dataesr/scientific-paragraphs-categorization huggingface dataset:
Differences in metabolites between experimental conditions were assessed by standard two-sample t-tests. Transcriptional datasets analyses: For the TCGA DLBCL set, level 3 raw expression counts of 48 patients were downloaded from TCGA. The WCM DLBCL cohort and cell lines RNA-seq data were aligned using STAR (v2. 3) to human reference genome (version hg19/GRCh37), and raw counts were calculated with HT-seq. Normalized read counts as provided by DESeq2 were extracted from the three datasets for posterior analyses. Spearman correlations were calculated in R and plotted with ggplot2. 44 samples corresponding to BL and PMBL profiled on Affymetrix HG U133 Plus 2 arrays were downloaded from http://llmpp. nih. gov/BL/ and were used as deposited by the authors. Proteomics dataset analyses: Data from the chemical precipitation of HSP90 cargoes from the cytoplasm of DLBCL cell lines OCI-LY1 and OCI-LY7 was obtained from previous experiments in our lab  [30]  and the union of the HSP90 interactome in the two cell lines was analyzed by KEGG pathway enrichment analysis in WebGestalt  [50].
Lymphoma samples and clinical data Fifty-five cases of t(14;18) 2 nodal FL, grades 1 to 3A were collected from the University of T übingen (Germany), National Cancer Institute, National Institutes of Health (Bethesda, MD), Hospital Clinic (Barcelona, Spain), Hospital Henri-Mondor (Creteil, France), Wilheminenspital (Vienna, Austria), University Hospital of Wales (Cardiff, United Kingdom), and University of Siena (Italy). The cases were reviewed by 5 of the authors (E. C. , B. G. F. , O. B. , E. S. J. , and L. Q. -M. ), according to the criteria of the 2017 World Health Organization classification. 1  Ten of the cases have been published. 26 All cases had at least 50% tumor cells. The morphology, growth pattern, cytology, and immunohistochemical staining for CD20, CD79a, BCL6, BCL2, CD10, CD3, MIB1, and CD23 were evaluated in formalin-fixed, paraffin-embedded (FFPE) tissue sections.
CN analysis DNAs extracted from FFPE were hybridized on the MIP-assay using Oncoscan FFPE according to standard protocols (Thermo Fisher Scientific, Schwerte, Germany). For comparison, previously published CN data on 35 cases of NMZL  24  and 42 of PTFL  26  were used, as well as data on t(14;18) 1 and t(14;18) 2 FL (for details see supplemental Methods). 6, 7 Gains and losses and CNN-LOH regions were evaluated and visually inspected by using Nexus Biodiscovery version 9. 0 software (Biodiscovery, Hawthorne, CA). Chromothripsis-like patterns were evaluated according to previously described criteria. 29 The human reference genome was GRCh37/hg19. The CNAs with a minimum size of 100 kb and CNN-LOHs larger than 5 Mb were considered informative.
NGS analysis Next generation sequencing (NGS) analysis was performed on the Ion Torrent PGM and S5 from Thermo Fisher Scientific. NGS libraries were amplified by using 2 primer pools of 2 Ion AmpliSeq Custom Panels covering 20 genes that have been shown to be frequently mutated in FL (panels NGS1 and NGS2; supplemental Tables  1  and 2 ). 2, 5, 30, 31 The custom panels were designed with the Ion AmpliSeq Designer from Thermo Fisher Scientific (version 3. 4). The library preparation, sequencing, and raw data analyses are described in the supplemental Methods.
Acknowledgements The authors thank  Sema Colak ,  Esther Kohler ,  Claudia Hermann , and  Noelia Garcia  for excellent technical assistance and the  IDIBAPS Genomics Core Facility. This work was supported, in part, by grants from the  Deutsche Forschungsgemeinschaft (DFG ;  QU144 and  QU144/1-1  [I. M. ]),  Fondo de Investigaciones Sanitarias Instituto de Salud Carlos III  ( Miguel Servet Program   CP13/00159  and  PI15/00580 , [I. S. ]). J. E. R. -Z. was supported by a fellowship from the  Generalitat de Catalunya Agencia de Gesti ón de la Universidad y la Investigaci ón (AGAUR)   FI-DGR 2017 (2017 FI_B01004 ). E. C. is an  Academia Researcher of the "Instituci ó Catalana de Recerca I Estudis Avançats" (ICREA)  of the  Generalitat de Catalunya. This work was partially developed at the  Centro Esther Koplowitz, Barcelona, Spain.
Selection of primer/probe sets Methods detecting ZIKV/CHIKV were selected from the literature based on three criteria: TaqMan technology, amplicon size (50-150 bp), and targeted sequence. The oligonucleotides were subjected to alignment analysis on a limited set of CHIKV/ZIKV sequences retrieved from NCBI, using Clustal Omega (EMBL-EBI). Primer/probe sequences were adapted if necessary to allow higher sequence coverage. Subsequently, primer/probe complementarities, melting temperature (Tm), and self-annealing were evaluated for each method using Multi Primer Analyzer software with standard settings (Thermo Fisher Scientific). Methods passing this selection stage were tested in a singleplex assay in a primer/probe concentration titration experiment (five primer/probe concentration combinations) using positive reference materials specific for the virus under analysis (Table  1 ).
Use of the bioinformatics tool Coverage of the ZIKV and CHIKV genome sequences by the four chosen methods (individually and in duplex) was analysed using the SCREENED web tool  (Vanneste et al. , 2018)  with default advanced options. A config file containing the oligonucleotide and reference amplicon sequences (Table  2 ) was used for the analysis. As the reference amplicon sequence input in SCREENED is a codon DNA strand, the CHIKV-a probe, originally designed to anneal to the anti-codon DNA strand, was introduced as its reverse complement. Full-length nucleotide sequences for ZIKV were retrieved from the NCBI Virus Resource database (5/11/2017) allowing for any host, region/country, genome region, and isolation source; and for CHIKV from the NCBI Nucleotide database using '"Chikungunya virus"[porgn:__txid37124] AND "complete À À À À DNA (Lambl) Alexeieff LGC (ATCC 1 50803D TM ) À À À À Cryptosporidium parvum DNA Strain Iowa LGC (ATCC 1 PRA67 TM ) À À À À Entamoeba histolytica DNA Schaudinn LGC (ATCC 1 30459D TM ) À À À À Human Human DNA / Thermo Fisher Scientific À À À À CHIKV, chikungunya virus; ZIKV, Zika virus.
Human and animal rights The study presented, including the publication of data related to arbovirus infection, was approved by the Comité Consultatif d'Ethique de Nouvelle-Calédonie (February 2015) and by the Comité de Protection des Personnes Ile de France I  (March 2015-13851 and January 2018-14793). The human biological samples used in this study were obtained from patients after receiving written informed consent.
Acknowledgements This work was supported by the project  ORIENT-EXPRESS  financed by the  WIV-ISP (presently named Sciensano; RP- PJ  project number  0000754 ) and partially by the  Arbo - VIRTUESS  project financed by the  Actions Concertées Interpasteuriennes  (project number  ACIP 2014-053 ). These funding sources had no involvement in the study design, data collection/analysis/ interpretation, or writing of the article. The authors are grateful to the  service Mycology & Aerology (IHEM/BCCM collection -Sciensano) and B. Lambrecht (Sciensano)  for providing materials, and  S. Lamoral  ( Sciensano ) for technical support. The authors would also like to thank  A. Perilhou  and  N. Jolly  ( Centre for Translational Science, Institut Pasteur Paris ) for their expertise on ethical issues.
Section 2 presents the instrumentation and datasets involved in the study. Passive and active remote sensing measurements are mainly used, completed by meteorological model outputs and the dataset of the Airparif air quality measuring network. We highlight the most polluted winter days of the past decade in Sect. 3 from archived ground-based PM 10 measurements and analyse the associated weather situations. In Sect. 4 we analyse in depth the measurements performed during winter 2016/2017, which included those of a N 2 -Raman ground-based lidar. This period was associated with two exceptional APEs whose characteristics are described in Sect. 5: we take advantage of the entire lidar dataset of these APEs. It covers a wide range of aerosol load, from pollution-free days to severely polluted days.
During daytime, the lidar-derived AOT is checked against the one measured by the sun photometer of the AERONET Paris site at concomitant times. The AOTs, combined with the elastic channel, lead to the retrieval of the aerosol extinction and backscatter coefficient profiles (AEC and ABC, respectively) and to their ratio, also called the lidar ratio (LR). In the inversion process, the extrapolation of AOT measurements from the N 2 -Raman wavelength to the elastic wavelength assumes a constant Ångström exponent with height for the particles in the atmospheric column; furthermore a mean value of the Ångström exponent is taken for each APE. The Ångström exponent is derived from the AErosol RObotic NETwork database (AERONET, http://aeronet. gsfc. nasa. gov/, last access: 5 June 2020) for the Paris site.
CATS The spaceborne lidar Cloud-Aerosol Transport System (CATS, https://cats. gsfc. nasa. gov/, last access: 5 June 2020) was operating on board the International Space Station (ISS) from January 2015 to October 2017. CATS has been recently evaluated by ground-based lidar measurements from the EARLINET network  (Proestakis et al. , 2019). The data used in this work come from the mode 7. 2 HSRL Demo of the CATS mission. Namely, this acquisition mode uses the backscattered light emitted at 532 and 1064 nm and the depolarization of the 1064 nm channel. In this study, we use the operational product of version L2O_V3-00 (NASA, 2017) with aerosol typing based on lidar ratio considerations. It comes as a complement to CALIOP data, strengthening the credibility of their concomitant results. Note that CALIOP and CATS have a different typology for aerosol subtyping and associated lidar ratio.
6b  and 8a ), which may bias the AERONET operational products  (Chew et al. , 2011). As far as lidar data are not disturbed these profiles are kept in the figure. For the two winter pollu- Table  2. The 8 most severely polluted winter days of the past decade. For each day we give both PM 2. 5 and PM 10 measured at ground level (Airparif network) in the format max/mean/min, where max and min are the hourly maximum and minimum value measured at a given background station during the day and Mean is the daily average over all background stations. Meteorological parameters (ECMWF ERA5) at ground level are also given: pressure (P ), temperature (T ), relative humidity (RH), wind speed (WS), and wind direction (WD) with the standard deviation associated with the daily average. The AOT field appears to be homogeneous within its spatial extension.
The aerosol typing derived from CATS at  03:00 UTC on 20 January and CALIOP at 02:00 UTC on 21 January is identical to the one previously retrieved for the event of December (Table  3 , Fig. 7a ) although the LR retrieved from the ground-based lidar decreases. Such a discrepancy is not significant when considering the expected uncertainty on the LR given by CALIOP (25 sr). 5 In situ ground-based measurements versus lidar inversed data 5. 1 PM 10 and PM 2. 5 at ground level The temporal evolutions of surface PM during the two particulate pollution events of winter 2016-2017 are analysed. Figure  10  displays both PM 2. 5 and PM 10 during the APEs of December 2016 and January 2017. Only the background stations (BS) are taken into account. For PM 10 and PM 2. 5 , an hourly average is calculated over all these stations in the  Paris vicinity.
Relationship between aerosol optical properties and PM 2. 5 The parameter legally used to gage an APE is PM 10 (no. 2008/50/CE and 2004/107/CE); however,  Randriamiarisoa et al. (2006)  demonstrate that the accumulation mode (PM 2. 5 ) contributes the most to optical properties of an aerosol population in the Paris area. Thus, in search of a correlation between Figs. 6 and 10, we chose PM 2. 5 over PM 10. We consider the dataset combining the two events displayed in Figs. 6, 7, and 10. It ranges from pollution-free days to severely polluted days and thus covers a wide range of AEC, AOT, and PM 2. 5 values.
Integrated optical properties Figure  11  shows the scatter plot between the PM 2. 5 measured at ground level and the total AOT lid. The linear regression conducted on all data of Fig. 10  (dashed grey line), i. e. AOT and PM 2. 5 day and night for the two pollution episodes sampled by lidar, shows no correlation, with a Pearson cor-  relation coefficient R 2 ≈ 0. 16. However, a group of points stands out from this dataset and is associated with a PBL top below 600 m a. m. s. l. It appears that most of these points are also associated with PM 2. 5 values above the information threshold (50 µg m -3 ). The linear regression conducted without this set of points (black solid line of Fig. 11 ) shows a better correlation (R 2 ≈ 0. 66). It suggests that the direct correlation between AOT and PM 2.
Compared with the previous method, the presence of aerosols in the free troposphere does not bias the linear relationship established. However, this approach should not be generalized too quickly for well-developed PBLs that may have high relative humidity at their top. In the case of hydrophilic aerosols, as is often the case for Paris pollution aerosols  (Randriamiarisoa et al. , 2006) , the AEC max may be found near the top of the PBL. Note that at low winter temperatures, aerosols are generally less acidic and therefore less hydrophilic  (Jaffrezo et al. , 2005). Here we find 358 ± 229 m as the averaged difference between PBL height and the altitude of AEC max over all the available profiles. This mean distance is associated with a high standard deviation resulting from the high variation in the PBL height within the considered dataset (∼ 640 ± 250 m a.
Discussion In Fig. 14  a third independent dataset is added to test the relevance of the linear fit shown in Fig. 13. Similar to the two major APEs, this third dataset is also sampled during the WASLIP experiment. It covers 8 d from 3 to 10 December 2016, and the data processing methodology is the same as the one used for the two major pollution events. This third period corresponds to an intermediate pollution situation with PM 2. 5 between 20 and 60 µg m -3 , included in the range of the two other polluted periods (PM 2. 5 from ∼ 5 to 90 µg m -3 ). Figure  14  shows that this new independent dataset fits pretty well in the 95 % prediction interval of the Fig. 13  linear regression. The Pearson correlation coefficient of the new linear regression applied on this third dataset is R 2 = 0. 5.
Furthermore, the application of a linear regression on all points, in-    cluding the new dataset, yields a significant Pearson correlation coefficient R 2 = 0. 67. Regarding the slopes, each linear regression shows equivalent steering coefficients. These slopes are highly dependent on the chemical composition of the aerosols as shown by  Raut and Chazette (2007). They correspond to the mean specific cross section of the sampled aerosols, which is highly variable and a function of the emission sources, but also of the aerosol ageing processes within the atmospheric environment. Here a value of 9. 4 m 2 g -1 is found for the two major pollution events and 9. 3 m 2 g -1 when the computation is made for all data in Fig. 14. These values are higher than what can be found in the literature and in particular compared to the results of  Raut and Chazette (2009)  (Table  1 , maximum of 7.
1 m 2 g -1 ) and their review in Table  2. These higher values could be explained by the use of PM 2. 5 in this study rather than PM 10 ; indeed, the same computation with PM 10 for the dataset of major APEs leads to a specific extinction cross section of 7. 0 m 2 g -1. Furthermore, the y intercepts are slightly different between the two datasets. An approach using remote sensing of aerosols with the choice of a good optical proxy can give an estimate of the surface pollution in terms of PM 2. 5 but should be used with caution because of the possible tempo-Figure  14. Relationship between PM 2. 5 (x axis) and AEC max within the PBL (y axis). Orange dots are the same dataset presented in Fig. 13. The orange solid line corresponds to the linear regression computed from the orange points (same as Fig.
13 ) and the light orange area illustrates its 95 % confidence interval. Blue crosses are data sampled from 3 to 10 December 2016. The blue solid line is the result of the linear regression calculated on this independent dataset. The purple solid line is the result of the linear regression calculated on all the points (blue crosses and orange dots). The correlation coefficients can be found in the top left-hand corner. ral variability in the nature of aerosols, especially between long-range pollution events and local pollution. The most promising approach is the direct assimilation of the raw lidar observation into a chemistry-transport model including measurement modelling  (Wang et al. , 2013 (Wang et al. , , 2014)).
Conclusions In this paper the lidar-derived optical properties of two major APEs of the winter of 2016/2017, found to be part of the most severe pollution events of the 2007-2017 decade over Paris, are investigated. This work is achieved through a synergy between (i) ground-based active and passive remote sensing devices, (ii) spaceborne instruments, (iii) air quality network measurements, and (iv) meteorological reanalyses. The data collected for this study highlight the maximum AEC in the PBL as an optical parameter that offers the possibility to assess the surface concentration of PM 2. 5. Although limited to a winter period, this lidar dataset comes to enrich the scientific literature, which was lacking severe winter pollution data. These episodes are rare (8 d in 1 decade, split between four separate events) but harmful for the citizens' health and still difficult to forecast.
Acknowledgements. The  Centre National d'Etude Spatial (CNES)  helped maintain the Raman-lidar instrument. The authors would like to thank  Pascal Genau  ( CNRS/LATMOS ) and  Cristelle Cailteau-Fischbach  ( UPMC/LATMOS ) for their support in operating the lidar data and the welcome at the QualAir station. François Dulac  ( CEA/LSCE ) and the five anonymous referees of this work are gratefully acknowledged for their constructive remarks, comments, and suggestions during the review process of this work. The authors would like to thank the  Airparif network  for collecting data. The authors would like to thank the  AERONET network  for sun photometer products (at https://aeronet. gsfc. nasa. gov/,last access: 5 June 2020). The authors acknowledge the MODIS Science, Processing, and Data Support teams for producing and providing MODIS data (at https://modis.
gsfc. nasa. gov/data/dataprod/,last access: 5 June 2020), the  Atmospheric Science Data Center (ASDC)  at  NASA Langley Research Center (LaRC)  for the data processing and distribution of CALIPSO products (level 4. 20, at https: //search. earthdata. nasa. gov/search,last access: 5 June 2020), and  CATS  products (level 2, at https://search. earthdata. nasa. gov/search,last access: 5 June 2020). ECMWF data used in this study have been obtained from the  Copernicus Climate Change Service Climate Data Store  (https://cds. climate. copernicus. eu/cdsapp#!/home, last access: 5 June 2020). Financial support. This work was supported by the  Commissariat à l'Energie Atomique  et aux énergies alternatives (CEA). Review statement. This paper was edited by  Nikos Hatzianastassiou  and reviewed by five anonymous referees.
Baseline readings at intermediate masses were also measured and subtracted from peak readings. Following isotopic analysis, raw data were regressed to mass spectrometer inlet time, corrected for blanks (determined by heating an empty Al foil wrapping, or in the static volume of the crushers), neutron interferences, radioactive decay and mass discrimination (the latter only for Ar isotopes), and converted to moles using calibrated volumes of air standard, analyzed daily during the period of the experiments. Methods used for converting neutron-produced noble gas isotopes to parent halogen, K, and Ca concentrations follow those described by  Ruzié-Hamilton et al. (2016). Reported elemental ratios are in molar (M) units and uncertainties are one standard deviation unless otherwise stated.
Acknowledgments and funding We thank  Zunli Lu  and an anonymous reviewer for constructive reviews, and  Anita Cadoux  for editorial assistance. S. G. acknowledges funding from a  NERC  Ph. D. studentship that supported the analyses of the Hamersley chert samples. R. B. is grateful for a  JSPS  Fellowship that funded his stay at the  University of Tokyo during  which the manuscript was prepared.
Acknowledgments S. E. acknowledges support from the  DiRAC Institute in the Department of Astronomy at the University of Washington. The  DIRAC Institute  is supported through generous gifts from the  Charles and Lisa Simonyi Fund for Arts and Sciences , and the  Washington Research Foundation. This research was in part funded by the  FWF Austrian Science Fund  project  S11603-N16 ,  EPL  from project  S11608-N16.
classical extraction) and to assess the effect of fenofibrate (fenofibrate-treated cells vs. non-treated cells). The average CVs obtained for polar metabolites and all classes of lipids for the two extraction methods were also compared using Student's t-tests to test the difference in variability between the two methods. For lipid classes, the relative abundance of lipid species within each class (expressed as a percentage of the total class) was compared for the two extraction procedures and for the fenofibrate-treated vs. non-treated cells using the Khi2 test. Differences were considered significant when p-value p < 0. 05. Statistical analyses were performed using R software. Data were represented as mean ± standard deviation with Graphpad Prism 8. 4. 1 software. All raw data have been uploaded to the Metabolights repository (URL http://www.
In this study, we aim to fill the gap on the knowledge of S. exigua larval olfaction through the analysis of a comprehensive RNA-seq dataset that includes several larval tissues. We expand the number of putative chemosensory-related genes described in S. exigua and propose a unifying gene nomenclature based on reconstructed phylogenetic trees and following names used in the annotated Spodoptera frugiperda genome  (Gouin et al. , 2017). As previous exposure to volatile organic compounds (VOCs) has been shown to influence OR gene expression in S. exigua adults  (Wan et al. , 2015) , we also examined whether such a regulation occurs in S. exigua larvae. We show that some ORs are strongly up-regulated after being exposed to any of the odorants tested, irrespectively of the larval behaviour they trigger.
De novo assembly and annotation of chemosensory-related genes Paired-end (PE) raw reads were trimmed and used for de novo assembly using Trinity v2. 3. 1  (Grabherr et al. , 2013)  with --min_kmer_cov 2 parameter. Trinity-assembled contigs were further clustered with Corset  (Davidson and Oshlack, 2014)  and the longest transcript from each Corset cluster was selected to obtain the final assembly. De novo assembled transcriptome is available at NCBI TSA database with accession number PRJNA634227. Annotation of transcripts encoding odorant receptors (ORs), ionotropic receptors (IRs), gustatory receptors (GRs), odorant-binding proteins (OBPs) and chemosensory proteins (CSPs). was performed with iterative blast searches using the amino acid sequences predicted from the S. frugiperda genome  (Gouin et al.