dataesr/scientific-paragraphs-categorization huggingface dataset:
For the simulations in this study, oversampling the WaCM data on a 1 km × 1 km grid has the additional practical advantage of giving a spatial gridding that is equivalent to the 1 km × 1 km gridding assumed here for the pre-processed SWOT data. The footprint size from the filtering in the pre-processing of WaCM data is still 5 km. The standard deviation of the errors of WaCM estimates of each velocity component is therefore still σ u,v = 0. 354 m s -1 , but the velocity component errors on a 1 km × 1 km grid are spatially correlated. The effects of correlation of the measurement errors on the vorticity estimated from WaCM data on a 1 km × 1 km grid are incorporated in the propagation-of-error analysis in Appendix G. 2. The results are summarized in the second column of Table  2. The oversampling increases the standard deviation of the vorticity errors by about a factor of 3 to a value of about 2.
This type of sampling errors was neglected in Sec. 6 by considering the idealized case of SWOT and WaCM measurement swaths that sample the full CCS model domain on each satellite overpass. An estimate of the spatially smoothed field within an actual measurement swath is imperfect if the distance between the estimation location and a swath edge is less than half of the span of data that are included in the weighting function of the smoother. The imperfections worsen the closer the estimates are to the swath edge. This source of error is worse for some smoothing algorithms than for others and the magnitudes of the errors can depend on the detailed structure of the field near the edge of the dataset. Edge effects are especially problematic when smoothing with a half-power filter cutoff wavelength that is comparable to or larger than the swath width.
08 × 10 -5 s -1. (G. 13a) Compared with the planetary vorticity of f = 8. 77 × 10 -5 s -1 at the central latitude 37 • N of the CCS model domain, this is σ ζ (37 • N) 5km = 0. 807f. (G. 13b) It is shown in Appendix H that it is advantageous to oversample the WaCM data on a grid spacing of ∆x = ∆y = 1 km because the response function of centered differences on the finer grid retains more of the high wavenumber variability in the vorticity signal. The footprint size from the filtering in the preprocessing of WaCM data is still 5 km. The standard deviations of the errors of WaCM estimates of the velocity components are therefore still σ 2 u and σ 2 v , but the velocity component errors on a 1 km × 1 km grid are spatially correlated. We will continue to assume, however, that the errors of the two velocity components are uncorrelated with each other, even on the oversampled ∆x × ∆y = 1 km × 1 km grid.
Acknowledgments We thank  Ralph Milliff ,  Bo Qiu ,  Ernesto Rodríguez  and  Lee-Lueng Fu  for many helpful editorial comments and suggestions that improved the manuscript. This research was funded by  NASA  Grants  NNX13AD78G ,  NNX14AM72G ,  NNX13AE32G ,  NNX14AM66G ,  NNX16AH76G ,  NNX14AM71G  and  NNX17AH54G. The two North Atlantic Ocean simulations in this study were performed using HPC resources from  GENCI-TGCC  with support from Grant  2017-A0010107638  for  Jonathan Gula.
Background Animal venoms are complex mixtures of protein and peptide toxins, enzymes, and other compounds. Because they are both potent and interact speci cally with the cell wall and membrane compounds of different cells, venom constituents are attractive candidates for the development of novel therapeutics and pesticides (1), although they remain largely unexplored. These rich molecule blends contain many antimicrobial peptides (AMPs) (2), and AMP sequences are available from the Antimicrobial Peptide Database (APD) databank (http://aps. unmc. edu/AP) (3). AMPs are very promising and are sure to be innovative additions to the arsenal of weapons against resistant bacteria (4, 5). AMPs have complex and wide-ranging mechanisms of action. They can directly target bacterial membranes, damaging cell integrity and consequently causing osmotic imbalance.
Confocal microscopy Bacterial cultures were done in the same conditions as described above, but they were incubated with pseudonajide tagged with FITC. After incubation, cells were washed with saline solution then 3 µL was added to glass slides for confocal analysis. The images were acquired using a Leica SP8 DMI 6000 CS confocal microscope, and ImageJ software was used for image analysis.
Viability of 100% represents no cytotoxicity or inhibition of cell growth, while under 25-30% is considered cytotoxic and 0% represents acute cytotoxicity. colorectal adenocarcinoma (Caco-2); breast adenocarcinoma (MDA-MB231); colorectal carcinoma (HCT116); prostatic adenocarcinoma (PC3); lung carcinoma (NCL-H727); and breast cancer (MCF7). List Of Abbreviations AMPs antimicrobial peptides APD Antimicrobial Peptide Database CFU colony-forming units DAG diacylglycerol DMSO dimethyl sulfoxide FITC uorescein isothiocyanate qRT-PCR quantitative real-time PCR RT reverse transcriptase SEM scanning electron microscopy TEM transmission electron microscopy.
Life Cycle Inventory The mineral phosphate fertilizer scenario (Sref) is the LCI for TSP production from the ecoinvent v2. 2 database (RER: triple superphosphate, as P2O5, at regional storage)  (ecoinvent, 2007). Sludge-based phosphate fertilizer scenarios were modeled based on a French WWTP. The main characteristics of the WWTP are shown in Table  1. LCI input data for sludge-based fertilizer scenarios were modeled using the ecoinvent v2. 2 database. LCI data of avoided fertilizers come from ecoinvent v2. 2 database and are modeled as ammonium nitrate (RER: Ammonium nitrate, as N, at regional storage) and triple superphosphate (RER: triple superphosphate, as P2O5, at regional storage). Flowcharts are described in detail in Supporting Information 1 (SI1). Sludge-based fertilizer scenarios assume a 30-year lifetime for wastewater and sludge treatment infrastructure and a 50-year lifetime for the sewer network according to  Risch et al.
LCI data used to allocate environmental burdens to sludge production LCI data used to assess sludge production are provided in this section. LCI data from sewer network and wastewater treatment line as well as the allocation factor used to allocate the LCI data to sludge production are provided in the following subsections. 2. 2. 1. 1. LCI data for the sewer network and wastewater treatment line LCI data for the sewer network were provided by the WWTP owner. LCI data for the sewer network are modeled according to  Risch et al. (2015). LCI data of the wastewater treatment line come from technical documents provided by the WWTP owner. LCI output data for the wastewater treatment line are composed of emissions, which are calculated according to nitrogen, carbon and P mass balances using transfer coefficients from the literature and expert opinion  (Doka, 2007).
Allocation factors A summary of these allocation factors is shown in In S1-BioAcid and S2-Crystal, the dewatered low-P sludge is stored for six months in a concrete area in the WWTP. During storage, sludge emits ammonia (NH3), N2O, CH4 and CO2. Emission factors for storage come from  Gourdet et al. (2017). After storage, the sludge is transported and spread on soil. Nitrogenous emissions are calculated according to the IPCC (2006), while P emissions are calculated according to  Nemecek and Kägi (2007). Heavy metals in sludge are included, but since no data on them were available, values from French legislation on sludge spreading are used  (Journal Officiel, 1998). In S3-AshDec and S4-Gifhorn, the dewatered low-P sludge is incinerated. Data for incineration in both scenarios come from Jossa and Remy (2015).
Life cycle inventory output data for the sludge-based phosphate fertilizer scenarios (S1, S2, S3 and S4) -Values are presented per functional unit: 1 kg of P produced Author-produced version of the article published in Science of the Total Environment, 2019, 656, 55-69. The original publication is available at http://www. sciencedirect. com/ doi : 10. 1016/j. scitotenv. 2018. 11. 356 Emissions were calculated from transfer coefficients, provided in Supporting Information 2 b Emission factors for thickening, dewatering and storage came from.
Acknowledgements The authors kindly thank their colleagues  Jean-Pierre Canler  and  Guillermo Baquerizo  from the  MALY Research Unit  and  Marie-Line Daumer  from the  OPAALE Research Unit at IRSTEA  for providing LCI data on the WWTP and the P recovery processes. The authors would like to thank  Michael Corson  for proofreading the English of this paper as well as the two anonymous reviewers for their valuable comments. Part of this study was financially supported by  ONEMA (French National Agency  for water and aquatic environments).
Visual Stimulation All stimuli were presented on a Dell U2413 LED monitor with custom written programs in Matlab using the Psychophysics Toolbox  (Brainard, 1997; Kleiner et al. , 2007). The monitor had dimensions of 51. 8 X 32. 4 cm (1920 X 1200 pixels) and a screen resolution of 37 pixels/cm. The refresh rate of the monitor was 60 Hz. Mantises were fixed onto a stand with the help of the component fixed onto their backs which fit into a corresponding component on the stand. Mantises were upside-down and held onto a cardboard disc with their legs. They were thus freely able to move their heads and forelegs, but the viewing distance was fixed. All stimuli were presented on a screen placed at a distance of 10 cm in front of the mantis. Stimuli were output in the green and blue channels with output in these channels weighted to adjust for the transmission of the filters (Fig.
The dependent variable was the probability of striking and the individual identity of the mantis was used as a random effect. Since the probability of striking was a binary decision (yes or no) we modelled the data using a binomial logistic link function. All data were analysed with the statistic software RStudio (version 1. 1. 383). Experiment 1: Results from the MID-left and MID-right conditions were pooled as were results from the Constant-left and -right conditions. Motion condition (MID or Constant) was built into the model as a factor as was Disparity. Models were run with or without interaction between these two factors. Models were compared on the basis of the Bayesian Information Criterion (BIC). Experiment 2: Size and Disparity were built in as factors into the model with each having three levels.
Demographics and health characteristics Demographic variables included age, gender, marital status (married/living with a partner vs not married), education (university degree vs less), and annual household income (low <20,000 € or £, medium ≥20,000 and <40,000 € or £, high 40,000+ € or £, or declined to answer). NHWS respondents provided data on body mass index (BMI), smoking status, alcohol use, and exercise behavior for health characteristics. Information was collected according to country of residence.
Strengths and limitations The notable strengths of the study include the relatively large sample size to estimate the burden of disease across several European cities and the survey-specific information available to identify at-risk patients and their burden. However, the present study also has a few limitations that should be considered when interpreting the results. First, MDD is defined using self-reported data and could not be objectively confirmed via claims data, electronic medical records, or another independent source. Thus, findings may be influenced by any inaccuracies in participants' recall about diagnoses, medications taken, and other study variables. Second, the cross-sectional design of the study precludes the ability to infer causality from the results or to ascertain whether the relationships between study variables change over time.
p. more likely than non-beneficiary children to complete secondary school. However, the authors found no evidence of differential secondary school graduation test score performance, conditional on completion. Duque, Rosales-Rueda, and Sanchez (2018)  similarly combine administrative data from the secondary school graduation test (up to 2014) and the SISBEN scores, as well as incorporating data on the universe of Colombia's public schools (up to 2015). The paper focuses on children born between 1988 and 2000, and aims to analyze whether absolute program impacts are higher for children exposed to adverse weather events (similar to what  Adhvaryu et al. (2018)  do for Mexico). The absolute impact of the CCT is identified at ages between ages 14 and 27 through a fuzzy RDD, after merging the different administrative datasets.
Data processing Bioinformatic analyses were performed using the RNA-seq pipeline from Sequana  [10]. Reads were cleaned for low-quality and adapter sequences using Cutadapt version 1. 11. Only sequences of at least 25 nt in length were considered for further analyses. Bowtie version 0. 12. 7 (parameters -chunkmbs 400 -m 1) was used for the alignment on the reference genome (M. tuberculosis H37Rv, from NCBI). Genes were counted using featureCounts version 1. 4. 6-p3 from the Subreads package (parameters: t gene -g ID -s 1). Count data were analyzed using R version 3. 4. 1  [11]  and the Bioconductor package DESeq2 version 1. 18. 1  [12]. The normalization and dispersion estimation were performed with DESeq2 using the default parameters and statistical tests for differential expression were performed applying the independent filtering algorithm.
Two independent analyses have been performed to study the H37Rv and ITM-04-1195 strains separately. For each of them, a generalized linear model was set in order to test for the differential expression between the time points and/or treatments. For each pairwise comparison, raw p-values were adjusted according to the Benjamini and Hochberg (BH) procedure  [13]  and genes with an adjusted pvalue lower than 0. 001 were considered differentially expressed. RNA-Seq data have been deposited at the Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo) under the accession number GEO ID: GSE123294.
Genome-wide impact on gene expression levels To investigate the transcriptional impact of MTB to exposure to delamanid, triplicates of a laboratory (H37Rv) and a DLM susceptible clinical (ITM-04-1195) MTB strain were spiked during 6 h and 24 h with the drug or its solvent DMSO. The viability of the cultures was followed post-sampling by measuring the optical density over time (Fig. S1 ). Illumina sequencing was performed on total RNA extractions, leading to the detection of transcripts for 3927 and 3918 genes for H37Rv and ITM-04-1195, respectively. Variance Stabilizing Transformation (VST) and subsequent Principal Component Analysis (PCA) showed a good correlation between the biological replicates for both strains (Fig. S2 ). Global differential analysis of the transcriptional data showed that delamanid significantly (log 2 FoldChange > |1|, P-adjusted < 0.
1 ). For example, after 6 h of incubation, an increasing number of 356 (37. 5%), 502 (66. 9%) and 582 (77. 6%) of the significant up-and down-regulated genes were found to have equally higher and lower expression levels in the presence of respectively 0. 2 μg/ml, 0. 4 μg/ml and 2. 0 μg/ml pretomanid (log 2 FoldChange > |0. 5| in the microarray dataset  [7] ) (Table  2 ). This effect of increasing similarity at higher concentrations correlates with the use of high concentrations of delamanid during our experimental setup. In addition, the heat map of transcriptional profiles demonstrates similarity with the responses induced by potassium cyanide (KCN). Using 5 μg/ml and 20 μg/ml of this inhibitor of aerobic respiration yielded respectively 420 (56. 0%) and 441 (58. 8%) genes (log 2 FoldChange > |0.
On the other hand, a significant induction of two other cytochromes cyp135A1 (Rv0327c) and cyp 138 (Rv0136) could be noticed (Table  S1 ). Apart from these direct effects of respiratory poisoning, a large number of indirect expression patterns were observed (Fig. 2 ). Fe-S clusters are thought to act as regulatory proteins that sense iron deprivation but also reactive oxygen species and gases like O 2 and NO  [15]. In our dataset, we notice a significant up-regulation of the SUF operon (Rv1460-66), the exclusive mycobacterial [Fe-S] cluster assembly system, after 6 h of drug exposure. This effect increased further after 24 h, which might indicate the accumulation of reactive species over time. Similarly, a small induction of CsoR (Rv0967) and RicR (Rv0190) might indicate the release of copper as a result of respiratory stress, while the glycerol-3-phosphate dehydrogenase glpD1 (Rv2249c) which is involved in aerobic respiration and oxidation of glycerol was induced as well.
In contrast, the data revealed that aerobic respiration was affected in the treated cells. Many of the identified responses were equally observed in transcriptomic data of respiratory inhibitors. As for pretomanid, this demonstrates that respiratory poisoning has a crucial role in the bactericidal effect of delamanid. However, due to the re-oxidizing effect of oxygen in aerobic environments, this poisoning is probably mainly important for the bactericidal effect of delamanid in anaerobic conditions and might therefore contribute to the activity of delamanid on dormant, non-replicating cells. The direct target of delamanid remains to be uncovered. For pretomanid, the effect on cell wall biosynthesis was more pronounced using low, suboptimal drug concentrations. Therefore, it can be suggested that in our dataset, the massive impact of high concentrations of delamanid on aerobic respiration masked the actual mode of action in aerobic conditions.
Acknowledgements We thank  Otsuka Novel Products GmbH (Munich, Germany)  for providing us with pure delamanid powder and the Belgian Co-ordinated Collections of Micro-organisms as co-ordinator (BCCM) for the distribution of the ITM-04-1195 strain. The Transcriptome and  Epigenome Platform  is a member of the  France Génomique consortium  ( ANR10-NBS-09-08 ). This research was supported by an  International Network of Institute Pasteur: ACIP  grant  N°03-2016  from the  Institute Pasteur in Paris.
For each strain, intestine colonization was followed after 1, 3-and 9-days post-inoculation by determining the CFU from fresh fecal pellets. Both WT and complemented strains exhibited a similar and a sustained colonization profile across the time course while snf5 mutant exhibited a significantly reduced degree of intestinal occupancy (Fig  6A ). At day 10, the mice were sacrificed, and the degree of colonization was measured in the stomach, the cecum and the colon. The obtained data showed that snf5 exhibited a reduced level of colonization in all tested organs as compared to the WT and the revertant strains (Fig 6B  -6D ). Since snf5 levels were mostly below the detection threshold (1 CFU), we used qPCR to reassess its depletion from the cecum. The qPCR data recapitulated the previous finding and confirmed the colonization defect of snf5 mutant in the cecum (Fig  6E).
Colony size and doubling-time measurements Overnight cultures of the WT, snf5 and the complemented strains were washed twice with PBS and diluted before plating on YPD-agar, YPS-agar and YP-Galactose-agar. After 3 days of incubation at 30˚C under either normoxic (21% O 2 ) or hypoxic conditions (5% O 2 ), plates were imaged and the diameters of at least 50 colonies were determined using ImageJ software  [76]. To assess doubling times, overnight cultures were washed twice with PBS and diluted to an OD 600 of 0. 25 in YPD, YPS and YP-Galactose. Growth assay curves were performed in 96-well plate using a Cytation 5 plate-reader at 30˚C under normoxic or hypoxic conditions, with OD 600 readings taken every 10 min for 48h. Doubling times were measured during the exponential growth of each strain.
Total RNA was eluted, assessed for integrity on an Agilent 2100 Bioanalyzer prior to cDNA labeling, microarray hybridization and analysis  [77]. The GSEA Pre-Ranked tool (http://www. broadinstitute. org/gsea/) was used to determine statistical significance of correlations between the transcriptome of the snf5 mutant with a ranked gene list or GO biological process terms as described by Sellam et al. [24]. Differentially expressed transcripts in S2 Table were identified using Welch's t-test with a false-discovery rate (FDR) of 5% and 1. 5-fold enrichment cut-off. All Microarray data are available at Gene Expression Omnibus (GEO)  [78]  with the accession number GSE137655. For qPCR experiments, cell cultures and RNA extractions were performed as described for the microarray experiment. cDNA was synthesized from 1μg of total RNA using High-Capacity cDNA Reverse Transcription kit (Applied Biosystems).
Acknowledgments We are grateful to  Aaron Mitchell  ( Carnegie Mellon University ),  Dominique Sanglard  ( Le Centre Hospitalier Universitaire Vaudois (CHUV)-UniversiteeLausanne ),  Doreen Harcus  ( National Research Council Canada ),  Karl Kuchler  ( Medical University of Vienna ) and  Guilhem Janbon  ( Institut Pasteur-Paris ) for providing strains.
2a-c ) confirmed the RNA-seq results from human lung tissue (Fig. 1a ), i. e. , increased levels of MIRLET7D targets in IPF fibroblasts concomitant with fibrosis markers (Supplementary Fig. 2c, left ). In addition, alternative mapping of our RNA-seq data to NONCODE database (Supplementary Fig. 2c , right) revealed increased expression of ncRNAs associated to MIRLET7D targets in IPF fibroblasts. Our RNA-seq in human primary fibroblasts was confirmed by expression analysis of representative MIRLET7D targets by qRT-PCR (Fig. 2b ). Furthermore, promoter analysis of the same MIRLET7D targets by chromatin immunoprecipitation (ChIP; Fig. 2c ) showed decreased levels of various subunits of the RNA exosome complex (EXOSC10, EXOSC5, and EXOSC1), the heterochromatin mark H3K27me3 (trimethylated Lys-27 of histone 3), and the enzyme mediating this histone modification (EZH2), whereas the levels of transcription initiating S5 phosphorylated RNA polymerase II (POLII) increased in IPF, compared with Ctrl fibroblasts.
Further details of statistical analysis in different experiments are included in the figures and figure legends. Briefly, two biological replicates of RNA-seq were analyzed by deep sequencing. For the rest of the experiments presented here, samples were analyzed at least in triplicates and experiments were performed three times. Statistical analysis was performed using Excel Solver and Graph Prism (v. 5). All data are represented as mean ± SD. T-test was used to determine the levels of difference between the groups and P-values for significance. P-values after T-test, *P ≤ 0. 05; **P < 0. 01, and ***P < 0. 001. Cell culture, transfection, and treatment. Primary fibroblast from Ctrl and IPF patients were cultured in complete MCDB131 medium (8% fetal calf serum (FCS), 1% L-glutamine, penicillin 100 U/ml, streptomycin 0.
In this assay, recombinant, full-length HDAC1 protein as a positive control and nuclear and cytoplasmic extracts from Ctrl and IPF primary fibroblasts were incubated with fluorophore-conjugated substrates, at a concentration equivalent to the substrate Km (6 μM for HDAC1). Reactions were performed in assay buffer (50 mM HEPES, 100 mM KCl, 0. 001% (v/v) Tween-20, 0. 05% (w/v) BSA, 200 μM Tris (2-carboxyethyl)phosphine (TCEP) pH 7. 4) and followed for fluorogenic release of 7-amino-4-methylcoumarin from substrate upon deacetylase and trypsin enzymatic activity. Fluorescence measurements were obtained at 470 nm approximately every 5 min using a multilabel plate reader. Data were analyzed on a plate-by-plate basis for the linear range of fluorescence over time. The kit contains Trichostatin A as negative controls.