dataesr/scientific-paragraphs-categorization huggingface dataset:
In these situations, Jalview will by default automatically create a backup file (called, e. g. , myfile. fa. bak001). Backup behavior can be changed in the "Backups" section of Jalview's preferences panel: by default only the three most recent backups will be retained. 6. Alignment of large sets of sequences is CPU intensive and can therefore take considerable time. Jalview's public alignment services provided by the University of Dundee in Scotland, UK, do not permit execution of alignments of greater than 1000 sequences with up to 1000 amino acids each. To perform larger alignments, it is necessary to either (a) download and configure a local instance of a compatible Jalview web services system or (b) align sequences using an external program and then import the result. (a) Downloading and configuring a local instance of Jalview Web Services.
A new web services system is currently being developed, and instructions will be made available via Jalview's built-in help on how to download and deploy these new services once they are put into production. (b) Exporting sequences for alignment and reimporting the result. Sequences may be exported via the File menu's "Save as" option in the alignment window or for the current selection via the pop-up menu (opened by rightclicking the selected area). Once the alignment has been performed, it is straightforward to import the aligned result to a new alignment view, but there are potential problems: l Alignment programs may have constraints on sequence name length, the range of characters permitted, and reject inputs containing duplicate sequence names. l Import of the aligned sequences as a new alignment will not retain sequence metadata from the original Jalview alignment view such as CDS relationships, database cross-references, sequence features, and secondary structure annotation rows.
l Hidden columns in the original view will not be accounted for or included in the result of the alignment (see Note 9). Problems with sequence names can be worked around through the use of custom scripts, but are outside the scope of this chapter. Assuming sequence identifiers have been preserved, then there are two ways to work around the loss of sequence metadata. Metadata originally retrieved by Jalview from an external database can be retrieved once again. The "Fetch DB Refs" option in the Web services menu provides options to retrieve records from either all standard databases or a specific one (e. g. UniProt for protein sequences), but this should be used with care since both options may take some time for large alignments. A second workaround is to manually export features and annotation from the original view and import the resultant Jalview features file (or GFF3 file) and Jalview annotations file to the newly imported aligned sequence set.
7. Jalview provides a Realignment option when performing alignments via the Clustal W and Clustal Omega web service programs. Normally, Jalview removes all gap characters from sequences passed to an alignment program, but for Realignment, gap characters will be preserved. The precise behavior depends upon which Clustal alignment program is used: l Clustal W identifies aligned regions of the input as a range of sequences of equal length (including any gap characters) at the beginning of the input data. All other sequences in the input data are then aligned to that first block (with inserts into the block created as necessary). This process is quick, and preserves the original aligned region, provided that region was reordered to appear at the top of the alignment view when the alignment was submitted.
14. Jalview includes the algorithms "UPGMA" and Neighbor-Joining for the generation of dendrograms from distance matrices computed over a range of columns in an MSA. These functions are accessed from the "Calculate Tree or PCA. " dialog in the Calculations menu. A variety of score functions are provided  [21]  including protein substitution matrices such as BLOSUM62 and a Percent Identity score suitable for DNA. Trees calculated by external programs may also be imported as New Hampshire (Newick) and New Hampshire "Extended" format flat files-Jalview will attempt to automatically match leaves to sequences based on the displayed sequence IDs. 15. In addition to the Jalview Desktop application, Jalview is also available as a web-based application: JalviewJS. Launched in late 2019, JalviewJS is the Jalview application compiled to JavaScript  [2]  and adapted to run in-page either as the fullfeatured "Desktop" application or as interactive MSA visualization components designed for embedding in web pages.
Acknowledgments JBP and GJB wrote the manuscript. SLD created training videos based on exercises in the Jalview Manual developed by JBP and DMAM. All other authors contributed to Jalview development in the period 2009-2019. Development of the Jalview sequence analysis platform is coordinated at the  University of Dundee  with support from the  UK's Biotechnology and Biological Sciences Research Council  ( BB/L010742/1 ), the  Scottish Funding Council , and the  Wellcome Trust  ( 101651/Z/13/Z ). We also gratefully acknowledge contributions and feedback from the  Jalview user community  and student project support from the  Google Summer of Code  (2011 and 2012).
The study of articulatory gestures has a wide spectrum of applications, notably in speech production and recognition. Sets of phonemes, as well as their articulation, are language-specific; however, existing MRI databases mostly include English speakers. In our present work, we introduce a dataset acquired with MRI from 10 healthy native French speakers. A corpus consisting of synthetic sentences was used to ensure a good coverage of the French phonetic context. A real-time MRI technology with temporal resolution of 20 ms was used to acquire vocal tract images of the participants speaking. The sound was recorded simultaneously with MRI, denoised and temporally aligned with the images. The speech was transcribed to obtain phoneme-wise segmentation of sound. We also acquired static 3D MR images for a wide list of French phonemes.
In  18, 19  , a radial encoding scheme was used together with a compressed SENSE reconstruction. The technique  20  makes use of radial sampling and the regularized nonlinear inversion reconstruction. Another approach, which does not necessarily assume a non-cartesian encoding, was used for dynamic 3D imaging of the vocal tract  14, 21. Since the technologies listed above are not easily available, data sharing could greatly accelerate research in the field. In this context, multiple databases exist for English speakers. Real-time MRI datasets, where 460 sentences were pronounced by 4 and 10 speakers, are presented in  22  and  16  , respectively. The databases  23  and  24  which were acquired from 17 and 8 speakers respectively, include both real-time and 3D static MRI. An emotional speech dataset recorded from 10 speakers was published in  15.
Recently, an extremely rich dataset counting 75 English speakers was presented  25. However, MRI datasets representing other languages are very limited. 2D dynamic MRI with temporal resolution of 7 frames per second of one female Portuguese speaker was published in  26. Static 3D MR images of five Japanese vowels pronounced by one male speaker are presented in  27. A dataset of 3D vocal tract shapes was published recently  28  for two German native speakers. A 2D dynamic with 3D static MRI database including 2 male French speakers was also acquired earlier  29. Nevertheless, the available data does not allow exhaustive investigation of these languages. Moreover, all the existing publicly available databases offering high spatio-temporal resolution dynamic MRI, exploit similar acquisition technologies due to the fact that they are acquired by the same research team.
Availability of datasets of different qualities could serve to get better precision in some aspects. In this work we report on a multi-modal MRI database consisting of 2D real-time and 3D static MR images of the vocal tract of 10 French speakers. The protocol used for the real-time MRI acquisitions for our dataset was successfully used by multiple groups in the context of the study of the articulators' motion  [30] [31] [32] [33]. While performing investigations on the vocal tract organs, it is crucial to consider the diversity of their movements during speech production. Standard French language includes 35 phonemes (18 consonants, 14 vowels, 3 semi-vowels) which form 1290 diphones  34  and many complex consonant clusters. To cover this variability as much as possible, a corpus was previously developed  29.
The corpus allows to explore numerous phenomena specific to the French language such as nasal vowels, uvular /ʁ/  35  , French /y/, short /ɥ/, and strong anticipation of labial features  36. The dataset includes annotations of the speech and of spontaneous swallowing and will thus provide researchers with data having a good coverage of the French phonetics to further explore French speech production and physiological processes taking place in the vocal tract vicinity.
Methods participants and speech task. The participants were 5 male and 5 female native French speakers (aged 29 ± 8 years) without any speech or hearing problems. Presence of any metal in the vocal tract vicinity, which may generate susceptibility artifacts, was also an exclusion criterion. Relevant patient characteristics are listed in Table  1. A set of mid-sagittal images demonstrating speakers' anatomy is presented in Fig. 1. All participants provided written informed consent, including written permission to publish the materials of this experiment. The data was recorded under the approved ethical protocol "METHODO" (ClinicalTrials. gov Identifier: NCT02887053). The study was approved by the institutional ethics review board (CPP EST-III, 08. 10. 01). The previously designed corpus  29  was presented in form of a pdf-file (see Supplemental Materials) which was projected on a screen in the MRI room so that a speaker could read it during the experiment without difficulty.
The presentation was sent to the volunteers before the experiment so that they had at least one day to get familiar with the sentences which sometimes sound strange even if they are all well-formed French sentences. Additionally, the speakers were carefully instructed directly before the acquisition to guarantee correct understanding of the task. Data acquisition and alignment. The MRI data was recorded at Nancy Central Regional University Hospital on a Siemens Prisma 3 T scanner (Siemens, Erlangen, Germany). The speakers were in supine position and the Siemens Head/Neck 64 coil was used. For the 2D real-time we used radial RF-spoiled FLASH sequence  13  with TR = 2. 22 ms, TE = 1. 47 ms, FOV = 22. 0 × 22. 0 cm, flip angle = 5°, and slice thickness was 8 mm. Pixel bandwidth was 1670 Hz/pixel.
Image size was 136 × 136, and in-plane resolution was 1. 6 mm. Images were recorded at a frame rate of 50 frames per second and reconstructed with a nonlinear inverse technique presented in  13. This method represents a formulation of a nonlinear optimisation problem with respect to both image and coil sensitivity maps which is solved iteratively with regularized Gauss-Newton method. The protocol used for dynamic data acquisition differs from the protocols of the published publicly available databases, and thus the quality is also somewhat different. Radial encoding trajectories are shorter than spiral ones, so that the repetition time is lower in our case (2. 22 ms comparing to 6. 004 ms in the latest dataset  25  ) which probably decreases the manifestation of the off-resonance effects. Also, some residual aliasing artifacts can be remarked in case of the datasets  16  and  15.
3 ). This was done using Transcriber 1. 5. 2 (http://trans. sourceforge. net/en/presentation. php). This software generates. trs files which include the timestamps and the corresponding text. Text annotations and audio signal were synchronized by a forced alignment automatic speech recognition system Astali (http://ortolang108. inist. fr/astali/) trained on French. It used the. trs text annotations together with the denoised sound to perform the temporal segmentation (both word-wise and phoneme-wise). The phonemes are stored inside a file using SAMPA phonetic annotation system  39.
Data Records The data is available on figshare  40. Each of the 10 folders contains the data of a speaker. To summarize, a folder (except the second one) contains 16 dynamic and 76 static series. Each of the dynamic series counts 1800 to 2200 frames and has duration about 1 minute, which results to overall 34800 images (approximately 15 minutes) per subject. Each static series consists of 36 slices. The dataset is organized as follows: the root directory contains ten speakers' folders with names "PXX" (XX here is a patient code). The speaker data is divided into three folders: DCM_2D, DCM_3D and OTHER. Inside DCM_2D and OTHER folders there are 16 subfolders with names SYY (YY is a series number) which correspond to the different dynamic acquisitions. Files stored inside the subfolders of DCM_2D are DICOM files with the MRI 2D dynamic data, and files contained inside subfolders of the OTHER folder are the corresponding denoised cropped sound, TEXT_ALIGNMENT_PXX_SYY.
The latter was used to check. consistency of the teeth positions (as determined by UP and DOWN). The correspondence between the folder names and the speech task performed in frames of an MRI series, is given in the Online-only Tables  1 2 3. The dataset structure is also illustrated in Fig. 4. The text alignment data are the Transcriber sentence annotation files (. trs files) which include pronounced text and the timestamps of start and end of each sentence on the one hand, and word and phoneme segmentation files (. textgrid Pratt files)  41  on the other hand. The code which allows reading these files is provided. For P2, 3D static images of consonants phonation were not acquired because of technical reasons, thus only dynamic 2D data and 3D static images of vowels and silent positions were included.
Technical Validation The dynamic 2D images were visually inspected by the researchers. In general, the images quality was good. Some minor artifacts can be observed as it is pointed out in Fig. 5. Some partial volume effects occurred because of relatively large slice thickness (8 mm). Despite the temporal filter, some residual radial aliasing artifacts were observed. Very fast articulatory gesture can lead to blurring, for example when the tongue tip approaches the alveolar region for /t/, /d/ or /l/. The video-sound alignment was verified by a researcher having more than 20 years of experience in the field (author Y. L. ). This was done by cross checking of the sound track and images using the videos included to the database as described in Methods section. In the case a misalignment found, a proper shift was applied to the sound.
Acknowledgements This research was funded by the projects  ArtSpeech and Full3DTalkingHead  of  ANR  ( Agence Nationale de la Recherche ), France; CPER "IT2MP", "LCHN" and  FEDER. We thank  Claire Dessale  and the  CIC-IT team  for their help with the legal issues and the team of MRI technicians of the  Nancy Brabois Central Regional University Hospital.
code availability A MATLAB code which generates a video from the dynamic data is available on GitHub https://github. com/ IADI-Nancy/ArtSpeech. This code also allows reading alignment annotation. trs and. textgrid files, and reading audio and dicom files. It uses mPraat third party toolbox which is available on bbTomas GitHub https://github. com/bbTomas/mPraat. The code was tested on Linux distribution of MATLAB2018b and MATLAB2020a. A Python toolbox for. textgrid files parsing also exists  42.
DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein-protein interactions. Currently, the MLR DockTScore is available at www. dockt hor. lncc. br. Structure-based drug design and virtual screening have become common approaches for drug discovery. The predictive performance of scoring functions is essential for such methodologies 1-3. However, accurate prediction of protein-ligand binding affinity remains a major challenge for current scoring functions. Despite the improvement over the last years of empirical, force-field or knowledge-based scoring functions, most of them still show unsatisfactory correlation with the experimental binding affinity or are based on meaningless description of protein-ligand interactions exhibiting overestimated accuracies in some cases  [4] [5] [6].
Empirical scoring functions are based on a set of individual contributions or interaction descriptors calibrated by regression or statistical approaches using a training set of experimental affinity data for protein-ligand complexes  7, 8. Improvement of scoring functions can be achieved by developing new terms, training on larger high-quality datasets or using sophisticated machine learning-based algorithms for regression analysis, e. g. XGBoost and LightGBM boosting approaches  [9] [10] [11] [12] [13]. Next, solvation and entropy contributions are key for ligand binding  [14] [15] [16] [17] [18] [19] [20]. Although several previous scoring functions have considered such effects  14, 15, 17, 19  common limitations of scoring functions are related to often neglecting them  10, [21] [22] [23].
New scoring functions based on more precise physics-based descriptors to better represent protein-ligand recognition process are thus needed. Furthermore, a number of studies demonstrated that scoring functions performance is very heterogeneous across different target classes  [22] [23] [24] [25] [26]. Target-specific scoring functions have shown to achieve better affinity prediction performance than general scoring functions trained over diverse protein families  [21] [22] [23] [27] [28] [29]. In this work, we developed a set of new empirical scoring functions, named DockTScore, to estimate protein-ligand binding affinity by explicitly accounting for physics-based interaction terms contributing to the binding free energy. Our models are based on the MMFF94S force field and trained and validated on highquality large datasets properly curated.
Data sets for target-specific scoring functions. We selected a random subset from the PDBbind v2013 refined set according to specific ranges of the EC Number, (Enzyme Commission Number (EC Number) is a system of enzyme nomenclature that numerically classifies enzymes based on the chemical reaction catalyzed. ) ranging from 3. 4. 11. 0 to 3. 4. 25. 69, to create a dataset for training and testing the scoring function focused for proteases, resulting in a subset composed of 783 structures (Table  S1 ). To create the dataset for inhibitors of protein-protein interactions (iPPIs), we took the X-Ray-based iPPIs dataset previously described in Kuenemann and colleagues  41  , which was composed of 85 protein-ligand complexes. Here, we collected the binding affinity data from the original sources and manually prepared each complex using the Protein Preparation Wizard from Maestro (Maestro, version 9.
7, Schrödinger, LLC, New York, NY, 2014). From the initial 85 iPPIs dataset, 25 complexes were removed due to their low resolution (value higher than 2. 5 Å), the presence of covalently bound ligands or absence of affinity data. The remaining 60 structures were suitable for training and testing the specific scoring functions for iPPIs (Table  1 ). S1 ). For the general scoring functions, the core set (N = 195) was extracted from the refined set, initially containing 2959 complexes. Thus, the random selection of complexes for the independent test and training sets was performed exclusively with the remaining 2764 complexes. The random 75% of the 2764 complexes used to train the general scoring functions is called "General::random" training set (N = 2073, Table  S1. In addition, we tested the influence of the training data set size on the predictive capacity for the general scoring functions.
Thus, we also trained general scoring functions using all the 2764 protein-ligand complexes (called here "General::all", Table  S1 ). In this case, the predictive performance was evaluated only on the v2013 core set (N = 195). For proteases, the training set was composed of 587 complexes and the test set was composed of 196 distinct complexes, not being used during the training step. Given the smaller size of the iPPI dataset, we characterized the composition of both training and test sets according to the protein families and the range of the binding affinity data (Table  1 ). Complexes of MDM2-like/P53 interacting with small ligands are the most frequent with 20 available structures, followed by complexes of Bromodomain4/Histone (11 complexes) and Blc2-like/BAX (10 complexes). Preparation of the structures.
Protein-ligand complexes of the v2013 refined set consist of the complete unit taken from Protein Data Bank (PDB)  42  (rcsb. org) and is available as prepared structures following an automatic procedure with some manual inspection performed by Li and colleagues  38. Originally, the protein-ligand complexes were prepared following a simple protonation scheme considering a neutral pH: (i) all carboxylic acid and phosphate groups were deprotonated, and (ii) all aliphatic amine, guanidine and amidine groups were protonated. As well known, the correct assignment of both protein and ligand protonation/tautomeric states is crucial for correct binding mode and affinity predictions, but is a very time-consuming task for a large number of ligands  [43] [44] [45]. In this work, we applied an improved protocol for the preparation of the structures of the v2013 refined set using the Protein Preparation Wizard from Maestro (Maestro, version 9.
7, Schrödinger, LLC, New York, NY, 2014). Protonation assignment and hydrogen-bond optimization were performed using ProtAssign and PROPKA  46  considering the presence of the bound ligand. Protonation and tautomeric states of the ligand were calculated using Epik 47 (Epik, version 2. 7, Schrödinger, LLC, New York, NY, 2014). Metal ions were considered as cofactors, and all waters were removed from the structures. Finally, energy minimization was performed to optimize the hydrogen atoms positions. A special attention was paid for the preparation of the core set due to its importance for the benchmarking studies. The protonation/tautomeric states of the binding-site residues and the bound ligand of the core set were further visually inspected and appropriate corrections were made guided by the original reference corresponding to the respective crystallographic structure and the Protoss program  48.
This strategy reduces the risk of overfitting on the training set and is controlled by the complexity parameter C, which is user-defined together with ε. Random Forests (RF) were introduced by Breiman in 2001 as a powerful strategy for ensemble learning  33. The RF combines several random trees (numTrees) in a bagging ensemble model, often leading to excellent results in diverse classification problems  33, 62. The output variable of a RF model is usually an average value of the predictions of the regression trees (as used in this work), where the node splitting is performed using a finite subset of features randomly chosen (numFeatures). All the machine-learning procedures were carried out using the Weka v3. 8. 3 package  30. We explored diverse configurations of SMOReg and RF on a tenfold cross-validation procedure.