dataesr/scientific-paragraphs-categorization huggingface dataset:
Representativeness of EuroSIDA and COHERE When weights were applied to COHERE/EuroSIDA data to match the key demographic characteristics of the TESSy population, in general our data underestimated LP (Fig. 1 ). We could not include Estonia, Russia and Ukraine in this sensitivity analysis due to a lack of data from either COHERE/EuroSIDA or TESSy. The overall estimate of LP in the cohorts was 48. 4% (95%CI 47. 9-48. 9), which increased slightly to 49. 2% (95%CI 48. 7-49. 7), after accounting for differences in age, sex and HIV transmission category between COHERE/EuroSIDA and the TESSy reference population. Austria, Germany and Italy had a lower percentage of LP after weighting; all other countries had a higher percentage of LP after comparison with TESSy data, with the biggest increase in Poland (9.
This study extends our previous work by applying estimates of LP from cohorts to surveillance data, which may lack longitudinal follow-up and data on clinical outcomes. This allowed us to estimate the country-specific burden of LP and to provide estimates of excess morbidity and mortality occurring in the first year following HIV-diagnosis, attributable to LP. We estimate that during 2010-2016 a total 12,050 excess AIDS-events/deaths within the first year of HIV-diagnosis could potentially have been avoided if late presenters had been diagnosed with HIV and started treatment similar to those diagnosed early in COHERE and EuroSIDA. This likely represents an under-estimate of the clinical benefit of starting ART earlier. Our findings are based on cohort data in years when a large proportion of patients did not start ART until their CD4 cell count was below 350 cells/mm 3 , and largely before the results of the INSI GHT START study led to changes in clinical management in Europe  [1].
Furthermore, low event rates may cover incomplete reporting, despite our efforts to ensure high data quality. Also, we only considered new events within 12 months after HIV-diagnosis, and our analyses do not account for potentially avoidable events before or at HIV-diagnosis. Thus, our numbers underestimate the actual number of potentially avoidable AIDS-events/deaths. A strength of this study is the country-level estimates of LP from seventeen countries across Europe, including countries in Eastern Europe where data are limited  [42]. However, despite the large size of the COHERE and EuroSIDA cohorts, we were only able to include data from five countries outside Western Europe and we had comparatively small numbers from those countries. The limitations of extrapolating event rates from relatively few participants to the whole HIV-diagnosed population should be kept in mind when interpreting results.
Further, for Russia there were no official statistics on the number of new HIV-diagnoses and we used estimates from sources available online. Increasing the availability and robustness of data from Eastern Europe remains an urgent priority. For our main analysis, we assumed that persons in COHERE/EuroSIDA were representative of all HIVdiagnosed persons. In sensitivity analyses, we then used TESSy data to reproduce the demographic characteristics (age, sex and HIV transmission category) of the whole HIV-diagnosed population. Estimates were similar, but the burden of LP and excess AIDSevents/deaths attributable to LP were slightly higher using this weighted analysis. We chose not to use additional factors, such as ethnicity or CD4 count at presentation, because the sample size used for weighting within additional groups would have been small in some countries.
Further, we were not able to use this sensitivity analysis in Estonia, Russia or Ukraine due to lack of data in COHERE/EuroSIDA and/or TESSy. Although our analyses indicate that our cohorts capture a representative sample of the whole HIV-diagnosed population, sites in EuroSIDA and COHERE are not selected at random and may not be representative of all clinics across the included countries. Finally, a limitation of our study is that different Fig. 2  Estimated excess events per 1,000,000 inhabitants in each country. The figure shows the estimated excess AIDS-events/deaths attributable to late presentation, when differences in population size are accounted for. The sensitivity analysis gives the estimated excess events, when people with a new AIDS event within 6 months of HIV-diagnosis are excluded.
The population estimates in each country are based on data from Eurostat (number of persons having their usual residence in a country on 1st January 2016)  [24]. *For Russia, the population estimate from 2014 (most recent available) was used The Late Presentation Working Groups in EuroSIDA and COHERE BMC Infectious Diseases (2020) 20:728 inclusion periods were used for COHERE (2010-2014) and EuroSIDA  (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016). This was done to increase the number of people included, and thus the robustness of estimates from Central and Eastern Europe. While the LP-rate has not changed significantly over the past the awareness of timely ART, the components of ART-regimens and the overall quality of care may have improved over time.
Acknowledgements The Late Presentation Working Groups in EuroSIDA and COHERE:  Andrea Antinori ,  Barbara Bartmeyer ,  Marek Beniowski ,  Fabrice Bonnet ,  Johanna Brännström ,  Jordi Casabona ,  Antonella Castagna ,  Genevieve Chene ,  Dominique Costagliola ,  Antonella d'Arminio Monforte ,  Julia Del Amo Valero ,  Stephane De Wit ,  Gerd Faetkenheuer ,  Hansjakob Furrer ,  Jacek Gasiorowski ,  Corinne Jadand ,  Ole Kirk ,  Kamilla Grønborg Laut ,  Mario Lazanas ,  Ignacio Suarez-Lozano ,  Jens Lundgren ,  Julia Mikhalik ,  Amanda Mocroft ,  Santiago Moreno ,  Cristina Mussini ,  Niels Obel ,  Dzmitry Paduta ,  Dorthe Raben ,  Peter Reiss ,  Andrew Riordan ,  Caroline Sabin ,  Jelena Smidt ,  Carlo Torti ,  Tatiana Trofimova ,  Anna Vassilenko ,  Marta Vasylyev ,  Josiane Warszwski ,  Linda Wittkop ,  Alexei Yakovlev ,  Robert Zangerle.
The dust sources in North and South America and Australia altogether account for about 25 % of total emissions. The suspending dust particles can directly influence the planetary radiation budget and indirectly impact the climate by interfering with cloud properties and cloud processes. Dust particles, as well as other ice nucleating particles (INPs), can aide the formation of ice crystals in the heterogeneous ice nucleation regime. Due to their effective ice nucleating capability and abundant concentration, mineral dust particles are considered the most important INP  (Kanji et al. , 2017). Recent studies found that atmospheric dust is also linked to the activity of tropical cyclones and rainfall  (Reed et al. , 2019; Thompson et al. , 2019). A comprehensive dataset of dust properties is of significant importance for understanding the effects of dust on the ecosystem and for reducing the uncertainties of climate models.
, 2009). However, very few field campaigns have been carried out for studying Asian dust. Compared to Saharan dust, the characteristics of Asian dust were not adequately explored. The earliest field campaign characterizing Asian dust dates back to 1989 when an experiment was carried out in Tajikistan for studying desert dust properties and the impact on meteorological conditions. The CADEX (Central Asian Dust EXperiment) project was planned to provide a dataset of optical and microphysical properties of dust from Central Asia. A multiwavelength Mie-Raman polarization lidar was deployed in Dushanbe, Tajikistan. This results in  Hofer et al. (2017)  and  Hofer et al. (2020)  provided important dust properties, such as vertically resolved lidar ratios, linear depolarization ratios and mass concentrations.
The polarization calibration is performed following the ± 45 • method (Freudenthaler https://doi. org/10. 5194/acp-20-13817-2020 Atmos. Chem. Phys. , 20, 13817-13834, 2020 et al. , 2009). During the DAO campaign, the polarization calibration was performed at least once per day. The Ångström exponent of the extinction coefficient and backscatter coefficient are calculated with Eq. (  2 ): Å = - log p(λ 1 ) -log p(λ 2 ) log λ 1 -log λ 2 , ( 2 ) where p(λ) represents the optical parameters, such as aerosol optical depth (AOD), extinction or backscatter coefficient at wavelength λ, and Å represents the Ångström exponent of the corresponding parameters p(λ). The statistical error of lidar derived parameters is estimated using the method presented in  Hu et al. (2019). The data presented in this study are recorded at nighttime, so the background radiation is negligible.
Sun-sky photometer Three sun-sky photometers are deployed in the Kashi observation site. One is affiliated with AERONET (AErosol RObotic NETwork;  Holben et al. , 1998) , and the other two are affiliated with SONET (Sun-Sky Radiometer Observation Network). SONET is a ground-based sun photometer network with the extension of a multiwavelength polarization measurement capability to provide long-term columnar atmospheric aerosol properties over China  (Li et al. , 2018). The three sun-sky photometers provide complementary measurements by following different measurement protocols. In all, they can measure daytime aerosol optical depth (denoted as AOD hereafter) at 340, 380, 440, 675, 870, 1020 and 1640 nm, polarized/unpolarized sky radiances at 440, 675, 870 and 1020 nm, and moon AOD as well. The succeeding data treatment and retrieval are performed following the protocols and standards of AERONET or SONET depending on the affiliation of the instruments.
Satellite data Satellite data have complementary advantages due to their large spatial coverage compared to ground-based remote sensing techniques. In order to monitor dust activities in the Taklamakan desert, we use the UV aerosol index (UVAI hereafter) derived from the OMPS (Ozone Mapping Profiler Suite) on board the Suomi-NPP (National Polar-orbiting Partnership) satellite  (Flynn et al. , 2004; Seftor et al. , 2014). OMPS provides full daily coverage data, and the overpass time for the Kashi region is around 06:30 UTC. The UVAI is calculated using the signal in the 340 and 380 nm channels  (Hsu et al. , 1999). UVAI = -100 × log 10 I 340 I 380 meas -log 10 I 340 I 380 calc , (3) where I 340 and I 380 represent the backscattered radiance at 340 and 380 nm. The subscripts "meas" and "calc" respectively represent the real measurements and model simulation in a pure Rayleigh atmosphere.
In naturally formed dust particles, these corners or edges may be trimmed by aeolian or fluvial erosion. This could be one reason why previous dust observations never found PLDRs greater than 0. 30 at 355 nm, while near the source and in heavy dust event, we suppose that the lifted dust may contain a fraction of big and morphologically complicated particles which have strong depolarizing effects. Since Taklamakan dust observations in the source region are quite rare, more observational data are needed for complementing the dataset of dust characteristics.
Acknowledgements. We acknowledge the colleagues at the  Institute of Remote Sensing and Digital Earth  for their kind help and for the financial support. We thank the colleagues from the  GPI RAS (Prokhorov General Physics Institute of the Russian Academy of Sciences ),  Service National d'Observation PHOTONS/AERONET-EARLINET , a component of the ACTRIS infrastructure, the  ESA/IDEAS+  and  Labex CaPPA  projects, and  IAP (Institute of Atmospheric Physics)  for the participation and the efforts they have made for the campaign. The  Russian Science Foundation  (project  16-17-10241 ) is acknowledged for providing lidar data processing algorithms. Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. ANR-11-LABX-0005 ). Review statement. This paper was edited by  Matthias Tesche  and reviewed by two anonymous referees.
Data availability. The satellite data from OMPS and AIRS can be found in NASA's GES DIS service center. The meteorological data, GDAS data and the HYSPLIT dispersion model are available at the NOAA ARL site (https://ready. arl. noaa. gov/HYSPLIT. php, last access: 1 February 2020;  Stein et al. , 2015). All the other data presented in this study are available upon any request. Author contributions. The project was supervised by PG and ZL. QH, TP and IV were in charge of the lidar operation and maintenance. QH, IV and HW performed the data analysis. QH wrote the paper. KL provided the sun-sky photometer data. MK helped in the lidar operation and instrument preparation.
MS diet prevents commensal E. coli overgrowth in colonic mucosa. As we observed a decrease in glycosylation pathways (essential for bacterial growth and attachment), and a decrease in SIgA secretion upon MS diet feeding, we analyzed the fecal E. coli load in mice fed a control diet or an MS diet. qPCR approach revealed a 3. 3 × 10 3 -fold decrease in E. coli population in mice fed an MS diet when compared to mice fed a control diet (p < 0. 001) (Fig. 3c ). These molecular data were confirmed by cultural approach by quantification of Enterobacteria on Drigalsky agar plate (selective for Gram negative bacteria) where we observed a 5. 8 × 10 3 -fold decrease in Enterobacteria load in mice fed an MS diet (p = 0. 003), independently of the genotype of the mice (Fig. 3d ). Immunostaining on colonic section confirmed these observations.
4b ). Nevertheless, no difference was observed in the number of ileum-associated AIEC LF82 between the groups (Fig. 4c ). These data agree with the due attention that CEACAM6 gene is not expressed in ileal mucosa in this model (Fig. 1c ). Nonetheless, a 11. 51-fold decrease was noted in the number of bacteria associated to colonic mucosa of mice from the group fed MS diet compared to mice fed a control diet (p < 0. 001) (Fig. 4d ). These data show that the micro-environment established by MS diet is not favorable for AIEC bacteria attachment and long-term colonization. To better characterize the inflammatory response induced during the course of infection, we quantified the micro-inflammatory marker lipocalin-2 (Lcn-2) in the stools, during infection. We observed a significant increase in Lcn-2 levels, throughout the infection study, in mice fed control diet (from day 1 to day 3 post-infection).
Methods crohn's disease patients. Overall, 28 CD patients were prospectively and consecutively enrolled between September 2015 and September 2016. Clinical parameters including disease location and Crohn's disease endoscopic index of severity (CDEIS) are detailed in Table  1. Blood samples were taken prior to the endoscopy and sera were used to measure folate (B9 vitamin) concentration with an Advia Centaur folate kit (Chemiluminescence and competition test) using XPT-Centaur analyzer (Siemens, Tarrytown, USA). Samples presenting a maximal rate of 40 ng/ml of folate were included in the study. The samples above this value were considered as outliers and were removed from the study, based on the data from the literature  17, 19. Stools were collected in the morning the day before the endoscopy to reduce intra-individual variations, and were used to measure Calprotectin and Chi3-L1 by chemiluminescence DiaSorin LIAISON Calprotectin assay and Human Chitinase 3-Like 1 ELISA kit (R&D systems; Minneapolis, MN, USA) respectively, according to the manufacturer's instructions.
By cultural method. Gram negative bacteria were quantified in stools of mice using Drigaslky gelosis. Briefly, stools were weighted and homogenized in 1 ml of PBS for 10 min. The suspension was serially diluted in PBS and 25 µl of each dilution were plated onto Drigalsky gelosis. Colonies were numbered 24 h later and the data were expressed as CFU/g of stool. By molecular quantification. Genomic DNA was extracted from mice stools using Nucleospin Soil kit (MACHEREY-NAGEL) following the manufacturer's instructions. The 16S rDNA was targeted by qPCR for quantification of E. coli by TaqMan approach using Dream Taq Master Mix (Thermo Fischer Scientific). The data are expressed as the quantity of 16S E. coli relative to total 16S Eubacteria. Primers used and PCR mix are specified in Supplementary Table  S5.
One hundred µL of PE-conjugated mouse anti-IgA (IgA Monoclonal Antibody (11-44-2), PE, eBioscience; diluted 1/12. 5 in staining buffer) were added to each tube and staining was performed on ice for 30 min. Three washes were performed with staining buffer before analysis on a BD LSRII. Data were then analyzed with BD Facs Diva and FlowJo. The PE positive gate was placed according to isotype control staining. intestinal epithelial cells enrichment. CEACAM6 specific CpG methylation level was measured on enriched-IECs from colonic mucosa. Colons were flushed and washed 3 times with ice-cold PBS. 0. 5 cm colonic sections were incubated in PBS-EDTA 2 mM for 1 h at 4 °C with slow agitation. PBS-EDTA was removed and the colonic sections were washed 3 times with PBS before release of colonic crypts by 1 min hand shaking.
17 µM of a specific primer related to CpG5. Thermocycling conditions were: 94 °C for 5 min followed by 35 cycles of 94 °C for 10 s, 54 °C for 5 s and 60 °C for 10 s. The extended primers labelled with different fluorescent dyes were run on an ABI 3,500 capillary electrophoresis instrument and analyzed with GeneMapper software (Applied Biosystems, Evry, France). Peak area ratios were calculated to measure the relative percentage of methylation. This protocol was adapted from  31.
Statistical analysis. Values are expressed as the mean ± SEM of 'n' number of experiments or median. Statistical analysis were performed using GraphPad Prism 6 for Windows version 6. 07 (GraphPad Software, San Diego, CA, USA) software package for PC. Single comparisons were performed by unpaired Mann-Whitney. One way ANOVA was used when specified. A value of p < 0. 05 was considered as statistically significant. Twosided Spearman test was used for correlations analysis.
Acknowledgements This study was supported by the  Ministère de la Recherche et de la Technologie ,  Inserm  ( UMR 1071 ),  INRAe  ( USC-2018 ),  Région Auvergne Rhône Alpes ,  FEDER  ( AAP Nouveau Chercheur-1640NCAA-AV0016772 ),  Fondation pour la Recherche Médicale  ( FRM, "Contrat doctoral", ECO201906008953 ) and by the  French government IDEX-ISITE  initiative  16-IDEX-0001  ( CAP 20-25 ), the  National Program "Microbiote" Inserm , and  Association François Aupetit (AFA). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript. We would like to thank  Fanny De Clercq  who took part of this project,  Sandrine Brugiroux ,  Romain Villéger  and  Gwenaelle Roche  for technical help and  Abdelkrim Alloui  from animal facility.
A high stress treatment level of 225 ppb was selected as an extreme condition. This is higher than most in situ observations, but close to the 300 ppb level that has often been used in previous reports on ozone-stress studies to produce a strong, reproducible physiological response in model plants  [41] [42] [43]. By investigating O 3 stress involved in oak decline, unique molecular-level stress responses by NRO can be determined. Finally, de novo assembly of the RNA sequence data followed by functional annotation of the differentially expressed transcripts was conducted to build a catalog of transcripts in response to O 3 stress for NRO.
Results Transcriptome sequencing output, de novo assembly and transcriptome quality More than 334 million raw reads were generated, including 639 Mb from the 454 platform, 2. 5Gb from the Illumina MiSeq platform, 23. 1Gb from the Illumina Hiseq 2000 platform and 42. 3Gb from the Illumina HiSeq 2500 platform. RNA libraries were sequenced from a wide variety of NRO tissues to provide good coverage of the gene space (334,073,559 reads) (Additional file 1: Table  S1 ). To produce a high-quality reference transcriptome, only the longer reads (originating from 454 and Illumina MiSeq) were used for assembly while the data generated from the HiSeq 2500 platform were used exclusively for differential gene expression analysis. After trimming low-quality bases, adapter removal, transcriptome assembly, and removal of redundant sequences, 52,662 putative transcripts with an average length of 778 bp and N50 length of 1244 bp were generated (Additional file 2: Fig.
S1 ). Transdecoder predicted an open reading frame (ORF) in 38,610 (73%) of the putative transcripts. In order to verify completeness of the transcriptome assembly, putative transcripts were >compared with the Embryophyta database of orthologs (n = 1440) by BUSCO; 988 (68. 6%) of the single-copy orthologs have a complete match within the oak transcriptome sequences. Another 166 (11. 5%) of the singlecopy orthologs were found as fragments, and 286 (19. 9%) were missing from the oak transcriptome assembly. While no reference genome is available for Q. rubra nor any other species from the red oak clade (subgenus Quercus sect. Lobatae)  [44] , three reference genomes from oak species in other clades are available: Q. lobata (Quercus sect. Quercus)  [45] , Q. robur (Quercus sect. Quercus)  [46] , and Q.
suber (Cerris sect. Cerris)  [47]. To assess sequence divergence between this NRO assembly and gene models from the reference genomes, read mapping through Conditional Reciprocal Best BLAST was performed. The proportion of NRO putative transcripts with a match to a gene model in the three oak species genomes was 68. 2% to Q. lobata, 82. 4% to Q. robur, and 66% to Q. suber, revealing no clear pattern of gene conservation associated with taxonomic relationship. It will be interesting for subsequent phylogenomics studies to determine if the variation in NRO putative transcripts mapping frequency among the species is different among sections of the genus Quercus reflects evolutionary distances versus quality and completeness of gene annotations among reference genomes.
Sequence annotation Homology-based functional assignments were obtained for a total of 37,535 and 37,880 putative transcripts from NCBI and IPS databases, respectively. Integration of results from both databases yielded annotation for 42,703 (81%) of the putative transcripts. The most common protein matches from NCBI BLAST originated from other woody plant species: Juglans regia, Ziziphus jujuba, Theobroma cacao, Prunus persica, and Vitis vinifera. Although an E-value cut-off of 1e-5 was used for the BLAST alignments, the majority of the sequence hits were strongly supported by much lower E-values (Additional file 2: Figure  S1 ). Gene Ontology (GO) terms were assigned to a total of 29,528 (69. 1%) annotated putative transcripts. To give a broad overview of annotations, GO term assignments were re-mapped to secondtier GO terms, yielding 70 total terms (Additional file 3: Figure  S2 ), which included: 21,623 putative transcripts that were assigned to terms in the biological process ontology (BP), 20,073 putative transcripts that were assigned to terms in the cellular component ontology (CC), and 24,819 putative transcripts that were assigned to terms in the molecular function (MF) group.
Analysis of DEGs The high depth RNA sequencing data was used to profile alterations in gene expression caused by O 3 stress. Significant DEGs between treatment and control tissue samples were defined at an adjusted p-value cut-off of 0. 05 and |log2 (fold change)| > 1. Two-year-old NRO seedlings were exposed to four levels of O 3 (control, 80 ppb, 125 ppb, 225 ppb), and leaf tissue samples from four biological replicates were taken at three time points  (7 h, 14d, 28d). The leaves at the control and 80 ppb levels appeared similar, with no visual injury. Injury was noted at the 125 and 225 ppb exposures. Leaves had the dark red interveinal stippling that is characteristic of moderate O 3 damage of hardwoods (Additional file 4: Figure  S3 ). Across all elevated O 3 treatment levels, 4136 DEGs were detected with 2142 transcripts upregulated and 1994 downregulated (Table  1 ).