dataesr/scientific-paragraphs-categorization huggingface dataset:
Online tools for batch querying and data visualization The batch query function enables the user to search the whole database for the occurrences of TFBSs together with their respective methylation status within a bed le. The new functionality returns the global TFBS and context-speci c partner information for the chosen cell line and enriched transcription factors. In order to ease interpretation, the tool produce selected information and gures for the provided genomic locations. Downloading the results requires a single click. Ultimately, the batch query constitutes a robust bioinformatics tool, considering context-speci c transcription factor information, which is accessible to all scientists. Researchers looking for a more customizable analysis or more comfortable with R can drill down further into a sample of interest with the API TFregulomeR  (Lin et al.
, 2020). This R package can access the entirety of the MethMotif database, and to generate all the gures and les available through the MethMotif website (with internet access). Starting with the genomic information of interest, it is possible to analyze the TFBS between cell lines and/or in different CpG contexts. TFregulomeR further enables the deconvolution of TFBS by segregating a list of the cofactors most likely binding the protein of interest. Finally, TFregulomeR streamlines e cient annotation of context-speci c data with genomic location and GREAT gene ontology information. Users can download this package through Github (https://github. com/benoukra ab/TFregulomeR) and begin exploring its many functions.
Conclusion And Perspectives In our initial release, we introduced the rst integrative transcription factor database which merged TFBS motifs (ChIP-seq) and DNA methylation (WGBS) (Xuan  Lin et al. , 2019). In this new release of MethMotif, we have increased the amount of available TFBS, cell lines, WGBS, and ChIP-seq covered. To date, MethMotif remains the only transcription factor database that provides context speci c TFBS information. Our revised TF card now includes a cofactor information panel covering methylation, cofactor, and cell line contexts. Keeping with our original design philosophy, the new cofactor card provides pre-generated gures supplying context-speci c information on TFBS motifs, DNA methylation, genomic location, and associate gene ontology. MethMotif thereby greatly increase the information content on the primary transcription factor and binding site speci city.
Acknowledgements The authors thank the  Center for Health Informatics and Analytics (CHIA) at Memorial University  as well as the  Digital Research Alliance of Canada (the Alliance)  with its regional partner  ACENET  for providing high-performance computing resources and database hosting. Authors contributions: M. J. D. , Q. X. X. L. and T. B. designed the platform; M. J. D. and Q. X. X. L. programmed the website and performed all analysis; M. J. D. , Q. X. X. L. , S. S. , D. T. and T. B. interpreted the data; M. J. D. , S. S. , D. T. , and T. B. wrote the manuscript. T. B. directed the study.
Methods and findings Based on rainfall erosivity and surface flow and transport sub-models, we developed a simplified model to assess basin-wide sediment yields on a monthly basis by upscaling the point rainfall input. Our calibration dataset of monthly data (Mg km -2 month -1 , available for the years  1961 and 1963-1969 ) revealed that our model satisfactorily reproduces the net soil erosion in the study area (R 2 = 0. 81). For the period 1950-2020, the reconstruction of an annually aggregated time-series of monthly net erosion data (297 Mg km -2 yr -1 on average) indicated a moderate decline in sediment yield after 1999. This is part of a long-term downward trend, which highlights the role played by land-use changes and reforestation of the mountainous areas of the basin.
Data sources For rainfall and net erosion, we have referred to the data provided by the Annals Project (https://www. isprambiente. gov. it), available through the ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale) portal and updated by the regional administrations (until 2020 for Tuscany, https://www. sir. toscana. it/consistenza-rete). For the MRB, net erosion data are available for the years 1961 and 1963-1969, which was used for the calibration of the erosion model. The reference rainfall station of Iera, centrally locate in the MRB, was used for model calibration and reconstruction of net erosion for the period 1950-2020. Until 1980, the monthly vegetation cover percentage was calibrated annually on a constant basis, with monthly variation from January to December around the annual mean value of ~75%  [35].
Spreadsheet-based model development was performed using the free online statistical software STATGRAPHIC (http://www. statpoint. net/default. aspx), with graphical support from WESSA (https://www. wessa. net/tsa. wasp) and CurveExpert Professional 1. 6 (https://www. curveexpert. net). The full set of raw data and the equations that support the findings of this study are available in S1 File.
isprambiente. it/viewersgi2); b) analysis from ArcGIS-ESRI Geostatistical Analyst on the ESDAC (European Soil Data Centre) dataset (https://esdac. jrc. ec. europa. eu/content/global-rainfall-erosivity) and c) adaptation from Magra River Basin Authority [32]". https://doi. org/10. 1371/journal. pone. 0262132. g001.
Thickening of intima-media complex in the common carotid artery is a biomarker of atherosclerosis. To automatically measure this thickness, we propose a region-based segmentation method, involving a supervised deep-learning approach based on the dilated U-net architecture, named caroSegDeep. It was trained and evaluated using 5-fold cross-validation on two open-access databases containing a total of 2676 annotated images. Compared with the methods already evaluated on these databases, caroSegDeep established a new benchmark and achieved a mean absolute error twice smaller than the interobserver variability.
II. METHOD The proposed solution builds on the U-net  [12]  architecture, with dilated convolutions on the bottleneck to increase the receptive field  [8]. It works in two steps and uses two identical networks with different weights (Fig. 2 ). First, the user's two mouse clicks define the region of interest (ROI) to be segmented. Next the far wall is detected. Eventually the IMC segmentation is done in the vicinity of the detected location. The code is available at https://github. com/nl3769/caroSegDeep.
III. RESULTS To assess each network on data not seen during its training, a 5-fold cross-validation was performed: the database was divided into five subsets of equal size, and five pairs of Θ F W and Θ IM C networks were trained and tested independently using in turn three subsets for training (60%), one subset for validation (20%) and one subset for testing (20%). The results obtained on the test subsets of these five pairs were merged to assess the performance of the method on the entire database. The second step (actual IMC segmentation) of the proposed cascade approach assumes that the median axis resulting from the first step (far wall detection) is approximately correct: if the far wall is wrongly detected, the ICM segmentation is likely to fail. Therefore, we first quantified the success rate of the first step alone, then incorrect median axes were manually redrawn using a home-made graphical interface before assessing the accuracy of the second step.
Robustness of the far wall detection: Images where the distance between the predicted and reference median axes was greater than 0. 8 mm were selected for visual inspection, which confirmed that 36 of them (1. 3% of the database) were failures, i. e. curves unusable to initialize the IMC segmentation step. Hence, the success rate was of 98. 7%. In the 36 images with failures, the median axis was interactively reinitialized, in preparation for the subsequent segmentation step. Accuracy of the IMC segmentation: The IMC segmentation inaccuracies were calculated with respect to the annotations performed by the expert A1. We assessed the median absolute difference (MAD) on IMT, which is the target measure, as well as the Hausdorff distance on the LI and MA contours. These errors were compared with the inter-and intra-observer variabilities, A2 vs.
A1 and A1' vs. A1, respectively. Table  I  summarizes the segmentation errors separately calculated for images from the CUBS 1 and CUBS 2 databases, which allows a comparison with other methods evaluated on these databases in previous studies  [5] ,  [6]. Thus, the previous benchmark on the CUBS 1 database was a MAD value on IMT equal to 114 ± 117 µm, while on the CUBS 2 database the best conventional method achieved 139 ± 119 µm and another Unet-based method obtained 178 ± 120 µm.
IV. DISCUSSION We proposed and fairly evaluated on open-access databases an almost-automatic (two user mouse-clicks) deep-learning method devised to extract the contours of the intima-media complex in longitudinal B-mode ultrasound images of the carotid artery. The proposed approach is patch-based, which allows for segmenting variable-width ROIs of without resizing the images. The far-wall localization step was successful in all but 1. 3% of the images. This robustness is a prerequisite for overall correct segmentation. The latter achieved errors smaller than the interobserver variability, both in terms of estimated thickness and contour location. Compared to the methods already evaluated on the same data  [5] ,  [6] , caroSegDeep established a new benchmark. As the proposed method is based on supervised learning, it has the potential to increase its performance by using larger and more diverse data for training.
In particular, the largest errors occurred in the presence of calcified plaques, which were present in few images. This work was oriented towards asymptomatic plaque-free subjects, images with plaques were not expected. Nevertheless, we expect that results might be improved by re-training the networks on a database enriched with such images. This avenue deserves investigation. Another direction might exploit multiple annotations available for each image to increase the robustness by learning how to account for such uncertainties.
INACCURACIES (MEAN ± STD) FOR CUBS 1 AND CUBS 2 DATASETS: MEAN ABSOLUTE DIFFERENCE (MAD) FOR THICKNESS QUANTIFICATION (IMT) AND HAUSDORFF DISTANCE (HD) FOR CONTOUR LOCATIONS (LI AND MA). THE ERRORS OF THE PROPOSED CAROSEGDEEP METHOD WITH RESPECT TO REFERENCE ANNOTATIONS ARE COMPARED TO THE INTER-AND INTRA-OBSERVER VARIABILITIES.
Acknowledgements The authors acknowledge  Philippe Andrey  ( UMR 1318 ,  Institut Jean-Pierre Bourgin ,  INRA ,  AgroParisTech ,  CNRS ,  Université Paris-Saclay ,) for his help in writing some small parts of the workflow code. The authors would like to thank  Christelle Baptiste ,  David Pot  and  Jean-Luc Verdeil  for providing us with the images of sorghum cross sections colored with fasga and  Herman Hofte  for miscanthus samples.
Funding The IJPB benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ). This work was financially supported by Pro-maïs and is part of the  DECLIC  project (http:// pro-mais. org/ les-progr ammes-de-reche rche-de-promais). Field trial was also carried out in the frame of DECLIC. This study was supported by the  French Government  Grant  ANR-11-BTBR-0006 BIOMASS FOR THE FUTURE , managed by the  French National Research Agency  under an " Investments for the Future" program  (Grant  ANR-11-IDEX-0003-02 ).
Some recent advances in biomolecular simulation and global optimization have used hybrid restraint potentials, where harmonic restraints that penalize conformations inconsistent with experimental data are combined with molecular mechanics force fields. These hybrid potentials can be used to improve the performance of molecular dynamics, structure prediction, energy landscape sampling, and other computational methods that rely on the accuracy of the underlying force field. Here, we develop a hybrid restraint potential based on NapShift, an artificial neural network trained to predict protein nuclear magnetic resonance (NMR) chemical shifts from sequence and structure. In addition to providing accurate predictions of experimental chemical shifts, NapShift is fully differentiable with respect to atomic coordinates, which allows us to use it for structural refinement.
By employing NapShift to predict chemical shifts from the protein conformation at each simulation step, we can compute an energy penalty and the corresponding hybrid restraint forces based on the difference between the predicted values and the experimental chemical shifts. The performance of the hybrid restraint potential was benchmarked using both basin-hopping global optimization and molecular dynamics simulations. In each case, the NapShift hybrid potential improved the accuracy, leading to better structure prediction via basin-hopping and increased local stability in molecular dynamics simulations. Our results suggest that neural network hybrid potentials based on NMR observables can enhance a broad range of molecular simulation methods, and the prediction accuracy will improve as more experimental training data become available.
NapShift uses protein sequence and dihedral angles as inputs to a neural network and outputs chemical shifts for each backbone atom in the protein. Differentiating the energy penalty between the calculated CS and the experimental results with respect to the atomic coordinates gives us a restraint force to pair with an empirical force field. To analyze the changes in the underlying energy landscape introduced by a restraint potential, we employed two small benchmark protein systems: tryptophan zipper 1 (PDB  13  code 1LE0  14  ), a 12-residue peptide that folds into a β-hairpin with specific packing of tryptophan side chains, and a 15-residue peptide based on the sequence of residues 56 to 70 of human platelet factor 4 (PDB code 1DN3  15  ), which was experimentally shown to form a stable α-helix in sodium dodecyl sulfate (SDS).
We used a combination of BH global optimization  [16] [17] [18]  and discrete path sampling  [19] [20] [21]  to sample the energy landscape and search for the native folded structure of each protein, using both the original molecular mechanics force field and the hybrid force field, with varying contributions from the NMR restraint potential. After benchmarking these two initial systems, we explored a more challenging test case, namely, an 18-residue peptide (DP5  22  ) designed by Araki and Tamura to exhibit equal propensities for the α-helical and β-hairpin forms. Using calculated NMR restraints, we guided BH global optimization simulations toward either the α-helix (PDB code 2DX3) or the β-hairpin (PDB code 2DX4) to explore the effect that the hybrid potential has on a multifunnel energy landscape.
To determine the effectiveness of hybrid restraints on larger globular proteins, we used the NapShift hybrid potential for BH global optimization of ubiquitin. Ubiquitin has been experimentally  23  and computationally  24  shown to transition between two distinct conformations: the native conformation (Ub) and a sparsely populated conformation, where the Cterminal tail is retracted (Ub-CR). Using both the original force field and the hybrid potential, we attempted to locate the native Ub conformation by starting a BH global optimization run from the Ub-CR conformation. We also incorporated this methodology into the GRO-MACS  25  software for molecular dynamics (MD) simulations. We found significant gains in accuracy for MD simulations with a NapShift hybrid potential for the tryptophan zipper 1 as well as for ubiquitin with orthogonal NMR data.
II. METHODS A. Datasets. NapShift was parameterized using a set of 3237 protein entries for which PDB structures and Biological Magnetic Resonance Data Bank  26  (BMRB) CS are known. PDB files containing multiple structural models were treated by considering only the first model. Individual CS values showing more than 5% relative error, calculated as ϵ rel = |ϵ|/|x| (where |ϵ| = |xx approximate | is the absolute error and x ≠ 0), were excluded from the training set. This protein database was decomposed into 2987 entries for training and validation, and 250 entries for testing the accuracy of the method (see Tables  S3  and S4  in the Supporting Information for the PDB structures employed in this work. ).
Our ANNs consisted of: (i) an input layer, which receives the input vectors; (ii) a single hidden layer, which identifies a mapping between the inputs and outputs; and (iii) an output layer that provides the final prediction (Figure  1a ). We tested the use of mono-, tri-, penta-, and heptapeptidebased structural parameters to generate the input vectors for the ANN training. The best compromise between the accuracy and complexity of the calculation was found for tripeptides, in line with the previous development of SPARTA+. 8  Thus, for a given residue i of the protein, the parameters employed in the input layer included: (i) the amino acid type and (ii) the torsion angles ϕ, ψ, χ 1 , and χ 2 of the residues i, i -1, and i + 1 (Figure  1b ). The protein sequence was processed in the ANN via BLOSUM62 to align the input sequence to the database.
For every CS input, forces were calculated and applied individually for all atoms involved in the dihedral angle of the tripeptide employed in the NapShift calculation: ϕ, ψ, χ 1 , and χ 2. The total potential energy, V Total , used in our calculations is therefore given by = + V V V Total FF CS (12) where V FF is the potential energy of the underlying biomolecular force field. We have implemented the NapShift hybrid restraint potential in the GMIN  30  and OPTIM  31  programs for use with the Amber  [32] [33] [34]  and CHARMM  35  molecular mechanics force fields. The NapShift potential is also implemented in the GROMACS  25  software package for MD simulations.
A tolerance parameter ϵ was also introduced to account for systematic error in the ANN prediction when evaluating the energy penalty  = > < l m o o o o o o o o n o o o o o o o o , , 0, , , ij ij ij ij ij ij ij ij ij ij ij ij ij where = + (Error ) ij ij j upp experimental (16) = (Error) ij ij j low experimental (17) This parameter creates a flat-bottom restraint function, where all predictions within a certain tolerance of the experimental value will lie at the bottom of the potential. This softens the contribution of the hybrid restraint forces in the case that the NapShift-predicted CS slightly deviates from the reference values. Error j for each backbone atom type j corresponds to the root-mean-square deviation (RMSD) error of the NapShiftpredicted chemical shifts for the given atom type over the testing dataset.
F. Restrained Molecular Dynamics Simulations. To study the dynamics of proteins in or near their free energy minimum, we implemented CS restraints in the GROMACS package for MD simulations,  25  which allows for the use of a variety of force fields for proteins, water molecules, and lipids, as well as a large number of integration methods, such as replica averaging  52  and meta-dynamics. 53  The restraints were imposed by adding an experimentally driven energy term to the standard force field (eq 12), where the experimental term was modeled as a harmonic potential based on the calculated CS value (eq 11). The harmonic restraints were again implemented as flat-bottom potentials (eq 15), where no restraining force was applied within the experimental error limits of the measured values. The weight K of the restraint energy with respect to the empirical force field was increased from zero up to a maximum value during the initial equilibration simulation, and subsequently maintained constant during final sampling.
Journal of Chemical Theory and Computation III. RESULTS A. Predicting CS from Structure Using NapShift. Six NapShift ANNs were defined for the atoms N, C, Cα, Cβ, H, and Hα, using tripeptides derived from 2987 protein structures for which CS assignment was available. The performance of NapShift was tested using a set of 250 proteins that were not used in the training/validation phase (see Table  S4  in the Supporting Information). This set was randomly selected from an initial database of 3237 PDB files, each associated with a corresponding BMRB entry. The only requirement for the 250 entries was that they were deposited after 2010, which provided an unbiased comparison with other programs such as SPARTA+ (published in 2010). The RMSD of CS values predicted for the 250 testing set showed an improved accuracy upon increasing the length of the input peptide n from 1 to 7 (Figure  2a ).
This result is consistent with the analysis of the global potential energy landscape using discrete path sampling  [19] [20] [21]  as V Total is the total potential energy, V CS is the chemical shift restraint energy, O1 is an order parameter denoting the number of native backbone hydrogen bonds found in the structure (maximum four), and O2 is an order parameter denoting the number of distances between centers of mass of neighboring pairs of TRP side chains that match the PDB structure to within ±0. 5 Å for the two closest pairs and ±1. 0 Å for the other pair (maximum three). The RMSD is calculated between the experimental structure (pdb code 1LE0  14  ) and the BH-predicted structure. 4a ) has a prominent low-energy funnel containing non-native conformations (i. e. , structures with the incorrect hydrogen-bonding pattern and tryptophan side-chain packing).
This result suggests that experimental restraints could be used in place of more complicated solvent models to reduce computational cost and overall model complexity. 3. Designed Peptide 5. DP5 22 is an 18-residue designed peptide, experimentally shown to exist in two distinct folded conformations: an α-helix (PDB code 2DX3) and a β-hairpin (PDB code 2DX4). A previous computational study used snapshots from molecular dynamics simulations to suggest that DP5 has a multifunnel energy landscape, where competing αhelix and β-hairpin conformations make it difficult for global optimization methods to identify a native structure from the sampled minima. 68  The authors of this previous study began MD simulations from each of the two experimental PDB structures, rather than attempting to sample the two competing conformations starting from an extended structure, as the relatively complex multifunnel energy landscape causes broken ergodicity problems.
Residues 3 through 10 were identified as α-helical by the STRIDE secondary structure assignment software,  67  which is consistent with the experimental structure. Although the total RMSD of the global minimum to the experimental structure was slightly higher than for the structure obtained from the unrestrained BH run (Table  IV ), this deviation was purely due to differences in the highly flexible C-terminal region of the structure (Figure  6a ). When considering only the α-helical residues, the RMSD of the restrained structure was 2. 98 Å, while the RMSD of the V Total is the total potential energy, V CS is the chemical shift restraint energy, and O3 is an order parameter denoting the number of residues that are labeled as α-helical by the STRIDE secondary structure assignment software.