dataesr/scientific-paragraphs-categorization huggingface dataset:
MOLeCULAR STUDieS AnD MODe OF inHeRiTAnCe In total, mutational studies of 25 candidate genes identified 58 different mutations as detailed in Table  1. Among them, 30 were novel (53. 57%) with no records in three major databases including HGMD (The Human Gene Mutation Database), LOVD (Leiden Open Variation Database), and IDbases (databases for immunodeficiency-causing variations). Three mutations have been already reported for the first time in other patients from Tunisian origin  (17) (18) (19)  and nine mutations were recurrent. The identified mutations include 21 missense mutations, 10 nonsense mutations, 10 splice-site mutations, 12 deletions, 1 duplication, and 1 insertion. Interesting and rare mutational mechanisms included one complex mutation (2 pb insertion and 5 bp deletion) in IGHM gene  (20) , and one de novo STAT1 mutation was identified in AD chronic mucocutaneous candidiasis disease  (21).
Knottins, or inhibitor cystine knots (ICKs), are ultrastable miniproteins with multiple applications in drug design and medical imaging. These widespread and functionally diverse proteins are characterized by the presence of three interwoven disulfide bridges in their structure, which form a unique pseudoknot. Since 2004, the KNOTTIN database (www. dsimb. inserm. fr/KNOTTIN/) has been gathering standardized information about knottin sequences, structures, functions and evolution. The website also provides access to bibliographic data and to computational tools that have been specifically developed for ICKs. Here, we present a major upgrade of our database, both in terms of data content and user interface. In addition to the new features, this article describes how KNOTTIN has seen its size multiplied over the past ten years (since its last publication), notably with the recent inclusion of predicted ICKs structures.
Since its launch in 2004, our KNOTTIN database (4) concentrates sequences, structures and bibliographical data about ICKs, except the few proteins belonging to the growth factor cystine-knots group. Data about cyclotides can also be found in the more specialized database CyBase  (5, 6). Our knottin-dedicated database is valuable given the specific properties of these miniproteins in terms of sequence, structure and function. Indeed, the knottins very low sequence identity between families and high sequence plasticity (except for the cysteines) require specific procedures to correctly identify and classify these proteins. In the same way, their structural pseudoknot, very low content of regular secondary structures and particular protein hy-drophobic core formed by disulfide bonds, require adapted 2D and 3D representation.
The whole diversity of knottins functions (such as neurotransmitters, analgesics, anthelmintics, anti-erectile dysfunction, antimalarials, antimicrobials, antitumor agents, protease inhibitors, toxins and insecticides) also has to be properly represented and documented, by gathering relevant functional annotations and bibliographic data. Finally, the active community of researchers working on the applied and theoretical aspects of knottins needs easy access to softwares dedicated to the analysis of knottins, which is provided by the computational tools available on our KNOTTIN website. Here, we present an upgraded version of KNOTTIN (www. dsimb. inserm. fr/ KNOTTIN/), 10 years after its last publication  (7). This report describes the new features of the database, the way it has evolved and shown usefulness over the past decade, and future developments.
Website navigation The content of the KNOTTIN database can be browsed with the horizontal navigation bar of the web interface. The 'Experimental 3D structures' and 'Sequences & 3D models' menus give access to experimental and theoretical models, respectively. In these pages, users can select one or several proteins, to visualize either their aligned sequences or 3D structures. The latter can also be done under the 'Sequence alignments' menu, which displays pre-compiled multi-sequence alignment files. In each of these three menus, the knottins are grouped by families, which have been determined based on sequence similarity and biological activity. Each knottin of the database is also categorized based on the length of its loops between the knot-forming cysteines (i. e. five loops for the knottins, and six for the cyclotides).
Querying the system The database can also be searched. Under the 'Sequence search' menu, amino acids sequences can be searched with the BLAST algorithm. The theoretical and experimental models of KNOTTIN can also be searched, under the 'Conformation search' menu, based on different structural features, such as torsion angles, secondary structure content and solvent accessibility. Sequences and structures can also be accessed via the 'Information search' menu, which offers users the possibility of searching the database by using different criteria, such as family, keywords, crystallography techniques or the aforementioned knottins nomenclature. The database also gathers the literature about the knottin proteins, which can be accessed under the 'Article search' menu based on criteria, such as articles authors, publication date and keywords.
Specific tools Besides the database, the KNOTTIN website is also a platform that regroups under the 'Tools' menu softwares dedicated to the analysis of knottin proteins. These computational methods are Knoter1D and Knoter3D  (7) , which are aimed at identifying knottins based on their 1D sequences and 3D structures, respectively. The third section of the 'Tools' menu is a portal to our Knoter1D3D web server  (8)  for the prediction of knottin 3D structures based on their sequence. The KNOTTIN web site also provides access to statistics about the database content, citations and web traffic. General information about knottins and the database usage are also available for users. Finally, the 'Links' menu contains hyperlinks to knottinrelated web resources--such as the Cyclotide Webpage (www. cyclotide.
PRACTICAL USE Over the past decade, KNOTTIN has been cited by multiple articles and reviews. In most of the cases, our database is cited either as a source of information on knottins, or when introducing knottin proteins and their structural characteristics, various functions, or their presence in a wide range of species. Numerous citations of KNOTTIN are also related the families of knottins defined in the database, to which authors refer when identifying or quantifying knottins of interest. This shows that our database, throughout the years, has successfully served as a useful overview on the field of inhibitor cystine knots. In addition to the data stored in KNOTTIN, the computational tools available on the website have also been cited, in particular Knoter1D and Knoter3D (for example in  (12) (13) (14) ).
NEW FEATURES Since its launch, the KNOTTIN database has seen its size grow (from 385 sequences in 2004, to 3320 in 2017, and from 85 to 214 natives structures), as the number of available sequences in UniProt and native structures in the PDB increased. However, the amount of experimentally determined structures of knottins remains relatively limited compared to the number of sequences, due to the difficulties related to the purification and crystallography of these proteins. This lack of available structures is a critical concern regarding the knottin-based drug design, which mainly lies on the study of 3D structures. To overcome this issue, one of the main features of this upgraded version of KNOTTIN is the systematic and automatic modeling and inclusion in the database of theoretical models produced with our aforementioned Knoter1D3D tool.
This addition of predicted structures for every knottin sequence has increased by one order of magnitude the size of our database in terms of Flowchart describing how the KNOTTIN database is generated. The Knoter1D and Knoter3D processes have been defined in the previous release of KNOTTIN  (7) ; the Knoter1D3D process is also described in our previous work  (8). The UniProt data are automatically extracted, by using a Perl script, from the corresponding UniProt web pages. structures, with currently contains >3000 theoretical models. In details, these theoretical models constitute a valuable source of structural data, especially for knottin families for which there is no experimental structure available, such as the bacterial knottins. The prediction of knottin structures has been integrated as a step in the pipeline for generating the data of KNOT-TIN (Figure  1 ).
When a protein sequence from UniProt is identified as a knottin by Knoter1D, it is used as an input for Knoter1D3D to produce theoretical models, which will be added to the database--along with related UniProt data (such as, sequence, descriptor, species, tissue, authors, PMID, keywords) and a multi-sequence alignment with other knottins of the same family computed with ClustalW  (16). The rest of the KNOTTIN pipeline concerns the detection and the subsequent addition of native knottin structures to the database. Thus, when a knottin structure is identified in the PDB by Knoter3D, the coordinate file is 'standardized': each residue is renumbered so that the knotted cysteines (except Cys IV) correspond to the positions 20, 40, 60, 80 and 100; the coordinate file is also reoriented by superimposition of the knotted cysteines onto those of the structure of the squash seed trypsin inhibitor (PDB code: 2btcI).
These coordinate files are added to the database, along with data regarding structural properties (such as torsion angles, secondary structures, solvent accessibility) computed with STRIDE (17) and PDBgeo (described in  (18) ). It should be noted that predicted structures of knottins can also be found in other databases of protein theoretical models (such as SWISS-MODEL  (19)  and Mod-Base (  20 )), but these data are do not match the models from KNOTTIN, neither quantitatively nor qualitatively. Indeed, according to the Protein Model Portal (www. proteinmodelportal. org), which contains models from SWISS-MODEL and ModBase, there are only 468 predicted structures of knottins in these two databases. The much greater number of models in KNOTTIN is explained by the fact that our Knoter1D3D comparative modeling procedure can accurately predict knottin structures when the template-to-sequence identity is as low as 10% (8), which is rather common among knottins.
Moreover, our use of a modeling method optimized for knottins necessarily improves the quality of the theoretical models, compared to those from other databases generated with regular comparative modeling procedures, which are not adapted to the particular structural features of knottins. This new version of the KNOTTIN database also comes with several technical improvements. The whole web interface has been entirely redesigned with the aim of being more user-friendly and compatible with modern devices. Notably, natives and predicted structures can now be visualized on the website thanks to the JavaScript-based molecular viewer JSmol  (21) , which therefore does not require users to have Java installed. By default, structures are displayed as a 'cartoon' representations, with each knottins loops colored differently.
The numbering of the cysteines I to VI is displayed, and the disulfide bonds ('SS') are represented as 'balls and sticks' and colored differently, depending on whether they are knotted or not. The regular secondary structure elements are hidden by default, but users have the possibility to change our graphical presets by right-clicking the viewer (Figure  2 )--as they would do with a local molecular viewer. This interactivity of JSmol also allows saving the session with the current parameters, as well as exporting the molecule as an image. Finally, some other improvements have been brought to the KNOTTIN website, such as a page in the 'Statistics' menu dedicated to the articles that cite KNOTTIN, and an updated version of the multi-sequence alignments viewer Mview  (22). The possibility of downloading the whole database content (sequences, native and predicted structures, and multi-sequence alignments) as a compressed archive is also a new feature.
It has been implemented with the aim of being useful for researchers wanting to carry out statistical studies about knottins. These data can also be used as training or benchmark datasets in the development of new computational methods dedicated to knottin proteins. Under the same 'Data' menu, users can now contribute to the maintenance and update of the database, by proposing either a new protein sequence (optionally with additional information or coordinate file) or a published article about knottins. This new functionality is achieved through web forms that users can fill out; the input is then manually verified, before being integrated to the database.
CONCLUSIONS AND PERSPECTIVES This new version of the KNOTTIN website is distinct from the former by the updated content of its database, as well as by its new interface and the inclusion of new data types. The KNOTTIN database now contains more than 3000 sequences of knottins, and has greatly extended its reach with the addition of predicted structures for all of these sequences. To cope with the daily increase of the number of sequence in the UniProt database, future efforts will be put in the full automation of the update pipeline. Regarding the latest data, it is interesting to observed that, while D458 Nucleic Acids Research, 2018, Vol. 46, Database issue sequences have been found in animals, plants, fungi, bacteria and viruses, knottins are still absent from archaea, which converges with previous findings  (13).
However, these systems produce results of limited spectral performance because the high and  low energy datasets are overlapping and the possibilities for material decomposition are limited to an analysis of only two basis images  2, 3. In spectral photon-counting CT, information is derived from photon-counting detectors that discriminate photon energies of each detected photon  2, 8. Therefore, an important advantage used herein of SPCCT over dual-energy CT is that the detected x-rays can be divided into multiple different energy windows to distinguish multiple materials. Additionally one or more boundaries of the energy bins can be freely adjusted to match the K-edge of contrast media such as AuNP or to best match the pathology being imaged  2, 9, 10, 20, 30. Furthermore, it is generally expected that SPCCT imaging will have lower radiation doses than conventional CT  2, 31.
A photograph of the system is shown in Fig. 1B. Material decomposition and quantitative measurements. The material decomposition process was based on a method originally developed for dual-energy CT  43  that was extended to photon-counting CT  44  , which is independent from the conventional images. The conventional CT images and material decomposition images were derived from SPCCT data using the detected information of the transmitted spectrum in all energy bins. The material decomposition from the multi-bin photon-counting data included a maximum-likelihood based material decomposition of the attenuation into three material images, i. e. water, iodine and gold  45. The material decomposition process provided water, iodine and gold images in mg/ml, allowing measurements of the concentration of a specific material.
Image analysis. For the phantom study, image analysis was performed using MATLAB (MathWorks. Inc. ). Samples were automatically detected on conventional images and circular regions of interest were automatically drawn in the middle of each tube. The same ROIs were used on all images generated on a given data set and mean and standard deviation were computed for each ROI. In vivo images were analyzed using ImageJ software  46. The attenuation values in Hounsfield Units (HU) and the concentrations of iodine and gold were recorded from a 2 mm slice, by manually drawing regions of interest (ROIs) of at least 50 pixels in all rabbits for selected organs (i. e. the left ventricle. right kidney. renal pelvis and spleen). The ROI's were defined on conventional CT images prior to retrieval of the gold and iodine concentrations per organ to avoid operator bias.
These ROIs were used later during the NF runs for computing the fMRI NF feature. During the active blocks of the MI_pre run, participants were asked to perform kMI of their right-hand. They were suggested to imagine their right-hand clenching by trying to recall the sensation they had in their right hand when actually executing the movement in the previous run. The goal of this run was for the participants to practice motor imagery. The data from this run was also intended to be used later for assessing the NF learning effect if any. During the active blocks of the NF runs (NF1, NF2, NF3), the screen displayed a white ball moving in the vertical (condition A), or horizontal (condition B) or both dimensions (condition C) and a square in the top-right corner of the screen representing the target to reach.
Data Acquisition/Technical Setup EEG and fMRI data were simultaneously acquired with a 64channel MR-compatible EEG solution from Brain Products (Brain Products GmbH, Gilching, Germany) and a 3T Verio Siemens scanner (VB17) with a 12-channel head coil. Foam pads were used to restrict head motion. EEG data was sampled at 5 kHz with FCz as the reference electrode and AFz as the ground electrode. fMRI acquisitions were performed using echoplanar imaging (EPI) with the following parameters: repetition time (TR)/echo time (TE) = 2000/23 ms, 210 × 210 mm 2 FOV, voxel size = 2 × 2 × 4 mm 3 , matrix size = 105 × 105, 32 slices, flip angle = 90 • ). Visual instructions and feedback were transmitted using the NordicNeurolab hardware and presented to the participant via an LCD screen and a rear-facing mirror fixed on the coil.
As a structural reference for the fMRI analysis, a high resolution 3D T1 MPRAGE sequence was acquired with the following parameters: TR/TI/TE = 1900/900/2. 26 ms, GRAPPA 2, 256×256 mm 2 FOV and 176 slabs, 1 × 1 × 1 mm 3 voxel size, flip angle = 9 •. Our multimodal EEG/fMRI-NF system  (Mano et al. , 2017 ) integrates EEG and fMRI data streams via a TCP/IP socket. The EEG data is pre-processed with BrainVision Review (Brain Products GmbH, Gilching, Germany) software for gradient and ballistocardiogram (BCG) artifact correction (see Section Real-Time Data Processing) and sent to Matlab (The MathWorks, Inc. , Natick, Massachussets, United States) for further processing. The fMRI data is pre-processed online for slice-time correction and motion correction with custom Matlab code adapted from SPM8 (FIL, Wellcome Trust Centre for Neuroimaging, UCL, London, UK).
Real-Time Data Processing Online gradient artifact correction and BCG correction of the EEG data were done in BrainVision Recview (Brain Products GmbH, Gilching, Germany) software. The gradient artifact correction in Recview is based on the average artifact subtraction method  (Allen et al. , 2000). We used an artifact subtraction template of 2,000 ms and four templates for template drift correction. The data was then down-sampled to 200 Hz and low pass filtered at 50 Hz (48 db slope) with a Butterworth filter. The data were subsequently corrected for BCG artifact  (Allen et al. , 1998). The pulse model was searched in the first 15 s of the data. The pulse detection was based on a moving template matching approach with minimal pulse period of 800 ms, minimum correlation threshold of 0. 7, and amplitude ratio range from 0.
6 to 1. 2 relative to the pulse model. For pulse correction, a moving template was computed by averaging the 10 previously detected pulses, and the correction was done on a window length of [-100 ms, 700 ms] relatively to the R-peak. This corrected data was then sent to Matlab. Every 125 ms the EEG laterality index was computed according to the following equation: eeg lat (t) = nLbp (t) -nRbp(t) nLbp (t) + nRbp(t) (1) Where nLbp (t) [respectively, nRbp (t)] is the normalized band power in the µ (8-12 Hz) band at the left motor electrode C1 (respectively at the right motor electrode C2) at time t. We defined nLbp and nRbp so that they would be higher than 1 when a desynchronization happened at the corresponding electrode: The fMRI signal was pre-processed online for motion correction, slice-time correction and then the fMRI laterality index was computed according to the following definition  (Chiew et al.
fMRI Data Analysis The fMRI data from each of the six runs (MLOC, MI_pre, NF1, NF2, NF3, MI_post) was pre-processed and analyzed with AutoMRI  (Maumet, 2013) , a proprietary software for fMRI analysis automation based on SPM8. Pre-processing included slice-time correction, spatial realignment, co-registration to the 3D T1, followed by spatial smoothing with a 8 mm Gaussian kernel. A first-level and second-level general linear model (GLM) analysis was performed. The first-level GLM included the canonical HRF for the task as well as its temporal and dispersion derivatives. For the second-level GLM analysis, the individual data were normalized to the Montreal Neurological Institute (MNI) template and grouped using a mixed effects linear model. The activations maps were corrected for multiple comparisons using Family-Wise error (FWE) correction (p < 0.
EEG Data Analysis For offline analysis, EEG signal was pre-processed using BrainVision Analyzer II software: data was cleared from gradient and CB artifact using the artifact subtraction method  (Allen et al. , 2000) , down-sampled to 200 Hz, filtered between 8 and 30 Hz using a Butterworth zero phase filter (48 db slope), segmented in 1 s segments, and segments affected by motion were removed. The EEG offline laterality index was then computed from this offline cleaned data in Matlab. For each of the three NF conditions (A: EEG-NF, B: fMRI-NF, C: EEG-fMRI-NF), we performed a repeated measure ANOVA of the averaged offline EEG laterality index between the three experimental conditions (A, B, and C) and paired t-tests of the averaged offline EEG laterality index. The NF blocks were averaged by considering the values between the first and the nineteenth second of the block.
FUNDING This work has received a French government support granted to the  CominLabs excellence laboratory  and managed by the  National Research Agency  in the " Investing for the Future" program  under reference  ANR-10-LABX-07-01. It was also financed by  Brittany region  under  HEMISFER  project, and the  National Research Agency  with the  REBEL  project and grant  ANR-15-CE23-0013-01. MRI data acquisition was supported by the  Neurinfo   MRI research facility  from the  University of Rennes I. Neurinfo  is granted by the  European Union (FEDER) , the  French State , the  Brittany Council, Rennes.
So, we now have an evidence that Fermat's little theorem not only has a proof in the Calculus of inductive constructions with universes, but also in constructive Simple type theory. Such a result was expected, but note that neither proving Fermat's little theorem in MATITA, that contains dependent products, inductive types, universes, etc. nor proving it in HOL LIGHT, that contains the excluded middle, extensionality, choice, etc. , provides a proof in such a weak theory. On the more practical side, this has permited to export this library from DEDUKTI to HOL LIGHT, ISABELLE/HOL, HOL4, COQ, etc. [36] , HOL LIGHT, ISABELLE/HOL, and HOL4 sharing a common input language: OPENTHEORY  [27]. The size of this library is 1. 5 Mo. It contains around 340 lemmas. It is checked in DEDUKTI in a few milliseconds.