dataesr/scientific-paragraphs-categorization huggingface dataset:
The WGBS libraries were processed using the nf-core/ methylseq v1. 5 pipeline (10. 5281/zenodo. 2555454) combining bwa-meth v0. 2. 2  (Pedersen et al. , 2014)  as an aligner and MethylDackel v0. 5. 0 (https://github. com/dpryan79/ MethylDackel) for the methylation calling. The default parameters were used for the entire workflow with the exception of the methylation calling where the following arguments were used: - --maxVariantFrac 0. 4 --minOppositeDepth 5 --CHG --CHH --nOT 3,3,3,3 --nOB 3,3,3,3 -d 3. Only cytosines with a minimum coverage of 3x were kept for the subsequent analysis. Further comparisons between the methylated cytosines and the genome annotation were performed using BEDtools v2. 27. 1  (Quinlan and Hall, 2010).
5. Population structure for both SNP and indel data was then characterized using the admixture model and independent allele frequencies in STRUCTURE v2. 3. 4  (Pritchard et al. , 2000). Dendrograms of both SNP and Indel data were generated under the UPGMA method using the R package poppr  (Kamvar et al. , 2014). The forty accessions were planted in a three replication, randomized complete block design in a greenhouse maintained at 21/20 °C and 16 hour days. Ten seeds per replicate were planted in 13. 3-cm 2 pots in Sungrow propagation potting mix. Seedlings were thinned to one plant per pot after emergence. Winter annual accessions require vernalization to induce flowering, so all winter accessions were placed in a growth chamber maintained at 4 °C with 16-h light for a period of 21 days about 4 weeks after emergence.
Structural variants using Iso-seq data Single-molecule real-time (SMRT) isoform sequencing (Iso-seq) based on PacBio (Pacific Biosciences, Menlo Park, CA) generated reads was used to investigate unambiguous full-length isoforms for two pennycress wild accessions, MN108 and Spring32-10. Total RNA extraction was performed on the green seed, hypocotyl, seedling root and flower tissues from pennycress plants grown in a climate-controlled growth chamber maintained 21/20 °C during 16-h:8-h day-night setting. Approximately 250 ng of total RNA was obtained and subjected to the Iso-seq Express Library Workflow (Pacific Biosciences, Menlo Park, CA). cDNA is synthesized from full-length mRNA with the NEBNext Single Cell/ Low Input RNA Prep Kit (New England Biolabs, Ipswich, MA) followed by PCR amplification.
The amplified cDNA is converted into SMRTbell templates using the PacBio SMRTbell Express Template Prep Kit 2. 0 for sequencing on the Sequel System. Sequencing was performed at the University of Minnesota Genomics Center Facility (Minneapolis, MN). The polished high-quality FASTA file obtained from Iso-seq3 was aligned to pennycress version 2 (T_arvense_v2) with min-imap2  (Li, 2018). The resulting SAM file was sorted and collapsed using the cDNA_Cupcake package to obtain an input GFF file such that each transcript has exactly one alignment and at most one ORF prediction. Sqanti3_qc. py, part of the SQANTI3 package  (Tardaguila et al. , 2018) , was deployed on the resulting GFF file along with the reference genome in the FASTA format and a GTF annotation file. This returned a reference corrected transcriptome, transcript-level and junction-level files with structural and quality descriptors, and a QC graphical report.
, 2020) , and the QTL region was surveyed for candidate genes. Comparison with YUN_Tarv_1. 0 Synteny between T_arvense_v2 and YUN_Tarv_1. 0 was assessed with minimap2 alignments and the resulting dotplot generated with the R package dotPlotly (https://github. com/tpoorten/ dotPlotly). The k-mer analysis of quality and completeness was carried out for each assembly with Merqury v1. 3  (Rhie et al. , 2020; Table S7, S8 and S9) , using both the PCR-free Illumina HiSeq reads generated in this study and those obtained from  Geng et al. (2021)  under the accession SRR14757813 in the NCBI Sequence Read Archive.
Acknowledgements We thank  Win Phippen ,  Thomas Gatter ,  Prabin Bajgain ,  Korbinian Schneeberger ,  Ra ul Wijfjes ,  MPI DB Genome Center, Vienna Biocenter Core Facilities (VBCF) ,  University of Minnesota Genomics Center (UMGC) ,  Minnesota Supercomputing Institute (MSI) , and all EpiDiverse network members and beneficiaries. We acknowledge the hard work of many who contributed to this.
Funding This material is based upon work that is supported by the  Minnesota Department of Agriculture  (J. A. , K. F. , R. C. ) and by the  National Institute of Food and Agriculture, U. S. Department of Agriculture , under award numbers  2018-67009-27374  (J. A. , R. C. , K. F. ), and  2019-67009-29004  (M. D. M, J. S. ) and the  Agriculture and Food Research Initiative Competitive  Grant No. 2019-69012-29851  (M. D. M, R. C. , J. S. ). This research was supported by the  U. S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Genomics Science Program  grant no. DE-SC0021286  (M. D. M, R. C. ). This work was further funded by the  Austrian Academy of Sciences  (C. B. , I. R. A. , K. J. , D. R. C. ); the  Max Planck Society  (D. W. , A. C. G. , P. C.
We estimated the effects of mark loss and recycled individuals on parameter estimates using a multistate Cormack-Jolly-Seber (MSCJS) framework. We explored parameter bias through simulations of a metapopulation system with different capture and survival rates. We also illustrated how mark loss can be easily estimated and accounted for using an empirical long-term (10 years) CMR dataset of bats, individually identified using both PIT tag technology as marks that can be lost, and multilocus genotypes as 'permanent marks'. 3. The results from our simulated scenarios demonstrated that the occurrence of bias and the parameters concerned were highly dependent on the study system, and no general rules on parameter behaviour can be established a priori. The model structure and the interdependency among parameters make it challenging to predict how bias could affect estimates.
Multi-state models were developed to deal with situations where the "state" (e. g. location, behaviour, physiology, reproductive or social status) of an individual may affect its survival or detection probability, and where the individual can change "state" during life (reviewed in  Lebreton et al. , 2009). These models became popular and widely used because of their flexibility to address a large range of study systems and biological questions, and because they can be used in a relatively straightforward manner by biologists given the development of user-friendly software (e. g. Mark, E-surge, Bugs). They are used to address many ecological and evolutionary hypotheses based on variations in life history traits (state transitions) throughout an individual's life  (Nichols & Kendall (1995)  see also  Cam (2009)  for an extensive discussion on the subject), or density dependence effect  (Schofield & Barker, 2008) , co-evolution  (Benkman et al.
The MSCJS model share assumptions with the CJS model  (Cormack, 1964; Jolly, 1965; G. Seber, 1965) , particularly in relation to mark loss. In addition, states are assumed to be recorded without error and survival, transition and observation probability must be the same for all individuals at a given occasion and state. To illustrate our approach with an empirical example, we also used our decade-long mark recapture data set of PIT-tagged and genotyped Greater mouse-eared bat (Myotis myotis), a taxonomic group particularly susceptible to PIT-tags loss  (Freeland & Fry, 1995). We used 'genotype' as an individual permanent mark to estimate bias between models accounting/not accounting for mark loss and recycling, and suggested recommendations for future studies. Similar to single state CJS models, we predicted that the MSCJS model would potentially be subject to underestimation of survival and overestimation of transitions in case of tag loss and recycling  (Nichols & Hines, 1993).
Data simulation To quantify the potential bias induced by mark loss on parameter estimates in the MSCJS framework, we defined several scenarios corresponding to realistic situations inspired from the literature (Supporting Information 1). We considered a study period of 10 years, during which an We allowed stochastic annual variations in survival rate for each age class. To set the most common values for survival parameters, we surveyed 700 species in six vertebrate classes-Actinopterygii, Chondrichthyes, Amphibia, Aves, Reptilia, and Mammalia. We used the existing database for tetrapods  (Conde et al. , 2019)  and also compiled data from the literature for fish and bat species (Supporting Information 1, Table  S1 ). The distribution of survival rates encompasses a large range of values (Supporting Information 1, Fig.
, 1999; Fokidis et al. , 2006) , for each scenario, we investigated a range of mark loss rates commonly reported in the literature (Supported Information 1, Table  S2 ). We simulated three mark loss probabilities (0. 05, 0. 25, 0. 4) during the first year after marking and a constant 0. 05 rate thereafter, which generated a diversity of cases of mark loss and recycled individuals (Supported Information 2,Fig. S3 ). We simulated fifty datasets for each of the 12 combination of parameters, and ran two MSCJS models: one with recycled individuals, which mimicked the situation where it was not possible to identify individuals which have lost their mark; the second, accounting for mark loss, which mimicked situations where a permanent mark was used to identify individuals (i. e. no recycling possible).
In this last model we only added to the previous model retention of mark as a Bernoulli process: with R i,t the retention rate of individual i at time t and pr j the probability of retention j (j in {1,2,3}) that depend both of the age of the individual i and the status of the mark at time t-1. In total, 1,200 simulations were performed (Supported Information 2, Fig. S1 ). The computational codes for a fully reproducible example dataset are provided in the Supporting Information 2.
Data analysis In the 600 simulated dataset, taking into account mark loss (without recycling), we identified individuals who lost their mark prior to running the model, and fitted a Bayesian MSCJS model, with priors and codes detailed in Supporting Information 2 (ModelA. jags). In the 600 simulated dataset, not accounting for tag loss, as individuals who lost their mark cannot be identified (i. e. recycled), we simply cut life histories each time mark loss occurred and added a new line into the dataset as a recycled individual. We then fitted a similar Bayesian MSCJS model, but removed the part concerning mark loss estimation (Supporting Information 2, ModelW. jags). The model codes and the initial values used for parameter estimation are provided in Supporting Information 2. All prior distributions used for the model without recycling were the same as those used for the model with recycled individuals.
Real data analyses To illustrate the simulations, we analysed an empirical dataset from a 10-year study of CMR data on the Greater-mouse eared bat (Myotis myotis) in Brittany  (France; 2010 -2019). A total of 2,561 individuals were marked in 5 roosts: La Roche Bernard (47˚31'N, 2˚18'W), Férel (47˚28'N, 2˚20'W), Noyal-Muzillac (47˚35'N, 2˚27'W), Béganne (47˚35'N, 2˚14'W) and Limerzel (47˚38'N, 2˚21'W). The bats were individually tagged using PIT tags, ID-100C (Troven®), with a unique 10digit code. These small passive tags (2. 12x11mm, 0. 1gr) allowed identification from passive readers. All individuals caught in roosts without PIT-tags were systematically tagged, which included both, individuals who lost their tag and those which were never tagged before, and genotyped as a second and permanent marking method.
To analyse these data, we fitted a multisite model (a special case of multi-state models when states are sites and transition are movement between sites. ), similar to the MSCJS model used for simulated data (Supporting Information 3). Emigration from the five studied subpopulations was assessed using capture and resighting data obtained between capture occasions in swarming and wintering sites. We accounted for individual heterogeneity, including a random effect in the estimation of survival probability, a widespread phenomenon in free populations and a known source of bias in CMR models. (Gimenez et al. , 2018). We defined eight possible detection states which allowed us to estimate separately, capture and resighting probabilities (see Supporting Information 3, Table  S1 ). As empirical data suggested the possible movement of individuals between all subpopulations and outside, we did not constrain transition between subpopulations except for movement of juveniles from D toward A, B, C which was not possible and therefore set to 0.
In this study, we also tested the effect of surgical adhesive (Vetbond®) after PIT-tag injection, in order to assess tag-loss reduction in comparison with selfhealing  (Lebl & Ruf, 2010; van Harten et al. , 2020). In this model, tag retention probabilities were modelled like in the simulation study (eqn. 1), by accounting for time since marking (two discrete classes: first year or subsequent years), individual age class (juvenile or adult), and the use of surgical adhesive (yes or no) As per the simulated datasets, two models were run, a first one taking into consideration tag loss and recycling, and a second model ignoring this information.
Computation details We used JAGS  (Plummer, 2003)  with the jagsUI package  (Kellner, 2016 ) from R 3. 6. 0 (R Core Team, 2019) for analyses of simulated and empirical datasets. We used four MCMC chains with 150,000 iterations each and drew samples from posterior distributions after discarding the first 50,000 iterations (burn-in). We retained every twentieth iteration generating 20,000 samples from the posterior distribution for each parameter. Chain convergence was assessed with the Gelman-Rubin statistic  (Brooks & Gelman, 1998). Among 1,200 simulations, some showed R-hat values > 1. 1, indicating a convergence failure. Converge problems concerned few parameters and the corresponding R-hat values were still low. To avoid prohibitively long computing time, we did not attempt to increase the number of iterations to achieve the complete convergence of MCMC chains for these parameters in the simulations concerned.
Our results are based on 50 simulated datasets per scenario, and it is expected that lack of convergence for these few parameters has no substantial influence on our results. For assessing bias in parameter estimates when mark loss or recycling is not accounted for, we computed the Earth Mover Distance (EMD), using the EMD-L 1 algorithm  (Ling & Okada, 2007) , a distance function that quantifies the difference between two distributions. This metric measures the minimum cost of turning, point by point, a distribution into another. We also estimated a ROPE (Region Of Practical Equivalence,  Kruschke, 2018)  for each scenario to assess the degree of difference between distribution represented by the EMD metric. To define this ROPE for each scenario, we randomly built 1,000 pairs of models from the 50 simulations and calculated the associated 1,000 EMDs from the posterior distributions of the estimated parameters (Supporting Information 2, Fig.
Indeed, in datasets with few recycled individuals, i. e. with low survival and capture rates, transition and resighting probabilities can be severely biased if mark loss is high. This suggests that results from studies where survival and capture are low should also be interpreted with caution if mark loss is suspected but not taken into consideration. Severity of bias can also vary over time, with bias in survival decreasing with time as in our simulated datasets. This is partly due to the mark loss pattern we chose and stresses the fact that, even in studies conducted over short periods, parameters can be substantially biased. In cases where mark loss increases with time since marking, we would expect an increase of bias with time. The simulation and the empirical study results together highlight how the complexity and interdependence of parameters can compound or counteract estimation biases in the absence of mark loss modelling.
In our experience and according to the literature, PIT-tags are prone to being shed regardless of the studied taxa, often in the short but sometimes in the long term. Mark loss should therefore be carefully considered in all CMR analyses and possibly also in other studies using similar datasets, and should be explicitly modelled when necessary for more accurate estimations of population dynamics. assistance in the fieldwork. Sampling was carried out in accordance with the French ethical and sampling guidelines issued in 3 successive permits delivered by the Préfet du Morbihan (Brittany) awarded to E. J. P. , F. T. and S. J. P. for the period 2010-2020.
Acknowledgements This project was funded by an  Irish Research Council   Postdoctoral Fellowship Grant   GOIPD/2018/256  awarded to F. T. , a  European Research Council   Research Grant   ERC-2012-StG311000  and an  Irish Research Council Laureate Award  awarded to E. C. T. The French field study was supported by the  European Regional Development Fund   EU000141  and a  Contrat Nature  grant awarded to  Bretagne Vivante  (BV). We would like to thank the volunteers of  BV  for.
, 2018). The average distance between the focal tree and its four nearest-neighbor trees (meanDist) was computed to characterize geographic isolation. These variables  Shen et al. 10. 3389/ffgc. 2022. 1007473 TABLE 1  Environmental variables used to predict the probability of occurrence, species richness and abundance of epiphytic spermatophytes and ferns on individual trees in a 1. 1 ha tropical rainforest (Yunnan, SW China). were computed using the nnIndex and fsasN4 functions from the forestSAS package  (Chai, 2021). The crown illumination index was divided into 5 scales (1: no direct light, 2: < 10% of lateral light, 3: 10-90% of overhead light, 4: ≥ 90% overhead light, 5: crown completely exposed,  Dawkins and Field, 1978). Liana infestation was evaluated using  Rutishauser et al. (2011)  index (0-5).
The X-Y coordinates of each tree were obtained from the XTRES. A topographic map (Elevation) was produced with the raster package  (Hijmans, 2021)  from measurements made by the autopilot vehicle (LiAIR VUX-1350, Beijing, China) equipped with VUX-1UAV Laser (RIEGL Laser Measurement Systems GmbH, Horn, Austria) at 10 m intervals. This 10 m resolution topographic map was employed to derive the topographic position index (TPI, characterizing the relative topographic position of each focal tree as the difference between its elevation and the mean elevation of all other trees  (Gallant and Wilson, 2000) , the slope (in degrees), and the orientation (Eastwest and Northsouth, in radiant) by SAGA-GIS v7. 9. 1. Light intensity ('L' , W/m 2 ), air temperature ('T' , • C), relative humidity ('RH' , %) and photosynthetic active radiation ('PAR' , µmol•m -2 •s -1 ) were recorded at 1-hr intervals from 12 trees at five height zones (tree base, middle trunk, inner canopy, middle canopy and outer canopy) from July 2017 to December 2019.
To predict the spatial variation of light and microclimatic conditions from the data collected by 54 dataloggers, we modeled hourly variation in T, L, RH and PAR in an X-Y-Z space (thus including tree height and elevation) using Random Forest  (Shen et al. , 2022)  as implemented by the randomForest package  (Liaw and Wiener, 2002)  in R v4. 0. 4 (R Core Team, 2021). The microclimatic conditions that prevail on each tree substantially vary from the base to the canopy. Between 2 and 62 m above ground, day (8 a. m. -7 p. m. ) relative humidity ranged between 53. 6 and 99. 9% and day temperature between 12. 0 and 31. 7 • C  (Shen et al. , 2022) , challenging the description of the global microclimatic conditions that prevail at each host tree. Each epiphytic fern and spermatophyte community was, however, restricted to a specific height zone on a tree (Figure  1B ).
Statistical analyses All statistical analyses were conducted in R v 4. 0. 4 (R Core Team, 2021). To test the null hypothesis that epiphytes are randomly distributed among trees, we randomized 1,000 times the distribution of epiphyte individuals across the 1,334 host trees and computed 1,000 random occupancy rates. Based on this, we determined whether the observed occupancy rate significantly differed from values expected by chance, i. e. , whether the observed occupancy rate was lower than 95% of the 1,000 random occupancy rates. To control for DBH, we assigned each individual tree to one of three DBH classes and repeated the above procedure for each DBH class. To determine whether a given tree is likely to be occupied by epiphytes, how many epiphytic species it is likely to host and at which abundance, we applied classification and regression Random Forest, respectively, using the randomForest package  (Liaw and Wiener, 2002).
These metrics characterize the difference in accuracy between full models and models, wherein individual variables are successively removed. High, positive values characterize variables that substantially contribute to the model, whereas negative values characterize variables that do not contribute to the model. To evaluate the models, we applied 100 repeated splitsampling cross-validation, where 70% of the data are used to calibrate the models and the remaining 30% to compute the different evaluation metrics from the "train" function of the caret package  (Kuhn, 2021). We set the number of trees to 1,000. We computed the average, across the 100 crossvalidation replicates, of the following statistics to evaluate model accuracy: sensitivity (true positive rate, ranging between 0 and 1), specificity (true negative rate, ranging between 0 and 1) and two statistics derived from sensitivity and specificity (Area Under the ROC Curve, AUC and True Skill Statistic, TSS), for classification-type models and RMSE (Root Mean Square Error), MAE (Mean Absolute Error) and R 2 for regressiontype models.
Acknowledgments We thank  Deng Yun  for providing a photograph of the canopy crane,  Ji Wang  for assistance with fieldwork,  Profs. Qiang Liu  and  Yun-Hong Tan  for assistance with identification,  Kyle Tomlinson  for suggestions on data analysis, and the  National Forest Ecosystem Research Station at Xishuangbanna  and the  Sino BON-Forest Canopy Biodiversity Monitoring Network  for providing research platform and environmental data.
Comparison of NMR data acquired (Figures  S5-S13 ) and the literature identified this compound as either Calein A or B  (Herz et al. 1980; Quijano et al. 1979). As the signals for protons 8 and 9 overlapped, 1 H, HSQC and HMBC spectra were recorded in benzene-d6 to definitively determine the relative position of acetate and angelate groups, allowing identification as calein A (Figure  S14A ). Relative configuration was confirmed by NOESY (Figure S14B). Although 1 has been isolated from several Calea species, it has not previously been isolated from C. pinnatifida, in which two other caleins have been described, namely calein D  (Ferreira et al. 1980 ) and calein C  (Caldas et al. 2018). Compound 2 was identified as the polyketide metabolite acetylportentol (Figure  1 ), heretofore known as a lichen constituent  (Huneck et al.
High Performance Thin Layer Chromatography (HPTLC) analysis From each of the six fractions, four aliquots were weighed and dissolved in absolute ethanol to reach a concentration of 0. 5 mg/mL, as was the standard butein. Samples were spotted on precoated silica gel 60GF 254 aluminium sheets (20 x 10 cm) using the automated sample applicator CAMAG Automatic TLC Sampler III (ATS3), which was programmed through WINCATS software. Before use, plates were prewashed with isopropanol and dried at room temperature. The operating conditions were as follows: syringe delivery speed: 10 mm/s; injection volume: 30 µL; bandwidth: 6 mm; start position: 15 mm and distance from bottom of plate: 15 mm. The plates were then developed in chloroform / ethyl acetate (75:25), and after drying, plates were scanned at six wavelengths: 220, 260, 280, 310, 340 and 370 nm.