dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall, 44% of the progeny were rr and 56% were rs (Figure  2 , Dataset S1), and this ratio was not significantly different from the 50:50 ratio expected according to Mendelian inheritance (G = 2. 78, df = 1, p > 0. 1) and not significantly different across the three families (GH = 0. 45, df = 2, p > 0. 7). The CtABCB1 genotype was strongly associated with survivorship on Bt poplar overall (Figure  2 , G = 194. 98, df = 2, p < 0. 0001), with nonsignificant differences among families (GH = 0. 003, df = 2, p > 0. 9). For crosses performed in 2015, 99% of the progeny were either rr and R (surviving on Bt poplar), or rs and S (killed by Bt poplar)-a nearly perfect correlation (Figure  2 , Dataset S1). For crosses performed in 2011, the correlation was somewhat lower with 91% of progeny being either rr and R, or rs and S.
Progeny were either rr (with two copies of the four-base-pair deletion) or rs (heterozygous, with one copy of the four-base-pair deletion and one copy of the wild-type allele). Overall, 44% of the progeny were rr and 56% were rs (Figure  2 , Dataset S1), and this ratio was not significantly different from the 50:50 ratio expected according to Mendelian inheritance (G = 2. 78, df = 1, p > 0. 1) and not significantly different across the three families (G H = 0. 45, df = 2, p > 0. 7). The CtABCB1 genotype was strongly associated with survivorship on Bt poplar overall (Figure  2 , G = 194. 98, df = 2, p < 0. 0001), with nonsignificant differences among families (G H = 0. 003, df = 2, p > 0. 9). For crosses performed in 2015, 99% of the progeny were either rr and R (surviving on Bt poplar), or rs and S (killed by Bt poplar)-a nearly perfect correlation (Figure  2 , Dataset S1).
5% agarose gels before being cleaned up using the DNA Clean and Concentrator-5 kit (Zymo Research Europe, Freiburg, Germany). Sanger sequencing was carried out on an ABI 3730xl DNA Analyzer (Applied Biosystems, Foster City, CA, USA). The resulting sequencing chromatographs were inspected individually and genotypes were assessed as described in Figure  S3. Results of the phenotyping and genotyping of the backcrosses as well as data analysis are summarized in Dataset S1. Data analysis employed G-statistics as described by Sokal and Rohlf  [55] ). Trace files are available in Supplementary Dataset S2 (family 48), Supplementary Dataset S3 (family 58) and Supplementary Dataset S4 (backcrosses 2011) which can be downloaded at  [56].
Comparison between CtABCB1 cDNA sequences derived from either the susceptible or the resistant populations, Figure S2: Predicted protein sequence of CtABCB1, Figure S3: Determination of the genotype for the backcrosses between susceptible and resistant C. tremula, Figure S4: Neighbor-joining tree of full-transporter ABCB protein sequences from Coleoptera, Figure S5: CLUSTAL Alignment of ABCB protein sequences from Coleoptera, Table S1: Primers used in this study and their function, Dataset S1: Details of the phenotyping and genotyping of the backcrosses, Dataset S2: Trace files corresponding to the genotyping of family 48, Dataset S3: Trace files corresponding to the genotyping of family 58, Dataset S4: Trace files corresponding to the genotyping of "backcrosses 2011. ".
Acknowledgments: We are grateful to  Bianca Wurlitzer ,  Domenica Schnabelrauch  and  Claudine Courtin  for technical support and to the greenhouse teams of the  Max Planck Institute for Chemical Ecology  and of the  INRA Zoologie Forestière  for taking care of the plants. We thank  Matan Shelomi ,  Max Planck Institute for Chemical Ecology , for comments on an earlier version of this manuscript. This work was supported by the  Max-Planck-Gesellschaft.
Furthermore, NTN1 was downregulated (Fig 1C, fold change (FC) ≤ 0. 5) in 43% of cases and the "NTN1low" samples were hypermethylated (Fig 1G; P = 3 × 10 À2 , twosided Mann-Whitney test) when compared with samples exhibiting no NTN1 downregulation (FC ≥ 1. 3). Using the same approach for DAPK1, we observed that samples exhibiting DAPK1 downregulation (29% of the samples, Fig 1D ) were also hypermethylated (P = 3 × 10 À4 ) when compared with the other samples (Fig  1H ). These epigenetic modifications did not seem to be independent events since in NTN1-hypermethylated samples (n = 23, FC ≥ 2), DAPK1 was also hypermethylated (mean FC = 2. 22), while in NTN1-hypomethylated samples (n = 13, FC < 0. 7), DAPK1 was not hypermethylated (mean FC = 0. 93). The relationship between DAPK1/NTN1 downregulation and DNA hypermethylation was also observed in a larger number of breast cancer samples (n = 807) available on TCGA data portal.
The analysis indicated that the mean percentage of CpG methylation of the DAPK1 and NTN1 pyrosequenced regions were inversely correlated (Pearson's r = À0. 66, P = 0. 003, and Pearson's r = À0. 55, P = 0. 008, respectively) with their levels of expression  (Fig EV1A and B). Altogether, these data suggested that DNA methylation is involved in the downregulation of NTN1 and DAPK1 in human breast cancers. To determine whether this concomitant change in DAPK1 and NTN1 expression was also observed at the protein levels, DAPK1, UNC5B, and netrin-1 were measured by immunohistochemistry (IHC) using tissue microarrays (70 sections) from human breast ductal carcinoma (Super Bio Chips). This analysis revealed, that, netrin-1-low samples also exhibited low levels of DAPK1 (v 2 test, P = 0. 04). In contrast, UNC5B levels were the same, irrespective of netrin-1 levels  (Fig EV1C and D).
This result was validated in the TCGA cohort of breast tumors samples (n = 807), where a correlation between DAPK1 and NTN1 expression was observed (odd ratio = 4. 71, P = 0. 03, Fig  EV1E ). In order to establish an in vitro experimental model mimicking the DNA methylation alterations observed in breast cancer tissues, DNA methylation patterns of two cancer cell lines were determined by parallel sequencing. Pull-down assays were conducted using the MDA-MB-231 cell line derived from human breast cancer, and the HMLER cell line generated through the in vitro transformation of human mammary cells  (Elenbaas et al, 2001; Morel et al, 2008). Methylated DNA fragments were selected using a recombinant protein containing the Methyl-CpG-binding domain of MBD2 of MBD2 (Methyl-Cap-seq), from MDA-MB-231 cell line derived from Altogether, these data strongly suggest that DNA hypermethylation is involved in the transcriptional silencing of DAPK1 and NTN1 in human breast cancer cells.
Alternatively, these data would suggest that tumor-growing conditions can overcome, at least partially, the anti-tumor effect of DAC. Experimental approaches  (Rodriguez-Paredes & Esteller, 2011b; Yang et al, 2012; Azad et al, 2013)  and clinical trials (ClinicalTrials. gov http://www. clinicaltrials. gov) using combinations of DAC with other epigenetic modifiers and/or cytotoxic agents provide interesting strategies for tumor growth reduction. However, these approaches have overlooked an important aspect of DAC, which is the re-expression of potentially masked targets and pathways. The data reported here provide not only evidence for the importance of ligand/DRs pairs in the regulation of tumor development but also advocate a new strategy based on the specific targeting of genes re-expressed following a DNA hypomethylating treatment.
The sequencing plate was kept at 80°C for 2 min and at room temperature for 5 min. Pyrosequencing reactions were performed in a PyroMark Q96 system using PyroGold reagents (Qiagen). Results were analyzed using the PyroMark Software. In a substantial part of human cancers, netrin-1 (NTN1) is upregulated and this upregulation is inhibiting apoptosis induced by its so-called dependence receptors, DCC and UNC5H, and thus promotes tumor progression. However, in other cancers, the selective inhibition of this dependence receptor death pathway relies on the silencing of proapoptotic effector proteins. A large fraction of human breast tumors exhibits simultaneous DNA methylation-dependent loss of expression of NTN1 and of DAPK1, a serine threonine kinase known to transduce the netrin-1 dependence receptor pro-apoptotic pathway.
Acknowledgements We are grateful to  Drs. A Puisieux  and  AP Morel  for the HMLER cells, and to  Sandrine Viala  for her nice and efficient work. We thank  Dr B. Manship  for editing of this manuscript and helpful discussions. This work was supported by institutional grants from the  CNRS , the  University of Lyon , the  Centre Léon Bérard , the  Ligue Contre le Cancer , the  INCA , the  ANR , the  ERC , the  EC FP7 Hermione-2man  and from the  Fondation Bettencourt. MG was supported by a fellowship grant from the  LabEx DEVweCAN.
The mRNA levels corresponding to the genes for the KP enzymes were quantified for each cell type and used as a surrogate indicator of protein expression. mRNA levels corresponding to KMO were 7000 times higher in the transfected cells than in the wildtype cells. HEK293 cells express KMO endogenously and the other kynurenine pathway enzymes but KMO activity was not detectable in these cells (data not shown), correlating with the observations of Alberati-Giani et al. 12  Several kynurenine pathway enzymes demonstrated upregulation in the KMO cells, in particular, the gene corresponding to TDO. TDO catalyses the first and rate limiting step in tryptophan metabolism. TDO upregulation in KMO-overexpressing cells may imply the existence of a feedback mechanism to enable homoeostasis of kynurenine concentrations in the cell.
Materials and Methods Cloning KMO(V5-6His): Full-length human KMO (GenBank Accession No. NM_003679) (Cys 452 variant) was amplified from an image clone by Taq polymerase-amplified PCR. A KOZAK sequence for translational initiation was incorporated by the forward PCR primer. The native stop codon was removed by the reverse PCR primer. The gene was ligated into the pcDNA5/FRT/V5/HisTOPO vector (Life Technologies, Renfrew, UK) by TOPO cloning reaction to facilitate expression by the Flp-In system. The vector C-terminal V5 and 6 histidine tags were incorporated in the protein construct. 10His-E2-Crimson-KMO: The fusion construct gene 10His-E2-Crimson-KMO was synthesised by Genscript and provided in vector pUC57. This construct incorporated a 10 × polyhistidine tag (CATCATCATCACCATCATCATCATCATCAC), far-red fluorescent protein E2-crimson (sequence obtained from Clontech, CA, USA) followed by an enterokinase site (GATGACGATGACAAG) and full-length human KMO (GenBank Accession No.
LC-MS analysis was carried out using the TSQ Quantum Discovery triple quadrupole mass spectrometer (Thermo Fisher Scientific, Hemel Hempstead, UK) using a pentafluorophenyl (PFP) fused pore column (Waters, Hertfordshire, UK), at 40 °C. The injection volume was 10 μl and the flow rate was 500 μl/min. The method had a run time of 4 min and d5 tryptophan was used as an internal standard. Qualifier and quantifier peaks were identified for 3HK and for d5 Tryptophan. Data was acquired and processed using Xcalibur 1. 4 and LC Quan 2. 0 SP1 (Thermo Scientific, Leicestershire, UK) software packages. Staining cells for immunofluorescence. HEK-KMO(V5-6His) cells were stained in transparent 96-well plates (Whatman, GE Healthcare, Buckinghamshire, UK). Wheat germ agglutinin Alexa Fluor 488 conjugate (Life Technologies) was used to stain the plasma membrane of the cells.
A 40x water immersion objective (NA 0. 9) was used for all imaging. Z stack imaging. A Leica SP5C spectral confocal laser scanning microscope (Leica Microsystems, Milton Keynes, UK) was used to obtain high-resolution Z stack images for 3D imaging analysis. Co-localisation images were obtained using a × 40 objective Plan-Apochromat oil immersion lens and the Argon (488 nm) and 633 nm lasers at Nyquist sampling rate. Data was collected using Leica Application Suite Advanced Fluorescence (LAS AF) software. The images were analysed using FACSDiva software (BD Bioscience, Oxford, UK) to determine the Pearson coefficient for co-localisation. JC-1 assay. Non-transfected HEK293 cells and HEK-KMO(V5-6His) cells were plated out separately into 96-well plates at 5 × 10 4 cells per well and incubated overnight at 37 °C, 5% CO 2 , 95% O 2 in DMEM with 10% FBS, 1% L-glutamine, 1% penicillin-streptomycin.
Cells of each type were incubated with medium containing either 500 μM 3HK or control medium (no additives) and incubated overnight for 24 h in a warm room at 37 °C, 5% CO 2 at maximum humidity. A Leica DMIRBE inverted microscope (Leica Microsystems) situated in the warm room was utilised to obtain images using a × 10 objective lens at 5 min intervals for 24 h. Images were captured using Leica QWIN software and composite videos exported using Quicktime player. Live cell imaging. HEK293 cells were plated out at 1 × 10 4 cells/ml on poly-Dlysine-treated coverslips placed in the wells of a six-well plate in OPTI-MEM medium and incubated overnight for 16 h at 37 °C, 5% CO 2. The following day, cells were transiently transfected with pcDNA-10His-E2-Crimson-KMO plasmid DNA using lipofectamine 2000 in OPTI-MEM medium.
For visualisation of apoptotic cells, the Annexin V fluorophore was excited at 488 nm and the emission was recorded using a narrow emission filter BP 515-565 nm. Cells were incubated for 24 h with a time-lapse of 3 min between image acquisition. Data was collected using the corresponding LSM 5 software program (Zeiss, Oberkochen, Germany). Statistical analysis. Differences between controls and experimental samples were analysed using an unpaired Student's t-test using GraphPad Prism software (GraphPad Software Inc. , CA, USA).
Despite alterations in mitochondrial Ca 2 þ uptake in hepatocytes from liver-specific MICU1 KD mice, no gross functional, morphological and/or histological differences were apparent between Ctrl and KD livers (Fig. 2a , LL, Supplementary Fig. 3c  and Supplementary Table  1 ). Gene expression profiling failed to show major differences between KD and Ctrl livers (accession number GSE69801, and Supplementary Fig. 4a, b , LL). Thus, patho-physiological consequences of MICU1 deletion may become apparent only under conditions of acute or chronic stress. MICU1 requirement for liver regeneration. We used 70% PHx  21  as a surgically induced liver-specific stress model  22. Liver regeneration after PHx is a unique tissue repair response that induces a synchronized replication of differentiated hepatocytes in the remnant liver, while maintaining liver-specific functions  23, 24.
For neutrophil identification, sections were immunostained using Anti-Ly6G (BD Pharmingen, 551459, 1:400), which was detected with Anti-Mouse-HRP (ThermoFisher Scientific), counterstained with haematoxylin, rehydrated and mounted. Quantification of BrdU incorporation was performed using 'CellProfiler cell image analysis software' and neutrophil count and necrotic foci were quantified using 'ImageJ software'. For quantification, 15 Â 20 fields were scored per animal. Biochemical analysis. For western blotting, freeze-clamped tissue was homogenized in lysis buffer containing 25 mM HEPES (pH 7. 4), 150 mM NaCl, 2. 5 mM EGTA, 10% glycerol, 0. 5% deoxycholic acid (DOC), 1% IGEPAL, 0. 1% SDS, 1% Triton X-100, 2 mM sodium floride, 2 mM imidazole, 1. 15 mM sodium molybdate, 2 mM activated sodium orthovanadate, 4 mM sodium tartrate dihydrate, 1 mM sodium pyrophosphate, 1 mM b-glycerophosphate and cOmplete protease inhibitor cocktail (Roche Diagnostics, Basel, Switzerland).
Cell lysates were prepared in RIPA buffer supplemented with proteases inhibitors (leupetin, antipain, pepstatin, phenylmethyl sulphonyl fluoride, Sigma). Proteins were analysed by western blotting after separation by SDS-PAGE on 10% TGX gels (Bio-Rad) or 4-12% Bis-Tris NuPAGE gradient gels (Life Technologies). The following antibodies were used for primary overnight incubation: MICU2 (Abcam (Ab)-ab101465, 1:500), Cyclophilin D (Ab-ab110324, 1:1,000), MICU1 (Sigma (Sg)-HPA037480, 1:400), Cytochrome c (BD Pharmingen-556432, 1:500), MCU (Sg-HPA037480, 1:500); CyclinD1 (Thermo Scientific (TS)-MA1-39546, 1:50), Hsp70 (TS-MA1-91159, 1:1,000); Bax (Cell Signaling Technology (CST)-14796, 1:1,000), cFOS (CST-4384, 1:1,000), cJUN (CST-9165, 1:1,000), p cJUN (Ser73) (CST-3270, 1:1,000), Caspase-3 (CST-9662, 1:1,000), CREB (CST-9197, 1:1,000), pCREB (CST-9198, 1:1,000), JNK54 (CST-9252, 1:1,000), p JNK54 (Thr185/Tyr185) (CST-9251, 1:1,000), PARP (CST-9532, 1:1,000), STAT3 (CST-4904, 1:1,000), p STAT3, (CST-9145, 1:1,000), GRB2 (Santa Cruz Biotechnology-255, 1:1,000), GAPDH (Millipore-MAB374, 1:1,000).
Membranes were scanned on the Odyssey scanner (Licor) or Kodak Image Station 440CF. Western blot densitometries were performed using ImageJ (NIH) or Image Studio lite software and normalized to levels of GRB2, GAPDH or Hsp70. Uncropped scans of the most important western blots are shown in Supplementary Fig. 7. ELISA kits were used to measure serum levels of IL-6 (eBiosciences) and TNF-a (eBiosciences) according to the manufacturer's instructions. Tissue triglyceride was extracted with ethanolic KOH as described  42. Triglycerides (serum and tissue) were measured using Stanbio Triglyceride Liquicolor reagent (Stanbio) as per the manufacturer's instructions. Nuclear factor-kB DNA-binding activity in 100 mg of total tissue lysate was measured using the transcription factor assay from Cayman Chemical.
Serum direct bilirubin and total bilirubin were measured using the bilirubin assay kit (Sigma). RNA isolation and quantitative PCR (qPCR). Total RNA was isolated from frozen liver samples (B15 mg) using Animal Tissue RNA Purification Kit (Norgen Biotek) and from isolated hepatocytes or MEFs using TRIzol reagent (Ambion). RNA concentration was measured by ND-1000 (NanoDrop). cDNA was synthesized using SuperScript III (Invitrogen) and used for qPCR reactions using SYBR Green (Invitrogen) on an ABI Prism 7000 sequence detection system (Life Technologies). Data were analysed using the comparative DDC t method. C t of the gene of interest was normalized to that of b-actin. Primers used are: Mice MICU1 (F-5 0 -AACAGCAAGAAGCCTGACAC-3 0 , R-5 0 -CTCATTGGGCGTTAT GGAG-3 0 ), Mice MICU2 (F-5 0 -GGAGCGTAAAACACTGGTC-3 0 , R-5 0 -GTAAG CAAGAAAAGATACTCGG-3 0 ), Mice MCU (F-5 0 -TACTCACCAGATGGCG TTC-3 0 , R-5 0 -GTCCTCTAACCTCTCCAC-3 0 ), b-actin (F-5 0 -CAACACCCCAGC CATG-3 0 , R-5 0 -GTCACGCACGATTTCCC-3 0 ).
C t values were calculated by the Real-Time PCR Analysis Software (Fluidigm) and software-designated failed reactions were discarded from analysis. Relative gene expression was determined by the DDC t method. Modified geNorm algorithm in R was used to identify the set of most stable reference genes, Idh3B and Mrpl16 were selected for normalization. Microarray gene expression analysis. Global gene expression analysis was performed using the Affymetrix Mouse Gene 2. 0 ST arrays following the standard manufacturer's protocol. The raw expression data were log-transformed and normalized across all the samples using the Robust Multiarray Averaging algorithm. Normalized data are deposited in GEO database (accession number GSE69801). Statistical analysis. Data are expressed as mean ± s. e. m. Experiments were performed at least three times, in duplicates or more.
Acknowledgements NIM811 was a generous gift from  Dr John Lemasters  ( Medical University of South Carolina, Charleston, SC ). We thank  Lauren Anderson-Pullinger ,  Peter Lynch  and  Jared Deutsch  for expert technical assistance and  Dr Sophie Astrof  for her advice and useful discussions on neonatal lethality. M. P. was a recipient of postdoctoral fellowships from  La Fondation pour la Recherche Medicale (FRM)  and from the  American Heart Association. The study was funded by  NIH  grants  RO1 GM102724  to G. H. and  R01 AA01887  to J. B. H. and by start-up funds to E. L. S. The creation of the MICU1-deficient mouse was funded by a  TJU  pilot grant. The Comparative Pathology and Mouse Phenotyping Shared Resource (CPMPSR),  Department of Veterinary Biosciences  and the  Comprehensive Cancer Center , the  Ohio State University , Columbus, OH, is supported in part by an  NCI grant  ( P30 CA016058 ).
A set of hindcast simulations with the new NEMO3. 6 ocean-ice model in the ORCA1 grid and forced by the DFS5. 2 atmospheric data was performed from 1958-2012. We focussed on simulations that differ only in their sea-ice component: the old standard version LIM2 and its successor LIM3. Main differences between these sea-ice models are the parameterisations of sub-grid-scale sea-ice thickness distribution, ice deformation, thermodynamic processes, and sea-ice salinity. Our main objective was to diagnose the ocean-ice sensitivity to the updated NEMO-LIM sea-ice physics. Results of such analysis have not been published for this new NEMO version. In the polar regions, NEMO-LIM3 compares better with observations, while NEMO-LIM2 deviates more, producing too much ice in the Arctic, for example. Differences between NEMO-LIM2 and NEMO-LIM3 do not change in simulations even when the freshwater adjustments are turned off.
Reference data When quantitatively assessing the modelled sea-ice and upper ocean realism, we included comparisons with satellite-based and reanalysis products of sea-ice concentration, thickness and velocity. Since 1979, space-borne passive microwave sensors have produced a nearly continuous and consistent record of ice concentration which provides a good basis for model validation. For sea-ice concentration we used the NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 2  (Meier et al. , 2013) , which covers both polar regions at a 25 km grid cell size. The observed sea-ice extent data, which were based on satellite observed sea-ice concentrations, are the NSIDC Sea Ice Index  (Fetterer et al. , 2002). For sea-ice velocity analysis, the OSI-SAF product by  Lavergne et al.
Additionally, we used the most recent World Ocean Atlas 2013 (WOA13)  (Boyer et al. , 2013)  averaged over the years 2005-2012. WOA13 lacks the Arctic observations included in PHC3, but has more recent observations elsewhere. Therefore, at least outside the Arctic Ocean, the WOA13 data temporally matches better with our NEMO-LIM comparison period of 2003-2012. As we will show in Section 5, WOA13 and PHC3 mainly differ in terms of Arctic SSS, while otherwise climatological differences are relatively small from the NEMO-LIM assessment perspective. Notably,  Wang et al. (2016b)  used PHC3 in their ocean-ice model Arctic intercomparison study, and using PHC3 here as well makes qualitative comparisons of our results with theirs straightforward. Finally, PHC3 was used to determine the initial hydrographic conditions of our model  simulations, and comparisons between 2003-2012 LIM3 climatology and PHC3  show how much our simulations diverged from their initial state in 45 years.
Code and data availability The NEMO version 3. 6 version incorporates LIM2 and LIM3. 6 sea-ice models, and can be downloaded from the NEMO web site (http://www. nemo-ocean. eu/) at this address: http://forge. ipsl. jussieu. fr/nemo/svn/branches/2015/nemo_v3_6_STABLE. The model input data can be obtained following the references described in section 2. 5. The output of model simulations and the computer scripts used to produce the results presented in the paper, including the figures, are available from the corresponding author upon request.
Acknowledgements. We acknowledge the creators of low resolution sea-ice drift product of the  EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF , www. osi-saf. org). We thank  Dr Laurent Brodeau  for providing us the very useful Barakuda software package to diagnose NEMO simulations. The work of Lensu and Uotila was supported by the  Academy of Finland  (contracts 264358 and  283034 ).