dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, unambiguous signatures of intrinsic variabil-ity on a timescale of years can be seen directly in the visibility data (Section 2. 4). Because GRMHD simulations naturally model both the source structure and its variability, they provide an important pathway for drawing conclusions from the observed variability. As a preliminary demonstration for a comprehensive study that will be published separately, we consider a small subset of the EHT Image Library (EHTC V). The simulations are parameterized with the black hole spin a * , the electron temperature parameter R high (see Section 4. 1) and the accretion state -strongly magnetized magnetically arrested disk (MAD,  Narayan et al. 2003) , or standard and normal evolution (SANE) flow, such as those considered in Sections 4. 1 and 6. 1.
A separate concern is whether the orientation of crescent models fitted to the VLBI data is consistent with the image domain η (Equation  11 ). In the case of the synthetic data sets considered in Section 4, the two GRMHD data sets exhibited quite large biases while the two MODEL data sets showed a high level of consistency, so this issue requires further study. Characterizing GRMHD simulations in terms of VLBI observables is the subject of continued work.
ACKNOWLEDGEMENTS The authors of the present paper thank the following organizations and programs: the  Academy of Finland  (projects  274477 ,  284495 ,  312496 ); the  Advanced European Network of E-infrastructures for Astronomy with the SKA (AENEAS)  project, supported by the  European Commission   Framework Programme Horizon 2020 Research and Innovation action  under grant agreement  731016 ; the  Alexander von Humboldt Stiftung ; the  Black Hole Initiative at Harvard University , through a grant ( 60477 ) from the  John Templeton Foundation ; the  China Scholarship Council ;  Comisión Nacional de Investigación Científica y Tecnológica (CONICYT, Chile , via PIA ACT172033, Fondecyt projects  1171506  and  3190878 ,  BASAL AFB-170002 , ALMA-conicyt 31140007);  Consejo Nacional de Ciencia y Tecnología  ( CONACYT, Mexico , projects  104497 ,  275201 ,  279006 ,  281692 ); the  Delaney Family via the Delaney Family John A.
The SMT is operated by the  Arizona Radio Observatory , a part of the  Steward Observatory of the University of Arizona , with financial support of operations from the  State of Arizona  and financial support for instrumentation development from the  NSF. The SPT is supported by the  National Science Foundation  through grant  PLR-1248097. Partial support is also provided by the  NSF Physics Frontier Center  grant  PHY-1125897  to the  Kavli Institute of Cosmological Physics at the University of Chicago , the  Kavli Foundation  and the  Gordon and Betty Moore Foundation  grant  GBMF 947. The SPT hydrogen maser was provided on loan from the GLT, courtesy of ASIAA. The EHTC has received generous donations of FPGA chips from  Xilinx Inc. , under the  Xilinx University Program. The EHTC has benefited from technology shared under open-source license by the  Collaboration for Astronomy Signal Processing and Electronics Research (CASPER).
MODELING APPROACHThe sparse nature of the pre-2017 data sets precludes reconstructing images in the manner employed for the 2017 data (EHTC IV). However, the earlier data are still capable of providing interesting constraints on more strictly parameterized classes of models. Figure5shows the 2013 data set overplotted with a best-fit ring model 3 (in blue; 5 degrees of freedom) and asymmetric Gaussian model (in red; 4 degrees of freedom). Both models attain similar fit qualities, as determined by Bayesian and Akaike information criteria (see, e. g. , Liddle 2007 ). 3 This is the maximum likelihood estimator for the slashed thick ring model (RT), as discussed in Section 3. 1.
Background: There is an urgent need to rapidly distinguish COVID-19 from other respiratory conditions, including influenza, at first-presentation. Point-of-care tests not requiring laboratory-support will speed diagnosis and protect health-care staff. We studied the feasibility of using breath-analysis to distinguish these conditions with near-patient gas chromatography-ion mobility spectrometry (GC-IMS). Methods: Independent observational prevalence studies at Edinburgh, UK, and Dortmund, Germany, recruited adult patients with possible COVID-19 at hospital presentation. Participants gave a single breathsample for VOC analysis by GC-IMS. COVID-19 infection was identified by transcription polymerase chain reaction (RT-qPCR) of oral/nasal swabs together with clinical-review. Following correction for environmental contaminants, potential COVID-19 breath-biomarkers were identified by multi-variate analysis and comparison to GC-IMS databases.
Participants were excluded if they were not breathing spontaneously and/or receiving respiratory support. Each patient was swabbed on presentation for RT-qPCR testing and admitted if clinically indicated. Patient clinical data were collected using the Research Electronic Data Capture web application  [15] , see Table  1. The RT-qPCR test was discarded if patient opt-out from the study, according to the study protocol. Dortmund participants were similarly recruited between April 11 and June 18, 2020, from patients presenting to the emergency department or outpatient clinic exhibiting respiratory symptoms. Each participant was swabbed on presentation or within 24 h of admission for RT-qPCR testing. Patient clinical data were collected using a questionnaire and recorded in Excel, Table  1. A COVID-19 diagnosis was made by senior physicians (MJR and KB in Edinburgh and by FH, RE, OM, MT and SY in Dortmund) based on clinical presentation, q-PCR test results and radiological findings.
The procedure is not considered an aerosol-generating procedure (AGP)  [16]. On completion of exhalation, a 5 cm 3 sample of their end-tidal breath was withdrawn into the syringe, the syringe immediately removed from the sampler, connected into the GC-IMS inlet, and 1 cm 3 automatically injected on to the GC column for analysis. Quality control (QC) covered 15 spectral and operational parameters assessed from a daily environmental sample collected every morning (RIE) or for every breath-sample (KD; Table  S  2). The breathacetone signal was also assessed to confirm that sampling occurred correctly -a weak or missing acetone signal indicating a fault in sample collection and/or analysis, resulting in data being excluded.
Data modelling The workflow for data processing and statistical analysis is shown in Fig. 1. Edinburgh data were extracted into a. csv format file using GC-IMS data processing software (LAV G. A. S. , Dortmund) with normalised relative drift time, to compensate for between-run instrument variation  [17, 18]. The data were then processed using MATLAB to generate integrated signal volumes for every isolated-feature and these were placed into a 2-dimensional matrix for further datamodelling. An average of 80 features were extracted from each breath-sample. Dortmund data were analysed with G. A. S. VOCCal software (Gas Dortmund) with retention times and drift time normalisation to compensate for the inter-run instrument variation. Signal peaks were integrated manually. Some features, such as methanol, were obscured by the reactant ion peak signal; this complication was resolved by subtraction of the reactant ion peak signal.
Approximately, 80 features were extracted. from each breath-analysis. A comparison of example breath-data obtained from the two centres is shown in Fig. 2. The drift-time aligned breath-data from the 33 Edinburgh participants were placed into a data-matrix and candidate COVID-19 breath-markers identified from a three-step multi-variate analysis data work-flow, based on the use of the SIMCA-P+ software with integrated 7-fold cross validation groups to protect against overfitting (Version 16. 1, Umetrics, UK). The work-flow comprised of: data-scaling, removal of non-discriminatory features, and isolation of discriminatory features. The log(10) data were Pareto-scaled before orthogonal partial least square discriminant analysis (OPLS-DA) was applied to identify and exclude non-discriminatory features from further analysis, identifying six exhaled VOC's candidate markers (Table  2 ).
At this stage data from 8 patients were excluded from further analysis due to either: a) no q-PCR tests results (n = 5) were available or there was no agreement between q-PCR test results and medical diagnosis (n = 3). Breath-data from 65 Dortmund participants were visually identified and placed into a peak gallery where they were statistically     2 ), The findings from the two independent studies were shared and an adjudicated VOC panel selected for running a PCA stratification model (Table  2 , Figs. 1À3) with participants with definite diagnosis, for whom q-PCR and the adjudicated diagnosis agreed (90 participants: 25 RIE and 65 KD). Finally, the data obtained from PCA models, was assessed with hierarchical clustering analysis which categorizes similar samples in the same categories. The results are presented in the form of the dendrogram, based on Ward's minimum variance method, sorted by score size.
Results Ninety-eight patients were recruited, of whom 21/33 (63. 6%) and 10/65 (15. 4%) were adjudicated to have COVID-19 in Edinburgh and Dortmund, respectively (Table  1 ). The diagnoses of non-COVID-19 patients included exacerbation of asthma and COPD (RIE 9. 1%, KD 3. 1%), bacterial pneumonia (RIE 3. 0%, KD 4. 6%), and others including cardiac conditions (RIE 24. 3%, KD 18. 5%). 69. 1% of those testing negative at Dortmund were returned to primary care and did not receive a formal hospital diagnosis. Six VOC in the Edinburgh GC-IMS data: ethanal, octanal, acetone, acetone/butanone mixed cluster, and methanol (level 2 identification  [19]  against 2014 NIST-RI data base and    2 , Fig. 3 ). An additional, as yet unidentified, VOC (Feature 144) was also noted at a retention time of 583 s and relative drift time of 1.
The identification of similar discriminator chemistry across the two different groups is an additional observation on the attributes of two independent sites with different cohorts with independent testing and data processing methods arriving at a single conclusion. We found preliminary data of a relationship between breath-biomarkers on presentation À in self-ventilating patients -and COVID-19 severity over their hospital stay. However, the sample-times in this study were not coordinated against onset of symptoms, since participants were recruited on their presentation on hospital, meaning that samples were obtained from participants at different stages of COVID-19. Experiments with time series sampling, involving a larger COVID-19 cohort, will be required to determine whether breath-analysis is able to reliably predict severity and progressive COVID-19.
However, GC-IMS is vulnerable to background contamination; care is therefore needed in selecting where to site the instruments and how to operate them to mitigate this issue. Disinfection and cleaning routines require careful management. Diet does influence exhaled VOC's and has the potential to produce confounding data and false discovery if not carefully managed. In these studies, dietary factors were dispersed and randomised across both study sites and across each day. Individuals' dietary signatures were processed out during the statistical modelling stage and the absence of a systematic sampling schedule prevented the introduction of a dietary artefact through inadvertent synchronisation with meal times.
Feature 144 was markedly elevated in the most serious cases (figure S 4) suggesting it might be useful as a marker of severity. GCÀMS studies are required to identify it because it was not found in current IMS databases  [28]. Its identification may provide useful information for understanding the unusual severity of COVID-19 compared to other viral pneumonias  [14]. In conclusion, we report here that near-patient GC-IMS analysis detects changes in breath-signatures of COVID-19 patients that may be able to differentiate them from non-COVID-19 respiratory and cardiac conditions and that are consistent with the disease's multi-system manifestations. Larger studies are required to test this hypothesis, together with complementary GCÀMS studies. If shown to be reliable, it offers the possibility for rapid identification or exclusion of COVID-19 in emergency departments or primary care that will improve management of patients and safety of healthcare staff.
Acknowledgements The authors acknowledge gratefully: the staff and patients of the  Royal Infirmary of Edinburgh and Klinikum Dortmund  for their support and participation in this study;  Kerry Rosenthal  for statistical consultation; and  Chief Scientist Office, Scotland for Core R&D  funding. EB reports grants from EDRF Priority 1 delivered through the  Welsh Government  (SMARTCymru 2014À2020 West Wales and the Valleys;  2020/ED/081 ) and personal fees from  IMSPEX Diagnostics Ltd , during the conduct of the study. Data Sharing. All the experimental data, csv extracted data files and processed data may be accessed on request to CLPT.
Effects of ACE inhibitors on intra-tumor immune cells composition To gain further insight on the molecular mechanisms underlining these observations, RNA sequencing was performed for 29 patients (26 from the no ACE inhibitor group and 3 from the ACE inhibitors group). We assessed immune cell composition in this cohort using CIBERSORT, a bioinformatics algorithm used to evaluate immune cell composition based on gene expression profiles. This software was able to estimate 19 immune cell types on the 22 available. We observed that the levels of M1 macrophages, activated mast cells, NK cells, eosinophils and memory CD4 activated T cells were nearly undetectable in ACE inhibitors group in comparison to control group (Figure  3 ). While this tendency is not significant (due to the low number of patients in ACE inhibitors group), it suggests a negative effect of ACE inhibitors on immune response.
Patients All patients with metastatic NSCLC (n = 178) at Georges Francois Leclerc center, Dijon, France treated with ICIs were identified based on chart records between June 2013 and December 2018, until data lock of August 2019. Patients treated in the first line setting with pembrolizumab or in second or further lines with nivolumab or durvalumab were included. Medical records were retrospectively analyzed to determine any oral or intravenous concomitant drug use during the treatment with anti PD-1 mAb. Use of cardiac, diabetesrelated drugs, antibiotics (longer than 7 d) or corticoids (higher than 10 mg equivalent prednisolone, for more than 7 d) starting after the beginning of anti PD-1 therapy were recorded. Clinicopathological characteristics were collected for all patients. Tumor response was evaluated by computed tomography (CT) scan, every four cycles.
RNA sequencing analysis Total RNA was extracted from formalin-fixed paraffinembedded (FFPE) tumor slices (5 × 5 µm) using the Maxwell 16 LEV RNA FFPE Purification kit (Promega), following manufacturer's instructions. Libraries were prepared from total RNA with the TruSeq Stranded Total RNA using Ribo-Zero (Illumina) according to manufacturer's instructions. Once qualified, paired-end libraries were sequenced using 2 × 75 bp output on a NextSeq 500 device (Illumina). The abundance of transcripts from RNA-seq data was quantified through the kallisto program. 45 Quality control was assessed through FastQC software (http://www. bioinfor matics. babraham. ac. uk/projects/fastqc). All quality controls were checked for the 29 patients. Gene-level count matrices were then created with the DESeq2 library.
46 Low-count genes were pre-filtered by removing genes with less than 10 reads. Data were normalized as TPM (transcript per million reads). Data were normalized as TPM (transcript per million reads). Corresponding data are available upon request. Immune infiltrates were evaluated using CIBERSORT LM22 software. Gene expression data with standard annotations were uploaded to the CIBERSORT web portal (http://cibersort. stanford. edu), and the algorithm was run using the LM22 signature and 1000 permutations. 47.
Statistical analysis Patients' characteristics were described according to hospital and compared using Fisher or chi-squared test for categorical data and t-test for continuous data. Patient's characteristics were described according to angiotensin converting enzyme inhibitor (ACE) use. Progression-free survival (PFS) was calculated as the time from the date of treatment initiation to the first recorded evidence of disease progression by CT scan, clinical evaluation or death. Overall survival (OS) was calculated as the time from the date of treatment initiation to the date of death whatever the cause. Survival curves were estimated using the Kaplan-Meier method and compared with the Log-rank test (univariate analysis). Univariate and multivariable Cox regression models were used to determine HRs and 95% confidence intervals (CIs) for PFS and OS.
Acquisition was performed on a Cytoflex 13 C cytometer (Beckman Coulter) and cells were analyzed using Cytexpert software (Beckman Coulter). The Mean Fluorescence Intensity (MFI) of specific markers was studied. Substance P concentration was evaluated in cell supernatants using substance P ELISA assay (KGE007 -R&D systems) according to manufacturer's instructions. Dataset comparisons were performed with GraphPad Prism 8, using paired Student's t tests (test group compared to control group). All P values were two tailed.
Cells were then stained with a APC anti-mouse CD45 (Miltenyi Biotec, 130-110-660), a BV650 anti-mouse Ly6G (127,641), a BV785 anti-mouse CD11b (101,243), a BV605 anti-mouse F4/80 (123,133), a A488 anti-HLA-DR (107,615), a APC-A700 anti-mouse CD206 (141,734) from biologend and Fixable Viability Dye (FVD) eFluor 780 (eBioscience) for 15 minutes at RT. After washing, cells were analyzed with a CytoFlex (Beckman Coulter) and a CytExpert software. TAMs were characterized with a CD45 + , CD11b + , Ly6G -, F4/80 + , HLA-DR + phenotype and CD206 MFI was measured in these cells.
13 C NMR analysis The 13 C NMR analyses were used to identify some compounds notably thymoquinone, carvacryl methyl oxide and thymohydroquinone. These analyses were performed using a Bruker AVANCE 400 Fourier Transform spectrometer operating at 100. 63 MHz for 13 C and equipped with a 5 mm probe, in deuterated chloroform (CDCl 3 ), with all shifts referred to internal tetramethylsilane (TMS). 30 mg of essential oil diluted in 300 ml of CDCl 3. 13 C NMR spectra were recorded with a pulse width (PW) of 5 ms (flip angle 45°), an acquisition time of 2. 7 s for 128 K data table with a spectral width (SW) of 25,000 Hz (250 ppm) and digital resolution 0. 183 Hz/pt. The number of accumulated scans was 2500 for each sample (about 50 mg of EO in 0. 5 ml of CDCl 3 ).
elongatum. Our study highlights that it can be very useful to investigate separately several individual plants from different populations of one species, in order to reveal the different chemical groups within and between populations and to detect certain rare compounds. On the other hand, the geographical and climatic data constitute important parameters for a better understanding of the biosynthesis of EOs of plants occurring under different conditions, even if it's difficult to generalize the potential effects. This work provides also a great database concerning the chemical polymorphism of EOs of the endemic O. elongatum. The important chemical variability should be considered an advantage to select the best genetic material to be involved in any breeding program for this endemic species.
The percent reduction of AK lesions is also a relevant endpoint as it takes into account the number of lesions at baseline, a factor that is known to influence the complete clearance rate  [10, 11]. However, it is not clearly shown how the number of lesions at baseline can influence the achievement of clearance. The present analysis used data from a previously reported systematic literature review (SLR) (Ezzedine et al. , forthcoming manuscript, 2020) to assess the effect of baseline lesion count on the complete clearance rate achieved with different 5-fluorouracil (5-FU) formulations, alone or with 10% salicylic acid solution.
Materials and methods The conduct of the systematic review has been reported in detail elsewhere (Ezzedine K, et al. , forthcoming manuscript, 2020). In brief, published reports of randomized controlled trials (RCTs) in adult patients (≥18 years) with AK affecting the face, ears, or scalp and receiving field-directed therapy  (  Further potential trials were identified by manual searching of relevant conference proceedings, bibliographies of included publications and ClinicalTrials. gov. In addition, information on four pivotal trials with 5-FU 4%, two of which have been reported previously  [12] , was provided by the marketing authorization holder.
Trial characteristics Seventy-five eligible RCTs reported in 151 publications were included in the SLR, of which 31 met the feasibility criteria for inclusion in quantitative comparisons of 5-FU, and 12 assessed 5-FU (4%, 5%, and 0. 5%) interventions in AK (Figure  1 ). Nine of the 12 trials included data for baseline lesion count and complete clearance rate, and five of these trials met the criteria in terms of evaluation of treatment effect at 4 weeks off treatment (Figure  2 )  [13] [14] [15] [16] [17]. A summary of these trials is shown in Table  2. The five trials were phase 2 or 3 trials with either a single-or double-blind design. Treatments included 5-FU 4% or 5% versus placebo  [15, 17] , 5-FU a 128 non-priority trials from database searching and 15 trials from ClinicalTrials. gov that were included, but not extracted.
The Task Force on Measurements and Modeling (TFMM-CLRTAP) therefore initiated an assessment of the added value of paired urban and regional-remote sites in Europe. Experimental data from paired sites were used to allocate urban pollution to the different spatial scale sources. The paired sites selected for this study provided chemically speciated PM 10 or PM 2. 5 data simultaneously measured at urban-traffic and regional-remote sites. In some cases, (e. g. Spain, ES) these measurements were continuously performed over long periods, whereas in other cases the measurements were performed for a limited time period. The periods presented here were comparable in Switzerland  (CH, 2008 (CH, -2009) )  and the Netherlands  (NL, 2007 (NL, -2008)) , whereas more recent data were used for Spain  (ES, 2010 (ES, -2014)) , Germany  (DE, 2013 (DE, -2014) )  and France  (FR, 2013 (FR, -2014)).
PMF model PMF (EPA PMFv5. 0) was applied to the collected daily PM speciated data for source identification and apportionment. PMF was applied to the PM chemically speciated data from ES, CH, and FR. For NL and DE, we used the PMF analysis already presented in  Mooibroek et al. (2011)  and  van Pinxteren et al. (2016) , respectively, and then apply the Lenschow approach to the PMF outputs. Detailed information about PMF can be found in the literature (e. g. Paatero and Tapper, 1994; Paatero, 1999; Paatero and Hopke, 2003, 2008; Hopke, 2016). PMF is a factor analytical tool that reduces the dimension of the input matrix (i. e. the daily chemically speciated data) to a limited number of factors (or sources). It is based on the weighted least-squares method and uses the uncertainties of the input data to solve the chemical mass balance equations.