dataesr/scientific-paragraphs-categorization huggingface dataset:
Automatic Annotation Rigid designators of named entities, such as Gene, Protein, Tissues, and Developmental Phases were automatically pre-annotated with the AlvisNLP pipeline using relevant Ath databases (e. g. TAIR 1  ) and customized lexicons. The goal of automatic pre-annotation was to speed-up the manual annotation process. The evaluation of the automatic annotation compared to the gold standard annotation shows a F-score equal to 0. 41, with a high precision (0. 89) and low recall (0. 26) due to a lack of relevant lexicon for most entity types.
Participating Systems Seven teams from 4 continents submitted their results to the test of the SeeDev binary task that are: DUTIR (Dalian University of Technology, China), LIMSI (CNRS, France), LitWay (Xidian University, China), ULisboa (LaSIGE, Universidade de Lisboa, Portugal), UniMelb (University of Melbourne, Australia), VERSE (University of British Columbia, Canada) and UTS (University of Turku, Finland). Their main background domains are Bioinformatics, Machine Learning, Natural Language Processing and Biology according to their responses to a survey. Table  4  summarizes the scores obtained by the participant systems ranked by F1-score (detailed results are available on the SeeDev site). The results of the DUTIR system are not displayed because they experienced a last minute hitch and ranked last.
relations that occur in a single sentence. The difference of the results obtained for intra-sentence dataset are less than 1 point, except for Limsi that gains 0. 056 points; indeed, Limsi is the only team that attempts to predict inter-sentence relations whereas all other participant systems predicted only intra-sentence relations. Given the proportion of inter-sentence relations in the test set (4%), the penalty of ignoring them could have been considered as bearable. In order to assess the difficulty to predict the correct relation type, we computed the F-scores when considering the category of the relations instead of the actual type (first line per category in bold and italic in Table  5 ). This did not yield a significant improvement although some participants were able to successfully predict events in categories with high biological relevance, such as the Regulation category ( Litway F1: 0.
Acknowledgments This work was supported by the  French National Institute for Agricultural Research (Inra) , the  Center for Data Science (CDS) , the  OpenMinTeD  project ( EC/H2020-EINFRA 654021 ) and the  Institute for Living Systems (IMSV) , funded by the  IDEX Paris-Saclay   ANR-11-IDEX-0003-02. The IJPB benefits from the support of the  Labex Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ).
Systematic structure probing experiments (e. g. SHAPE) of RNA mutants such as the mutate-and-map (MaM) protocol give us a direct access into the genetic robustness of ncRNA structures. Comparative studies of homologous sequences provide a distinct, yet complementary, approach to analyze structural and functional properties of non-coding RNAs. In this paper, we introduce a formal framework to combine the biochemical signal collected from MaM experiments, with the evolutionary information available in multiple sequence alignments. We apply neutral theory principles to detect complex long-range dependencies between nucleotides of a single stranded RNA, and implement these ideas into a software called aRNhAck. We illustrate the biological significance of this signal and show that the nucleotides networks calculated with aRNhAck are correlated with nucleotides located in RNA-RNA, RNA-protein, RNA-DNA and RNA-ligand interfaces.
To capture this signal, it is essential to design a formal framework that calculates correlations between the genetic robustness of the structural profile--obtained from MaM experiments--and the evolutionary information available for this molecule--usually contained in MSAs. This paper attempts to look beyond RNA structure determination, and introduces a novel concept to leverage the information embedded in experimental structure probing datasets of mutant RNAs. We apply neutral theory principles  (9)  to detect functional dependencies between distant nucleotides in a single stranded RNA. More precisely, we first use MaM data to identify mutations that significantly destabilize the native structure of the molecule, i. e. the mutations associated with the most divergent SHAPE profiles. Then, we retrieve from RNA MSAs (Rfam database  (10) ) homologous sequences containing those destabilizing mutations, and compare their nucleotide distribution to the e104 Nucleic Acids  Research, 2016, Vol.
44, No. 11  PAGE 2 OF 11 background distribution observed in the Rfam MSA. Finally, the ensemble of positions with highest mutual information is used to reveal nucleotide networks of functional dependencies. This protocol aims to capture non-trivial covariations or geometric conservations that are key to guarantee the stability and specificity of the binding site structure. We implement our model in a software named aRN-hAck. We illustrate potential applications of the signal captured with our theoretical framework, and apply aRNhAck to analyze MaM datasets available on the RNA Mapping Database  (11). Our experiments reveal non-trivial longrange dependencies within ncRNA primary structures of 5S ribosomal RNA, the yeast phenylalanine tRNA and the cobalamin, adenine and glycine riboswitches.
Compensatory mutations may also be witnessed in positions which are not immediately structurally (in a broad definition, including noncanonical motifs and pseudoknots) related to the disrupting mutation. In this case, we hypothesize that these mutations are most likely involved in the quaternary structure, and may reside at the interface between the chain of interest and other molecules in the formation complexes. The detection of such interactions is therefore the primary application of our method. To that purpose, we rely on covariation models, an information theoretical tool that has been successfully used for RNA for sequence alignment and structure prediction  (15). The Rfam database  (10)   Normalized point-wise mutual information. To identify those pairs of nucleotides at specific positions which vary together, we use the normalized point-wise mutual information (NPMI) measure  (15).
Implementation Our software, aRNhAck, is implemented in python 2. 7. To identify mutations of interest, the threshold of SHAPE profile disruption was tested between the 95 and 99 percentiles. The parameter was set to 10 creating windows of size 21 to measure the local SHAPE profile disruptions. The parameter ␥ was evaluated for values between 0 and 30. The whole implementation is freely available at: http: //csb. cs. mcgill. ca/arnhack It requires BioPython (16) for reading the MSAs, networks  (17)  for modeling the graphs and matplotlib  (18)  for visualizing the results. The 3D complex analysis used in our validation is performed using the Python API provided by the PyMol software (19).
Datasets We evaluated our methods on molecule for which we could obtain simultaneously (i) an MaM experiment dataset, (ii) a determined 3D structures interacting with other chain(s) and (iii) a Rfam alignment Table  1  shows the quality of the alignment of the mutate-and-map sequences with their corresponding Rfam family. This search resulted in six RNAs: the 5S ribosomal RNA, the c-di-GMP riboswitch, the cobalamin riboswitch (Puzzle 6), the phenylalanine tRNA, the adenine riboswitch and the glycine riboswitch. However, the experimental structure of the glycine riboswitch found in the PDB contains an artificial stem loop binding a protein used to stabilize the RNA structure and facilitate the crystallization. Since this protein is missing in the MaM experiments, we decided to exclude this riboswitch from our test set.
Nonetheless, we show our results in the supplementary material (Supplementary Figure  S7 ). The 5S ribosomal RNA is the family RF00001 on Rfam. Its seed alignment consist of 713 sequences. The family also provides the consensus structure. The MaM protocol was applied to the consensus sequence of four structures which have as PDB IDs: 2WWQ (20), 3OAS and 3OFC (21), and 3ORB  (22). Those four determined structures have almost the same sequence with slight differences in the length on their 5 and 3 extremities. The yeast phenylalanine tRNA is included in the Rfam family RF00005 which has 960 seed sequences from various tRNAs. Its structure has been crystallized in presence of magnesium and manganese (PDB ID: 1EHZ). Although, for a complete characterization of its structural context and interactions with other molecules, we also considered structures of the two tRNAs in the structure of the yeast 80S ribosome-tRNA complexes (PDB ID: 3J78).
The c-di-GMP riboswitch is present in family RF01051 in Rfam, which contains 156 sequences in its seed alignment, and a consensus structure. The consensus sequence e104 Nucleic Acids Research, 2016, Vol. 44, No. 11 PAGE 6 OF 11 was also built from four structures, with PDB IDs: 3IWN (23) and 3MXH, 3MUV, 3MUT  (24). Importantly, c-di-GMP is known to bind a pocket inside the three-way junction at positions 11 -13, 40 -41 and 85 of the sequence on which the MaM experiments were run  (25, 26) , and the MaM experiment was done in presence of its ligand. It is also worth noting that, in order to facilitate the crystallization, the hairpin loop L2 of this molecule has been artificially designed to bind the U1A protein. Here, we included only the positions binding the ligand. Nonetheless, for completeness, we also show in the supplementary material the results including the stem loop L2 binding interface.
The MaM cobalamin riboswitch sequence can be found in the Rfam family RF00174 which has 430 seed sequences. The PDB contains the structure bounded to its ligand (PDB ID: 4GXY). Noticeably, the MaM experiments were done in the presence of cobalamin ligands. The adenine riboswitch belongs to family RF00167 which has 133 seed sequences. The structure with the adenine ligand has PDB ID: 1Y26. Three different MaM experiments were conducted on this molecule. Experiments Adenine 2 and Adenine 3 where done in presence of the ligand, and are used in this paper. The third experiment Adenine 4 has been performed in absence of the ligand, and thus was omitted from this benchmark since disruptive mutations cannot be used to detect key structural elements of the ligand-bound structure. Nonetheless, the results are indicated in the supplementary material.
Experimental design The Infernal 1. 1 (27) software was used with default parameter values to: (i) create a covariance model for each alignment, and; (ii) align the sequence from the MaM experiment with the generated covariance model. The consensus secondary structure was then restricted to gapless positions within the aligned sequence w. For each mutation over the SHAPE profile percentile cutoff ␦, the dataset was composed of the regions of interest given ␥ , i. e. the set of positions returned by the Algorithm 3. We used different strategies to determine the interaction sites (i. e. positive dataset), depending of the nature and context of these interactions. All interactions were manually verified. For the 5S RNA, we implemented a PyMOL script to extract nucleotides of each PDB model, whose position any of their atom is at most at 5å from any atom of another chain the the complex.
An implementation of this script is included in the distribution of aRNhAck For the tRNA, we extracted positions that are at most 5 Å away from another chain in the two tRNAs found inside the structure of the yeast 80S ribosome-tRNA complexes (PDB ID: 3J78). However, because those were not phenylalanine tRNAs, we aligned them to the MaM sequence with Lo-caRNA  (28) , and used this alignment to map the interaction sites on the latter. We identified the positions 1, 19, 34 -36, 56 -57, 73 -76 (containing the anticodon) in this positive set. Among them, only the anticodon and T--C-G, known to bind the 5S RNA in the 50S ribosomal subunit  (29) , motif appeared to us to be strongly conserved. Thus, we considered only these two interactions sites in our experiments and presented the results separately.
For completeness, the results obtained on other positions have been included in the supplementary material. Finally, we also confirmed the location of the anticodon using tRNAscan-SE  (30). For the riboswitches, we used Ligand Explorer (31) to identify nucleotide at most 5 Å from the ligand in their respective crystal structures. The set of all positions is found in the Supplementary Table  S1. All other remaining positions compose the negative dataset. The positions not present in the model were ignored. This highlights one of the challenges of this benchmark. For the 5S rRNA, out of 121 positions, two models had 3 nucleotides missing, one had 4 missing and the other 6. For c-di-GMP, out of 103 positions, one model had 8 nucleotides missing, two others 21 and the last 22. Which explains some discrepancies between the models.
Computationally predicted structural disruptions yield weaker signal To justify the use of SHAPE experimental data, we evaluate the performance of a fully automated pipe-line in which mutations altering the RNA conformational landscape are predicted with a computer software instead of MaM data. Here, we predict these destabilizing mutations with re-muRNA  (32). Alternatively, for longer sequences, RNAsnp (33) can also be used. The Boltzmann conformational ensemble B w of a sequence w is the probability distribution of valid RNA secondary structures on the sequence w. Given a wild type sequence wt and a mutant m, remuRNA (32) computes the relative entropy (or Kullback-Leibler divergence) between the two probability distributions B wt and B m. The latter provides an estimate of the destabilization of the conformational landscape induced by the mutation.
Given the set of all secondary structure S, the relative entropy is defined as: We report our results in Figure  4B. Here again, our data unveil a signal that shows a correlation between the mutation identified with aRNhAck and the RNA-binding interfaces. Nonetheless, the strength of the signal extracted with remuRNA is of lower magnitude than the one achieved with the SHAPE experiments and the MaM protocol. An exception is the tRNA for which aRNhAck achieves better performance with remuRNA than MaM data. We conjecture that this could be due to a difficulty of the MaM protocol to capture a clear signal on these structures. To further validate our model, we applied this protocol based on remuRNA prediction on a dataset made of Rfam families with experimentally determined 3D structures (See 'Materials and Methods' section).
C4 cropland Measured data and simulated results are presented for the 2003-2006 period for the Mead1 site (Fig. 7 ). For Mead, the applied novel features with BBGCMuSo included C 4 enzyme-driven photosynthesis, transient simulation, planting, harvest, irrigation, drought-related plant senescence, genetically programmed leaf senescence, standing dead biomass pool, fruit pool (in this case, maize yield), non-zero soft stem allocation, and acclimation. Note that the allocation-related "current growth proportion" parameter that controls the content of the storage pool (non-structured carbohydrate reserve for next year's new growth) was zeroed here in order to avoid natural plant growth in the spring. In other words, in the case of planting, the storage pool has to be turned off as maize growth is only possible after sowing.
Deciduous broad-leaved forest For the Jastrebarsko forest site, measurement data were available for the period from 2008 to 2014. But, as the measurement height was changed in spring 2011 (flux tower was up-graded due to tree growth), to assure the homogeneity of the measurement data we used data from 2008-2010 only. For simulation at Jastrebarsko, several novel features were used from BBGCMuSo v4. 0. For example, transient simulation, alternative tipping bucket soil water balance calculation, groundwater effect on stomatal conductance and decomposition, drought-related plant senescence, thinning, fruit allocation, bulk denitrification, soil moisture limitation calculation, soil stress index, soil texture change through the soil profile, and respiration acclimation were all used in the simulation.
, 2014)  and, in combination with hypoxic conditions in the root zone, reflects negatively on GPP and TER (Fig. 8a, b ). Soil water content simulation in BBGCMuSo version shows improvement for the leaf-off season (November-April) when, unlike the original version, soil water saturation in BBGCMuSo simulation becomes evident (Fig. 8c ). BBGCMuSo still overestimates soil moisture status in the summer. According to the original Biome-BGC, the forest never experiences soil saturation, which is contrary to the actual situation in winter and early spring. The original Biome-BGC provides only SWC data for one layer of the entire rooting zone (0-100 cm in this case), and therefore it cannot be directly compared with the actual measurements or output from BBGCMuSo which correspond to the 0-30 cm soil layer.
Also, it should be emphasized that the parameters  of soil bulk density, SWC of saturation, field capacity, and wilting point for soil layers were not available. BBGCMuSo calculated those parameters based only on the provided parameters for soil texture. Simulated LAI value also decreased to more realistic values in BBGCMuSo due to model improvements (Fig. 8d ). Based on litterfall data (0. 408 kg m -2 ;  Marjanović et al. , 2011b)  and average specific leaf area of 15 m 2 kg -1 for oak  (Morecroft and Roberts, 1999) , the average maximum LAI at Jastrebarsko is estimated to be 6. 1 m 2 m -2 , just slightly above what is estimated by BBGCMuSo. BBGCMuSo NPP estimates are also in good agreement with those from field observation  (Marjanović et al. , 2011a) , unlike those of the original version which overestimate NPP (Fig.
, 2015). Additionally, plant traits (e. g. , specific leaf area, leaf C : N ratio, photosynthesis-related parameters) vary considerably in space, so even though plant trait data sets are available, they might not capture the site-level parameters. These issues raise the need for proper infrastructure for parameter estimation (or, in other words, calibration or model inversion). The need for model calibration is also emphasized as we introduced a couple of empirical parameters in BBGC-MuSo (e. g. , those related to plant senescence, water stress thresholds, denitrification drivers, disturbance-related mortality parameters) that need optimization using measurement data from multiple sites. In other words, though BBGCMuSo parameterization is mostly based on observable plant traits, model calibration can not be avoided in the majority of the cases  (Hidy et al.
CALMAR, a new library for adjustment has been developed. This code performs simultaneous shape and level adjustment of an initial prior spectrum from measured reactions rates of activation foils. It is written in C++ using the ROOT data analysis framework,with all linear algebra classes. STAYSL code has also been reimplemented in this library. Use of the code is very flexible : stand-alone, inside a C++ code, or driven by scripts. Validation and test cases are under progress. Theses cases will be included in the code package that will be available to the community. Future development are discussed. The code should support the new Generalized Nuclear Data (GND) format. This new format has many advantages compared to ENDF.
Implemented Method The main objective for developing a new adjustment code was the determination of the two parameters for a multigroup spectrum: • the shape, classically computed by well known adjustment codes listed in the ASTM standard  [1]. • the level of this spectrum: absolute level of the reactor is not necessarily known from alternate measurements than foils activity. Basically, the CALMAR implemented algorithm finds an optimum scaling factor to be applied to the prior spectrum. Then the code determines the shape of the spectrum with a least square formula.
Uncertainties Uncertainties are computed using the conditional expectation formalism. The STAYSL code and its derivatives make use of the Schur complement (or block matrix inversion lemma) for computing the adjusted covariance matrix. In CALMAR algorithm, the inversion lemma is used many times to determine: • scaling factor variance ( Ĉ ) • the covariance matrix of the shape ( ) with the remaining available experimental uncertainties. One part has been already taken into account to determine the scaling factor. • the covariance matrix between the shape and the scaling factor ( Ĉ, ). The expectation and the covariance of the scaled spectrum is computed using these three previous items. One pays attention to the fact that C and are correlated, so expectation of the product should be computed as follow (  7 ) E Ĉ = E Ĉ E + Ĉ,.
Execution of the Code The use of the CALMAR library is versatile and three modes are available now: • stand-alone code with text data files • function calls to the library within a C ++ code. In this case no files are needed. • C ++ script through the ROOT interpreter. The last mode is the more interesting because the script is in fact an input job file: it contains all the directives. Some more outputs can be added to the job file to compute some more rates (gas production, DPA,. ). The coupling with other codes is also possible, with all the support of ROOT framework. For example, the TRIPOLI4 Monte-Carlo code  [6]  supports various output formats including XML and ROOT trees. The CALMAR script can process easily these data as an input for the adjustment code.
Test Cases A validation over experimental datasets is in preparation. Code will be tested over experimental datasets of foils from thermal to fast. A dataset obtained in CALIBAN reactor is described in  [9]. A comparison with the STAYS'L code will be done. These test cases will be included in the CALMAR package. This type of package has been provided with the IRDF90 library as Neutron Metrology File (NMF-90,  [10] ) including codes and test cases. In our case, we will provide updated datasets with the new IRDFF library.
Nuclear Data Format Today, cross section processing is done by an external tool with the same functionalities as the X333 tool provided with IRDF90  [11]. The intended developments of the CALMAR library are to include ENDF reading routines in order to directly process the IRDFF library. Nuclear data processing is still consuming time, even for end-users and not really standardized. The general NJOY  [12]  tool has many parameters, increasing the risk of a mistake for dosimetry end-users. The paragraph describing NJOY in  [13]  is quite symptomatic of code complexity and a post processor (NJpp) is still needed to interface other codes. On the other side, specialized tools provided with dosimetry library (X333) are not maintained (updated for new format) any more. JANIS is user friendly tool but lacks automation features.
The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over 2 decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior.