dataesr/scientific-paragraphs-categorization huggingface dataset:
2. The empirical foreign wet CIA (H 2 O-N 2 + H 2 O-O 2 ) and dry CIA (N 2 -N 2 + O 2 -O 2 + N 2 -O 2 ) that we are using (see equations 30 and 31 in ?, and the red, green and light-blue curves in Article number, page 6 of ?? Fig. 3. APEX Atmospheric observing run carried out on December 6, 2020. The panels from top to bottom show: atmospheric parameters from the observing log (blue and green); uncorrected spectra from the observations CLASS file (black); corrected spectra (blue, see text) plus ATM model prediction for airmass = 1. 0 (red) and line positions for N 2 O and O 3 ; ATM model simulations (red) and data vs model residuals according to Eq. (  3 ) (blue dots, smoothed to 1 GHz); and atmospheric opacity breakdown according to those model results (several colors in the bottom panel).
Summary and conclusions We obtained the first kilohertz-resolution atmospheric spectra in the 450 µm atmospheric window from a ground-based submm observatory. The conditions during the observations were relatively dry and stable. The performance of the receiver in terms of stability and sensitivity has produced data suitable for comparison with the atmospheric model currently implemented at ALMA, APEX, and other observatories, allowing us to check its overall validity, and also verify how well the weakest features of the atmospheric spectrum are reproduced. The observations provide a very high level of spectral detail. The calibration offsets have been satisfactorily corrected, and the slowly varying atmospheric conditions are monitored and taken into account in the simulations. Very good agreement is obtained with the ATM simulations, proving that this model is adapted: no fundamental changes are necessary, for its use in the explored frequency range for astronomical sites located at ∼5000 meters above sea level.
Model calculations and discussion In this section, we present ATM model calculations -which include all species mentioned in the previous section -performed to be compared with the final re-calibrated data presented in the third panel of Fig. 3. We are not trying to fit the data using a single static atmospheric profile because, as already mentioned in Sect. 3, the atmospheric conditions were slightly changing during the observing run. On the other hand, the model calculations should make simple assumptions and rely upon a very limited number of parameters, with satisfactory results, for an operational code such as ATM. In particular, we adjusted the abundances and vertical distribution of some molecules (see Fig. 5 ) in order to get a reasonable match to their observed lines. The data allow some discrimination between different seasonal and/or day/night abundances and vertical distributions.
Summary and conclusions We obtained the first kilohertz-resolution atmospheric spectra in the 450 µm atmospheric window from a ground-based submm observatory. The conditions during the observations were relatively dry and stable. The performance of the receiver in terms of stability and sensitivity has produced data suitable for comparison with the atmospheric model currently implemented at ALMA, APEX, and other observatories, allowing us to check its overall validity, and also verify how well the weakest features of the atmospheric spectrum are reproduced. The observations provide a very high level of spectral detail. The calibration offsets have been satisfactorily corrected, and the slowly varying atmospheric conditions are monitored and taken into account in the simulations. Very good agreement is obtained with the ATM simulations, proving that this model is adapted: no fundamental changes are necessary, for its use in the explored frequency range for astronomical sites located at ∼5000 m above sea level.
Acknowledgements. The APEX SEPIA receiver is a joint development by the  Group of Advanced Receiver Development (GARD, Gothenburg, Sweden)  from the  Onsala Space Observatory (OSO, Sweden) , the  Netherlands Research School  for Astronomy (NOVA, The Netherlands), and the  European Southern Observatory (ESO). The authors thank  ESO  for funding this work through contract  CFP/ESO/19/25417/HNE. JRP also thaks  ESO  for funding the implementation of ATM in the ALMA software under contract  14977/ESO/07/15694/YWE. JRP thanks  Observatoire de Paris  for supporting his work for this paper through its program "chercheurs invités" from Oct 10 th to Dec. 10 th 2021, and  Ministerio de Ciencia e Innovación of Spain  for funding support through projects  PID2019-106110GB-I00 ,  PID2019-107115GB-C21 / AEI / 10.
Efficacious vaccines usually prevent FCV-associated disease but not infection, and vaccine efficacy varies on the prevailing FCV field strain. Therefore, if problems persist in fully vaccinated cats, a change of the vaccine strain(s) should be considered. Moreover, FCV vaccines have lower efficacy against highly virulent strains. Ideally, further efforts will aim to develop FCV vaccines that prevent infection, or at least all potential detrimental clinical manifestations. This guideline will continue to be updated regularly on the ABCD homepage (www. abcdcatsvets. org) as new data become available. A previous version of this ABCD guideline was published in 2009  [190]. The current review is an update of this previous version.
Fruit Collection The sampling population used in this study includes both elite and minor cultivars of date palm with most originating from the West Asian population and a smaller number representing the North African gene pool  (Hazzouri et al. , 2019). The original sampling of tree-ripened fruits from 148 date palms in this study was first reported in  Hazzouri et al. (2019;  Supplementary Table  1 ) and data for sugar, organic acid, and moisture content provided in that study. Date palms in this study consisted of adult female fruit-bearing palms located at two farms in the United Arab Emirates. The minimum age range is from roughly 10 years on the Al Falassi farm (Al Schweib, Abu Dhabi, UAE) and 17 years on the farm in Al Hamria (Ras-Al-Khaima, UAE). Both use drip irrigation with ∼750 liters of water per palm every two weeks.
The mass spectrometer operated in electron impact (EI+) ionization mode at 70 eV with a scan range of 40-350 Dalton. Data were analyzed with MassHunter version B 08. 00 (Agilent Technologies, Palo Alto, United States). Peaks were identified by comparing the obtained mass spectra with those of the National Institute of Standards and Technology (NIST, Gaithersburg, Maryland, United States) 14 database. Co-injection of series of alkanes, from C8 to C20 (Sigma-Aldrich), was used for the calculation of Kovats retention indices that were then compared with those found in the literature (Pubchem website 1  and NIST). After careful revision and removal of artifacts and pollution coming from fiber and column, the list of volatiles was reduced to 80 molecules. The raw data consisted of 3 technical replicates of each of the 148 date palm fruit samples and 80 volatiles (see section "Data Availability").
g. , alcohols). The raw data matrix with three technical replicates was then processed to remove low quality data points using the following procedure. First, any volatile from a single fruit sample with coefficient of variation (CV) > 50% across the three technical replicates was evaluated to determine if one of the technical replicates deviated substantially (as defined below) from the other two. Deviations were calculated for each of the minimum and maximum technical replicates as a percentage of the median replicate as | (replicate-median replicate)| /median replicate. In cases where the minimum or maximum replicates deviated from the median replicate by < 35% and the other by > 55%, the replicate with the larger deviation from the median was excluded. Second, in cases where the CV was > 50% for the three technical replicates, but one of the replicates could not be identified as low quality using the above criterion, information across volatiles to determine if one of the three technical replicate runs consistently yielded low quality data points across volatiles was used.
In this case, the technical replicate corresponding to the low quality run was excluded for volatiles with CV > 50% for the date palm sample. After cleaning of the raw SPME/GC mass spectroscopy (SPME/GCMS) data, the means of the technical replicate m i values were calculated per volatile per sample. These values are estimates of the number of butanol equivalents of each volatile per fruit sample with units µg/100 g and were included as trait values in a volatile 148 × 80 phenotype matrix with 3. 28% (412/12,580) missing data. This matrix was used in downstream analysis of volatiles. Finally, a second matrix was created for comparison of volatiles to other fruit traits collected for a subset of the same date palm samples. The volatile matrix was merged with fruit organic acid, sugar composition, and fruit color data from  Hazzouri et al.
2. 8. 0; Gu, 2016)  package in R. Prior to clustering and heatmap generation, volatile trait values were adjusted by adding a constant to traits with zero values (using the same procedure as in the PCA) followed by Z-score transformation of log-transformed values. Z-score values less than -3 or greater than 3 were set to -3 and 3, respectively prior to calling the Heatmap function. Hierarchical clustering on both the sample and volatile axis of the heatmap was performed using Euclidean distances. Clustering of volatiles was performed with the pvclust package (v. 2. 2-0) in R using 1-Spearman's rank correlation as the distance measure. Node support was estimated as approximately unbiased ("AU"; see pvclust documentation) P-values obtained from 1,000 bootstrap replicates. The output was then plotted using the dendextend R package (v.
Clonal Analysis of Volatile Composition We tested the hypothesis that vegetative clone pairs are more similar in their volatile profiles than expected by chance using a randomization procedure. Each replicate of the procedure consisted of randomly choosing n pairs of samples from the 148 samples in our volatile dataset and assigning them as pseudo-clone pairs, where n corresponds to the observed number of clones (Supplementary Table  3 ). We then calculated the Euclidean distance between volatile profiles separately for each pair of pseudo-clones and determined the mean for the n pseudoclone pairs. This procedure was repeated 10,000 times. The P-value was calculated as the proportion of the 10,000 replicates where the mean Euclidean distance between pseudo-clone pairs was less than the observed mean.
Samples considered clones in this analysis included only those that could be verified based on whole genome sequencing. Analysis of broad sense heritability was conducted using the approach of  Einspahr et al. (1963)  and  Zsuffa (1975)  on clones in our analysis represented by two palms. Analysis of Variance (ANOVA) summary statistics for the calculations were obtained using the aov function in the base R (v. 4. 1. 1) stats package and data tidying performed with the Broom package (v. 0. 7. 10) in R.
a RI lit retention index coming from the NIST 14 database. b Compound previously reported in date fruit by A = El Arem et al. (2011), B = Reynes et al. (1996), C = Khalil et al. (2017), D = Mezroua et al. (2017), E = Hatem et al. (2018), F = Arif and Lombarkia (2018), G = Narain (2007), H = Jaddou et al. (1984), I = El Arem et al. (2012), J = Harrak et al. (2005). c No overlapping compounds were found with Siddeeg et al. (2019) and Aldulaimi et al. (2020).
ACKNOWLEDGMENTS We thank  Ahmad Al Falassi  who graciously opened his farm to our research and kindly supported our efforts throughout the project. We also thank the  UAE Ministry of Environment and Climate Change  who granted access to the farm in  Al Hamria ,  Ras-Al-Khaima  and  Sallah Abdulla Moussa  for facilitating its use. We also thank the engineers at both farms for their assistance in accomplishing this work. We thank the  Date Palm Research Unit at the UAEU  for various roles in facilitating this work. We thank  Robert R. Krueger ,  Khaled Masmoudi  and the deceased  Hussam S. M. Khierallah  for helpful discussions on aspects of date palm agriculture and fruit development at the outset of this project. Gina M. Pham  for helpful discussions on data curation and analysis, and  Justin B.
Acknowledgements We thank  S. Bayat  ( INSERM ) for help during the test phase,  P. Masson  ( LADAF ) for dissections of donors' bodies,  H. Reichert  ( ESRF ) and  R. Tori  for general support of the project and  C. Muzelle ,  R. Homs ,  C. Jarnias ,  F. Cianciosi ,  P. Vieux ,  P. Cook ,  L. Capasso  and  A. Mirone  for their help in setup developments and improvements. We also thank  R. Engelhardt ,  A. Muller Brechlin ,  C. Petzold ,  N. Kroenke , and  M. Kuhel  for help with histology and autopsies. This project has been made possible in part by grants number  2020-225394  from the  Chan Zuckerberg Initiative DAF , an advised fund of  Silicon Valley Community Foundation , The ESRF -funding proposals md1252 and md1290, the  Royal Academy of Engineering  ( CiET1819/10 ) and the  MRC  ( MR/R025673/1 ).
I also used data from stations around the Mont-Blanc massif. Stations CI17-CI23 of the CIFALP project  (Zhao et al. , 2018)  were in operation between 2018/11/11 -2019/12/3. Stations CI18-CI20 were reinstalled at the same location on 2019/12/20  (Helmstetter and Guéguen, 2020). Three stations   (Vernay et al. , 2019) , which provides a reanalysis of meteorological 177 and snow cover data. This model adjusts an estimate from a numerical weather predic- Red lines show the horizontal error ellipses at the 68% confidence level. It provides the hourly rate of snowfall on the Mont-Blanc massif for different ranges of elevations and slope orientations. The GLACIOCLIM station has only measured atmospheric pressure since 2019/9/13. In Figure  2 , we can see very regular peaks in the seismogram repeating on average every 160 s.
Some instruments (nodes  N11-N35 , new stations  CI18-CI20 ) belong to the  French National Pool of Portable Seismic Instruments (Sismob-RESIF). Sismob is member of the  French seismological and geodetic network Resif-Epos , a  Research Infrastructure (RI)  managed by  CNRS-Insu. I thank  Michel Bouchon ,  Florent Gimbert ,  Ugo Nanni ,  James Hollingsworth ,  Emmanuel Chaljub ,  Mathieu Causse ,  Luc Moreau  and  Roland Bürgmann  for interesting discussions. Chris Carr ,  Yoram Terleth  and two anonymous reviewers provided very insightful comments that helped to improve the manuscript. This work was supported by the  SAUSSURE  project (https://saussure. osug. fr)( ANR-18-CE01-0015 ) funded by the  Agence National de la Recherche (ANR). This work was also partially funded by the  SISM@LP-Swarm project (POIA-FEDER  Grant, funded by the  European Commission FEDER ,  Région SUD  and  Agence Nationale de la Cohésion des Territoires).
Data Availability Statement Some seismological data are available from the Federation of Digital Seismograph Networks (FDSN) network with network codes CH, GU, IV, FR, ZO, 1D, 8C. FDSN data centers are available from https://www. fdsn. org/networks. The list of all seismic stations used and network codes are given in Table  1. Some data are distributed by FDSN with restricted access (network codes XT, 8D). Data from temporary stations B01-B04, DOM and MDG on glaciers in the massif are not  (yet)  open. Catalogs of repeating LFIs and cluster locations are provided at https:// doi. org/10. 5281/zenodo. 6822165. Meteorological data are available from GLACIO-CLIM observatory (https://glacioclim. osug. fr) and S2M database  (Vernay et al. , 2019). We used nonlinloc software  (Lomax et al. , 2000)  to locate LFIs.
s. l. But there are many gaps in the snowfall data during snowfall episodes. Therefore, I use data from the S2M database Data for network codes ZO, 1D, 8C, GU, IV, CH and FR are freely available via FDSN web services. (MFERR, VFER2 and AMID2) were installed by ETH Zurich in 2019 to detect and 162 locate earthquakes in the Mont-Blanc massif. I also used permanent seismic stations 163 located further away from the Mont-Blanc massif, from the Switzerland Seismologi-164 cal Network (Swiss Seismological Service, 1983), the network of North Western Italy 165 (University of Genova, 1967), the INGV Seismological Data Centre(INGV, 2006), and 166 the French broadband network,(RESIF, 1995). 167 3. 3 Snowfalls and Atmospheric Pressure 168 I used meteorological data to analyze the influence of precipitation and atmo-169 spheric pressure on the occurrence of LFIs because snowfalls have been suggested to.
Before this date, I use data from the closest Meteofrance station that measures atmospheric pressure located at Bourg Saint Maurice, 24 km south of Mont-Blanc. I checked that both stations provide similar values after shifting the Bourg Saint Maurice pressure to correct for the effect of elevation. When both stations are in operation the correlation coefficient between the pressure measured at Argentière and Bourg-Saint- Maurice is 0. 88. 4 Methods 4. 1 Detection, Classification and Selection of Repeating LFIs I detected a first sequence of low-frequency icequakes by simply looking at a one-hour long signal recorded by station B01 on 2017/12/19 starting at 22:00 UTC.
Sample preparation and sequencing Samples were selected from a large set of opportunistically-collected material to achieve a sample set that was relatively balanced by sex, year of hatching and sampling date. Faecal samples were processed using previously published methods  [41]. Briefly, enrichment for encapsidated viral nucleic acids relative to eukaryotic cells and bacteria was conducted by centrifugation and selection of supernatant. Filtration was not performed so that any large viruses and bacterial data could be also retained. Residual DNA was digested with DNase I  [41]. The Boom method was used for extraction of DNA and RNA nucleic acids  [42]. Reverse transcription was performed using non-ribosomal random hexamers  [43]. Second strand DNA synthesis was performed with Klenow fragment DNA polymerase.
Viral genome reconstruction Sequencing adaptors were removed and reads were filtered for quality (q30 quality and read length >45nt) using cutadapt 1. 18  [44]. Cleaned reads were assembled de novo into contigs using SPAdes 3. 12. 0 (k-mer lengths 21, 33, 55, 77)  [45]. Taxonomic assignment was achieved on contigs of length >900 nt through searches against the NCBI RefSeq viral database (downloaded in July 2019) using DIAMOND 0. 9. 22 with an e-value cutoff of <10 -5  [46]. All contigs that matched eukaryotic virus sequences were selected and used as queries to perform reciprocal searches against the NCBI non-redundant protein sequence database with an e-value cut-off of <10 -3 in order to eliminate likely false positives  [47]. Virus sequences were classified into viral operational taxonomic units (vOTUequivalent to contigs).
Phylogenetic analyses Phylogenetic trees were estimated using maximum likelihood methods for all vOTUs that might represent novel species, in order to place them within currently known viral diversity and to explore their host range. Representative sets of polymerase-or capsid-coding proteins were extracted from the NCBI non-redundant database for each taxonomic group in which the vOTUs were classified. For segmented viruses such as the Nodaviridae, in which genes for these proteins occur on different segments, we used both protein sequences. Amino acid sequences were aligned using MUSCLE 3. 8. 425 (16 iterations) with default settings  [52]  or MAFFT v7. 388 with the L-INS-i algorithm  [50, 51]. Genomic sequences downloaded from NCBI that could not be reliably aligned with the sequences generated here due to high amino acid divergence were removed and the dataset subsequently realigned.
No reference sequences were removed during this process. All alignments were trimmed manually to focus on more conserved genome regions and to remove regions that could not be reliably aligned. Amino acid substitution model testing was performed using ProtTest 3. 4. 2 [54]. Phylogenetic trees were constructed in RAxML 8. 2. 11  [55]  using 1000 bootstrapped replicates. Trees were mid-point rooted and visualized with FigTree 1. 4 (http://tree. bio. ed. ac. uk/software%20/figtree/). Phylogenetic placement was used to determine whether the putative host of each Fig. 1  Comparison of the composition of faecal prokaryotic communities of C. olor, at the phylum level, to the results obtained by other metagenomic studies conducted on wild Anatidae species. The bibliographic search was conducted of PubMed on 16 September 2019 using the Adjutant package in R, using the search '(bird OR avian) AND (bact metagenomic OR 16S OR bact microbio)' (1424 papers).
Compositional analysis to support host assignments of newly discovered eukaryotic viruses To independently corroborate the assigned host taxa for the picornaviruses and related ssRNA viruses detected in the study, discriminant analysis using virus composition (4 genomic mono-nucleotide frequencies and 16 dinucleotide observed / expected representations, generating 20 total parameters) was conducted using parameters based on 277 representative full genome sequences from the order Picornavirales and for caliciviruses (sequences from the ICTV Virus Metadata Resource (VMR), MSL36; https://talk. ictvonline. org/taxonomy/vmr/). This method has been previously shown to be effective at discriminating between host type (plants, vertebrates, invertebrates)  [56]. Compositional analyses were 94. 6% accurate at assigning vertebrate, invertebrate and plant hosts in the VMR control dataset (Table  S1 ).
Statistical analyses First, data representing the number of reads per prokaryotic or viral taxa per sample were standardised to allow inter-sample comparisons. All statistical analysis was performed using R v 3. 6. 1 and RStudio 1. 2. 5019 software  [60, 61]. Samples were discarded if they were outliers in term of the completeness of their prokaryotic rarefaction curves at the family scale: samples whose rarefaction curves did not asymptote were removed. Discarding of outliers based on viral rarefaction curves was not undertaken because most viral rarefaction curves did not plateau. Next, we limited taxonomic binning artefacts and potential inter-sample contamination by applying an abundance threshold of >1/1,000,000 reads/sample for prokaryotes and >1/10,000,000 reads/sample for viruses. To enable comparison between viral taxa that have different genome lengths, the number of virus reads was divided by the length of the viral contig to which it mapped (kb).
We conducted assessments of abundance (in terms of read counts) of both prokaryotes and viruses, and assessments of prevalence and diversity for prokaryotes only because viral rarefaction curves did not asymptote. In analyses of viruses, we focus on only coding regions (CDS) that could be assembled completely, or already characterised viral taxa. The impact of bird age on prokaryotic community α-diversity was evaluated using the Shannon and Simpson diversity indices, and on βdiversity using Bray-Curtis dissimilarity index. The effect of bird age, sex, and seasonality on the composition of prokaryotic communities was also determined by one-factor PERMANOVAs with 10,000 permutations on Bray-Curtis matrix, using the adonis function of the VEGAN package  [62]. Permutational tests of dispersions (PERDISPs) using the function permutest.