dataesr/scientific-paragraphs-categorization huggingface dataset:
To this end, the accurate segmentation of new lesions is an essential prerequisite to quantifying lesional changes and measuring features, such as new lesion volumes and locations. However, manual delineation of such lesions is tedious, time-consuming, and expensive, especially because experts need to deal with 3D images and several modalities; therefore, accurate computer-assisted methods are needed to automatically perform this task. Longitudinal MS lesion segmentation, however, remains very challenging since MS images often change subtly over time within a patient, and new lesions can be very small although they vary dramatically in shape, structure, and location across patients. The MSSEG-2 MICCAI 2021 challenge  (Confavreux et al. , 1992; Vukusic et al. , 2020)  aims to develop effective datadriven algorithms for the segmentation of new MS lesions by providing a dataset of 40 pairs of 3D FLAIR images acquired at two different time points (with varying intervals) and registered in the intermediate space between the two time points.
Experiments All the models are implemented using PyTorch  (Paszke et al. , 2019)  and PyTorch Lighting  (Falcon, 2019)  frameworks and trained on NVIDIA P100 GPUs. We evaluate the performance of Pre-U-Net for MS lesion segmentation on the MSSEG-2 dataset. We follow the same training workflow in all the experiments. In the following, we first provide the details of this workflow, then present the evaluation protocol and the results.
Setup. Data A total of 40 and 60 MS patients are represented in the MSSEG-2 training and test set, respectively. For each patient, two longitudinal 3D FLAIR images are acquired at different time intervals (e. g. , 1 year, 3 years) and registered in the intermediate space between the two time points. New lesions that a patient developed between the two time points were manually delineated by multiple raters, and the consensus The third column indicates the median value of voxel size for each axis. The fourth column indicates the median number of voxels along each axis. ground truths were obtained through a voxel-wise majority voting (see Figure  1 ).
Inference A test image in the inference is first subjected to z-score intensity normalization and resampled to a voxel size of 1 mm 3. The prediction is then made using a sliding window approach with a 50% overlap and a window size of 128 × 128 × 128 (which is equal to the patch size used in training). For a given voxel from overlapping windows, the mean of the predictions is simply taken as the final value (the SlidingWindowInferer module from MONAI was used to perform the sliding window inference). The resulting probability map is resampled back to the original voxel size and finally thresholded by 0. 5 to obtain a binary segmentation map.
Conclusion We devised a U-Net-like architecture consisting of preactivation blocks, called Pre-U-Net, for longitudinal MS lesion segmentation. We successfully trained our models by using data augmentation and deep supervision, alleviating the problem of data insufficiency and class imbalance. The effectiveness of Pre-U-Net was evaluated in segmenting and detecting new white matter lesions in 3D FLAIR images on the MSSEG-2 dataset. Pre-U-Net achieved a Dice score of 40. 3% and F 1 score of 48. 1%, outperforming the baselines, U-Net and Res-U-Net. In particular, Pre-U-Net is, as reflected by F 1 scores, more effective than the baselines at detecting new lesions, and it is competitive with U-Net in terms of segmentation performance, as evidenced by Dice and HD scores.
Funding This research leading to these results has received funding from  EU   H2020 MSCA-ITN-2018 :  INtegrating Magnetic Resonance SPectroscopy and Multimodal Imaging for Research and Education in MEDicine (INSPiRE-MED ), funded by the  European Commission  under Grant Agreement # 813120. This research also received funding from the  Flemish Government  ( AI Research Program ). AI- KU Leuven Institute for AI, B-3000, Leuven, Belgium.
, 2020a). TLR7 is the only X-linked gene, the other 14 genes being autosomal. These are the only known loci in humans for which inborn errors have been reported that (a) are recessive, as opposed to dominant, (b) are biochemically complete (as opposed to partial deficiencies), and (c) impair type I IFN immunity. All reported causal variants at 13 of these 15 loci have a global Genome Aggregation Database (Gno-mAD, v2. 1) minor allele frequency (MAF) <10 -4 (IFIH1 and TLR3 being the exceptions, with causal variants having MAFs of ≤6. 7 × 10 -3 and 1. 7 × 10 -3 , respectively). The penetrance of most these genotypes for most associated viral diseases is incomplete. We thus tested the hypothesis that these 15 recessive inborn errors of type I IFN immunity may underlie moderate, severe, or critical COVID-19 in at least some children, who may not necessarily have suffered from other unusually severe viral illnesses before COVID-19.
1%) were considered to have been born to consanguineous parents, based on homozygosity rates >1%. The proportion of patients born to consanguineous parents was significantly higher than that in controls (Fisher's exact test, P = 3. 5 × 10 -8 ), but, in terms of absolute numbers, there were more controls (75) than cases (26) born to consanguineous parents. We analyzed the WES or WGS data for these individuals, checking for all 15 recessive inborn errors of type I IFN immunity. We then selected homozygous and potential compound heterozygous nonsynonymous and essential splice site variants at the 14 autosomal loci, and hemizygous variants at the TLR7 locus. We searched for very rare (MAF <10 -4 ) variants among the GnomAD nonsynonymous or splicesite variants with a combined annotation-dependent depletion score greater than the gene-specific mutation significance cutoff, for the 15 genes  (Itan et al.
, 2022). The 12 patients originated from 10 kindreds and four countries (Belgium, Russia, Iran, and Turkey; Table  1 ). Eight of the 12 patients were tested for auto-Abs against type I IFNs, and all were negative  (Bastard et al. , 2021a; Bastard et al. , 2020). We also identified one control with a hemizygous missense variant of TLR7 (H782D), another with a homozygous missense variant of IFIH1 (Q415K), and a third with a homozygous missense variant of IRF7 (L128M). These three controls originated from three different countries: Brazil, France, and Singapore (Table  1 ). We also screened our sick children for very rare (MAF <10 -4 according to gnomAD database) homozygous pLOF variants of the 337 genes known to underlie AR or XR IEIs  (Tangye et al. , 2020; Tangye et al. , 2021). We did not identify additional candidate recessive defects.
, 2021) , these five patients bring us to a total of 12 patients (10. 7%) in our pediatric COVID-19 cohort with recessive complete inborn errors of type I IFN immunity, including 9. 0% of boys for XR traits and 4. 5% of children with AR traits. As a means of obtaining a better estimate of this latter proportion, we screened for pairs of individuals with first-to third-degree relationships (kinship ≥0. 0442), using the appropriate option of King software  (Manichaikul et al. , 2010)  to estimate the pairwise relatedness between patients. We found two additional pairs of distant relatives in the patient cohort (besides P6/7 and P8/9), and 33 in the control cohort. after the exclusion of one sample per pair, we estimated the frequency of AR type I IFN IEI in our pediatric cohort at 9. 3% (10 of 108 unrelated patients).
Primary sequencing data were demultiplexed with the Illumina HAS2. 2 pipeline, and genotype as P2-P4 (homozygous for P216fs*14). SV40 fibroblasts from a patient with complete STAT2 deficiency (G522R/R506*) and another patient with complete IFNAR1 deficiency (homozygous V225fs) served as positive controls, whereas SV40 fibroblasts from two healthy donors (C1 and C2) served as negative controls. Experiment was done once with four technical replicates. Error bars indicate the SD of four technical repeats. (B) The fold-induction of ISGs (IFIT1, MX1, and IFI27) was determined by qPCR, with normalization against the housekeeping gene GUSB, and then untreated mock infection, with experiments performed in parallel with A. Experiments were repeated twice. Error bars indicate the SD of the repeats. sample-level quality control was performed for base quality, coverage, duplicates, and contamination (FREEMIX <0.
05 by VerifyBamID). We used the Genome Analysis Software Kit (GATK; v3. 4-46 or 4) best-practice pipeline to analyze our WES data  (DePristo et al. , 2011). We aligned the reads obtained with the human reference genome (hg19), using the maximum exact matches algorithm in the Burrows-Wheeler Aligner  (Li and Durbin, 2009). PCR duplicates were removed with Picard tools (http:// picard. sourceforge. net). The GATK base quality score recalibrator was applied to correct sequencing artifacts. Genotyping was performed with GATK GenotypeGVCFs in the interval intersecting all the capture kits ±50 bp. Sample genotypes with a coverage <8×, a genotype quality <20, or a ratio of reads for the least covered allele (reference or variant allele) over the total number of reads covering the position (minor read ratio) <20% were filtered out.
We filtered out variant sites that (a) fell in lowcomplexity or decoy regions, (b) were multiallelic with more than four alleles, (c) had >10% missing genotypes in our cohort, and (d) spanned >15 nucleotides. Variant effects were predicted with the Ensembl Variant Effect Predictor  (McLaren et al. , 2016)  and the Ensembl GRCh37. 75 reference database, retaining the most deleterious annotation obtained from Ensembl protein coding transcripts overlapping with RefSeq transcripts.
In Firth's regression, a penalty term is assigned to the standard maximum likelihood function used to estimate the parameters of a logistic regression model  (Firth, 1993 ). Firth's regression can handle genes for which there are no carriers among cases or controls. With no covariates, this corresponds to adding 0. 5 to every cell of a 2 × 2 table of allele counts vs. case-control status. We accounted for ethnic heterogeneity by including the first five principal components of the PCA in Firth's logistic regression model. Analyses were also adjusted for sex. We checked that our adjusted burden test was well calibrated by also analyzing the enrichment in rare (MAF <0. 0001) homozygous synonymous variants. We performed PCA with Plink v1. 9 software on WES and WGS data, with the 1000 Genomes Project phase 3 public database as a reference, using >15,000 exonic variants with MAF >0.
01 and call rate >0. 99. We estimated pairwise relatedness between patients and controls using the related option of King software  (Manichaikul et al. , 2010)  and screened for pairs of related individuals, up to the third degree (kinship ≥0. 0442). We also estimated the homozygosity rate of the patients and controls from the WES data as the proportion of the autosomal genome in runs of homozygosity  (Belkadi et al. , 2016). We identified runs of homozygosity with PLINK  (Purcell et al. , 2007)  using a 1,000-kb window and 50 single-nucleotide variations in the window. For this analysis, we used ∼167,000 single-nucleotide variations with a gnomAD frequency >0. 05. In vitro assays of STAT2 production and function HEK293T cells were used to seed 6-well plates and were transfected with pCMV-STAT2 WT/mutant variants for 24 h before stimulation with 10,000 U/ml IFN-α2a for 30 min (130-093-874, hIFNa2a; Miltenyi Biotec).
SARS-CoV-2 infection experiments The SARS-CoV-2 NYC isolate (GenBank accession no. OM345241) was obtained from a de-identified patient. The virus isolate was amplified through 6-7-d passages in Caco-2 cells at 37°C. After each passage, virus-containing supernatant was harvested, clarified by centrifugation (3,000 g for 10 min), and filtered through a disposable vacuum filter system with 0. 22-μm pores. The passage 3 stock, used in this study, had a titer of 3. 4 × 10 6 PFU/ml determined on Vero E6 cells with a 1% methylcellulose overlay, as described in a previous study  (Mendoza et al. , 2020). Human SV40 fibroblasts were stably transduced with pTRIP-CD271-2A-ACE2 and positively selected to obtain >90% CD271 + cells (130-099-023; Miltenyi Biotec). Cells were used to seed 96-well plates at a density of 7,000 cells per well, with or without 10 3 IU/ml IFN-α2b (Intron A; MSD).
Acknowledgments We thank the patients' families for agreeing to participate in our research. The  Laboratory of Human Genetics of Infectious Diseases  is supported by the  Howard Hughes Medical Institute ; the  Rockefeller University ; the  St. Giles Foundation ; the  National Institutes of Health (NIH ;  R01AI088364  and  R01AI63029 ); the  National Center for Advancing Translational Sciences ;  NIH   Clinical and Translational Science Award program  ( UL1 TR001866 ); a  Fast Grant from Emergent Ventures ; the  Mercatus Center at George Mason University ; the  Yale Center for Mendelian Genomics ; the  GSP Coordinating Center  funded by the  National Human Genome Research Institute  ( UM1HG006504  and  U24HG008956 ); the  Yale High Performance Computing Center  ( S10OD018521 ); the  Fisher Center for Alzheimer's Research Foundation ; the  JPB Foundation ; the  Meyer Foundation ; the  French National Research Agency (ANR)  under the  "Investments for the Future" program  ( ANR-10-IAHU-01 ); the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ); the  French Foundation for Medical Research  ( EQU201903007798 ); the  ANR GenMISC  ( ANR-21-COVR-039 )  ANRS-COV05 ,  ANR GENVIR  ( ANR-20-CE93-003 ), and  ANR AABIFNCOV  ( ANR-20-CO11-0001 ) projects; the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no.
Harold and Leila Y. Mathers Charitable Foundation ; the  Meyer Foundation ; and the  Bawd Foundation. J. Le Pen  was supported by the  Francois Wallace Monahan Postdoctoral Fellowship at The Rockefeller University  and the  European Molecular Biology Organization Long-Term Fellowship  ( ALTF 380-2018 ). P. Bastard was supported by the  MD-PhD program  of the  Imagine Institute  (with the support of the  Fondation Bettencourt-Schueller). H. C. Su  and  H. Jing  are supported by the  Intramural Research Program  of the  National Institute of Allergy and Infectious Diseases, NIH. G. Novelli  and  A. Novelli  from the  COVID Human Genetic Effort  are supported by  Regione Lazio  (Research Group Projects 2020) no. A0375-2020-36663 ,  GecoBiomark. I. Meyts  is a Senior Clinical Investigator at the  Research Foundation-Flanders  and is supported by the  CSL Behring Chair of Primary Immunodeficiencies , by the  CSL- Behring Research Grant , by the  KU Leuven   C1  grant  C16/18/007 , by a  VIB   GC PID grant , by the  FWO  grants  G0C8517N ,  G0B5120N , and  G0E8420N  and by the  Jeffrey Modell Foundation.
This project has received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 948959 ). This work is supported by  ERN-RITA. S. Delafontaine is supported by personal  FWO  grant  11F4421N. L. Renia and L. F. P. Ng were supported by the  Singapore National Medical Research Council COVID-19 Research Fund  ( COVID19RF-001 ;  COVID19RF-0008 ;  COVID19RF-060 ) and A*STAR COVID-19 research funding ( H/20/04/g1/006 ). The  Canarian Sequencing Hub  is funded by  Instituto de Salud Carlos III  ( COV20_01333  and  COV20_01334 ), the  Spanish Ministry of Science and Innovation  ( RTC-2017-6471-1 ;  AEI/FEDER, UE) , and  Cabildo Insular de Tenerife  ( CGIEU0000219140  and "Apuestas científicas del ITER para colaborar en la lucha contra la  COVID-19 ").
Acknowledgements The financial contributions of the  Agence Nationale de la Recherche  (projects MemPepSyn  14-CE34-0001-01 , InMembrane  15-CE11-0017-01 ,  Biosupramol 17-CE18-0033-3 , Naturalarsenal  19-AMRB-0004-02  and the  LabEx Chemistry of Complex Systems   10-LABX-0026_CSC ), the  University of Strasbourg , the  CNRS , the  Région Grand-Est  and the  RTRA International Center of Frontier Research in Chemistry  are gratefully acknowledged.
Statistical analysis For all experiments, the difference between 2 groups was calculated with the Mann-Whitney test. Kruskal-Wallis test with the original false discovery rate (FDR) method of Benjamini and Hochberg was used for multiple comparisons. For matching data, a mixed-effect analysis with the original FDR method of Benjamini and Hochberg was used. We used GraphPad Prism 8 (GraphPad Software, La Jolla, Calif). All graphs show mean and SEM.
*These authors contributed equally to this work. E. B. was supported by a fellowship from the  ''Fondation pour la Recherche M edicale''  (grant no. FDT201904008017 ). Work at  Institut Toulousain des Maladies Infectieuses et Inflammatoires  was supported by grants from  Soci et e Francaise d'Allergologie  (grant no. SFA2017 ), the  Agence Nationale de la Recherche  (grant no. ANR-18-CE15-0004-01  to J. -C. G. and grant no. ANR-20-CE15-0027-01  to S. L. ), and  Fondation Recherche M edicale  ( equipe labellis ee  DEQ20180339187 ). Disclosure of potential conflict of interest: The authors declare that they.
ACKNOWLEDGMENTS The authors are grateful to  Drs. Simone Conti  and  Aravinda Munasinghe  for helpful discussions. Computer resources were provided by  National Energy Resource Scientific Computing Center (NERSC) , which is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231 , and by  Oak Ridge Leadership Computing Facility , which is a  DOE Office of Science User Facility  supported under Contract  DE-AC05-00OR22725. Release Number  LLNL-JRNL-793877.
We also evaluated the cytotoxic activity on Vero cells of the most interesting biofilm inhibitors. In an effort to gain knowledge on the structure-activity relationship (SAR) of chromone carboxamides as potential anti-PqsR agents, we designed a series of direct chromone-CO-NH-R amides and retro chromone-NH-CO-R amides (series 1 and 2, Figure  1 ), bearing various substituents in terms of electronic effects, size, and physicochemical properties. The strategy of exchanging an amide for a retro-amide has been successfully used in the field of antimicrobial research with the identification of retro-amides with increased activity against C. albicans [21] and M. tuberculosis  [22]. However, despite the large amount of literature and data on chromone carboxamides, their retro-amide counterparts have not been extensively explored  [19].
Docking of Chromone 2-Carboxamides with PqsR We used the AutoDock Vina software to predict the affinity of target compounds for the PqsR protein  [24]. PqsR, also named MvfR (Multiple Virulence Factor Regulator), is a global activator controlling the expression of many virulence factors in P. aeruginosa  [13, 25, 26]. A monomeric unit of PqsR is composed of two subdomains connected by a βsheet region β4  [25]. The ligand-binding domain is a hydrophobic cavity inserted between helix α4 and sheets β4-5-7. The active site is composed of the two pockets A and B (Figure  3A ), mostly formed by aliphatic amino acid residues  [25, 27]. The superficial A pocket is located between sheets β5-7 while the deep B pocket is buried in the hinge region of PqsR, between the two subdomains. ) and in vitro anti-biofilm activity data for direct chromone carboxamides (3′a-i).
Docking of Chromone 2-Carboxamides with PqsR We used the AutoDock Vina software to predict the affinity of target compounds for the PqsR protein  [24]. PqsR, also named MvfR (Multiple Virulence Factor Regulator), is a global activator controlling the expression of many virulence factors in P. aeruginosa  [13, 25, 26]. A monomeric unit of PqsR is composed of two subdomains connected by a βsheet region β4  [25]. The ligand-binding domain is a hydrophobic cavity inserted between helix α4 and sheets β4-5-7. The active site is composed of the two pockets A and B (Figure  3A ), mostly formed by aliphatic amino acid residues  [25, 27]. The superficial A pocket is located between sheets β5-7 while the deep B pocket is buried in the hinge region of PqsR, between the two subdomains.
Docking of Chromone 2-Carboxamides with PqsR We used the AutoDock Vina software to predict the affinity of target compounds for the PqsR protein  [24]. PqsR, also named MvfR (Multiple Virulence Factor Regulator), is a global activator controlling the expression of many virulence factors in P. aeruginosa  [13, 25, 26]. A monomeric unit of PqsR is composed of two subdomains connected by a βsheet region β4  [25]. The ligand-binding domain is a hydrophobic cavity inserted between helix α4 and sheets β4-5-7. The active site is composed of the two pockets A and B (Figure  3A ), mostly formed by aliphatic amino acid residues  [25, 27]. The superficial A pocket is located between sheets β5-7 while the deep B pocket is buried in the hinge region of PqsR, between the two subdomains. Ilangovan et al.
Docking of Chromone 2-Carboxamides with PqsR We used the AutoDock Vina software to predict the affinity of target compounds for the PqsR protein  [24]. PqsR, also named MvfR (Multiple Virulence Factor Regulator), is a global activator controlling the expression of many virulence factors in P. aeruginosa  [13, 25, 26]. A monomeric unit of PqsR is composed of two subdomains connected by a β-sheet region β4  [25]. The ligand-binding domain is a hydrophobic cavity inserted between helix α4 and sheets β4-5-7. The active site is composed of the two pockets A and B (Figure  3A ), mostly formed by aliphatic amino acid residues  [25, 27]. The superficial A pocket is located between sheets β5-7 while the deep B pocket is buried in the hinge region of PqsR, between the two subdomains. Ilangovan et al.
identified Ile149, Phe221, Tyr258, Ile263, and to a lesser extent, Ile186, Leu207, and Ile236 residues as essential amino acids in the interaction between PqsR and the 2-nonyl-4-hydroxy-quinoline (NHQ) ligand, a PQS homolog with a C9 side chain  [25]. Docking simulations were carried out with the apo form of the PqsR-CBD (PDB code: 4JVC  [25] ) by keeping all protein residues rigid, except for Ile149, Leu189, Ile236, Tyr258, and Thr265  [25, 28]. The docking search space was larger than the A and B pockets of the PqsR-CBD. We considered the binding free energy (Ebind, kcal. mol -  , Table  1 ) and inhibition constant (Ki, μM, Table  S1 ) of the best ranked conformations as the main parameters (score) for the analysis of AutoDock results. Compounds with the highest affinity are those with the lowest binding energy.
We also considered the protonated forms at pH = 7. 2 for amine-containing structures. Docking results are reported in Tables  2  and 3. Docking of PQS gave a predicted binding energy (Ebind) of -8. 1 kcal. mol -  for the best pose, in which PQS is deeply bonded in the B pocket (Figure  3B ), through hydrophobic interactions with the residues Ala102, Ile149, Ala168, and Pro238, contrary to NHQ which occupies the two pockets (Figure  3A ). As expected, PQS interactions were mainly hydrophobic  [25, 29, 30]. Our docking results showed PQS forms several H-bonds between its carbonyl group and Gln194 and Ser196, and between its 3-hydroxyl group and Leu197. Docking simulations were carried out with the apo form of the PqsR-CBD (PDB code: 4JVC  [25] ) by keeping all protein residues rigid, except for Ile149, Leu189, Ile236, Tyr258, and Thr265  [25, 28].
Protein Structure File and Ligand Preparation The X-ray crystal structure of the P. aeruginosa PqsR protein was downloaded from the Protein Data Bank website (PDB code: 4JVC, 203 amino acid residues, resolution: 2. 5 Å, completeness: 99. 2%) and used for structure-based virtual screening  [28]. Avogadro software [v1. 2. 0] was used for the geometric optimization of chemical structures through the UFF force field minimization field in 10, 000 steps with a conjugate gradient algorithm. A conformational analysis was performed in order to find the lowest energy conformation, using the MMFF94 force field of MarvinSketch [v14. 10. 6. 0]. The minimized ligand structures were then used for docking calculations.