dataesr/scientific-paragraphs-categorization huggingface dataset:
Global Sensitivity Analysis Results A GPR emulator was first developed as approximation of the coupled PROSPECT4-SAIL-MODTRAN model given 12 input variables and 1000 samples taken from the analysis dataset. GPR was used because of superior performances as opposed to other MLRAs  [31] [32] [33]. This was also the case here: GPR clearly outperformed competing algorithms such as neural networks and kernel ridge regression (results not shown). Training the GPR emulator took less than 3 min. It reached an overall accuracy with RMS errors (RMSE) of 1. 06 and normalized RMS errors (NRMSE) of 2. 39%. When plotting the NRMSE along the spectral range, it appears that accuracies are consistent with the exception of the water absorption regions around 1400 and 1900 nm where accuracies are somewhat poorer (errors around 6%) (results not shown).
Biophysical Variables Retrieval GPR models were developed for the variables Cab, Cw and LAI using the retrieval training data with noise added for nine bands at 20 m (without B8). These models were then validated against simulated reference data for: (1) the TOC scale, (2) TOA reference data, and (3) TOC dataset with noises according to Sen2Cor atmospheric correction errors. At the TOC scale, it was no surprise that retrievals against the TOC reference dataset led to excellent validation results with for Cab and Cw an R 2 of 0. 94-0. 97 (Table  5 ). LAI was poorer validated, with a R 2 of 0. 68, due to saturation for higher LAI values, i. e. , above 3. This suggests that the LAI model is suboptimally trained. What is more of importance in the context of this study is that results only degrade slightly when upscaling to TOA data, thus with the inclusion of atmospheric variables in the LUT.
Excellent results are again obtained for Cab and Cw (R 2 of 0. 91-0. 95), while poorer yet consistent results are achieved for LAI (R 2 of 0. 62). When moving back to the TOC scale, but now with adding noise levels according to Sen2Cor atmospheric correction errors, the results tend to degrade further. Given that this exercise is conducted with simulated data, the latter scenario is considered closer to reality. Comparison of these results may suggest that retrieving biophysical variables directly from TOA radiance data can be more beneficial, however, that is yet to be evaluated when applying to real data. Table  5. Retrieval performance results against 5'000 LUT reference datasets for biophysical variables retrieval from surface reflectance (TOC), TOA radiance (TOA) and surface reflectance with noise levels after atmospheric correction (ATM).
For the TOC and TOA retrieval datasets 2% Gaussian noise was added, while for the TOC-ATM retrieval datasets noises are added according to  [50]. An easy way to gain insight into the functioning of the GPR models at TOC and TOA scale is by means of inspecting the sigmas (σ b ), i. e. , the band relevance, of the trained GPR models. They have been plotted in Figure  6  for the three biophysical variables. The lower the σ b is, the more important the band. Accordingly, the σ b reveal the most important wavelengths with spectral information used for the development of the models. The following observations can be made:.
• The TOC and TOA models were trained by simulated data using RTMs that deal with spectral variability of homogeneous vegetated surfaces. Although 20 soil spectral signatures were added to the training, that is definitely not enough to cover the natural variability of non-vegetated surfaces at S2 spatial resolution for complete images. For instance, the models are not trained for water bodies and man-made surfaces. Ideally, spectral variability of all kinds of non-vegetated surfaces should be added to the training dataset. Similarly, most likely the model performs poorly over heterogeneous vegetated surfaces such as forests. • Another way how to further optimize the training LUT for operational mapping is by using sample distributions that reflect reality more, e. g. , normal or log-normal distributions for key variables.
, 2016; Khan et al. , 2010; López-Bellido et al. , 2011)  and pests and diseases  (Stoddard et al. , 2010). Winter faba bean experiments were conducted in southwestern France over several growing seasons  (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015)  with limited pest, disease and weeds pressure, This dataset provides a rich basis to adapt STICS to faba bean and analyse the capacity of the model to unravel the role of abiotic factors in driving yield and N 2 fixation variability. Our first objective was to calibrate and assess the performance of STICS to simulate N acquisition by soil mineral uptake and biological N 2 fixation, crop development and shoot biomass production, grain yield formation, N content in grain and straw, and soil water and mineral N contents along the whole profile during crop growth and after harvest.
By doing so, we explored the hypothesis that the generic model -(i. e. with the same formalism for all crops) can take into account the specificities of faba bean (N 2 fixation) and can be robustly calibrated for this grain legume. The second objective was to analyze the ability of STICS to generate a robust prediction for an independent dataset and allow a credible agronomic diagnosis of yield and N 2 fixation variability due to abiotic stresses. For this objective, our hypothesis was thatthe spatiotemporal adequacy of soil/crop relationships (e. g. N nutrition based on both mineral-N uptake and N2 fixation) and the realism of the implemented process equations allow for an accurate prediction of abiotic stresses.
Overview of the model The soil-crop model (as well as cropping system model) STICS  (Brisson et al. , 2008 (Brisson et al. , , 2002 (Brisson et al. , , 1998) )  was chosen for its agro-environmental purpose, crop genericity (allowing the simulation of crop successions with the same model) and robustness. STICS simulates carbon, water and nitrogen dynamics in the soil-crop system on a daily time step over the crop cycle. Unlike crop models that combine crop modules with different structures, e. g. DSSAT (Decision Support System for Agrotechnology Transfer,  Jones et al. , 2003)  or APSIM ( Agricultural Production Systems sIMulator,  Keating et al. , 2003) , STICS uses a common and generic structure for different crop species. Species-specific sets of parameters define the differences between species.
, 2012). Site, year, and management factors (cultivar, crop density, incorporation of a cover crop before planting and sowing date) defined 35 Site-Year-Management units (Table  2 ). Site-year-management units covering a broad range of soil types, growing seasons and management situations  (He et al. , 2017)  were chosen for the calibration dataset (Table  2 ). The remaining units, differing in growing season and/or management (compared with calibration dataset) formed the evaluation dataset (Table  2 ).
Stress factors and variability in grain yield and total N 2 fixed Stress factors calculated by STICS (see section 2. 1. 2 and Table  S2 ) were averaged over periods ranging from -60 to + 60 days before/after the beginning of grain filling. For each period, the Pearson correlation coefficient (R 2 ) between observed grain yield (total fixed N 2 respectively) and stress factor affecting grain yield (N 2 fixation respectively) over the period was computed. The period corresponding to the maximum R 2 was determined. For this analysis, calibration and evaluation datasets were pooled.
Crop development and LAI Calibration led to satisfactory prediction of the dates of emergence, end of juvenile phase, beginning of grain filling and physiological maturity. Relative bias was low (0 -10%) with fair EF values (Figure  3 ), indicating the relevance of calculating crop development based on vernalisation and photoperiodic and thermal time-course formalisms. Simulated LAI values for the calibration dataset were close to observed values with a low relative bias of 1% and a satisfactory EF of 0. 69. Prediction error was however great due to a bias of a few days in the senescence dynamic that had large consequences on the daily agreement between observed and simulated LAI values (rRMSE=56%) (Figure  4a ).
Above ground biomass, N 2 fixation and N acquisition Predicted biomass agreed with observation throughout the growth seasons in both calibration and validation datasets (EF>0. 77 and -7% < bias < 0%). Prediction error was high due to senescence defoliation between grain physiological maturity and harvest that was difficult to precisely simulate (rRMSE of 41 and 31% for calibration and evaluation respectively). There was a large variation in faba bean biomass at harvest (1. 5 -11. 6 t ha -1 and 2. 8 -7. 2 t ha -1 for calibration and evaluation datasets respectively) depending on site, year and management (Figure  4c, d ) and the model was able to reproduce this behaviour. The calibration of parameters related to N 2 fixation (Table  3  and Table 4 ) allowed to reach a good agreement of simulated versus observed values throughout crop growth (Figure  4e ,f) (EF = 0.
Grain yield and grain N content Observed grain yield greatly varied depending on site-year-management (0. 42 -4. 7 t ha-1 , Figure  5a ). The model performed well in simulating grain yield (EF=0. 66 with low relative bias) for calibration dataset. Model performance remained satisfactory during evaluation, with an EF value of 0. 65 and a relative bias of -1% (Figure  5b ). Simulated grain N content was slightly underestimated (relative bias of -10%) in the calibration dataset with a satisfactory EF value of 0. 60 (Figure  5c ). In the evaluation, grain N content was predicted with a low relative bias (3%). The EF was low (0. 27) but the range of observed values for evaluation (65-162 kg N ha -1 ) was narrower than for calibration (21-200 kg N ha -1 ).
Impact of temporal dynamics of stress factors With regard to grain yield, the water stress factor (i. e. water supply/demand ratio, see section 2. 1. 2 and Table  S2 ) had the strongest explanatory power. Indeed, when averaged over a period of 6 days before the beginning of grain filling (i. e. the end of grain onset), it explained 78% of the observed grain yield variability in the overall dataset (Figure  6a ). The model fairly reproduced the temporal dynamic of above ground biomass (Figure  7d , e, f). Water stress during the end of grain onset (Figure  7a, b, c ) affected the growth rate of above ground biomass (Figure  7d , e, f), which in turn influenced the final number of grains (Figure  7g , h, i). In the end, the model was able to simulate contrasting water stresses that occurred in different soil types (valley/hillside) for a range of climatic conditions and their final impact on the number of grain and grain yield (Figure  7 ).
, 2008). Secondly, the dataset used for model calibration may not be originally intended to cover model calibration issues (as it was the case in our study), so that most of crop model parameters are not directly measured (e. g. radiation use efficiency). Thirdly, the need for similar environmental conditions, varieties and exact correspondence of the measured variable to the process represented by the model limit the number of parameters that can be derived from a literature review. For example, the radiation use efficiency parameters in STICS (efcroijuv, efcroiveg and efcroirepro, see Table  3 ) correspond to potential conditions with no stress, and the presence or absence of stress is not necessarily mentioned in the literature. Finally, there is a risk of error accumulation when extensively relying on approximate or conflicting literature values  (Wallach et al.
, 2011). For all these reasons, recourse to optimisation is advisable. The strength of our approach lies in the rational stepwise optimisation procedure chosen. Rather than an empirical trial and error procedure  (Flénet et al. , 2004; Hartkamp et al. , 2002) , we chose a sequential optimisation procedure that is fully documented and is easily replicable for other crops and cultivars with the STICS model. =Breaking down the optimisation into several stages and using it at the module-scale rather than the scale of the entire model allow to avoid unrealistic simulations where errors in parameter values would offset each other  (Bechini et al. , 2006; Grant, 2001). Taking into account within-season dynamic measurements to successively calibrate the different model components allows to estimate few parameters simultaneously and therefore to reduce numerical problems  (Guillaume et al.
1. 2 and Table  S2 ). This careful check of the behaviour of different model functional modules (Figure  7 , Figure  8 ) strongly suggests that the STICS model accurately captured the underlying processes driving grain yield formation, N 2 fixation and water stress. We can therefore be confident in the diagnosis drawn from our site-yearmanagement units with regard to water stress. Thus, the generic STICS processes describing yield formation and water stress, when calibrated for faba bean with the proper options activated, have proven useful in explaining variability in yield for this particular legume crop, without the need to build new processes into the generic model. The good fit obtained with the evaluation dataset indicates that the current set of parameters could be reliably used in other situations different from the ones in the calibration.
MBE and rMBE indicate whether the model under/overestimate a given variable, and gives the absolute and relative magnitude and direction of the bias. EF is based on the comparison of the simulation model with a constant model (i. e. the average of measured data) and ranges between -∝ and one. Negative EF values indicate that the constant model performs better than or as well as the simulation model. A value of one indicate a perfect model. EF is a good indicator to compare model simulations with two different sets of parameters (e. g. before/after calibration) against the same dataset. It is however of little value to compare two simulations with the same set of parameters against two different datasets, because it depends on the variance of the observed dataset. RMSE and rRMSE are the model absolute and relative prediction error.
Acknowledgments: This research was supported by the  European Commission (REA)  through the  LEGATO  project ( FP7-613551 ) and the  French National Research Agency (ANR)  through the  LEGITIMES French project  ( ANR-13-AGRO-0004 ), the 7 th  PIREN-Seine program  and the  Climate-CAFE   European  project (selected by the European  FACCE-JPI ERA-NET Plus program ). We thank the  Centre Régional de Recherche et d'Expérimentation en Agriculture Biologique de Midi-Pyrénées (CREAB-MP) in Auch, France , for making available their faba bean dataset. We are grateful to  Loïc Prieur ,  Didier Rafaillac ,  Michel Labarrère  and several trainees who assisted in data collection and  Didier Chesneau  and  Eric Lecloux  who performed the extraction and the analysis of soil mineral N. We thank  Patrice Lecharpentier  for his help with the Optimistics software.
By periodically repeating the unit cell shown in Figure  1b , the complete pentamode structure can be obtained. In this paper, based on the finite-element method, we performed a series of numerical simulation calculations using COMSOL Multiphysics (Version 5. 3, COMSOL, France). Specifically speaking, we firstly numerically calculated the phonon band structure of this model in the structural mechanics module, based on the Bloch theorem and by solving the elasto-dynamic equation. Then, combining the generalized Hook's law and the elastic wave equations, we can retrieve the elasticity tensor and then the mechanic modulus from the expressions  [28] : C 44 = ρ v T,z 110 2 (1) C 12 = ρ v L 110 2 -C 44 -ρ v T,xy 110 2 (2) C 11 = 2ρ v T,xy 110 2 + C 12 (3) G = C 44 (4) B = (C 11 + 2C 12 )/3 ( 5 ) where ρ represents the mass density of the pentamode structure, which is given by the volume filling fraction f times the mass density ρ 0 of the constituent material, i.
Results and Discussion In order to quantify the performances of the new structure we first computed the dispersion relation. The constituent material of the pentamode structure we consider here is a polymer with mass density ρ 0 = 1190 kg/m 3 , Poisson's ratio ν = 0. 4 and Young's modulus E = 3 GPa. The phonon band structure was calculated with COMSOL Multiphysics using the structural mechanics module. The results of the pentamode structure are shown in Figure  2. To make clear the vibration modes of the dispersion relation curves, the different modes are colored differently (see caption of Figure  2 ). The branches 1 and 2 (depicted in red) correspond to the first elastic transverse modes. Similarly, the branch 3 (depicted in blue) corresponds to the longitudinal mode. It becomes immediately clear that the light grey region in the phonon band structure is the single-mode band gap, in which the transverse waves are inhibited and only the longitudinal wave can propagate.
Funding: This research was supported by the  National Natural Science Foundation of China  (No. 51172178 ). The APC was funded by the  EIPHI Graduate School  (contract " ANR-17-EURE-0002 ") and by the  French Investissements d'Avenir program , project  ISITEBFC (contract  " ANR-15-IDEX-03 "). Acknowledgments: Y. H. gratefully acknowledges financial support from  China Scholarship Council.
Entity normalization, or entity linking in the general domain, is an information extraction task that aims to annotate/bind multiple words/expressions in raw text with semantic references, such as concepts of an ontology. An ontology consists minimally of a formally organized vocabulary or hierarchy of terms, which captures knowledge of a domain. Presently, machine-learning methods, often coupled with distributional representations, achieve good performance. However, these require large training datasets, which are not always available, especially for tasks in specialized domains. CONTES (CONcept-TErm System) is a supervised method that addresses entity normalization with ontology concepts using small training datasets. CONTES has some limitations, such as it does not scale well with very large ontologies, it tends to overgeneralize predictions, and it lacks valid representations for the out-of-vocabulary words.
Description of the CONTES Method CONTES  [7]  implements a supervised machine learning approach which aims to improve the generalization limitations of previous machine learning methods, especially when few training datasets are available, with respect to the number of classes. CONTES is an open-source tool written in Python (https://github. com/Arnaud-Ferre/CONTES). The core of the method relies on two different semantic vector spaces: (1) semantic vector representations of expressions from a corpus (SSC for Semantic Space of Corpus), (2) semantic vector representations of concepts from an ontology (SSO for Semantic Space of Ontology). CONTES can use all kinds of semantic vector representations for SSC computations, such as distributional representations. It particularly uses Word2Vec word embeddings  [9].
Dimension reduction and concept embeddings The CONTES method has been evaluated as a normalization task of BB from the BioNLP Shared Task of 2016 (http://2016. bionlp-st. org/tasks/bb2). This task consists of normalizing entity mentions, in the raw text, with concepts of the OntoBiotope ontology. OntoBiotope is an ontology of microorganism habitats, and captures classifications used by biologists to describe microorganism isolation sites (e. g. , GenBank, GOLD, ATCC), and contains around 2,000 concepts, which is relatively small, compared to other biomedical ontologies, but relatively high, for a machine-learning classification problem. In comparison, the Gene Ontology contains around 50,000 concepts and the Unified Medical Language System meta-thesaurus contains more than one million concepts.
Dimensionality-reduction techniques We aimed to reduce the dimension of the SSO to make CONTES scalable to larger ontologies. We have used three well-known techniques to reduce the dimension of the semantic space of the ontology: principal component analysis (PCA), multidimensional scaling (MDS), and t-distributed Stochastic Neighbor Embedding (t-SNE). PCA and MDS have already been tested, with similar results  [7]. Here, t-SNE is a nonlinear method to reduce dimensions, with the aim to retain the similarity of vectors between the initial space and the embedded space. All these methods can be used with the machine learning Python library scikit-learn (https://scikit-learn. org/ stable/).
Out-of-vocabulary words New words currently required to recalculate the word embeddings in CONTES. Methods like FastText are designed to obtain embeddings for words even if they are unknown from the training data. For our experiments, we considered FastText  [11]  as a solution to tackle the problem of out-of-vocabulary words. The FastText method is based on the skip-gram model  [9, 11] , and takes into account subword information to produce word embeddings. It also considers each word as a bag of character n-grams and gives the word representation by summing the representations of the character n-grams  [11]. To investigate the effects of unknown words, FastText is added to CONTES as another method, and we have conducted experiments with a subset of the datasets of the BB normalization task.
The Database of Protein Disorder (DisProt, URL: https://disprot. org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies. The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved.
Similarly, only 45% of the residues of all human proteins are covered by multiple sequence alignment-based Pfam-A protein family annotations  (6). These values suggest that we have only a vague notion about the structure and function of the majority of proteins in our databases. As a significant fraction of the dark proteome and non-Pfam annotated proteins and protein regions are intrinsically disordered (the concepts having become almost synonymous), our best approach for illuminating the dark proteome is to predict disorder from sequence, and experimentally characterize the underlying structural ensembles  (7). The prediction of protein disorder from sequence was on the menu of the Critical Assessment of Protein Structure Prediction (CASP), a community-wide experiment of predicting protein structures from sequence  (8) , for many years.
In the enzyme UDP-␣-D-glucose-6-dehydrogenase (UGDH, Dis-Prot DP02338), the C-terminal disordered tail has such a role, fine-tuning the energy landscape of the protein and stabilizing a sub-state that has a high affinity for an allosteric inhibitor  (16, 17). It is without doubt that we cannot afford to ignore this intrinsically disordered, yet functionally important part of the proteome. Not only does structural disorder play an exquisite role in cellular signaling and regulation  (18) , it is also often implicated in disease  (19, 20). Consequently, IDPs also represent important drug targets: a largely unexplored frontier in developing molecular medicine is the rational design of drugs against IDPs  (21, 22). Due to these challenges, it is important to update and upgrade DisProt, the primary database of protein disorder.