dataesr/scientific-paragraphs-categorization huggingface dataset:
The lognormal can fit monotonically increasing, decreasing or unimodal data distributions: Cat = e -0. 5 × ⎛ ⎜ ⎜ ⎝ ln ( C c 0 ) c b ⎞ ⎟ ⎟ ⎠ 2 (4) where C 0 and C b are estimated parameters and C is observed absolute change in catechin content calculated as the difference between pre-and post-inoculated tissue concentrations (mgAE g -1 of phloem). Catechin concentrations were normalized relative to the overall minimum value in the dataset prior to fitting models. We fit regression models with datasets describing our sample of surviving, non-attacked (N = 71) and reference, beetle-killed trees (N = 25). Models were fit using maximum likelihood techniques (Canham and  Uriarte, 2006). Simulated annealing  (Goffe et al. , 1994) , a global optimization algorithm, was used to determine maximum-likelihood based estimates of model parameters.
We used the following likelihood function to estimate probability of survival given a particular model form and the dataset of observations: log likelihood = ∑ n i=1 { log(p i ) log(1-p i ) if tree i survived if tree i died } (5) where p i is the probability that an individual tree survived and (1 -p i ) is the probability that a tree died. Computations of log likelihood using Eq. (  5 ) assume independence of observations. However, in contrast with traditional parametric approaches, parameter estimation and model comparisons based on likelihood and parsimony are generally unaffected by autocorrelated data structures  (Hubbell, 2001; Uriarte et al. , 2004). We constructed a series of nested models based on different permutations of terms in Eq. (  1 ). Competing models were compared using Akaike information criterion (AIC) with a bias correction for small sample size  (Johnson and Omland, 2004).
Percent change in catechin concentration represents the difference between pre-and post-treatment content, normalized relative to the overall mean concentration change in the dataset. The sun-illuminated stem covariate represents the fraction of the main trunk fully exposed to the sun, shown here as a percent. Crown defoliation due to foliar shedding or chlorosis was estimated for all shoots in the productive portion of the crown. Models were fit with the original untransformed data. All factors (panels A through D) were plotted within the range of the observational data. Shaded regions delineate asymmetric likelihood-based support limits. mechanistic explanation for this pattern is apparent, and synthesis rates for this compound were similar among surviving and killed trees. Faccoli and Schlyter (2007)  did attribute antifeedant properties to taxifolin, but the magnitude of the detected effect in that experiment was small compared to the diet-suppressing influence of catechin.
Funding This research was supported by grants: " Building up an excellent scientific team and its spatiotechnical background focused on mitigation of the impact of climatic changes  to forests from the level of a gene to the level of a landscape at the  FFWS CULS Prague ", No. CZ. 02. 1. 01/0. 0/0. 0/15_003/0000433  financed by OP RDE; " Factors of Norway spruce survival during large-scale disturbance in NP Šumava " No. A_21_09  funded by  Internal Grant Agency FFWS CULS in Prague ; "Probabilistic modelling of tree survival in Norway spruce during severe bark beetle outbreaks based on tree growth history, climate variation, and neighborhood factors" funded by  FFWS CULS in Prague ; " Development of integrated modern and innovative diagnostic and protection methods of spruce stands with the use of semiochemicals and methods of molecular biology ", No.
Matrix solution was loaded in duplicate onto each MALDI-TOF plate with or without a bacterial test standard (Bruker Protein Calibration Standard I) to control loading on the MS target plate, matrix quality, and MALDI-TOF apparatus performance  (Nebbak et al. , 2017). MALDI-TOF MS parameters. The protein mass profiles of each sample were obtained using a microflex LT spectrometer with the flexControl software (Bruker Daltonics) with special parameters recommended by  Kumsa et al. (2016). The spectra obtained were visualized by the flex-Analysis 3. 3 software and transferred to ClinProTools version v. 2. 2 and MALDI Biotyper v. 3. 0 (Bruker Daltonics, Germany) for analysis  (Karger et al. , 2012). Spectrum analysis and reference database creation. For the spectrum analysis, the ClinProTools v2.
2 and flexAnalysis v3. 3 software programs were applied to evaluate the reproducibility of the MS spectra obtained from the same arthropods' species and assess intraspecies specificity  (Fotso Fotso et al. , 2014; Nebbak et al. , 2016). After the morphological and molecular confirmation of arthropod identification, two to five high-quality reproducible profiles with a high peak intensity of each species conserved in 70% ethanol were selected and loaded into the MALDI Biotyper 3. 0 to create as reference spectra  (Kumsa et al. , 2016; Boucheikhchoukh et al. , 2018). As for the upgrade arthropod MALDI-TOF database, reference spectra were established by spectra at least two specimens per species of both genders using the algorithm MALDI Biotyper software v3. 0 (Bruker Daltonics;  Diarra et al.
, 2017; Boucheikhchoukh et al. , 2018). Blind test. All specimens were subjected to a blind test, except those used as reference spectra. The log score values (LSVs) were calculated by the MALDI Biotyper software v. 3. 0 (Bruker Daltonics) to estimate the reliability of species identification. The LSVs ranged from 0 to 3; these corresponded to the degree of similarity between the spectra submitted by the blind test and the MS reference spectra in the database. The identification was considered reliable with an LSV of at least 1. 8  (Yssouf et al. , 2013; Kumsa et al. , 2016). The identification of blind tested samples was assessed by taking the highest LSVs associated with a spectrum quality.
Standard PCR for pathogens identification The positive qPCR samples were subjected to standard PCR and sequencing. For the identification of Piroplasmida (Theileria sp. and Babesia sp. ), Anaplasmataceae (Anaplasma sp. and Ehrlichia sp. ), and Borrelia sp. , 1100 bp of the 18S rRNA gene, 520 bp fragment of the 23S rRNA gene, and 1200 bp of the 16S rRNA gene were used, respectively  (Bottieau et al. , 2012; Dahmani et al. , 2017; Dahmana et al. , 2019). The PCR reactions were performed on a thermal cycler (Applied Biosystems, Paris, France) using the AmpliTaq Gold® 360 Master Mix (Thermo Fisher Scientific, USA), according to the manufacturer's recommendations. In each reaction, negative and positive controls were used. Moreover, 1. 5% agarose gel stained with SYBR® Safe (Invitrogen, USA) electrophoresis was applied to confirm the amplification and visualized by Image Lab software (Bio-Rad, Marnes-La-Coquette, France).
Sequencing and phylogenetic analyses The purification of PCR products was applied using NucleoFast 96 PCR plates (Macherey-Nagel, Düren, Germany), according to the manufacturer's recommendations. The purified PCR products were sequenced using the BigDye Terminator Cycle Sequencing Kit (3130×1 Genetic Analyzer, ABI PRISM). The sequences obtained were assembled and edited by ChromasPro software (ChromasPro 1. 7, Technelysium Pty Ltd. , Tewantin, Australia), and the corrected sequences were compared with the reference sequences available in GenBank by the BLASTn (https://blast. ncbi. nlm. nih. gov/Blast. cgi). The obtained sequences of Egyptian EVB genotypes and arthropods were recorded in GenBank. These sequences obtained and sequences available in GenBank were aligned using CLUSTAL W in MEGA software X  (Kumar et al.
MALDI-TOF MS analyses of arthropods For H. equina, the random MALDI-TOF test revealed that the obtained spectra did not exist in our database and all tested flies were identical to each other. However, the random MALDI-TOF test of Rhipicephalus spp. revealed that the obtained spectra were not identical to each other and they did not exist in our database. Therefore, the reference database was essential for the accurate identification of arthropod specimens. After molecular confirmation, H. equina and R. annulatus genotypes from Egypt were introduced as reference MS spectra into the laboratory database. Once the database updated, a blind test was performed on the rest of arthropods. The microflex LT MALDI-TOF MS enabled the identification of 105/105 (100%) for H. equina (1. 22 ≤ LSVs ≤ 2. 48, Fig.
Molecular identification of arthropods Molecular identification was applied on specimens selected for the creation of reference MS spectra. For H. equina, based on the CO1 gene, standard PCR and sequencing were applied and succeeded in identifying sucking flies as H. equina (GenBank: MK737646) with 99% (589/590) identity to those of H. equina from Portugal (GenBank: MN868873). In addition, DNA markers (CO1 and 16S rRNA genes) were used in standard PCR and sequencing on Rhipicephalus spp. specimens selected for the creation of reference MS spectra. The BLAST analysis revealed that R. annulatus was the only Rhipicephalus sp. detected in this study with different genotypes. By the 16S rRNA gene, R. annulatus (GenBank: MK737647 and MK737648) was identified and collected from horses and donkeys in Beni Suef province with 99% (430/434) identity to those of R.
annulatus derived from cattle from Beheira province, Egypt (Gen-Bank: KY945491). In further identification through the CO1 gene, the BLAST search confirmed that Rhipicephalus sp. was R. annulatus (Gen-Bank: MZ505561 and MZ505565) with 99% (802/809) similarity to R. annulatus from cattle from the United States (GenBank: KX228534). The phylogenetic position of the five obtained R. annulatus compared with other reference tick species is illustrated in Fig. 4.
annulatus collected from donkeys. The BLAST research reported the presence of two different genotype groups that were attributed to two genera of Anaplasmataceae (Anaplasma and Ehrlichia) in R. annulatus. A new A. platys genotype (GenBank: MN614105) was detected in one R. annulatus with 100% (415/415) identity with A. platys-like genotype detected in cattle and sheep from the same locality of Beni Suef province (GenBank: MN626397 and MN626398) and clustered in the same clade (bootstrap value 99, Fig. 5 ). In addition, this genotype had 99% (409/415) identity with those detected in the blood of New Caledonian dogs (GenBank: KM021425). Another Anaplasma sp. that was detected in R. annulatus was Anaplasma marginale genotype (GenBank: MN614106) with 100% (380/380) identity to A. marginale detected in the blood of cattle from Algeria (GenBank: MH321194).
Moreover, two Ehrlichia spp. with three different genotypes were found in R. annulatus; a new genotype close to Candidatus Ehrlichia rustica was derived from one R. annulatus (GenBank: MN614107) with 99% (413/ 415) identity to those of E. rustica found in Amblyomma variegatum from Côte d'Ivoire (GenBank: KT364330), and Candidatus E. rustica genotype was derived from three R. annulatus (GenBank: MN614109) with 100% (415/415) identity to the same reference. Finally, a novel potential Ehrlichia sp. was derived from three R. annulatus (GenBank: MN614110) with 98% (404/413) identity with Candidatus Ehrlichia urmitei detected in Rhipicephalus bursa in France. In the phylogenetic tree, the sequence of novel potential Ehrlichia sp. clustered in a well-supported branch (bootstrap value 55) with Ehrlichia ruminantium (Fig.
5 ). The phylogenetic position of these genotypes is illustrated in Fig. 5. For Borrelia sp. , the result of qPCR revealed that 5 of 166 (3%) were positive for Borrelia sp. , including 3. 3% in R. annulatus (2/61) and 2. 9% (3/105) in H. equina. Using 16S rRNA gene sequencing, we identified it as B. theileri that derived from two R. annulatus. The result revealed that a new genotype of B. theileri (GenBank: MN619805) was identical to B. theileri previously detected in sheep and cattle from the same locality of Beni Suef province (GenBank: MN621893 and MN621894). The phylogenetic position of this B. theileri genotype is illustrated in Fig. 6.
Pathogen detection in Equidae All blood samples were screened by qPCR to determine the presence of pathogen DNA of piroplasmids, Anaplasmataceae, Rickettsia spp. , Borrelia spp. , C. burnetii, and Bartonella spp. DNAs of Piroplasmida and Anaplasma spp. were detected in horses and donkeys, while equines were free from DNA of other pathogens (Table  3 ). For Anaplasma sp. , 33 of 335 (9. 8%, 28 horses and five donkeys) were positive by the qPCR system. We obtained good-quality 23S rDNA   collected from Egypt (GenBank: MN625935). The position of these genotypes was shown in the phylogenetic tree (Fig. 5 ). Piroplasmida DNA was detected by qPCR in 27 of 335 animals (8. 1%, 25 horses and two donkeys). As a result, PCR and sequencing of the 18S rRNA gene portion were performed and allowed the identification of 15 of the 27 qPCR positive samples (4.
5%, 13 in horses and two in donkeys). According to the BLAST search, we identified three Piroplasma species: T. equi, a new potential Theileria sp. "Africa," and Theileria ovis. In horses, we identified two Piroplasma sp. (T. equi and Theileria sp. "Africa"). By the BLAST research, we obtained two genotypes of T. equi; one genotype was derived from one horse, and another genotype was derived from three horses (GenBank: MN625897 and MN625898) with 100% (922/922) identity to those of T. equi detected in horses from Israel (GenBank: MK063843 and MK063842, respectively). Moreover, we found four genotypes of Theileria sp. "Africa" (GenBank: MN625900, MN625901, MN625902, and MN625903) that clustered in a separate clade with a good bootstrap support with the other Theileria sp. "Africa" previously detected in African horses (Fig.
7 ). In addition, by the BLAST analyses, one genotype from horse was considered to be a new genotype with 99% (917/918) identity to T. equi detected in horse blood from Brazil (GenBank: MG052913). Moreover, three genotypes were identified as T. equi with 100% (923/923) identity to those detected in horse blood from Turkey and Sudan (GenBank: MG569896, MG569893, and AB515309, respectively). In donkeys, T. ovis shared 100% (919/919) identity with sheep and buffalo previously detected in the same province in Egypt (GenBank: MN625886 and MN625887).
Acknowledgment and Funding This study was supported by the  Institut Hospitalo-Universitaire (IHU) Méditerranée Infection , the  National Research Agency  under the program " Investissements d'avenir ", reference  ANR-10-IAHU-03 , the  Région Provence-Alpes-Côte d'Azur and European funding FEDER PRIMI. The authors acknowledge funding from the  Science and Technology Development Fund (STDF)  and  Institut Francais d'Egypte (IFE)  (ID:  30652 ) for the support of this research.
Acknowledgements We thank  Baptiste Sorin  and our other colleagues from the  BIOEPAR Dynamo team  for their comments and advice on this work. We also acknowledge the two reviewers for their encouragement to expand the scope of this study. We are most grateful to the  Bioinformatics Core Facility of Nantes BiRD , member of  Biogenouest ,  Institut Français de Bioinformatique (IFB)  ( ANR-11-INBS-0013 ), and to the  INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale bioinformatics Facility  [42]), for the use of their computing resources. This work was also granted access to the  HPC resources of IDRIS (Jean Zay supercomputer)  under the allocation 2021-101889 made by  GENCI.
Funding This work was carried out with the financial support of the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101000494  (DECIDE) and with a grant of the  Carnot institute France Futur Élevage  (project  SEPTIME ). This publication only reflects the author's view and the  Research Executive Agency  is not responsible for any use that may be made of the information it contains. This work was also supported by the  French region Pays de la Loire  ( PULSAR grant ).
The exchanges were audio-recorded for internal report writing, and used for further analysis. We analyzed the characteristics shared by the farming systems identified by the workshop participants, using the audio recordings and report to stay close to the collective expression of principles shared among the workshop participants. We analyzed the exchanges concerning individual practices to capture their diversity in more detail. We conducted a thematic analysis of the notes reporting the exchanges. The diversity of practices described for each topic was reported with the aim of illustrating the diversity within a shared principle. We used this empirical data to better characterize the nature of the shared principles.
Acknowledgments The authors are very grateful to the editors and reviewers ( L. Schaefer ,  A. Zernack , and  L. Capra ) for their insightful revisions, comments, and suggestions for improving this work. The authors thank  N. Hueber  and all of the BRGM team for helping collect field data. The authors thank the inhabitants of Salazie for allowing us access to their properties. The authors would like to acknowledge  Reunion Island's Regional Council  and the  Institut National de l'Information Géographique et Forestière (IGN)  for.
The criteria for extracting principal components were an eigenvalue > 1 and a minimum cumulative variance of 50%. The extracted principal components were described with the variable residuals with loadings > 0. 50 or < -0. 50. A hierarchical clustering on principal components (HCPC) was performed on the principal components extracted from the PCA to identify clustered groups of pigs based on social behaviors. The Euclidean distance was used between individuals and the Ward's criterion was applied as clustering method  60. Characterization of clusters with non-social individual parameters. Statistical tests were computed using the HCPC function of the 'FactorMineR' package to identify the principal components and variables used in the multivariate analyses that characterized the clusters. Principal components and the supplementary quantitative variable (total number of social interactions) characterizing the clusters were identified using v-tests -a v-test greater than 1.
Acknowledgements The authors thank  R. Comte ,  D. Grivault ,  M. Oiry  and  C. Robert  for their technical assistance in the laboratory, with a special thanks to  C. Mustière  and  F. Thomas  for developing the method for 5-HT analysis. The authors gratefully acknowledge the personnel from the 'Porganic' experimental organic station, led by  S. Ferchaud , for taking excellent care of the animals and for their help in field measures, with a special thanks to  T. Terrasson  for managing video material and recordings.
qPCR experiments Quantitative PCR reactions (qPCR) were performed in a final volume of 20 μl consisting of 1 μl of the cDNA, 15 μM of the specific primers  (Feria et al. , 2016)  and 10 μl of SensiFAST SYBR No-ROX kit (Bioline). PCR was conducted on the MiniOpticonTM Real-Time PCR Detection System (Biorad), and the threshold cycles (Ct) were determined using Bio-Rad CFX Manager software for all treatments. To normalize the obtained values actin expression was used as an internal control (actin rRNA: forward 5 ′ -CCAGCTCATCTGTTGAGAAG-3 ′ , reverse 5 ′ -CCTGTGAACAATCGATGGAC-3 ′ ).
Acknowledgments We thank  Prof. Sofía García-Mauriño  and  Prof. José A Monreal  from the  University of Seville  for discussion and advice. This research was supported by  Spanish Ministerio de Economía y Competitividad  (( AGL2012-35708 ;  AGL2016-75413-P ;  AGL2016-75413-P ) and by  Junta de Andalucía  ( P12-FQM-489  and  PAI group BIO298 ).
Acknowledgements We apologize to all our colleagues in this field whose work could not be cited due to a limited number of references. This work was supported by funds from the  Cluster of Excellence on Plant Sciences of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy -EXC-2048/1project ID 390686111 to M. B. and by funds from the  French National Program   EC2CO  ( Ecosph ere Continentale et Côti ere ) to JA. We thank three anonymous referees for providing constructive comments on the manuscript.
Statistical analysis of the data The rating criteria analysed were RF and Number/g. In the first step, data from the plants inoculated with X. diversicaudatum and X. index were considered together. A two-factor ANOVA was carried out on data from combinations of the factors 'accession' and 'nematode species' using XLSTAT software (version 2014. 5. 03; Addinsoft, Paris, France). F values and probabilities Pr > F for each factor are reported in Supplementary data (see Results section). A multiple comparison analysis was performed on the data using the Fisher LSD multiple range test at P ≤ 0. 05. Next, the four accessions were analysed separately for X. diversicaudatum and X. index using a single-factor ANOVA completed by the multiple-comparison analysis and the Fisher LSD multiple range test at P ≤ 0.
CONCLUSION Our study allowed to get the first data regarding the host suitability of different grapevine accessions to X. diversicaudatum. We evaluated the host suitability of X. index-resistant material derived from muscadine together with X. index-resistant and -susceptible reference accessions, to a population of X. diversicaudatum obtained from woody host plants. We showed that the host suitability of these materials paralleled that of X. index, which suggests that resistance determinants to both nematode vectors might be the same or linked. Our results illustrated a poorer host suitability of grapevine materials to the X. diversicaudatum population used than to X. index. In order to take into account the higher diversity of X. diversicaudatum than X. index, other populations of the former species, and in particular a population obtained from grapevine, will have to be evaluated.
Many giant viral genes and gene products related to energy production and metabolic pathways have been identified recently  (Aherfi et al. , 2022; Ha et al. , 2021; Moniruzzaman et al. , 2020 ; Figure  1 ). In a detailed study examining a database of 2436 annotated Nucleocytoviricota, including metatranscriptomic data from California's surface waters, hundreds of viruses, mainly from the Mimiviridae and Phycodnaviridae families, were identified. Many expressed viral transcripts in this dataset are genes involved in glycolysis (e. g. phosphofructokinase  [EC 2. 7. 1. 11] , glyceraldehyde 3-phosphate dehydrogenase [EC 1. 2. 1. 12], phosphoglycerate mutase  [EC 5. 4. 2. 11] ), gluconeogenesis (e. g. phosphoenolpyruvate carboxykinase  [EC 4. 1. 1. 32 ] and fructose-1,6-bisphosphatase  [EC 3. 1.
, 2011; Coutinho et al. , 2017; Lindell et al. , 2005; Sharon et al. , 2009; Sullivan et al. , 2006). These proteins include photosystems I and II that drive the complete photosynthetic process during phage infection, with the overall effect of promoting maximal phage replication  (Fridman et al. , 2017). Most NCLDV groups appear to be present ubiquitously in marine environments, based on metagenomics  (Endo et al. , 2020). The majority of these are closely related to the families Mimiviridae and Phycodnaviridae: of the more than 6,700 polymerase B genes (PolB; a conserved marker of NCLDVs) assembled from the Tara Oceans dataset, 5091 were related to Mimiviridae and 981 to Phycodnaviridae  (Endo et al. , 2020). However, other studies suggest that certain NCLDVs may be endemic to the regions in which they were identified  (Needham et al.
Acknowledgments We are indebted to  Dagoberto Sinisterra ,  Omar Marı ´n ,  Andres Lopez, Sebastian Narvaez ,  Paula Palacios ,  Nicole Vargas ,  Bryan Ospina ,  Anderson Arenas ,  Sebastian Cañas  and  Marı ´a Fernanda Rondo  ´n for their help during the hard work of nest excavation, to  Julian Flavell  for english revision, to  Wilmar Torres , from The  Universidad del Valle, Graduate Program in Biology , for statistical support, and to  Nataly Amarillo  for her help in designing Fig 5. We also thank four anonymous referees for helpful comments on a previous version of the manuscript.