dataesr/scientific-paragraphs-categorization huggingface dataset:
Methylation array hybridization for 5mC analysis We generated methylation datasets for 22 placenta samples using the Illumina Infinium Human Methylation450 BeadChip arrays, which simultaneously quantifies ~1. 7% of all CpG dinucleotides. Two paired CVS-term placenta samples were hybridized to the Infinium MethylationE-PIC (EPIC) arrays since the former platform was made obsolete in 2017, as were the samples from multiple anatomical sites and twin/triplet pregnancies. Bisulphite conversion was performed according to the manufacturer's recommendations for the Illumina Infinium Assay (EZ DNA methylation kit, ZYMO, Orange, CA). The converted DNA was used for hybridisation following the Illumina Infinium HD methylation protocol at genomic facilities of the Cancer Epigenetics and Biology Program (Barcelona, Spain).
Data filtering and analysis of methylation signals In addition to the HM450k datasets generated in-house, we also included 45 placenta sample datasets (31 preeclampsia and 14 term controls), from GSE57767, which were collected at Hospital of the University of Pennsylvania (Additional file 1)  [20]. Before analyzing the data, we excluded possible sources of technical biases that could influence results. We applied signal background subtraction, and inter-plate variation was normalised using default control probes in BeadStudio (version 2011. 1_Infinium HD). We discarded probes with a detection p value > 0. 01, containing single nucleotide polymorphisms (SNPs) within the interrogation or extension base as well as those with potential cross-reaction due to multiple sequence homologies. We also excluded probes that lacked signal values in one or more of the DNA samples analyzed.
Statistical analyses Differences between groups were checked by two-tailed unpaired Student's t tests or Mann-Whitney-Wilcoxon tests depending on data distribution. These differences were considered significant at p < 0. 05. The similarity between methylation profiles was measured by the Pearson correlation coefficients. For multiple comparisons, we used Benjamini and Hochberg corrections. All clinical data were introduced in a Statistical Package for Social Sciences (SPSS, IBM) software v17. 0 database. Variables showing associations that were significant in the univariant analysis were subsequently introduced in multiple regression models to adjust for possible interactions or confounding factors. Results were considered significant if the p value < 0. 05.
Polymorphic events at placenta-specific DMRs are not more frequent in pregnancy complications We utilized the same Illumina Infinium HM450K array data to explore the profiles of placenta-specific DMRs. In total, we analyzed 112 regions incorporating 763 probes, which represents the total number of confirmed placenta-specific DMRs from our previous work  [15, 16]  and those reported in two additional studies  [17, 18]. Consistent with previous reports, this revealed that the majority of placenta-specific DMRs have some samples with low-level methylation, indicative of a polymorphic event (Fig. 2a ; Additional file 3). To confirm these observations, 32 placenta-specific DMRs associated with paternal expression were analyzed by pyrosequencing. For two loci, AIM1 and N4BP2L1, we found significant differences between the mean methylation levels for the IUGR group compared to controls (IUGR mean 66.
d Quantitative RT-PCR for transcripts regulated by affected DMRs. The violin plots represent the expression levels of 50 control placenta samples with normal birthweight and methylation. Samples with hypomethylation are highlighted as green data points, while hypermethylated samples are in red. e Allelic expression analysis was performed for the H19 transcript using the rs2839704 SNP, with allelic contributions quantified by pyrosequencing correlations (mean Pearson's r = 0. 95; range 0. 95-0. 96) than between unrelated placenta samples suggesting that methylation does not vary greatly between sampling sites. Finally, we compared placenta samples from dizygotic twins and triplets. This revealed that the correlations between samples of the same gestations, therefore sharing the same in utero environment and maternal exposures, were also more similar (mean Pearson's r 0.
Cell lines MDA-MB231 (NCI-DTP Cat# MDA-MB-231, RRID: CVCL_0062), T47D (NCI-DTP Cat# T-47D, RRID:CVCL_0553), SKBR3 (ATCC Cat# HTB-30, RRID: CVCL_0033), and MCF7 (NCI-DTP Cat# MCF7, RRID: CVCL_0031) cell lines were grown in RPMI-1640/Glu-tamax™ (Gibco) supplemented with 10% FBS and 100 U/ml penicillin/streptomycin (Gibco). The NK92 (ATCC Cat# PTA-6670, RRID:CVCL_2142) cell line and NK cells isolated from healthy donors (NKd) by positive selection (CD56 Positive Selection Kit; Stem Cell) were cultured in RPMI 1640/GlutaMax™ (Gibco) supplemented with 10% FBS and 300 U/mL rhIL2. All cell lines were originally obtained from ATCC, tested for Mycoplasma infection by PCR (Venor GeM OneStep; Minerva Biolabs) every 3-6 months and used at low passage (<50).
Hybridization was then performed on microarray using linearly amplified labeled cRNA, following the manufacturer protocol and Agilent SureHyb Chamber. After washing in acetonitrile, slides were scanned using an Agilent G2565CA microarray scanner with defaults parameters. Microarray images were analyzed using Feature Extraction software (FES; version 10. 7. 3. 1) from Agilent technologies. Defaults settings were used. For the data processing and analysis, raw data files from FES were imported into R with LIMMA (an R package from the Bioconductor project)  33  , and processed as follow: gMedianSignal data were imported, controls probes were systematically removed, and flagged probes (gIsSaturated, gIsFeatpopnOL, gIsFeatNonUnifOL) were set to NA. Inter-array normalization was performed by quantile normalization.
To get a single value for each transcript, we took the mean of each replicated probes summarized data. Missing values were inferred using KNN algorithm from the package "impute" from R bioconductor. Normalized data were then analyzed. To assess differentially expressed genes between two groups, we start by fitting a linear model to the data. Then we used an empirical Bayes method to moderate the standard errors of the estimated log-fold changes. The top-ranked genes were selected based on an absolute fold-change >2 and an adjusted p-value (FDR) <0. 05. Data have been deposited in the ArrayExpress database at EMBL-EBI (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-8143.
Western blot Total protein extracts were prepared from cells treated or not with CP-31398 during the indicated time, γ-irradiated (5 Gy) or co-incubated with NKd cells at the indicated effector:target ratio during 30 min, lysed by RIPA buffer containing a cocktail of protease and phosphatase inhibitors (Thermo Scientific). Proteins were denatured 10 min at 95 °C in Laemmli buffer before SDS-PAGE separation on 4-20% precast gels (BioRad) and transfer on nitrocellulose membrane (Thermo Scientific). Blots were probed with the indicated primary Ab and HRP-conjugated secondary Ab. Western blot quantification was performed using the Image-J densitometry software.
Flow cytometry analysis of autophagy To evaluate autophagy induction, MDA-MB231 ± CP-31398 (48 h) were incubated for 30 min at 37 °C in dilution buffer containing Cyto-ID-green (Cyto-ID Autophagy Detection Kit; Enzo Life Science) according to the manufacturer's instructions. Cells were then washed with dilution buffer and immediately analyzed with a BD Accuri™ C6 flow cytometer. Data analysis was performed with the FlowJo software.
Flow cytometry analysis of cell surface markers To analyze FAS, DR4, DR5, ULBP-1, ULBP-2/5/6, ULBP-3, ULBP-4, ICAM-1, or MHC-Class I expression at the cell surface, MDA-MB231 ± CP-31398 cells were incubated 30 min at 4 °C with appropriate isotype control or one of the specific antibody indicated above. In some case, a secondary antibody conjugated to Alexa Fluor®488 (Life Technologies) was used. Cells were then fixed with PBS 1X containing 2% formaldehyde (PolySciences Inc. ) before analysis with a BD Accuri™ C6 flow cytometer. Data analysis was performed with the FlowJo software.
Fluorescence microscopy analysis MDA-MB231 cells stably transfected with a vector encoding Tomato-LC3 using FuGENE (Promega) were grown on collagen-coated glass coverslips (Sigma). After 48 h ± CP-31398 treatment, cells were fixed and stained with the indicated primary and secondary Ab conjugated to Alexa Fluor®488 or 647 (Life Technologies). Coverslips were mounted with Vectashield/DAPI (Vector Laboratories). Image acquisition was performed with a IX83 epifluorescence microscope and data analyzed with Cell-Sens Dimension software (Olympus). The colocalization between Tomato-LC3 + autophagosomes and the protein of interest was measured and is displayed as a pearson's correlation coefficient R(r) from at least 100 cells from three independent experiments. The number of autophagosome per cell was also evaluated in at least 250 cells from three independent experiments.
Monitoring of the autophagosome to autolysosome maturation by tandem sensor The fusion between autophagosomes and autolysosomes was measured with a Premo Autophagy Tandem Sensor RFP-eGFP-LC3B kit (Thermo Scientific) according to the manufacturer's instructions. Briefly, MDA-MB231 cells were seeded in a 6-well culture plate and infected with LC3B BacMam reagent (30 viral particles per cell) during 24 h. Cells were then treated with chloroquine (16 h) or CP-31398 (48 h). To induce starvation, cells were cultured 24 h in serum-free medium. Cells were then analyzed with a IX83 microscope and CellSens Dimension software (Olympus). Autophagosomes (in yellow) and autolysosomes (in red) were counted in at least 100 infected cells from three independent experiments.
Chromium release assay NK cell cytotoxic activity was measured by a 4 h  51  Cr release assay performed in triplicate, as previously described  27. Data are expressed as the percentage of specific  51  Cr release from target cells, calculated as (experimental release-spontaneous release)/(maximum release-spontaneous release) × 100. Inhibition of the PFN/ Gzm-mediated cytotoxic pathway was performed by preincubating NK cells for 2 h with 100 nM concanamycin A (CMA)(Sigma-Aldrich). Functional effects of ZB4 anti-Fas neutralizing mAb was also tested by pre-incubating target cells for 2 h before the assay. (see figure on previous page) Fig. 1  The reactivation of p53 transcriptional activity by CP-31398 increases p53-mutated breast cancer cell susceptibility to NK-mediated lysis. a MDA-MB231 cells express high level of mutated p53 (p53 R280K ) lacking transcriptional activity as shown by the absence of p53-dependent transactivation of its target gene p21 even after γ-irradiation.
Most of these genes are upregulated following 24-48 h CP-31398 treatment. Genes indicated in red encode proteins belonging to the ULK complex. d RT-qPCR validation of ULK1, ULK2, ATG101, ATG13, FIP200, ATG5, ATG12, GABARAPL2, and ZFYVE1 expression following CP-31398 treatment (24 and 48 h) compared to untreated cells (F. C. fold-change). e CP-31398 treatment (24-48 h) increases the expression of several proteins belonging to the ULK complex (ULK1, ATG101, ATG13, FIP200). ATG5 was used as a control. The normalized ratio protein of interest/Actin was calculated by densitometry analysis and display. Data are representative of three independent experiments. f PFT-α (an inhibitor of p53 transcriptional activity) inhibits CP-31398-dependent induction of ULK1 expression, while ULK2 expression is not affected.
Data are representative of at least three independent experiments (a, b, e) or are the mean ± s. d. of at least three independent experiments (c, d, f, g). The p values (d, g) were determined using one-way ANOVA. *P < 0. 05 change in Bcl-X L , XIAP, Bcl-2, cIAP-1 or survivin amount (or in Bax, Mcl-1, or cIAP-2 protein level used as control) (Fig. 7a ). We thus hypothesized that the CP-31398induced autophagic flux (defined as the autophagic degradation activity  44  ) is blocked and that the absence of degradation could be due to the absence of fusion between autophagosomes and lysosomes. To validate this hypothesis, we performed a LC3 turnover assay using western blot and CQ. Indeed, in response to CQ, which inhibits lysosome acidification and autophagosomelysosome fusion, LC3-II accumulates even under normal conditions because the turnover/degradation of LC3-II by basal autophagy is blocked.
Acknowledgements The authors thank  Denis Martinvalet  ( University of Padova, Italy ) for the granzymes,  Gerard Pierron  and  Sylvie Souquere  ( Gustave Roussy cellular imaging facility ) for the electron microscopy analysis,  Gustave Roussy Genomic platform members (Villejuif, France ) for the microarray analysis,  Bassam Janji  ( Luxembourg Institute of Health ) and  Agnete Engelsen  ( University of Bergen, Norway ) for helpful discussions. This work was supported by  INSERM, the french "Ligue Nationale Contre Le Cancer" (Equipe labellisée)  and by PhD fellowships from the  french "Ligue Nationale Contre Le Cancer" (M. C. N)  and the  "Association pour la Recherche sur le Cancer" (T. BS. S). The " taxe d'apprentissage" program  ( Direction de la Recherche-Gustave Roussy ) supported the microarray analysis.
Mutation visualization Sequences obtained from control and cancerous tissues were aligned using BioEdit software version 7. 1. 9 (Hall, 1999). In our analysis, each sample could present several mutations, and a given mutation could occur in several samples. Under these conditions, it can be difficult to determine which mutations are shared across samples and to identify possible co-occurring mutations. We used a visualization method based on rainbow boxes  (Lamy et al. , 2016; Lamy et al. , 2017) , a recently developed visualization technique for overlapping data sets. Rainbow boxes can be used to visualize up to 100 elements and 250 sets; we applied this technique to the visualization of the set of mutations present in each sample. Here, we considered samples as the elements and mutation locations as the sets.
Predicted pathogenicity of missense mutations Mutations in MT-CYB were identified in 27. 3% of oral cavity cancer tissues, with 29 missense mutations reported (Table  2 ). Among the mutations found, 16 (55. 17%), including p. Ala53Asp, p. Ala53Ser, p. Asn54Ser, p. Thr58Ile, p. Pro60Thr, p. His61Pro, p. Pro64Arg, p. Glu65Gly, p. Glu65Asp, p. Trp66Leu, p. Leu69Val, p. Phe70Leu, p. Phe70Val, p. Arg76Ala, p. Arg76Pro, and p. Leu86His , were considered pathogenic. These amino acid changes were induced by 32 mutations, including 9 (28. 12%) transitions and 23 (71. 87%) transversions. The most common pathogenic mutation (7 patients) was an arginine to proline substitution at position 76 (p. Arg76Pro). This mutation is novel and has not been previously reported in the database MITOMAP (www. mitomap.
However, there are many examples of synonymous mutations that have been implicated in various human diseases, including heritable conditions  (Sauna and Kimchi Sarfaty, 2011) , suggesting parallel functional effects in cancers. Awareness that somatic synonymous mutations may include causal variants, i. e. driver mutations, is beginning to take hold. In this study, the mutations in the gene encoding cytochrome b follow the neutral theory of molecular evolution. Most mutations are selectively neutral and would have occurred at random. The MT-CYB mutations are somatic, and the majority are synonymous substitutions. We used a new method for visualizing mutations shared by patients, based on rainbow boxes. This is the first application of rainbow boxes in this context. This type of visualization was found to be useful for searching for mutation co-occurrence and could be used on many other datasets.
Functional Impact of Amino Acid Substitutions in Cytochrome b Protein Identified in Patients with Oral Cavity Cancer Pathogenic mutations are shown in bold; pathogenic mutations not previously described in mitomap database (www. mitomap. org) are shown in bold and italic; neut: neutral; tol, tolerable; pd, probably damaging; del, deleterious; pod, possibly damaging; apf, affect protein function; med, medium; ben, benign transitions.
Acknowledgements The authors are grateful to  Department of Oral and Maxillofacial Surgery at the Affiliated Hospital Aristide Le Dantec  for the availability of the samples. The study was approved by the  University Cheikh Anta Diop Research Ethics committee  (Reference:  0272/2018/CER/UCAD ). The Helsinki Declaration has been read and approved. This study was funded by the  Population Genetics and Management Team of the Department of Animal Biology of the Faculty of Science and Technology, Cheikh Anta Diop University, Dakar.
Data analysis Data were analyzed in Mplus 7. 4 with Full Information Maximum Likelihood (FIML)  [25]. FIML allows the estimation of the parameters even in the presence of incomplete (i. e. , missing) data on some variables. Therefore, individuals with at least one data point in any HOME measure or in the outcome were included in the analysis. Estimated models are represented in Fig  2. Trajectories of HOME (mediator). As a preliminary step, we estimated the trajectories of each of the 6 HOME dimensions using growth models composed of an intercept and a linear slope latent factor (both random effects). All estimated models had a good fit to the data, i. e. non-significant chi-square of model fit, Comparative Fit Indices and Tucker-Lewis indices >0. 95, Standardized Root Mean Square Residual and the Root Mean Square Error of Approximation <0.
Effect of HOME on child outcomes (path b). We estimated the effect of the mediators on child outcomes by regressing the intercept and slope growth factors (b 1 and b s in Fig 2 ) on the outcomes. Indirect (mediation) effect. Finally, we estimated the indirect effect of treatment group membership on child outcomes via the mediators using the distribution of the product approach, i. e. , computing unbiased confidence intervals of the a � b product. The mediation model is described using the mathematical equation in the S4 File. Complementary exploratory analyses. Several sets of complementary analyses were conducted. First, mediation analyses were performed using an alternative approach relying on causally-defined indirect effects. Specifically, we used the procedures described by Imai and colleagues  [27, 28]  and implemented in the R package mediation  [29]  to estimate the Average Causal Mediation Effect of the intercept and slope parameters previously estimated.
For example, a recent study of mediation effects in an early intervention program in Colombia  [45]  finds that improvements in material investments (e. g. number of toys and books) were important for children's cognitive outcomes and improvements in material and time investments (number of times read to the child) were important for socio-emotional outcomes. In our study we only have data on the interactions that took place during the course of the interview. Another plausible explanation for our lack of mediation effects is the presence of moderation effects which may restrict the positive impact of the improved home environment to specific subgroups of children. Our complementary exploratory analyses suggested that maternal age, maternal depressive symptoms, and child gender may be important moderators to test in future investigations with adequate statistical power.
Acknowledgments We would like to thank the  Northside Partnership  who provided funding for the Preparing for Life evaluation through The  Atlantic Philanthropies  and the  Department of Children and Youth Affairs , and the  Children's Research Network  for providing funding through the  Prevention and Early Intervention Grant Scheme  ( CRN-PEI-2017 ). We are also grateful to all those who participated in and supported this research, especially the participating families and community organizations, the PFL intervention staff, and the  Expert Advisory Committee. Thanks also to  Prof. James Heckman ,  Prof. Colm Harmon ,  Prof. Cecily Kelleher ,  Prof. Sharon Ramey , and  Prof. Craig Ramey  for their guidance and advice throughout the project, and the  Early Childhood Research Team  at  UCD Geary Institute for Public Policy  for their contributions to the work.
The aim of this paper is to compare the two largest forest fires that occurred in Greece in July 2018 using metrics for burned area and burn severity mapping, derived only from free satellite data. Sentinel-2 satellite images of the European Space Agency (ESA) within the Copernicus program provide a spatial resolution of 10 m, which facilitates more accurate monitoring of environmental phenomena such as forest fires. The processing of the satellite images and the calculation of the metrics was performed using SNAP software, which is an open-source software developed by ESA. The mapping of the obtained results was performed in the QGIS software, which is also an open-source software. The delimitation of the burned area and the classification of the severity of both wildfires was performed using the Relativized Burn Ratio (RBR) satellite index.
The resident population of these localities was respectively 1,446 and 200 inhabitants in 2011  (Hellenic Statistical Authority, 2011). The data used for this study are four Sentinel-2 products. Two data sets of pre-fire and post-fire satellite imagery were obtained for the study of the Kineta fire, and other two for the Mati fire. For the Kineta fire two Sentinel-2A Level 2A tiles (Tile ID: T34SFH) were acquired between on July 3, 2018 (before the main event) and August 19, 2018 (after the main event). In the same way, for the Mati fire one Sentinel-2A Level 2A (Title ID: T34SFH) and another one Sentinel-2B Level 2B tiles (Tile ID: T34SGH) was acquired between on July 3, 2018 (before the main event) and August 4, 2018 (after the main event). This four Sentinel-2 scenes have been downloaded free from the official Copernicus Open Access Hub website, operated by the European Space Agency (ESA) and the Copernicus Programme  (Copernicus Open Access Hub, 2018).
Identification and extraction of burned areas Processing satellite data requires a GIS (Geographic Information System) environment. For data processing have been used the software SNAP (Sentinel-2 Toolbox), QGIS and Excel for subsequent statistical calculations. For the Level 2A products that have been used in this study the atmospheric correction has already been applied. Solar radiation reflected by the Earth's surface to satellite sensors is affected by its interaction with the atmosphere. The objective of applying an atmospheric correction is to determine true surface (Bottom-Of-Atmosphere, BOA) reflectance values from the Top-Of-Atmosphere (TOA) reflectance values, by removing atmospheric effects  (Mousivand et al. , 2015). Atmospheric correction is especially important in cases where multi-temporal images are compared and analysed as it is in this case (pre-fire and post-fire images).
The SNAP software offers the integration of Sen2Cor algorithm for performing atmospheric correction. Sen2Cor is a processor for Sentinel-2 Level 2A product generation and formatting; it performs the atmospheric, terrain and cirrus correction of Top-Of-Atmosphere Level 1C input data. Sen2Cor creates Bottom-Of-Atmosphere, optionally terrain and cirrus corrected reflectance images; additional, Aerosol Optical Thickness, Water Vapour, Scene Classification Maps and Quality Indicators for cloud and snow probabilities (Main-Knorn, 2017). Atmospheric correction using Sen2Cor algorithm is a computationally heavy process and takes several time to be completed depending on the machine. However, since April 2017 the Level-2A products have already been generated and are available to download for acquisitions over Europe (such as this case).