dataesr/scientific-paragraphs-categorization huggingface dataset:
[29]. However, when SNP calls are compared across different platforms that use different technology, such as between the Infinium 20 K and the Axiom 480 K apple SNP arrays, concordance rates may be more variable. This variability is likely due in large part to differences in the chemistry, probe lengths, probe densities used across these platforms, and/or differences in the genotyped germplasm. Concordance rates between the Illumina Infinium 20 K and Affymetrix Axiom Apple 480 K SNP array data were reported as 96 to 98%, based on 53 common individuals  [34]. This high rate is promising and is in line with those found in other organisms: an average concordance of 96 to 98. 8% was reported in human  [37] , sheep  [38] , and swine  [39]. However, levels of concordance were not documented at the individual SNP level, as would be needed for accurate data integration.
Additionally, none of these studies reported on the technical or biological reasons for the observed SNP call discordances. In comparative work prior to this, compatibility between array platforms has been determined by evaluating the concordance of SNP calls of genetically duplicate samples genotyped on both platforms, which is usually limited to few individuals. Such evaluations would be made more useful by considering SNP call accuracy via assessments of Mendelian inconsistent and Mendelian consistent errors  [40]  across direct parent-offspring relationships. This approach could increase the number of informative comparisons and could also expand the data chain to multiple successive generations. Moreover, the use of inheritance patterns surpasses analyses on duplicate genotypes in determining the precise genotype of an individual, allowing, for instance, the revealing of null alleles.
The power of compatibility studies may increase even further by an integrated Mendelian error analyses on a mixed data set, rather than within array analyses. The identification and troubleshooting of Mendelian inconsistent and consistent errors have been previously described using Infinium SNP array data in apple, cherry, and peach  [29]. Extensive pedigree information exists for apple from breeding records (e. g. from websites such as https://hort. purdue. edu/newcrop/pri/), pomological textbooks (e. g  [41]. ), historic pedigree reconstruction studies  [6, 20, 27, 29, [31] [32] [33] [42] [43] [44] [45] , and may also be revealed by the available SNP data. Mendelian inconsistent and consistent errors in SNP array data can result from the presence of secondary polymorphism(s) on probe sites and/or the presence of duplicated or paralogous sequences.
This study was undertaken in view of our need for an integrated high-quality dataset of Illumina Infinium® 20 K and Affymetrix Axiom® 480 K SNP array data in apple (Malus × domestica). While creating the integrated and highly curated dataset, we took the opportunity to thoroughly evaluate observed discordances and inaccurate SNP calls. Thus, the goals of this study were i) to qualify and quantify the compatibility, defined as SNP calls that are both accurate and concordant, of SNP calls across both arrays and ii) to evaluate reasons for observed probe failures and discordant SNP calls in order to improve SNP array data interpretation and probe/platform development. Towards these goals, this study included classical concordance evaluations across individuals genotyped on both platforms, as well as accuracy evaluations by detecting and evaluating Mendelian inconsistent and consistent errors across pedigrees on a mixed dataset.
Genetic map and Infinium data curation There were 10,295 SNPs that passed the Infinium SNP data curation steps and thus were included in the genetic map. Of these, 94. 1% (9685) were SNPs retained from the iGL map (Table  1 ; Additional file 1). For 12. 4% (1206) of the SNPs retained from the iGL map, new positions were assigned, and these new positions were all within their respective genetic bins on the iGL, and also within a single centimorgan (cM) of their original position except for six SNPs (Additional file 2), and except for the first 5-6 Mb of LG1, where SNPs were ordered according to other ongoing studies on the Golden Delicious doubled haploid v1. 1 (GDDH13) and the antherderived trihaploid Hanfu line (HFTH1) whole genome sequences (WGSs) (Van de Weg, personal communication). The level by which co-segregation patterns could be examined varied per SNP and some SNPs were only polymorphic in a small number of individuals.
Discussion Our results demonstrate a high degree of SNP compatibility (81. 7%) between the Infinium® 20 K SNP array and the Affymetrix Axiom® Apple 480 K SNP array across 10,295 SNPs we deemed robust in Infinium data (Table  3 ). Our initial SNP call concordance rate for duplicate genotypes across the two platforms, 97. 1%, was in line with what had been previously reported for these arrays  [34]. Our further efforts towards resolving discordances and excluding SNPs with inaccurate SNP calls on either array have now allowed the creation of a combined dataset including SNPs with compatibility clustering and thus will have an exceedingly low SNP call error rate, even for individuals that were only genotyped on one of the two arrays. This highly curated combined dataset will be useful in subsequent studies.
Researchers using data from both arrays could try to avoid issues by only using the 6417 SNPs that did not require any additional adjustments, though this would result in either a smaller number of SNPs available or a higher level of missing data present in the combined dataset, which this study sought to avoid. However, manual reclustering of the 1995 cluster plots was very time consuming. The most common manual curation performed was to set some percentage of individuals to missing data in the Axiom data due to their errant positions between clusters in cluster plots (B and D in Table  3  and Additional file 4). This issue occurred in 52% of the 1995 SNPs requiring manual curation to make them compatible and could possibly have been greatly reduced or largely eliminated by using more stringent threshold settings in the Axiom Analysis Suite  [62].
[34]  excluded 8. 4% of the SNPs that required no adjustment to make compatible in this study (noted per SNP in Additional file 1). As such automated clustering solutions are currently unavailable, manual curation methods were used in this study. We considered the effort of manual curation worth our time, as we wished to obtain an integrated SNP array dataset with a maximum number of compatible SNPs to use for downstream analyses. However, this manual curation was time consuming, with diminishing returns for each unit time spent on addressing the increasingly complex cases. Others seeking to create such an integrated SNP array dataset with highly accurate SNP calls may do well to establish a desired balance between time spent and level of curation while considering the requirements of the dataset in the context of their research aims.
Conclusions We identified 8412 SNPs that could be used to construct an accurate integrated Infinium and Axiom apple SNP array dataset. This specific result will aid future studies involving tracing the inheritance of haplotypes in a combined array dataset. Problematic clustering encountered in our study was primarily due to secondary polymorphisms, alternate probe binding locations interfering with probes' target sequences, and differences between probes and target genomic sequences. The proximity of the sequence difference(s) to the target SNP between probes and their target and non-target sequences was identified as a major factor in SNP inclusion and compatibility. However, secondary polymorphisms identified roughly on the final 40 (of 50) positions on the Infinium array and the final seven positions on the Axiom array did not impact SNP inclusion and compatibility.
Genetic map revision, curation of Infinium SNP data, and filtering of SNPs We used a revised version of the iGL map  [15]  to ensure adequate evaluation of the accuracy of SNP data at the later stages of this research. We revised the SNP sorting order and cM positions for SNPs in this map using the process described in Vanderzande et al. [29]  using physical coordinates obtained by blasting  [65]  the 50 nt long probe sequences from all SNPs on the Infinium array  [3]  against the GDDH13 WGS  [16]. BLASTn parameters used are as follows: Expected Value Threshold 0. 001, Word Size 11, Match/Mismatch Scores 1 and -2, Gap Costs; existence 5 and extension 2. Parameters were chosen to generate a dataset neutral to bias in detection of orthologs and paralogs  [66]. To ensure the accuracy of the physical positions, results from the more extensive, 121 long probe source sequences  [3]  were used in a few instances where no results were found using only the 50 nt probe sequences.
Identification and evaluation of discordant SNPs in duplicated individuals Only SNPs that passed the previously described curation steps on the Infinium array data were considered for evaluating discordant SNPs in duplicate individuals. SNP calls for Axiom genotyped individuals were obtained using Axiom Analysis Suite software using the default diploid settings. Discordant SNP calls were identified for all pairs of individuals that were genotyped on both arrays. They were further evaluated to determine the nature of the discordancy and whether manual cluster adjustments could be made to improve concordance. In cases of ambiguity regarding which of the discordant calls was accurate, pedigree information was used to evaluate inheritance or co-segregation using FlexQTL™. Confirmed null alleles in Infinium data were not tallied as discordant SNP calls with Axiom data, as they had been hand called during the Infinium data curation step.
Identification of higher order SNP data quality issues affecting concordance and accuracy Following the previous investigation of discordance between duplicated genotypes, discordance and accuracy were investigated in a single, mixed Infinium-Axiom dataset using all germplasm included in this study. Individuals were filtered to include only those having published parent-offspring relationships (Additional file 3). This dataset was imported into FlexQTL™ to identify Mendelian inconsistent and consistent errors which were then systematically evaluated as described in Vanderzande et al. [29]. Through this evaluation SNPs were identified that had irresolvably inaccurate calls. SNPs with inaccurate Infinium data were reclassified as excluded. Following this reclassification, SNPs with inaccurate Axiom data were deemed incompatible and classified as described in the section "Classification of compatible and incompatible SNP clustering".
To accomplish this, we first identified secondary polymorphisms among 53 individuals from the 480 K Axiom array SNP discovery panel  [34]  (Group 5 in Additional file 3) by aligning their sequence data to the GDDH13 WGS  [16]  using the Burrows-Wheeler Alignment tool  [68]  in conjunction with SAMtools  [69]  and identified all variants within the physical coordinates for the 50 nt long Infinium probes, as identified through the previously described BLAST hits. Secondary polymorphisms were considered if they were present in at least 10 % of the sequenced individuals. We used the resulting data to demonstrate how secondary polymorphisms influenced SNP calling quality in two ways. First, for each relevant (in) compatibility classification, we identified one or more representative SNPs. Next, their sequence-based polymorphism data was examined alongside cluster plot data to determine whether the observed clustering problems resulting in inaccurate and thus incompatible SNP calls were likely caused by secondary polymorphisms, as to provide explicit example cases.
Second, information on the positions of secondary polymorphisms was correlated to the inclusion/exclusion rate in Infinium data and by compatibility of Axiom data with included Infinium SNPs. Additionally, to determine the effects of major differences between a probe and an intended target sequence, we also evaluated all cases of included SNPs that had probes with E-values greater than 1. 0E-12. To minimise the risk of examining artefacts due to high E-values being caused by local sequence errors in the GDDH13 WGS  [16] , we also considered BLAST hits from the HFTH1 WGS  [53]  for this step, which became available during this study. (www. fruitbreedomics. com), which was co-funded by the EU seventh Framework Programme.
Availability of data and materials All accessions included in this study are listed in Additional file 3. The Axiom data used in this study was made available through Muranty et al. [32]. Curated Infinium data for duplicates and pedigree ancestors available at the Genome Database for Rosaceae (https://www. rosaceae. org/publication_ datasets, accession number tfGDR1050). The highly curated integrated apple SNP array dataset will be shared through following publications on downstream analyses.
Cell Growth Cell growth was monitored using an infrared probe (Excell210, CellD, Roquemaure, France) continuously measuring absorbance at 880 nm and storing data every minute. The absorbance data were calibrated in dry weight. Dry cell weight was determined by filtering 10 mL of bacterial suspension (straight out of the bioreactor) through a 0. 20 µm polyethersulfone membrane (Supor ® , PALL Biotech, Saint-Germain-en-Laye, France). The filter was then dried for 24 h at 80 • C. Measurements were obtained in triplicate. Mass concentrations were finally converted to mol C L -1 (carbon-mol of biomass per liter), assuming the simplified unit-carbon biomass formula CH 1. 8 O 0. 5  [30].
Model Parameter Identification The system of kinetic equations for a single fermentation experiment included 24 parameters: five yield coefficients, five inhibition parameters, five growth/production rates, five Monod-like saturation constants, three shape factors, and one lag time. Due to a limited number of experimental data and to facilitate the identification procedure, a single value was adopted for the inhibition parameter (K Im ) and the Monod saturation constant (K Sm ) of the four identified metabolites. Moreover, 10 parameters were fixed for all experiments: the shape factors, the yield coefficients, and the Monod saturation constants (K SX and K Sm ). For each fermentation, lag time was determined by graphical readout. This simplification of fixing parameters independent of operating conditions is supported by two assumptions often used in the literature: (1) metabolite production yields are constant and therefore independent of culture conditions  [41]  and (2) the saturation constant of the Monod model depends only on the nature of the substrate  [33, 38] , which was the same in all experiments of this study.
Aims: To quantify how fine-grain (within-plot) beta diversity differs among biomes and vegetation types. Study area: Palaearctic biogeographic realm. Methods: We extracted 4,654 nested-plot series with at least four different grain sizes between 0. 0001 m² and 1,024 m² from the GrassPlot database spanning broad geographic and ecological gradients. Next, we calculated the slope parameter (z-value) of the power-law species-area relationship (SAR) to use as a measure of multiplicative beta diversity. We did this separately for vascular plants, bryophytes and lichens and for the three groups combined (complete vegetation). We then tested whether z-values differed between biomes, ecological-physiognomic vegetation types at coarse and fine levels and phytosociological classes. Results: We found that z-values varied significantly among biomes and vegetation types.
Introduction The Palaearctic biogeographic realm (sensu  Olson et al. 2001)  is the largest terrestrial realm on Earth, covering more than 52 million square kilometres or approximately 40% of the ice-free terrestrial surface of the globe. Its elevational extent ranges from the lowest (423 m b. s. l. ; Dead Sea) to the highest (8,848 m a. s. l. ; Mt. Everest) point of the terrestrial surface and is thus exceptionally large. The resulting environmental heterogeneity includes eight of the ten biomes recognised by  Bruelheide et al. (2019)  and translates into a huge diversity of vegetation types, ranging from single-species stands to those that hold the world records for vascular plant species richness at most grain sizes below 100 m 2  (Wilson et al. 2012; Roleček et al. 2021). The GrassPlot database  (Dengler et al 2018; Biurrun et al.
2009; Dembicz et al. 2021). In fact, the z-value measures multiplicative β-diversity, standardized by the grain size ratio  (Polyakova et al. 2016; Dembicz et al. 2021). β-diversity quantifies the dissimilarity between local communities and, together with mean α-diversity, makes up the diversity of a larger area (γ-diversity)  (Whittaker 1960). Understanding patterns and drivers of β-diversity is thus of high importance both for ecological theory and biological conservation. According to  Storch (2016) , z-values are closely related to the mean occupancy of species, i. e. the rarer species are in the finest grain size on average, the higher the z-values are. Dembicz et al. (2021)  analysed which factors influence small-grain z-values using the GrassPlot database and found that herb layer cover, elevation, rock and stone cover, latitude and land use had the greatest explanatory power.
Vegetation-plot data We used plot data from the collaborative vegetation-plot database GrassPlot  (Dengler et al. 2018; Biurrun et al. 2019 ; https://edgg. org/databases/GrassPlot). GrassPlot assembles vegetation-plot data from grasslands and other non-forest vegetation types (rocks and screes, deserts, ruderal communities, etc. ) from the Palaearctic biogeographic realm. We retrieved all nested-plot series from GrassPlot (v. 2. 04 on 20 March 2020) that contained at least four different grain sizes (4,654 series, consisting of 164,578 individual plots ranging from 0. 0001 to 1,024 m 2 ). All series had information on vascular plants, 890 on terricolous (soil-dwelling) bryophytes, 894 on terricolous lichens, and 862 on all three taxonomic groups (i. e. total species richness of the vegetation, hereafter termed complete vegetation).
SAR modelling We fitted a power function to each dataset (i. e. a species group within a nested-plot series) using the non-transformed "S-space" (S = c A z ) and the "logarithmic S-space" (log 10 S = log 10 c + z log 10 A) with S = species richness, A = area in m², and c and z the fitted "intercept" and "slope" parameters, respectively. Both approaches are valid, have been widely used in the literature, and have different advantages and limitations (see  Dengler 2009; Dengler et al. 2020a ). Due to the different treatment of the error structure, the parameter estimates in the two mathematical spaces usually deviate. Generally speaking, fitting in S-space gives more weight to a good fit at larger grain sizes, whereas fitting in log S-space gives more weight to a good fit at smaller grain sizes and typically reduces heteroscedasticity.
2007; Dembicz et al. 2021 ) and biotic invasions  (Powell et al. 2013). Our results, broken down into different typologies (Suppl. material 3), provide a set of "benchmarks", which future local-oro continental-scale studies in Palaearctic open habitats can utilise, in a similar way to the α-diversity benchmarks provided in the GrassPlot Diversity Explorer (https://edgg. org/index. php/databases/GrasslandDiversi-tyExplorer; Biurrun et al. 2021). For the missing or less represented units, Suppl. material 3 can be understood as a call to collect such nested-plot data, preferentially using the EDGG standard  (Dengler et al. 2016) , and contribute them to the collaborative GrassPlot database  (Dengler et al. 2018; Biurrun et al. 2019 ) so that we can draw an even more complete picture in the future.
Acknowledgements We thank all vegetation scientists who carefully collected the multi-scale plant diversity data from Palaearctic Grasslands available in GrassPlot. The  Eurasian Dry Grassland Group (EDGG)  and the  International Association for Vegetation Science (IAVS)  supported the EDGG Field Workshops that generated a core part of the GrassPlot data. The  Bavarian Research Alliance (via the BayIntAn scheme)  and the  Bayreuth Center of Ecology and Environmental Research (BayCEER)  funded the initial GrassPlot workshop during which the database was established and the current paper was initiated (grants to J. D. ). I. B. , J. A. C. and I. G. -M. were supported by the  Basque Governement  ( IT936-16 ). Data of R. K. P. were collected with support of  US NSF  grant #  BSR-8506098. A. N. was supported by the  University of Mazandaran  under a continuous "Master Research Plan".
Influence of the Surveillance System's Maturity on the Process Outputs These two case studies tackle surveillance systems with contrasting degrees of maturity, as they are under development in Vietnam and already well-established in France. Despite the differing maturities of their systems, participants in both cases emphasized that the performance of a One Health surveillance system depends essentially on the quality of each of the sectoral programs that are to integrate. Hence, in Vietnam, despite the pressing plea of international organizations in favor of a fully integrated AMR surveillance (3), the participatory process allowed participants to affirm their own positioning centered on more basic needs within each of the One Health components. Thus, they considered the strengthening of surveillance capacities in existing programs as a priority in the mid-term, before considering any data integration.
In France, the quality of the data produced by the 18 existing programs was also identified as an essential prerequisite to achieve the objective of the desired One Health surveillance system. Interestingly, integration itself was then considered under the lens of an increase of information utility. The question of information utility was tackled in the French case study from its user's standpoint, an aspect that was absent from workshops in Vietnam. This sharp contrast was directly linked to the system's maturity. The French system's greater stabilization, in terms of information production, allowed stakeholders to better focus on its use and impact. This user-based vision of health surveillance value and required improvement appears to be a quite recent concern, with methodologies that remain to be elaborated  (13).
ACKNOWLEDGMENTS We would like to warmly thank the participants in the series of workshops held in France and Vietnam, as well as the  National Institute of Veterinary Research and the French Agency for Food, Environmental and Occupational Health and Safety  for hosting these workshops in conditions conducive to discussion and innovation. We would also like to thank  Panomsak Promburom ,  Emeline Hassenforder , and  Nicolas Salliou  for their contribution to the development of the socio-technical framework.
, 2015). In 2016, water and soil samples taken from a bank of the Arakawa River, Japan led to the characterization of tokyovirus  (Takemura, 2016) , and samples of sewage collected in the Pampulha lagoon, Brazil led to the characterization of Brazilian marseillevirus  (Dornas et al. , 2016). In 2017, noumeavirus  (Fabre et al. , 2017)  and kurlavirus  (Chatterjee and Kondabagil, 2017)  were isolated from a muddy sample of fresh water collected in a pond near Noumea airport, New Caledonia and a sewage water sample collected in Mumbai, India, respectively. In 2018, two marseilleviruslike viruses were isolated from soil samples collected in an aboriginal village (Serendah village) in Peninsular Malaysia  (Tan et al. , 2018)  and from a fresh water pond bed soil in Shanghai (GenBank MG827395).