dataesr/scientific-paragraphs-categorization huggingface dataset:
We are grateful to  Daniel Pillot  and  Herman Ravelojaona  ( IFPEN ) for their technical and scientific support in Rock-Eval ® analysis, a trademark registered by  IFP Energies Nouvelles. The anonymous reviewers as well as the associate editor  Sebastian Naeher  are thanked very much for their critical comments and constructive advice that helped to improve this publication. Moreover, we thank the  GeoB Core Repository at MARUM  and  PANGAEA  for archiving the sediments and data used in this work. We thank  Ezemvelo KZN Wildlife  and iSimangaliso  Wetland Park Authority  for permitting us to work at Lake St Lucia. Samples were collected under the registered project, " A multi-proxy investigation into past and present environmental change at Lake St Lucia ". Financial support. This work was financially supported by the  German Federal Ministry of Education and Research (BMBF, Bonn, Germany)  under the project " Tracing Human and Climate impacts in South Africa" (TRACES ), project number  03F0798A.
Confocal microscopy The acorns of Set#3 were rinsed three times with PBS after fixation, immersed in 15 ml PBS containing 50 lg ml À1 of wheat germ agglutinin (WGA)-AlexaFluor488 conjugate (Life Technologies), incubated 2 h at 37°C, and rinsed again three times with PBS. The samples were observed under a confocal microscope (Olympus Fluoview FV1000 with multiline laser FV5-LAMAR-2 and HeNe(G) laser FV10-LAHEG230-2). Pictures were taken with x, y and z coordinates at 405, 488 and 594 nm and with 910, 920, 940 or 960 objectives. Images were merged (RGB) using IMAGEJ software  (Schneider et al. , 2012). Pictures were created using Z PROJECT STACKS  (Campisano et al. , 2014) , cropped and the light/contrast balance was improved  (Glassner et al. , 2015). Images presented in this publication correspond to the average colonization level observed.
The offset term represents a variable with a known effect, which is commonly introduced in countdata models with a predefined coefficient (here, 1) to account for variations in sampling effort (here, sequencing depth)  (Kotze et al. , 2012; Chiquet et al. , 2018) , including in microbiome data models  (Xia et al. , 2018). Variations in fungal community composition were analyzed by using permutational multivariate analyses of variance (PERMANOVAs) with 9999 permutations, performed with the adonis function of the VEGAN package v. 2. 4. 6  (Oksanen et al. , 2019). Compositional dissimilarities among acorn samples were estimated using quantitative and binary versions of the Jaccard index  (Jaccard, 1901)  and visualized with principal coordinate analyses (PCoAs) using the PHYLOSEQ package v. 1.
Separate models for acorns in the canopy and acorns on the ground were fitted when the interaction was significant. In addition, we investigated whether changes in fungal community composition after acorn fall were due to either the substitution of canopy-associated fungal species by ground-associated fungal species or gain of ground-associated fungal species without loss of canopy-associated fungal species, by partitioning Jaccard binary dissimilarities among acorns of the same mother tree using the BETAPART package v. 1. 5. 1  (Baselga & Orme, 2012). The proportion of fungal species of acorns on the ground also found in acorns in the canopy was calculated for each mother tree. Test of H3: The lifestyle of fungal species influences their response to variations in the seed environment HMSCs  (Ovaskainen et al.
Random effects at each hierarchical level (oak population, mother tree and seed) were also introduced to model variations in ASV sequence counts that can neither be attributed to the four ecological predictors nor to sequencing depth. To test H3, we included available knowledge on fungal lifestyle in the T matrix. For each ASV, we included the trophic mode (saprotroph and/or plant pathogen) and the degree of specialization toward acorns of each ASV. The putative trophic mode of each ASV was determined using the FUNGuild database  (Nguyen et al. , 2016)  as a first approach. The information provided by FUNGuild was verified and completed by an extensive literature search. The degree of specialization of ASVs toward acorns was defined as the log-ratio of ASV relative abundance in acorns vs other sample types (i.
Response of seed fungal species to elevation and seed fall depended on their lifestyle (H3) The HMSCs showed that fungal lifestyle influenced the response of fungal ASVs to environmental variation, in accordance with our hypothesis (H3). High elevation selected for saprotroph Seeds were either collected in the tree canopy or on the ground. Materials from the seed microenvironment (twigs and leaves, or litter and upper soil) were also collected. The fungal community of all four sample types was analyzed using a metabarcoding approach. Only amplicon sequence variants (ASVs) assigned to Ascomycota (A) or Basidiomycota (B) with the UNITE database were kept. Average relative abundances of all ASVs were computed for each sample type, after merging ASVs assigned to the same fungal species. Only ASVs assigned to species with a confidence level (CL) > 80% are shown in the table.
Acknowledgements We thank  Albin Lobo  ( University of Copenhagen, Denmark ) and  Jean-Marc Louvet  ( Biogeco, INRA-University of Bordeaux, France ) for their assistance during sampling campaigns. We are also very grateful to  Martine Martin-Clott  e,  C eline Lalanne ,  Patrick Leger ,  Adline Delcamp  ( Biogeco, INRA-University of Bordeaux, France ) and  Jessica Vallance  (SAVE,  INRA-Bordeaux Sciences Agro, France)  for providing materials and reagents for processing the samples and advice on molecular biology protocols. We thank  Samuel Pennarun  and all members of the Genotoul sequencing facility ( Get-PlaGe, Toulouse, France ) for performing MiSeq sequencing, and the  Genotoul bioinformatics facility  ( Bioinfo Genotoul, Toulouse, France ) for providing computing and storage resources.
We also thank  Erwan Guichoux  and all members of the  Genome Transcriptome facility of Bordeaux (PGTB, Cestas, France)  for performing the SNP genotyping. Thanks to  Franck Deniel  and  Ga€ etan Burgaud (ESIAB, Universit e de Bretagne Occidentale, France)  for giving us marine fungal strains used as positive controls. Very constructive comments from three anonymous reviewers helped improve the manuscript. We would like to acknowledge  George Washington University  for its contribution to the funding of molecular biology analyses. We also thank the  INRA Meta-Omics of Microbial Ecosystems (MEM)  metaprogramme ( Learn-biocontrol  project), the  INRA Ecosystem Services (EcoServ)  metaprogramme ( IBISC  project), the LABEX  COTE  ( MICROMIC  project;  ANR-10-LABX-45 ), the  LABEX CEBA  ( ANR-10-LABX-25-01 ) and the  Aquitaine Region  ( Athene  project, no.
Data availability All raw sequences obtained from the sequencing of seeds of sessile oak and their biotic microenvironment are available from the National Center for Biotechnology Information Sequence Read Archive (http://www. ncbi. nlm. nih. gov/sra) under accession number PRJNA551388. The code, ASV tables and metadata (including ASV richness, sequencing depth, site and sample descriptions, and genotypic data) are available as an archive at https://doi. org/10. 15454/0CNFWS.
, 2010; 2013 )  have been proposed. Atlas methods provide a template of aggregated myocyte orientation. Atlas methods are based on the high correlation of left ventricular (LV) shape and aggregated myocyte orientation. The template of aggregated myocyte orientation is personalized by mapping it onto a new shape. However, incorporating additional, patient-specific microstructural information remains challenging. A mapping, combining the atlas and additional input data, is needed. We propose a data-driven low-rank and personalizable structure model. It provides a more detailed representation than classical rule-based approaches with the possibility of model personalization in contrast to atlas methods. Our approach is derived from high-resolution, ex-vivo porcine cDTI data extracting detailed 3D volumetric information about predominant myocyte orientation.
Data acquisition Ten healthy and five porcine hearts with chronic myocardial infarction (9 weeks after 120 min occlusion of the left circumflex coronary artery) were imaged on a clinical 1. 5 T MR system (Achieva, Philips Health-Care, Best, the Netherlands) using an 8channel head coil. The hearts were arrested with a potassiumchloride overdose, explanted and fixated by retrograde perfusion (150-180 cm water column) approximately 15 min after arrest with the 4% formalin solution. During fixation, the aorta, pulmonary veins and venae cavae were clamped to ensure fixative perfusion through the microvasculature bed and to keep both ventricular chambers under hydrostatic pressure for shape preservation. Experimental procedures were in accordance with the Swiss animal protection law and conformed to Directive 2010/63 EU of the European Parliament and of the Council on the Protection of Vertebrate Animals used for experimental and other scientific purposes.
Imaging was performed using a 3D multishot diffusion-weighted spin echo sequence with echo planar imaging readout and the following parameters: spatial resolution 0. 75 × 0. 75 × 0. 75 mm 3 zero-filled to 0. 5 × 0. 5 × 0. 5 mm 3 , TR/TE 1 s/84 ms, one unweighted ( b = 0 s/mm 2 ) image volume, 8 diffusion encoding directions at b = 150 s/mm 2 and 24 directions at b = 10 0 0 s/mm 2 , number of EPI profiles 25, bandwidth 664/21 Hz (freq. /phase encoding), 2 signal averages. The total scan duration ranged from 20 to 24 h. Signal to noise ratio (SNR) was estimated by computing the mean signal divided by the standard deviation of noise within the masked myocardium, for the b = 0 image and the diffusion weighted images. To this end, the mean signal was computed on the averaged magnitude data  ( Firbank et al.
, 1999; Murphy et al. , 1993 ). To ensure Gaussian noise distribution, the two signal averages, containing uncorrelated noise, were subtracted and the standard deviation of the difference signal was computed across the myocardium. Data was split into three groups, eight healthy data sets were used to derive the parametric myocyte model (training), two healthy hearts were used as test cases (test). Five hearts with chronic myocardial infarction were used for testing model robustness to pathology involving remodelling (infarct). The structural reorientation in the infarction region results in a locally, noncoherent microstructure orientation, not following the double helical pattern  ( Sosnovik et al. , 2009; 2014; Mekkaoui et al. , 2018 ). Thus, by fitting the model to these hearts, we can investigate the flexibility of the model and the global influence of local variations.
Data preparation Data preparation includes semi-automatic segmentation of the LV myocardium, assignment of shape-adapted coordinates, and projection of the first cDTI eigenvector onto a local coordinate system. The semi-automatic segmentation of the LV myocardium combines graph-based segmentation using manually selected seed points  ( Canny, 2004 )  and edge detection methods  ( Lim, 1990; Parker, 2010 ). Additional information from cDTI such as fractional anisotropy (FA) and first eigenvector orientation of the diffusion tensor are used to exclude trabeculae and papillary muscles. The final segmentation is manually corrected. Secondly, a shape-adapted physiological coordinate system is derived, defining each position by a transmural, circumferential and longitudinal coordinate. This global LV coordinate system ensures that the same positions with respect to the LV anatomy are assigned the same coordinates across individual heart shapes.
An SVD across hearts is applied to these degrees of freedom of the onedimensional functions of the PGD basis, after subtracting the mean over the database. Each 1D function is represented by a set of SVD basis functions. N SVD is the number of SVD basis functions representing one 1D function of the PGD basis, e. g. , F m (t) = f m,mean (t) + N SVD n f =1 w F m ,n f • f m,n f (t). The SVD basis functions f m,n f , g m,n g , h m,n h represent the major feature variations across hearts. The resulting representation is f d;PGD (t, c, l) = N PGD m =1 f m,mean (t) + N SVD n f =1 w F m ,n f • f m,n f (t) • g m,mean (c) + N SVD n g =1 w G m ,n g • g m,n g (c) • h m,mean (l) + N SVD n h =1 w H m ,n h • h m,n h (l). (2) A minimization problem can then be solved to identify the weights w giving the best fit to the data.
Model fitting For personalization of the aggregated myocyte model (PGD: Eq. (  2 ) or POD: Eq. (  4 ) ), the weights (PGD: w F m ,n f , w G m ,n g , w H m ,n h or POD: w m,t i ) are fit to measured data. Computational cost can be split into the time needed for model extraction from the training data and the time for weight adaptation and reconstruction of the estimated myocyte aggregate orientation field. Times were measured on an 8-core Intel(R) Core(TM) i7-10700 K, 3. 8 GHz desktop computer. The PGD of a volumetric data set took 65 min for each projection, on average, without parallelization. Reconstruction of the PGD model took 16. 3 min for 90 0,0 0 0 data points, on average. For the PGD model, a PGD of the target data has to be performed prior to model fitting. Computation of the POD took 2 min for each projection, on a single core.
Error metric To evaluate the model estimations of the principle myocyte aggregate orientation (MA), we compare the angular difference between the approximation at the positions of the voxel centers to the first eigenvector of the diffusion tensor from the highresolution measurements ( MA). As an error metric, the median over all data points on the myocardium is used. For qualitative evaluation and visualization we show helix angle maps. The helix angle is defined as the angle between the local circumferential axis and the projection of the principle myocyte direction onto the local longitudinal-circumferential plane  ( Stoeck et al. , 2018; Scollan et al. , 1998 ).
Rule-based model (RBM) The rule-based model is based on  Bayer et al. (2012)  and is adapted to the LV only. To obtain the local aggregated myocyte orientation the local coordinate system (section II. B) was rotated. The circumferential axis was counterclockwise rotated by the local helix angle around the transmural axis, and subsequently by the local transverse angle around the longitudinal axis. The local helix and transverse angle are linear functions of the transmural coordinate. To personalize the rule-based model to the porcine data, the boundary conditions of helix and transverse angle at endo-and epicardium were adapted to the cDTI data sets, applying a leastsquare fit of the linear functions.
The comparison of MA between the approximations of the PGD model, POD model, atlas, and rule-based methods is presented in Fig. 6. The PGD model shows the smallest average error for all data sets, healthy training data, healthy test set and infarct test set, respectively. MA is 9. 7 • , 10. 1 • and 11. 3 • when averaged over the training cases, healthy test case and infarct test cases, respectively. The error is higher for the POD model, followed by the atlas and RBM. Compared to the error using the PGD model, the average error is 34 % higher for the POD model, 62 % higher for the atlas and 97 % higher for the RBM for the healthy test set. For PGD model, POD model and atlas the error for the training data is smaller than for the test sets with a difference of less than 2. 1 • , whereas for the RBM, not derived from the training data, the average error on the training data set is 2.
05 ) for all models. For the PGD model the error decreases with a steep gradient and levels of at l = 0. 15. For POD and atlas approximations the error halves within the two most apical bins ( l < 0. 1 ). However, an elevated error is visible in the apical third of the ventricle. RBM approximation shows a slow error decrease that reaches a plateau at l = 0. 65. A small increase of the error at the base ( l > 0. 85 ) is present for all models. The local maxima of the transmural variation at the endoand epicardial surfaces and the prominent peak of the longitudinal variation at the apex confirm the results shown in Figs. 4  and 5. The PGD and POD model based on basis functions extracted from porcine data have been applied to one human ex-vivo data set to test the feasibility to approximate human myocardial structure.
Fig. 8  shows the same plots as Figs. 3 (a), 4,5,6 and 7 for the human heart. Fig. 8  (a) shows the error dependence on the number of included basis functions. The markers in the surface plot indicate the approximation of the human data without personalization of the PGD model, directly using the mean PGD modes, extracted from the porcine training data sets. The mean of the first PGD mode results in MA of 16. 4 •. Adding more modes without personalization results in a high error of 22. 7 •. To compensate this deviation of mean PGD modes extracted from the porcine data and the human data, a scaling factor, estimated during fitting to the human data, was introduced for the mean. The weighted mean (0 SVD modes) results in a MA of 15. 19 • , adding the weighted mean of higher PGD modes does not change the approximation error (improvement compared to one weighted PGD mode < 0.
5 • per mode. The POD model results in a low error of 12 • with only one mode. With 11. 2 • for 4 POD modes the error levels off, with minor improvements of less than 0. 1 • per mode for higher POD modes. The helix maps in Fig. 8  (b) show a qualitatively good agreement between the original cDTI data and both PGD and POD approximations. However, both models smooth the data. The cDTI data shows a small dark blue patch at the endocardial surface with negative helix angle due to its non-cyclic definition. This is not captured by both models, the POD over-smooths the helix values at the endocardium. The MA maps show elevated errors at the apex and in the region with a steep helix at the endocardium, more prominent for the POD model. Qualitative results agree with observations form the porcine data sets.
8 (d). The bars represent MA for the optimal mode configuration. The PGD model shows the best performance with MA = 9. 4 •. The error is 17 % higher for the POD model, 29 % higher for the RBM model and 129 % higher for the porcine atlas. The marker for the PGD model indicates the un-personalized first mean PGD mode, extracted from the porcine data. Without personalization, MA is 75 % higher, compared to the personalized PGD model. Personalization improves the MA by 50 % for the RBM, comparing the fit to the human data and the adaptation to the average boundary values from the porcine data. The RBM shows the largest improvement, when approximating the human data set, compared to the ability to represent the porcine data set. The spatial variation of MA is shown in Fig. 8 (e). Both models result in similar spatial variations.
In contrast to the circumferential variation for the porcine data in Fig. 7  , no locally elevated error is observed at the inferior LV-RV intersection. The 95-percentile value is elevated for c > 0. 8. The transmural plot presents an error increase for t > 0. 7 towards the epicardium, with a predominant increase of 75-and 95-percentile. The error at the endocardium is not increased for the PGD model. For the POD model the 75-and 95 percentiles show a minor increase towards the endocardium, less pronounced than for the porcine data. The longitudinal error variation shows a peak at the apex with IQR > 35 • , for l < 0. 1. For both models the error deceases with increasing longitudinal coordinate, leveling off at l < 0. 35. The region of high errors at the apex is larger than for the porcine test cases.
Discussion The small median angular difference between the measurements and the model estimations for the healthy training data set (PGD: 9. 7 ± 1. 5 • / POD: 11. 3 ± 1. 3 • ) as well as the rapid decline of the angular error towards a plateau with 4 PGD and 4 SVD modes show that the predominant myocyte orientation can be represented in a low-rank basis. Contrary to the PGD approach, the POD method does not present such a distinct plateau for the error as a function of number of modes, however the small improvement of less than 2. 5% per added mode for more than 8 modes and the overall small median error supports the feasibility of the approach. For the combined PGD and SVD method, the PGD modes represent the major characteristics of the microstructure within each heart. Higher order modes capture the characteristics extracted from the residual and therefore reflect features with smaller global impact.
The scar region covers 7. 1 ± 2. 8% of the myocardium and has a mass of 5. 4 ± 2. 4 g. The local error in the area of the infarction is slightly higher compare to the remote healthy myocardium (case average: PGD: 1. 2 • /POD: 2. 5 • ). This variation is within the range of the standard deviation over the AHA sectors for the healthy test cases, approximating the local in-homogeneity across the myocardium. However, for two out of five cases the local error in the area of the myocardial infarction is higher, with maximal difference values of MA PGD: 3. 8 • /POD: 8. 3 •. This locally elevated error within the infarction may be attributed to (a) a pathologically reduced fractional anisotropy and thus larger variability in eigen vector orientations derived from noisy cDTI data and (b) structural characteristics in the area of the myocardial infarction, not represented by the models derived from healthy training cases.
DTI measurements have become the most commonly used tool to non-destructively assess microstructure in organs. These measurements are suffering from low SNR and in the realm of e. g. in-vivo imaging a precision of 15. 5 • (systole)/31. 9 • (diastole)  ( Aliotta et al. , 2018 )  was reported. Despite the data-induced error, its non-linear propagation and the reported mid-myocardium stiffening with increasing helix  ( Palit et al. , 2015b ) , underlines the need for more realistic myocyte aggregate orientation representations, reducing the total model error. Error reduction in patient specific representations of myocyte aggregate orientation is therefore a prerequisite for image-based, patient-specific bio-mechanics modelling. The PGD and POD models are based on a functional basis extracted form an ex-vivo DTI training data set, these basis functions describe common characteristics across the hearts, thus potentially suppressing noise in imaging data by truncation of the basis.
The importance of a heterogeneous myocyte aggregate orientation  ( Rodríguez-Cantano et al. , 2019; Eriksson et al. , 2013 )  suggests that an atlas and the data-driven PGD and POD models have an advantage, compared to a global rule-based approach. Further, the PGD and POD model add natural variability along the circumferential and longitudinal coordinates. A human show case was used to test the feasibility to transfer the PGD and POD model, using basis functions obtained from porcine data, to human aggregated myocyte orientation. The resulting MA is in the same range as the results on the porcine data, suggesting the applicability of the porcine derived modes to human data. The PGD model uses the low-rank PGD basis averaged over the porcine training data set. This is personalized by fitting the SVD basis functions to the human data set.
Without personalization (0 SVD modes), the error for the first PGD mode agrees well with the results for the porcine data. This suggests a good match of the first PGD mode, representing the most prominent global structural characteristics of the underlying human case. However, adding higher PGD modes without personalization worsens the median MA, whereas it is improved or constant for the porcine data. Thus, the mean of those structural characteristics with increasing detail cannot be transferred directly from the porcine to human data. The devaition from the mean PGD mode, extracted from the porcine data set, to the human test case, could be compensated by a scaling factor for each 1D function of the mean PGD mode. If the same scaling factors could be used for other human test cases, remains an open question.