dataesr/scientific-paragraphs-categorization huggingface dataset:
In summary, we have demonstrated that the use of stitched multiplex analysis in combination with MPA PASS software is a powerful tool for creating, exploring, and analyzing data from hundreds of combinations of markers that can have great utility in guiding downstream quantitative single-EV methods or bulk EV subset analysis methods. This methodology holds utility for screening a large variety of markers simultaneously on EV-containing samples, with the main limitations being sample quantity and the semiquantitative nature of the assay itself. While super-resolution analysis (single-molecule detection) of single EVs is a highly quantitative assay with a demonstrated ability to identify clinical differences in samples, the clinical utility of such super-resolution assays is limited  (Lennon et al.
Multiplex samples referred to as 'neat' were aliquoted from PPP into multiplex bead mixture in the volumes outlined in the database (see key resources table) and results sections. EVs isolated from PPP using size exclusion chromatography (qEV-5, Izon Bioscience) were performed by adding 500 mL to the top of a qEV with fractions collected in 500 mL volumes. Fraction 7-9 were pooled, and the volumes incubated with multiplex beads are outlined in the database (see key resources table) and results sections. EV-Clean, a multimodal resin composition, in this case CaptoCore 700 (Cytiva Life Sciences, Cat. 17548101), prepared EVs were created by incubating 50 mL of PPP with 100 mL of resin for 30 minutes at room temperature, as previously described  (Welsh et al. , 2020c). The supernatant was removed from the resin and incubated with multiplex beads in the volumes indicated within the database (see key resources table) and results sections.
The EV-capture bead mixture was washed using vacuum manifold and immediately resuspended in 150 mL buffer to each well. Wells were reverse pipetted to resuspend beads. Antibody (information including volume, concentration, manufacturers, catalogue numbers can be found in Table  1  and respective MPA PASS. xls database files for each assay) was then added to each well and incubated, protected from light and shaking, for two hours at room temperature. Samples were reverse pipetted before washing using the vacuum manifold and immediately resuspending in 150 mL MACSPlex buffer, this was repeated. Each well was resuspended with 75 mL MACSPlex buffer, reverse pipetted and transferred to 96-well V-bottom polypropylene plates. This was repeated to ensure maximal bead recovery from the 1. 2 mm filter plates, giving a final volume of 150 mL.
Flow cytometry Beads were triggered using a forward light scatter threshold and optimal gains for each detector (CytoFLEX S, Beckman Coulter, USA; Aurora, Cytek Bioscience, USA) were found by performing voltration on 8-peak beads (Cat. 422903, BioLegend, USA). A detailed protocol for bead gating strategies using FlowJo Software can be found in Methods S2 and at: https://doi. org/10. 17504/ protocols. io. bm3gk8jw. Briefly, Figure  1  outlines the types of signal that can originate from the multiplex assays (Figure  1A ), shows how singlets are gated (Figure  1B ), and the fluorescent 'bar-coding' of the multiplex array (Figure  1C ) and the specificity of each population (Figure  1D ).
Nanoparticle tracking analysis Detectable sample concentration was approximated using a NanoSight LM10 instrument (Malvern, UK), equipped with a 405 nm LM12 module and EMCCD camera (DL-658-OEM-630, Andor). Video acquisition was performed with NTA software v3. 4, using a camera level of 14. Three 30 second videos were captured per sample. Post-acquisition video analysis used the following settings: minimum track length = 5, detection threshold = 4, automatic blur size = 2-pass, maximum jump size = 12. 0. Exported datasets were compiled and plotted using scripts written in MATLAB (v9. 7. 0. 1261785 (R2019b) Update 3, The Mathworks Inc, Natick, MA). MPA PASS software A standalone software package for multiplex analysis was developed using MATLAB (v9. 7. 0. 1261785 (R2019b) Update 3, The Mathworks Inc, Natick, MA).
Normalization methods Normalization methods shown in Figure  1C  are as shown below and named separation index 1 and 2 (SI1-2), fold change (FC), and background subtraction (BS). Each method uses the median of positive bead, ỹ, and negative bead, x. All methods are available for use within the MPA PASS package. Results in the manuscript used fold-change to avoid the creation of negative numbers thereby allowing logarithmic transformation of data for conducting downstream analyses without the need to alter or exclude datapoints. Limitations of the study Despite being a powerful method to identify markers useful for immune-affinity isolation methods or downstream single particle analysis, multiplex analysis itself is semi-quantitative and the changes in signal intensity can occur for a variety of reasons.
ACKNOWLEDGMENTS We thank the translational clinical programs and staff at the  United States National Institutes of Health (NIH) National Cancer Institute (NCI)  and the  National Institute of Neurological Disorders and Stroke (NINDS) Clinics. J. W. , T. T. , B. K. , J. K. , J. S. , J. L. G. , J. B. T. , M. M. , T. G, T. C. , C. H. , A. A. , H. M. , M. L, K. C. , J. G, A. L. , J. B. , E. R. , K. A. , and J. J. were supported by the  Intramural Research Program  of the  NIH, NCI, Center for Cancer Research. S. J. , S. M. , and M. P. were supported by the intramural  NIH ,  NINDS program. I. G. , A. M. , A. A-M. , and J. J. were supported by the  NIH Common Fund. J. J. specifically acknowledges  NIH   ZIA BC011502 ,  ZIA BC011503 ,  U01 HL126497 , and  UG3 TR002881 , the  Prostate Cancer Foundation , and the  NCI-Curie Partnership Program  (J.
, 2019). In this study, we hypothesize that remote sensing-based assessments of aboveground biomass (AGB) can decrease the dependency on expert knowledge when setting up CWTs and as such contribute to the automation of the land cover class mapping into PFTs for land surface models. The main rationale is that the aboveground biomass content of an ecosystem provides information on the fraction of tree PFTs of that ecosystem. In this context, the objective of this study are to (1) construct a framework of data assimilation in which biomass remote sensing products can be routinely used to update an existing or create a new CWT, (2) constrain a crosswalking table used to convert the ESA-CCI Global Land Cover map into a PFT map and (3) propagate the credible interval from using a CWT in the production of PFT maps to the simulation results of biomass and albedo maps derived from a land surface model.
Overview CWTs  (Poulter et al. , 2015)  are used to convert the 43 land cover types distinguished on the ESA-CCI land cover product into generic plant functional types (13 PFTs in  Poulter et al. (2015)  distinguished by large-scale land surface models such as the ORCHIDEE model  (Krinner et al. , 2005)  used in this study. These generic PFTs are further grouped and/or divided to match each model-specific PFT classification, using additional grid-cell information to separate grassland and. Approach to assimilate the information held by aboveground biomass (AGB) maps into PFT maps. Remote sensing AGB and land cover products are jointly assimilated to obtain cross-walking tables that can be used to make PFT maps. Due to the uncertainty analysis in the data assimilation approach, an ensemble of cross-walking tables and PFT cover fraction maps can be produced.
Aboveground biomass map This study also makes use of a continental map of AGB of African savannas and woodlands for the year 2010  (Bouvet et al. , 2018). The map has a 25 m resolution and is built from the 2010 L-band data of the Phased Array L-band Synthetic Aperture Radar (PALSAR) on the Advanced Land Observing Satellite (ALOS, 2022) satellite. Covering the African continent required about 180 data strips of which 91 % were acquired between May and November 2010. The remaining 9 % of the domain was filled with imagery from 2009 and 2008. The data have been processed by the Japan Aerospace Exploration Agency (JAXA, Japan Aerospace Exploration Agency, 2022) using the large-scale mosaicking algorithm described in  Shimada and Ohtaki (2010) , including orthorectification, slope correction and radiometric calibration behttps://doi.
org/10. 5194/gmd-15-2599-2022 Geosci. Model Dev. , 15, 2599-2617, 2022 tween neighboring strips and multi-image filtering described in  Bouvet et al. , 2018. The continental AGB map was derived as follows: (1) stratification into wet and dry season areas in order to account for seasonal effects in the relationship between PAL-SAR backscatter and AGB, (2) the development of a statistical model relating the PALSAR backscatter to observed AGB, (3) Bayesian inversion of the direct model to obtain AGB and its credible interval for pixels where no observations are available and (4) masking out non-vegetated areas using the ESA-CCI Land Cover dataset (but see Sect. 2. 1. 1). The resulting AGB map was visually compared with existing AGB maps  (Saatchi et al. , 2011; Baccini et al. , 2012; Avitabile et al.
Pre-processing One known limitation of the original AGB map  (Bouvet et al. , 2018)  is the signal saturation and in some cases the decrease of the signal  (Mermoz et al. , 2015)  occurring in L-band SAR for AGB values higher than 85 t ha -1. In order to overcome this issue, a second AGB map was created based on two other ancillary datasets: a map of tree cover  (Hansen et al. , 2013)  and a map of tree height  (Simard et al. , 2011). The AGB was estimated by deriving an empirical relationship between biomass, available from airborne lidar estimates, and the product of tree cover and tree height. The second version targets dense forest areas such as in the Congo Basin and is used to adjust the AGB values at locations where signal saturation occurred. Because of a coarser resolution from the tree height map (0.
, 1. 1 Gt) of the total AGB of Africa. Filtering retrained 15 out of the 31 land cover types including bare land. These 15 land cover types (Table  1 ) represent 96 % of the surface of Africa and 98 % of its AGB. One additional issue had to be dealt with the spatial resolution of the land cover map (9 ha) largely differed from the resolution of the AGB map (0. 01 • × 0. 01 • , 100 ha). Therefore, each observational point on the AGB map is represented by 11 pixel × 11 pixel on the land cover map. To simplify the overall data assimilation methodology (see Sect. 3. 2), we chose to use only AGB pixels (100 ha) which have a unique land cover type (i. e. , pure pixels, in terms of land cover type). To this aim, the variety of land cover types across the 11 pixel × 11 pixel within each AGB pixel (i.
, 2018) : F lc,w , F lc,b and F lc,h were defined as fractions of, respectively, woody vegetation, bare soil and herbaceous vegetation within a given land cover type; their values thus range between 0 and 1, and their sum is equal to 1. For this reason, a Dirichlet distribution was used to describe the probability distribution of the woody, bare soil and herbaceous cover fractions: Bp p ∼ N µ, σ b 2 lc , a (F lc,w , F lc,b , F lc,h ) ∼ Di(θ lc,t , θ lc,b , θ lc,h ). (9) OpenBUGS  (Thomas, 2010) , the software that was used in this study, cannot use a Dirichlet distribution as a stochastic node. This constraint can be overcome by making the cover fractions dependent on each other: F lc,w = q lc,1 (10) F lc,b = q lc,2 • (1 -q lc,1 ) (11) F lc,h = (1 -q lc,1 ) • (1 -q lc,2 ). ( 12 ) Let q lc,i , with i = 1,.
The method developed in this study helped to estimate the uncertainty of cross-walking tables which can now be used to benchmark further methodological developments. Moreover, the method identified bare soil cover fraction would be required to reduce the uncertainty of future cross-walking tables and the plant functional type maps they generate. Financial support. This research has been supported by the Centre National d'Etude Spatiales (CNES) through the TOSCA program (BIOMASS-Valorisation project; purchase order BC_T61CDD). Review statement. This paper was edited by Carlos Sierra and reviewed by two anonymous referees.
Figure1. Approach to assimilate the information held by aboveground biomass (AGB) maps into PFT maps. Remote sensing AGB and land cover products are jointly assimilated to obtain cross-walking tables that can be used to make PFT maps. Due to the uncertainty analysis in the data assimilation approach, an ensemble of cross-walking tables and PFT cover fraction maps can be produced. Subsequently, the land surface model ORCHIDEE can be run for different PFT maps to quantify the uncertainty from propagation of the uncertainty from remote sensing products into a model simulation.
Acknowledgements. This study was primarily financed by the  French space agency ,  Centre National d'Etude Spatiale (CNES) , through the  "BIOMASS-Valorisation" project from the TOSCA research program , which contributed to the funding of  Guillaume Marie  and  Cécile Dardel. The  Marie Sklodowska Curie Fellowship CLIMPRO (MSCA-Fellowship EU 895455 ) partly funded  Guillaume Marie.
Code availability. All R scripts and ORCHIDEE tag 2. 0 (rev 6592) source code are available at https://zenodo. org/ badge/latestdoi/345907299 (last access: 25 May 2021) or https://doi. org/10. 5281/zenodo. 4785328 (Marie et al. , 2021). ORCHIDEE tag 2. 0 (rev 6592) code is also available from https://forge. ipsl. jussieu. fr/orchidee/wiki/GroupActivities/ CodeAvalaibilityPublication/ORCHIDEE_tags_2. 0_gmd_2021_ Africa (last access: 29 March 2022). Data availability. The biomass map of Africa created by CES-BIO can be downloaded at https://www. theia-land. fr/en/product/ african-biomass-map (last access: 29 March 2022). It consists of a GIF file in which Africa is spatially discretized in pixels of 1 km × 1 km. The unit is a ton of dry mass per hectare (t ha -1 ). The contact person is alexandre.
bouvet@cesbio. cnes. fr. The land cover map is freely available from http://www. esa-landcover-cci. org (last access: 15 November 2020). The ecoregion map used in this study is freely available from https://databasin. org/datasets/68635d7c77f1475f9b6c1d1dbe0a4c4c/ (last access: 23 March 2022;  Olson et al. , 2022). Author contributions. GM, BSL and PP designed the experiments and GM carried them out. GM developed the OpenBUGS model code and performed the simulations. GM and BSL prepared the manuscript with contributions from all co-authors.
Summary • In apple (Malus Â domestica), most cultivars are diploid, though a sizeable number are triploids, which tend to be stronger growing, more robust, and bear larger fruit. However, triploidy is also associated with strongly reduced fertility. Some recorded pedigrees for historical apple cultivars include triploids as parents of diploids, despite this reputation of poor fertility. This information, coupled with some initiatives using triploids in breeding efforts, result in confusion about how possible or common it is for triploids to be parents of diploid offspring. To date, no studies have systematically evaluated and identified pedigrees of triploid apple cultivars to resolve these contradictions. • Here, we describe a method to make triploid genotype calls using Illumina Infinium single nucleotide polymorphism (SNP) array data through a novel Python script: ploidyClassifier.
Unreduced gamete-donating parents (UGDPs) that produced 2n gametes have been confirmed or identified in several studies through the use of SSR data  (Evans et al. , 2011; Larsen et al. , 2017; Ramos-Cabrer et al. , 2007; Storti et al. , 2013) , DArT markers  (Ordidge et al. , 2018) , and SNP markers  (Muranty et al. , 2020; Vanderzande et al. , 2017). However, none of these studies has systematically evaluated pedigrees in which triploids are the recorded parents of diploid cultivars. Additionally, probing large SNP datasets this way may shed some light into the origins and historic breeding use of triploid apple cultivars, as exemplified for 'Ribston Pippin'  (Muranty et al. , 2020). In this study, we demonstrate a method for making accurate genotype calls for triploids using the Illumina Infinium apple 20K SNP array  (Bianco et al.
| Plant material A set of 3594 accessions (2715 unique) genotyped on the Illumina apple Infinium 20K array  (Bianco et al. , 2014)  and/or the Affymetrix apple Axiom 480K array  (Bianco et al. , 2016)  were evaluated in this study (Table  S1 ). Germplasm included in the study was assigned MUNQ (Malus UNiQue genotype) codes  (Denancé et al. , 2020) , as previously described  (Muranty et al. , 2020) , based on SSR data, where available. For accessions that did not have SSR data available for MUNQ attribution, MUNQ codes were assigned if their SNP data was over 99. 5% identical with samples that have been previously assigned MUNQ codes. No triploids genotyped on the 480K SNP array were included in this study, as the method described in this study for making triploid genotype calls was developed specifically for material genotyped on Illumina SNP arrays.
| SNP genotyping A genetic map composed of 10,295 SNPs deemed to be of acceptable quality that was previously described  (Howard, Troggio, et al. , 2021)  was used in this study, with 20 SNPs excluded that had null alleles in more than 50 diploid individuals. Genotype calls for diploid samples were made as previously described  (Howard, Troggio, et al. , 2021; Vanderzande et al. , 2019). This process included visual inspection of B-allele frequency plots to assess ploidy as a check to the automated ploidy calling used in the Python script described below. Samples with poor or problematic allele call quality (as described in  Vanderzande et al. , 2019)  and duplicates were excluded from all analyses. Duplicates were identified as having the same genotype calls for at least 99. 9% of SNPs. SNP genotyping for triploids was conducted using a custom Python script named ploidyClassifier.
25, 0. 5, and 0. 75) and with a prior sigma of 0. 02 to represent the typical distribution of the B-allele-frequency histogram of a tetraploid accession. All these models were built using the lmfit (Non-Linear Least-Squares Minimization and Curve-Fitting) module  (Newville et al. , 2014)  for Python. The best fitting model was chosen using the Bayes Information Content (BIC). The script then reclassifies SNP calls for triploids to consider allele dosage. Homozygous SNP calls are then re-coded as AAA and BBB, and heterozygous SNP calls are divided into AAB and ABB depending on the value of Theta. Triploid SNP calls are designated AAB when the value of Theta is lower than the median of the values of Theta for heterozygous diploids for the considered marker and the genotype is set as ABB when the value of Theta is higher than the median of the values of Theta for heterozygous diploids for the considered marker.
could have also inherited two thirds of its 3n chromosome complement from an individual diploid if that diploid was both its RGDP and one grandparent of its UGDP1. Thus, an individual was considered a possible UGDP2 if it shared at every SNP either both its alleles or one of its alleles at least twice for at least 99. 5% of homozygous SNPs with a triploid. Each triploid with an identified or confirmed UGDP1 was further evaluated to identify its RGDP. To identify these RGDPs, the SNP alleles that must have originated from this RGDP were deduced by subtracting the SNP alleles attributable to the UGDP1 from the triploid offspring. For example, if the triploid's SNP alleles were "AAB," and the identified UGDP1 had SNP alleles "AA," then the SNP allele from the RGDP must have been "B. " These deduced RGDP contributions were then compared with the diploid dataset to identify the RGDPs.
| Testing SNP call accuracy in triploids SNPs were considered problematic if they demonstrated at least four Mendelian inconsistent inheritance errors  (Sobel et al. , 2002)  across triploids that had either UGDPs or both UGDPs and RGDPs identified. Cluster plots of these SNPs were evaluated to determine the nature of the SNP call inconsistency. Data for SNPs deemed to be inaccurately called were set to missing and the UGDP and RGDP identification process was repeated using the subset of SNPs that remained to identify any relationships that had been previously rejected. Further genotype call accuracy evaluations and a demonstration of triploid phasing were made by comparing genotype calls for triploid 'Jonagold' to its reported UGDP 'Golden Delicious' and RGDP 'Jonathan'  (Gianfranceschi et al.
| Use of triploid data for diploid pedigree reconstruction Genotypic profiles for unidentified, putative UGDP1s were deduced and used for diploid pedigree reconstruction (Figure  2a ). This SNP allele deduction was accomplished using the homozygous calls of these triploids. In other words, when the triploid's SNP calls were "AAA" or "BBB," the UGDP1's SNP alleles must be "AA" or "BB" for those SNPs, respectively. Genotypic profiles deduced this way would have many instances of missing data, but there were still sufficient genotype calls available for pedigree reconstruction. Deduced genotypic profiles for putative UGDP1s were compared with the diploid dataset to identify parent-offspring relationships, as described in  Vanderzande et al. (2019). Any individual from the diploid dataset that had a parent-offspring relationship with a putative UGDP1 was assumed to be either an offspring or a parent of a putative UGDP1 or the RGDP of the original triploid offspring.
| Triploid allele calling Our results demonstrated that a high number of SNPs on the Illumina Infinium 20K SNP array could be accurately called for triploids using ploidyClassifier. Previously,  Chagné et al. (2015)  demonstrated the ability to differentiate triploids (and aneuploids) from diploids using Illumina Infinium SNP array data, but that study did not attempt to make genotype calls for triploids. Methods for allele calling in triploids have been previously demonstrated for the Thermo Fisher GeneTitan platform for Atlantic salmon (Salmo salar)  (Grashei et al. , 2020) , but our study is the first to do so on the Illumina Infinium platform. Following the filtering of SNPs with problematic triploid clustering (see Figure  S1 ), the resulting data were of high quality, with very few Mendelian inconsistent errors across identified or confirmed triploid pedigrees (Table  S3 ).
It is likely that additional genotypic data for more cultivars would enable the confirmation of an UGDP1 origin for many of the other triploids that currently lack identified UGDP1s in the dataset. Triploid production through UGDP2s (i. e. , with heterozygosity reduced through recombination) is far rarer, as there were only three instances of possible UGDP2s in this study and only a single confirmed UGDP2 (Table  S4 ). The only confirmed UGDP2 in this study was a breeding selection and was included intentionally in the study because it represented the only confirmed case of an UGDP2 across the data available for this study. The three instances of possible UGDP2s could not be confirmed because, unlike the breeding selection, no pedigree records were available for them and two alternative hypotheses for the observation of possible UGDP2s were possible.
Shoots and roots were oven-dried at 70 • C to a constant weight to determine shoot and root dry biomasses. The plant water content (WC) was calculated as follows: WC = [(FW -DW)/FW] × 100; where FW = fresh weight, DW = dry weight  [65]. To assess the average projected leaf area, the lettuce plants were photographed in top view 21 and 34 days after transplanting. The projected leaf area was calculated by analyzing the photographs using the threshold color plugin from ImageJ software (http://imagej. nih. gov/ij/ accessed on 29 September 2021).