dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequences and Phylogenetic Analyses Bioedit 7. 2. 5 software  [29]  and ClustalW (version 1. 83)  [30]  were used to compare and align nucleotide sequences of the complete genomes of seven Moroccan H9N2 isolates. The phylogenetic tree was constructed by the maximum likelihood method, using the Mega 6. 06 software  [31]. The Blast  [32]  and Bioedit programs  [30]  were used to determine the sequence identity and compare the Moroccan strains with those selected from Genbank.
Molecular Characterization and Phylogenetic Analysis of the Eight Viral Segments Viruses from highly positive samples were isolated. The genome of 7 of them was fully sequenced with an IlluminaMiSeq System  [28]. Phylogenetic analysis showed that our Moroccan H9N2 viruses isolated from chickens were in the same cluster as the other Moroccan viruses detected in 2016, and grouped into G1 lineage. They were compared with relevant virus sequences available in GenBank. Based on HA and NA phylogenetic trees, our Moroccan viruses were closely related to viruses previously isolated in the Emirates (2015), Morocco (2016), Burkina Faso (2017), and Algeria (2017), with bootstrap values of 100 and 60 for HA and NA, respectively (Figure  1 ). Regarding the internal genes, they grouped with the Moroccan viruses of 2016-2017, Algerian viruses of 2017, and Ghana viruses of 2017-2018 (Figure  A1 ).
L I Q N AS13 T L I Q N AS14 A L I Q N AS29 V L I Q N AS32 A L I Q N AS71 A L I Q N AS76 A Q I Q N AS77 A Q I Q N HA, hemagglutinin; RBS, receptor-binding site. * H3 numbering; ** GenBank accession number: SCA48100. Among mutations associated to resistance to antiviral molecules, no changes from SF1 strain were identified for NA H274Y substitution  [37]  or M2 S31N mutation  [38]  (Table  3 ).
Acknowledgments We would like to thank  Ms Vanessa Veron  and  Estelle Verhille  ( Centre de Ressources Biologie-Tumorothèque ICO, Saint-Herblain, FR ) for their help in the preparation of the TMA sections. We also thank  Ms Manon Taupin  ( Research Pathology Platform, ICO, Saint-Herblain, FR ) for her advices in immunohistochemsitry. This was supported by an internal ICO funding.
The dataset was collected using a Krios Titan transmission electron microscope (Thermo Fisher Scientific) equipped with an XFEG at 300 kV and CS-corrector using the automated data-collection software EPU (Thermo Fisher Scientific). 4 images per hole with defocus range of -0. 5 --2. 5 µm were collected with K3 detector (Gatan) operated in super resolution mode. Image stacks with 60 frames were collected with the total exposure time of 4 sec and total dose of 84. 9 e -/Å 2. 12284 images were acquired and 9840 of them were used for processing. Motion correction and CTF estimation have been performed in CTFFIND4  63  and MotionCorr2 64 during image acquisition using TranSPHIRE 65. The filament picking was performed using crYOLO 66. On the next step, 2. 22 million helical segments were classified in 2D using ISAC  67  to remove erroneous picks.
The dataset was collected using a Krios Titan transmission electron microscope (Thermo Fisher Scientific) equipped with an XFEG at 300 kV and CS-corrector using the automated data-collection software EPU (Thermo Fisher Scientific). 4 images per hole with defocus range of -0. 5 --2. 5 µm were collected with K3 detector (Gatan) operated in super resolution mode. Image stacks with 60 frames were collected with the total exposure time of 4 sec and total dose of 82. 3 e -/Å 2. 5342 images were acquired and 2758 of them were used for processing. Motion correction and CTF estimation have been performed in CTFFIND4  63  and MotionCorr2 64 during image acquisition using TranSPHIRE 65. The filament picking was performed using crYOLO  66. On the next step, 2. 51 million helical segments were classified in 2D using ISAC  67  to remove erroneous picks.
by NMR in solution, revealing a long, flexible N-terminus (residues 1 -101, Fig S2) and an ART domain (TcART) spanning residues 102 -281 (Fig 1b). TcART comprises eight antiparallel β-strands (Fig 1c) of which the perpendicularly oriented triads β1/3/8 and β2/7/6 are connected by two hydrogen bonds between Y112 in β1 and A195 at the end of β2 as anchoring points. These essential β-sheet segments follow the overall organization of the conserved ART domain fold 23 (Fig 1c, Fig. S3), including conservation of the two characteristic Y112-A195 hydrogen bonds whose equivalents are present in most ART structures (see e. g. PDB codes 1GIQ, 5ZJ5, 4Z9D, 4TLV, 1PTO, 1WFX, 6E3A, 6RO0).
Acknowledgements We thank  O. Hofnagel  and  D. Prumbaum  for assistance with data collection and maintaining the EM facility,  F. Merino  for help in building atomic models for the cryo-EM structures,  S. Bergbrede  for the excellent technical assistance,  D. Friedrich  and  P. Schmieder  for help in NMR measurements,  W. Linke  and  A. Unger  for providing us with muscle acetone powder,  S. Rospert  for providing us with anti-RPS9 serum,  N. Duclert-Savatier  for technical help on consensus calculations, and  S. Pospich ,  Y. Belyi ,  R. S. Goody  for fruitful discussions. This work has been funded by the  Max Planck Society  (S. R. ). A. B. was supported by an  EMBO  long-term fellowship and a stipend of the Humboldt foundation. H. O. acknowledges funding by the  German Research Foundation (DFG) , grant Os  106/17-1.
Data Availability The coordinates for the cryo-EM structures of the TcART-F-actin complex and ADPR-F-actin have been deposited in the Electron Microscopy Data Bank under accession numbers EMD-14532 and 14533. The corresponding molecular models for TcHVR, ADPR-F-actin, and the TcART-F-actin complex have been deposited at the wwPDB with accession codes PDB 7ZBQ, 7Z7H and 7Z7I. TcHVR chemical shifts and peak lists were deposited at the BMRB, accession code 34717. The raw data generated during the current study are available from the corresponding authors on reasonable request. Individual data points from the graphs at Fig 2b,.
Fungal material The strain E224 of Cophinforma mamane (D. E. Gardner) A. J. L. Phillips & A. Alves was isolated from the fresh leaves of Bixa orellana L. collected in November 2013 by C. Amasifuen and M. Haddad, from the National Reserve of Allpahuayo Mishana in the Amazonian forest of Loreto, Peru (GPS coordinates: 3 58'2. 3'' S, 73 25'3. 9'' W). The isolation, identification and storage processes followed have been previously described  (Triastuti et al. , 2019; Barakat et al. , 2019). Briefly, the sterilized tissues of the leaves were placed on a Petri dish containing MEA and chloramphenicol (100 mg/L). The ITS sequence is available in the GenBank database under the accession number MG457709. 1. The cryotubes are preserved at À80 C in 30% glycerol in the fungal collection of the UMR 152 laboratory.
Data processing LC-MS data from each experiment (epigenetic modifiers and amino acids) were separately processed with MS-DIAL version 4. 7  (Tsugawa et al. , 2015)  for mass signal extraction between 100 and 1500 Da from 0. 5 to 12. 5 min. MS1 and MS2 tolerances were set to 0. 01 and 0. 025 Da, respectively, in centroid mode for each dataset. Peaks were aligned on a QC reference file with a retention time (RT) tolerance of 0. 05 min and a mass tolerance of 0. 025 Da. Minimum peak height was set to 70% below the observed total ion chromatogram (TIC) baseline for blank samples. MS-DIAL data was cleaned with MS-CleanR workflow using default parameters  (Fraisier-Vannier et al. , 2020 ): all filters with a minimum blank ratio set to 0. 8, a maximum relative standard deviation (RSD) set to 30 and a relative mass defect (RMD) ranging from 50 to 3000.
The maximum mass difference for feature relationships detection was set to 0. 005 Da and maximum RT difference was set to 0. 025 min. The Pearson correlation links were considered with correlation !0. 8 and statistically significant a ¼ 0. 05. Two peaks were kept in each cluster: the most intense and the most connected to other ions. The data was normalized, first, by the Total ion chromatogram (TIC) using MS-DIAL and then re-normalized based on total peak area using MS-CleanR workflow. The kept features were annotated with MS-FINDER version 3. 5  (Tsugawa et al. , 2016). The MS1 and MS2 tolerances were set to 0. 005 and 0. 05 Da, respectively. Formula finder were processed with C, H, O, N, P and S atoms. Annotation was done with the use of internal generic databases from MS-FINDER: LipidMaps, YMDB, ChEBI, NPA, NANPDB, COCONUT, KNApsacK and UNPD.
Only annotations with a score greater than 5 were retained throughout the analysis while unannotated features are referred to as "unknown". Data were finally exported as. msp file for MSMS spectral information and. csv files for metadata information (peak area, retention times, annotation results, etc. ). The MSP and metadata files generated after the MS-CleanR workflow were imported into MetGem (version 1. 3. 4) (Olivon et al. , 2018). The data were filtered by removing all MS/MS peaks within ±17 Da of the precursor m/z. MS/MS spectra were window filtered by using the top 6 peaks in the ±50 Da window throughout the spectrum. MS/MS fragment ion tolerance was set to 0. 2 Da. A mass spectral similarity network was created where edges were filtered to have a cosine score above 0. 7 and at least six matched peaks.
Acknowledgements This work was supported by the  Institut de Recherche pour le D eveloppement of France (IRD)  through the  Allocations de Recherche pour une Th ese au Sud (ARTS)  grant awarded to R. P. The authors also thank the logistic support of  Pharma-Dev laboratory at Paul Sabatier University in Toulouse, France and LMI-LaVi laboratory (UPCH-IRD ) at  Cayetano Heredia University in Lima, Peru.
, 1998; Krizhevsky et al. , 2012). With respect to manual methods, ConvNet approaches can improve consistency, be less prone to errors and save a lot of time. While a few studies have shown the usefulness of ConvNet-based automatic SNc ROI segmentation approaches, they were tested on rather small datasets of PD patients by training the model using data augmentation techniques  (Krupička et al. , 2019; Le Berre et al. , 2019; Shinde et al. , 2019). Therefore, there is a need for a robust automatic SNc segmentation framework tested on a larger dataset of PD patients, involving fewer training data and no data augmentation that can still yield diagnostic performance similar to established ground truth, manual SNc segmentation performed by experts. In this study, we propose a framework, NigraNet, that facilitates fully automatic SNc segmentation by employing a ConvNet-based architecture based on a modified U-net  (Ronneberger et al.
Participants In this cross-sectional, prospective, observational, case-control study, patients with early-stage idiopathic PD and HV were recruited from May 2015 to March 2020 in the ICEBERG study (ClinicalTrials. gov Identifier: NCT02305147) at the Paris Brain Institute (ICM). The PD patients were included based on the UK Brain Bank Criteria made by a movement disorder specialist  (Hughes et al. , 1992). They were aged between 18 and 75 years with no/minimal cognitive disturbances (Mini-Mental State Examination > 24) and a disease duration of less than 4 years. The HV did not have any current or prior history of psychiatric or neurological disorders. The local ethics committee approved this study and all participants provided written informed consent (IRB of Paris VI, RCB 2014-A00725-42).
MRI data acquisition The participants were scanned using a 3 Tesla PRISMA scanner (Siemens, Erlangen, Germany) and a 64-channel receive-only head coil. The MRI protocol included whole brain three-dimensional T1-weighted (T1-w) imaging and axial turbo spin echo two-dimensional (2D) T1-w neuromelanin-sensitive imaging with a field of view restricted to the midbrain. Neuromelanin-sensitive images were carefully positioned in the axial plane perpendicular to the long axis of the brainstem and covering the locus coeruleus and the SNc. Whole brain T1-w images were acquired using a sagittal Magnetization Prepared 2 RApid Gradient Echo (MP2RAGE) with a 1-mm isovoxel size  (Marques et al. , 2009)  and neuromelanin-sensitive images were acquired with the following parameters: with TR/TE/flip angle: 890 ms/13 ms/180 • , 3 averages, voxel size: 0.
Image analysis Image analysis was performed in MATLAB (MathWorks Inc, MA, USA, vR2017b) using FSL (FMRIB, UK, v5. 0) and Statistical Parametric Mapping (SPM, UK, v12). Image preprocessing and postprocessing were performed using MRtrix3 (UK, v3. 0). The manual SNc and background ROIs were delineated using the FreeSurfer image viewer (MGH, USA, v5. 3. 0).
1  (Gaurav et al. , 2021). (ii) Automatic Segmentation Procedure: An image-processing pipeline was developed for automatic segmentation by implementing the DL model in Python 3. 6. 0 using Scikit-learn (v0. 21. 0) and Keras library (v2. 2. 4) with the TensorFlow framework (v1. 14) at the backend. The DL model was trained on NVIDIA-quadro P6000 (v418. 67) with CUDA 10. 1 and 24 GB GPU memory. (a) Data preprocessing: All the training images were resliced to a dimension of 240 × 240 × 48. The manually segmented SNc ROIs as input features were thresholded, binarized and normalized before feeding into our training network. (b) NigraNet architecture details: A complete schematic of the network architecture of NigraNet has been illustrated in Fig. 1. The network consisted of an encoder in the form of contracting path and a decoder in the form of expansive path without employing any fully connected layer.
Hence, from our pool of 199 manually segmented SNc ROIs of PD patients and HV, a random dataset of 60 images were randomly split using the shuffle function into 54 principal training images and 6 external validation images. (d) Data prediction: We ran the model to predict the SNc probability mask on the remaining 139 participants which were not used for training or validation. (e) Data postprocessing: With an aim to obtain the best segmentation performance, we binarized the predicted SNc masks by thresholding with a probability of 0. 5 so that we do not have over estimation of SNc. Then we employed the maskfilter function of the MRtrix3 package  (Tournier et al. , 2019)  in order to retain the largest connected component in the left and right SNc, yielding the final automatic ROI used in further analyses.
Quantitative analysis The quantitative measurements were calculated on both manual and automatic SNc ROIs using our in-house algorithm written in MATLAB. (Gaurav et al. , 2021)  SNc volume (Vol) was obtained as the number of voxels in the neuromelanin-based ROI in the image slices multiplied by the voxel size. The total intracranial volume (TIV) was estimated as the summation of white matter, grey matter and cerebrospinal fluid volumes derived from SPM12. Thereafter, we calculated the corrected SNc volume (C vol ) by dividing Vol by the TIV in order to normalize for individual variations in head sizes. normalized signal intensity (NSI) and contrast-to-noise ratio (CNR) were calculated by dividing the mean signal in the SNc by the background ROI signal. In order to correct for slice-to-slice intensity variations in the 2D TSE acquisition, and for putting the SNc signal measurements on a scale that is comparable across participants and over time, we computed normalized signal intensity (NSI) and contrast-to-noise ratio (CNR) for each slice normalizing the mean signal in SNc relative to the background signal using the following formulas  (Chougar et al.
Statistical analyses Statistical analyses were performed using R (R Core Team 2019, v3. 6. 1) and MATLAB. We performed the between-group comparisons of demographic and clinical variables using the parametric Student's t-test whereas the inter-group differences in sex distribution were assessed using the Chi-square test. Parametric tests were performed as the imaging data were normally distributed according to the Shapiro-Wilk normality test. For both automatic and manual methods, we tested the between-group difference in Vol, C vol , NSI and CNR using a one-way general linear model (GLM) -analysis of covariance (ANCOVA) by considering the group (PD and HV) as the between-group factor with both age and sex as covariates. Diagnostic performance to classify individuals as PD or HV based on SNc neuromelanin measures was assessed using receiver operating characteristic (ROC) analysis.
Imaging results (Table 1) The automatic segmentation prediction took less than a second for a single participant on a modern GPU (Fig. 1 ). Quantitative analysis: For both automatic and manual methods, we found a highly significant difference in all SNc measurements of Vol, C vol , NSI and CNR between PD and HV (p < 0. 001 for GLM-ANCOVA and post-hoc t-tests, Fig. 2 ). For automatic and manual methods, respectively, we observed a percentage difference of -27. 4 % and -17. 7 % for SNc Vol, -30. 0 % and -22. 2 % for C vol , -15. 8 % and -14. 4 % for NSI, -17. 1 % and -16. 0 % for CNR in PD compared with HV (Table  1 ). ROC analyses: To discriminate PD patients from HV in the training dataset (n = 60), the ROC analyses provided areas under the curve (AUC) of 0. 96 and 0. 85 for Vol, 0. 95 and 0.
88 for C vol , 0. 85 and 0. 85 for NSI and 0. 85 and 0. 85 for CNR for automatic and manual methods respectively (Fig. 3 ). Overall, in the entire dataset comprising both training and testing data (n = 199), AUC was 0. 83 and 0. 74 for Vol, 0. 85 and 0. 76 for C vol , 0. 79 and 0. 78 for NSI and 0. 77 and 0. 76 for CNR for automatic and manual method respectively. Effect of sex: Overall, there was a highly significant effect of sex for both C vol and CNR measurements using both the automatic and manual methods (p < 0. 001 for GLM-ANCOVA). Further separate post-hoc analysis demonstrated that both groups had significantly larger C vol in females than in males (p < 0. 001 for t-test). In addition, the female patients also showed significantly larger CNR than the male patients using both the methods (p < 0.
Discussion This study demonstrated a DL-based robust, fast, fully automatic segmentation model of the SNc using neuromelanin-sensitive MRI. The diagnostic performance of the neuromelanin-based volume and signal intensity using the NigraNet automatic method showed comparable results with the manual method while demonstrating superior performance in C vol measurements. These measurements were correlated clinically with disease duration for NSI, and LEDD for both NSI and CNR. The NigraNet framework comprised a simple network architecture employing dilated convolutional layers that was tested on a large database of early-stage idiopathic PD patients rather than advancedstage PD patients as in the previous studies (Le  Berre et al. , 2019; Shinde et al. , 2019). It demonstrated reliable predictions despite training on a small number of neuromelanin-sensitive images.
AI-based models for MRI segmentations Previous studies using DL-based SNc segmentation included relatively smaller datasets of 36 PD patients,  (Krupička et al. , 2019)  45 PD patients  (Le Berre et al. , 2019)  or 60 PD patients  (Shinde et al. , 2019). This study using  ConvNet(Le Berre et al. , 2019)  obtained a percentage of Vol reduction in PD compared with HV of 46. 4 % using automatic method and 42. 8 % using the manual method, and AUC of 0. 95 for both automatic and manual measurements. In our present study, PD patients had a lower percentage Vol decrease and AUC relative to HV than the aforementioned studies, likely because we examined early PD patients. Studies also reported better performance of SNc segmentation using a U-net model compared to threshold segmentation  (Krupička et al.
, 2019; Le Berre et al. , 2019)  for discriminating patients from HV, particularly in the superior segmented SNc slices, with a Vol reduction of 26. 2 % between HV and PD for all slices, which was in line with ours  (Krupička et al. , 2019). More studies utilizing inferior, middle and superior SNc slices separately are warranted to confirm these results  (Krupička et al. , 2019). The study  (Le Berre et al. , 2019)  examined patients with more advanced PD with disease duration of 10. 0 ± 6. 2 years in the principal dataset and 15. 8 ± 13. 3 years in the external dataset. Another study  (Shinde et al. , 2019)  included patients with moderately advanced PD with disease duration of 6. 3 ± 4. 1 years where ConvNet showed better performance than the radiomics based classifiers. Although this study  (Shinde et al.
Conclusion We presented a framework NigraNet utilizing small MRI training dataset to fully automatize the SNc segmentation procedure by eliminating the human factor as much as possible in order to process more data in a rather shorter time with an increased precision and more reproducible results. We showed comparable performance with ground truth for detecting neuromelanin-based volume and signal changes in the SNc. Considering the consistency, accuracy and speed of our approach, this study could be a crucial step towards the implementation of a time-saving, non-rater-dependent, fully automatic method for studying neuromelanin changes in clinical settings and large-scale neuroimaging studies.
Funding sources This work was supported by grants from  DHOS-Inserm, France Parkinson ,  Ecole des NeuroSciences de Paris (ENP) ,  Fondation pour la Recherche Médicale (FRM) , and the  Investissements d'Avenir ,  IAIHU-06  ( Paris Institute of Neurosciences -IHU ),  ANR-11-INBS-0006 ,  Fondation d'Entreprise EDF, BIOGEN Inc. ,  Fondation Thérèse  and  René Planiol ,  Fondation Saint-Michel ,  Unrestricted support for Research on Parkinson's disease from Energipole  ( M. Mallart ) and  Société Française de Médecine Esthétique (M Legrand ).
Linear parameter α To provide an example of the NanOx capacity to model local lethal events, we computed the linear parameter α for one illustrative cell line: normal lung fibroblast (V79) cells from a Chinese hamster, irradiated with hydrogen, helium, carbon, neon and argon ions of various energies. The calculation was performed by tuning the ELLF, F(z) (see Eq. 8), on the basis of a representative dataset consisting of 15 experimental α values via the Migrad minimization algorithm  [24]  implemented in ROOT  [25]. The representative dataset included the following radiations: photons, protons (2. 6 and 7. 7 MeV), helium ions (2. 9 and 9. 2 MeV/u), carbon ions (12, 28. ELLF consists in obtaining the set of parameters h, z 0 , σ that minimize the χ 2 , computed as: χ 2 Nα k 1 tN,t k α c -tN,t k α c, rep SE k tN,t k α c, rep ⎡ ⎢ ⎣ ⎤ ⎥ ⎦ 2 , (44) where N α is the number of α values in the representative dataset; the ( tN,tk α) c are computed with Eq.
26; SE k ( tN,tk α) c, rep is the standard error (set to 30% for all cases  [15] ) associated with the ( tN,t k α) c, rep values that are derived from the representative dataset through the following iterative procedure: the first step consists in converting the α values in the representative dataset ( t k α rep ) into representative mean cell survival probabilities to a single particle impact ( t k S 1,rep ) with the following equation  [14] : t k α rep Σ a• t k LET 1 -t k S 1,rep (45) The second step consists in using the mean cell survival probability S 1 to a single particle impact as defined in the formalism of NanOx: S 1 tN,t k α c c k P c k ( ) • exp -n p 1 tN,t k α c × tN,c k S 1,G , (46) where tN,c k S 1,G is the cell survival probability to global events with a single particle impact (and the configuration c k ).