dataesr/scientific-paragraphs-categorization huggingface dataset:
About 200 metabolites of polyphenols not yet recorded in any database were added, associated with the original literature. PhytoHub can now be searched to get the list of metabolites observed or expected in biofluids after consumption of a given food. For example, when searched for apple, a list of 195 metabolites is obtained, along with the information necessary to identify them in urine or plasma metabolomic profiles. In addition, for compounds whose metabolism has not yet been studied in humans, the most likely host and microbial metabolites can be predicted by Biotransformer (http://biotr ansfo rmer. ca/), a new open-access tool that applies prediction rules elaborated from machine-learning algorithms and expert knowledge including PhytoHub data  [43]. Integration of more chemical, biological and spectral data for phytochemical metabolites in metabolomic databases will be key for a better understanding of food phytochemical ADME and associated inter-individual variation.
Obtaining hypotheses of identification for unknowns using databases is one step in the process. Hypotheses must be validated by comparison of experimental MS/MS spectra or retention time with those of authentic standards. Unfortunately, many standards are expensive or not commercially available. A collaborative platform called Food-ComEx (http://foodc omex. org/) is facilitating the sharing of standards, and several chemists from POSITIVe already proposed some precious synthesized metabolites of polyphenols there  [44]. MS/MS fragmentation is widely used to support or discard hypotheses of identification, while information associated to the retention time has not been considered enough. We evaluated the potential of retention time prediction by PredRet  [45]. This online tool can predict the retention time of compounds in a chromatographic system as soon as they have been experimentally determined in other registered chromatographic systems.
We compiled retention time data for 471 plant food metabolites in 24 chromatographic systems used in 19 platforms across Europe. Predictions were very accurate, with a median prediction error of 0. 03-0. 76 min depending on the systems. Such level of precision represents a great help to accelerate the identification process, with the possibility to discard hypotheses outside the predicted retention time window. PredRet is free to use for new contributors, and the number and the accuracy of predictions will increase with the number of shared data. Coordination of efforts and large collaborative initiatives are essential to improve and harmonize the assessment of individual exposures to plant food bioactive compounds using untargeted metabolomics. The COST Action POSI-TIVe has been instrumental in building a community with shared interests, which will hopefully continue to interact and expand efforts to collectively address the most important remaining challenges.
The impact of UGT genotypes on the endogenous concentrations of these compounds, the incidence of associated cancers and response to select drugs has been reported (for review see  [39] ). Although the bidirectional relationship between UGT and plant bioactive has been reported  [54, 55] , the impact of UGT genotype on plant bioactive concentrations and efficacy remains to be established. The ongoing COB trial (NCT01922869) is taking a more comprehensive approach and is sequencing 112 genes, involved in all stages of plant bioactive metabolism from digestion to elimination, to establish their impact on flavonoid ADME over 48 h. No human GWAS investigation of plant bioactive metabolism is available.
Methodological approaches for the identification of gene variants of interest with respect to plant food bioactives Once genes of interest are identified, large publicly available databases including HapMap and more recently the 1000 Genome Project (where the genomes of a large number of people sequenced and their genotypic data stored) are used to select potentially important variants. Their selection should be on the basis of their known or predicted impact on protein concentration, structure or function, or pathogenicity, or variants which tag and act as genetic biomarkers for a particular gene region. In addition to the gene itself, variants in adjacent regulatory regions, for example transcription factor binding site (TFBS) and enhancers, which can influence gene expression, are also likely to be important.
Therefore, variants in genes regions within 10 Kb from the transcription start sites 1 3 (TSS) and transcription end site (TES) for each gene should be included. The second step is to determine the effect of these variants. The Variant Effect Predictor (VEP)  [56]  tool is used to provide information regarding variants' locations, consequences (e. g. ,missense or frameshift), minor allele frequencies (MAFs) and pathogenicity prediction established using Sorting Intolerant from Tolerant (SIFT), Polyphen, and others. The next step is to search publicly available datasets that have records of studies showing associations of the selected variants with disease incidence or biomarkers. Some of the commonly used datasets include GWAS, dbSNP, Humsavar and pharmacogenetic databases. Using all this gathered information, the final step, is to select a tagging SNP for each haplotype block (250 kb max) together with SNPs in the inter-block regions, to provide a comprehensive list of variants covering the whole gene region.
Priorities for SNPs' selection include: 1. SNPs previously published in online databases, 2. SNPs with predicted pathogenicity established using SIFT and Polyphen, 3. SNPs in promotor regions, exons, 3'UTR and regulatory regions, and 4. SNPs with a MAF ≥ 0. 5 Such an approach will result in a list of SNPs which can be used to probe human clinical trial and cohort data, for genotype*ADME*phenotype associations. At present, research is at the early stages of identifying the main genetic determinants of plant bioactive metabolism and efficacy. It is hoped that specific genotypes will emerge which could identify subgroups who are likely to most benefit from increased intake of selected plant bioactive compounds. When evidence is sufficient, such information may be used in routine health examinations and possibly be used for individual dietary recommendations.
The search strategy employed was so-called "gene-centric microbial genome and metagenome mining". The method consists of (1) screening literature for the crucial microbial metabolic steps that were identified by experts working on the specific compound classes, (2) if absent, screen for microbial enzymes and their encoding genes that are known to perform a comparable metabolic step, (3) use JGI's (Joint Genome Institute) integrated microbial genome and microbiome (IMG/ER) to filter known HMP (Human Microbiome Project) genomes for these genes (https ://img. jgi. doe. gov/imgm_hmp) and export those and (4) use the exported genes to query HMP metagenomes, the 10 million gene catalogue from the former MetaHIT FP7 project or other metagenome databases and subsequently obtain a stratification of human individuals based on their microbiome metagenomic's information.
This approach of stratifying individuals according to their personal microbial (meta) genomic profiles has rarely been attempted before. Several knowledge gaps were identified primarily relate to a lack of accurate gene annotation and database implementation. Since not all plant bioactive compounds could be covered, a set of model compounds (isoxanthohumol, lignans, flavan-3-ols, ellagitannins, isoflavones, rutin, chlorogenic acid and anthocyanin) was chosen to evaluate employing the gene-centric metagenomic screening strategy. We illustrate our approach with the microbial metabolism of lignans and isoxanthohumol. As mentioned in the section above, enterolactone has been extensively investigated with regard to health effects mainly related to hormonal-dependent cancers. Enterolactone is formed by microbiota-dependent metabolism of plant lignans, in several steps as illustrated by secoisolariciresinol (SECO) diglucoside converted to enterodiol and to enterolactone in the final step.
While Eubacterium limosum ZL-II is known to convert SECO into enterodiol, its genome is unfortunately not completely available. Chen et al. [60] , therefore, used the genome of E. limosum KIST612 as a reference to further characterize the four-component O-demethylase. We used the same reference organism's proteome and genome to screen the IMG database for available putative O-demethylase encoding genes. Interestingly, IMG database exploration for functional protein-encoding genes upstream a metabolic pathway yielded a lower number of positive bacterial genomes. To exemplify, screening for the more general methyltransferase II resulted in positive hits for 2010 bacterial genomes, the corrinoid protein gave 272 bacterial genomes, while methyltransferase I-which probably has higher substrate specificity-only yielded 96 bacterial genomes (Fig.
3b ). This reduction in number of bacterial genomes increases the feasibility of stratifying individuals based on their phylogenetic microbiome composition and making predictions on their probability of harboring a SECO-converting microbiome. A similar gene-centric mining of metagenomic databases can serve as a basis for stratifying individuals based on the presence of functional genes and explore whether specific determinants (ethnicity, diet, health status, sex, age, etc. ) form a confounding factor for this stratification. However, this proposal to use gene-centric metagenomic mining does not suffice to identify metabotypes that group individuals based on their ability to generate a high plant-based bioactive concentration in vivo. In-depth and independent validation with separate intervention studies, including the incorporation of all metadata from study participants, need to further prove the usefulness of this approach and indicate to what extent the microbiome is actually involved in a bioactive's ADME and predict which individuals would experience the largest health effects based on their microbial metagenome.
The identification of putatively involved microbes is still complicated due to some specific issues. The gene-centric metagenomic screening relies on available information of metabolic pathways, which are for many compounds still uncharacterized. Moreover, if functional genes are less well conserved across microbial genomes, gene-centric metagenome mining could yield false negatives depending on the cutoffs that are used. Third, knowledge of gene sequence information does not immediately lead to gene annotation. In such case, KEGG pathways cannot be explored and database screening can only be conducted based on the gene identifier (Gene ID). In addition to the fact that it is difficult to connect an individual's metagenome with its metadata, it is clear that future research progress in this field will require constantly updated databases and proper gene annotations.
When this is not the case, no interpolation is needed, see for example, the work of Sá et al. 14  To validate the results, different cases are presented to compare SIMP-ALL with the SIMP interpolation functions and their relation with the H-S bounds. Finally, some numerical examples are computed to see the influence and convenience of the interpolation function on the optimal topologies. Additionally, a MATLAB code of the SIMP-ALL interpolation is provided. This work is organized as follows. Section 2 presents the bimaterial topology optimization problem. Section 3 introduces the concept of the topological derivative and presents the topological derivative for isotropic materials in terms of the shear and bulk modulus. H-S bounds are briefly presented in Section 4 and connected with the topological derivative.
The examples are computed in MATLAB© with a standard PC (3. 40 GHz processor in a 64-bit architecture). However, some differences appear when  1 becomes smaller. The optimal topologies when using the SIMP interpolation require large number of iterations and finite-element solver evaluations to converge and gray areas appear. This is probably due to a small penalization value appeared in that cases (see Figure  2 ). In the case of SIMP adaptive, in which the penalization value is rather large, although it seems to be almost free of numerical instabilities, nonstandard topologies appear. Likewise, a significant computational effort in terms of iterations is required to converge. In contrast, the SIMP-ALL interpolation behaves as adequately in terms of optimal topology and computational effort as FIGURE  4  Cantilever beam example for a sequence of different material-void interpolations using different interpolation functions.
However, as it becomes smaller, the SIMP-ALL method has shown to obtain up to 20% stiffer topologies, free of gray areas and smaller computational cost. In comparison with SIMP, this result shows that the SIMP-ALL method is auto-penalizing in a wider range of cases. From the author's point of view, rather than an alternative, the SIMP-ALL interpolation method can be understood as a generalization of the SIMP method, adequate for all kind of interpolation materials, with physical meaning, free of heuristic parameters, and with no extra computational effort. In this sense, SIMP-ALL interpolation bridge also the gap between the topological derivative community and the SIMP community, allowing the first to use regularized algorithms and the second to use the topological derivative concept. A MATLAB implementation of the SIMP-ALL interpolation is available at https://github.
ACKNOWLEDGEMENTS The research leading to these results has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no. 712949  ( TECNIOspring PLUS ) and from the  Agency for Business Competitiveness  of the  Government of Catalonia. We thank  Dr Samuel Amstutz  for assistance with developing the methodology, for helping on deeply understand the topic and for the comments that greatly improved the manuscript.
| Model description We describe the model following the ODD (Overview, Design concepts, and Details) protocol for individual-based models  (Grimm et al. , 2006 (Grimm et al. , , 2010)). The model was implemented in NetLogo 4. 1. 3 and released under NetLogo 5. 3. 1  (Railsback & Grimm, 2019; Wilensky, 1999). The NetLogo program and all data files required to run the model are available under https ://www. comses. net/codeb ases/69863 caa-2f8e-4412-a564-a2826 d9d38 d3/relea ses/1. 0. 0/.
Emergence Foraging (stayer or mover) or nesting (investment or conservative) strategies directly determine rookery reproductive output via individual behavior. Intuitively, the best individual strategy would be to feed on feeding patches close to the rookery, thus reducing the cost of migration. However, with conspecifics depleting the close patches, different strategies might be beneficial. The rookeries' reproductive outputs consequently emerged from individual behavior while searching for patches and deciding on nesting energy allocation. Furthermore, the time interval between every breeding event emerged from energetic constraints, as well as the distribution of the spatial feeding patch usage that we could compare with tracking data from field surveys.
Feeding patches Locations of feeding patches were set up by combining maps from two distinct sources: the World Atlas of Seagrasses  (Green & Short, 2003)  and the Agulhas and Somali Current Large Marine Ecosystem project (ASCLME; www. asclme. org). Mapped seagrass beds were transformed into feeding patches (grid cells) at locations corresponding to the location of the main mapped sea grasses beds. Additional feeding patches were added along the coast of Somalia as this place is known to host vast areas of seagrass bed that are not mapped in the cited datasets (S. Andréfouët, personal communication).
Oceanic currents To model oceanic currents, we are using an annual climatology map that reflects the mean current velocities in the region. This map was computed by combining GEKCO surface current daily datasets  (Sudre, Maes, & Garçon, 2013). We did not consider any seasonal effect at this stage. To represent the 2D currents vector maps in the model, in the RGB (red, green, blue) tuple that is used to encode colors in NetLogo, the green component was left at zero and the values of the red and blue component were used to represent, respectively, the eastward and the northward components of the sea surface currents (Figure  2 ).
Additional feeding patches were added along the (10) T n,i = S N ⋅ T n, max (11) i,t+1 = i,t +Δ i,n (12a) Φ p,t+1 = Φ p,t +Δ Φp,t (12b) Δ Φp,t = Φ p,t 1 -Φ p,t ∕Φ max -N p,t Φ p,t (13a) Δ Φp,t ≈ 0 (13b) Φ ≈ Φ max ∕2 (13c) N p,t = N T ∕N F (14) = ⋅ N T ∕N F ⋅ Φ max ∕ Φ max -Φ max ∕2 = 2 ⋅ ⋅ N T ∕N F (15a) if d p,𝜎 < d 𝜎, max :Φ p,t+1 = Φ p,t -Δ Φp,t (15b) Δ Φp,t = i ⋅ ⋅ d , max ∕d p, ⋅ Φ p,t (15c) d p, = p y -y coast of Somalia as this place is known to host vast areas of seagrass bed that are not mapped in the cited datasets (S. Andréfouët,.
National Forest Inventory Data The French NFI is a continuous countrywide inventory  [1, 61]. The two-phase sampling plan is based on a permanent systematic 1-kilometer-square unit grid. The first-phase annual sample is drawn randomly for one-tenth of the grid (10 km 2 ). For each 1-kilometer square grid cell one to four points may be drawn, thus constructing an annual sample of ~80,000 points. The first phase points are photo interpreted on 0. 5 m resolution infrared aerial photographs for land cover and land use, and produce results for land cover. The second phase is a subsample of the first taken in different proportions. For forests, the standard second phase sample is obtained by taking one point out of two (~6500 points) that are visited in the field. Field observations are obtained from concentric plots of 6, 9, 15, and 25 m radius.
For the purpose of this research, data from 775 plots measured during the 5-year period 2010-2014 were used. This period has been selected to match the acquisition date of the remotely sensed data whose reference year was 2014. A total of 11 forest attributes were considered as response variables: growing stock volume, basal area, density, and quadratic mean diameter, production volume (estimated based on measurements of the last 5 growth rings), volumes of broadleaved, conifers, oak, Scots pine, other broadleaved and other conifers. Summary statistics for the plot variables are provided in Table  1. Two photogrammetric elevation models (2008 and 2014) are available for a subset (~44%) of the study area (N = 346 plots, Table  1 ), and were used to assess the effect of canopy height changes on the precision of estimates.
3 m, total height, as well as diameter increment during the last five years. Additional measurements including timber height, mid-diameter and mid-timber diameter are taken on a subsample of plots to fit volume models  [1]. For the purpose of this research, data from 775 plots measured during the 5-year period 2010-2014 were used. This period has been selected to match the acquisition date of the remotely sensed data whose reference year was 2014. A total of 11 forest attributes were considered as response variables: growing stock volume, basal area, density, and quadratic mean diameter, production volume (estimated based on measurements of the last 5 growth rings), volumes of broadleaved, conifers, oak, Scots pine, other broadleaved and other conifers. Summary statistics for the plot variables are provided in Table  1.
Auxiliary Data Three sources of auxiliary data available at no cost were considered: a forest type (FT) map (BD Forêt ® v2) constructed by the French National Institute of Geographic and Forest Information (IGN), DAP models of the forest canopy surface, and Landsat images. All auxiliary variables were processed at a 30 m spatial resolution corresponding to the Landsat image pixel size and to the field plot area used for dendrometric measurements. The FT map product BD Forêt ® is a vector layer constructed by photo-interpretation of near-infrared aerial photographs. The map includes a total of 32 classes and a minimum polygon size of 0. 5 ha. The nomenclature includes a description of the vegetation type with the dominant species, according to tree cover classes. The map corresponding to the study region has been constructed using aerial images acquired in 2008.
The forest contours were used to construct a forest mask. The mask was further intersected with the 30 m grid, and only the grid cells completely in the forest mask were considered for the analysis  [10]. The FT map attributes were summarized for each grid cell and aggregated into three classes corresponding to pure broadleaved, pure conifers and mixed stands. The DAP Digital Surface Models (DSM) were normalized using a co-occurring ALS Digital Terrain Model (DTM) to generate Canopy Height Models (CHM). ALS data were not considered as auxiliary data for describing the forest structure as their availability and cost prevent regular updates. The ALS data were acquired by IGN between January 17 and March 30, 2014, as part of the land survey with a point density of 2 pts. m -2 and a flight line overlap of 50%.
The point cloud was processed using the iterative TIN algorithm of the Terrascan software (Terrasolid, https://www. terrasolid. com, last consulted October 25, 2018), followed by visual inspection and manual correction whenever necessary. The algorithm was run using a terrain angle of 88 • , iteration angle of 6 • , and an iteration distance of 1. 4 m. The point data were delivered by 1 km x 1 km tiles, and were buffered using a 30 m threshold. Ground classified points were used to construct a digital terrain model with a 1 m resolution using inverse distance weighted interpolation. The DAP DSMs were constructed using aerial images acquired in 2013 (eastern part of the area, from July 06 to 12), 2014 (western part of the area, from May 18 to July 16), and 2008 (western part of the area only).
The 2013-2014 images were acquired using an IGN camera (sensor V28T, 14,650 x 10,700 pixels of 6. 8 µm each) mounted with a Zeiss lens having a 125 mm focal length. The images were acquired at 6,400 m above ground level, leading to a 35 cm resolution at the ground level. The 2008 images were acquired on August 30 using a DMC camera (sensor of 13,824 x 7680 pixels of 9 µm each) and a Zeiss 90 mm focal length lens. The acquisition was done at an altitude of 5,500 m, leading to a 54 cm resolution at the ground level. For all flights, the image overlap was 60% along and 25% across tracks. The image orientation was done by IGN as part of the production process. The dense image matching was done using MicMac open source photogrammetric software  [62]. MicMac is based on a multi-resolution-optimization approach and used a coarse to fine matching strategy based on image pyramids to improve the processing time and control the similarity of matches between levels.
Euclidean distance should be preferred when no variable selection is performed; and (5) the k-NN technique in conjunction with model-assisted estimators produced a significant improvement in precision of inventory parameter estimates. This work demonstrated the potential of MSNFI approaches in complex broadleaved dominated forests such as those found in France. This opens up the possibilities of more forest attribute estimation for smaller spatial domains. Future work will focus on a downscaling approach involving both model-assisted and model-based approaches. No 633464, as well as CHM-ERA project from the French National Research Agency (ANR) as part of the "Investissements d'Avenir" program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE).