dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of candidate genes (CGs) and Enrichment analysis of CGs Twenty-three candidate genes (CGs) were identified by using the VennDiagram package (Figure  5 ). Then R software was used to perform GO and KEGG pathway analysis of the CGs. GO of CGs was analyzed based on BP, CC, MF. 1215 GO terms were significantly enriched (P<0. 05), 1148 in BP, 28 in CC, 39 in MF. Top 20 terms were shown in Figure  6. The data of top 20 GO analysis were listed in Table  5. Based on these GO terms data, we found that most significantly terms were response to lipopolysaccharide, response to molecule of bacterial origin, membrane raft, membrane microdomain, BH domain binding and death domain binding, suggested that SFJDC could treat NCP with multiple synergies. The pathways that were significantly affected by SFJDC in the process of treating NCP were identified by KEGG pathway.
Licochalcone a, a flavonoid extracted from licorice toot, was known for its anti-inflammatory, anti-cancer, anti-oxidative and anti-bacterial bioactivity  [39]. Acacetin, a flavone compound, played an important role in antiinflammatory and anti-peroxidative  [40]. In addition, they have high OB and acacetin from 2 herbs (PR, IR) of SFJDC. The three main ingredients were anti-inflammatory and COVID-19 caused by a series of inflammatory storms. Hence, they might be the crucial effective compounds of SFJDC according the network. PPI network of SFJDC against NCP were visualized using Cytoscape software to obtain the candidate target genes. In order to obtain the more accurate genes, two parameters including DC and BC were used to screen nodes and structure a new network. 169 genes were eventually identified for SFJDC against NCP including 156 other human genes and 13 target genes.
IFIH1. We identified 74 individuals from 51 families segregating a total of 27 likely pathogenic mutations in IFIH1. Ten adult individuals, 13. 5% of all mutation carriers, were clinically asymptomatic (with seven of these aged over 50 years). All mutations were associated with enhanced type I interferon signaling, including six variants (22%) which were predicted as benign according to multiple in silico pathogenicity programs. The identified mutations cluster close to the ATP binding region of the protein. These data confirm variable expression and nonpenetrance as important characteristics of the IFIH1 genotype, a consistent association with enhanced type I interferon signaling, and a common mutational mechanism involving increased RNA binding affinity or decreased efficiency of ATP hydrolysis and filament disassembly rate.
, 2015). Although it was initially considered that SMS was a distinct, mutation-specific disorder, subsequent reports indicate that SMS and the neuroinflammatory phenotypes seen in the context of IFIH1 gain-of-function constitute part of the same disease spectrum  (Buers, Rice, Crow, & Rutsch, 2017; Bursztejn et al. , 2015). Type I interferonopathy associated IFIH1 mutations are either absent from control databases, or only present at very low frequency. However, we have noted previously that in silico algorithms are not always reliable in differentiating IFIH1 disease-causing variants from benign polymorphisms  (Ruaud et al. , 2018). Such difficulty in assigning molecular pathogenicity is compounded by marked variability in disease expression, sometimes even within the same family, and the observation of complete non-penetrance in certain pedigrees  (Rice et al.
, 2014). Given this background, we considered it important to provide an update of our experience of sequencing individuals for pathogenic IFIH1 mutations associated with a type I interferonopathy state. In total, we describe molecular and clinical data relating to 74 individuals from 51 families, identifying 27 likely pathogenic mutations that cluster close to the ATP binding region of the protein. Our data confirm variable expression and nonpenetrance as important characteristics of these mutant genotypes, and the consistent association with enhanced type I interferon signaling as assessed by interferon-stimulated gene (ISG) expression, referred to as the interferon score.
| Subjects Patients were ascertained through direct contact and/or collaborating physicians across clinical research laboratories in the UK and France  (Crow) , the USA (Vanderver), and Italy (Orcesi). n. 20170035275 of 23/10/2017). Amino acid substitutions were considered as pathogenic mutations when they were seen in the context of a neuroimmune/autoinflammatory state (including AGS, a spastic-dystonic syndrome, nonsyndromic spastic paraparesis or SMS), and when two or more of the following applied: observation of the same variant in an unrelated family; de novo occurrence; documented increase in ISG expression; in vitro data consistent with IFIH1 gain-of-function.
| Mutational analysis Mutations were identified on a variety of next-generation sequencing platforms. Where Sanger sequencing was undertaken, primers were designed to amplify the coding exons of IFH1, with mutation annotation based on the reference cDNA sequence NM_022168. 2. Variants were assessed using the in silico programs SIFT (http://sift. jcvi. org), Polyphen2 (http://genetics. bwh. harvard. edu/pph2/), and CADD (https://cadd. gs. washington. edu), summarized in VarCards (http://varcards. biols. ac. cn/). Population allele frequencies were obtained from the gnomAD database (http://gnomad. broadinstitute. org).
| Molecular data We collected data on 74 individuals from 51 families, identifying 27 distinct mutations in total (Figure  1 ; Table  1 ). Fourteen mutations were recorded in a single proband, seven in more than one individual belonging to a single-family, and six in more than one family. Of these six recurrent mutations, the p. Arg720Gln, p. Arg779Cys, and p. Arg779His substitutions were observed most frequently (6, 8, and 10 times, respectively). Twentytwo mutations were recorded to have occurred de novo in at least one individual, whilst four mutations were only ascertained in familial cases demonstrating autosomal dominant transmission (two mutations, p. Ala489Thr and p. Gly495Arg, were transmitted from a father in whom the mutation arose de novo). Three mutations, p. Thr331Arg, p. Arg779Cys, and p.
| DISCUSSION Here we present data on 74 individuals, 41 previously unreported, from 51 families, with a putative gain-of-function mutation in IFIH1. Consistent with previous descriptions, we observed a spectrum of phenotypes, encompassing AGS, spastic-dystonia, spastic paraparesis, SMS and clinical nonpenetrance. Phenotypic variability was common, both in the context of familial inheritance and mutations seen recurrently across families so that no obvious genotype-phenotype correlations could be ascertained. Acute regression was noted in almost one-third of symptomatic mutation carriers, occurring after the age of 1 year in seven patients demonstrating completely normal development to that time. Beyond acute regression, a slower onset of disease, and subsequent progression, was seen in patients demonstrating a spastic paraparesis phenotype.
The study was approved by the Leeds (East) Research Ethics Committee (10/H1307/132), the Comité de Protection des Personnes (ID-RCB/EUDRACT: 2014-A01017-40), IRB study protocol (Myelin Disorders Bioregistry Project: IRB# 14-011236) and the local ethics committee of the IRCCS Mondino Foundation, Pavia, Italy (3549/2009 of 30/9/2009 and 11/12/2009;.
ACKNOWLEDGMENTS Yanick J. Crow  acknowledges The  University of Maryland Brain  and  Tissue Bank  of the  NIH NeuroBioBank. Yanick J. Crow  acknowledges the  European Research Council  ( 786142-E-T1IFNs ), a state subsidy managed by the  National Research Agency (France)  under the  "Investments for the Future" program  bearing the reference  ANR-10-IAHU-01  and the MSDAvenir fund (DEVO- DECODE  Project). Tracy A. Briggs  acknowledges the  National Institute for Health Research (NIHR; NIHR Transitional Research Fellowship ,  TRF-2016-09-002 ; with the views expressed were those of the author and not necessarily those of the  NHS , the  NIHR  or the  Department of Health). Adeline L. Vanderver  is supported by the  Kamens endowed chair for Translational Neurotherapeutics  and the  Myelin Disorders Bioregistry Project.
Funding information Public Health Research Programme , Grant/Award Number:  TRF-2016-09-002 ;  National Center for Advancing Translational Sciences  of the  National Institutes of Health , Grant/Award Number:  KL2TR001879 ;  CURE Pennsylvania Frontiers in Leukodystrophy , Grant/Award Number:  U01HD082806 ;  H2020   European Research Council , Grant/Award Number:  786142-E-T1IFNs ;  Agence Nationale de la Recherche , Grant/Award Number:  ANR-10-IAHU-01 ; MSDAvenir fund, Grant/Award Number: DEVO- DECODE  Project;  NIHR Transitional Research Fellowship , Grant/Award Number:  TRF-2016-09-002.
, 2005; Riedel, Sagata, Suhardjono, Tänzler, & Balke, 2013; Smith et al. , 2008; Tänzler, Sagata, Surbakti, Balke, & Riedel, 2012). While this application was not the initial aim of DNA barcoding, it has been suggested that aside from specimen identification, biodiversity inventory may benefit from a universal molecular method of species delineation  (Hajibabaei, Singer, Hebert, & Hickey, 2007; Hebert & Gregory, 2005). At the core of all DNA barcoding initiatives lies the construction and validation of reference libraries that can be further used to assign unknown specimens to known species  (Hubert et al. , 2008; Ratnasingham & Hebert, 2007). However, reference libraries available in public repositories such as BOLD or GenBank can host a substantial portion of taxonomic misidentifications  (Bridge, Roberts, Spooner, & Panchal, 2003; Vilgalys, 2003)  leading to ambiguous identifications at the species level  (Ardura, Planes, & Garcia-Vazquez, 2013; Bortolus, 2008).
We further used this carefully crafted DNA barcode library for the assignment of 245 new DNA barcode records to the species level. In order to detect potential new OTUs or the impact of incomplete coverage of the coalescent trees of each OTUs, the four species delimitation methods were applied to the entire dataset consisting of 1,072 DNA barcodes. In case new OTUs were detected or OTU delimitation was revised, an updated reference library was build including representative sequences of each new OTU. A second specimen assignment analysis was further performed using this updated library. Using an iterative procedure of re-examination through species delimitation and specimen assignment methods on the whole dataset (1,072 sequences), we generated a fit-for-use reference library and quantified the impact of incomplete sampling on specimen assignment.
| Sampling, sequencing, and data repository A total of 245 specimens were collected by visiting fish markets and scuba diving using polespears or captured using various gears including seine nets, cast nets, and gill nets across 25 sites in the Indo-Australian Archipelago (23 sites in Indonesia and 2 sites in Papua New Guinea, Figure  1 ). Specimens were photographed and individually labeled, and voucher specimens were preserved in a 5% formalin solution or a 70% ethanol solution. A fin clip or a muscle biopsy was taken for each specimen and fixed in a 96% ethanol solution for further genetic analyses. Both tissues and voucher specimens were deposited in the National Collections at the Research Centre for Biology (RCB) and Research Centre for Oceanography (RCO) from the Indonesian Institute of Sciences (LIPI).
The PCR products were purified with ExoSap-IT ® (USB Corporation) and sequenced in both directions. Sequencing reactions were performed using the "BigDye ® Terminator v3. 1 Cycle Sequencing Ready Reaction," and sequencing was performed on an automatic sequencer ABI 3130 DNA Analyzer (Applied Biosystems). The sequences and collateral information (photographs, voucher collection number, and collection data) are publicly available in BOLD  (Ratnasingham & Hebert, 2007)  and are available in the projects BIFV and WPRFM (Table  S2 ) and as a dataset (https ://doi. org/10. 5883/ DS-BIFMU ). DNA sequences were submitted to GenBank; accession numbers are accessible directly at the individual records in BOLD.
1 to visually inspect genetic distances and DNA barcode clusters (Figure  S1 ). For the sake of clarity, species identified based on morphological characters are referred to as species while species delimited by DNA sequences are referred to operational taxonomic unit (OTU), defined as diagnosable molecular lineages  (Avise, 1989; Moritz, 1994; Vogler & DeSalle, 1994). OTUs were delimitated using four different algorithms:   2. 3 (Kapli et al. , 2017; Zhang et al. , 2013) , and (d) General Mixed Yule-Coalescent (GMYC) in its multiple rate version (mGMYC) as implemented in the R package Splits 1. 0-19  (Fujisawa & Barraclough, 2013). Both RESL and ABGD used DNA alignments as input, while a maximum likelihood (ML) tree was used for mPTP and a Bayesian chronogram was reconstructed for mGMYC based on a strict-clock model using a 1.
Three specimen assignment methods implemented in the R package BarcodingR version 1. 0. 2 (Zhang, Hao, Yang, & Shi, 2017)  in R 3. 4. 0 (R Core Team, 2017) were used to assign unknown specimen to known species: (a) the back-propagation neural networks method (BP), a machine learning approach inferring species membership using DNA sequence data based on a neural network algorithm  (Zhang, Sikes, Muster,& Li, 2008; Zhang & Savolainen, 2009) ; (b) the fuzzy set-based approach method (FZ), a distance method based on a K-nearest neighbor (KNN) search algorithm with a fuzzy membership function to estimate the probability of a query sequence belonging to the nearest neighbor reference DNA barcode sequence  (Zhang, Muster, et al. , 2012) ; and (c) the alignment-free kmer-based method (FZKMER), suitable for both coding and noncoding portions of the genomes using machine learning  (Zhang, Feng, et al.
probilities" also in a range of 0-1. For the sake of clarity, we will refer hereafter to both the bp. probabilities and the FMF values as "probabilities. " As the different methods can lead to conflicting results, a consensus has been established for the multiple methods using the consensus. identify function of the barcodingR package. We considered that a consensus emerged if at least two methods were converging. Finally, we computed the ratio of the intraspecific K2P genetic distance of a selected OTU to the nearest neighbor K2P genetic distance for each method to test whether the selected OTU was the least distant possible and spot potential false positives (i. e. , specimens incorrectly assigned). The delimitation and the assignment methods were used iteratively: Species delimitation methods were first applied to the 827 DNA barcode reference library, and specimen assignment methods applied to the 245 newly generated DNA barcodes.
A second round of species delimitation was performed on the whole dataset, that is, the 827 DNA barcode reference library and the 245 newly generated DNA barcodes (1,072 sequences), in order to check for the consistency of the delimitation schemes. We added one representative sequence of each new OTU retrieved and removed/added the corresponding sequences when OTUs were merged/split following this second iteration of delimitation to perform a second round of specimen assignment of the newly generated DNA barcodes. The comparison of the two rounds of species delimitations and specimen assignments also allowed us to appraise the impact of the taxonomic coverage of the reference library on the accuracy of the specimen identifications, that is, whether OTU attributed by specimen assignment methods corresponds to the OTU given by the species delimitation methods, and to evaluate the behavior of the probabilities associated with the true (correctly assigned) and false (erroneously assigned) positives.
| Second round of species delimitationrevising OTU delimitation by incorporating the new unknown specimens The second round of species delimitation applied to the joint dataset of 1,072 DNA barcodes yielded more OTUs in general with 113 using RESL, 161 using ABGD, 82 using mPTP, and 110 using mGMYC (vs. 105, 148, 70, and 120 for the first round; Figure  S1  and Table  S3 ) with a consensus consisting of 121 OTUs, that is eight additional OTUs (OTUs 96, 105, 116 to 121) compared to the first round of species delimitation. A few OTUs observed during the first round of delimitation were merged during the second round (OTUs 90 and 96, and OTUs 33 and 39). In addition, this second round of delimitation showed that the 245 unknown specimens belong to 27 different OTUs (Table  S4 ) corresponding to 12 known OTUs described in N G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G 0.
Yet, computing a consensus showed that a third of the false positives appeared as "ambiguous identification. " Moreover, probabilities associated with false positive were no larger than 0. 5 and 0. 75 for FZKMER and FZ, respectively, when all three methods converged toward the same identity. BP was the method generating most false positives among the three methods investigated in this study and can be considered as the least reliable for the present dataset. FZ method was the only method that did not display any false positives after the second round of species delimitation irrespective of whether the identification was supported by the two other methods or not. This result is in agreement with the recommendation of  Zhang et al. (2017)  to the use of the FZ method to avoid potential false positives in cases of incomplete taxon coverage in a reference library.
DATA AVA I L A B I L I T Y S TAT E M E N T These sequence and collateral data have been deposited in BOLD (projects BIFV and WPRFM) and are publicly available as a dataset (BIFMU, https ://doi. org/10. 5883/DS-BIFMU ). DNA sequences have also been submitted to GenBank; accession numbers are accessible directly at the individual records in BOLD. R script to perform the assignments used in the manuscript is available on GitHub https :// github. com/edelr ieutr ottin/ Assig ning_Grey_mullets and Zenodo.
Finally, and following labeling, cells were trypsinized and stained with DAPI for dead cell exclusion. Around 3*10 6 cells were sorted (BD FACSAria III; BD Biosciences), using the FACSDiva software or sterile sorted (BD Influx; BD Biosciences), using the BD FACS software. Four populations were defined according to the relative abundance of NCT-SiR and GFP-or mEOS3. 2-(green) PSEN1: P5 (NCT low/PSEN1 low), P6 (NCT high/PSEN1 low), P7 (NCT low/PSEN1 high) and P8 (NCT high/ PSEN1 high). The different populations were collected for subsequent culture and further validated using western blot analysis and confocal microscopy.
4 NA oil objective lens and an Andor iXon 885 EM-CCD camera. Image acquisition was done with five grid rotations and image reconstruction was made with ZEN software. For QuASIMoDOH analysis, widefield images were used, with one ROI per cell selected to exclude edges. Background was removed by thresholding by Mode  (Paparelli et al. , 2016). QuASIMoDOH was run in batch for all ROIs. Statistical analysis of the inhomogeneity parameter was done in GraphPrism. For nearest-neighbor analysis and density measurement, image channels were aligned using Tetraspeck beads as fiducials in the ImageJ plugin B-unwarpJ. The removal of structured artifacts was done by manually adjusting the intensity histogram. Masks of intensity peaks were made by the ImageJ plugin H-watershed. Coordinates of particle centroids were extracted and used in a costume-written MATLAB routine to calculate the nearest-neighbor distance.
Molecular Counting by Photon Statistics (CoPS) PM sheets were imaged in a confocal setup Microtime 200 (PicoQuant) using an inverted microscope (IX73, Olympus) equipped with an oil objective lens (UPlanSApo 100 Â 1. 4 NA, Olympus). A fibercoupled pulsed diode laser (LDH-D-C-485, PicoQuant) was used for 485 nm excitation at 10 MHz repetition rate. Fluorescence was separated from the excitation light by a dichroic mirror (Zt488/ 640rpc, Chroma) and split into four fractions by three 50:50 beam splitters (Beck Optronic Solutions Limited). Additional fluorescence filters (LP488, Semrock, and SP750, Chroma) were used in front of each of the four SPAD detectors (SPCM-AQRH-14-TR, Excelitas). Data were acquired with a timecorrelated single-photon counting unit (HydraHarp 400, PicoQuant). Images were further analyzed with costum-written MATLAB routines (The Mathworks Inc).
Photo-Activated Localization Microscopy (PALM) Intact cells stably rescued with mEOS3. 2-PSEN1 and PM sheets derived thereafter were used for PALM. Imaging was done in TIRF mode in a home-build wide-field setup with an inverted microscope equipped with Nikon Perfect Focus System, a 100 Â 1. 49 NA APO-TIRF objective lens, an EM-CCD camera (9100-23B; Hamamatsu) and a 1. 6x expansion lens (100 nm pixel size). During recording, the 405 nm activation laser intensity was manually adjusted to obtain the proper density of single-molecules per frame. Localizer software  (Dedecker and Neely, 2012)  was used for image reconstruction and extraction of coordinates. Cluster analysis by DBSCAN was done in qSR analysis platform  (Andrews, 2017)  with a 100 nm distance and a minimum of 10 localizations per cluster.
Images were done using objective Apo TIRF 100 X oil NA 1. 49 and a sensitive Evolve EMCCD camera (Photometrics, Tucson, USA). Photo-activation of mEOS3. 2 was done using the Ilas 2 scanner system and 405 nm laser diode. The 37˚C atmosphere was created with an incubator box and an air heating system (Life Imaging Services, Basel, Switzerland). This system was controlled by MetaMorph software (Molecular Devices, Sunnyvale, USA). Twenty cycles of 50 images at 0. 05 s per frame were taken with a 2. 5 s delay between each cycle. During SPT acquisitions, tracking based on wavelet segmentation were done using WaveTracer a MetaMorph (Molecular Devices, Sunnyvale, USA) add-on developed at the Interdisciplinary Institute of Neuroscience (IINS -UMR5297 -CNRS/University of Bordeaux)  (Kechkar et al. , 2013).
, 2007; Izeddin et al. , 2012; Kechkar et al. , 2013). The instantaneous diffusion coefficient was calculated for each trajectory from a linear fit of the first four points of the MSD. The type of motility was inferred by the range of the a coefficient given the MSD fit r 2 = t a +k  (Sibarita, 2014; Manzo and Garcia-Parajo, 2015). Immobile tracks (D 0. 01 mm 2 /s) were excluded, confined diffusion was defined as a <0. 1, anomalous as 0. 1 a 0. 9, Brownian as 0. 9 a 1. 1, and directed as a >1. 1  (Sibarita, 2014). The extraction of tracking data was done in MATLAB (The MathWorks, Inc).