genera accounted for 66% of all ABFV reads and explained more than 70% of the variance among samples (Fig. 1 ). Eighty-seven species from 49 genera were identified. Among all genera, Streptococcus and Pseudomonas were most diverse, with 14 species and 11 species detected, respectively. Of note, the number of species identified was partially determined by the richness of diversity of sequences included in the reference database; thus, well-studied genera (such as Streptococcus and Pseudomonas) could be more diverse at the species level.
01, Fig. 4B ). As the proportion of reads could have e Cells in the BALF were collected and stained with Wright Giemsa's stain, and cells were counted under a microscope. f GOLD, Global Initiative for Obstructive Lung Disease criteria. g Data represent frequencies of exacerbations for COPD patients in the previous 4 years (2014 to 2018) after the collection of BALF samples. h For discrete data, the contingency table was created and the Fisher exact test was used for the significance test; thus, we were testing whether a specific classification (e. g. , male or female) was associated with one of the three active lung microbiome subgroups. i For continuous data, the Kruskal-Wallis rank sum test was applied; thus, we were testing whether a given feature was different among three different active lung microbiome subgroups.
ACKNOWLEDGMENTS We are thankful to  Aiping Wu  ( Center of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking, China ) for his help on transcriptome data analysis and to  Mark Stoneking  ( Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany ) for comments and proofreading. We are also thankful to  Ying Wang  and  Lan Chen  for their help in laboratory management. This study was supported by  CAMS Innovation Fund for Medical Sciences  ( 2016-I2M-1-014 ,  2017-I2M-3-017 ), the  Fundamental Research Funds for the Central Universities  ( 2016ZX310060 ,  2016GH320002 ),  National Key R & D Project  " Precise Medicine Research " from  Ministry of Science and Technology and National Health and Family Planning Commission  ( 2016YFC0903800 ), the  Program for Changjiang Scholars and Innovative Research Team in University  (IRT13007), and  Fondation Mérieux.