dataesr/scientific-paragraphs-categorization huggingface dataset:
3  from Paper I 30 ) for the lines under study and could not be reliably retrieved at the current sensitivity level of the spectrometer. Therefore, we set ∆ 2 = 0 in the model and only ∆ 0 was varied. Still, the self-shifting coefficient cannot be reliably retrieved from these spectra. In pure CO they do not include the central part of the line (Fig. 9 ) and CO-Ar spectra are recorded with low partial pressure of CO. Overall the pressure shifting effect in gases under study is very small and comparable with the measurement uncertainty. The scarce data on line shifting retrieved from the resonator spectrometer spectra will be discussed together with the related RAD spectrometer results in the next section. Measured intensities of the R(0) and R(1) lines at various partial pressures of CO are shown in Fig.
2,R0 from video data and γ CO-Ar 2,R1 from resonator data) the deviation is near 3σ. The final set of experimentally determined line shape coefficients is obtained from analysis of the data from all three instruments. Weighted averaging is used in most cases. The result is shown in Table  7  together with ab initio data, and available data from other sources. Note that all the retrieved parameters are recalculated to 296 K using the ordinary power law. The value of the temperature exponent n γ = 0. 78 obtained from ab initio calculations (Section 4) is used for both lines. For simplicity, the same exponent value is used for recalculation of all other collisional line shape parameters. Since the temperature variations are small (the correction does not exceed 1 % of the parameter value in most unfavorable cases), the potential inaccuracy of the exponent value results in much smaller changes and thus can be neglected in the total error budget.
08 (22) and 230 538 000. 32 (32) kHz in a perfect agreement with the frequencies of these transitions from Winnewisser et al. 22. To compare the results retrieved from the analysis of the RAD and the resonator spectrometer data, we normalized the observed shift of the R(0) and R(1) lines from the aforementioned unshifted positions by the total gas pressure. The result is shown in the right panel of Fig. 15. The figure demonstrates that data obtained from both instruments agree with each other and with results of ab initio calculations (Table  7 ).
Intensity Figure  12  presents the measured integrated intensity for both lines. Weighted averaged results from line-by-line and multifit procedures agree within standard deviation of data points. The final values (from multifit, as it is less prone to systematic errors) are slightly smaller than the HITRAN2020 data for both lines. For the R(0) line, the difference is statistically significant. The obtained value is smaller by about 0. 5 %: S exp /S HIT RAN = 0. 994(2) and the difference is about 3 times larger than the measurement uncertainty. For the R(1) line, the relative deviation is very similar but in this case it is comparable to the uncertainty of the experimental result: S exp /S HIT RAN = 0. 996 (4). Note that, in the HI-TRAN database, the declared intensity uncertainty for these lines is less than 1 %.
g. Koshelev et al. 62  ). Our trial clearly demonstrates that the improvement in the line shape representation can be achieved only if both Γ 0 and Γ 2 are considered as efficient (or ad hoc) parameters of the qSD model  64. It supplies from 5 to 6 times better description of the ab initio profiles than any other way of retrieving Γ 0 and Γ 2 parameters from the theoretically calculated dependence Γ(υ). A better representation of the line by the SI profile is the result of a poor fit of the quadratic function Eq. (  13 ) to the real SD of collisional cross-section (Fig. 17 ). This fact indicates the potential problem of populating spectroscopic databases by calculated γ 0 and γ 2 coefficients, which can lead to worsening of the observed spectra modeling in comparison with the use of the SI approach.
Numerical modeling of the spectrum in the vicinity of the center of the collisionally-broadened line on the basis of the results of ab initio calculations using generally accepted theoretical profiles, revealed a potential problem associated with filling the spectroscopic databases with theoretical coefficients for the quadratic speed-dependence model. The use of the resonator spectrometer for CO spectrum recordings at pressures of broadening gas up to 2 atmospheres made it possible to identify potential manifestations of problems in modeling the spectrum using the traditional approach based on the impact approximation and the approximation of binary collisions. 12 )) of the R(0) and R(1) lines calculated at 297. 5 K, 1000 Torr and various models: qSD-profile with X 0 and X 2 parameters calculated using Eq.
Acknowledgements Experimental data from RAD and video spectrometers were obtained with support from the  Russian State  Project  0030-2021-0016. Measurements of the R(1) line at elevated pressures with the resonator spectrometer and joint analysis of all data were performed within the RSF project  22-17-00041. SA acknowledges support from the  Ministry of Science and Higher Education of the Russian Federation , project No. 121031500297-3.
HMs can be analyzed by MALDI  46  and were identified in biological tissue slices by MALDI-MSI  47. The use of a specific chlorinated matrix allows for the ionization of HMs species by the addition of one or several atoms of chloride. To our knowledge, this method was never used for the characterization of HMs in biosolid samples. Another focus for the reuse of biosolids is their pathogen content and the associated risk to human health and the environment. Pathogen resistance is highly monitored and could occur due to the use of biosolids for land application  48. As demonstrated by its wide use in hospitals for daily microbiology  49  , MALDI proves very useful for the identification of pathogens. Indeed, metabolomics of human pathogens is increasingly documented with several databases  [50] [51] [52] [53]  available for the annotation of human disorders  54, 55.
MALDI-MSI is a powerful tool for environmental sciences MALDI-MSI is a dynamic field, and recent developments have increased sensitivity (MALDI²), confirming the suitability of this technology for environmental science applications to detect low amounts of contaminants. The implementation of MALDI on rapid instruments reduces the acquisition time, allowing for very fast analysis. Data analysis and contaminants annotation is facilitated by recent advancements in commercially available softwares (SCiLS Lab coupled to MetaboScape), even in a nontargeted approach. Advances in the field of trapped ion mobility spectrometry (TIMS) will be very useful to differentiate isomers of contaminants that might have different biological activities/toxicities. Several studies have proven that MALDI-MSI is suitable for the detection of environmental POPs in biological samples  24, 25.
MALDI-MSI data processing Raw data from MALDI-MSI acquisitions were imported from. mis files into SCiLS Lab MVS 2022a Premium 3D using the Import Wizard with instrument type « Bruker Magnetic Resonance Mass Spectrometry (MRMS) instruments », sqlite centroid spectra, automatic m/z range and automatic axis parameters. Segmentation was performed on the imported features with root mean square normalization and weak denoising to define the sample and off-sample regions. The sample region was exported in the. srd format suitable for import into Meta-boScape 2021b for annotation. In MetaboScape, the peak list from the original. d data was loaded along with the. srd file from SCiLS Lab 2022a. Speckle size and n/Roll were chosen to obtain the highest coverage possible on the samples (Supplementary Table  6 ), and the intensity threshold was set at 500.
The [M + H]+ was set as the primary ion; [M+Na]+, [M + K]+, [M + NH4]+ were set as seed ions. For POPs and pathogen metabolites, the obtained dataset was annotated using suspect lists derived from the Norman Database (https://www. normannetwork. com/nds/), Yeast Metabolome Database (YMDB, http://www. ymdb. ca/), E. coli Metabolome Database (ECMDB, https://ecmdb. ca/) and an internal database for targeted LC-HRMS analysis containing 2072 contaminants (848 pesticides and 1224 toxicants; Bruker Pesti-cides&Tox Screener). For HMs analysis, a suspect list of the expected chlorinated HMs ions was created and used for annotation. The annotations were performed with a threshold of maximum 3 ppm deviation on the exact mass of analytes, and a maximum of 30 for the mSigma value assessing the good fitting of isotopic profiles.
Quantification of POPs and HMs The quantification of copper and telmisartan in biosolid samples was performed using analytical standards and following previously published methods  47, 58. A standard curve was established by spotting 0. 2 µL (telmisartan) and 1 µL (copper) of standard solutions on the ITOcoated glass slide to reach 2 to 15 pmol (telmisartan) and 0. 5 to 10 µg (copper) on each spot. For each biosolid sample, one replicate was dedicated to quantification, and a spot of 15 pmol (telmisartan) and 10 µg (copper) was applied on the biosolid samples. During data processing, the signal obtained for each spot on the slide was recovered in SCiLS Lab 2022a and a standard curve was established using the quantification tool provided by the software. The on-sample signal for the 15 pmol/10 µg spots was measured, background signal from each sample itself was removed from the intensity of the signal measured on the 15 pmol/10 gµg spot, and extinction coefficients were calculated by comparing the intensity of the signal between the 15 pmol/10 µg spots on the slide and on each sample.
Five biological replicates were prepared for each biosolid sample. The analysis was performed using liquid chromatography (LC) coupled to high-resolution mass spectrometry (HRMS) on a DioneX Ultimate 3000 (Thermo, Massachusetts, USA) coupled to a Q-TOF Impact II (Bruker, Germany). The samples were analyzed using both targeted and nontargeted screening approaches. For the targeted approach, the method allowed for the identification of POPs using a database containing 2072 contaminants (848 pesticides and 1224 toxicants; Bruker Pesticides&Tox Screener), their retention time, their exact mass and the exact mass of their fragments in broad-band collision-induced dissociation (bbCID) fragmentation mode. Samples were analyzed on a C18 column (AcclaimTM RSLC 120 C18, 2. 2 µm, 120 A, 2. 1 × 100 mm, Dionex bonded silica products) equipped with an Acquity UPLC-BEH C18 precolumn (1.
LC-HRMS/MS data processing POPs and metabolites detected in the samples using MALDI-MSI were searched for in the LC-HRMS/MS raw data to confirm their identifications. The compounds from which a retention time was confirmed were identified to level 1 of the Schymanski classification  40. For the nontargeted method, the annotations were performed using Metaboscape 4. 0 (Bruker), which creates a bucket containing all adduct forms for a mass of interest, which corresponds to level 5 (exact mass) in the Schymanski classification. To obtain the dataset, an intensity threshold of 1000 was set up. The annotations were achieved using the criteria of mass deviation (Δm/z) under 3 ppm and a mSigma value (assessing the good fitting of isotope patterns) under 30. Tentative identifications were achieved using suspect lists derived from the annotated compounds found in MALDI-MSI datasets.
Acknowledgements The authors would like to acknowledge the  Agence de l'Eau Rhin Meuse , the villages of Lutter and Falkwiller, the  Communauté de communes de Moselle Sud and Portes de Meuse , the facilities of  La Wantzenau , Meistratzheim and Sausheim for access to biosolids from the constructed wetland and activated sludge systems. The authors also wish to thank the  Laboratoire d'Etude des Eaux (LEE)  for its help. L. M. discloses support for the research of this work from  Agence de l'Eau Rhin Meuse (AERM)  [grant number  183696 ]. Figures 1a and2a were created with BioRender. comwith full license to publish.
Results The SkyPole algorithm was tested on data recorded with a polarimetric camera equipped with a fisheye lens. The camera was placed on the roof of a laboratory in Marseille, France (43. 286987 • N, 5. 4032786 • E) giving a quasi-hemispherical view of the sky dome. The SkyPole algorithm described in Fig. 1D  was applied to DoLP images of the sky in order to locate the NCP and assess the accuracy of the method. As the NCP is located in the sky at an azimuth equal to that of the geographical north and an altitude equal to the observer's latitude, the latitude and north bearing measured were compared with the ground truth values obtained from the solar ephemeris. As shown in Fig. 2 , the results obtained by applying the SkyPole method to experimental data (20) were consistent with the simulated results presented in Fig.
Materials and Methods The SkyPole method was tested with data collected from a Rayleigh sky simulation. We also tested our method with experimental data obtained with a calibrated Lucid Vision Lab PHX050S-QC, division of focal plane polarimetric camera. Our data were first preprocessed with a circular averaging filter. Next, we implemented the absolute difference in DoLP between several moments of time. We then applied a threshold-based binarization algorithm. Next, in order to obtain the NCP, several binary images were added. The NCP was located by searching for the maximum valued point, namely the intersection between all the radial invariance axes (cf. Fig. 1D ). Last, the coordinates of the point were transformed to north and latitude coordinates and compared with the ground truth values.
Sequencing (i) Genomes of phage-resistant clones. Single clones were allowed to grow overday in LB supplemented with 0. 7 mM EDTA, to limit capsule production. We performed DNA extraction with the guanidium thiocyanate method, with few modifications  (Pitcher et al. , 1989). RNAse A treatment (37 °C, 30 min) was performed before DNA precipitation. Each clone (n=15) was sequenced by Illumina with 150pb paired-end reads, yielding approximately 1 Gb of data per clone. The reads were compared to the reference genome using breseq v0. 33. 2, default parameters. (ii) wcaJ gene. PCR of wcaJ was performed using the primers that hybridized 150 base pairs upstream and downstream of the wcaJ gene; K2. wcaJ. 150-5 (5'-GGCG TTCC AGCA AGGG TTAT C -3') and K2. wcaJ. 150-3 (5'-ACGT TCGC GCTT AAAT GTG-3'), respectively.
Acknowledgements The sequencing work was made at the  Biomics Platform ,  C2RT ,  Institut Pasteur, Paris, France , supported by  France Génomique  ( ANR-10-INBS-09 ) and IBISA. We thank  Alex Hall  for critical reading of the manuscript. We would also like to thank the reviewers for their contribution which critically improved the manuscript. This work was funded by an  ANR JCJC (Agence national de recherche)  grant [ ANR 18 CE12 0001 01  ENCAPSULATION] awarded to OR. The laboratory is funded by a  Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  (grant  ANR-10-LABX-62-IBEID ) and the  FRM  [ EQU201903007835 ]. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Methods A technical working group (TWG) was formed to develop a BRM to support the application of laboratory biological risk management and improve laboratory sustainability by providing an evidence base for biosafety measures. The TWG conducted a gap analysis for a selected list of priority pathogens on procedures related to diagnostic testing and associated research for those pathogens, including but not limited to sample processing, testing, animal models, tissue processing, necropsy, culture, storage, waste disposal, and decontamination. The TWG screened databases, websites, publications, reviews, articles, and reference libraries for relevant data to achieve this. The main research domains used to perform the literature searches were the ABSA database, Belgian Biosafety Server, US centres for disease, control and prevention reports, WHO reports, PubMed, and internet searches for terms related to biosafety matters, including, for example, inactivation, decontamination, laboratory-acquired infections (LAIs), laboratory releases, and modes of transmission.
In addition, two specimens of Coeloseris  Vaughan, 1918 , a monotypic genus currently ascribed to Agariciidae Gray, 1847  (Veron, 2000; Waheed et al. , 2015a) , were collected and investigated. Indeed, previous molecular data indicated that the genus is sister to a lineage including Euphyllia glabrescens  (Chamisso & Eysenhardt, 1821)  and Galaxea astreata  (Lamarck, 1816)   (Arrigoni et al. , 2017) , while morphological observations by  Kitahara et al. (2012b)  indicated that Coeloseris mayeri  Vaughan, 1918  lacks the main morphological characters shared by all extant agariciid genera. We sequenced three mitochondrial loci and investigated a total of 23 morphological characters, including skeletal macromorphology, micromorphology/microstructure, and polyp structure. Molecular and morphological phylogeny reconstructions of Euphylliidae were generated and compared.
Molecular analyses Total genomic dna was extracted using the DNeasy® Blood and Tissue kit (Qiagen Inc. , Hilden, Germany) from coral tissues preserved in ethanol or using a phenol-chloroform-based method with a phenol extraction buffer (100 mM Tris-Cl pH 8, 10 mM edta, 0. 1% sds) from samples conserved in chaos solution  (Fukami et al. , 2004; Huang et al. , 2011). dna sequence data was collected for three mitochondrial gene fragments: the barcoding portion of the cytochrome c oxidase gene (coi), the small subunit of ribosomal rna (12S rRNA), and the large subunit of ribosomal rna (16S rRNA). The three markers were selected because they proved to be informative for testing the monophyly of scleractinian taxa in previous studies (e. g. ,  Fukami et al. , 2008; Kitahara et al. , 2010; Stolarski et al.
, 2011; Kitahara et al. , 2012b). We used the primers lco1490-hco2198  (Folmer et al. , 1994)  for coi, antmt12SF-antmt12SR  (Chen & Yu, 2000)  for 12S rRNA, and LP16SF-LP16SR (Le  Goff-Vitry et al. , 2004)  for 16S rRNA. pcr protocols from  Stolarski et al. (2011)  were followed. All pcr products were purified with Illustra ExoStar (ge Healthcare, Buckinghamshire, UK) and directly sequenced using an abi 3130xl Genetic Analyzer (Applied Biosystems, Carlsbad, CA, USA). Sequences obtained in this study were deposited in ncbi under accession numbers OQ301692-OQ301709 (coi), OQ303739-OQ303765 (12S rDNA), and OQ301759-OQ301783 (16S rDNA) (supplementary table  S1 ). For the phylogenetic analyses, our newly generated sequences were aligned with Euphylliidae sequences from previous studies  (Romano & Palumbi, 1996; Romano & Cairns, 2000; Chen et al.
, 2002; Fukami et al. , 2008; Barbeitos et al. , 2010; Lin et al. , 2011; Niu et al. , 2016; Akmal et al. , 2017; Chuang et al. , 2017)  and several other representatives of the "Complex" clade downloaded from GenBank (supplementary table  S2 ). We did not include coi sequences of euphylliids published by  Luzon et al. (2017 Luzon et al. ( , 2018)) , notably including some species not analysed in this study such as Fimbriaphyllia paraancora  (Veron, 1990)  and Fimbriaphyllia yaeyamensis  (Shirai, 1990) , because the coi regions targeted by  Luzon et al. (2017 Luzon et al. ( , 2018) )  and in our study overlapped only by about 50 bp. The sequence of the corallimorpharian Discosoma nummiforme Rüppell & Leuckart, 1828 (accession number kp938434) was selected as outgroup following  Fukami et al.
(2008)  and  Lin et al. (2014). Multiple alignments were carried out using mafft v. 7. 130b  (Katoh & Toh, 2008; Katoh et al. , 2009; Katoh & Standley, 2016)  and the iterative refinement method e-ins-i. General statistics concerning the three genetic markers were calculated with DnaSP v. 5. 10. 01  (Rozas et al. , 2003; Librado & Rozas, 2009). Sequence data sets from each locus were analysed individually as well as in a concatenated matrix. Phylogenetic analyses were conducted under three optimality criteria, namely maximum likelihood (ml), Bayesian inference (bi), and maximum parsimony (mp). ml phylogeny hypotheses were obtained using RAxML v. 8  (Stamatakis, 2014)   graphical front-end raxmlGUI v. 2. 0. 8  (Edler et al. , 2021). The gtrgamma substitution model was applied and the branch support was assessed by 1,000 rapid bootstrap replicates.
Prior to bi analyses, the most suitable nucleotide substitution models were selected for each locus using jModelTest v. 2. 1. 9  (Guindon & Gascuel, 2003; Posada, 2008; Darriba et al. , 2012) , testing for 24 models, and choosing the best model based on the Akaike Information Criterion (aic). The software selected the gtr + G for coi, the gtr + I + G for 12S rRNA, and the hky + G for 16S rRNA. Under these evolution models, MrBayes v. 3. 2. 6  (Huelsenbeck & Ronquist, 2001; Ronquist & Huelsenbeck, 2003; Ronquist et al. , 2012)  was used for each data set to generate four Markov chains of 10 million iterations in two runs, logging one tree per 100 generations. mcmc convergence among runs was assessed using Tracer v. 1. 7  (Rambaut et al. , 2018) , and the first 20,001 trees were discarded as burn-in.
The matrix composed of 23 morphological characters (see supplementary data S1) was used for phylogenetic analyses and included a total of 12 species of Euphylliidae and Coeloseris mayeri. Pachyseris speciosa was selected as outgroup based on our molecular phylogenetic tree and previous genetic studies (e. g. ,  Fukami et al. , 2008; Kitahara et al. , 2016). mp phylogenetic analysis was run using tnt v. 1. 5  (Goloboff, 1999; Goloboff et al. , 2008) , under the same settings described for the molecular phylogeny reconstructions. Tree searches were performed with 10,000 random addition sequence replicates, each employing 100 cycles of sectorial searches, ratcheting, drifting, and tree fusing. Clade stability was determined through 10,000 bootstrap replications and a final strict consensus was obtained.
2017 Luzon et al. , 2018)) , Gyrosmilia (group V-E), and Coeloseris (group V-F) as monophyletic lineages with low to high branch supports. No sequences of Ctenella were available on GenBank for these two mitochondrial loci. The coi phylogenetic tree resolved Fimbriaphyllia (group V-A sensu  Luzon et al. 2017 Luzon et al. , 2018)) , Euphyllia (group V-C sensu  Luzon et al. 2017 Luzon et al. , 2018)) , and Coeloseris (group V-F) as distinct clades, while all Galaxea sequences formed a large unresolved polytomy within Euphylliidae. In addition, our sequences of Gyrosmilia and the GenBank sequence of Ctenella (accession number AB441208) were nested together in a single group (BIpp = 0. 98, MLbs = 74, MPbs = 54).
With sequences from mitochondrial markers coi and cytB,  Fukami et al. (2008)  showed that C. chagius belonged to clade V sensu  Fukami et al. (2008). Following the molecular phylogenetic tree of  Fukami et al. (2008) ,  Budd et al. (2012)  assigned all members of clade V sensu  Fukami et al. (2008) , thus including Ctenella, to Euphylliidae. Although Ctenella was not collected for this study, we included this taxon in both molecular and morphological phylogeny reconstructions by using the coi sequence of C. chagius published by  Fukami et al. (2008)  (accession number AB441208) and investigating macromorphology of the holotype of C. chagius  (bmnh 1928. 3. 1. 61)  and the polyp structure. Both analyses corroborated Ctenella as a member of Euphylliidae and showed a sister relationship with Gyrosmilia.