dataesr/scientific-paragraphs-categorization huggingface dataset:
The temperature and humidity meso-scale heterogeneities do not play a significant role, at least on average over the regions analysed in the present study. These conclusions hold for all tested GCM-scale resolutions and for both COARE 3. 0 and ECUME surface flux parametrizations. These conclusions are based on a numerical framework that uses operational forecasts from the 2. 5 km resolution CPM AROME, over two tropical domains. Our results thus possibly depend on the model and domains used here and therefore should be further tested with other CPMs and over contrasted regions. The use of relevant observation datasets could also provide further robustness of our results. In further work, it will be of interest to extend the present results to the smaller boundary-layer scale, using large datasets based on large-eddy simulations.
ACKNOWLEDGMENTS This work was supported by the French national research agency (Agence National de la Recherche) through the ANR-COCOA project "COmprehensive Coupling approach for the Ocean and the Atmosphere" (grant: ANR-16-CE01-0007) and the European Union Horizon 2020 H2020-CRESCENDO project "Coordinated Research in Earth Systems and Climate: Experiments, Knowledge, Dissemination and Outreach" funded under the programm SC5-01-2014: Advanced Earth-system models; grant agreement No 641816. The INSU-LEFE DEPHY project "Développement et Évaluation des PHYsiques des modèles atmosphériques" allowed for helpful interactions with participating researchers. The authors would like to thank both referees for their careful reading of this work and their relevant comments.
Box and whisker plot of the relative contributions of each term of Equations (22)-(24), for the subdataset in which the meso-scale contribution F MS ∕F  ≥ 10%: (a) momentum flux, (b) sensible heat flux, and (c) latent heat flux. Red points denote the mean, red lines the median, the blue box the interquartile range, and the whisker the 5 to 95%ile range 5. 5%. When applied to the entire dataset (right column), the flux reconstruction does not introduce large errors in conditions with weak meso-scale contribution to the GCM-scale flux, and thus also strongly reduces the flux errors.
U R E 10 A priori flux reconstructions versus the SM fluxes F  for (a, b) momentum, (c, d) sensible heat, and (e, f) latent heat. In the left column, only the subdataset F MS ∕F  ≥ 10% is considered, while in the right column, the entire dataset is considered. Blue points denote T 1a (GCM-computed flux), red points T 1a + T 1b , black points T 1a + T 1b + T 2a , and brown points GCM-computed flux (T 1a ) plus the partial gustiness contribution ( C Δ(U -Ũ), see discussion (Section 6)). In the upper part of each panel, the relative residual flux and the NRMSE computed between the different reconstructions and the SM fluxes follow the same colour code as above Normalized flux reconstructions based on Equations (25), (26) and (27) for each wind subrange for (a) the subdataset F MS ∕F  ≥ 10%, and (b) the entire dataset.
Funding information  ANR-COCOA (COmprehensive Coupling  approach for the Ocean and the Atmosphere), Grant/Award Numbers:  ANR-16-CE01-0007 ;  Horizon 2020 H2020-CRESCENDO  ( Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach ) , Grant/Award Numbers: Grant/Award Numbers :  SC5-01-2014  (Advanced Earth-system models), 641816.
In 13 cases (3. 2%), HER2 IHC could not be reliably interpreted due to the absence of a positive external control. After performing DISH on the latter three groups, we identified 24 HER2-positive EC (5. 9%; 95% CI: 3. 6-8. 2%) (Figure  1 ). Subclonal HER2 expression was observed in 81 cases (20%), including 11 HER2-positive EC (45. 8%). Incomplete membranous immunoreactivity was seen in 101 tumors (24. 9%), including 6 HER2-positive EC (25. 0%). All HER2 IHC slides were additionally scored by an adjusted version of the American Society of Clinical Oncology and College of American Pathologists (ASCO/CAP) 2007 guideline for breast cancer. The HER2 IHC results were compared to the HER2 amplification status by DISH (data shown in Table  S2 ). Age, histology, and tumor grade differed significantly between patients with HER2positive and HER2-negative EC (Table  1 ).
Importantly, we found that the correlation between HER2 positivity and p53abn EC is significantly stronger than the correlation between HER2 positivity and serous histology (p < 0. 0001). The interaction between HER2 overexpression and mutant p53 status has been described in other cancers. A study using cancer cell lines with introduced gain-of-function TP53 mutations found an increase in HER2 mRNA expression levels as well as HER2 protein levels  [28]. Analysis of the publicly available breast cancer dataset (GSE22358) confirm significantly higher HER2 mRNA levels in p53 mutant samples (p = 0. 046) compared to p53 wildtype samples  [29]. Mechanistically, mutant p53 has shown to be associated with enhanced transcriptional activity of HSF1 (heat shock transcription factor 1) that targets chaperon Hsp90 which in turn stabilizes the HER2 and p53 proteins, thereby further reinforcing oncogenic signaling  [30].
In our study, we did not detect a statistically significant difference in survival between patients with HER2-positive and HER2-negative EC after adjustment for p53 status. However, our study was not powered to detect small differences in prognosis. We can therefore not rule out that HER2 status has minor negative prognostic impact in HREC. Importantly, after extending the PORTEC-3 dataset with the TCGA cohort, we still did not find a significant independent prognostic value of the HER2 status in multivariable analysis (including the molecular classification). This makes it less likely that there is a small prognostic difference that we did not detect. Although previous studies showed prognostic value of HER2 overexpression and/or amplification in EC  [19] , these findings may have been confounded by an underlying mutant p53 status.
Upfront central pathology review was performed by reference gynecopathologists to confirm eligibility. The study was approved by the Dutch Cancer and medical ethics committees at participating. Patient, tumor, and treatment characteristics were not significantly different between PORTEC-3 trial patients included in and excluded from the molecular analyses, as reported previously  [9]. In addition, we used publicly available endometrial cancer data from TCGA series to further extend our cohort  [40]. Clinicopathological and molecular data as well as patient outcome data from all patients with available copy number variation (CNV) data were obtained from the Genome Data Analysis Center  [41].
Next-Generation Sequencing For the purpose of previous publications, targeted next-generation sequencing (NGS) using AmpliSeq Cancer Hotspot Panel v5 was performed on PORTEC-3 tumor samples. Detailed description of DNA isolation and sequencing was described previously  [9]. In cases with a failed DISH assay, the final HER2 status was determined by visual examination of copy number plots generated for every individual sample for the presence of unequivocal ERBB2 amplification. All cases with low-quality NGS data were considered ineligible for the examination of ERBB2 amplification. The presence of pathogenic PIK3CA and PTEN mutations was evaluated independent of and blinded for HER2 status. A minimum coverage threshold of 100 reads and variant allele frequency of 0. 1 were considered. Pathogenicity of non-synonymous PIK3CA and PTEN mutations was assessed using the public databases COSMIC  [44]  and ClinVar  [45] , as well as in silico tools SIFT  [46]  and PolyPhen  [47].
Statistical Analysis Statistical analyses were performed with SPSS (Statistical Package of Social Science) version 25 (IBM, Armonk, NY, USA). Associations between groups were analyzed using the χ 2 statistics or Fisher's exact test for categorical variables, and the Mann-Whitney U test for continuous variables. The Spearman's Rho correlation coefficient was calculated to compare the correlations between HER2 amplification and histological and molecular characteristics. The difference between the correlations was tested by transforming the correlations to z-scores; then, we calculated the ratio of the difference to the standard error and compared this ratio to the standard normal distribution  [48]. Five-year recurrence-free survival (RFS) and overall survival (OS) were estimated using the Kaplan-Meier's methodology and compared between groups using the log-rank test.
Tumor cells inevitably encounter various types of stress, including hypoxia, limited glucose and amino acids shortage  (14, 45). ATF4 is often found to be up-regulated in tumor cells to promote adaptation of cells to these challenges  (46). Interestingly, the METTL5 expression level is noticeably up-regulated in tumors, especially solid tumors, compared with the normal control tissues in various TCGA tumor datasets (Fig. S5B ). Importantly, the expression of the METTL5 cofactor, TRMT112, is also up-regulated and correlates well with the expression pattern of METTL5 in these tumors (Fig. S5C ). We speculate that up-regulation of METTL5 may serve to ensure 18S rRNA methylation in order to sustain a high level of ATF4 protein expression (through regulation of translation efficiency of ATF4) to counter the stress in tumors.
Ribo-Seq data analysis The clean reads were obtained by trimming the adapters and filtering the low-quality sequences (Q30) from raw FASTQ sequences of input and ribosome profiling using trim_galore (https://www. bioinformatics. babraham. ac. uk/projects/trim_galore). For input samples, the clean reads were then mapped to mouse reference genome (mm10) using STAR  (49). The mapping results in BAM format were used as the input of featureCounts program from the Subread package (50) to calculate the raw read count for CDS region of each gene in GENCODE mouse gene mode (M21). For the clean Riboseq data, the clean ribosome-protected fragments (RPFs) were collapsed into FASTA format by fq2collapedFa. pl (51). Ribo-toolkit (https://bioinformatics. caf. ac. cn/) was used to perform various analyses based on the collapsed RPF tags and raw read counts from input samples, which include tRNA and rRNA removal, RPF quality controls, codon occupancy analysis and translation efficiency analysis.
In brief, the RPFs tags were first aligned to rRNA sequences from Ensembl database (release 91) and tRNA sequences from GtRNAdb databases  (52)  to exclude the tags derived from rRNAs and tRNAs. The filtered RPF tags were then mapped to mouse reference genome using STAR  (49)  and the count of unique-mapped RPF tags on CDS region of each GENCODE gene mode (M21) was computed using featureCounts. The translation efficiency (TE) was calculated by dividing normalized RPF abundance by its normalized mRNA abundance. A threshold of ΔTE (fold change of TE) > ±1. 5 and FDR <0. 05 was used to define the differentially translated genes  (53). For codon occupancy analysis, the genome unique mapped tags were aligned against mouse transcript sequences (GENCODE M21) using Bowtie with a maximum one mismatch allowed  (54).
Computation of MFE MFE was computed using two programs: RNAfold, included in the ViennaRNA software package version 2. 1. 5 ; and Quickfold, from the Mfold web server (http://mfold. rna. albany. edu/?q=DINAMelt /Quickfold). For very short sequences, we found that the MFEs computed by Quickfold (Mfold) were sometimes positive. In these cases, global free energy were set to 0 kcal/mol.
Gene Ontology and Gene Set Enrichment Analysis Gene Ontology analysis of differentially translated genes was conducted using DAVID GO  (55). To identify the enriched ontology terms, a significance threshold based on Benjamini-Hochberg adjusted pvalue (FDR) < 0. 05 was used. For Gene Set Enrichment Analysis (GSEA)  (56) , the Java implementation from Broad institute (http://software. broadinstitute. org/gsea/index. jsp) was used to evaluate the statistical enrichment of gene ontology sets in the MSigDB C5 gene ontology collection. Briefly, the translation gene list containing 10,446 genes was pre-ranked by fold change, which was then used as the input of the Java implementation to calculate the normalized enrichments score and adjust p-values based on classic mode.
TCGA data analysis RNA-Seq expression data and small RNA-Seq data for 33 TCGA tumor types were downloaded from Genomic Data Commons Data Portal (GDC) of TCGA (http://cancergenome. nih. gov/). The gene expression matrix was then constructed by merging the TPM (Transcripts Per Million) values of all RNAseq samples. The expression correlations between METTL5 and TRMT112 among TCGA tumors were conducted by using Pearson correlation coefficient. rRNA is critical for maintaining efficient translation of ATF4. In the METTL5 KO cells (right: light blue box), basal translation of uORF1 and CDS is greatly hindered, presumably to be due to ribosome conformation changes caused by the lack of m 6 A modification in the 18S rRNA. Upon ER stress, ATF4 mRNA is efficiently translated by the m 6 A-decorated ribosome in WT cells, followed by acute up-regulation of a set of its downstream targets (CHOP etc.
METTL5 is upregulated in various tumors. The gene expression data-sets were retrieved from TCGA and the differences in various tumor and normal tissues were compared using GEPIA2 software(http://gepia2. cancer-pku. cn/). Red: tumor, green: normal tissue; C. Expression of TRMT112 is up-regulated in various tumors and correlates well with METTL5 expression. The gene expression data of TRMT112 were also downloaded from TCGA and the correlation of Figure Legends.
Acknowledgments: We thank  Erdem Sendinc  and  Bill Neidermyer  for assistance with setting up the ribosome profiling experiments and their insightful advice. We also thank  Santa Cruz Biotechnology ,  Inc. for providing antibodies. This work was supported by an  R35  grant from the  NCI  ( 1R35CA210104 ) and by funds from  BCH  to YS. This work was also supported in part by grants from the  NIH  to X. C. ( R01 GM049245 ), and a  CPRIT  grant to X. C. ( RR160029 ).
The design of the FIMR consists of a glass tube with a resistive coating on the inside surface and four quadrupole rods mounted radially on the outside. With an RF (radio frequency) field, ions are collimated to the central axis, improving the detection efficiency of product ions. The mass resolving power of the 1. 2 m long TOF mass analyzer was 12 000-13 000 m dm -1 during the whole campaign. Data were recorded with a time resolution of 5 s. Background measurements using ultra-high-purity nitrogen (UHP N 2 ) were automatically performed every hour. The temperature, relative humidity (RH), wind speed, and ambient pressure were continuously monitored at 3. 4 m a. g. l. , whereas the solar radiation was measured at 15. 6 m a. g. l. from a mast located at the site. The mixing ratios of nitrogen oxides (NO x ) and ozone (O 3 ) were measured at 4 m a.
Data analysis and quantification of multiple compounds Data analysis was performed using the software package "Tofware" (https://www. tofwerk. com/software/tofware/, last access: 12 February 2020) that runs in the Igor Pro environment (WaveMetrics, OR, USA). Tofware enables timedependent mass calibration, baseline subtraction, and assignment of a molecular formula to the identified ions by highresolution analysis. Signals were averaged over 30 min before mass calibration. Due to the high resolving power of the LTOF (long time of flight) mass analyzer, isobaric ions were more clearly separated. Examples of peak identification are given in Fig. S1  in the Supplement. The Vocus was calibrated twice a day during the campaign with a mixture (70 ppb each) of terpenes (m/z 137: α-/β-pinene + limonene; m/z 135: p-cymene) that was diluted using UHP N 2.
Vocus PTR-TOF capabilities in the forest While  Krechmer et al. (2018)  and  Riva et al. (2019a)  have described the novel setup and performance of the Vocus PTR-TOF and its application during a lab study, the instrument capability has not been fully explored in an ambient environment. Based on the CERVOLAND deployment, we provide here the first overview of gas-phase molecules measured by the Vocus PTR-TOF in the forest. For a better visualization of the complex data set from real atmosphere, mass defect plots (averaged over the whole campaign) are shown in Fig. 2  with the difference between the exact mass and the nominal mass of a compound plotted against its exact mass. With the addi- tion of hydrogen atoms, the mass defect increases, while the addition of oxygen atoms decreases the mass defect.
Acknowledgements. The authors would like to thank the  PRIME-QUAL program  for financial support (ADEME, convention # 1662C0024 ). This study has also been carried out with financial support from the  French National Research Agency (ANR)  in the frame of the " Investments for the Future" program , within the Cluster of  Excellence COTE  ( ANR-10-LABX-45 ) of the  University of Bordeaux. Special thanks to  Elena Ormeño-Lafuente  ( IMBE ) for the loan of the BVOC calibration gas cylinders and  Christophe Chipeaux  and  Denis Loustau  ( ISPA-INRA ) for their precious help in providing meteorological data and access to the  ICOS station facility. Financial support. This research has been supported by the  H2020 European Research Council  (grant nos. ATM-GTP (742206 ), COALA (638703), and CHAPAs (850614)) and the  Academy of Finland  (grant nos.
Numerical Method All CAA calculations were performed with the help of the sAbrinA solver  [22] [23] [24]  , which is a CAA code originally developed two decades ago at ONERA. Over the years, this solver has been extensively validated and continuously applied to numerous noise problems, achieved within multiple frameworks (industry contracts, European projects, international cooperation efforts, etc. ). The sAbrinA solver is a structured, time-accurate CAA code that solves either full or linear Euler equations, in a conservative and perturbed form. Concerning discrete operators, the code employs a third-order, multi-stage, Runge-Kutta marching scheme in time, as well as spatial derivation and filtering operators that rely on highly accurate Finite-Difference (FD) schemes. Indeed, besides its use of 7-point stencil/6 th order standard FD schemes, sAbrinA solver may also employ the so-called Intrinsically Optimized Finite Differences (IOFD) schemes  26.
This new class of optimized FD operators are of very high accuracy thanks to an optimization process based on a minimization of the scheme's leading-order truncation error (rather than on a classical optimization of the scheme's spectral properties). In the present study, both standard and IOFD schemes were used, the former being employed for the calculation cases of section 5, whereas the latter were used for the simulations of sections 3 and 4. Concerning meshes, the code deals with multi-block structured grids with one-to-one interfaces, and it is fully parallelized using the Message Passing Interface (MPI) standard. In particular, all computations presented hereafter made use of these multi-blocks/multi-cores features. Finally, the solver includes the usual boundary conditions (reflection by solid walls, etc.
Again, to separately estimate the effects of each QFF component (side plates, collector plate, nozzle) or features (jet flow heterogeneities), several calculations were conducted; whilst the CFD-derived forcing source term was kept the same, the CAA grid and/or mean flow were adjusted for each calculation case, to properly isolate each installation effect. Calculations were run for a number of time steps covering the temporal extent of the CFD data record (12,600 steps), corresponding to 20 periods of the primary shedding frequency fprim.
Acknowledgments The present study was achieved as part of a joint effort between  NASA  and  ONERA  (namely, the  NASA-ONERA International Agreements on Reducing Aircraft Noise  and on Understanding and Predicting the Source of Nose Landing Gear Noise). In that regard, the author first acknowledges  NASA Langley Research Center (LaRC)  for welcoming him as a Visiting Researcher and providing him full support, including computational resources and data. The authors is indebted to  NASA/LaRC  personnel; first, the following researchers:  Dr. Lockard ,  Dr. Khorrami , and  Dr. Choudhari  for their key guidance and invaluable support throughout this work,  Dr. Hutcheson  and  Mr. Stead  for the detailed information regarding the QFF experimental set up, and  Dr. Tinetti  and  Dr. Bahr  for their fruitful exchanges on the matter.
However, not much is known about the transcriptional consequences of these persistent DNA methylation changes  [15]. Furthermore, while the alterations at multiple molecular layers, from epigenetics to metabolomics, have been investigated in relation to current smoking in adults  [19] [20] [21] [22] , there is lack of information about the multi-layer molecular changes associated with in utero exposure or with the exposure to SHS in children. Regarding adult SHS exposure, only DNA methylation candidate studies are available  [23]. Here, we aimed to identify multi-layer molecular signatures associated with exposure to tobacco smoke in two early life susceptibility windows, in utero due to maternal smoking and in childhood through exposure to SHS. For this, we used molecular data from 1203 children of the Human Early Life Exposome (HELIX) study, including child blood DNA methylation and transcription, plasma proteins, and sera and urinary metabolites.
Study population The Human Early Life Exposome (HELIX) study is a collaborative project across 6 established and ongoing longitudinal population-based birth cohort studies in Europe  [24] : the Born in Bradford (BiB) study in the UK  [25] ; the Étude des DÉterminants prÉ et postnatals du dÉveloppement et de la santÉ de l'Enfant (EDEN) study in France  [26] ; the INfancia y Medio Ambiente (INMA) cohort in Spain  [27] ; the Kaunas cohort (KANC) in Lithuania  [28] ; the Norwegian Mother, Father and Child Cohort Study (MoBa)  [29] ; and the RHEA Mother Child Cohort study in Crete, Greece  [30]. Around age 8 years, the HELIX follow-up visit of the offspring took place using harmonized questionnaires and sampling protocols (n = 1301). HELIX children with complete data on prenatal and postnatal variables of exposure to tobacco smoking and with data on at least one omics platform were selected for the present study (n = 1203).
Definitions of exposure to tobacco smoking in pregnancy Maternal smoking habits during pregnancy were obtained from existing data collected in each cohort using non-harmonized questionnaires administered to the mothers in at least the first and third trimester of pregnancy. Two variables for active maternal smoking during pregnancy were generated: (i) any maternal smoking during pregnancy if the mother had smoked at any time during pregnancy ("yes/no"), and (ii) sustained maternal smoking, if the mother had smoked, at least, in the 1st and in the 3rd trimester ("yes/no"). The mean number of cigarettes smoked per day during pregnancy was estimated averaging the mean number of cigarettes per day in the first and third trimesters. Maternal exposure to SHS in pregnancy (mother-SHS: "yes/no") was assessed through questionnaire and was slightly different between cohorts: (i) exposure at home, work, or leisure places (INMA); (ii) exposure at home or work (MoBa, RHEA, BiB); (iii) exposure without specifying location (EDEN); and (iv) partner smoking (KANC).