dataesr/scientific-paragraphs-categorization huggingface dataset:
To reduce the error associated with low frequency sequences, and also focus analyses on predicted levels of richness  67  , we used two types of analysis. First, we identified the frequency of potential contaminant reads in the positive control. Second, we compared chironomid eDNA richness with variable levels of relative abundance filtering (no filtering, 0. 01 and 0. 02%), against historical records of richness (genus level only available) for Llyn Padarn (based on summer surveys for Llyn Padarn, 2003-2013). Consequently, abundance filtering was performed on the OTU tables at the level that most closely emulated expected chironomid richness and within the limits associated with empirically observed low-level contamination in the sequencing dataset. The validity of the Chironomidae OTUs identified by BLAST and retained after abundance filtering was checked using a phylogenetic approach.
To assess variation of richness over time polynomial regression was performed using R version 3. 2. 4 (2016). The PRIMER-E software  70  was used to calculate b-diversity based on the Sørensen index for total diversity and Animalia only diversity detected from aqueous eDNA samples and for Chironomidae OTUs for both sample types. Non-metric multi-dimensional scaling (nMDS) and Hierarchical Clustering (HC) analysis were used to represent community similarity between samples. Analysis of similarity (ANOSIM) was used to test for significant effects of time in relation to community composition. Chironomidae OTU read abundance (eDNA versus community DNA). To explore relationships between the numbers of metabarcoding sequence reads, individual OTUs and methodology (eDNA versus community DNA), we used a generalized additive model (GAM), with time as a smoothing term, using the R-package mgcv  71.
In the GAM model, abundance, calculated as total normalized reads per OTU and standardized per method (to allow for across method comparison), was assessed in relation to OTU identity and method (eDNA versus community DNA). In addition, we assessed the ecological relationship between OTU abundance (log transformed) in Llyn Padarn and species frequency (that is, abundances derived from ecological assessment) across the UK, by performing a two-way ANOVA, using the lm function in R. UK species frequencies were derived from a Chironomidae inventory of 435 species across 220 UK lakes  37. We restricted the species frequency data to 97 sites where species frequency was inventoried at the national level and observed in this study.
Acknowledgements This work was funded by the  Environment Agency (EA) UK , a  Knowledge Economy Skills Scholarship  (KESS), a  Natural Environment Research Council (NERC) NBAF  pilot project grant ( NBAF824 2013-14 ) and the  Freshwater Biological Association (FBA ;  Gilson Le Cren  Memorial Award 2014). We thank the  EA and Bangor University  for support and in particular,  Wendy Grail ,  John Evans ,  Emlyn Roberts  and  EA  staff for facilitating provision of eDNA grade laboratory working spaces, equipment, and taxonomic identification of chironomid specimens;  High Performance Computing Wales  for allowing use of their systems;  Les Ruse  and APEM for identification of Chironomidae specimens for Barcoding;  Natural Resources Wales  for providing historical data for Llyn Padarn. We also acknowledge the support of  NERC  Highlight  Topic grant   NE/N006216/ 1.
Patients and sample collection Patients with severe burns were admitted to Edouard Herriot Hospital (Lyon, France) and included in a placebo-controlled, randomized, double-blind study that has been described elsewhere  [20]. Patients aged between 18 and 75 years, with a total burn surface area >30% were included if they presented with onset of severe shock (norepinephrine >0. 5 μg/kg/min) between 24 and 72 h after injury. Pregnancy, trauma, initial sepsis and cardiac insufficiency were exclusion criteria. The protocol was accepted by the ethical committee on 15 February 2005 and was registered at ClinicalTrials. gov (NCT00149123). All healthy volunteers (HV) and patients (or next of kin) gave written informed consent before inclusion in the study. Thirty-two patients were enrolled in the clinical study.
Microarray analysis Microarray normalization and statistical analysis were performed using R/Bioconductor (R v3. 0. 0)  [21, 22]. Quality assessment was performed using the simpleaffy (v2. 36. 1)  [23]  and arrayQualityMetrics (v3. 14. 0)  [24]  packages. The simpleaffy package provided quality controls before the normalization process by checking density distributions, estimating the average background intensities and assessing the RNA quality measures. The arrayQualityMetrics package generated a report of quality metrics from both raw data and normalized microarray data. We used principal component analysis  [25]  to identify poor-quality arrays. After removing outlier samples the raw data were normalized, adjusted for background noise and summarized using the guanine cytosine robust multi-array (GCRMA) algorithm with default parameters  [26].
Batch effects were removed using COMBAT  [27]. Filtering was performed based on MAS5. 0 p values. MAS5. 0 p values were estimated for each probe set, corresponding to significant differences between perfect match and mismatch intensities. Then we kept only probe sets for which all p values were <0. 2. We finally applied a batch correction using COMBAT to remove the effect of varying delays between admission and each sampling. MIAME-compliant microarray data are available on the Gene Expression Omnibus (GEO) website [GEO:GSE77791].
Microarray statistical analyses The first analysis compared samples obtained from patients before they received any treatment to samples from HV. For all probe sets we performed moderated t tests (Limma package version 3. 16. 0) [28] ; we adjusted the p values for multiple testing using the Benjamini-Hochberg correction  [29]  to ensure a false discovery rate below 5%. Adjusted p values <0. 05 were considered significant. We then assessed gene modulation associated with burn shock across time, using only microarrays from patients on placebo. We performed moderated F tests  [28]  and corrected for multiple testing using the Bonferroni correction. Adjusted p values <0. 05 were considered significant. Finally we assessed the impact of hydrocortisone on gene expression over time. We first performed moderated t tests to compare the two groups at each sampling (Benjamini-Hochberg correction  [29] , adjusted p value <0.
Functional annotation analyses Functional analyses were performed using Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, Redwood City, USA) and Gene Ontology (GO)  [30]. For the core analysis all differentially expressed probe set ID were mapped to their corresponding gene and molecular objects in the IPA Knowledge Base. Fisher's exact test was used to estimate p values for diseases and biological function enrichment analyses, and the p values were adjusted using the Benjamini-Hochberg method  [29]. To assess statistical significance in pathway analyses, we used repeated measure analysis of variance (ANOVA), with one between-group variable (treatment) and two within-group variables (time and probe) with the following model: Expression value e Treatment þ Time þ Probe þ Treatment : Time The modulation of pathway expression over time and according to treatment was considered significant for p values <0.
Statistical significance of the observed differences was assessed using repeated measures analysis of variance taking into account treatment, time and gene (probes) effects. We observed significant modulation over time (p value < 10 -6 ), and according to hydrocortisone administration (p value < 10 -6 ). S1 to S4 sample times day 0, day 1, day 5 and day 7, respectively mesenchymal cells, respectively. Regarding the blood transcriptome of burn patients, a first dataset (GSE19743, buffy-coat samples) from 57 patients and 63 healthy volunteers (analyzed in  [37] [38] [39] ) showed that genes modulated within 10 days after injury were mainly related to immunity. Genes modulated at a later stage (11-49 days) were also involved in metabolism and apoptosis  [39]. Our results were consistent regarding the identified functional pathways.
We showed that modulation was triggered early after burns as most genes were already modulated within 72 h of injury. A second dataset described the pooled-leukocytes transcriptome of 112 burn patients sampled within 7 days  [40]. This study assessed prognostic factors and found a 39-gene signature associated with a burn size >40%. These genes were related to platelet activation, TNF production, cellular adhesion, migration, and degranulation. Interestingly, our dataset shared 8 out of the 10 most modulated genes (LCN2, LTF, THBS1, ITGA2B, CD24, TCN1, BPI and SLC51A). Finally, we observed that most of the Fig. 3  Modulation of gene expression induced by hydrocortisone administration after burn shock. a Heatmap representation of expression in genes specifically modulated by hydrocortisone. Gene expression (rows) is color-coded from blue to orange.
Acknowledgements We thank  Dr Marc Bertin-Maghit ,  Dr Christophe Magnin , and  Dr Laure Fayolle-Pivot , for their help in recruiting the patients in the prospective study. This project is part of  Advanced Diagnostic for New Therapeutic Approaches (ADNA) , a program dedicated to personalized medicine, coordinated by  Institut Mérieux  and supported by the  French public agency BPI France.
19. Briefly, the DNA methylation age is computed as a weighted average of the age-related CpGs, with weights defined using a penalized regression model (Elastic-net regularization)  18. The few missing values were imputed using the k-nearest neighboring (KNN) imputation algorithm implemented in the R Bioconductor package impute  43. Age acceleration (AA) was defined as the difference between epigenetic and chronological age. Positive values of AA (that is epigenetic age is higher than the chronological age) indicate accelerated aging and vice versa. Since AA could be correlated with chronological age and WBC percentage, we computed the so-called 'intrinsic' AA  20  , defined as the residuals from the linear regression of AA with chronological age and WBC percentages. The latter were estimated using the Houseman algorithm  44.
Finally, the associations of SES with AA were further verified after exclusion of incident cancer/cardiovascular cases in EPIC Italy, and individuals exposed to at least one NCD risk factor, leading to the analysis of 1,549 NCD risk factor free individuals. Raw methylation data may be obtained upon request to giovanni. fiorito@iigm. it. All participants gave written informed consent for their samples to be used in genetic and epigenetic studies of health. This study was reviewed and approved by the HuGeF Ethic Committee. This study was conducted following the principles of the Declaration of Helsinki and its subsequent revisions.
Acknowledgements This research was supported by the 'Lifepath' grant to  Paolo Vineis  at  Imperial College, London, Silvia Polidoro at the HuGeF Foundation, Silvia Stringhini at Lausanne's University Hospital ,  Mika Kivimaki  at  University College London ,  Richard Layte  and Cathal McCrory at the  Trinity College Dublin, Dublin, Ireland  ( European Commission   H2020  grant, Grant number:  633666 ). Giovanni Fiorito ,  Silvia Polidoro ,  Giuseppe Matullo  and  Simonetta Guarrera  are supported by the  Italian Institute for Genomic Medicine (IIGM) ,  Torino, Italy  with support from  Compagnia di San Paolo. Silvia Stringhini  is supported by the  Swiss National Science Foundation  ( Ambizione Grant  n°  PZ00P3_147998 ). Laura Baglietto  is supported by a  Marie Curie International Incoming Fellowship  within the  7th European Community Framework Programme.
Panagiotis Georgiadis  and  Soterios A. Kyrtopoulos  were supported by the  European Union  grant Envirogenomarkers (Grant number  226756 ). Mika Kivimaki  is supported by the  UK Medical Research Council  ( K013351 ) and  NordForsk. The MCCS component of the work was funded by the  Australian National Health and Medical Research Council , including project grant numbers  1011618 ,  1026892 ,  1026522 ,  1050198 ,  623206 , and  1043616 , and program grant numbers  209057  and  1074383.
MEthods: pArtIcIpAnts, IntErvEntIons And outcoMEs study setting The FLORALI-2 study is taking place in 28 mixed medical and surgical ICU in France (list on https:// clinicaltrials. gov/ ct2/ show/ NCT02668458? term= Florali+ 2& rank=1) belonging to the European REVA network and CRICS-TRIGGERSEP network.
MEthods: dAtA collEctIon, MAnAgEMEnt And AnAlysIs data collection and management Data are collected and recorded on an e-CRF by a trained investigator or research assistant at each centre. A blank copy of the e-CRF can be printed from the e-CRF; this enables the investigator or research assistant to fill it out with the data of the included patients, which will thereby be captured. Once data collection has been completed, the investigator or research assistant shall sign and date the copy. This document will constitute an integral part of the patient's medical records; as such, it shall be retained permanently. Data recorded in the e-CRF that originate in source documents must be consistent with each other; if they are not, the differences have got to be justified and documented. With the aim of homogenising measurement quality, the recording and collection of patients' data in all centres, a dedicated portable SpO 2 monitor (Covidien, Nelcor DS 100A) and single-patient-use digital sensors (Covidien, Max-A-I) will be provided to all the participating centres to ensure that all centres have equivalent monitoring.
Open Access Before intubation, the following data are collected and registered: demographic data, comorbidities, type of admission, diagnostic of respiratory failure, main reason for endotracheal intubation, oxygen support before the decision of intubation, clinical parameters, severity scores using the Simplified Acute Physiology Score (SAPS) II and the SOFA score at inclusion and 24 hours after admission, chest X-ray description (parenchyma involvement of the four quadrants, normal aspect, interstitial infiltrates, alveolar infiltration or condensation) and arterial blood gas. After randomisation, the following parameters are recorded: nature and number of operators and their training, clinical parameters, SpO 2 variations (from preoxygenation to H1 after intubation, at H6-12), arterial blood gases (30 min and at H6-12 after intubation), criteria of difficult intubation evaluated by the MACOCHA score  18  ; during the intubation procedure, lowest SpO 2 , drop <80% of SpO 2 , SpO2 ≥92%, SpO2 at 100%, feasibility of preoxygenation strategy, duration of preoxygenation, laryngoscopy, number of attempts, Cormack-Lehane grade,  19  traction force on laryngoscope, Sellick manoeuvre, modified Intubation Difficulty Scale score.
20 Immediate complications will be collected, including moderate complications, regurgitation (aspiration of gastric content), oesophageal intubation, cardiac arrhythmia (supra or ventricular arrhythmia) requiring therapy, dangerous agitation (Richmond Agitation-Sedation Scale score >3), dental injury; severe complications, arterial hypotension and treatments initiated, cardiac arrest, death; new infiltrate, gastric distension on postendotracheal intubation chest X-ray. During ICU stay or at day 28, the following data will be recorded: SAPS II, SOFA score (within 24 hours after admission), day 1 to day 3, ventilator-associated pneumonia, ICU length of stay, mechanical ventilation duration, ICU and mortality at day 28.
statistical methods All the analyses will be performed by the study statistician, in accordance with the International Conference on Harmonisation and Good Clinical Practice guidelines. A predefined statistical analysis plan will be followed. The analysis will be performed on an intention-to-treat basis after validation by a blind review committee of the inclusion and exclusion criteria for each patient. All the analyses will be conducted by the biostatistics department of the Poitiers University Hospital using statistical software (SAS, V. 9. 3; SAS Institute; Cary; North Carolina, USA, and R V. 2. 14. 1). A two-tailed P value of less than 0. 05 will be considered as indicating statistical significance.
Descriptive analysis of patient groups at baseline The continuous variables will be summarised with the classic parameters of descriptive analysis (median, interquartile ranges and extreme values or mean and SD), while indicating the number of missing data. The category variables will be presented in the form of absolute frequency and percentage in each modality. The criteria of subject selection will be verified on the basis of the data recorded in the case reports. Wrongly included subjects such as those lost to follow-up will be described. Deviations from the protocol will be described and analysed on a case-by-case basis.
EthIcs And dIssEMInAtIon consent or assent The patient is included after having provided a written informed consent to the investigator according to the decision of the central ethics committee. If the patient is not able to understand the information given, he/she can be included if the same procedure is completed with a next of kin. After the patient's recovery, he/she will be asked if he/she agrees to continue the trial. confidentiality Data will be handled according to French law. All original records will be archived at trial sites for 15 years. The clean database file will be anonymised and kept for 15 years.
Access to data All investigators will have access to the final data set. Participant-level data sets will be made accessible on a controlled access basis. dissemination policy Findings will be published in peer-reviewed journals and presented at local, national and international meetings and conferences to publicise and explain the research to clinicians, commissioners and service users. dIscussIon Severe oxygen desaturation, commonly defined as a drop in SpO 2 below 80%, is a life-threatening complication frequently encountered during intubation of critically ill patients in the ICU. 1 18  Preoxygenation is of great importance to prevent oxygen desaturation, especially in patients with hypoxaemia. 5 Although breathing through a valve-bag facemask is the usual procedure for preoxygenation in the operating room, other strategies have been proposed in ICUs, especially NIV in patients with acute hypoxaemic respiratory failure.
Radiation code ARTDECO The radiative transfer is calculated using ARTDECO (Atmospheric Radiative Transfer Database for Earth Climate Observation), a numerical tool developed at LOA (Lille University) which gathers several methods to solve the radiative Atmos. Chem. Phys. , 17, 10811-10835, 2017  www. atmos-chem-phys. net/17/10811/2017/ transfer equation and data sets (atmospheric profiles, optical properties for clouds and aerosols, etc. ) for the modelling of radiances and radiative fluxes in the Earth's atmosphere under the plane-parallel assumption. Data and a user guide are available on the AERIS/ICARE Data and Services Center website at http://www. icare. univ-lille1. fr/projects/artdeco. In this paper, the radiative transfer equation is solved using the discrete-ordinates method DISORT  (Stamnes et al.
, 1988)  in the solar spectrum (0. 25-4 µm) and the thermal spectrum (4-100 µm). The spectral resolution is 400 cm -1 in 0. 25-0. 69 µm, 100 cm -1 in 0. 69-4 µm and 20 cm -1 in 4-100 µm, which gives 303 wavelength bands in total. Gaseous absorption by H 2 O, CO 2 and O 3 is taken into account and represented by the correlated k-distributions  (Dubuisson et al. , 2005; Kratz, 1995). In ARTDECO, the coefficients of the k-distribution are calculated using a line-by-line code  (Dubuisson et al. , 2006)  from the HITRAN 2012 spectroscopic database  (Rothman et al. , 2013). The use of correlated k-distribution makes it possible to accurately account for the interaction between gaseous absorption and multiple scattering with manageable computational time. In addition, the impact of the absorption continua is modelled using the MT_CKD model  (Mlawer et al.
0641 Z (8) r eff = 23. 4 • 10 0. 0177 Z (9) These relationships were derived from aircraft measurements of the droplet spectrum in stratocumulus clouds, covering the range -40 to -20 dBZ. The relationships are not valid in the presence of drizzle, which strongly increases Z as droplets grow larger. Drizzle presence typically occurs when Z > -20 dBZ (e. g. Matrosov et al. , 2004). We therefore use the value of LWC and r eff obtained at Z = -20 dBZ for higher Z. The relationships are plotted in Fig. 2. LWC and r eff are estimated in each radar gate from cloud base to cloud top using these relationships, assuming no attenuation of the radar signal. For the lowest altitudes, where the radar data cannot be used, we apply the reflectivity of the lowest usable gate (usually at ≈ 50 m). The LWP of the MWR is then applied as a scaling factor to improve the estimate of LWC.
Acknowledgements. This research is supported financially by the  French ministry of defence -Direction Gérérale de l'Armement , and by the company Meteomodem. The authors would like to acknowledge  SIRTA  for providing the observational data used in this study, obtained in the framework of the  ACTRIS-2 project  (funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  654109 ). We thank the  ICARE Data and Services Center  for providing access to the ARTDECO model with associated data sets. ARTDECO has been developed with financial support (TOSCA programme) from the  French space agency, "Centre National d'Etudes Spatiales" (CNES). We thank  Jean-Baptiste Renard  for his help with the instrument LOAC, enabling us to measure the droplet size distribution in fog.
Data availability. Radar, ceilometer and radiosonde data as well as the measurements of radiative fluxes at 10 m, surface meteorological parameters and visibility are available from the SIRTA public data repository, which is accessible online at http://www. sirta. fr. The data policy and a data download are available from the website. The data from the MWR and the data used for calculating the sensible heat fluxes are available on request on the SIRTA website: http://sirta. ipsl. fr/data_form. html. The data and code of ARTDECO are available on the AERIS/ICARE Data and Services Center website: http://www. icare. univ-lille1. fr/projects/artdeco.
Background: Severely ill patients might develop an alteration of their immune system called post-aggressive immunosuppression. We sought to assess the risk of ICU-acquired infection and of mortality according to the absolute lymphocyte count at ICU admission and its changes over 3 days. Methods: Adults in ICU for at least 3 days with a shock or persistent low blood pressure were extracted from a French ICU database and included. We evaluated the impact of the absolute lymphocyte count at baseline and its change at day 3 on the incidence of ICU-acquired infection and on the 28-day mortality rate. We categorized lymphocytes in 4 groups: above 1. 5 × 10 3 cells/µL; between 1 and 1. 5 × 10 3 cells/µL; between 0. 5 and 1 × 10 3 cells/µL; and below 0. 5 × 10 3 cells/µL. Results: A total of 753 patients were included.