dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The project was funded by the  U. S. Department of Energy (DOE) Joint Genome Institute , a  DOE Office of Science User Facility , and supported by the  Office of Science of the U. S. DOE  under Contract No. DE-AC02-05CH11231  within the framework of the  Mycorrhizal Genomics Initiative  (CSP #  305 ),  Metatranscriptomics of Forest Soil Ecosystems project  ( CSP #   570 ), and the  1000 Fungal Genome projects  ( CSP  #  662  and  1974 ). This research was also supported by the  Laboratory of Excellence ARBRE  ( ANR-11-LABX-0002-01 ), the  Region Lorraine , the  European Regional Development Fund , and the  Plant-Microbe Interfaces Scientific Focus Area in the Genomic Science Program , the  Office of Biological and Environmental Research in the US DOE Office of Science. L. G.
N. is funded by the  Momentum Program of the Hungarian Academy of Sciences  (Grant No. LP2019/13-2019 ) and the  National Research, Development and Innovation office  (Contract No:  GINOP-2. 3. 2-15-2016-00052 ). G. S. received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement no. 714774  and the grant  GINOP-2. 3. 2. -15-2016-00057. We also thank the many colleagues whom provided fungal strains and material, including  Jan Colpaert ,  Pierre-Emmanuel Courty ,  Joanna F. Dames ,  Patricia Jargeat ,  Björn Lindahl ,  Asuncion Morte ,  Jose-Eduardo Marque-Galvez ,  Andy Taylor , and  Yukari Kuga.
Data availability Genome assemblies and annotations for the organisms used in this study are available via the JGI fungal genome portal MycoCosm (mycocosm. jgi. doe. gov). In addition, the newly sequenced genome assemblies and annotations have been deposited to GenBank (see Supplementary Data 1 for accession codes/BioProjects). The complete transcriptome datasets are available at the GEO at the National Center for Biotechnology Information (http://www. ncbi. nlm. nih. gov/geo/). The accession codes for accessing the data deposited at GEO database are provided in the "Methods/Phylostratigraphy" section and in the caption of Supplementary Fig. 6. All other data supporting the findings of this study are available within the article and its Supplementary information files or are available from the corresponding authors upon request.
Data We collected publicly available data on 6,903 confirmed cases from 271 cities of mainland China, that were available online as of February 8, 2020. The data were extracted in Chinese from the websites of provincial public health departments and translated to English (Table  S5 ). We then filtered the data for clearly indicated transmission events consisting of: (i) a known infector and infectee , (ii) reported locations of infection for both cases, and (iii) reported dates and locations of symptom onset for both cases. We thereby obtained 468 infector-infectee pairs identified via contact tracing in 93 Chinese cities between January 21, 2020 and February 8, 2020 (Figure  S1 ). The index cases (infectors) for each pair are reported as either importations from the city of Wuhan ( N = 239), importations from cities other than Wuhan ( N = 106) or local infections ( N = 122).
Estimating serial interval distribution For each pair, we calculated the number of days between the reported symptom onset date for the infector and the reported symptom onset date for the infectee. Negative values indicate that the infectee developed symptoms before the infector. We then used the fitdist function in Matlab  (19)  to fit a normal distribution to all 468 observations. It finds unbiased estimates of the mean and standard deviation, with 95% confidence intervals. We applied the same procedure to estimate the means and standard deviations with the data stratified by whether the index case was imported or infected locally. Estimating the basic reproduction number ( R 0 ) Given an epidemic growth rate r and normally distributed generation times with mean ( ) and standard deviation ( ), the basic reproduction number is given by (  6 ).
Model Comparison We used maximum likelihood fitting and the Akaike information criterion (AIC) to evaluate four candidate models for the COVID-19 serial interval distributions: normal, lognormal, Weibull and gamma. Since our serial interval data includes a substantial number of non-positive values , we fit the four distributions both to truncated data in which all non-positive values are removed and to shifted data in which 12 days are added to each observation (Figure  S1  and Tables  S2-S3 ). The lognormal distribution provides the best fit for the truncated data (followed closely by the gamma and Weibull). However, we do not believe there is cause for excluding the non-positive data and would caution against making assessments and projections based on the truncated data. The normal distribution provides the best fit for the full dataset (shifted or not) and thus is the distribution we recommend for future epidemiological assessments and planning.
Short Tandem Repeat for A. fumigatus (STRAf) Typing and Analysis STRAf typing is based on the amplification of nine highly polymorphic microsatellite markers (STRAfs 2A, 2B, 2C, 3A, 3B, 3C, 4A, 4B, 4C). These nine STRAf loci were amplified with three triplexed PCRs as previously described  [13]. PCR products were diluted 10-fold with water for molecular biology and then analyzed on the Applied Biosystems 3130 Genetic Analyzer (ThermoFisher, Waltham, MA, USA) with GeneScan™ 400 HD ROX™ size standard according to the manufacturer's instructions (Thermofisher ® , Waltham, MA, USA). Amplicon sizes were determined with GeneMapper software (version 5, ThermoFisher, Waltham, MA, USA) and then transformed in repeat number. STRAf analysis was then performed using RStudio software (version 3. 2. 2, Boston, MA, USA).
fumigatus  [8]. Some environments supporting the growth, sexual reproduction, genetic variation and containing residues of azole fungicides could cause these complex mutations to emerge, amplify and spread. So, ARAf may very well emerge in specific environments and spread to other countries due to natural factors such as wind, or anthropogenic factors such as human travel and commercial trade  [11, 23]. This gene flow could explain the fact that genotypically close ARAf, or those having the same genotype, were found in different countries  [18, 19, 21]. A comparison of genotypes from sawmill ARAf with genotypes from other A. fumigatus isolates in a London database (https://afumid. shinyapps. io/afumID/, 4049 A. fumigatus isolates collected worldwide) showed that some sawmill ARAf (A25, R21, G15, P12 with the same MLG) presented the same genotype as a clinical Australian isolate (data not shown).
Among them, isolate P12 is one of the two isolates found in one sawmill that did not treat wood, but rather imported foreign wood from Russia that had already been treated with fungicides (sawmill P). Curiously, these two ARAf shared the same MLG as other sawmills ARAf. According to these results, the gene flow could not be excluded here and it may be possible that these ARAf emerged in a favorable environment outside of France and then, when imported, spread to the Eastern France sawmills. Conversely, an MLG identified twice in a single sawmill (D) is not linked to any profile in the London database. The distribution of sawmill ARAf and ASAf genotypes, with a lower diversity of ARAf isolates, also seems to concur with two hypotheses reported by other authors to explain the emergence and distribution of ARAf : the specific genetic background and predisposition of some clones to develop azole-resistance, and the better ability of ARAf isolates to accept the azole-resistant genes via mating and recombination  [11, 14].
The analysis uses data from proton-proton (pp) collisions recorded by the ATLAS experiment in 2016 at a centre-of-mass energy of 13 TeV. Two signal models are used to study the sensitivity of the ATLAS detector to such LLPs. The first is a simplified RPV SUSY model in which a squark-antisquark pair is produced, each decaying into a long-lived neutralino which results in a pair of charged leptons and a neutrino. The second is a toy model where a LLP, denoted by Z , is produced in q q annihilations and decays into a pair of charged leptons. These models were selected to study how the kinematic properties of a three-or two-body decay affect the signal efficiencies. The search is intended to be as model-independent as possible and is not optimized for these particular signal models. Previously, the ATLAS Collaboration searched for displaced dilepton vertices in the inner tracking volume of the ATLAS detector at √ s = 8 TeV  [15]  and for oppositely charged dimuons using only muon-spectrometer tracks at √ s = 13 TeV  [16].
Dataset and simulated events This analysis uses the pp collision data recorded in 2016, corresponding to an integrated luminosity of 32. 8 fb -1 obtained while all parts of the detector were operational. The uncertainty in the integrated luminosity is 2. 2%  [24] , obtained using the LUCID-2 detector  [25]  for the primary luminosity measurements. The sensitivity of the ATLAS detector to LLPs decaying into a pair of leptons was studied using Monte Carlo (MC) simulations of two different signals. In the RPV SUSY simplified model, a pair of leftand right-handed squarks of the first two generations was produced via the strong interaction. These eight squarks were assumed to be mass-degenerate and decay into their SM partner and the lightest supersymmetric particle (LSP), which is a bino-like neutralino ( χ0 1 ).
The HepData entry  [57]  associated with this publication has additional efficiency maps and material to reinterpret the results of this search in other models. For the interpretations of the RPV SUSY model, two independent scenarios are considered, each corresponding to an LSP decay that is mediated by a single dominant RPV coupling, λ 121 or λ 122. Figure  3  shows the overall signal efficiencies for each scenario, which can be as high as 40% for a 1. 3 TeV neutralino and 12% for a 50 GeV neutralino. The selection criteria that have the largest impact on the signal efficiencies are the reconstruction efficiency of displaced vertices, the trigger and preselection requirements, and, in the case of ee and eµ vertices, the material veto. Upper limits at the 95% confidence level (CL) are calculated with the HistFitter  [58]  package by testing various signal hypotheses using the CL s prescription  [59]  with a profile likelihood ratio as the test statistic, whose probability density function is determined with pseudo experiments.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS ,  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF , and  MPG, Germany ;  GSRT, Greece; RGC, Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ;  CNRST ,.
Statistical Analysis The data set is presented as mean values with the standard error of the mean (SEM) or the interval of confidence (IC) at 95% level. The statistical analysis for multiple comparisons was obtained by using the one-way ANOVA test followed by Tukey's post-hoc test at 95% confidence. The analysis of the factors' effects on iron bioaccessibility, Pearson's correlation, and all the above-cited analyses were done by using the Minitab ® 18. The statistical analysis of the ferritin formation data was performed using the GraphPad Prism 8 software package.
More formally, we denote the ratios as R i,j and the differences as D i,j. They are defined in the equations 2 and 3: R i,j = µ i µ j , (2) D i,j = µ i -µ j. (3) Then, a univariate selection step is performed to reduce the complexity of the classifier. Reducing complexity helps us to avoid over-fitting problems. The F-test of ANalysis Of VAriance (ANOVA)  [24]  is used to extract the k most discriminant features. It consists of the computation of a p-value, the probability that two distributions are the same. The lower the p-value is, the more discriminant the feature is. The classification consists of a simple SVM classifier with a linear kernel. Leave one patient out cross-validation is performed. At every iteration, all spectra except from one patient are used to train the classifier. The complete pipeline is written in Python thanks to the Scikit-learn library  [25].
After this transformation, all spectra are normalized according to their l 2 norm, meaning that all spectra are divided by their sum of squares. In a practical situation, the endoscopist inserts the fiberscope in the operating channel of the endoscope, as described in image 2. This operating channel is usually used to insert tools like pincer or cutting/grasping instruments. The endoscopy usually lasts 1 or 2 minutes during which the multispectral cameras acquire images every second approximatively. A specific acquisition software has been developed in C# programming language with. NET 4. 5 framework and the library xiAPI that allows the control of the multispectral cameras  [30]. This software manages the data saving and adapts the exposition time of the cameras automatically. The cameras have been optically calibrated such that the focus is approximately 2cm.
Then we can select the temporally nearest multispectral image for the analysis. The image should then be filtered with a low-pass filter to reduce the noise. Although it exists a various number of filtering like the use of wavelets  [31]. In our case, we have chosen to use a simple pyramidal filter:            1 2 3 4           . Then, spectra are taken on a grid by considering 1 pixel over 7 (vertically and horizontally). We gathered data for 16 patients. The table 1 classifies them according to their histological results. For all these patients, 200 valid spectra were randomly taken among the valid spectra to have the same number of spectra for each patient. the image is removed. The two figures 4 show median spectra of the 16 patients after calibration, respectively for VIS camera (figure  4a ) and NIR (figure  4b ).
Conclusion In this paper, a new prototype is introduced to acquire endoscopic NBI videos and multispectral videos during in vivo stomach exploration. The prototype is used to acquire spectra on 16 patients. For each of these patients, 200 spectra are selected, leading to a dataset of 3200 spectra. Spectra obtained with multispectral cameras are processed with a classification pipeline containing a wavelength reduction algorithm, the computation of three types of features (reduced bands, subtractions and divisions) and a leave one patient out classification with an SVM classifier with a linear kernel. The classifier can recognize a healthy stomach wall from chronic gastritis and intestinal metaplasia with a good precision of 80%. Moreover, two exciting wavelength ranges are identified in the red and near-infrared ranges: [745, 755 nm] and [780, 840 nm].
To the best of our knowledge, the analysis of this ensemble of three types of tissues (i. e. chronic gastritis, intestinal metaplasia, and healthy) has never been considered in a previous paper. These promising results have to be confirmed on a larger dataset. The prototype can also be improved to have better quality for the images and, thus, to be able to exploit textural information caught by the multispectral cameras. Finally, unmixing algorithms can be used to get information on the relative concentration of chromophores and thus having a better understanding of the biological changes that appear with pre-cancerous lesions.
, 2017). Moreover, they represent the most commonly used combination of tests for nanoparticles genotoxicity studies  (Magdolenova et al. , 2014). From a methodological point of view, the comet assay was performed on cells exposed at the air-liquid interface (ALI) in order to obtain a differentiation state as close as possible to the cellular exposure conditions observed in vivo. ALI cell culture is a useful method for investigating the harmful effects of PM on the respiratory airway as primary cells exhibit differentiation and pseudostratified striation similar to those observed in vivo  (Cho et al. , 2018). On the other hand, the micronucleus test requires at least 1. 5 to 2 cell divisions to reveal micronucleus formation  (OECDa, 2016). However, our preliminary assays demonstrated that, cells, once at the ALI, differentiate but no longer divide, and thus, preventing the induction of the micronuclei (data not shown).
A FACSCanto II flow cytometer (BD Biosciences) providing 488 nm and 633 nm excitation, running FACSDiva ™ v7. 0 software, was used for data acquisition and analysis. Frequencies of mutant reticulocytes (RET) and mutant red blood cells (RBC) were determined by examining at least 1 x 10 6 of total RET or RBC, respectively, per animal. All mutant cell frequencies, %RET, averages and standard error calculations were performed with the Excel ® spreadsheet developed by the supplier (Litron).
Statistical Analysis For each test, differences with a p<0. 05 were considered statistically significant. For the in vitro and in vivo comet assay, it has previously been shown that the tail intensity does not follow a Gaussian distribution  (Bauer et al. , 1998). Thus, the non-parametric Mann-Whitney U-test was used to evaluate the statistical difference between groups (i. e. between each concentration vs. the respective negative control). All statistical analyses were performed with StatView  Software (version 5. 0, SAS Institute Inc. , SAS Campus Drive, Cary, North Carolina 27513, USA). For the in vitro micronucleus test, the statistical significance of difference between groups (i. e. each concentration vs. the negative control) was determined using the CHI-squared test. For the in vivo micronucleus test, statistical analyses were performed for micronucleus number with StatView ® Software using the non-parametric Mann-Whitney U-test.
humans corresponds to a very different exposure dose in these two species). Moreover, Chen et al. (2010) have shown that in road tunnels, during rush hours (i. e. during holidays between 8 am and 11 am), PM 2. 5 concentration can increase up to 132 µg/m 3 (46. 5 µg/m 3 for PM 0. 1 ). According to the very recent update (April 2018) of the WHO ambient (outdoor) air quality database containing average annual estimates for PM 2. 5 exposure globally (covering more than 4000 cities in 108 countries), the concentration of atmospheric PM 2. 5 of 121. 83 µg/m 3 corresponds to a moderate to high exposure to these particles. In all regions of the world, apart from the high-income region of the Americas, populations are exposed to levels of PM 2. 5 that are higher than the annual mean value of 10 μg/m 3 recommended in the WHO air quality guideline.
ACKNOWLEDGMENTS This work benefited grants from the  Region Hauts-de-France  (PRSE, Convention  N°13004366 ), the  French Agency for Food, Environmental and Occupational Health and Safety (ANSES) (PNREST ANSES ,  CANCER ITMO AVIESAN , Convention  N°2014/1/183 ) and the  European Funds for Regional Economical Development (CPER CLIMIBIO). We thank  E. Vernet  for technical assistance. We thank  Dr Helene Bauderlique  from the  BioImaging Center Lille  for facility access to the cytometer  BD Canto II  ( BICeLcampus Pasteur , 1 rue du  Prof Calmette , 59019 Lille).
(2018). We use optical and MIR colours  (Yan et al. 2013)  to select optically red sources. The final sample is restricted to those AGNs for which X-ray spectroscopy is available (47 sources). In section 3. 1 we fit the X-ray spectra to quantify the X-ray absorption of the sources. In addition we construct spectral energy distributions (SEDs) using optical, near-infrared(NIR), and MIR photometry. We use the CIGALE code  (Ciesla et al. 2015)  to fit the SEDs and get an estimation of various absorption indices (AGN emission, torus inclination). We also complement our analysis with available optical spectra (see section 3. 3). Finally, we compare the results of different wavelengths and techniques for each red AGN.
Infrared AGN candidates WISE  (Wright et al. 2010 ) completed an all-sky coverage in four  MIR bands: 3. 4, 4. 6, 12, and 22 µm (W1, W2, W3 and W4 bands, respectively). Various colour criteria used these IR bands to efficiently identify AGN candidates. For instance,  Mateos et al. (2012)  suggested a selection method using three WISE colours. Stern et al. (2012)  used the W1 and W2 bands and applied the criterion W1 -W2 ≥ 0. 8 to select AGNs with W2 < 15. 05 in the COSMOS field. Assef et al. (2013)  extended the latter criterion and provided a selection of AGNs for fainter WISE sources using the WISE All-Sky data release catalogue. Assef et al. (2018)  modified these criteria to incorporate data obtained during the post-cryogenic main mission extension (AllWISE catalogue). In this study, we use approximately 500 000 WISE detections included in the latest WISE catalogue (AllWISE) that lie within the XMM-XXL area to select IR AGN candidates, applying the criteria of  Assef et al.
X-ray spectra We examine the X-ray absorption of the 47 red AGNs in our sample. To achieve that, we apply X-ray spectral-fitting using the XSPEC v12. 10 software  (Arnaud 1996). We use the Cash statistical analysis (C-stat; Cash 1979) on the spectra binned to 1 count/bin, which has been shown to recover the actual spectral parameters in the most accurate way even for very low-count spectra (e. g. Krumpe et al. 2008). The model applied to the spectral data is a simple power law, A(E) = KE -Γ , absorbed by both the Galactic photoelectric absorption and the intrinsic absorption. Γ is the photon index of the power law and K is the flux at 1 keV in units of photons/keV/cm 2 /s. The Galactic absorption is taken from the Leiden/Argentine/Bonn (LAB) Survey of Galactic HI  Kalberla et al. (2005). The X-ray spectra for the 47 AGNs are presented in Figures A.
1-A. 47. The best-fit values are presented in Table  3. In Fig. 1  we present N H versus X-ray luminosity. Figure  2  presents the N H distribution of the sources, taking into account the N H upper limits  (Isobe et al. 1986). For that purpose, we use the Astronomical SURVival Statistics (ASURV;  Lavalley et al. 1992 ) software package, which adopts the maximum-likelihood Kaplan-Meier estimator to take into account censored data  (Feigelson & Nelson 1985). The derived mean value is log N H /cm -2 = 21. 80 ± 0. 13. The distribution shows that the red sources are almost equally divided between obscured (N H > 10 22 cm -2 ) and unobscured AGNs. The limit of N H = 10 22 cm -2 is often used in X-rays because lower column densities can be produced by dust lanes in the galaxy instead of the torus  (Malkan et al.
SED fitting We calculate the contribution of the AGNs to the power output of the host galaxy using the Code Investigating GALaxy Emission (CIGALE;  Burgarella et al. 2005; Noll et al. 2009 ). We provide the CIGALE code version 2018. 0 with multi-wavelength flux densities, using optical, NIR, and MIR photometry. Table  4  presents the models and the parameters used by CIGALE for the SED fitting of our X-ray sample. The  Fritz et al. (2006)   7  and the SEDs are shown in Figures A. 1-A. 47 (for more details on the SED fitting, see also  Masoura et al. 2018). As mentioned in the previous section, Herschel photometry is available for nine of the sources in our sample. We fit the SEDs of these sources with and without FIR data to check whether the lack of FIR photometric bands affects the SED fitting measurements.