dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, the match-up exercise using the SYVEL (r 2 = 0. 95, NRMSE = 14%, slope = 0. 7) and MAGEST (r 2 = 0. 8, NRMSE = 16%, slope = 0. 8) autonomous stations provided satisfactory results, proving that the selection of the algorithms was appropriate. Still, additional in situ measurements in these areas would improve the calibration of the models and provide better validation results. The switching bound selection method presented here can be easily applied to any turbid coastal water area associated with wide turbidity ranges, without the need for in situ measurements. The bounds can be directly selected by comparing the water reflectance in the green, red and NIR wavebands directly derived from satellite data to detect the saturation points and infer the switching values (Figures  7  and 8 ).
Acknowledgments: This work was funded by the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 606797  ( HIGHROC  project). USGS  and  NASA  are acknowledged for the Landsat-8 imagery. NASA is also thanked for the VIIRS and MODIS Aqua data, and for the SeaDAS processing software. We thank the  CESBIO  and  CNES  for providing MACCS-corrected Landsat data on the THEIA web portal. In situ reflectance and SPM field data were acquired in the frame of the following projects: SeaSWIR (funding:  BELSPO agency PI: Els Knaeps), Rivercolor  (funding: CNES; PI: V. Lafon),  Gigassat  (funding:  ANR-12-AGRO-0001-05  and  ANR-12-AGRO-0001-08 ; PI: F. Pernet), and  TURBO  (funding:  CNRS/INSU PNTS-2015-07 ; PI:  P. Gernez ). We also acknowledge the MAGEST and SYVEL network teams for the availability of the turbidity time series in the Gironde and Loire estuaries.
Open Science Ultimately, mathematicians, computational scientists, and plant biology must unite at the level of jointly collecting data, analyzing it, and doing science together. Open and timely data sharing to benchmark code is a first step to unite these disciplines along with building professional interfaces to bridge between the disciplines  (Bucksch et al. , 2017; Pradal et al. , 2017). A number of platforms provide open, public access to datasets, figures, and code that can be shared, including Dryad 16  , Dataverse 17  , and Figshare 18. Beyond the ability to share data is the question of open data formats and accessibility. For example, in remote sensing research it is unfortunately common that proprietary data formats are used, which prevents their use without specific software. This severely limits the utility and community building aspects of plant morphological research.
Beyond datasets, making code openly available, citable, and user-friendly is a means to share methods to analyze data. Places to easily share code include web-based version controlled platforms like Bitbucket 19  or Github 20 and software repositories like Sourceforge 21. Furthermore, numerous academic Journals (e. g. , Nature Methods, Applications in Plant Sciences, and Plant Methods) already accept publications that focus on methods and software to accelerate new scientific discovery  (Pradal et al. , 2013). Meta-analysis datasets provide curated resources where numerous published and unpublished datasets related to a specific problem (or many problems) can be accessed by researchers 22. The crucial element is that data is somehow reflective of universal plant morphological features, bridging the gap between programming languages and biology, as seen in the Root System Markup Language  (Lobet et al.
(C) A detail from a single TLS point cloud. Individual branches (yellow) 20 m above ground can be inspected from the point cloud with centimeter level resolution to estimate their length and thickness. Images provided by EP (Finnish Geospatial Research Institute in the National Land Survey of Finland). ALS data was obtained from the National Land Survey of Finland Topographic Database, 08/2012 (National Land Survey of Finland open data license, version 1. 0).
The TLS point cloud is so dense that individual tree point clouds (orange) and parts from them (yellow) can be selected for detailed analysis. (C) A detail from a single TLS point cloud. Individual branches (yellow) 20 m above ground can be inspected from the point cloud with centimeter level resolution to estimate their length and thickness. Images provided by EP (Finnish Geospatial Research Institute in the National Land Survey of Finland). ALS data was obtained from the National Land Survey of Finland Topographic Database, 08/2012 (National Land Survey of Finland open data license, version 1. 0).
2016) 20 https://github. com/ (retrieved May 29, 2016) 21 https://sourceforge. net/ (retrieved May 29, 2016) 22 BAAD: a Biomass And Allometry Database for woody plants, https://github. com/dfalster/baad (retrieved May 29, 2016) 23 http://www. cyverse. org/ (retrieved August 20, 2016) 24 http://dirt. iplantcollaborative. org/ (retrieved August 20, 2016) 25 https://www. tacc. utexas. edu/ (retrieved August 20, 2016).
Head models We employed two different head models for this study. The first was a 3-compartment BEM that served to validate the theoretical findings (see Figs. 1 2 3 ). The BEM was constructed in the Brainstorm software package  (Tadel et al. , 2011)  for Matlab (Mathworks, Natick, MA). The anatomy of the head was selected as the "ICBM-152" standard, which was constructed by non-linearly averaging 152 individual heads in a common coordinate system  (Mazziotta et al. , 1995; Fonov et al. , 2011). The Brainstorm package relies on the FreeSurfer tool for segmenting the head into its constituent tissue categories  (Fischl, 2012). The BEM was constructed with the following (default Brainstorm) parameters: scalp: relative conductivity of 1 S/m, skull: relative conductivity of 0. 0125 S/m, brain: relative conductivity of 1 S/m.
Public availability of data The head model, as well as the code to perform reciprocal TES, are available for download in Matlab format at: http://www. jd-lab. org/resources. that intensity can be significantly increased (2 or 3×) while only sacrificing a small amount of focality (1 or 2 cm). A good tradeoff between intensity and focality was found here to be c = 10 10 or c = 10 11. Increasing c also led to a reduction in the number of electrodes recruited by the optimal montage (denoted by color of markers), which was approximately 4-7 at c = 10 10. The resulting electric field had pronounced intensity at both occipital and inferior temporal targets. However, the strength of the field was dampened at the inferior temporal sources (0. 066 V/m) relative to that at the occipital sources (>0. 12 V/m), presumably reflecting the difficulty in targeting ventral regions with TES.
Protein flexibility is often implied in binding with different partners and is essential for protein function. The growing number of macromolecular structures in the Protein Data Bank entries and their redundancy has become a major source of structural knowledge of the protein universe. The analysis of structural variability through available redundant structures of a target, called multiple target conformations (MTC), obtained using experimental or modeling methods and under different biological conditions or different sources is one way to explore protein flexibility. This analysis is essential to improve the understanding of various mechanisms associated with protein target function and flexibility. In this study, we explored structural variability of three biological targets by analyzing different MTC sets associated with these targets.
Introduction Proteins are highly flexible macromolecules and their dynamic properties are crucial to many biological processes. This flexibility is often implied in interactions between different partners and is essential for protein function, e. g. , enzyme catalysis and activity regulation  [1] [2] [3]. The analysis of the protein flexibility is relevant to decipher the variety of mechanisms associated with a function (e. g. , allostery, aggregation, and oligomerization) and protein plasticity, as described in  [4, 5]. The growth in the speed of macromolecular structure determination techniques has increased the number of Protein Data Bank (PDB)  [6]  entries to approximately 122,000 three-dimensional (3D) protein structures. Because most newly solved structures fall within existing families, a large number of structures exhibit high redundancy, i.
e. , more than half share at least 95% sequence identity. Even if this redundancy is considered valuable as in investigating families of homologous sequences  [7, 8] , the dominant approach for the data mining of the PDB considers redundancy as non-informative  [9] , resulting in an artificial reduction in the variability of the structural space. The different structures associated with a target, referred as multiple target conformations (MTC), can correspond to available PDB structures obtained under different conditions using nuclear magnetic resonance (NMR) models and X-ray crystallography or to theoretical models obtained using homology modeling or to dynamic series of 3D views obtained using molecular dynamics simulations that sample exhaustively the target conformation landscape. These ensembles of 3D view of one target correspond to different MTC subsets when obtained by different sources and approaches.
We extracted the inhibitor-binding site of the uPA catalytic domain complexed with an inhibitor (PDB ID 3I6G) using PockDrug-Server  [46]  to analyze the uPA catalytic domain structural variability linked to the inhibitor binding. The p53 DBD target. The p53 protein exerts a tumor suppressor function primarily as a transcription factor by regulating the expression of a set of genes in the cell. More than 50% of human cancers are associated with mutations in p53 and 90% of them occur in the DBD. The human p53 is a 393-residue protein that can be structurally and functionally divided into four domains. The p53 DBD is located in the central region of the protein and contains approximately 200 residues. Its conformation consists of a β-sandwich composed of two antiparallel β-sheets with two large loops referred to as L2 and L3.
In parallel, two additional subsets were built to analyze the structural variability associated with a set of homology models obtained using the MODELLER 9 program  [51]. These subsets, "P53-HM QM " and "P53-HM HM ", are composed of wild-type models built by homology modeling using two mutated p53 DBD X-ray structures as templates: one super stable quadruple mutant (QM) and one hexamutant (HM) structure. To analyze the structural variability of p53 protein linked to inhibitor binding, we extracted its inhibitor-binding site from a holo p53 structure (PDB ID 4AGM) using PockDrug-Server  [46]. Immunodeficiency virus type 1 protease target. PR1 is an effective drug target for acquired immune deficiency syndrome treatment because it is involved in the maturation of the viral proteins  [52]. PR1 is a homodimer composed of 99 amino acids in each chain.
The secondary structure variability is illustrated in the neq SL graphic, where each MSLA position is colored according to its secondary structure or secondary structure changes (Fig 3A ). Step 5-Visualization and localization of variable positions onto a target 3D structure. SA-conf locates the structurally variable positions onto one target structure (the first ID of the MSA output file) based on a script generated using PyMOL software  [61]. To do so, this 3D structure is colored according to three structural position types: strictly conserved, weakly variable or variable  (Fig 3B). Mutated positions are also highlighted in the stick representation.
). We also added red stars "*" to localize the residues involved in the binding site detected by the PockDrug webserver  [46]. (B) Graphical representation of the human uPA domain complexed with a chemical inhibitor (PDB ID: 3IG6). The protein is displayed as a cartoon (left figure) and as a surface (right figure) and is colored according to the structural variability of the positions: structurally conserved positions are colored in magenta, weakly structurally variable positions are colored in cyan, structurally variable positions where SL changes do not imply secondary structure changes are colored in "navy blue", and structurally variable positions where SL changes imply secondary structure changes are colored in dark blue. Residues located at mutated positions are displayed as sticks. Ligand 438 (HETATM code) is displayed in stick form with its Cα atoms colored in yellow.
SA-conf implementation SA-conf is a freely available combined Python and R programming language. that run on a GNU/Linux system. These programs require Python  [62] , the Biopython Python package  [63]  and working installations of the R  [64] , PyMOL  [61] , ClustalW  [53]  and T-coffee  [54]  programs. The underlying data and SA-conf executable script, with a set of pre-compiled binaries are available at http://www. mti. univ-paris-diderot. fr/recherche/plateformes/logiciels. Even if the duration of the SA-conf process depends on the number and length of the considered MTC, SA-conf is a notably quick tool that can analyze a large dataset. For example, the run of SA-conf using as input the "uPA set" (105 PDB chain IDs) with a pre-computed MSA on the same computer lasted 3. 09 min in Ubuntu on an Intel R Xeon(R) CPU E5-2609 0 @ 2.
Conformation set mining To capture the target structural variability without introducing bias into the analysis, it is essential to work on a clean MTC subset. SA-conf is able to mine diverse 3D conformation sets and is used to firstly prepare clean subsets, as illustrated below on uPA and p53 targets. To build an uPA MTC subset, we first ran SA-conf on a list of 107 PDB IDs corresponding to UniProt ID P00749 (human uPA). The structure overview provided by SA-conf highlighted the heterogeneity of the structure set. It includes a mixture of 54% monomers and 45% oligomers with certain heteromer complexes, and most of the structures are complexed with a ligand. The SA-conf MSA showed that some of the 187 chains are mutant forms and only 105 chains matched with the uPA catalytic domain (S3 Fig).
A clean MTC set, "uPA set" was built by manually selected these 105 uPA chains. It includes chains in apo and holo forms, wild-type and mutant sequences (Table  1 ). To build a clean heterogeneous MTC p53 DBD subset, we first ran SA-conf on a heterogeneous list of 78 PDB IDs associated with UniProt ID P04637 (70 human cellular tumor antigen p53) and UniProt ID P02340 (8 mouse cellular tumor antigen p53). The SA-conf MSA highlights several chains that do not match with the p53 DBD sequence (S4 Fig). We built the "P53 set" by retaining only one chain by PDB files matching with p53 DBD sequence. This set correspond to a heterogeneous subset composed of 76 X-ray and two NMR structures (PDB IDs: 2FEJ and 2MEJ), in bound (complexed with different partners such as p53 DBD, another protein, DNA or small molecule) or free form, corresponding to wild-type or mutant forms and human or mouse p53 DBD chains (Table  1 ).
For each subset, we defined most variable regions according to the variability of the studied dataset. For instance, nine most structurally variable regions were detected from uPA set (Fig  3A ) and defined as regions with at least four (l = 4) successive variable positions or at least two (l = 2) with one highest variable position (neq SL > 4). Using the SA-conf output that locates structural variables position onto one uPA structure, we observed that four of them are short loop fragments, included within long and solvent-accessible loops (Fig 3B  and 3C ). Thus, these long loops are composed of a succession of weakly and highly variable regions in agreement with Regad et al. (  2010 ) who showed that the long loops are not random coils  [38]. SA-conf also allows the detection of structural variability in terms of secondary structure changes.
All these results suggest the existence of intrinsic flexibility to facilitate the interactions. We also extracted p53 intrinsic flexibility by analyzing SA-conf results obtained on the two homology modeling sets P53-HM HM and P53-HM QM (Table  1 ). These two sets exhibit a weak structural variability associated with short structurally variable regions matching roughly with intrinsic flexibility detected using P53-NMR (Fig 5  , Table 2 ). This is partly explained by spatial restraints and structural proximity constraints associated to Modeller software. We note that   [47]. Residues involved in the interface between p53 DBD and 53BP1 proteins were estimated using 2P2I Inspector server  [68]  with the 1GZH PDB ID as input. They are identified by "*". Ligand-binding site was estimated using PockDrug server  [46]  with the 4AGM PDB ID as input.
For instance, the p53 homology models tend to result in very regular C-terminal α-helix conformations and the p53 NMR set have structures with particular conformations at some positions relative to other structure set. These particular Correspondence between known important PR1 regions and detected variable regions using PR1 and PR1-NMR sets. Residues involved in the dimerization interface of PR1, estimated using 2P2I Inspector server  [68]  with the 1HXB PDB ID as input, are identified by "•". Ligand-binding site was estimated using PockDrug server  [46]  with the 1HXB PDB ID as input, are identified using "*". Structural regions correspond to regions defined by Sadiq et al. (  2010 )  [72]. The eight and seven structural variable regions extracted from the PR1 and PR1-NMR sets are respectively named R1 PR1 -R8 PR1 and R1 PR1-NMR -R7 PR1-NMR.
However, the p53 NMR set have regions more variable in length and magnitude than the P53 set. This result can be partly explained by the greater number of constraint conformations in the heterogeneous P53 set due to the crystallographic technique. These local and global differences detected by SA-conf between p53 Xray and NMR conformations, have also been reported by Lukman and collaborators on a large p53 DBD conformer dataset  [67]  or on a large diverse set  [73]. In addition, in all p53 MTC subsets except in the P53-NMR set, one short variable region (close to the ligand-binding site) is observed. This suggests that the variability of this region is most probably explained by the deformation involved by ligand interactions, not observed using the P53-NMR set. All seven identified P53-NMR variable regions match with a portion of the heterogeneous P53 variable regions and five are located within the interaction regions (Fig  5 ).
More specifically, and as described in  [19] , the existing visualization programs such as PyMOL  [61]  can provide sequence or structure alignment but are not suited for the automation and comparison of a large MTC set. Molecular mechanics programs such as GROMACS  [76]  and CHARMM  [77]  analysis can provide structural quantification for structural ensembles most often associated with a molecular dynamics simulation but are expensive with respect to time and can be performed only on a restricted number of target forms. Other programs or web servers dedicated to the computation of multiple structure alignments such as MUSTANG-MR  [13]  are able to analyze two or more structures with different sequences but with a high computational cost. Based on a SA simplification, recent multiple structure alignment methods, such as MulPBA  [14] , reduce the computational cost but can have still some difficulties in obtaining global convergence and providing a good multiple structural alignment using a large and diverse dataset.
Additionally, these approaches do not provide structural variability quantification. Considering tools dedicated to the analysis and quantification of the structural variability, we found three direct competitors able to obtain automatic, precise, and detailed structural statistics of MTC sets: CoDNaS  [16, 18] , GSA-tool  [23] , PSSweb  [19, 20]. A detailed comparison of the technical features of these four tools dedicated to the structural variability analysis is presented in Table  3. CoDNaS  [16, 18]  is a database of protein conformational diversity that analyses the structural diversity of a protein's native state available in the PDB. GSATools explores the conformational space of target models determined using molecular dynamics simulations based on a SA  [23]. Consequently, it focuses on the analysis and quantification of the intrinsic structural variability of an MTC with identical sequence and obtained under the same conditions.
The wellknown metric rmsf calculated by PSSweb exhibits variable correlation coefficients ranked from 0. 01 to 0. 77 on the different subsets. These results demonstrate that rmsf and B-factor parameters quantify complementary information from the exponent of Shannon entropy. The SAconf exponent of Shannon entropy presents the interest to measure both the quantity of average information of a position and its uncertainty in terms of observed local conformations. Hence, this criterion is effective to detect highly variable positions as an rmsf metric but also positions that exhibit little variability. In this way, the exponent of Shannon entropy is a particularly adapted criterion for mining unbalanced or representativeness protein datasets. It can detect structural variability information due to a few particular observed conformations of a target, and the associated MSLA map then allows a direct identification of the particular conformations.
In the case of P53, SAconf tool applied on combined Xray, NMR and homology models data highlights similar results to those obtained on MTC sets obtained by molecular dynamics simulations  [65, 67]. The advantage of SA-conf is that is can be apply to structure sets available in the PDB (crystallographics and NMR structures), but also to theoretical models such homology models and to MTC obtained using dynamics simulations to analyze a large conformational space of the target. Finally, this work underlines that structural variability analysis is an important source of structural knowledge of the protein universe.
We also added red stars "*" to localize the residues involved in the binding site detected by the PockDrug webserver [46]. (B) Graphical representation of the human uPA domain complexed with a chemical inhibitor (PDB ID: 3IG6). The protein is displayed as a cartoon (left figure) and as a surface (right figure) and is colored according to the structural variability of the positions: structurally conserved positions are colored in magenta, weakly structurally variable positions are colored in cyan, structurally variable positions where SL changes do not imply secondary structure changes are colored in "navy blue", and structurally variable positions where SL changes imply secondary structure changes are colored in dark blue. Residues located at mutated positions are displayed as sticks. Ligand 438 (HETATM code) is displayed in stick form with its Cα atoms colored in yellow.
They are labeled from R1 P53 to R8 P53 and correspond to respectively positions 15-17, 25-36, 53-57, 91-97, 135-139, 154-160, 173-174 and 192-202. We also added red stars "*" to localize the residues involved in the binding site detected by the PockDrug webserver [46]. (B) Graphical representation of the human p53 DBD domain complexed with the Large T antigen (colored in gray). (PDB ID: 2H1L). The protein is displayed as a cartoon (left figure) and as a surface (right figure) and is colored according to the structural variability of the positions: structurally conserved positions are colored in green, weakly structurally variable positions are colored in cyan, structurally variable positions where SL changes do not imply secondary structure changes are colored in "navy blue", and structurally variable positions where SL changes imply secondary structure changes are colored in dark blue.