dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion We provided a consolidated database of important traits of European seaweeds that is distinct in its completeness and taxonomic coverage. Because the database is standardised and fits within the broader framework of WoRMS that aims to include all taxa described, it serves not only as a significant resource for phycological research focusing on trait-based ecology or evolution, but also for general macro-ecological and macroevolutionary research in general. The database can help to explore ecological questions about relations among traits or help to unravel taxonomic and evolutionary patterns of traits in seaweeds. Because some trait values vary geographically and some traits values were documented at the genus level, the database may not always provide the highest variation at the species level.
Acknowledgements Authors Stefanie Dekeyzer ,  Wim Decock  and  Leen Vandepitte  have been supported through the  EMODnet Biology  project ( MARE/2012/10/SI2. 657705 ). This work makes use of data and/or infrastructure provided by VLIZ and funded by  Research Foundation -Flanders (FWO)  as part of the  Belgian  contribution to LifeWatch. This work used resources and facilities provided by  UGent  as part of the Belgian contribution to EMBRC-ERIC ( FWO GOH3817N  and  I001621N ) https://doi. org/10. 5194/essd -2022-329  Preprint. Discussion started: 14 October 2022 c Author(s) 2022. CC BY 4. 0  License.
In the present study, we applied Pairwise Maximum Common Subgraph Feature Generation (PMCSFG), an algorithm developed by our group to generate molecular features by computing maximum common substructures (MCS) under the block-and-bridge-preserving subgraph isomorphism of pairs of graph-encoded molecules  [28]. MCS has many applications in drug discovery  [29, 30]  including similarity searching  [31] , chemical space analysis  [32, 33] , and activity cliffs detection  [34]. As a case study, we used a publicly available benchmark dataset containing 6512 chemicals with known mutagenicity  [35]. Both qualitative and quantitative methods were used in this investigation. First, the most informative PMCSFG patterns were selected based on point-wise mutual information and were compared to well-known toxicophores for mutagenicity.
Second, we compared fingerprints generated by the PMCSFG algorithm with 12 state-of-the-art 2D chemical fingerprints by using them as features in several learning algorithms widely used in chemoinformatics: decision trees, k-nearest neighbors, rule learners, naïve Bayes, and support vector machines  [36, 37]. In order to validate the effectiveness of the models, we used cross-validation on the benchmark dataset and furthermore we report results on an external test set. The results show that PMCSFG has a similar performance as the best non-dictionary-based fingerprints, while using a tractable number of patterns.
| Data Benchmark dataset: Hansen. We used a publicly available benchmark dataset reported by Hansen et al  [35]. The dataset consists of 6512 compounds annotated with Ames mutagenicity and can be downloaded from http:// doc. ml. tu-berlin. de/toxbenchmark/. Although the dataset was already pretreated to remove duplicate structures and inorganic molecules, we cleaned the chemical data to normalize specific chemotypes (e. g. , nitro group, organophosphate moiety), to convert the structures to their aromatic form, and to add hydrogens on the heteroatoms. This resulted in a well-balanced dataset containing 3503 mutagenic and 3009 non-mutagenic compounds. External test set. To assess the predictivity of the classification models, we used an external test set. We collected 1125 non-redundant molecules annotated with Ames mutagenicity data (447 mutagenic and 678 nonmutagenic compounds), to measure the classification accuracy of our models on unseen data.
| Selection of the most informative MCS We scored the features by their point-wise mutual information (PMI). The PMI expresses the correlation of a pattern m to a target variable t in the following way: PMIðm; tÞ ¼ log 10 N � pðm; tÞ p m ð Þ � p t ð Þ where N denotes the number of molecules in the dataset, pðm; tÞ the number of mutagenic molecules that have feature m, p m ð Þ denotes the number of molecules a pattern m occurs in and p t ð Þ are the number of mutagenic molecules. Then, we used the MMRFS algorithm to extract the top-50 features while maximizing PMI and minimizing redundancy between the features  [41].
| Chemical fingerprints The features generated by fingerprint methods are used to encode each molecule in a dataset as a k-dimensional binary vector (with k the number of features), where a 1 is marked in the i-th position if the i-th feature occurs in the molecule and a 0 otherwise. In this study we only consider methods that directly produce binary vectors, which excludes e. g. , graph neural networks, graph attention networks, and the neighborhood subgraph pairwise distance kernel. We divide fingerprinting methods into five categories, based on the way features are generated. Dictionary-based fingerprints rely on features which have been identified a priori by domain experts as important fragments. MACCS keys  [25]  consist of 166 predefined structural keys which are considered as significant fragments for bioactivity of chemicals.
Pub-Chem FP  [26]  have 883 features corresponding to PubChem substructures. The remaining fingerprint types conceptually encode fragments based on the atom-bond structures in the dataset. Path-based fingerprints can enumerate all paths up to a certain length. Dendritic encodes the linear paths augmented with intersections of linear paths, with a maximum of five bonds per path to encode branched features  [27]. Torsion encodes features of four consecutively bonded non-hydrogen atoms along with the number of non-hydrogen branches, corresponding to a torsion angle  [42]. Radial-based fingerprints iteratively encode features that represent each heavy atom in larger and larger structural neighborhoods, up to a given diameter (2, 4, and 6 in this study). Extended-connectivity fingerprints  [43]  are generated directly from the dataset by first assigning an initial label to each atom and then applying a Morgan type algorithm  [44] , which was proposed as a method for solving the molecular isomorphism problem.
In another approach, MOLPRINT2D  [45] , each heavy atom in a structure is characterized by an environment that consists of all other heavy atoms within a distance of two bonds. Each member of the list is encoded into a string of the form Type-freq(Type)-d, where freq(Type) is the number of times a given atom type is found at a distance d from the central atom. The atom-typing scheme used is the Sybyl Mol2. Atom pair-based fingerprints encode features representing two atoms and their corresponding distance. Specifically, we use here the Pairwise approach  [46]  which considers the Carhart atom types and the topological distance separating them. The Pairwise Maximum Common Subgraph Feature Generation (PMCSFG) algorithm generates features by computing MCSs under the block-and-bridge-preserving subgraph isomorphism between molecules from a graphbased dataset  [28].
Experimental methodology The features are generated as follows. Given a dataset G, we first generate features only from the training set. Then, we propositionalize each example in G to a one-bit vector encoding representation: given a feature set of size k, each graph g 2 G is encoded as a k-dimensional binary vector, where a 1 is marked in the i-th position if the i-th subgraph is subgraph isomorphic to g. Note that we use the general subgraph isomorphism here to embed the patterns, in order to ensure the same treatment of all fingerprints. For PMCSFG, we selected 4000 unique MCSs by randomly sampling pairs of molecules. Sampling more than 4000 MCSs did not improve performance in an earlier study  [28]. For the other fingerprints, there are no other parameters to be set. In order to compare the different fingerprint methods, we used them as features in five different machine learning methods from the Weka  [47]  data mining tool: Support vector machines (SVM) combined with the Tanimoto kernel.
Naïve Bayes. We use the Naïve Bayes classifier as implemented in Weka  [53]. The learning task is to discriminate the harmless molecules from the mutagenic ones in the Hansen dataset. To evaluate the classification models, we use the area under the ROC curve (AUROC) score  [54]. For all experiments, a stratified 10-fold cross-validation is used. For all methods we used standard parameters, except for SVMs, where we tuned the regularization parameter out of 10 possible values through an internal 5-fold cross-validation on the training set.
Some of the top-ranked MCSs perfectly match known structural alerts for mutagenicity. As shown in Table  1 , we retrieve nitro-aromatic (410) and dinitro-aromatic groups (344), aromatic amine (2498), alkyl N-nitroso (358) and aryl N-nitroso groups (3552), aromatic hydroxylamine (762) and their derived esters (638), aromatic azo group (563), hydrazine (1539), alkyl ester of sulfonic acid (3945), acyl halide (2121) haloalkyl ether (2506), haloethyl amine (2131), polycyclic aromatic hydrocarbons (1010), quinone (1847), and nitrogen mustard and its derivatives including cyclophosphamide (1675). More specific structural alerts known or considered to be carcinogen are also overrepresented in the Hansen dataset. They correspond to the 2-aminobenzimidazole (2102), the dantron moiety (1028), and the substructure of the benzo[a]pyrene-7,8-dihydrodiol-9,10-epoxide (562); the latter being the metabolite of benzo[a]pyrene responsible for the disruption of the normal process of copying DNA by intercalating its double-helical structure.
When applied outside the learning dataset, MCS 1189 is able to match compounds without nitro substituent on the arylamide substructure. This highlights a weakness of our method since many molecules not reported as mutagens exhibit such substructures. As examples, we can cite the biocides Tibromsalan (bactericide), Boscalid (fungicide), and Picolinafen (herbicide). The other undefined MCS (1421) is more of interest because this unambigous substructure is not reported on ToxAlerts  [15] , an open expert-knowledge-based platform that contains more than 3000 toxicophores from the literature for several endpoints including mutagenicity (117). Nineteen out of the 24 supporting molecules exhibiting this MCS belong to the mutagenic class (Figure  3 ). The analysis of the supporting molecules showed that the vertex labeled C ar is always part of a tricyclic (aromatic) system.
| Quantitative analysis From the 4000 MCSs generated using the PMCSFG algorithm, we constructed molecular fingerprints and used them as features in learning models. Table  2  shows the results of the comparison with 12 state-of-theart 2D chemical fingerprints on the Hansen dataset. The different fingerprints perform similarly, with slightly higher results obtained by the dictionary-based fingerprints (MACCS keys and PubChem FP), the ECFP/ FCFP fingerprints and the PMCSFG fingerprints, although the differences are not statistically significant (assuming a normal distribution and requiring at least 3 standard deviations of difference to conclude that one method significantly outperforms another). Interestingly, the fingerprints with the most features do not necessarily have the best performance.
| Comments with respect to the state-of-the-art Many (Q)SAR models and structural alerts are available to assess the possible mutagenicity of substances  [59] [60] [61]. In this paper, we used the data set reported by Hansen which is the most popular data set for chemical Ames mutagenicity  [35]. It should be noted that the comparisons of the available models reported in the literature have sometimes been performed on subsets or supersets of the Hansen data set. A trend can nevertheless be observed: the predictive power of (Q)SAR models is high, often achieving AUC around 0. 8-0. 9, and our methodology is not an exception to the rule. For recent comparative studies on this topic the reader is referred to Benigni et al. [62]  and Yang et al. [63]. Regarding the identification of structural alerts, a dominant paradigm is based on the idea that a substructure is of lesser interest when it relies on too few occurrences in the data set.
Since redundant and non-discriminative substructures often overfit the model and deteriorate the classification accuracy, we tackled this issue by using the MMRFS algorithm  [41]. The MMRFS algorithm searches over the feature space in a heuristic way. A feature is selected if it is relevant to the class label and contains very low redundancy to the features already selected. In the present case, this feature selection algorithm tended to output a subset of MCSs whose elements were discriminative, different, and representative of the mutagenic compounds. The method demonstrated its efficiency as the 50 top-ranked MCSs matched around 40 known structural alerts for mutagenicity. In addition, due to their intrinsic properties it turned out that the MCS features were typically much larger than the ones generated with the other methodologies (9 atoms on average and up to 28 atoms for the Hansen dataset) and corresponded to recognizable molecular fragments.
In order to have a complete picture of the results obtained in this study we also report here the results obtained in the same dataset by using learning representation techniques; namely graph neural networks. Results reported in Li et al. [64] show that employing a graph neural network achieves an AUC of 0. 878. These results are to be expected since learning features in an end-to-end fashion conditioned on the downstream task is always more beneficial than handcrafted features. However, this does no invalidate the results we obtained in this study. One limitation of using learning representation techniques is explainability and interpretability of the representations. A major part of machine learning models is to be able to inform domain experts regarding the reasons behind model decisions.
| CONCLUSION In this paper, we compared 12 traditional chemical fingerprints to the PMCSFG fingerprint method, which learns fingerprints based on sampling maximum common subgraphs from a molecular dataset. Although we found no significant differences in predictive performance between the different PMCSFG has multiple advantages. First, they can be automatically and efficiently learned from data, unlike the dictionary-based fingerprints which are selected by hand. Second, compared to the non-dictionary-based fingerprints, the number of features can be easily controlled. Third, PMCSFG reaches the same predictive performance as the other fingerprints with a smaller number of features (except for the manually designed MACCS fingerprints). It turns out that the MCS features are typically much larger than the ones in the former fingerprints and correspond to recognizable molecular fragments.
This surface is characterized by three zones (fixed, thermostated, and reactive). (b) Same surface is covered with a hydrocarbon film. Such a rate of deposition is high as often in MD simulation. The effect of this unphysically high flux is compensated by the substrate thermostat, which prevents unphysically high-temperature increase due to energy transfer at impact. The species are sequentially released, from a random position above the substrate, toward the surface with velocities randomly selected in a Maxwell-Boltzmann velocity distribution at 300 K. [54, 55]  Five simulation runs (with different initial velocity sets) were also performed. All the MD simulations were carried out using the open-source code LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator). [56] VMD software was used to visualize the MD results and Python scripts were written for data processing.
ACKNOWLEDGMENTS The authors are grateful to the  Fédération Calcul Scientifique et Modélisation Orléans Tours (CaSciMo-dOT)  for the numerical resources made available to us. This work has been supported by the  National Scholarship Agency of Gabon (ANBG)  and the  French National Research Agency (ANR)  through the  MONA  project ( ANR-18-CE30-0016 ).
Luminescence dating The results of OSL measurements are given in Tables 1,2 and 3. Both the average dose model (ADM,  Guérin et al. , 2017)  and the central age model (CAM,  Galbraith et al. , 1999)  were used. The results are indistinguishable, within uncertainties. The Kernel plots of the D e distributions are presented in Supplementary Information Fig. S1. The distributions of equivalent dose values did not reveal any issues with poor bleaching or grain population mixing, even though this may be the result of an averaging effect due to the large number of grains measured together. According to  Guérin et al. (2017) , the ADM doses should be more accurate than CAM doses for determining burial ages; hence, we used the former dataset to calculate OSL ages. The radioelement content and external dose rates measured in situ, as well as the total dose rates, are presented in Table  2.
Acknowledgements. We thank the owner of the quarry,  M. de La Rochefoucauld , for allowing us to sample the section, as well as  J. P. Faivre  ( PACEA, CNRS-University of Bordeaux ) for financing OSL dating as part of the  NATCH  project ( Région Nouvelle-Aquitaine ) (2016 - 1R40204 -00007349 -00007350 ). This work was carried out within the framework of  J. Puyrigaud 's Masters thesis supervised by  P. Bertran.
For bioenergy, it calculates net power station energy produced and bioenergy carbon capture and storage (BECCS) potential. The model is described in  Hastings, Clifton-Brown, Wattenbach, Mitchell, and Smith (2009)  and  Shepherd, Littleton, et al. (2020). A block diagram of the model growth processes is shown in Figure  1  2. 1 | Spatial databases for modelling Databases were obtained and formatted for Europe-wide spatial simulations. Potential land that could be used for the cultivation of biomass crops in Europe was investigated. The Corine land cover map from Copernicus  (Buettner, 2014)  was used to create a mask of areas unsuitable for the cultivation such as bare rock, or areas that should not be used for cultivation such as forests, wetlands and shrubland. Only arable and pasture classes were considered suitable.
In addition, any areas with high carbon soils, typically above 15% organic matter or peatland were considered unsuitable. The soil properties were obtained from the harmonized world soil data base from IIASA  (Wieder et al. , 2014). We only used the dominant soil for each grid block. These soil parameters were used to calculate the wilting point, field capacity and soil carbon to a depth of up to 1 m for each grid pixel. The RCP2. 6 future climate scenario from the HADGEM 3 model from the UK Met Office  (Martin et al. , 2011)  for the period 2020-2030 in monthly time steps and on a 0. 5 degree grid was used as driving climate data.
| Spatial projections After satisfactorily validating the MiscanFor model for different sites and years with varying soils and climate, we applied the model spatially. There are two versions of the MiscanFor model, a single site version and a spatial version with exactly the same processes but with the capability to use databases at variable scale and extent. For the spatial model, climate, elevation, soil, land use and land constraint databases were prepared for Europe at 1 km resolution. The crop biomass processes and parameters for the validated site model were input to the spatial model. The energy produced by a miscanthus assuming a medium size power plant burning miscanthus bales uses the method of  Hastings, Clifton-Brown, Wattenbach, Mitchell, Stampfl, and Smith (2009). Carbon capture potential (CCS) is based on an assumption of 90% efficiency  (Albanito et al.
We have used this method to produce aggregated European totals rather than using marginal land use databases because the definition of marginal land changes depending on the crop or land use, and because land use databases are based on current/past land use, and we are producing future projections with a proportion of land conversion to biomass determined by a socio-economic IAM. Moreover, the IAM land area output is related to the RCP 2. 6 climate scenario, used in this study, to determine the biomass land area necessary for sufficient BECCS decarbonization to meet the Paris Agreement (United Nations, 2015) limit on global temperature increase under 2°C.
V1 V2 r  which are, in turn, related to variance in soil type and climate. Uncertainty in rapidly scaling-up biomass energy supply, especially in dry climates and in regions where future climate change could result in drier conditions, has important policy implications in bioenergy effectiveness for lowering atmospheric carbon  (Littleton et al. , 2022). Despite different climate and soil datasets, the uncertainty we obtained for European biomass projections is of the same range and spatial distribution as obtained in  Hastings, Clifton-Brown, Wattenbach, Mitchell, and Smith (2009). The European aggregated mean annual totals for biomass (Table  6 ) show a European capacity to produce nearly 90 Mt year -1 of miscanthus harvest yield from the conversion of 10% of less productive grassland and 10% of less productive arable land.
The calibrations were based on field trials grown on less productive land, where the definition of environmental restriction on growth is in keeping with the use of lower-grade arable land and grassland conversion to growing biomass. Climate projections from the RCP2. 6 climate scenario have been used, assuming a 2°C or lower global temperature increase and associated bioenergy land use change which assumes biomass crops for bioenergy with CCS being rolled out at scale in the next 10-20 years  (Littleton et al. , 2022). All hybrids use the same climate, soil and land use databases, the only differences are from the biomass calibrations from field trials. Seeded M. sin × sin and rhizome M. sac × sin European harvest potential show promise to outperform the current commercial crop M × g. Seeded M.
Finally, regarding limitations for viewing the European projections, the spatial limit of the databases and the components of the land mask do not allow us to view variation between fields, maps only allow for regional viewing at a 1 km resolution. The next steps could be to repeat the measurements a later year in a follow on project, using the same plus additional locations. This is particularly important regarding plant maturity. If it were also possible to link onto other projects such as the PBC4GGR, carbon fluxes will be combined with similar measurements to this study to understand the dynamics of RUE, which could provide further information on the dynamics of photosynthesis and respiration. Nevertheless, to see the limitations is not to downplay the successes of the field trials and the modelling.
ACKNO WLE DGE MENTS We would like to thank the industry partner Terravesta, developers of Miscanthus throughout the UK, the Dutch company  Van Dinter Semo , developers of M. sin × sin hybrids, and all collaborators and the members of the GRACE project funded by the  Bio-based Industries Joint Undertaking , grant agreement no. 745012. The authors thank  EPSRC ,  BBSRC  and  UK Supergen Bioenergy Hub  ( EP/S000771/1 ) who funded and supported this research. The work was made possible by a  Supergen Bioenergy  hub fellowship, funding ref. RG15855-UKRI  ( EPSRC ) via  Aston University  and the survey was carried out as part of project SUMMER-seed-propagated upscaling of Miscanthus-modelling and environmental response. This work was also made possible by  BBSRC  funding ( BB/V011553/1 ) for the  Perennial Biomass Crops for Greenhouse Gas Removal (PBC4GGR) Demonstrator project , and was also undertaken as part of the  UK Energy Research Centre research programme.