dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioinformatics and phylogenetic analysis A dataset comprising Mycobacterium tuberculosis lineage 2 isolates with intact RD181 (n = 618) and one H37Rv isolate was retrieved from NCBI database (https://www. ncbi. nlm. nih. gov/sra) using SRA Toolkit v3. 0. 0 (https:// github. com/ncbi/sra-tools) and parallel-fastq-dump v0. 6. 7 (https://github. com/rvalieris/parallel-fastq-dump). Quality of downloaded FASTQ files was assessed with FastQC v0. 11. 9 (https://github. com/s-andrews/FastQC). These 618 genomes included 8 Russian genomes and 610 genomes from 23 other countries  [1, 15, [17] [18] [19] [20] [21]  (see Table  S1  with accession numbers). The TBvar v1. 1. 5 workflow (https://github. com/dbespiatykh/TBvar) was used for mapping and variant calling. In brief, FASTQ reads were mapped to the reference M.
tuberculosis H37Rv genome (RefSeq accession no. NC_000962. 3) using BWA MEM v0. 7. 17  [22]  algorithm. Mapped reads were sorted by coordinates, converted to BAM format and indexed using SAMtools v1. 16. 1  [23]. Subsequently, duplicate reads were removed with Sambamba v1. 0  [24]. Mapping quality was assessed with SAMtools stats and mosdepth v0. 3. 3  [25]. All the following variant calling steps were performed with GATK4 v4. 3. 0. 0 package  [26]. All reports were aggregated with MultiQC v1. 10. 1  [27]. Variants effects were annotated with SIFT4G v19. 0. 2  [28]  and SnpEff v5. 1d  [29]. Lineages from called SNPs were assigned with TbLG v0. 1. 5 (https://github. com/dbespiatykh/tblg). TB-Profiler v4. 4. 2 was used to discover resistance mutations and for spoligotyping  [30]. To construct the phylogenies, the SNP alignment was extracted from the tab-delimited output of GATK VariantsToTable.
Repetitive regions were excluded using a mask from a previously published study (available at https://github. com/mbhall88/head_ to_head_pipeline/blob/master/analysis/baseline_variants/resources/compass-mask. bed) [31]. Recombinant regions from the SNP alignment were filtered out using Gubbins v3. 2. 1  [32]. The resulting alignment was cleaned with SNP-sites v2. 5. 1  [33]. Maximum likelihood (ML) phylogeny was inferred from 619 sequences with 16 220 nucleotide sites using IQ-TREE 2 v2. 2. 0. 3  [34]. Support values were inferred from 1 000 ultrafast bootstrap replicates (UFBoot  [35] ) with the "-bnni" argument and from 1 000 replicates for Shimodaira-Hasegawa (SH) approximate likelihood ratio test with the "-altr" argument. Bestfit model was determined by ModelFinder  [36]  with the "-m MFP" argument, best-fit model according to Bayesian information criterion (BIC) was K3Pu + F + ASC + R7.
M. tuberculosis H37Rv1998 (SRR20082811) was used as an outgroup. ML phylogeny was visualized with the ggtree v3. 7. 1. 002 [37] , ggtreeExtra v1. 4. 2  [38] , ggplot2 v3. 3. 6 (https://ggplot2-book. org/), ggstar v1. 0. 4 (https:// github. com/xiangpin/ggstar), ggplotify v0. 1. 0 (https:// github. com/GuangchuangYu/ggplotify), ggnewscale v0. 4. 7 (https://github. com/eliocamp/ggnewscale), ran-domcoloR v1. 1. 0. 1 (https://github. com/ronammar/ randomcoloR), and tidytree v0. 4. 2 (https://github. com/ YuLab-SMU/tidytree) packages for R v4. 1. 2 [39]. To construct minimum spanning tree (MST) SNP distance matrix was created using Seqtk v1. 3-r106 (https://github. com/lh3/seqtk) and snp-dists v0. 8. 2 (https://github. com/ tseemann/snp-dists). MST tree was inferred and visualized using ape v5.
7  [40] , igraph v1. 4. 1 (https://github. com/igraph/rigraph), ggnetwork v0. 5. 12 (https://github. com/briatte/ggnetwork), and ggplot2 v3. 4. 1 R packages. The NGS data (fastq files) were used for in silico spoligotyping using SpoTyping program  [41]. For the enrichment analysis Clusters of Orthologous Genes (COG) categories were annotated using eggNOGmapper v2. 1. 9 [42] , gene ontology (GO) categories with PANNZER tool  [43]  using Positive Predictive Value (PPV) cutoff of 0. 5, KEGG pathways were annotated using BioServices v1. 11. 2  [44]  Python library. Additionally, functional categories from the TubercuList database were also tested for enrichment  [45]. All enrichment analyses were performed in R using Fisher's exact test. The significance of amino acid substitutions was assessed using PAM1 (Point Accepted Mutation 1) values calculated by PhyResSE online tool.
mutations were found only in isolates of the Beijing 14717-15 cluster. These two phylogenetic SNPs of the Beijing 14717-15 cluster were screened for specificity in the proprietary Beijing global genome databases (> 6000 genomes [Dr. Joao Perdigao, Universidade de Lisboa, Portugal, and > 10,000 genomes [Dr Egor Shitikov, Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine, Russia]). As a result, these two SNPs were found robustly specific and unique for the Beijing 14717-15-cluster isolates.
Data Availability The data that support the findings of this study are presented in Supplementary Table  S1  that provides a complete list of accession numbers for all genomes used in this study. In particular, the genomes sequenced in our laboratory were deposited in NCBI Short Read Archive under accession numbers: SRR18591745, SRR18591744, SRR18591743, SRR18591742, SRR18591741, SRR7796667, SRR7796668.
2/ Materials and methods 2. 1 Selection criteria of the population and control group Medically-treated seizure-free MTLE-HS patients were identified from thirteen French tertiary epilepsy centres. Selection criterion were: 1) adult patients with well-defined MTLE, assessed by experimented epileptologist on medical and EEG data, 2) hippocampal sclerosis on structural brain MRI (characterized by a T1 hippocampal atrophy and a T2 hypersignal, visually assessed by a neuroradiologist, and approved by the epileptologist), 3) MTLE evolving for at least 5 years and 4) seizure freedom under medical treatment (including focal seizures without impaired awareness) for at least 2 years. The clinical and demographic data of these patients were retrospectively reviewed from medical charts. These selection criterions took into account two possibilities of drug response already described in MTLE-HS patients: 1) a delayed pharmacoresistance during the first years of evolution 9,10 and 2) alternating periods of seizure-freedom and seizure recurrence  3.
Recorded variables Sociodemographic data such as age, sex, level of education, profession, marital status were collected. Education levels were classified by referring to high school graduation (High school graduation or lower, one to 3 years of higher education, more than 3 years of higher education). Medical history included age at the onset of epilepsy, family history of epilepsy, psychiatric comorbidity (mood disorders, anxiety and psychosis), and the occurrence of an initial precipitating injury (meningitis, encephalitis, head trauma, febrile convulsion [both simple and prolonged]). We collected data on the semiology of seizures. We categorized auras according to their semiological description into seven categories: abdominal, experiential, autonomic, psycho-affective, visual, sensory and non specific.
For the medically-treated seizure-free group, we also collected the time to achieve seizure freedom, the response to the first ASM, current medication and the number of seizure-free years at the last evaluation. Semiological data for medically-treated seizure-free MTLE-HS patients were collected mainly from medical charts and in 13 cases from video-EEG recordings. Neuropsychological data were not available for the medically-treated seizure free MTLE-HS patients as they did not undergo a presurgical evaluation. We did not collect the standard 20-min EEG data that are not considered as a performant biomarker of seizure control in the literature. 1, 14, 15.
Therefore, we minimized the risk of error when categorizing a patient as "Seizure-free" by selecting a positive predictive value higher than 0. 9, to reduce the probability of inappropriately delay referral to pre-surgical assessment in patient misclassified as "seizurefree". With respect to the group balance, dataset was split in a 80%-set used to find the best algorithm, which was then applied on the testing set (20%), i. e. on patients independent from the classifier construction, in order to evaluate its performances. The process to select the best algorithm using repeated 3-fold cross-validation is detailed in the Supplementary Materials.
Comparisons between medically-treated seizure-free and drug-resistant MTLE-HS patients The proportion of missing data in the overall database was 11. 8% in both groups (mostly employment and marital status data). Sixty-four medically-treated seizure-free MTLE-HS patients from 13 French tertiary epilepsy centres and 200 drug-resistant MTLE-HS patients were included. Comparisons between medically-treated seizure-free and drug-resistant MTLE-HS patients are shown in Table  1.
Figures legends -Tutorial: How to download and run our classifier? -Get to https://github. com/mhouot/MTLE-HS_algorithm -You can download the all data and classifier or just run the app to test a patient. -Testing a patient without R download on: https://mhouot. shinyapps. io/mtle-hs_algorithm/ -To get the algorithm on your computer: -Download the free software R and R studio and install it on your computer from https://cran. r-project. org. -Then, you need to install the shiny package in R: Copy and paste in the R console if (!require('shiny')) install. packages("shiny") -To run the application, you have to load the library and use the function runGitHub(). Copy and paste in the R console: library(shiny) shiny::runGitHub("MTLE-HS_algorithm", "mhouot") -Choose between the two tabs: ACCEPTED MANUSCRIPT / CLEAN COPY.
If ratified, the now accepted taxonomic changes will be published online in the MSL and Virus Metadata Resource (VMR) (see below). At all stages of the process, each TaxoProp is posted publicly for comment (https://ictv. global/files/proposals/pending); ratified TaxoProps are archived at https:// ictv. global/files/proposals/approved. TaxoProp templates and instructions can be downloaded from https://ictv. global/taxonomy/ templates.
RANK TaxoProps take the form of Microsoft Word documents summarizing the evidence for the proposed taxonomic changes or additions, and Excel spreadsheets itemizing any new taxa, as well as changes to existing taxonomic names or their place in the taxonomic hierarchy. TaxoProps must propose taxon names, provide supporting evidence for proposed taxonomic placements, and in the case of new species taxa, must provide nucleotide sequence information via GenBank accession numbers of an exemplar virus isolate. All TaxoProps must be based on the availability of at least one near-complete genome sequence of an exemplar virus. The ICTV does not classify viruses characterized only by partial genome sequences. Particular care must be taken with the accuracy of the Excel spreadsheets, because the process of correcting even an incorrectly spelled taxon name can be lengthy and may require submission of a corrected TaxoProp.
ICTV REsouRCEs master species List and Virus metadata Resource The ICTV maintains a list of all virus taxa and their history, called the Master Species List (MSL). The MSL is updated each year following the ratification vote and is available from the ICTV website (https://ictv. global/msl). A complementary list, called the Virus Metadata Resource (VMR), provides information about at least one virus assigned to each species taxon, including the virus name, an accession number for all or part of the virus genome, the nature of the virus genome (DNA or RNA, single-or double-stranded and positive-or negative-sense), and the natural host (plant/animal/fungi/protist/bacteria or archaea), if known. This means that, by searching the VMR, it is possible to link virus names to the corresponding species and higher taxa and to link virus species taxa with the genome sequence of an exemplar virus (https://ictv.
global/help/vmr/ find_species_name). The VMR is updated several times a year and is available as a downloadable spreadsheet (https://ictv. global/vmr). The MSL and VMR also form the basis of the virus section of the National Center for Biotechnology Information (NCBI) Taxonomy Database  [19]. However, implementation of the new taxonomy releases into the NCBI database is not automatic, and short delays are expected each year. Thus, the most recent MSL and VMR databases should be used to confirm virus taxonomic assignments.
This work was also supported in part through the  Laulima Government Solutions, LLC , prime contract with the  NIH NIAID , under Contract No. HHSN272201800013C. J. H. K. performed this work as an employee of Tunnell Government Services (TGS), a subcontractor of Laulima Government Solutions, LLC, under Contract No. HHSN272201800013C. A. R. M. is a Program Director at the  U. S. National Science Foundation (NSF) ; the statements and opinions expressed herein are made in a personal capacity and do not constitute endorsement by  NSF  or the government of the United States. H. M. O. was supported by the  University of Helsinki and Academy of Finland  funding for FINStruct, part of  Biocenter Finland and Instruct-ERIC. D. L. R. is funded by the  MRC  ( MC_UU_12014/12 ). S. S. acknowledges support from the  Mississippi Agricultural and Forestry Experiment Station (MAFES) ,  USDA-ARS  project  58-6066-9-033  and the  National Institute of Food and Agriculture, US Department of Agriculture, Hatch Project , under Accession Number  1021 494.
, 2013) : SeaWiFS, MODIS, MERIS, and VIIRS. PHYSAT-Med retrieves the dominant group for a given satellite image pixel (4 km for the Mediterranean Sea) by identifying which phytoplankton group is the major contributor to the radiance anomaly. In other words, it associates a given phytoplankton type with its main bio-optical signature, while taking into account biases induced by other optically-active components, such as dust deposition events and rivers discharge. From this high-resolution database (about 6,600 daily images), 10-day and monthly maps of dominant phytoplankton groups were obtained by calculating the phytoplankton group that was dominant during the integration period (10-day or monthly, respectively) at each geographical pixel, not including "unidentified" pixels. The regional PHYSAT-Med product was validated by comparing the outputs of the algorithm with more than 5,000 in situ measurements analyzed by high-performance liquid chromatography (HPLC)  (Navarro et al.
Acknowledgments IH-C acknowledges financial support from the project  TRITOP  (Grant  UIB2021-PD06 ) funded by  University of the Balearic Islands  and by  FEDER(EU). AO thanks financial support from Projects  LAMARCA  ( PID2021-123352OB-C31 ) funded by  MICIN/AEI/10. 13039/501100011033/ FEDER ,  UE  and  Tech2Coast  ( TED2021-130949B-I00 ) funded by  MCIN/ AEI/10. 13039/501100011033  and  BY EU  ' NextGenerationEU/PRTR '. This work has been partially done in the framework of the  AEI  accreditation " Maria de Maeztu Centre of Excellence " given to  IMEDEA  ( CSIC-UIB)  ( CEX2021-001198 ). AB acknowledges support from the  National Science Foundation  (Grant  OCE-1658174 ). This study is based upon work from the  CSIC Interdisciplinary Thematic Platform Teledeteccion (PTI-TELEDETECT)  members.
Data Availability Statement Absolute geostrophic velocity data are available on the CMEMS web platform https://data. marine. copernicus. eu/products, https://doi. org/10. 48670/moi-00141. PHYSAT-Med data are referenced at  Navarro et al. , 2017  and are available at https://nimbus. imedea. uib-csic. es/index. php/s/6w6YzWBpTmWD8BW. The code to compute the particle trajectories and the Finite-Time Lagrangian diagnostics are available at https://zenodo. org/ record/7705122, https://doi. org/10. 5281/zenodo. 7705122 (Hernandez-Carrasco, 2023) with Creative Commons Attribution 4. 0 International licence. Figures are plotted using Matlab v2021 software available under licence at https://es. mathworks. com/academia/tah-portal/uib-31521075. html.
The first study presented using scRNAseq in asthma identified a novel mucous ciliated cell state, and dominance of type 2 helper T (Th2) cell signalling  [58]. Further work utilising scRNAseq showed heterogeneity within Th2 cell populations and identified a subset of pathogenic IL-9 expressing Th2 cells that was increased in allergic asthmatic individuals compared to allergic individuals without asthma  [59]. Strikingly, postallergen challenge, Th2 cells were only present in the airways in asthma, and airway epithelial cells demonstrated a dramatically altered transcriptional response in subjects with asthma but not in those with allergy alone  [60]. The online resource Human Lung Cell Atlas integrates multiple scRNAseq respiratory system datasets, facilitating disease comparisons at the single cell level with the potential to identify novel targets for intervention  [61].
Using machine learning, nasal fluid protein signatures were mapped to transcriptomic datasets, identifying subsets of severe asthmatics  [66]. Further, differentially expressed gene/protein pathway analysis in this study revealed potential novel therapeutic targets. Digital spatial profiling (DSP) is a complementary technique that adds a crucial layer of information, linking transcriptomics and proteomics to imaging. Dr Francesca Polverino (Houston, United States) described how spatial omics is a cutting-edge tool that allows structural navigation of the lung by digitally selecting regions of interest  [67]. Identification of gene and protein enrichments within a specific spatial context using the same input material can predict pathologies associated with specific lung regions. The first DSP study in COPD demonstrated that the immune checkpoint, PD-L1, was spatially clustered with protein markers of activated T cells, as well as genes involved in cancer progression.
Next, Dr Nahal Mansouri (Lausanne, Switzerland) focussed on the role of basophils in regulating tumours, combining multi-level analyses of scRNAseq with complex laboratory models enabled the exploration of the role of understudied basophils and their interaction with regulatory T cells. Clinically relevant interventions (antihistamines) disturbed the interactions between basophils/Tregs, promoting tumour progression in mice. This work highlighted a surprising role of cell-to-cell crosstalk that directly impacted the risk of metastasis in humans. Dr Amanda Oliver (Cambridge, United Kingdom) shared a recent integrated cell atlas of healthy and diseased lungs  [73]. The value of this database was demonstrated by combining scRNAseq and spatial transcriptomics to reveal novel circuits of cell communication between epithelial cells and CD4 + T cells.
Metabolite identification in untargeted metabolomics is complex, with the risk of false positive annotations. This work aims to use machine learning to successively predict the retention time (Rt) and the collision cross-section (CCS) of an open-access database to accelerate the interpretation of metabolomic results. Standards of metabolites were tested using liquid chromatography coupled with high-resolution mass spectrometry. In CCSBase and QSRR predictor machine learning models, experimental results were used to generate predicted CCS and Rt of the Human Metabolome Database. From 542 standards, 266 and 301 compounds were detected in positive and negative electrospray ionization mode, respectively, corresponding to 380 different metabolites. CCS and Rt were then predicted using machine learning tools for almost 114,000 metabolites.
R 2 score of the linear regression between predicted and measured data achieved 0. 938 and 0. 898 for CCS and Rt, respectively, demonstrating the models' reliability. A CCS and Rt index filter of mean error ± 2 standard deviations could remove most misidentifications. Its application to data generated from a toxicology study on tobacco cigarettes reduced hits by 76%. Regarding the volume of data produced by metabolomics, the practical workflow provided allows for the implementation of valuable large-scale databases to improve the biological interpretation of metabolomics data.
A confident and definitive identification (level 1) is hit when two or more orthogonal properties fit with data from authentic standard compounds in identical analytical settings. When the latter are unavailable, a comparison of experimental data with public libraries could lead to a putative compound annotation (level 2) or class annotation (level 3). Finally, unknown features discriminated with spectral data are classified at the lowest confidence level (level 4). Therefore, feature annotation is achieved by comparing experimental measurements to existing in-house or external databases of known metabolites to generate potential candidates  [10]. Several commercial or open-source databases containing spectral data in libraries (Human Metabolome Database (HMDB)  [11] , Metlin  [12]. ) were developed and are continuously updated by the scientific community.
However, confident and unequivocal structure identification could quickly be an issue when a candidate is not found (limited number of spectra) or when several candidates are proposed (false positives), increasing the probability of misidentification  [13, 14]. Therefore, it becomes important to use other readily obtained physicochemical properties for better metabolite identification. Ion mobility-mass spectrometry (IMS-MS) is a fast two-dimensional separation of ions based on their mobility in a buffer gas. Importantly, this mobility is structure-dependent and is not affected by equipment or experimental factors (matrix effects, variations in mobile phase composition, and chromatography settings, ionization mode, acquisition settings. ), unlike retention time (Rt) and mass spectrum  [15] , resulting in a high degree of repeatability and therefore facilitating database queries  [16].
The physical property measured in IMS-MS is the collision cross-section (CCS). Thus, it provides the orthogonal separation to improve signal-to-noise, resolution, and isomeric metabolite separation  [17] , participating in the reduction of misidentification. However, the favorable contribution of CCS is currently limited by the poor availability of CCS reference values  [18] [19] [20] [21] [22]. New experimental and computational approaches to predict those parameters for a large number of compounds is highly valuable. Several studies have developed, or applied machine learning-based prediction approaches  [23]. Softwares like AllCCS  [22] , CCS Predictor  [24] , DeepCCS  [25] , MetCCS Predictor  [26] , or LipidCCS predictor  [27]  can efficiently generate a model when molecular descriptors are provided  [15].
Rt is often decisive in feature annotation but usually relies on the availability of authentic chemical standards that are applied to experimental conditions. In untargeted metabolomics, the transferability of Rt database between laboratories is not achievable because of the absence of standardized assays across different laboratories. Multiple machine learning models for the prediction of Rt have already been described, including quantitative structure-retention relationship (QSRR) models  [31] [32] [33] [34] [35]. QSRR strategies have been used to accelerate the method development process by comparing predicted separation with different columns  [36]  or to enhance the confidence of identifications  [37]. Software packages, such as the QSRR Automator  [38] , exist to automate Rt prediction model creation.